Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured Embeddings

Abstract: Open-text (or open-domain) semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR). Unfortunately, large scale systems cannot be easily machine-learned due to lack of directly supervised data. We propose here a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words, which are mapped to more than 40,000 entities) thanks to a training scheme that combines learning from WordNet and ConceptNet with learning from raw text. The model learns structured embeddings of words, entities and MRs via a multi-task training process operating on these diverse sources of data that integrates all the learnt knowledge into a single system. This work ends up combining methods for knowledge acquisition, semantic parsing, and word-sense disambiguation. Experiments on various tasks indicate that our approach is indeed successful and can form a basis for future more sophisticated systems.


Impact of Mobility On QoS of Mobile WiMax Network With CBR Application

Abstract: The issue of mobility is important in wireless network because internet connectivity can only be effective if it's available during the movement of node. To enhance mobility, wireless access systems are designed such as IEEE 802.16e to operate on the move without any disruption of services. In this paper we are analyzing the impact of mobility on the QoS parameters (Throughput, Average Jitter and Average end to end Delay) of a mobile WiMAX network (IEEE 802.16e) with CBR application.


Autonomous Traffic Control System Using Agent Based Technology

Abstract: The way of analyzing, designing and building of real-time projects has been changed due to the rapid growth of internet, mobile technologies and intelligent applications. Most of these applications are intelligent, tiny and distributed components called as agent. Agent works like it takes the input from numerous real-time sources and gives back the real-time response. In this paper how these agents can be implemented in vehicle traffic management especially in large cities and identifying various challenges when there is a rapid growth of population and vehicles. In this paper our proposal gives a solution for using autonomous or agent based technology. These autonomous or intelligent agents have the capability to observe, act and learn from their past experience. This system uses the knowledge flow of precedent signal or data to identify the incoming flow of forthcoming signal. Our architecture involves the video analysis and exploration using some Intelligence learning algorithm to estimate and identify the flow of traffic.


3-Phase Recognition Approach to Pseudo 3D Building Generation from 2D Floor Plan

Abstract: Nowadays three dimension (3D) architectural visualisation has become a powerful tool in the conceptualisation, design and presentation of architectural products in the construction industry, providing realistic interaction and walkthrough on engineering products. Traditional ways of implementing 3D models involves the use of specialised 3D authoring tools along with skilled 3D designers with blueprints of the model and this is a slow and laborious process. The aim of this paper is to automate this process by simply analyzing the blueprint document and generating the 3D scene automatically. For this purpose we have devised a 3-Phase recognition approach to pseudo 3D building generation from 2D floor plan and developed a software accordingly. Our 3-phased 3D building system has been implemented using C, C++ and OpenCV library [24] for the Image Processing module; The Save Module generated an XML file for storing the processed floor plan objects attributes; while the Irrlitch [14] game engine was used to implement the Interactive 3D module. Though still at its infancy, our proposed system gave commendable results. We tested our system on 6 floor plans with complexities ranging from low to high and the results seems to be very promising with an average processing time of around 3s and a 3D generation in 4s. In addition the system provides an interactive walk-though and allows users to modify components.


Context-Capture Multi-Valued Decision Fusion With Fault Tolerant Capability For Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in practice, many applications need to process multi-valued data, such as temperature and reflection level used for lightening prediction. To address these challenges, the Markov modulated Poisson process (MMPP) and multi-valued logic are introduced into WSNs to perform context-capture multi-valued decision fusion. The overall decision fusion is decomposed into two parts. The first part is the context-capture model for WSNs using superposition MMPP. Through this procedure, the fusion center has a higher probability to get useful local decisions from sensors. The second one is focused on multi-valued decision fusion. Fault detection can also be performed based on MVL. Once the fusion center detects the faulty nodes, all their local decisions are removed from the computation of the likelihood ratios. Finally, we evaluate the capability of context-capture and fault tolerant. The result supports the usefulness of our scheme.


Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE)

Abstract: Recent research in ubiquitous computing uses technologies of Body Area Networks (BANs) to monitor the person's kinematics and physiological parameters. In this paper we propose a real time mobile health system for monitoring elderly patients from indoor or outdoor environments. The system uses a bio- signal sensor worn by the patient and a Smartphone as a central node. The sensor data is collected and transmitted to the intelligent server through GPRS/UMTS to be analyzed. The prototype (UMHMSE) monitors the elderly mobility, location and vital signs such as Sp02 and Heart Rate. Remote users (family and medical personnel) might have a real time access to the collected information through a web application.


Co-nondeterminism in compositions: A kernelization lower bound for a Ramsey-type problem

Abstract: Until recently, techniques for obtaining lower bounds for kernelization were one of the most sought after tools in the field of parameterized complexity. Now, after a strong influx of techniques, we are in the fortunate situation of having tools available that are even stronger than what has been required in their applications so far. Based on a result of Fortnow and Santhanam (JCSS 2011), Bodlaender et al. (JCSS 2009) showed that, unless NP \subseteq coNP/poly, the existence of a deterministic polynomial-time composition algorithm, i.e., an algorithm which outputs an instance of bounded parameter value which is yes if and only if one of t input instances is yes, rules out the existence of polynomial kernels for a problem. Dell and van Melkebeek (STOC 2010) continued this line of research and, amongst others, were able to rule out kernels of size O(k^d-eps) for certain problems, assuming NP !\subseteq coNP/poly. Their work implies that even the existence of a co-nondeterministic composition rules out polynomial kernels. In this work we present the first example of how co-nondeterminism can help to make a composition algorithm. We study a Ramsey-type problem: Given a graph G and an integer k, the question is whether G contains an independent set or a clique of size at least k. It was asked by Rod Downey whether this problem admits a polynomial kernelization. We provide a co-nondeterministic composition based on embedding t instances into a single host graph H. The crux is that the host graph H needs to observe a bound of L \in O(log t) on both its maximum independent set and maximum clique size, while also having a cover of its vertex set by independent sets and cliques all of size L; the co-nondeterministic composition is build around the search for such graphs. Thus we show that, unless NP \subseteq coNP/poly, the problem does not admit a kernelization with polynomial size guarantee.


The countable versus uncountable branching recurrences in computability logic

Abstract: This paper introduces a new simplified version of the countable branching recurrence of Computability Logic, proves its equivalence to the old one, and shows that the basic logic induced by it is a proper superset of the basic logic induced by the uncountable branching recurrence. A further result of this paper is showing that the countable branching recurrence is strictly weaker than the uncountable branching recurrence in the sense that the latter logically implies the former but not vice versa.


Iterative Constructions and Private Data Release

Abstract: In this paper we study the problem of approximately releasing the cut function of a graph while preserving differential privacy, and give new algorithms (and new analyses of existing algorithms) in both the interactive and non-interactive settings. Our algorithms in the interactive setting are achieved by revisiting the problem of releasing differentially private, approximate answers to a large number of queries on a database. We show that several algorithms for this problem fall into the same basic framework, and are based on the existence of objects which we call iterative database construction algorithms. We give a new generic framework in which new (efficient) IDC algorithms give rise to new (efficient) interactive private query release mechanisms. Our modular analysis simplifies and tightens the analysis of previous algorithms, leading to improved bounds. We then give a new IDC algorithm (and therefore a new private, interactive query release mechanism) based on the Frieze/Kannan low-rank matrix decomposition. This new release mechanism gives an improvement on prior work in a range of parameters where the size of the database is comparable to the size of the data universe (such as releasing all cut queries on dense graphs). We also give a non-interactive algorithm for efficiently releasing private synthetic data for graph cuts with error O(|V|^{1.5}). Our algorithm is based on randomized response and a non-private implementation of the SDP-based, constant-factor approximation algorithm for cut-norm due to Alon and Naor. Finally, we give a reduction based on the IDC framework showing that an efficient, private algorithm for computing sufficiently accurate rank-1 matrix approximations would lead to an improved efficient algorithm for releasing private synthetic data for graph cuts. We leave finding such an algorithm as our main open problem.


