The Wavelet Trie: Maintaining an Indexed Sequence of Strings in Compressed Space

Abstract: An indexed sequence of strings is a data structure for storing a string sequence that supports random access, searching, range counting and analytics operations, both for exact matches and prefix search. String sequences lie at the core of column-oriented databases, log processing, and other storage and query tasks. In these applications each string can appear several times and the order of the strings in the sequence is relevant. The prefix structure of the strings is relevant as well: common prefixes are sought in strings to extract interesting features from the sequence. Moreover, space-efficiency is highly desirable as it translates directly into higher performance, since more data can fit in fast memory. We introduce and study the problem of compressed indexed sequence of strings, representing indexed sequences of strings in nearly-optimal compressed space, both in the static and dynamic settings, while preserving provably good performance for the supported operations. We present a new data structure for this problem, the Wavelet Trie, which combines the classical Patricia Trie with the Wavelet Tree, a succinct data structure for storing a compressed sequence. The resulting Wavelet Trie smoothly adapts to a sequence of strings that changes over time. It improves on the state-of-the-art compressed data structures by supporting a dynamic alphabet (i.e. the set of distinct strings) and prefix queries, both crucial requirements in the aforementioned applications, and on traditional indexes by reducing space occupancy to close to the entropy of the sequence.


Markerless Motion Capture in the Crowd

Abstract: This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.


Visualizing Collective Discursive User Interactions in Online Life Science Communities

Abstract: This paper highlights the rationale for the development of BioViz, a tool to help visualize the existence of collective user interactions in online life science communities. The first community studied has approximately 22,750 unique users and the second has 35,000. Making sense of the number of interactions between actors in these networks in order to discern patterns of collective organization and intelligent behavior is challenging. One of the complications is that forums - our object of interest - can vary in their purpose and remit (e.g. the role of gender in the life sciences to forums of praxis such as one exploring the cell line culturing) and this shapes the structure of the forum organization itself. Our approach took a random sample of 53 forums which were manually analyzed by our research team and interactions between actors were recorded as arcs between nodes. The paper focuses on a discussion of the utility of our approach, but presents some brief results to highlight the forms of knowledge that can be gained in identifying collective group formations. Specifically, we found that by using a matrix-based visualization approach, we were able to see patterns of collective behavior which we believe is valuable both to the study of collective intelligence and the design of virtual organizations.


Learning to Predict the Wisdom of Crowds

Abstract: The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, "CrowdSense," that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers' votes that approximates the crowd's opinion.


Large-Scale Automatic Labeling of Video Events with Verbs Based on Event-Participant Interaction

Abstract: We present an approach to labeling short video clips with English verbs as event descriptions. A key distinguishing aspect of this work is that it labels videos with verbs that describe the spatiotemporal interaction between event participants, humans and objects interacting with each other, abstracting away all object-class information and fine-grained image characteristics, and relying solely on the coarse-grained motion of the event participants. We apply our approach to a large set of 22 distinct verb classes and a corpus of 2,584 videos, yielding two surprising outcomes. First, a classification accuracy of greater than 70% on a 1-out-of-22 labeling task and greater than 85% on a variety of 1-out-of-10 subsets of this labeling task is independent of the choice of which of two different time-series classifiers we employ. Second, we achieve this level of accuracy using a highly impoverished intermediate representation consisting solely of the bounding boxes of one or two event participants as a function of time. This indicates that successful event recognition depends more on the choice of appropriate features that characterize the linguistic invariants of the event classes than on the particular classifier algorithms.


Compensating Interpolation Distortion by Using New Optimized Modular Method

Abstract: A modular method was suggested before to recover a band limited signal from the sample and hold and linearly interpolated (or, in general, an nth-order-hold) version of the regular samples. In this paper a novel approach for compensating the distortion of any interpolation based on modular method has been proposed. In this method the performance of the modular method is optimized by adding only some simply calculated coefficients. This approach causes drastic improvement in terms of signal-to-noise ratios with fewer modules compared to the classical modular method. Simulation results clearly confirm the improvement of the proposed method and also its superior robustness against additive noise.


Bayesian Data Cleaning for Web Data

Abstract: Data Cleaning is a long standing problem, which is growing in importance with the mass of uncurated web data. State of the art approaches for handling inconsistent data are systems that learn and use conditional functional dependencies (CFDs) to rectify data. These methods learn data patterns--CFDs--from a clean sample of the data and use them to rectify the dirty/inconsistent data. While getting a clean training sample is feasible in enterprise data scenarios, it is infeasible in web databases where there is no separate curated data. CFD based methods are unfortunately particularly sensitive to noise; we will empirically demonstrate that the number of CFDs learned falls quite drastically with even a small amount of noise. In order to overcome this limitation, we propose a fully probabilistic framework for cleaning data. Our approach involves learning both the generative and error (corruption) models of the data and using them to clean the data. For generative models, we learn Bayes networks from the data. For error models, we consider a maximum entropy framework for combing multiple error processes. The generative and error models are learned directly from the noisy data. We present the details of the framework and demonstrate its effectiveness in rectifying web data.


Improving Customer Service in Healthcare with CRM 2.0

Abstract: The Healthcare industry is undergoing a paradigm shift from healthcare institution-centred care to a citizen-centred care that emphasises on continuity of care from prevention to rehabilitation. The recent development of Information and Communication Technology (ICT), especially the Internet and its related technologies has become the main driver of the paradigm shift. Managing relationship with customers (patients) is becoming more important in the new paradigm. The paper discusses Customer Relationship Management (CRM) in healthcare and proposes a Social CRM or CRM 2.0 model to take advantage of the multi-way relationships created by Web 2.0 and its widespread use in improving customer services for mutual benefits between healthcare providers and their customers.


Evaluating CRM Implementation in Healthcare Organization

Abstract: Recently, many healthcare organizations are adopting CRM as a strategy, which involves using technology to organize, automate, and coordinate business processes, in managing interactions with their patients. CRM with the Web technology provides healthcare providers the ability to broaden their services beyond usual practices, and thus offers suitable environment using latest technology to achieve superb patient care. This paper discusses and demonstrates how a new approach in CRM based on Web 2.0 will help the healthcare providers improving their customer support, avoiding conflict, and promoting better health to patient. With this new approach patients will benefit from the customized personal service with full information access to perform self managed their own health. It also helps healthcare providers retaining the right customer. A conceptual framework of the new approach will be discussed.


E-Health Initiative and Customer's Expectation: Case Brunei

Abstract: This paper is to determine the dimension of e-health services in Brunei Darussalam (Brunei) from customers' perspective. It is to identify, understand, analyze and evaluate public's expectation on e-health in Brunei. A questionnaire was designed to gather quantitative and qualitative data to survey patients, patient's family, and health practitioners at hospitals, clinics, or home care centers in Brunei starting from February to March, 2011. A 25-item Likert-type survey instrument was specifically developed for this study and administered to a sample of 366 patients. The data were analyzed to provide initial ideas and recommendation to policy makers on how to move forward with the e-health initiative as a mean to improve healthcare services. The survey revealed that there exists a high demand and expectation from people in Brunei to have better healthcare services accessible through an e-health system in order to improve health literacy as well as quality and efficiency of healthcare. Regardless of the limitations of the survey, the general public has responded with a great support for the capabilities of an e-health system listed from the questionnaires. The results of the survey provide a solid foundation for our on going research project to proceed further to develop a model of e-health and subsequently develop a system prototype that incorporate expectations from the people.


Edited Volumes, Monographs, and Book Chapters in the Book Citation Index (BKCI) and Science Citation Index (SCI, SoSCI, A&HCI)

Abstract: In 2011, Thomson-Reuters introduced the Book Citation Index (BKCI) as part of the Science Citation Index (SCI). The interface of the Web of Science version 5 enables users to search for both "Books" and "Book Chapters" as new categories. Books and book chapters, however, were always among the cited references, and book chapters have been included in the database since 2005. We explore the two categories with both BKCI and SCI, and in the sister social sciences (SoSCI) and the arts & humanities (A&HCI) databases. Book chapters in edited volumes can be highly cited. Books contain many citing references but are relatively less cited. This may find its origin in the slower circulation of books than of journal articles. It is possible to distinguish between monographs and edited volumes among the "Books" scientometrically. Monographs may be underrated in terms of citation impact or overrated using publication performance indicators because individual chapters are counted as contributions separately in terms of articles, reviews, and/or book chapters.


Who is Authoritative? Understanding Reputation Mechanisms in Quora

Abstract: As social Q&A sites gain popularity, it is important to understand how users judge the authoritativeness of users and content, build reputation, and identify and promote high quality content. We conducted a study of emerging social Q&A site Quora. First, we describe user activity on Quora by analyzing data across 60 question topics and 3917 users. Then we provide a rich understanding of issues of authority, reputation, and quality from in-depth interviews with ten Quora users. Our results show that primary sources of information on Quora are judged authoritative. Also, users judge the reputation of other users based on their past contributions. Social voting helps users identify and promote good content but is prone to preferential attachment. Combining social voting with sophisticated algorithms for ranking content might enable users to better judge others' reputation and promote high quality content.


