ppxscal/embedding_network huggingface dataset:
Multi-objective clustering ensemble for gene expression data analysis
Authors: Katti Faceli, Marcilio C. P. de Souto, Daniel S. A. de Araújo, André C. P. L. F. de Carvalho
Venue: Neurocomputing (2009)

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. Its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective clustering with automatic K-determination (MOCK), the algorithm most closely related to ours.
Automated Detection of Reference Structures in Law
Authors: Emile de Maat, Radboud Winkels, Tom van Engers
Venue: Proceeding of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference (2006)

Combining legal content stores of different providers is usually time, effort and money intensive due to the usually 'hard-wired' links between different parts of the constituting sources within those stores. In practice users of legal content are confronted with a vendor lock-in situation and have to find work-arounds when they want to combine their own content with the content provided by others. In the BSN project we developed a parser that enables the creation of a referential structure on top of a legal content store. We empirically tested the parsers' effectiveness and found an over 95% accuracy even for complex references.
Comparison of Traditional and Novel Discretization Methods for Advection Models in Numerical Weather Prediction
Authors: Sean Crowell, Dustin Williams, Catherine Mavriplis, Louis Wicker
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Numerical Weather Prediction has been dominated by low order finite difference methodology over many years. The advent of high performance computers and the development of high order methods over the last two decades point to a need to investigate the use of more advanced numerical techniques in this field. Domain decomposable high order methods such as spectral element and discontinuous Galerkin, while generally more expensive (except perhaps in the context of high performance computing), exhibit faster convergence to high accuracy solutions and can locally resolve highly nonlinear phenomena. This paper presents comparisons of CPU time, number of degrees of freedom and overall behavior of solutions for finite difference, spectral difference and discontinuous Galerkin methods on two model advection problems. In particular, spectral differencing is investigated as an alternative to spectral-based methods which exhibit stringent explicit time step requirements.
Kingdom of the Knights: evaluation of a seamlessly augmented toy environment for playful learning
Authors: Steve Hinske, Matthias Lampe, Nicola Yuill, Sara Price, Marc Langheinrich
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

Ubiquitous technologies offer new opportunities for digitally augmenting children's toys and play experiences. A key question is how augmented toy environments affect children's playful learning, and whether this differs from non-augmented play environments. This paper presents preliminary results of a user study we conducted to evaluate an augmented toy environment that we built --- the Augmented Knights Castle --- in terms of fun and storytelling, particularly when compared with an identical, non-augmented version. All sessions were observed, video-recorded and further feedback was elicited through small group interviews and questionnaires. Findings suggest ways in which digitally augmented play environments promote different kinds of activity from an equivalent non-augmented play environment.
GOSR: geographical opportunistic source routing for VANETs
Authors: Liu Zhongyi, Zhao Tong, Yan Wei, Li Xiaoming
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (2009)

Geographical source routing is a promising routing technique for VANETs, due to its adaptability for network dynamics and ability to handle topology holes. In traditional geographical source routing algorithms a best-known neighbor, typically the neighbor nearest to the next junction in a greedy fashion, is designated as the next hop. This approach may cause two drawbacks: (1) the designated neighbor might not receive the packet correctly and (2) non-neighbor nodes are never given opportunities to do forwarding. Due to the broadcast nature of wireless media, non-neighbor nodes can overhear the transmission and may offer better forwarding progress than the designated neighbor. In this paper, we resolve the above two problems by introducing the concept of opportunistic routing to geographical source routing. A new routing protocol, named Geographical Opportunistic Source Routing (GOSR), is developed. GOSR allows non-neighbor nodes as well as the best-known neighbor to become a forwarder. The notification cost of opportunistic routing is minimized by enforcing a scope from which candidate forwarders are selected. Defer timers are adopted to avoid conflicts caused by simultaneous transmissions by nodes in the designated scope. Simulation results show that GOSR can substantially reduce hop count and also improve end-to-end delivery ratio remarkably.
Identity and Trust Management Directions for User Controlled Light-Path Establishment
Authors: R. J. Hulsebosch, M. S. Bargh, J. H. van der Spek, P. H. Fennema, J. F. Zandbelt
Venue: Proceeding of the 2005 conference on Self-Organization and Autonomic Informatics (I) (2005)