Decentralized List Scheduling

Abstract: Classical list scheduling is a very popular and efficient technique for scheduling jobs in parallel and distributed platforms. It is inherently centralized. However, with the increasing number of processors, the cost for managing a single centralized list becomes too prohibitive. A suitable approach to reduce the contention is to distribute the list among the computational units: each processor has only a local view of the work to execute. Thus, the scheduler is no longer greedy and standard performance guarantees are lost. The objective of this work is to study the extra cost that must be paid when the list is distributed among the computational units. We first present a general methodology for computing the expected makespan based on the analysis of an adequate potential function which represents the load unbalance between the local lists. We obtain an equation on the evolution of the potential by computing its expected decrease in one step of the schedule. Our main theorem shows how to solve such equations to bound the makespan. Then, we apply this method to several scheduling problems, namely, for unit independent tasks, for weighted independent tasks and for tasks with precendence constraints. More precisely, we prove that the time for scheduling a global workload W composed of independent unit tasks on m processors is equal to W/m plus an additional term proportional to log_2 W. We provide a lower bound which shows that this is optimal up to a constant. This result is extended to the case of weighted independent tasks. In the last setting, precedence task graphs, our analysis leads to an improvement on the bound of Arora et al. We finally provide some experiments using a simulator. The distribution of the makespan is shown to fit existing probability laws. The additive term is shown by simulation to be around 3 \log_2 W confirming the tightness of our analysis.


User Empowerment in the Internet of Things

Abstract: This paper focuses on the characteristics of two big triggers that facilitated wide user adoption of the Internet: Web 2.0 and online social networks. We detect brakes for reproduction of these events in Internet of things. To support our hypothesis we first compare the difference between the ways of use of the Internet with the future scenarios of Internet of things. We detect barriers that could slow down apparition of this kind of social events during user adoption of Internet of Things and we propose a conceptual framework to solve these problems.


Using Variational Inference and MapReduce to Scale Topic Modeling

Abstract: Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference of LDA. In this paper, we propose a technique called ~\emph{MapReduce LDA} (Mr. LDA) to accommodate very large corpus collections in the MapReduce framework. In contrast to other techniques to scale inference for LDA, which use Gibbs sampling, we use variational inference. Our solution efficiently distributes computation and is relatively simple to implement. More importantly, this variational implementation, unlike highly tuned and specialized implementations, is easily extensible. We demonstrate two extensions of the model possible with this scalable framework: informed priors to guide topic discovery and modeling topics from a multilingual corpus.


Computational Hardness of Enumerating Satisfying Spin-Assignments in Triangulations

Abstract: Satisfying spin-assignments in triangulations of a surface are states of minimum energy of the antiferromagnetic Ising model on triangulations which correspond (via geometric duality) to perfect matchings in cubic bridgeless graphs. In this work we show that it is NP-complete to decide whether or not a surface triangulation admits a satisfying spin-assignment, and that it is #P-complete to determine the number of such assignments. Both results are derived via an elaborate (and atypical) reduction that maps a Boolean formula in 3-conjunctive normal form into a triangulation of an orientable closed surface.


Applying Data Privacy Techniques on Tabular Data in Uganda

Abstract: The growth of Information Technology(IT) in Africa has led to an increase in the utilization of communication networks for data transaction across the continent. A growing number of entities in the private sector, academia, and government, have deployed the Internet as a medium to transact in data, routinely posting statistical and non statistical data online and thereby making many in Africa increasingly dependent on the Internet for data transactions. In the country of Uganda, exponential growth in data transaction has presented a new challenge: What is the most efficient way to implement data privacy. This article discusses data privacy challenges faced by the country of Uganda and implementation of data privacy techniques for published tabular data. We make the case for data privacy, survey concepts of data privacy, and implementations that could be employed to provide data privacy in Uganda.


Teaching Introductory Electrical Engineering Course to CS Students in a Russian University

Abstract: This article is about the author's experience with developing and teaching an introductory electrical engineering course for students of Faculty (department) of Information Technology of a Russian university. The curriculum of this department conforms to typical computer science curricula of US engineering schools with a noticeable omission of comparable electrical engineering courses. When developing the course, I did my best to pay attention to learning preferences of the department's student body. I also hoped to contribute to a degree to meeting labor market demands for developers of electrical engineering CAD software. As for inspiration, I was enchanted with ideas of the Mead & Conway revolution, albeit indirectly related to my enterprise.


Annotating Simplices with a Homology Basis and Its Applications

Abstract: Let $K$ be a simplicial complex and $g$ the rank of its $p$-th homology group $H_p(K)$ defined with $Z_2$ coefficients. We show that we can compute a basis $H$ of $H_p(K)$ and annotate each $p$-simplex of $K$ with a binary vector of length $g$ with the following property: the annotations, summed over all $p$-simplices in any $p$-cycle $z$, provide the coordinate vector of the homology class $[z]$ in the basis $H$. The basis and the annotations for all simplices can be computed in $O(n^{\omega})$ time, where $n$ is the size of $K$ and $\omega<2.376$ is a quantity so that two $n\times n$ matrices can be multiplied in $O(n^{\omega})$ time. The pre-computation of annotations permits answering queries about the independence or the triviality of $p$-cycles efficiently. Using annotations of edges in 2-complexes, we derive better algorithms for computing optimal basis and optimal homologous cycles in 1-dimensional homology. Specifically, for computing an optimal basis of $H_1(K)$, we improve the time complexity known for the problem from $O(n^4)$ to $O(n^{\omega}+n^2g^{\omega-1})$. Here $n$ denotes the size of the 2-skeleton of $K$ and $g$ the rank of $H_1(K)$. Computing an optimal cycle homologous to a given 1-cycle is NP-hard even for surfaces and an algorithm taking $2^{O(g)}n\log n$ time is known for surfaces. We extend this algorithm to work with arbitrary 2-complexes in $O(n^{\omega})+2^{O(g)}n^2\log n$ time using annotations.


An Analysis of Chinese Search Engine Filtering

Abstract: The imposition of government mandates upon Internet search engine operation is a growing area of interest for both computer science and public policy. Users of these search engines often observe evidence of censorship, but the government policies that impose this censorship are not generally public. To better understand these policies, we conducted a set of experiments on major search engines employed by Internet users in China, issuing queries against a variety of different words: some neutral, some with names of important people, some political, and some pornographic. We conducted these queries, in Chinese, against Baidu, Google (including google.cn, before it was terminated), Yahoo!, and Bing. We found remarkably aggressive filtering of pornographic terms, in some cases causing non-pornographic terms which use common characters to also be filtered. We also found that names of prominent activists and organizers as well as top political and military leaders, were also filtered in whole or in part. In some cases, we found search terms which we believe to be "blacklisted". In these cases, the only results that appeared, for any of them, came from a short "whitelist" of sites owned or controlled directly by the Chinese government. By repeating observations over a long observation period, we also found that the keyword blocking policies of the Great Firewall of China vary over time. While our results don't offer any fundamental insight into how to defeat or work around Chinese internet censorship, they are still helpful to understand the structure of how censorship duties are shared between the Great Firewall and Chinese search engines.


Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

Abstract: We propose an extension of the Restricted Boltzmann Machine (RBM) that allows the joint shape and appearance of foreground objects in cluttered images to be modeled independently of the background. We present a learning scheme that learns this representation directly from cluttered images with only very weak supervision. The model generates plausible samples and performs foreground-background segmentation. We demonstrate that representing foreground objects independently of the background can be beneficial in recognition tasks.


MAC Centered Cooperation - Synergistic Design of Network Coding, Multi-Packet Reception, and Improved Fairness to Increase Network Throughput

Abstract: We design a cross-layer approach to aid in develop- ing a cooperative solution using multi-packet reception (MPR), network coding (NC), and medium access (MAC). We construct a model for the behavior of the IEEE 802.11 MAC protocol and apply it to key small canonical topology components and their larger counterparts. The results obtained from this model match the available experimental results with fidelity. Using this model, we show that fairness allocation by the IEEE 802.11 MAC can significantly impede performance; hence, we devise a new MAC that not only substantially improves throughput, but provides fairness to flows of information rather than to nodes. We show that cooperation between NC, MPR, and our new MAC achieves super-additive gains of up to 6.3 times that of routing with the standard IEEE 802.11 MAC. Furthermore, we extend the model to analyze our MAC's asymptotic and throughput behaviors as the number of nodes increases or the MPR capability is limited to only a single node. Finally, we show that although network performance is reduced under substantial asymmetry or limited implementation of MPR to a central node, there are some important practical cases, even under these conditions, where MPR, NC, and their combination provide significant gains.


Securing Tor Tunnels under the Selective-DoS Attack

Abstract: Anonymous communication systems are subject to selective denial-of-service (DoS) attacks. Selective DoS attacks lower anonymity as they force paths to be rebuilt multiple times to ensure delivery which increases the opportunity for more attack. In this paper we present a detection algorithm that filters out compromised communication channels for one of the most widely used anonymity networks, Tor. Our detection algorithm uses two levels of probing to filter out potentially compromised tunnels. We perform probabilistic analysis and extensive simulation to show the robustness of our detection algorithm. We also analyze the overhead of our detection algorithm and show that we can achieve satisfactory security guarantee for reasonable communication overhead (5% of the total available Tor bandwidth in the worst case). Real world experiments reveal that our detection algorithm provides good defense against selective DoS attack.