Proceedings of the first International Workshop On Open Data, WOD-2012

Abstract: WOD-2012 aims at facilitating new trends and ideas from a broad range of topics concerned within the widely-spread Open Data movement, from the viewpoint of computer science research. While being most commonly known from the recent Linked Open Data movement, the concept of publishing data explicitly as Open Data has meanwhile developed many variants and facets that go beyond publishing large and highly structured RDF/S repositories. Open Data comprises text and semi-structured data, but also open multi-modal contents, including music, images, and videos. With the increasing amount of data that is published by governments (see, e.g., data.gov, data.gov.uk or data.gouv.fr), by international organizations (data.worldbank.org or data.undp.org) and by scientific communities (tdar.org, cds.u-strasbg.fr, GenBank, IRIS or KNB) explicitly under an Open Data policy, new challenges arise not only due to the scale at which this data becomes available. A number of community-based conferences accommodate tracks or workshops which are dedicated to Open Data. However, WOD aims to be a premier venue to gather researchers and practitioners who are contributing to and interested in the emerging field of managing Open Data from a computer science perspective. Hence, it is a unique opportunity to find in a single place up-to-date scientific works on Web-scale Open Data issues that have so far only partially been addressed by different research communities such as Databases, Data Mining and Knowledge Management, Distributed Systems, Data Privacy, and Data Visualization.


Towards Real-Time Summarization of Scheduled Events from Twitter Streams

Abstract: This paper explores the real-time summarization of scheduled events such as soccer games from torrential flows of Twitter streams. We propose and evaluate an approach that substantially shrinks the stream of tweets in real-time, and consists of two steps: (i) sub-event detection, which determines if something new has occurred, and (ii) tweet selection, which picks a representative tweet to describe each sub-event. We compare the summaries generated in three languages for all the soccer games in "Copa America 2011" to reference live reports offered by Yahoo! Sports journalists. We show that simple text analysis methods which do not involve external knowledge lead to summaries that cover 84% of the sub-events on average, and 100% of key types of sub-events (such as goals in soccer). Our approach should be straightforwardly applicable to other kinds of scheduled events such as other sports, award ceremonies, keynote talks, TV shows, etc.


Computational linear algebra over finite fields

Abstract: We present here algorithms for efficient computation of linear algebra problems over finite fields.


Matrix Formula of Differential Resultant for First Order Generic Ordinary Differential Polynomials

Abstract: In this paper, a matrix representation for the differential resultant of two generic ordinary differential polynomials $f_1$ and $f_2$ in the differential indeterminate $y$ with order one and arbitrary degree is given. That is, a non-singular matrix is constructed such that its determinant contains the differential resultant as a factor. Furthermore, the algebraic sparse resultant of $f_1, f_2, \delta f_1, \delta f_2$ treated as polynomials in $y, y', y"$ is shown to be a non-zero multiple of the differential resultant of $f_1, f_2$. Although very special, this seems to be the first matrix representation for a class of nonlinear generic differential polynomials.


Indus script corpora, archaeo-metallurgy and Meluhha (Mleccha)

Abstract: Jules Bloch's work on formation of the Marathi language has to be expanded further to provide for a study of evolution and formation of Indian languages in the Indian language union (sprachbund). The paper analyses the stages in the evolution of early writing systems which began with the evolution of counting in the ancient Near East. A stage anterior to the stage of syllabic representation of sounds of a language, is identified. Unique geometric shapes required for tokens to categorize objects became too large to handle to abstract hundreds of categories of goods and metallurgical processes during the production of bronze-age goods. About 3500 BCE, Indus script as a writing system was developed to use hieroglyphs to represent the 'spoken words' identifying each of the goods and processes. A rebus method of representing similar sounding words of the lingua franca of the artisans was used in Indus script. This method is recognized and consistently applied for the lingua franca of the Indian sprachbund. That the ancient languages of India, constituted a sprachbund (or language union) is now recognized by many linguists. The sprachbund area is proximate to the area where most of the Indus script inscriptions were discovered, as documented in the corpora. That hundreds of Indian hieroglyphs continued to be used in metallurgy is evidenced by their use on early punch-marked coins. This explains the combined use of syllabic scripts such as Brahmi and Kharoshti together with the hieroglyphs on Rampurva copper bolt, and Sohgaura copper plate from about 6th century BCE.Indian hieroglyphs constitute a writing system for meluhha language and are rebus representations of archaeo-metallurgy lexemes. The rebus principle was employed by the early scripts and can legitimately be used to decipher the Indus script, after secure pictorial identification.


Distance Optimal Formation Control on Graphs with a Tight Convergence Time Guarantee

Abstract: For the task of moving a set of indistinguishable agents on a connected graph with unit edge distance to an arbitrary set of goal vertices, free of collisions, we propose a fast distance optimal control algorithm that guides the agents into the desired formation. Moreover, we show that the algorithm also provides a tight convergence time guarantee (time optimality and distance optimality cannot be simultaneously satisfied). Our generic graph formulation allows the algorithm to be applied to scenarios such as grids with holes (modeling obstacles) in arbitrary dimensions. Simulations, available online, confirm our theoretical developments.


Planning Optimal Paths for Multiple Robots on Graphs

Abstract: In this paper, we study the problem of optimal multi-robot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that computes minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resulting algorithms from these ILP models are complete and guaranteed to yield true optimal solutions. In addition, our flexible framework can easily accommodate other variants of the MPP problem. Focusing on the time optimal algorithm, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. Computational results confirm the effectiveness of our method.


A lightweight dynamic pseudonym identity based authentication and key agreement protocol without verification tables for multi-server architecture

Abstract: Traditional password based authentication schemes are mostly considered in single server environments. They are unfitted for the multi-server environments from two aspects. On the one hand, users need to register in each server and to store large sets of data, including identities and passwords. On the other hand, servers are required to store a verification table containing user identities and passwords. Recently, On the base on Sood et al.'s protocol(2011), Li et al. proposed an improved dynamic identity based authentication and key agreement protocol for multi-server architecture(2012). Li et al. claims that the proposed scheme can make up the security weaknesses of Sood et al.'s protocol. Unfortunately, our further research shows that Li et al.'s protocol contains several drawbacks and can not resist some types of known attacks, such as replay attack, Deny-of-Service attack, internal attack, eavesdropping attack, masquerade attack, and so on. In this paper, we further propose a light dynamic pseudonym identity based authentication and key agreement protocol for multi-server architecture. In our scheme, service providing servers don't need to maintain verification tables for users. The proposed protocol provides not only the declared security features in Li et al.'s paper, but also some other security features, such as traceability and identity protection.


On how percolation threshold affects PSO performance

Abstract: Statistical evidence of the influence of neighborhood topology on the performance of particle swarm optimization (PSO) algorithms has been shown in many works. However, little has been done about the implications could have the percolation threshold in determining the topology of this neighborhood. This work addresses this problem for individuals that, like robots, are able to sense in a limited neighborhood around them. Based on the concept of percolation threshold, and more precisely, the disk percolation model in 2D, we show that better results are obtained for low values of radius, when individuals occasionally ask others their best visited positions, with the consequent decrease of computational complexity. On the other hand, since percolation threshold is a universal measure, it could have a great interest to compare the performance of different hybrid PSO algorithms.


Simple Agents Learn to Find Their Way: An Introduction on Mapping Polygons

Abstract: This paper gives an introduction to the problem of mapping simple polygons with autonomous agents. We focus on minimalistic agents that move from vertex to vertex along straight lines inside a polygon, using their sensors to gather local observations at each vertex. Our attention revolves around the question whether a given configuration of sensors and movement capabilities of the agents allows them to capture enough data in order to draw conclusions regarding the global layout of the polygon. In particular, we study the problem of reconstructing the visibility graph of a simple polygon by an agent moving either inside or on the boundary of the polygon. Our aim is to provide insight about the algorithmic challenges faced by an agent trying to map a polygon. We present an overview of techniques for solving this problem with agents that are equipped with simple sensorial capabilities. We illustrate these techniques on examples with sensors that mea- sure angles between lines of sight or identify the previous location. We give an overview over related problems in combinatorial geometry as well as graph exploration.


Macroscopes: models for collective decision making

Abstract: We introduce a new model of collective decision making, when a global decision needs to be made but the parties only possess partial information, and are unwilling (or unable) to first create a globalcomposite of their local views. Our macroscope model captures two key features of many real-world problems: allotment structure (how access to local information is apportioned between parties, including overlaps between the parties) and the possible presence of meta-information (what each party knows about the allotment structure of the overall problem). Using the framework of communication complexity, we formalize the efficient solution of a macroscope. We present general results about the macroscope model, and also results that abstract the essential computational operations underpinning practical applications, including in financial markets and decentralized sensor networks. We illustrate the computational problem inherent in real-world collective decision making processes using results for specific functions, involving detecting a change in state (constant and step functions), and computing statistical properties (the mean).