This contribution investigates the role of identity management and trust in user-controlled provisioning of end-to-end light-paths across multiple optical networks managed by different (optical) network service providers. Lack of trust between the user's identity provider and the optical network service providers hampers effective exchange of user identity information needed for authorizing the reservation and utilization of network resources in such user-controlled light-path establishment. The paper proposes a Virtual Organization as a trusted third party to overcome this lack of trust and analyzes the possible roles it can play to facilitate trusted and secure exchange of identity credentials.
Issues in evaluation of stream learning algorithms
Authors: João Gama, Raquel Sebastião, Pedro Pereira Rodrigues
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. There are no golden standards for assessing performance in non-stationary environments. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of Predictive Sequential methods for error estimate - the prequential error. The prequential error allows us to monitor the evolution of the performance of models that evolve over time. Nevertheless, it is known to be a pessimistic estimator in comparison to holdout estimates. To obtain more reliable estimators we need some forgetting mechanism. Two viable alternatives are: sliding windows and fading factors. We observe that the prequential error converges to an holdout estimator when estimated over a sliding window or using fading factors. We present illustrative examples of the use of prequential error estimators, using fading factors, for the tasks of: i) assessing performance of a learning algorithm; ii) comparing learning algorithms; iii) hypothesis testing using McNemar test; and iv) change detection using Page-Hinkley test. In these tasks, the prequential error estimated using fading factors provide reliable estimators. In comparison to sliding windows, fading factors are faster and memory-less, a requirement for streaming applications. This paper is a contribution to a discussion in the good-practices on performance assessment when learning dynamic models that evolve over time.
Threat analysis for hardware and software products using HazOP
Authors: Burzin Daruwala, Salvador Mandujano, Narasimha Kumar Mangipudi, Hao-chi Wong
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

These days, hardware and software products must be designed with security in mind and threat modeling is a fundamental step towards properly securing sensitive areas on the system. The task of identifying all relevant threats, however, is not an easy challenge. Even though use cases may be properly documented and routinely used during the development cycle for elicitation of functional requirements, nonfunctional requirements, such as security, are often neglected due to the priority new functionality receives over other aspects of the product, as well as the complexity of analyzing and planning for the unexpected. Better methods are necessary to protect computing technologies whose complexity and attack surface keeps expanding with more and more features. In the field of process safety, a variety of techniques have emerged to assess and mitigate risk. Since the domains of safety and security share multiple similarities, various authors have suggested that safety techniques might find application in computer security. Actual applicability of these techniques to existing technologies, however, was yet to be demonstrated. This paper takes one such technique, HazOp, and applies it to Intel hardware and software shedding some light on the mechanization of use-case-based threat analysis. It shows how HazOp can be employed on a diverse range of technologies to correctly identify deviations from use cases that may represent a violation to the security objectives of the product. We document the advantages of this method over more traditional ones with regard to efficiency, extensibility and efficacy. The reader will also learn how to apply and, potentially, adapt HazOp to accelerate the discovery of system threats as well as the derivation of test scenarios for purposes of security evaluation.
Coronal loop detection from solar images
Authors: Nurcan Durak, Olfa Nasraoui, Joan Schmelz
Venue: Pattern Recognition (2009)