Lower Bounds for the Average and Smoothed Number of Pareto Optima

Abstract: Smoothed analysis of multiobjective 0-1 linear optimization has drawn considerable attention recently. The number of Pareto-optimal solutions (i.e., solutions with the property that no other solution is at least as good in all the coordinates and better in at least one) for multiobjective optimization problems is the central object of study. In this paper, we prove several lower bounds for the expected number of Pareto optima. Our basic result is a lower bound of \Omega_d(n^(d-1)) for optimization problems with d objectives and n variables under fairly general conditions on the distributions of the linear objectives. Our proof relates the problem of lower bounding the number of Pareto optima to results in geometry connected to arrangements of hyperplanes. We use our basic result to derive (1) To our knowledge, the first lower bound for natural multiobjective optimization problems. We illustrate this for the maximum spanning tree problem with randomly chosen edge weights. Our technique is sufficiently flexible to yield such lower bounds for other standard objective functions studied in this setting (such as, multiobjective shortest path, TSP tour, matching). (2) Smoothed lower bound of min {\Omega_d(n^(d-1.5) \phi^{(d-log d) (1-\Theta(1/\phi))}), 2^{\Theta(n)}}$ for the 0-1 knapsack problem with d profits for phi-semirandom distributions for a version of the knapsack problem. This improves the recent lower bound of Brunsch and Roeglin.


Fitting a Model to Data in Loss Tomography

Abstract: Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Although most of the estimators claim to be the maximum likelihood estimators, the claim is only partially true since the maximum likelihood estimate can be obtained at most for a class of data sets. Unfortunately, few people are aware of this restriction that leads to a misconception that an estimator is applicable to all data sets as far as it returns a unique solution. To correct this, we in this paper point out the risk of this misconception and illustrate the inconsistency between data and model in the most influential estimators. To ensure the model used in estimation consistent with the data collected from an experiment, the data sets used in estimation are divided into 4 classes according to the characteristics of observations. Based on the classification, the validity of an estimator is defined and the validity of the most influential estimators is evaluated. In addition, a number of estimators are proposed, one for a class of data sets that have been overlooked. Further, a general estimator is proposed that is applicable to all data classes. The discussion starts from the tree topology and end at the general topology.


A Glance into the Future of Human Computer Interaction

Abstract: Computers have a direct impact on our lives nowadays. Human's interaction with the computer has modified with the passage of time as improvement in technology occurred the better the human computer interaction became. Today we are facilitated by the operating system that has reduced all the complexity of hardware and we undergo our computation in a very convenient way irrespective of the process occurring at the hardware level. Though the human computer interaction has improved but it's not done yet. If we come to the future the computer's role in our lives would be a lot more rather our life would be of the artificial intelligence. In our future the biggest resource would be component of time and wasting time for a key board entry or a mouse input would be unbearable so the need would be of the computer interaction environment that along with the complexity reduction also minimizes the time wastage in the human computer interaction. Accordingly in our future the computation would also be increased it would not be a simple operating system limited to a computer it would be computers managing our entire life activities hence fall out of domain of present computers electronic based architecture .In this research paper we propose a model that shall be meeting the future human computer interaction needs possessing linguistic human computer interference environment based on surface technology, automation and photonic computing, which would be reliable, efficient and quicker satisfying all the future artificial intelligence pre requisites.


Online Anomaly Detection Systems Using Incremental Commute Time

Abstract: Commute Time Distance (CTD) is a random walk based metric on graphs. CTD has found widespread applications in many domains including personalized search, collaborative filtering and making search engines robust against manipulation. Our interest is inspired by the use of CTD as a metric for anomaly detection. It has been shown that CTD can be used to simultaneously identify both global and local anomalies. Here we propose an accurate and efficient approximation for computing the CTD in an incremental fashion in order to facilitate real-time applications. An online anomaly detection algorithm is designed where the CTD of each new arriving data point to any point in the current graph can be estimated in constant time ensuring a real-time response. Moreover, the proposed approach can also be applied in many other applications that utilize commute time distance.


Reversible arithmetic logic unit

Abstract: Quantum computer requires quantum arithmetic. The sophisticated design of a reversible arithmetic logic unit (reversible ALU) for quantum arithmetic has been investigated in this letter. We provide explicit construction of reversible ALU effecting basic arithmetic operations. By provided the corresponding control unit, the proposed reversible ALU can combine the classical arithmetic and logic operation in a reversible integrated system. This letter provides actual evidence to prove the possibility of the realization of reversible Programmable Logic Device (RPLD) using reversible ALU.


Detecting 2-joins faster

Abstract: 2-joins are edge cutsets that naturally appear in the decomposition of several classes of graphs closed under taking induced subgraphs, such as balanced bipartite graphs, even-hole-free graphs, perfect graphs and claw-free graphs. Their detection is needed in several algorithms, and is the slowest step for some of them. The classical method to detect a 2-join takes $O(n^3m)$ time where $n$ is the number of vertices of the input graph and $m$ the number of its edges. To detect \emph{non-path} 2-joins (special kinds of 2-joins that are needed in all of the known algorithms that use 2-joins), the fastest known method takes time $O(n^4m)$. Here, we give an $O(n^2m)$-time algorithm for both of these problems. A consequence is a speed up of several known algorithms.


Towards Completely Lifted Search-based Probabilistic Inference

Abstract: The promise of lifted probabilistic inference is to carry out probabilistic inference in a relational probabilistic model without needing to reason about each individual separately (grounding out the representation) by treating the undistinguished individuals as a block. Current exact methods still need to ground out in some cases, typically because the representation of the intermediate results is not closed under the lifted operations. We set out to answer the question as to whether there is some fundamental reason why lifted algorithms would need to ground out undifferentiated individuals. We have two main results: (1) We completely characterize the cases where grounding is polynomial in a population size, and show how we can do lifted inference in time polynomial in the logarithm of the population size for these cases. (2) For the case of no-argument and single-argument parametrized random variables where the grounding is not polynomial in a population size, we present lifted inference which is polynomial in the population size whereas grounding is exponential. Neither of these cases requires reasoning separately about the individuals that are not explicitly mentioned.


Data Aggregation and Privacy for Police Patrols

Abstract: With a widespread growth in the potential applications of Wireless Sensor Networks, the need for reliable security mechanisms for them has increased manifold. This paper proposes a scheme, Privacy for Police Patrols (PPP), to provide secure data aggregation that relies on multilevel routing. Privacy factors have been identified and implemented. Aggregates are prepared and the summary of information is gathered and stored in a repository. The above defined approaches are integrated in police patrol applications and preliminary results are obtained.


Sending Hidden Data via Google Suggest

Abstract: Google Suggest is a service incorporated within Google Web Search which was created to help user find the right search phrase by proposing the autocompleting popular phrases while typing. The paper presents a new network steganography method called StegSuggest which utilizes suggestions generated by Google Suggest as a hidden data carrier. The detailed description of the method's idea is backed up with the analysis of the network traffic generated by the Google Suggest to prove its feasibility. The traffic analysis was also performed to discover the occurrence of two TCP options: Window Scale and Timestamp which StegSuggest uses to operate. Estimation of method steganographic bandwidth proves that it is possible to insert 100 bits of steganogram into every suggestions list sent by Google Suggest service.


How Hidden Can Be Even More Hidden?

Abstract: The paper presents Deep Hiding Techniques (DHTs) that define general techniques that can be applied to every network steganography method to improve its undetectability and make steganogram extraction harder to perform. We define five groups of techniques that can make steganogram less susceptible to detection and extraction. For each of the presented group, examples of the usage are provided based on existing network steganography methods. To authors' best knowledge presented approach is the first attempt in the state of the art to systematically describe general solutions that can make steganographic communication more hidden and steganogram extraction harder to perform.


Lost Audio Packets Steganography: The First Practical Evaluation

Abstract: This paper presents first experimental results for an IP telephony-based steganographic method called LACK (Lost Audio PaCKets steganography). This method utilizes the fact that in typical multimedia communication protocols like RTP (Real-Time Transport Protocol), excessively delayed packets are not used for the reconstruction of transmitted data at the receiver, i.e. these packets are considered useless and discarded. The results presented in this paper were obtained basing on a functional LACK prototype and show the method's impact on the quality of voice transmission. Achievable steganographic bandwidth for the different IP telephony codecs is also calculated.


Is Cloud Computing Steganography-proof?