Overview of MC CDMA PAPR Reduction Techniques

Abstract: High Peak to Average Power Ratio (PAPR) of the transmitted signal is a critical problem in multicarrier modulation systems (MCM) such as Orthogonal Frequency Division Multiplexing (OFDM), and Multi-Carrier Code Division Multiple Access (MC CDMA) systems, due to large number of subcarriers. High PAPR leads to reduced resolution, and battery life. It also deteriorates system performance. This paper focuses on review of different PAPR reduction techniques with attendant technical issues as well as criteria for selection of PAPR reduction technique. To reduce PAPR the constraints are low power consumption, and low Bit Error Rate (BER). Spectral bandwidth is improved by better spectral characteristics, and low complexity/cost.


Stimulus and correlation matching measurement technique in computer based characterization testing

Abstract: Constructive theory of characterization test is considered. The theory is applicable to a nano devices characterization: current-voltage, Auger current dependence. Generally small response of device under test on an applied stimulus is masked by an unknown deterministic background and a random noise. Characterization test in this signal corruption scenario should be based on correlation measurement technique of device response on applied optimal stimulus with optimal reference signal. Co-synthesis solution of stimulus and reference signal is proposed.


Collective Creativity: Where we are and where we might go

Abstract: Creativity is individual, and it is social. The social aspects of creativity have become of increasing interest as systems have emerged that mobilize large numbers of people to engage in creative tasks. We examine research related to collective intelligence and differentiate work on collective creativity from other collective activities by analyzing systems with respect to the tasks that are performed and the outputs that result. Three types of systems are discussed: games, contests and networks. We conclude by suggesting how systems that generate collective creativity can be improved and how new systems might be constructed.


Eliminating the Weakest Link: Making Manipulation Intractable?

Abstract: Successive elimination of candidates is often a route to making manipulation intractable to compute. We prove that eliminating candidates does not necessarily increase the computational complexity of manipulation. However, for many voting rules used in practice, the computational complexity increases. For example, it is already known that it is NP-hard to compute how a single voter can manipulate the result of single transferable voting (the elimination version of plurality voting). We show here that it is NP-hard to compute how a single voter can manipulate the result of the elimination version of veto voting, of the closely related Coombs' rule, and of the elimination versions of a general class of scoring rules.


Low-Complexity Energy-Efficient Broadcasting in One-Dimensional Wireless Networks

Abstract: In this paper, we investigate the transmission range assignment for N wireless nodes located on a line (a linear wireless network) for broadcasting data from one specific node to all the nodes in the network with minimum energy. Our goal is to find a solution that has low complexity and yet performs close to optimal. We propose an algorithm for finding the optimal assignment (which results in the minimum energy consumption) with complexity O(N^2). An approximation algorithm with complexity O(N) is also proposed. It is shown that, for networks with uniformly distributed nodes, the linear-time approximate solution obtained by this algorithm on average performs practically identical to the optimal assignment. Both the optimal and the suboptimal algorithms require the full knowledge of the network topology and are thus centralized. We also propose a distributed algorithm of negligible complexity, i.e., with complexity O(1), which only requires the knowledge of the adjacent neighbors at each wireless node. Our simulations demonstrate that the distributed solution on average performs almost as good as the optimal one for networks with uniformly distributed nodes.


Analysis of Twitter Traffic based on Renewal Densities

Abstract: In this paper we propose a novel approach for Twitter traffic analysis based on renewal theory. Even though twitter datasets are of increasing interest to researchers, extracting information from message timing remains somewhat unexplored. Our approach, extending our prior work on anomaly detection, makes it possible to characterize levels of correlation within a message stream, thus assessing how much interaction there is between those posting messages. Moreover, our method enables us to detect the presence of periodic traffic, which is useful to determine whether there is spam in the message stream. Because our proposed techniques only make use of timing information and are amenable to downsampling, they can be used as low complexity tools for data analysis.


Convolutional Neural Networks Applied to House Numbers Digit Classification

Abstract: We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.


Approximately Optimal Auctions for Selling Privacy when Costs are Correlated with Data

Abstract: We consider a scenario in which a database stores sensitive data of users and an analyst wants to estimate statistics of the data. The users may suffer a cost when their data are used in which case they should be compensated. The analyst wishes to get an accurate estimate, while the users want to maximize their utility. We want to design a mechanism that can estimate statistics accurately without compromising users' privacy. Since users' costs and sensitive data may be correlated, it is important to protect the privacy of both data and cost. We model this correlation by assuming that a user's unknown sensitive data determines a distribution from a set of publicly known distributions and a user's cost is drawn from that distribution. We propose a stronger model of privacy preserving mechanism where users are compensated whenever they reveal information about their data to the mechanism. In this model, we design a Bayesian incentive compatible and privacy preserving mechanism that guarantees accuracy and protects the privacy of both cost and data.


Asynchrony and Collusion in the N-party BAR Transfer Problem

Abstract: The problem of reliably transferring data from a set of $N_P$ producers to a set of $N_C$ consumers in the BAR model, named N-party BAR Transfer (NBART), is an important building block for volunteer computing systems. An algorithm to solve this problem in synchronous systems, which provides a Nash equilibrium, has been presented in previous work. In this paper, we propose an NBART algorithm for asynchronous systems. Furthermore, we also address the possibility of collusion among the Rational processes. Our game theoretic analysis shows that the proposed algorithm tolerates certain degree of arbitrary collusion, while still fulfilling the NBART properties.


Access Graphs Results for LRU versus FIFO under Relative Worst Order Analysis

Abstract: Access graphs, which have been used previously in connection with competitive analysis to model locality of reference in paging, are considered in connection with relative worst order analysis. In this model, FWF is shown to be strictly worse than both LRU and FIFO on any access graph. LRU is shown to be strictly better than FIFO on paths and cycles, but they are incomparable on some families of graphs which grow with the length of the sequences.


Solution Representations and Local Search for the bi-objective Inventory Routing Problem

Abstract: The solution of the biobjective IRP is rather challenging, even for metaheuristics. We are still lacking a profound understanding of appropriate solution representations and effective neighborhood structures. Clearly, both the delivery volumes and the routing aspects of the alternatives need to be reflected in an encoding, and must be modified when searching by means of local search. Our work contributes to the better understanding of such solution representations. On the basis of an experimental investigation, the advantages and drawbacks of two encodings are studied and compared.


On probabilities of Risk type board game combats

Abstract: Risk is a well-known turn based board game where the primary objective is nothing less than the world domination. Gameplay is based on battles between armies located in adjacent territories on the map of Earth. The combat's outcome is decided by rolling dice, and therefore a probabilistic approach can be taken. Although several results are derived, the conclusions suggest that the gameplay is highly depending on luck.


Hybrid MPI/StarSs - a case study

Abstract: Hybrid parallel programming models combining distributed and shared memory paradigms are well established in high-performance computing. The classical prototype of hybrid programming in HPC is MPI/OpenMP, but many other combinations are being investigated. Recently, the data-dependency driven, task parallel model for shared memory parallelisation named StarSs has been suggested for usage in combination with MPI. In this paper we apply hybrid MPI/StarSs to a Lattice-Boltzmann code. In particular, we present the hybrid programming model, the benefits we expect, the challenges in porting, and finally a comparison of the performance of MPI/StarSs hybrid, MPI/OpenMP hybrid and the original MPI-only versions of the same code.


Conception of a management tool of Technology Enhanced Learning Environments

Abstract: This paper describes the process of the conception of a software tool of TELE management. The proposed management tool combines information from two sources: i) the automatic reports produced by the Learning Content Management System (LCMS) Blackboard and ii) the views of students and teachers on the use of the LCMS in the process of teaching and learning. The results show that the architecture of the proposed management tool has the features of a management tool, since its potential to control, to reset and to enhance the use of an LCMS in the process of teaching and learning and teacher training, is shown.


Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records

Abstract: RxNorm was utilized as the basis for direct-capture of medication history data in a live EHR system deployed in a large, multi-state outpatient behavioral healthcare provider in the United States serving over 75,000 distinct patients each year across 130 clinical locations. This tool incorporated auto-complete search functionality for medications and proper dosage identification assistance. The overarching goal was to understand if and how standardized terminologies like RxNorm can be used to support practical computing applications in live EHR systems. We describe the stages of implementation, approaches used to adapt RxNorm's data structure for the intended EHR application, and the challenges faced. We evaluate the implementation using a four-factor framework addressing flexibility, speed, data integrity, and medication coverage. RxNorm proved to be functional for the intended application, given appropriate adaptations to address high-speed input/output (I/O) requirements of a live EHR and the flexibility required for data entry in multiple potential clinical scenarios. Future research around search optimization for medication entry, user profiling, and linking RxNorm to drug classification schemes holds great potential for improving the user experience and utility of medication data in EHRs.


Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes

Abstract: We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel conditions after being physically instantiated by a 3D printer. That is, unlike other approaches such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. However, the representation used significantly restricted the range of morphologies explored. In this paper, we present initial explorations into the use of a simple generative encoding, known as Gielis superformula, that produces a highly flexible 3D shape representation to design VAWT. First, the target-based evolution of 3D artefacts is investigated and subsequently initial design experiments are performed wherein each VAWT candidate is physically instantiated and evaluated under approximated wind tunnel conditions. It is shown possible to produce very closely matching designs of a number of 3D objects through the evolution of supershapes produced by Gielis superformula. Moreover, it is shown possible to use artificial physical evolution to identify novel and increasingly efficient supershape VAWT designs.


Resource Buying Games

Abstract: In resource buying games a set of players jointly buys a subset of a finite resource set E (e.g., machines, edges, or nodes in a digraph). The cost of a resource e depends on the number (or load) of players using e, and has to be paid completely by the players before it becomes available. Each player i needs at least one set of a predefined family S_i in 2^E to be available. Thus, resource buying games can be seen as a variant of congestion games in which the load-dependent costs of the resources can be shared arbitrarily among the players. A strategy of player i in resource buying games is a tuple consisting of one of i's desired configurations S_i together with a payment vector p_i in R^E_+ indicating how much i is willing to contribute towards the purchase of the chosen resources. In this paper, we study the existence and computational complexity of pure Nash equilibria (PNE, for short) of resource buying games. In contrast to classical congestion games for which equilibria are guaranteed to exist, the existence of equilibria in resource buying games strongly depends on the underlying structure of the S_i's and the behavior of the cost functions. We show that for marginally non-increasing cost functions, matroids are exactly the right structure to consider, and that resource buying games with marginally non-decreasing cost functions always admit a PNE.


Learning From An Optimization Viewpoint

Abstract: In this dissertation we study statistical and online learning problems from an optimization viewpoint.The dissertation is divided into two parts : I. We first consider the question of learnability for statistical learning problems in the general learning setting. The question of learnability is well studied and fully characterized for binary classification and for real valued supervised learning problems using the theory of uniform convergence. However we show that for the general learning setting uniform convergence theory fails to characterize learnability. To fill this void we use stability of learning algorithms to fully characterize statistical learnability in the general setting. Next we consider the problem of online learning. Unlike the statistical learning framework there is a dearth of generic tools that can be used to establish learnability and rates for online learning problems in general. We provide online analogs to classical tools from statistical learning theory like Rademacher complexity, covering numbers, etc. We further use these tools to fully characterize learnability for online supervised learning problems. II. In the second part, for general classes of convex learning problems, we provide appropriate mirror descent (MD) updates for online and statistical learning of these problems. Further, we show that the the MD is near optimal for online convex learning and for most cases, is also near optimal for statistical convex learning. We next consider the problem of convex optimization and show that oracle complexity can be lower bounded by the so called fat-shattering dimension of the associated linear class. Thus we establish a strong connection between offline convex optimization problems and statistical learning problems. We also show that for a large class of high dimensional optimization problems, MD is in fact near optimal even for convex optimization.


Connectivity Oracles for Planar Graphs

Abstract: We consider dynamic subgraph connectivity problems for planar graphs. In this model there is a fixed underlying planar graph, where each edge and vertex is either "off" (failed) or "on" (recovered). We wish to answer connectivity queries with respect to the "on" subgraph. The model has two natural variants, one in which there are $d$ edge/vertex failures that precede all connectivity queries, and one in which failures/recoveries and queries are intermixed. We present a $d$-failure connectivity oracle for planar graphs that processes any $d$ edge/vertex failures in $sort(d,n)$ time so that connectivity queries can be answered in $pred(d,n)$ time. (Here $sort$ and $pred$ are the time for integer sorting and integer predecessor search over a subset of $[n]$ of size $d$.) Our algorithm has two discrete parts. The first is an algorithm tailored to triconnected planar graphs. It makes use of Barnette's theorem, which states that every triconnected planar graph contains a degree-3 spanning tree. The second part is a generic reduction from general (planar) graphs to triconnected (planar) graphs. Our algorithm is, moreover, provably optimal. An implication of Patrascu and Thorup's lower bound on predecessor search is that no $d$-failure connectivity oracle (even on trees) can beat $pred(d,n)$ query time. We extend our algorithms to the subgraph connectivity model where edge/vertex failures (but no recoveries) are intermixed with connectivity queries. In triconnected planar graphs each failure and query is handled in $O(\log n)$ time (amortized), whereas in general planar graphs both bounds become $O(\log^2 n)$.


Deterministic Function Computation with Chemical Reaction Networks

Abstract: Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. CRNs are widely used to describe information processing occurring in natural cellular regulatory networks, and with upcoming advances in synthetic biology, CRNs are a promising language for the design of artificial molecular control circuitry. Nonetheless, despite the widespread use of CRNs in the natural sciences, the range of computational behaviors exhibited by CRNs is not well understood. CRNs have been shown to be efficiently Turing-universal when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates. We introduce the notion of function, rather than predicate, computation by representing the output of a function f:N^k --> N^l by a count of some molecular species, i.e., if the CRN starts with n_1,...,n_k molecules of some "input" species X1,...,Xk, the CRN is guaranteed to converge to having f(n_1,...,n_k) molecules of the "output" species Y1,...,Yl. We show that a function f:N^k --> N^l is deterministically computed by a CRN if and only if its graph {(x,y) | f(x) = y} is a semilinear set. Furthermore, each semilinear function f can be computed on input x in expected time O(polylog(|x|)).


Discrete Dynamical Genetic Programming in XCS

Abstract: A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems.


Fuzzy Dynamical Genetic Programming in XCSF

Abstract: A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems.


Software Mutational Robustness

Abstract: Neutral landscapes and mutational robustness are believed to be important enablers of evolvability in biology. We apply these concepts to software, defining mutational robustness to be the fraction of random mutations that leave a program's behavior unchanged. Test cases are used to measure program behavior and mutation operators are taken from genetic programming. Although software is often viewed as brittle, with small changes leading to catastrophic changes in behavior, our results show surprising robustness in the face of random software mutations. The paper describes empirical studies of the mutational robustness of 22 programs, including 14 production software projects, the Siemens benchmarks, and 4 specially constructed programs. We find that over 30% of random mutations are neutral with respect to their test suite. The results hold across all classes of programs, for mutations at both the source code and assembly instruction levels, across various programming languages, and are only weakly related to test suite coverage. We conclude that mutational robustness is an inherent property of software, and that neutral variants (i.e., those that pass the test suite) often fulfill the program's original purpose or specification. Based on these results, we conjecture that neutral mutations can be leveraged as a mechanism for generating software diversity. We demonstrate this idea by generating a population of neutral program variants and showing that the variants automatically repair unknown bugs with high probability. Neutral landscapes also provide a partial explanation for recent results that use evolutionary computation to automatically repair software bugs.


Planar F-Deletion: Approximation, Kernelization and Optimal FPT Algorithms

Abstract: Let F be a finite set of graphs. In the F-Deletion problem, we are given an n-vertex graph G and an integer k as input, and asked whether at most k vertices can be deleted from G such that the resulting graph does not contain a graph from F as a minor. F-Deletion is a generic problem and by selecting different sets of forbidden minors F, one can obtain various fundamental problems such as Vertex Cover, Feedback Vertex Set or Treewidth t-Deletion. In this paper we obtain a number of generic algorithmic results about Planar F-Deletion, when F contains at least one planar graph. The highlights of our work are - A constant factor approximation algorithm for the optimization version of Planar F-Deletion. - A randomized linear time and single exponential parameterized algorithm, that is, an algorithm running in time O(2^{O(k)} n), for the parameterized version of Planar F-deletion where all graphs in F are connected. The algorithm can be made deterministic at the cost of making the polynomial factor in the running time n*log^2 n rather than linear. - A polynomial kernel for parameterized Planar F-deletion These algorithms unify, generalize, and improve a multitude of results in the literature. Our main results have several direct applications, but also the methods we develop on the way have applicability beyond the scope of this paper. Our results -- constant factor approximation, polynomial kernelization and FPT algorithms -- are stringed together by a common theme of polynomial time preprocessing.


Speech Recognition: Increasing Efficiency of Support Vector Machines

Abstract: With the advancement of communication and security technologies, it has become crucial to have robustness of embedded biometric systems. This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional feature space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Support Vector Machines(SVMs) and Lindear Discriminant Analysis(LDA) with MFCCs and implementing such system in real-time using SignalWAVE.


Entry and Spectrum Sharing Scheme Selection in Femtocell Markets

Abstract: Focusing on a femtocell communications market, we study the entrant network service provider's (NSP's) long-term decision: whether to enter the market and which spectrum sharing technology to select to maximize its profit. This long-term decision is closely related to the entrant's pricing strategy and the users' aggregate demand, which we model as medium-term and short-term decisions, respectively. We consider two markets, one with no incumbent and the other with one incumbent. For both markets, we show the existence and uniqueness of an equilibrium point in the user subscription dynamics, and provide a sufficient condition for the convergence of the dynamics. For the market with no incumbent, we derive upper and lower bounds on the optimal price and market share that maximize the entrant's revenue, based on which the entrant selects an available technology to maximize its long-term profit. For the market with one incumbent, we model competition between the two NSPs as a non-cooperative game, in which the incumbent and the entrant choose their market shares independently, and provide a sufficient condition that guarantees the existence of at least one pure Nash equilibrium. Finally, we formalize the problem of entry and spectrum sharing scheme selection for the entrant and provide numerical results to complement our analysis.


Fair Allocation Without Trade

Abstract: We consider the age-old problem of allocating items among different agents in a way that is efficient and fair. Two papers, by Dolev et al. and Ghodsi et al., have recently studied this problem in the context of computer systems. Both papers had similar models for agent preferences, but advocated different notions of fairness. We formalize both fairness notions in economic terms, extending them to apply to a larger family of utilities. Noting that in settings with such utilities efficiency is easily achieved in multiple ways, we study notions of fairness as criteria for choosing between different efficient allocations. Our technical results are algorithms for finding fair allocations corresponding to two fairness notions: Regarding the notion suggested by Ghodsi et al., we present a polynomial-time algorithm that computes an allocation for a general class of fairness notions, in which their notion is included. For the other, suggested by Dolev et al., we show that a competitive market equilibrium achieves the desired notion of fairness, thereby obtaining a polynomial-time algorithm that computes such a fair allocation and solving the main open problem raised by Dolev et al.


Learning in Riemannian Orbifolds

Abstract: Learning in Riemannian orbifolds is motivated by existing machine learning algorithms that directly operate on finite combinatorial structures such as point patterns, trees, and graphs. These methods, however, lack statistical justification. This contribution derives consistency results for learning problems in structured domains and thereby generalizes learning in vector spaces and manifolds.


Design and Implementation of the End System to Intermediate System (ES-IS) Routing Information Exchange Protocol as a Loadable Kernel Module in Linux Kernel 2.6

Abstract: This paper presents a partial implementation of the ES-IS Routing Information Exchange Protocol packet processing in Linux Kernel 2.6, which is for use in conjunction with the Connectionless Network Protocol (CLNP) in Aeronautical Telecommunication Network (ATN). First, we show the data structures involved in the protocol operation. Second, we describe the map of the packet processing whose design has been developed in the research. Third, we explain how the protocol is implemented as a loadable kernel module. Finally, we conclude the implementation result based on performed tests.


Design and Implementation of the Connectionless Network Protocol (CLNP) as Loadable Kernel Modules in Linux Kernel 2.6

Abstract: In this paper, we present an implementation of CLNP ground-to-ground packet processing for ATN in Linux kernel version 2.6. We present the big picture of CLNP packet processing, the details of input, routing, and output processing functions, and the implementation of each function based on ISO 8473-1. The functions implemented in this work are PDU header decomposition, header format analysis, header error detection, error reporting, reassembly, source routing, congestion notification, forwarding, composition, segmentation, and transmit to device functions. Each function is initially implemented and tested as a separated loadable kernel module. These modules are successfully loaded into Linux kernel 2.6.


Secure the Clones

Abstract: Exchanging mutable data objects with untrusted code is a delicate matter because of the risk of creating a data space that is accessible by an attacker. Consequently, secure programming guidelines for Java stress the importance of using defensive copying before accepting or handing out references to an internal mutable object. However, implementation of a copy method (like clone()) is entirely left to the programmer. It may not provide a sufficiently deep copy of an object and is subject to overriding by a malicious sub-class. Currently no language-based mechanism supports secure object cloning. This paper proposes a type-based annotation system for defining modular copy policies for class-based object-oriented programs. A copy policy specifies the maximally allowed sharing between an object and its clone. We present a static enforcement mechanism that will guarantee that all classes fulfil their copy policy, even in the presence of overriding of copy methods, and establish the semantic correctness of the overall approach in Coq. The mechanism has been implemented and experimentally evaluated on clone methods from several Java libraries.


Optimal Capacity Relay Node Placement in a Multi-hop Wireless Network on a Line

Abstract: We use information theoretic achievable rate formulas for the multi-relay channel to study the problem of optimal placement of relay nodes along the straight line joining a source node and a sink node. The achievable rate formulas that we use are for full-duplex radios at the relays and decode- and-forward relaying. For the single relay case, and individual power constraints at the source node and the relay node, we provide explicit formulas for the optimal relay location and the optimal power allocation to the source-relay channel, for the exponential and the power-law path-loss channel models. For the multiple relay case, we consider exponential path-loss and a total power constraint over the source and the relays, and derive an optimization problem, the solution of which provides the optimal relay locations. Numerical results suggest that at low attenuation the relays are mostly clustered close to the source in order to be able to cooperate among themselves, whereas at high attenuation they are uniformly placed and work as repeaters. The structure of the optimal power allocation for a given placement of the nodes, then motivates us to formulate the problem of impromptu ("as-you-go") placement of relays along a line of exponentially distributed length, with exponential path- loss, so as to minimize a cost function that is additive over hops. The hop cost trades off a capacity limiting term, motivated from the optimal power allocation solution, against the cost of adding a relay node. We formulate the problem as a total cost Markov decision process, for which we prove results for the value function, and provide insights into the placement policy via numerical exploration.


Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

Abstract: Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.


Designing generalisation evaluation function through human-machine dialogue

Abstract: Automated generalisation has known important improvements these last few years. However, an issue that still deserves more study concerns the automatic evaluation of generalised data. Indeed, many automated generalisation systems require the utilisation of an evaluation function to automatically assess generalisation outcomes. In this paper, we propose a new approach dedicated to the design of such a function. This approach allows an imperfectly defined evaluation function to be revised through a man-machine dialogue. The user gives its preferences to the system by comparing generalisation outcomes. Machine Learning techniques are then used to improve the evaluation function. An experiment carried out on buildings shows that our approach significantly improves generalisation evaluation functions defined by users.


Change-Of-Bases Abstractions for Non-Linear Systems

Abstract: We present abstraction techniques that transform a given non-linear dynamical system into a linear system or an algebraic system described by polynomials of bounded degree, such that, invariant properties of the resulting abstraction can be used to infer invariants for the original system. The abstraction techniques rely on a change-of-basis transformation that associates each state variable of the abstract system with a function involving the state variables of the original system. We present conditions under which a given change of basis transformation for a non-linear system can define an abstraction. Furthermore, the techniques developed here apply to continuous systems defined by Ordinary Differential Equations (ODEs), discrete systems defined by transition systems and hybrid systems that combine continuous as well as discrete subsystems. The techniques presented here allow us to discover, given a non-linear system, if a change of bases transformation involving degree-bounded polynomials yielding an algebraic abstraction exists. If so, our technique yields the resulting abstract system, as well. This approach is further extended to search for a change of bases transformation that abstracts a given non-linear system into a system of linear differential inclusions. Our techniques enable the use of analysis techniques for linear systems to infer invariants for non-linear systems. We present preliminary evidence of the practical feasibility of our ideas using a prototype implementation.


Multipath-dominant, pulsed doppler analysis of rotating blades

Abstract: We present a novel angular fingerprinting algorithm for detecting changes in the direction of rotation of a target with a monostatic, stationary sonar platform. Unlike other approaches, we assume that the target's centroid is stationary, and exploit doppler multipath signals to resolve the otherwise unavoidable ambiguities that arise. Since the algorithm is based on an underlying differential topological theory, it is highly robust to distortions in the collected data. We demonstrate performance of this algorithm experimentally, by exhibiting a pulsed doppler sonar collection system that runs on a smartphone. The performance of this system is sufficiently good to both detect changes in target rotation direction using angular fingerprints, and also to form high-resolution inverse synthetic aperature images of the target.


(Non-)existence of Polynomial Kernels for the Test Cover Problem

Abstract: The input of the Test Cover problem consists of a set $V$ of vertices, and a collection ${\cal E}=\{E_1,..., E_m\}$ of distinct subsets of $V$, called tests. A test $E_q$ separates a pair $v_i,v_j$ of vertices if $|\{v_i,v_j\}\cap E_q|=1.$ A subcollection ${\cal T}\subseteq {\cal E}$ is a test cover if each pair $v_i,v_j$ of distinct vertices is separated by a test in ${\cal T}$. The objective is to find a test cover of minimum cardinality, if one exists. This problem is NP-hard. We consider two parameterizations the Test Cover problem with parameter $k$: (a) decide whether there is a test cover with at most $k$ tests, (b) decide whether there is a test cover with at most $|V|-k$ tests. Both parameterizations are known to be fixed-parameter tractable. We prove that none have a polynomial size kernel unless $NP\subseteq coNP/poly$. Our proofs use the cross-composition method recently introduced by Bodlaender et al. (2011) and parametric duality introduced by Chen et al. (2005). The result for the parameterization (a) was an open problem (private communications with Henning Fernau and Jiong Guo, Jan.-Feb. 2012). We also show that the parameterization (a) admits a polynomial size kernel if the size of each test is upper-bounded by a constant.