Abstract: The paper focuses on characterisation of information hiding possibilities in Cloud Computing. After general introduction to cloud computing and its security we move to brief description of steganography. In particular we introduce classification of steganographic communication scenarios in cloud computing which is based on location of the steganograms receiver. These scenarios as well as the threats that steganographic methods can cause must be taken into account when designing secure cloud computing services.


On the Universality of Online Mirror Descent

Abstract: We show that for a general class of convex online learning problems, Mirror Descent can always achieve a (nearly) optimal regret guarantee.


PM2PLS: An Integration of Proxy Mobile IPv6 and MPLS

Abstract: This paper proposes a handover scheme supporting Multi-Protocol Label Switching (MPLS) in a Proxy Mobile IPv6 (PMIPv6) domain that improves the mobility and gives Quality of Service (QoS) and Traffic Engineering (TE) capabilities in wireless access networks. The proposed scheme takes advantages of both PMIPv6 and MPLS. PMIPv6 was designed to provide NETwork-based Localized Mobility Management (NETLMM) support to a Mobile Node (MN); therefore, the MN does not perform any mobility related signaling, while MPLS is used as an alternative tunneling technology between the Mobile Access Gateway (MAG) and the Local Mobility Anchor (LMA) replacing the IP-in-IP tunnels with Label Switched Path (LSP) tunnels. It can also be integrated with other QoS architectures such as Differentiated Services (DiffServ) and/or Integrated Services (IntServ). In this study, we used MATLAB to perform an analysis to evaluate the impact of introducing MPLS technology in PMIPv6 domain based on handover latency, operational overhead and packet loss during the handover. This was compared with PMIPv6, and a PMIPv6/MPLS integration. We proved that the proposed scheme can give better performance than other schemes.


Event-Clock Automata: From Theory to Practice

Abstract: Event clock automata (ECA) are a model for timed languages that has been introduced by Alur, Fix and Henzinger as an alternative to timed automata, with better theoretical properties (for instance, ECA are determinizable while timed automata are not). In this paper, we revisit and extend the theory of ECA. We first prove that no finite time abstract language equivalence exists for ECA, thereby disproving a claim in the original work on ECA. This means in particular that regions do not form a time abstract bisimulation. Nevertheless, we show that regions can still be used to build a finite automaton recognizing the untimed language of an ECA. Then, we extend the classical notions of zones and DBMs to let them handle event clocks instead of plain clocks (as in timed automata) by introducing event zones and Event DBMs (EDBMs). We discuss algorithms to handle event zones represented as EDBMs, as well as (semi-) algorithms based on EDBMs to decide language emptiness of ECA.


Analysis on the computability over the efficient utilization problem of the four-dimensional space-time

Abstract: This paper formally proposes a problem about the efficient utilization of the four dimensional space-time. Given a cuboid container, a finite number of rigid cuboid items, and the time length that each item should be continuous baked in the container, the problem asks to arrange the starting time for each item being placed into the container and to arrange the position and orientation for each item at each instant during its continuous baking period such that the total time length the container be utilized is as short as possible. Here all side dimensions of the container and of the items are positive real numbers arbitrarily given. Differs from the classical packing problems, the position and orientation of each item in the container could be changed over time. Therefore, according to above mathematical model, the four-dimensional space-time can be utilized more truly and more fully. This paper then proves that there exists an exact algorithm that could solve the problem by finite operations, so we say this problem is weak computable. Based on the understanding of this computability proof, it is expected to design effective approximate algorithms in the near future. A piggyback work completed is a strict proof on the weak computability over general and natural case of the three-dimensional cuboid packing decision problem that all parameters are positive real numbers.


Performance and Convergence of Multi-user Online Learning

Abstract: We study the problem of allocating multiple users to a set of wireless channels in a decentralized manner when the channel quali- ties are time-varying and unknown to the users, and accessing the same channel by multiple users leads to reduced quality due to interference. In such a setting the users not only need to learn the inherent channel quality and at the same time the best allocations of users to channels so as to maximize the social welfare. Assuming that the users adopt a certain online learning algorithm, we investigate under what conditions the socially optimal allocation is achievable. In particular we examine the effect of different levels of knowledge the users may have and the amount of communications and cooperation. The general conclusion is that when the cooperation of users decreases and the uncertainty about channel payoffs increases it becomes harder to achieve the socially opti- mal allocation.


Functions as proofs as processes

Abstract: This paper presents a logical approach to the translation of functional calculi into concurrent process calculi. The starting point is a type system for the {\pi}-calculus closely related to linear logic. Decompositions of intuitionistic and classical logics into this system provide type-preserving translations of the \lambda- and \lambda\mu-calculus, both for call-by-name and call-by-value evaluation strategies. Previously known encodings of the \lam-calculus are shown to correspond to particular cases of this logical embedding. The realisability interpretation of types in the \pi-calculus provides systematic soundness arguments for these translations and allows for the definition of type-safe extensions of functional calculi.


Local Optima Networks of the Quadratic Assignment Problem

Abstract: Using a recently proposed model for combinatorial landscapes, Local Optima Networks (LON), we conduct a thorough analysis of two types of instances of the Quadratic Assignment Problem (QAP). This network model is a reduction of the landscape in which the nodes correspond to the local optima, and the edges account for the notion of adjacency between their basins of attraction. The model was inspired by the notion of 'inherent network' of potential energy surfaces proposed in physical-chemistry. The local optima networks extracted from the so called uniform and real-like QAP instances, show features clearly distinguishing these two types of instances. Apart from a clear confirmation that the search difficulty increases with the problem dimension, the analysis provides new confirming evidence explaining why the real-like instances are easier to solve exactly using heuristic search, while the uniform instances are easier to solve approximately. Although the local optima network model is still under development, we argue that it provides a novel view of combinatorial landscapes, opening up the possibilities for new analytical tools and understanding of problem difficulty in combinatorial optimization.


Local Optima Networks of NK Landscapes with Neutrality

Abstract: In previous work we have introduced a network-based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here we extend this formalism to neutral fitness landscapes, which are common in difficult combinatorial search spaces. By using two known neutral variants of the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned by a parameter, we show that our new definitions of the optima networks and the associated basins are consistent with the previous definitions for the non-neutral case. Moreover, our empirical study and statistical analysis show that the features of neutral landscapes interpolate smoothly between landscapes with maximum neutrality and non-neutral ones. We found some unknown structural differences between the two studied families of neutral landscapes. But overall, the network features studied confirmed that neutrality, in landscapes with percolating neutral networks, may enhance heuristic search. Our current methodology requires the exhaustive enumeration of the underlying search space. Therefore, sampling techniques should be developed before this analysis can have practical implications. We argue, however, that the proposed model offers a new perspective into the problem difficulty of combinatorial optimization problems and may inspire the design of more effective search heuristics.


Centric selection: a way to tune the exploration/exploitation trade-off

Abstract: In this paper, we study the exploration / exploitation trade-off in cellular genetic algorithms. We define a new selection scheme, the centric selection, which is tunable and allows controlling the selective pressure with a single parameter. The equilibrium model is used to study the influence of the centric selection on the selective pressure and a new model which takes into account problem dependent statistics and selective pressure in order to deal with the exploration / exploitation trade-off is proposed: the punctuated equilibria model. Performances on the quadratic assignment problem and NK-Landscapes put in evidence an optimal exploration / exploitation trade-off on both of the classes of problems. The punctuated equilibria model is used to explain these results.


NK landscapes difficulty and Negative Slope Coefficient: How Sampling Influences the Results

Abstract: Negative Slope Coefficient is an indicator of problem hardness that has been introduced in 2004 and that has returned promising results on a large set of problems. It is based on the concept of fitness cloud and works by partitioning the cloud into a number of bins representing as many different regions of the fitness landscape. The measure is calculated by joining the bins centroids by segments and summing all their negative slopes. In this paper, for the first time, we point out a potential problem of the Negative Slope Coefficient: we study its value for different instances of the well known NK-landscapes and we show how this indicator is dramatically influenced by the minimum number of points contained into a bin. Successively, we formally justify this behavior of the Negative Slope Coefficient and we discuss pros and cons of this measure.