Higher Order City Voronoi Diagrams

Abstract: We investigate higher-order Voronoi diagrams in the city metric. This metric is induced by quickest paths in the L1 metric in the presence of an accelerating transportation network of axis-parallel line segments. For the structural complexity of kth-order city Voronoi diagrams of n point sites, we show an upper bound of O(k(n - k) + kc) and a lower bound of {\Omega}(n + kc), where c is the complexity of the transportation network. This is quite different from the bound O(k(n - k)) in the Euclidean metric. For the special case where k = n - 1 the complexity in the Euclidean metric is O(n), while that in the city metric is {\Theta}(nc). Furthermore, we develop an O(k^2(n + c) log n)-time iterative algorithm to compute the kth-order city Voronoi diagram and an O(nc log^2(n + c) log n)-time divide-and-conquer algorithm to compute the farthest-site city Voronoi diagram.


Coupling Clinical Decision Support System with Computerized Prescriber Order Entry and their Dynamic Plugging in the Medical Workflow System

Abstract: This work deals with coupling Clinical Decision Support System (CDSS) with Computerized Prescriber Order Entry (CPOE) and their dynamic plugging in the medical Workflow Management System (WfMS). First, in this paper we argue some existing CDSS representative of the state of the art in order to emphasize their inability to deal with coupling with CPOE and medical WfMS. The multi-agent technology is at the basis of our proposition since (i) it provides natural abstractions to deal with distribution, heterogeneity and autonomy which are inherent to the previous systems (CDSS, CPOE and medical WfMS), and (ii) it introduces powerful concepts such as organizations, goals and roles useful to describe in details the coordination of the different components involved in these systems. In this paper, we also propose a Multi-Agent System (MAS) to support the coupling CDSS with CPOE. Finally, we show how we integrate the proposed MAS in the medical workflow management system which is also based on collaborating agents


EDCC 2012 - Fast Abstracts & Student Forum Proceedings

Abstract: Fast Abstracts at EDCC 2012 are short presentations, aiming to serve as a rapid and flexible mechanism to report on current work that may or may not be complete, introduce new ideas to the community, and state positions on controversial issues or open problems. This way, fast abstracts provide an opportunity to introduce new work, or present radical opinions, and receive early feedback from the community. Contributions are welcome from both academia and industry. The goal of the Student Forum is to encourage students to attend EDCC 2012 and present their work, exchange ideas with researchers and practitioners, and get early feedback on their research efforts. All papers were peer-reviewed by at least three program committee members, and the authors were provided with detailed comments on their work. In the end we had one accepted paper for the Student forum.


Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash

Abstract: It is shown that for cuckoo hashing with a stash as proposed by Kirsch, Mitzenmacher, and Wieder (2008) families of very simple hash functions can be used, maintaining the favorable performance guarantees: with stash size $s$ the probability of a rehash is $O(1/n^{s+1})$, and the evaluation time is $O(s)$. Instead of the full randomness needed for the analysis of Kirsch et al. and of Kutzelnigg (2010) (resp. $\Theta(\log n)$-wise independence for standard cuckoo hashing) the new approach even works with 2-wise independent hash families as building blocks. Both construction and analysis build upon the work of Dietzfelbinger and Woelfel (2003). The analysis, which can also be applied to the fully random case, utilizes a graph counting argument and is much simpler than previous proofs. As a byproduct, an algorithm for simulating uniform hashing is obtained. While it requires about twice as much space as the most space efficient solutions, it is attractive because of its simple and direct structure.


First-Order Quantifiers and the Syntactic Monoid of Height Fragments of Picture Languages

Abstract: We investigate the expressive power of first-order quantifications in the context of monadic second-order logic over pictures. We show that k+1 set quantifier alternations allow to define a picture language that cannot be defined using k set quantifier alternations preceded by arbitrarily many first-order quantifier alternations. The approach uses, for a given picture language L and an integer m > 0 the height-m fragment of L, which is defined as the word language obtained by considering each picture p of height m in L as a word, where the letters of that word are the columns of p. A key idea is to measure the complexity of a regular word language by the group complexity of its syntactic monoid. Given a picture language L, such a word language measure may be applied to each of its height fragments, so that the complexity of the picture language is a function that maps each m to the complexity of the height-m fragment of L. The asymptotic growth rate of that function may be bounded based on the structure of a monadic second-order formula that defines L. The core argument for that lower bound proof is based on Straubing's algebraic characterization of the effect of first-order quantifiers on the syntactic monoid of word languages by means of Rhodes' and Tilson's block product.


An Interference-Aware Virtual Clustering Paradigm for Resource Management in Cognitive Femtocell Networks

Abstract: Femtocells represent a promising alternative solution for high quality wireless access in indoor scenarios where conventional cellular system coverage can be poor. Femtocell access points (FAP) are normally randomly deployed by the end user, so only post deployment network planning is possible. Furthermore, this uncoordinated deployment creates the potential for severe interference to co-located femtocells, especially in dense deployments. This paper presents a new femtocell network architecture using a generalized virtual cluster femtocell (GVCF) paradigm, which groups together FAP, which are allocated to the same femtocell gateway (FGW), into logical clusters. This guarantees severely interfering and overlapping femtocells are assigned to different clusters, and since each cluster operates on a different band of frequencies, the corresponding virtual cluster controller only has to manage its own FAP members, so the overall system complexity is low. The performance of the GVCF algorithm is analysed from both a resource availability and cluster number perspective, and a novel strategy is proposed for dynamically adapting these to network environment changes, while upholding quality-of-service requirements. Simulation results conclusively corroborate the superior performance of the GVCF model in interference mitigation, particularly in high density FAP scenarios.


Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks over Unreliable Channels

Abstract: Wireless sensor networks have been increasingly used for real-time surveillance over large areas. In such applications, it is important to support end-to-end delay constraints for packet deliveries even when the corresponding flows require multi-hop transmissions. In addition to delay constraints, each flow of real-time surveillance may require some guarantees on throughput of packets that meet the delay constraints. Further, as wireless sensor networks are usually deployed in challenging environments, it is important to specifically consider the effects of unreliable wireless transmissions. In this paper, we study the problem of providing end-to-end delay guarantees for multi-hop wireless networks. We propose a model that jointly considers the end-to-end delay constraints and throughput requirements of flows, the need for multi-hop transmissions, and the unreliable nature of wireless transmissions. We develop a framework for designing feasibility-optimal policies. We then demonstrate the utility of this framework by considering two types of systems: one where sensors are equipped with full-duplex radios, and the other where sensors are equipped with half-duplex radios. When sensors are equipped with full-duplex radios, we propose an online distributed scheduling policy and proves the policy is feasibility-optimal. We also provide a heuristic for systems where sensors are equipped with half-duplex radios. We show that this heuristic is still feasibility-optimal for some topologies.


Real-Time Stochastic Processing Networks with Concurrent Resource Requirements

Abstract: Stochastic Processing Networks (SPNs) can be used to model communication networks, manufacturing systems, service systems, etc. We consider a real-time SPN where tasks generate jobs with strict deadlines according to their traffic patterns. Each job requires the concurrent usage of some resources to be processed. The processing time of a job may be stochastic, and may not be known until the job completes. Finally, each task may require that some portion of its tasks to be completed on time. In this paper, we study the problem of verifying whether it is feasible to fulfill the requirements of tasks, and of designing scheduling policies that actually fulfill the requirements. We first address these problems for systems where there is only one resource. Such systems are analog to ones studied in a previous work, and, similar to the previous work, we can develop sharp conditions for feasibility and scheduling policy that is feasibility-optimal. We then study systems with two resources where there are jobs that require both resources to be processed. We show that there is a reduction method that turns systems with two resources into equivalent single-resource systems. Based on this method, we can also derive sharp feasibility conditions and feasibility-optimal scheduling policies for systems with two resources.


Dynamic Template Tracking and Recognition

Abstract: In this paper we address the problem of tracking non-rigid objects whose local appearance and motion changes as a function of time. This class of objects includes dynamic textures such as steam, fire, smoke, water, etc., as well as articulated objects such as humans performing various actions. We model the temporal evolution of the object's appearance/motion using a Linear Dynamical System (LDS). We learn such models from sample videos and use them as dynamic templates for tracking objects in novel videos. We pose the problem of tracking a dynamic non-rigid object in the current frame as a maximum a-posteriori estimate of the location of the object and the latent state of the dynamical system, given the current image features and the best estimate of the state in the previous frame. The advantage of our approach is that we can specify a-priori the type of texture to be tracked in the scene by using previously trained models for the dynamics of these textures. Our framework naturally generalizes common tracking methods such as SSD and kernel-based tracking from static templates to dynamic templates. We test our algorithm on synthetic as well as real examples of dynamic textures and show that our simple dynamics-based trackers perform at par if not better than the state-of-the-art. Since our approach is general and applicable to any image feature, we also apply it to the problem of human action tracking and build action-specific optical flow trackers that perform better than the state-of-the-art when tracking a human performing a particular action. Finally, since our approach is generative, we can use a-priori trained trackers for different texture or action classes to simultaneously track and recognize the texture or action in the video.