Next Generation High Speed Computing Using Photonic Based Technolog

Abstract: In the present era of technology computer has facilitated the human life up to a great extent. The speed of computation has raised to astonish level but the pace of development of other technologies which have core dependency over computers have raised relatively exponentially huge, though the computer speed of computation is very fast with respect to human abilities but still it has to be increased a lot more to meet the future requirements. We have pushed electrons to their maximum limit to a stage that nothing further could be expected from electrons. Alternately one can use photon to replace the relatively sluggish electrons. An alternate that posses all feature that an electron holds but only millions of time faster and with a far more reliability in one way or the other stretching the computers speed to a stage that no one would have ever even wonder. In this research paper the photonics implementations in computation industry have been presented along with its scope as an alternate to electron with comparative study of electron and photon under the computation perspective, generalized working of silicon based optical computers, the application of photons and their crucial role in the upcoming times. Keywords: Photonic Technology, H


Estimation of Severity of Speech Disability through Speech Envelope

Abstract: In this paper, envelope detection of speech is discussed to distinguish the pathological cases of speech disabled children. The speech signal samples of children of age between five to eight years are considered for the present study. These speech signals are digitized and are used to determine the speech envelope. The envelope is subjected to ratio mean analysis to estimate the disability. This analysis is conducted on ten speech signal samples which are related to both place of articulation and manner of articulation. Overall speech disability of a pathological subject is estimated based on the results of above analysis.


Development and Modelling of High-Efficiency Computing Structure for Digital Signal Processing

Abstract: The paper is devoted to problem of spline approximation. A new method of nodes location for curves and surfaces computer construction by means of B-splines and results of simulink-modeling is presented. The advantages of this paper is that we comprise the basic spline with classical polynomials both on accuracy, as well as degree of paralleling calculations are also shown.


On the Undecidability of Fuzzy Description Logics with GCIs with Lukasiewicz t-norm

Abstract: Recently there have been some unexpected results concerning Fuzzy Description Logics (FDLs) with General Concept Inclusions (GCIs). They show that, unlike the classical case, the DL ALC with GCIs does not have the finite model property under Lukasiewicz Logic or Product Logic and, specifically, knowledge base satisfiability is an undecidable problem for Product Logic. We complete here the analysis by showing that knowledge base satisfiability is also an undecidable problem for Lukasiewicz Logic.


A Glance into the Future of Human Computer Interactions

Abstract: Computers have a direct impact on our lives nowadays. Human's interaction with the computer has modified with the passage of time as improvement in technology occurred the better the human computer interaction became. Today we are facilitated by the operating system that has reduced all the complexity of hardware and we undergo our computation in a very convenient way irrespective of the process occurring at the hardware level. Though the human computer interaction has improved but it's not done yet. If we come to the future the computer's role in our lives would be a lot more rather our life would be of the artificial intelligence. In our future the biggest resource would be component of time and wasting time for a key board entry or a mouse input would be unbearable so the need would be of the computer interaction environment that along with the complexity reduction also minimizes the time wastage in the human computer interaction. Accordingly in our future the computation would also be increased it would not be a simple operating system limited to a computer it would be computers managing our entire life activities hence fall out of domain of present computers electronic based architecture .In this research paper we propose a model that shall be meeting the future human computer interaction needs possessing linguistic human computer interference environment based on surface technology, automation and photonic computing, which would be reliable, efficient and quicker satisfying all the future artificial intelligence pre requisites.


Sorting Algorithms with Restrictions

Abstract: Sorting is one of the most used and well investigated algorithmic problem [1]. Traditional postulation supposes the sorting data archived, and the elementary operation as comparisons of two numbers. In a view of appearance of new processors and applied problems with data streams, sorting changed its face. This changes and generalizations are the subject of investigation in the research below.


Greedy Set Cover Estimations

Abstract: More precise estimation of the greedy algorithm complexity for a special case of the set cover problem is given in this paper.


Direct Sequence Spread Spectrum Steganographic Scheme for IEEE 802.15.4

Abstract: This work addresses the issues related to network steganography in IEEE 802.15.4 Wireless Personal Area Networks (WPAN). The proposed communication scheme employs illicit Direct Sequence Spread Spectrum code sequences for the transmission of steganographic data. The presented approach is a compromise between minimising the probability of covert channel disclosure and providing robustness against random errors and a high steganographic data rate. The conducted analyses show that it is possible to create a covert channel with a data rate comparable to the raw data rate of IEEE 802.15.4 without much impact on the perceived receiver sensitivity, the Chip Error Rate and the Bit Error Rate.


Long-Term Energy Constraints and Power Control in Cognitive Radio Networks

Abstract: When a long-term energy constraint is imposed to a transmitter, the average energy-efficiency of a transmitter is, in general, not maximized by always transmitting. In a cognitive radio context, this means that a secondary link can re-exploit the non-used time-slots. In the case where the secondary link is imposed to generate no interference on the primary link, a relevant issue is therefore to know the fraction of time-slots available to the secondary transmitter, depending on the system parameters. On the other hand, if the secondary transmitter is modeled as a selfish and free player choosing its power control policy to maximize its average energy-efficiency, resulting primary and secondary signals are not necessarily orthogonal and studying the corresponding Stackelberg game is relevant to know the outcome of this interactive situation in terms of power control policies.


A Stochastic Game Formulation of Energy-Efficient Power Control: Equilibrium Utilities and Practical Strategies

Abstract: Frequency non-selective time-selective multiple access channels in which transmitters can freely choose their power control policy are considered. The individual objective of the transmitters is to maximize their averaged energy-efficiency. For this purpose, a transmitter has to choose a power control policy that is, a sequence of power levels adapted to the channel variations. This problem can be formulated as a stochastic game with discounting for which there exists a theorem characterizing all the equilibrium utilities (equilibrium utility region). As in its general formulation, this theorem relies on global channel state information (CSI), it is shown that some points of the utility region can be reached with individual CSI. Interestingly, time-sharing based solutions, which are usually considered for centralized policies, appear to be part of the equilibrium solutions. This analysis is illustrated by numerical results providing further insights to the problem under investigation.


Interactive ontology debugging: two query strategies for efficient fault localization

Abstract: Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple "split-in-half" strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the "split-in-half" approach. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.


Enhancing Knowledge Sharing Between Educational Portals

Abstract: Information and knowledge in exchange in public networks is a crucial challenge that needs to be overcome in order to consolidate the benefits associated with such structures. We study the impact of the nature of the information exchanged over the possibilities of success of this process, basing ourselves on the analysis of the information produced by the members of the Network of National Educational Portals. One of the main challenges that the Network of National Educational Portals faces consists in finding effective ways of sharing information that can promote knowledge transfer between members of the network. We argue that a key factor that prevents information sharing is the use of performance metrics by portal responsibles to evaluate the results of their decisions. These metrics are highly sensitive, context-dependent, and produced through non-standardized methods, all of which reduce the willingness of knowledge sharing. We present a different approach: based on the Network of National Educational Portals case, we propose creating a comprehensive information system aimed at providing reliable and timely information in a systematic fashion. We believe that adopting standardized procedures and indicators of less sensitive nature, we can produce information for all partners without the shortcomings of the usual practices.


Fully De-Amortized Cuckoo Hashing for Cache-Oblivious Dictionaries and Multimaps

Abstract: A dictionary (or map) is a key-value store that requires all keys be unique, and a multimap is a key-value store that allows for multiple values to be associated with the same key. We design hashing-based indexing schemes for dictionaries and multimaps that achieve worst-case optimal performance for lookups and updates, with a small or negligible probability the data structure will require a rehash operation, depending on whether we are working in the the external-memory (I/O) model or one of the well-known versions of the Random Access Machine (RAM) model. One of the main features of our constructions is that they are \emph{fully de-amortized}, meaning that their performance bounds hold without one having to tune their constructions with certain performance parameters, such as the constant factors in the exponents of failure probabilities or, in the case of the external-memory model, the size of blocks or cache lines and the size of internal memory (i.e., our external-memory algorithms are cache oblivious). Our solutions are based on a fully de-amortized implementation of cuckoo hashing, which may be of independent interest. This hashing scheme uses two cuckoo hash tables, one "nested" inside the other, with one serving as a primary structure and the other serving as an auxiliary supporting queue/stash structure that is super-sized with respect to traditional auxiliary structures but nevertheless adds negligible storage to our scheme. This auxiliary structure allows the success probability for cuckoo hashing to be very high, which is useful in cryptographic or data-intensive applications.


Jeux stochastiques et contr\^ole de puissance distribu\'e

Abstract: Transmitters of a multiple access channel are assumed to freely choose their power control strategy in order to be energy-efficient. We show that in a stochastic game framework, we can develop energy-efficient distributed control strategies which only require partial knowledge of the entire system. Achievable utility equilibrium region is characterized and based on time-sharing, an explicit power control strategy is proposed.


Dynamics in Near-Potential Games

Abstract: Except for special classes of games, there is no systematic framework for analyzing the dynamical properties of multi-agent strategic interactions. Potential games are one such special but restrictive class of games that allow for tractable dynamic analysis. Intuitively, games that are "close" to a potential game should share similar properties. In this paper, we formalize and develop this idea by quantifying to what extent the dynamic features of potential games extend to "near-potential" games. We study convergence of three commonly studied classes of adaptive dynamics: discrete-time better/best response, logit response, and discrete-time fictitious play dynamics. For better/best response dynamics, we focus on the evolution of the sequence of pure strategy profiles and show that this sequence converges to a (pure) approximate equilibrium set, whose size is a function of the "distance" from a close potential game. We then study logit response dynamics and provide a characterization of the stationary distribution of this update rule in terms of the distance of the game from a close potential game and the corresponding potential function. We further show that the stochastically stable strategy profiles are pure approximate equilibria. Finally, we turn attention to fictitious play, and establish that the sequence of empirical frequencies of player actions converges to a neighborhood of (mixed) equilibria of the game, where the size of the neighborhood increases with distance of the game to a potential game. Thus, our results suggest that games that are close to a potential game inherit the dynamical properties of potential games. Since a close potential game to a given game can be found by solving a convex optimization problem, our approach also provides a systematic framework for studying convergence behavior of adaptive learning dynamics in arbitrary finite strategic form games.


The IHS Transformations Based Image Fusion

Abstract: The IHS sharpening technique is one of the most commonly used techniques for sharpening. Different transformations have been developed to transfer a color image from the RGB space to the IHS space. Through literature, it appears that, various scientists proposed alternative IHS transformations and many papers have reported good results whereas others show bad ones as will as not those obtained which the formula of IHS transformation were used. In addition to that, many papers show different formulas of transformation matrix such as IHS transformation. This leads to confusion what is the exact formula of the IHS transformation?. Therefore, the main purpose of this work is to explore different IHS transformation techniques and experiment it as IHS based image fusion. The image fusion performance was evaluated, in this study, using various methods to estimate the quality and degree of information improvement of a fused image quantitatively.


Frequency based Classification of Activities using Accelerometer Data

Abstract: This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.


Frequency Domain Approach for Activity Classification using Accelerometer

Abstract: Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.


Logical Concurrency Control from Sequential Proofs

Abstract: We are interested in identifying and enforcing the isolation requirements of a concurrent program, i.e., concurrency control that ensures that the program meets its specification. The thesis of this paper is that this can be done systematically starting from a sequential proof, i.e., a proof of correctness of the program in the absence of concurrent interleavings. We illustrate our thesis by presenting a solution to the problem of making a sequential library thread-safe for concurrent clients. We consider a sequential library annotated with assertions along with a proof that these assertions hold in a sequential execution. We show how we can use the proof to derive concurrency control that ensures that any execution of the library methods, when invoked by concurrent clients, satisfies the same assertions. We also present an extension to guarantee that the library methods are linearizable or atomic.


High Accuracy Human Activity Monitoring using Neural network

Abstract: This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.


A Game Theoretic Approach to Distributed Opportunistic Scheduling

Abstract: Distributed Opportunistic Scheduling (DOS) is inherently harder than conventional opportunistic scheduling due to the absence of a central entity that has knowledge of all the channel states. With DOS, stations contend for the channel using random access; after a successful contention, they measure the channel conditions and only transmit in case of a good channel, while giving up the transmission opportunity when the channel conditions are poor. The distributed nature of DOS systems makes them vulnerable to selfish users: by deviating from the protocol and using more transmission opportunities, a selfish user can gain a greater share of the wireless resources at the expense of the well-behaved users. In this paper, we address the selfishness problem in DOS from a game theoretic standpoint. We propose an algorithm that satisfies the following properties: (i) when all stations implement the algorithm, the wireless network is driven to the optimal point of operation, and (ii) one or more selfish stations cannot gain any profit by deviating from the algorithm. The key idea of the algorithm is to react to a selfish station by using a more aggressive configuration that (indirectly) punishes this station. We build on multivariable control theory to design a mechanism for punishment that on the one hand is sufficiently severe to prevent selfish behavior while on the other hand is light enough to guarantee that, in the absence of selfish behavior, the system is stable and converges to the optimum point of operation. We conduct a game theoretic analysis based on repeated games to show the algorithm's effectiveness against selfish stations. These results are confirmed by extensive simulations.


Bottom-Left Placement Theorem for Rectangle Packing

Abstract: This paper proves a bottom-left placement theorem for the rectangle packing problem, stating that if it is possible to orthogonally place n arbitrarily given rectangles into a rectangular container without overlapping, then we can achieve a feasible packing by successively placing a rectangle onto a bottom-left corner in the container. This theorem shows that even for the real-parameter rectangle packing problem, we can solve it after finite times of bottom-left placement actions. Based on this theorem, we might develop efficient heuristic algorithms for solving the rectangle packing problem.


Counting Perfect Matchings as Fast as Ryser

Abstract: We show that there is a polynomial space algorithm that counts the number of perfect matchings in an $n$-vertex graph in $O^*(2^{n/2})\subset O(1.415^n)$ time. ($O^*(f(n))$ suppresses functions polylogarithmic in $f(n)$).The previously fastest algorithms for the problem was the exponential space $O^*(((1+\sqrt{5})/2)^n) \subset O(1.619^n)$ time algorithm by Koivisto, and for polynomial space, the $O(1.942^n)$ time algorithm by Nederlof. Our new algorithm's runtime matches up to polynomial factors that of Ryser's 1963 algorithm for bipartite graphs. We present our algorithm in the more general setting of computing the hafnian over an arbitrary ring, analogously to Ryser's algorithm for permanent computation. We also give a simple argument why the general exact set cover counting problem over a slightly superpolynomial sized family of subsets of an $n$ element ground set cannot be solved in $O^*(2^{(1-\epsilon_1)n})$ time for any $\epsilon_1>0$ unless there are $O^*(2^{(1-\epsilon_2)n})$ time algorithms for computing an $n\times n$ 0/1 matrix permanent, for some $\epsilon_2>0$ depending only on $\epsilon_1$.


Symmetry Breaking in Neuroevolution: A Technical Report

Abstract: Artificial Neural Networks (ANN) comprise important symmetry properties, which can influence the performance of Monte Carlo methods in Neuroevolution. The problem of the symmetries is also known as the competing conventions problem or simply as the permutation problem. In the literature, symmetries are mainly addressed in Genetic Algoritm based approaches. However, investigations in this direction based on other Evolutionary Algorithms (EA) are rare or missing. Furthermore, there are different and contradictionary reports on the efficacy of symmetry breaking. By using a novel viewpoint, we offer a possible explanation for this issue. As a result, we show that a strategy which is invariant to the global optimum can only be successfull on certain problems, whereas it must fail to improve the global convergence on others. We introduce the \emph{Minimum Global Optimum Proximity} principle as a generalized and adaptive strategy to symmetry breaking, which depends on the location of the global optimum. We apply the proposed principle to Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategies (CMA-ES), which are two popular and conceptually different global optimization methods. Using a wide range of feedforward ANN problems, we experimentally illustrate significant improvements in the global search efficiency by the proposed symmetry breaking technique.


Soft Session Types (Long Version)

Abstract: We show how systems of sessions types can enforce interactions to be bounded for all typable processes. The type system we propose is based on Lafont's soft linear logic and is strongly inspired by recent works about session types as intuitionistic linear logic formulas. Our main result is the existence, for every typable process, of a polynomial bound on the length of any reduction sequence starting from it and on the size of any of its reducts.


Cartesian stiffness matrix of manipulators with passive joints: analytical approach

Abstract: The paper focuses on stiffness matrix computation for manipulators with passive joints. It proposes both explicit analytical expressions and an efficient recursive procedure that are applicable in general case and allow obtaining the desired matrix either in analytical or numerical form. Advantages of the developed technique and its ability to produce both singular and non-singular stiffness matrices are illustrated by application examples that deal with stiffness modeling of two Stewart-Gough platforms.


Singular surfaces and cusps in symmetric planar 3-RPR manipulators

Abstract: We study in this paper a class of 3-RPR manipulators for which the direct kinematic problem (DKP) is split into a cubic problem followed by a quadratic one. These manipulators are geometrically characterized by the fact that the moving triangle is the image of the base triangle by an indirect isometry. We introduce a specific coordinate system adapted to this geometric feature and which is also well adapted to the splitting of the DKP. This allows us to obtain easily precise descriptions of the singularities and of the cusp edges. These latter second order singularities are important for nonsingular assembly mode changing. We show how to sort assembly modes and use this sorting for motion planning in the joint space.