Sorted Range Reporting

Abstract: In this paper we consider a variant of the orthogonal range reporting problem when all points should be reported in the sorted order of their $x$-coordinates. We show that reporting two-dimensional points with this additional condition can be organized (almost) as efficiently as the standard range reporting. Moreover, our results generalize and improve the previously known results for the orthogonal range successor problem and can be used to obtain better solutions for some stringology problems.


A Tight Combinatorial Algorithm for Submodular Maximization Subject to a Matroid Constraint

Abstract: We present an optimal, combinatorial 1-1/e approximation algorithm for monotone submodular optimization over a matroid constraint. Compared to the continuous greedy algorithm (Calinescu, Chekuri, Pal and Vondrak, 2008), our algorithm is extremely simple and requires no rounding. It consists of the greedy algorithm followed by local search. Both phases are run not on the actual objective function, but on a related non-oblivious potential function, which is also monotone submodular. Our algorithm runs in randomized time O(n^8u), where n is the rank of the given matroid and u is the size of its ground set. We additionally obtain a 1-1/e-eps approximation algorithm running in randomized time O (eps^-3n^4u). For matroids in which n = o(u), this improves on the runtime of the continuous greedy algorithm. The improvement is due primarily to the time required by the pipage rounding phase, which we avoid altogether. Furthermore, the independence of our algorithm from pipage rounding techniques suggests that our general approach may be helpful in contexts such as monotone submodular maximization subject to multiple matroid constraints. Our approach generalizes to the case where the monotone submodular function has restricted curvature. For any curvature c, we adapt our algorithm to produce a (1-e^-c)/c approximation. This result complements results of Vondrak (2008), who has shown that the continuous greedy algorithm produces a (1-e^-c)/c approximation when the objective function has curvature c. He has also proved that achieving any better approximation ratio is impossible in the value oracle model.


A New Guess-and-Determine Attack on the A5/1 Stream Cipher

Abstract: In Europe and North America, the most widely used stream cipher to ensure privacy and confidentiality of conversations in GSM mobile phones is the A5/1. In this paper, we present a new attack on the A5/1 stream cipher with an average time complexity of 2^(48.5), which is much less than the brute-force attack with a complexity of 2^(64). The attack has a 100% success rate and requires about 5.65GB storage. We provide a detailed description of our new attack along with its implementation and results.


Automatic Sampling of Geographic objects

Abstract: Today, one's disposes of large datasets composed of thousands of geographic objects. However, for many processes, which require the appraisal of an expert or much computational time, only a small part of these objects can be taken into account. In this context, robust sampling methods become necessary. In this paper, we propose a sampling method based on clustering techniques. Our method consists in dividing the objects in clusters, then in selecting in each cluster, the most representative objects. A case-study in the context of a process dedicated to knowledge revision for geographic data generalisation is presented. This case-study shows that our method allows to select relevant samples of objects.


Performance Evaluation of QoS Parameters in Dynamic Spectrum Sharing for Heterogeneous Wireless Communication Networks

Abstract: Cognitive radio nodes have been proposed as means to improve the spectrum utilization. It reuses the spectrum of a primary service provider under the condition that the primary service provider services are not harmfully interrupted. A cognitive radio can sense its operating environment's conditions and it is able to reconfigure itself and to communicate with other counterparts based on the status of the environment and also the requirements of the user to meet the optimal communication conditions and to keep quality of service (QoS) as high as possible. The efficiency of spectrum sharing can be improved by minimizing the interference. The Utility function that captures the cooperative behavior to minimize the interference and the satisfaction to improve the throughput is investigated. The dynamic spectrum sharing algorithm can maintain the quality of service (QoS) of each network while the effective spectrum utilisation is improved under a fluctuation traffic environment when the available spectrum is limited.


A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines

Abstract: The placement of wind turbines on a given area of land such that the wind farm produces a maximum amount of energy is a challenging optimization problem. In this article, we tackle this problem, taking into account wake effects that are produced by the different turbines on the wind farm. We significantly improve upon existing results for the minimization of wake effects by developing a new problem-specific local search algorithm. One key step in the speed-up of our algorithm is the reduction in computation time needed to assess a given wind farm layout compared to previous approaches. Our new method allows the optimization of large real-world scenarios within a single night on a standard computer, whereas weeks on specialized computing servers were required for previous approaches.


Mariages et Trahisons

Abstract: A self-stabilizing protocol tolerates by definition transient faults (faults of finite duration). Recently, a new class of self-stabilizing protocols that are able to tolerate a given number of permanent faults. In this paper, we focus on self-stabilizing protocols able to tolerate Byzantine faults, that is faults that introduce an arbitrary behaviour. We focus on strict-stabilization in which the system have to contain the effects of Byzantine faults. Specificaly, we study the possibility to construct in a self-stabilizing way a maximal matching in a network where an arbitrary number of process may become Byzantine.


Fast and Robust Parametric Estimation of Jointly Sparse Channels

Abstract: We consider the joint estimation of multipath channels obtained with a set of receiving antennas and uniformly probed in the frequency domain. This scenario fits most of the modern outdoor communication protocols for mobile access or digital broadcasting among others. Such channels verify a Sparse Common Support property (SCS) which was used in a previous paper to propose a Finite Rate of Innovation (FRI) based sampling and estimation algorithm. In this contribution we improve the robustness and computational complexity aspects of this algorithm. The method is based on projection in Krylov subspaces to improve complexity and a new criterion called the Partial Effective Rank (PER) to estimate the level of sparsity to gain robustness. If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an O(KPNlogN) complexity and an O(KPN) memory footprint instead of O(PN^3) and O(PN^2) for the direct implementation, making it suitable for K << N. The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking. The estimation performances are tested on field measurements with synthetic AWGN, and the proposed algorithm outperforms non-sparse reconstruction in the medium to low SNR range (< 0dB), increasing the rate of successful symbol decodings by 1/10th in average, and 1/3rd in the best case. The experiments also show that the algorithm does not perform worse than a non-sparse estimation algorithm in non-sparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity. The algorithm is also tested against a method assuming a "discrete" sparsity model as in Compressed Sensing (CS). The conducted test indicates a trade-off between speed and accuracy.


Logic Characterization of Floyd Languages

Abstract: Floyd languages (FL), alias Operator Precedence Languages, have recently received renewed attention thanks to their closure properties and local parsability which allow one to apply automatic verification techniques (e.g. model checking) and parallel and incremental parsing. They properly include various other classes, noticeably Visual Pushdown languages. In this paper we provide a characterization of FL in terms a monadic second order logic (MSO), in the same style as Buchi's one for regular languages. We prove the equivalence between automata recognizing FL and the MSO formalization.


Regulation of off-network pricing in a nonneutral network

Abstract: Representatives of several Internet service providers (ISPs) have expressed their wish to see a substantial change in the pricing policies of the Internet. In particular, they would like to see content providers (CPs) pay for use of the network, given the large amount of resources they use. This would be in clear violation of the "network neutrality" principle that had characterized the development of the wireline Internet. Our first goal in this paper is to propose and study possible ways of implementing such payments and of regulating their amount. We introduce a model that includes the users' behavior, the utilities of the ISP and of the CPs, and the monetary flow that involves the content users, the ISP and CP, and in particular, the CP's revenues from advertisements. We consider various game models and study the resulting equilibria; they are all combinations of a noncooperative game (in which the ISPs and CPs determine how much they will charge the users) with a "cooperative" one on how the CP and the ISP share the payments. We include in our model a possible asymmetric weighting parameter (that varies between zero to one). We also study equilibria that arise when one of the CPs colludes with the ISP. We also study two dynamic game models and study the convergence of prices to the equilibrium values.


The computational complexity of Minesweeper

Abstract: We show that the Minesweeper game is PP-hard, when the object is to locate all mines with the highest probability. When the probability of locating all mines may be infinitesimal, the Minesweeper game is even PSPACE-complete. In our construction, the player can reveal a boolean circuit in polynomial time, after guessing an initial square with no surrounding mines, a guess that has 99 percent probability of success. Subsequently, the mines must be located with a maximum probability of success. Furthermore, we show that determining the solvability of a partially uncovered Minesweeper board is NP-complete with hexagonal and triangular grids as well as a square grid, extending a similar result for square grids only by R. Kaye. Actually finding the mines with a maximum probability of success is again PP-hard or PSPACE-complete respectively. Our constructions are in such a way that the number of mines can be computed in polynomial time and hence a possible mine counter does not provide additional information. The results are obtained by replacing the dyadic gates in [3] by two primitives which makes life more easy in this context.


Finding Small Sparse Cuts Locally by Random Walk

Abstract: We study the problem of finding a small sparse cut in an undirected graph. Given an undirected graph G=(V,E) and a parameter k <= |E|, the small sparsest cut problem is to find a subset of vertices S with minimum conductance among all sets with volume at most k. Using ideas developed in local graph partitioning algorithms, we obtain the following bicriteria approximation algorithms for the small sparsest cut problem: - If there is a subset U with conductance \phi and vol(U) <= k, then there is a polynomial time algorithm to find a set S with conductance O(\sqrt{\phi/\epsilon}) and vol(S) <= k^{1+\epsilon} for any \epsilon > 1/k. - If there is a subset U with conductance \phi and vol(U) <= k, then there is a polynomial time algorithm to find a set S with conductance O(\sqrt{\phi ln(k)/\epsilon}) and vol(S) <= (1+\epsilon)k for any \epsilon > 2ln(k)/k. These algorithms can be implemented locally using truncated random walk, with running time almost linear to the output size. This provides a local graph partitioning algorithm with a better conductance guarantee when k is sublinear.


The Join Levels of the Trotter-Weil Hierarchy are Decidable

Abstract: The variety DA of finite monoids has a huge number of different characterizations, ranging from two-variable first-order logic FO^2 to unambiguous polynomials. In order to study the structure of the subvarieties of DA, Trotter and Weil considered the intersection of varieties of finite monoids with bands, i.e., with idempotent monoids. The varieties of idempotent monoids are very well understood and fully classified. Trotter and Weil showed that for every band variety V there exists a unique maximal variety W inside DA such that the intersection with bands yields the given band variety V. These maximal varieties W define the Trotter-Weil hierarchy. This hierarchy is infinite and it exhausts DA; induced by band varieties, it naturally has a zigzag shape. In their paper, Trotter and Weil have shown that the corners and the intersection levels of this hierarchy are decidable. In this paper, we give a single identity of omega-terms for every join level of the Trotter-Weil hierarchy; this yields decidability. Moreover, we show that the join levels and the subsequent intersection levels do not coincide. Almeida and Azevedo have shown that the join of R-trivial and L-trivial finite monoids is decidable; this is the first non-trivial join level of the Trotter-Weil hierarchy. We extend this result to the other join levels of the Trotter-Weil hierarchy. At the end of the paper, we give two applications. First, we show that the hierarchy of deterministic and codeterministic products is decidable. And second, we show that the direction alternation depth of unambiguous interval logic is decidable.


Robust Geometric Spanners

Abstract: Highly connected and yet sparse graphs (such as expanders or graphs of high treewidth) are fundamental, widely applicable and extensively studied combinatorial objects. We initiate the study of such highly connected graphs that are, in addition, geometric spanners. We define a property of spanners called robustness. Informally, when one removes a few vertices from a robust spanner, this harms only a small number of other vertices. We show that robust spanners must have a superlinear number of edges, even in one dimension. On the positive side, we give constructions, for any dimension, of robust spanners with a near-linear number of edges.


A Query Language for Formal Mathematical Libraries

Abstract: One of the most promising applications of mathematical knowledge management is search: Even if we restrict attention to the tiny fragment of mathematics that has been formalized, the amount exceeds the comprehension of an individual human. Based on the generic representation language MMT, we introduce the mathematical query language QMT: It combines simplicity, expressivity, and scalability while avoiding a commitment to a particular logical formalism. QMT can integrate various search paradigms such as unification, semantic web, or XQuery style queries, and QMT queries can span different mathematical libraries. We have implemented QMT as a part of the MMT API. This combination provides a scalable indexing and query engine that can be readily applied to any library of mathematical knowledge. While our focus here is on libraries that are available in a content markup language, QMT naturally extends to presentation and narration markup languages.


Markov chain methods for small-set expansion

Abstract: Consider a finite irreducible Markov chain with invariant distribution $\pi$. We use the inner product induced by $\pi$ and the associated heat operator to simplify and generalize some results related to graph partitioning and the small-set expansion problem. For example, Steurer showed a tight connection between the number of small eigenvalues of a graph's Laplacian and the expansion of small sets in that graph. We give a simplified proof which generalizes to the nonregular, directed case. This result implies an approximation algorithm for an "analytic" version of the Small-Set Expansion Problem, which, in turn, immediately gives an approximation algorithm for Small-Set Expansion. We also give a simpler proof of a lower bound on the probability that a random walk stays within a set; this result was used in some recent works on finding small sparse cuts.


Dynamic Planar Point Location with Sub-Logarithmic Local Updates

Abstract: We study planar point location in a collection of disjoint fat regions, and investigate the complexity of \emph {local updates}: replacing any region by a different region that is "similar" to the original region. (i.e., the size differs by at most a constant factor, and distance between the two regions is a constant times that size). We show that it is possible to create a linear size data structure that allows for insertions, deletions, and queries in logarithmic time, and allows for local updates in sub-logarithmic time on a pointer machine.


Numerical Analysis of Diagonal-Preserving, Ripple-Minimizing and Low-Pass Image Resampling Methods

Abstract: Image resampling is a necessary component of any operation that changes the size of an image or its geometry. Methods tuned for natural image upsampling (roughly speaking, image enlargement) are analyzed and developed with a focus on their ability to preserve diagonal features and suppress overshoots. Monotone, locally bounded and almost monotone "direct" interpolation and filtering methods, as well as face split and vertex split surface subdivision methods, alone or in combination, are studied. Key properties are established by way of proofs and counterexamples as well as numerical experiments involving 1D curve and 2D diagonal data resampling. In addition, the Remez minimax method for the computation of low-cost polynomial approximations of low-pass filter kernels tuned for natural image downsampling (roughly speaking, image reduction) is refactored for relative error minimization in the presence of roots in the interior of the interval of approximation and so that even and odd functions are approximated with like polynomials. The accuracy and frequency response of the approximations are tabulated and plotted against the original, establishing their rapid convergence.


Model Checking with Probabilistic Tabled Logic Programming

Abstract: We present a formulation of the problem of probabilistic model checking as one of query evaluation over probabilistic logic programs. To the best of our knowledge, our formulation is the first of its kind, and it covers a rich class of probabilistic models and probabilistic temporal logics. The inference algorithms of existing probabilistic logic-programming systems are well defined only for queries with a finite number of explanations. This restriction prohibits the encoding of probabilistic model checkers, where explanations correspond to executions of the system being model checked. To overcome this restriction, we propose a more general inference algorithm that uses finite generative structures (similar to automata) to represent families of explanations. The inference algorithm computes the probability of a possibly infinite set of explanations directly from the finite generative structure. We have implemented our inference algorithm in XSB Prolog, and use this implementation to encode probabilistic model checkers for a variety of temporal logics, including PCTL and GPL (which subsumes PCTL*). Our experiment results show that, despite the highly declarative nature of their encodings, the model checkers constructed in this manner are competitive with their native implementations.


String Trees

Abstract: A string-like compact data structure for unlabelled rooted trees is given using 2n bits.


An Efficient Method For Multichannel Wireless Mesh Networks With Pulse Coupled Neural Network

Abstract: Multi cast communication is a key technology for wireless mesh networks. Multicast provides efficient data distribution among a group of nodes, Generally sensor networks and MANETs uses multicast algorithms which are designed to be energy efficient and to achieve optimal route discovery among mobile nodes whereas wireless mesh networks needs to maximize throughput. Here we propose two multicast algorithms: The Level Channel Assignment (LCA) algorithm and the Multi-Channel Multicast (MCM) algorithm to improve the throughput for multichannel sand multi interface mesh networks. The algorithm builds efficient multicast trees by minimizing the number of relay nodes and total hop count distance of the trees. Shortest path computation is a classical combinatorial optimization problem. Neural networks have been used for processing path optimization problem. Pulse Coupled Neural Networks (PCNNS) suffer from high computational cast for very long paths we propose a new PCNN modal called dual source PCNN (DSPCNN) which can improve the computational efficiency two auto waves are produced by DSPCNN one comes from source neuron and other from goal neuron when the auto waves from these two sources meet the DSPCNN stops and then the shortest path is found by backtracking the two auto waves.


Verification Condition Generation and Variable Conditions in Smallfoot

Abstract: These notes are a companion to [1] which describe - the variable conditions that Smallfoot checks, - the analysis used to check them, - the algorithm used to compute a set of verification conditions corresponding to an annotated program, and - the treatment of concurrent resource initialization code.


What's in an `is about' link? Chemical diagrams and the Information Artifact Ontology

Abstract: The Information Artifact Ontology is an ontology in the domain of information entities. Core to the definition of what it is to be an information entity is the claim that an information entity must be `about' something, which is encoded in an axiom expressing that all information entities are about some entity. This axiom comes into conflict with ontological realism, since many information entities seem to be about non-existing entities, such as hypothetical molecules. We discuss this problem in the context of diagrams of molecules, a kind of information entity pervasively used throughout computational chemistry. We then propose a solution that recognizes that information entities such as diagrams are expressions of diagrammatic languages. In so doing, we not only address the problem of classifying diagrams that seem to be about non-existing entities but also allow a more sophisticated categorisation of information entities.