MeLinDa: an interlinking framework for the web of data

Abstract: The web of data consists of data published on the web in such a way that they can be interpreted and connected together. It is thus critical to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We consider here the various techniques developed for that purpose and analyze their commonalities and differences. We propose a general framework and show how the diverse techniques fit in the framework. From this framework we consider the relation between data interlinking and ontology matching. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes, but would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments.


Leveraging Bus Mobility to Enable Communications in Urban Areas

Abstract: This paper shows that the deployment of an opportunistic network on any public transportation system leads to obtain a scalable and efficient urban communication platform. We use the term Bus Switched Networks (BSNs) to indicate this urban backbone that complements the services of 3G networks and enables to meet the application level requirements for a large class of applications by ensuring high delivery ratio and acceptable delays under different conditions of packet load. We sustain these arguments by providing three contributions. The first contribution is a novel and lightweight probabilistic routing protocol for BSN which we prove to be highly effective in satisfying the loose QoS required by urban-wide delay-tolerant information services. The second contribution is the proposal of URBeS, an analysis platform that, given a specific city served by public transportation, produces real bus mobility traces and traffic analysis for any given routing protocol. The last contribution is an extensive benchmark analysis on three real cities which have been selected to explore geo and structural diversity.


Metastability of Logit Dynamics for Coordination Games

Abstract: Logit Dynamics [Blume, Games and Economic Behavior, 1993] are randomized best response dynamics for strategic games: at every time step a player is selected uniformly at random and she chooses a new strategy according to a probability distribution biased toward strategies promising higher payoffs. This process defines an ergodic Markov chain, over the set of strategy profiles of the game, whose unique stationary distribution is the long-term equilibrium concept for the game. However, when the mixing time of the chain is large (e.g., exponential in the number of players), the stationary distribution loses its appeal as equilibrium concept, and the transient phase of the Markov chain becomes important. It can happen that the chain is "metastable", i.e., on a time-scale shorter than the mixing time, it stays close to some probability distribution over the state space, while in a time-scale multiple of the mixing time it jumps from one distribution to another. In this paper we give a quantitative definition of "metastable probability distributions" for a Markov chain and we study the metastability of the logit dynamics for some classes of coordination games. We first consider a pure $n$-player coordination game that highlights the distinctive features of our metastability notion based on distributions. Then, we study coordination games on the clique without a risk-dominant strategy (which are equivalent to the well-known Glauber dynamics for the Curie-Weiss model) and coordination games on a ring (both with and without risk-dominant strategy).


Solving Linear Constraints in Elementary Abelian p-Groups of Symmetries

Abstract: Symmetries occur naturally in CSP or SAT problems and are not very difficult to discover, but using them to prune the search space tends to be very challenging. Indeed, this usually requires finding specific elements in a group of symmetries that can be huge, and the problem of their very existence is NP-hard. We formulate such an existence problem as a constraint problem on one variable (the symmetry to be used) ranging over a group, and try to find restrictions that may be solved in polynomial time. By considering a simple form of constraints (restricted by a cardinality k) and the class of groups that have the structure of Fp-vector spaces, we propose a partial algorithm based on linear algebra. This polynomial algorithm always applies when k=p=2, but may fail otherwise as we prove the problem to be NP-hard for all other values of k and p. Experiments show that this approach though restricted should allow for an efficient use of at least some groups of symmetries. We conclude with a few directions to be explored to efficiently solve this problem on the general case.


Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Abstract: Consumers increasingly rate, review and research products online. Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.


Groupstrategyproofness of the Egalitarian Mechanism for Constrained Rationing Problems

Abstract: The key contribution of the paper is a comprehensive study of the egalitarian mechanism with respect to manipulation by a coalition of agents. Our main result is that the egalitarian mechanism is, in fact, peak group strategyproof : no coalition of agents can (weakly) benefit from jointly misreporting their peaks. Furthermore, we show that the egalitarian mechanism cannot be manipulated by any coalition of suppliers (or any coalition of demanders) in the model where both the suppliers and demanders are agents. Our proofs shed light on the structure of the two models and simpify some of the earlier proofs of strategyproofness in the earlier papers. An implication of our results is that the well known algorithm of Megiddo to compute a lexicographically optimal flow in a network is group strategyproof with respect to the source capacities (or sink capacities).


Consistent Query Answering via ASP from Different Perspectives: Theory and Practice

Abstract: A data integration system provides transparent access to different data sources by suitably combining their data, and providing the user with a unified view of them, called global schema. However, source data are generally not under the control of the data integration process, thus integrated data may violate global integrity constraints even in presence of locally-consistent data sources. In this scenario, it may be anyway interesting to retrieve as much consistent information as possible. The process of answering user queries under global constraint violations is called consistent query answering (CQA). Several notions of CQA have been proposed, e.g., depending on whether integrated information is assumed to be sound, complete, exact or a variant of them. This paper provides a contribution in this setting: it uniforms solutions coming from different perspectives under a common ASP-based core, and provides query-driven optimizations designed for isolating and eliminating inefficiencies of the general approach for computing consistent answers. Moreover, the paper introduces some new theoretical results enriching existing knowledge on decidability and complexity of the considered problems. The effectiveness of the approach is evidenced by experimental results. To appear in Theory and Practice of Logic Programming (TPLP).


Analogy perception applied to seven tests of word comprehension

Abstract: It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are often limited by the need for hand-coded high-level representations as input. An alternative approach is to use high-level perception, in which high-level representations are automatically generated from raw data. Analogy perception is the process of recognizing analogies using high-level perception. We present PairClass, an algorithm for analogy perception that recognizes lexical proportional analogies using representations that are automatically generated from a large corpus of raw textual data. A proportional analogy is an analogy of the form A:B::C:D, meaning "A is to B as C is to D". A lexical proportional analogy is a proportional analogy with words, such as carpenter:wood::mason:stone. PairClass represents the semantic relations between two words using a high-dimensional feature vector, in which the elements are based on frequencies of patterns in the corpus. PairClass recognizes analogies by applying standard supervised machine learning techniques to the feature vectors. We show how seven different tests of word comprehension can be framed as problems of analogy perception and we then apply PairClass to the seven resulting sets of analogy perception problems. We achieve competitive results on all seven tests. This is the first time a uniform approach has handled such a range of tests of word comprehension.


The Divergence of Reinforcement Learning Algorithms with Value-Iteration and Function Approximation

Abstract: This paper gives specific divergence examples of value-iteration for several major Reinforcement Learning and Adaptive Dynamic Programming algorithms, when using a function approximator for the value function. These divergence examples differ from previous divergence examples in the literature, in that they are applicable for a greedy policy, i.e. in a "value iteration" scenario. Perhaps surprisingly, with a greedy policy, it is also possible to get divergence for the algorithms TD(1) and Sarsa(1). In addition to these divergences, we also achieve divergence for the Adaptive Dynamic Programming algorithms HDP, DHP and GDHP.


Constant-time filtering using shiftable kernels

Abstract: It was recently demonstrated in [5] that the non-linear bilateral filter [14] can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [5] can be extended to few other linear and non-linear filters [14, 17, 2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in [5], we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum [15, 6], and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (non-shiftable) Gaussian kernel that is ubiquitously used in image filtering.


Instant e-Teaching Framework Model for Live Online Teaching

Abstract: Instant e-Teaching is a new concept that supplements e-Teaching and e-Learning environment in providing a full and comprehensive modern education styles. The e-Learning technology depicts the concept of enabling self-learning among students on a certain subject using online reference and materials. While the instant e-teaching requires 'face-to-face' characteristic between teacher and student to simultaneously execute actions and gain instant responses. The word instant enhances the e- Teaching with the concept of real time teaching. The challenge to exercise online and instant teaching is not just merely relying on the technologies and system efficiency, but it needs to satisfy the usability and friendliness of the system as to replicate the traditional class environment during the deliveries of the class. For this purpose, an instant e-Teaching framework is been developed that will emulate a dedicated virtual classroom, and primarily designed for synchronous and live sharing of current teaching notes. The model has been demonstrated using a teaching Arabic recitation prototype and evaluated from the professional user profession's perspectives.


Higher Order Programming to Mine Knowledge for a Modern Medical Expert System

Abstract: Knowledge mining is the process of deriving new and useful knowledge from vast volumes of data and background knowledge. Modern healthcare organizations regularly generate huge amount of electronic data stored in the databases. These data are a valuable resource for mining useful knowledge to help medical practitioners making appropriate and accurate decision on the diagnosis and treatment of diseases. In this paper, we propose the design of a novel medical expert system based on a logic-programming framework. The proposed system includes a knowledge-mining component as a repertoire of tools for discovering useful knowledge. The implementation of classification and association mining tools based on the higher order and meta-level programming schemes using Prolog has been presented to express the power of logic-based language. Such language also provides a pattern matching facility, which is an essential function for the development of knowledge-intensive tasks. Besides the major goal of medical decision support, the knowledge discovered by our logic-based knowledge-mining component can also be deployed as background knowledge to pre-treatment data from other sources as well as to guard the data repositories against constraint violation. A framework for knowledge deployment is also presented.


MediaWiki Grammar Recovery

Abstract: The paper describes in detail the recovery effort of one of the official MediaWiki grammars. Over two hundred grammar transformation steps are reported and annotated, leading to delivery of a level 2 grammar, semi-automatically extracted from a community created semi-formal text using at least five different syntactic notations, several non-enforced naming conventions, multiple misspellings, obsolete parsing technology idiosyncrasies and other problems commonly encountered in grammars that were not engineered properly. Having a quality grammar will allow to test and validate it further, without alienating the community with a separately developed grammar.


Correlation Estimation from Compressed Images

Abstract: This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2 to 4 dB.


Introducing Sourcements

Abstract: Sourcing processes are discussed at a high abstraction level. A dedicated terminology is developed concerning general aspects of sourcing. The term sourcement is coined to denote a building block for sourcing. No- tions of allocation, functional architecture and allocational architecture, equilibrium, and configuration are discussed. Limitations of the concept of outsourcing are outlined. This theoretical work is meant to serve as a point of departure for the subsequent development of a detailed theory of sourcing and sourcing transformations, which can be a tool for dealing with practical applica- tions.


Fence - An Efficient Parser with Ambiguity Support for Model-Driven Language Specification

Abstract: Model-based language specification has applications in the implementation of language processors, the design of domain-specific languages, model-driven software development, data integration, text mining, natural language processing, and corpus-based induction of models. Model-based language specification decouples language design from language processing and, unlike traditional grammar-driven approaches, which constrain language designers to specific kinds of grammars, it needs general parser generators able to deal with ambiguities. In this paper, we propose Fence, an efficient bottom-up parsing algorithm with lexical and syntactic ambiguity support that enables the use of model-based language specification in practice.


Finding All Allowed Edges in a Bipartite Graph

Abstract: We consider the problem of finding all allowed edges in a bipartite graph $G=(V,E)$, i.e., all edges that are included in some maximum matching. We show that given any maximum matching in the graph, it is possible to perform this computation in linear time $O(n+m)$ (where $n=|V|$ and $m=|E|$). Hence, the time complexity of finding all allowed edges reduces to that of finding a single maximum matching, which is $O(n^{1/2}m)$ [Hopcroft and Karp 1973], or $O((n/\log n)^{1/2}m)$ for dense graphs with $m=\Theta(n^2)$ [Alt et al. 1991]. This time complexity improves upon that of the best known algorithms for the problem, which is $O(nm)$ ([Costa 1994] for bipartite graphs, and [Carvalho and Cheriyan 2005] for general graphs). Other algorithms for solving that problem are randomized algorithms due to [Rabin and Vazirani 1989] and [Cheriyan 1997], the runtime of which is $\tilde{O}(n^{2.376})$. Our algorithm, apart from being deterministic, improves upon that time complexity for bipartite graphs when $m=O(n^r)$ and $r<1.876$. In addition, our algorithm is elementary, conceptually simple, and easy to implement.


Mechanism Design and Risk Aversion

Abstract: We develop efficient algorithms to construct utility maximizing mechanisms in the presence of risk averse players (buyers and sellers) in Bayesian settings. We model risk aversion by a concave utility function, and players play strategically to maximize their expected utility. Bayesian mechanism design has usually focused on maximizing expected revenue in a {\em risk neutral} environment, and no succinct characterization of expected utility maximizing mechanisms is known even for single-parameter multi-unit auctions. We first consider the problem of designing optimal DSIC mechanism for a risk averse seller in the case of multi-unit auctions, and we give a poly-time computable SPM that is $(1-1/e-\eps)$-approximation to the expected utility of the seller in an optimal DSIC mechanism. Our result is based on a novel application of a correlation gap bound, along with {\em splitting} and {\em merging} of random variables to redistribute probability mass across buyers. This allows us to reduce our problem to that of checking feasibility of a small number of distinct configurations, each of which corresponds to a covering LP. A feasible solution to the LP gives us the distribution on prices for each buyer to use in a randomized SPM. We next consider the setting when buyers as well as the seller are risk averse, and the objective is to maximize the seller's expected utility. We design a truthful-in-expectation mechanism whose utility is a $(1-1/e -\eps)^3$-approximation to the optimal BIC mechanism under two mild assumptions. Our mechanism consists of multiple rounds that processes each buyer in a round with small probability. Lastly, we consider the problem of revenue maximization for a risk neutral seller in presence of risk averse buyers, and give a poly-time algorithm to design an optimal mechanism for the seller.


A Semantic Relatedness Measure Based on Combined Encyclopedic, Ontological and Collocational Knowledge

Abstract: We describe a new semantic relatedness measure combining the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. Our measure achieves the currently highest results on the WS-353 test: a Spearman rho coefficient of 0.79 (vs. 0.75 in (Gabrilovich and Markovitch, 2007)) when applying the measure directly, and a value of 0.87 (vs. 0.78 in (Agirre et al., 2009)) when using the prediction of a polynomial SVM classifier trained on our measure. In the appendix we discuss the adaptation of ESA to 2011 Wikipedia data, as well as various unsuccessful attempts to enhance ESA by filtering at word, sentence, and section level.


Parallel Backtracking with Answer Memoing for Independent And-Parallelism

Abstract: Goal-level Independent and-parallelism (IAP) is exploited by scheduling for simultaneous execution two or more goals which will not interfere with each other at run time. This can be done safely even if such goals can produce multiple answers. The most successful IAP implementations to date have used recomputation of answers and sequentially ordered backtracking. While in principle simplifying the implementation, recomputation can be very inefficient if the granularity of the parallel goals is large enough and they produce several answers, while sequentially ordered backtracking limits parallelism. And, despite the expected simplification, the implementation of the classic schemes has proved to involve complex engineering, with the consequent difficulty for system maintenance and extension, while still frequently running into the well-known trapped goal and garbage slot problems. This work presents an alternative parallel backtracking model for IAP and its implementation. The model features parallel out-of-order (i.e., non-chronological) backtracking and relies on answer memoization to reuse and combine answers. We show that this approach can bring significant performance advantages. Also, it can bring some simplification to the important engineering task involved in implementing the backtracking mechanism of previous approaches.


Design of Arabic Diacritical Marks

Abstract: Diacritical marks play a crucial role in meeting the criteria of usability of typographic text, such as: homogeneity, clarity and legibility. To change the diacritic of a letter in a word could completely change its semantic. The situation is very complicated with multilingual text. Indeed, the problem of design becomes more difficult by the presence of diacritics that come from various scripts; they are used for different purposes, and are controlled by various typographic rules. It is quite challenging to adapt rules from one script to another. This paper aims to study the placement and sizing of diacritical marks in Arabic script, with a comparison with the Latin's case. The Arabic script is cursive and runs from right-to-left; its criteria and rules are quite distinct from those of the Latin script. In the beginning, we compare the difficulty of processing diacritics in both scripts. After, we will study the limits of Latin resolution strategies when applied to Arabic. At the end, we propose an approach to resolve the problem for positioning and resizing diacritics. This strategy includes creating an Arabic font, designed in OpenType format, along with suitable justification in TEX.


The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty

Abstract: Many real world domains require the representation of a measure of uncertainty. The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of Probabilistic Logic Programming (PLP), leading to languages such as the Independent Choice Logic, Logic Programs with Annotated Disjunctions (LPADs), Problog, PRISM and others. These languages share a similar distribution semantics, and methods have been devised to translate programs between these languages. The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive. As one alternative, the PRISM system reduces the complexity of query answering by restricting the form of programs it can evaluate. As an entirely different alternative, Possibilistic Logic Programs adopt a simpler metric of uncertainty than probability. Each of these approaches -- general PLP, restricted PLP, and Possibilistic Logic Programming -- can be useful in different domains depending on the form of uncertainty to be represented, on the form of programs needed to model problems, and on the scale of the problems to be solved. In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and Possibilistic Logic Programs. PITA relies on tabling with answer subsumption and consists of a transformation along with an API for library functions that interface with answer subsumption.