In this paper, we make an overview of a methodology for the automatic retrieval of images with coronal loops from the solar image data captured by the extreme-ultraviolet imaging telescope (EIT) onboard the spacecraft SOHO (Solar and Heliospheric Observatory). Our image retrieval system provides relevant data to astrophysicists who need such data to study the coronal heating problem. As part of building this system, we investigated various image preprocessing techniques, image based features, and classifiers to automatically detect coronal loops and to indicate their locations on the images. Despite many challenges related to the coronal loop characteristic, we obtained promising results, namely, 78% precision and 80% recall in loop retrieval.
A Deterministic Crowding Evolutionary Algorithm for Optimization of a KNN-based Anomaly Intrusion Detection System
Authors: F. de Toro-Negro, P. Garcìa-Teodoro, J. E. Diáz-Verdejo, G. Maciá-Fernandez
Venue: Proceeding of the 2008 conference on Applications of Data Mining in E-Business and Finance (2008)

This paper addresses the use of an evolutionary algorithm for the optimization of a K-nearest neighbor classifier to be used in the implementation of an intrusion detection system. The inclusion of a diversity maintenance technique embodied in the design of the evolutionary algorithm enables us to obtain different subsets of features extracted from network traffic data that lead to high classification accuracies. The methodology has been preliminarily applied to the Denial of Service attack detection, a key issue in maintaining continuity of the services provided by business organizations.
Loss network models and multiple metric performance sensitivity analysis for mobile wireless multi-hop networks
Authors: John S. Baras, Senni Perumal, Vahid Tabatabaee, Kiran Somasundaram, Punyaslok Purkayastha
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

We develop and evaluate a new method for estimating and optimizing various performance metrics of mobile wireless multi-hop networks, including MANETs. The method utilizes approximate (throughput) loss model that couples the physical, MAC and routing layers effects. The model provides quantitative statistical relations between the loss parameters that are used to characterize multiuser interference and physical path conditions on the one hand and the traffic rates between origin destination pairs on the other. The model considers effects of the hidden nodes, node scheduling algorithms, MAC and PHY layer failures and unsuccessful packet transmission attempts at the MAC layer in arbitrary time varying network topologies where multiple paths share nodes. The method then applies Automatic Differentiation (AD) to these implicit performance models, to compute sensitivities of various performance metrics with respect to network parameters. We demonstrate the method by applying it to time varying mobile network topologies, including reduced connectivity instances, with both random access MAC (contention mode of the 802.11) as well as reservation based MAC (USAP TDMA based protocol). We analyze throughput, delay and packet loss as metrics and investigate metric optimization and tradeoff analysis. Finally we provide numerical results for realistic mobile networks with time varying topologies.
Activity module development for Moodle: a sample activity module, edugame
Authors: Serkan Cankaya, Serkan Izmirli
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

Moodle, one of the most popular course management systems (CMSs), has a highly extendable infrastructure. Moodle is an open source CMS. Everyone can easily contribute to the development of Moodle with plug-ins, activity modules, blocks and so on. The most used contribution method is activity modules. Moodle has a list of activities by default (forums, quizzes, assignments, etc.). Teachers can use these activities in their classroom to support lessons. But sometimes default activities do not meet the needs of teachers. Therefore, activity module development can be important for efficient use of Moodle in some circumstances. In this study, Moodle infrastructure and development steps of a sample activity module, EduGame, will be explained in a step by step manner. EduGame activity module is a tool to integrate educational flash games into the Moodle. It also saves the game points of students into the Moodle database. Educational computer games are great ways for teachers to pique children's interest in the learning process. So integrating educational computer games to Moodle can be crucial for computer game based learning.
COA: finding novel patents through text analysis
Authors: Mohammad Al Hasan, W. Scott Spangler, Thomas Griffin, Alfredo Alba
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

In recent years, the number of patents filed by the business enterprises in the technology industry are growing rapidly, thus providing unprecedented opportunities for knowledge discovery in patent data. One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing. Availability of such ranking can substantially reduce enterprise IP (Intellectual Property) management costs. Unfortunately, the existing software systems in the IP domain do not address this task directly. Through our research, we build a patent ranking software, named COA (Claim Originality Analysis) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the "claims" section of a patent. Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics--citation count, patent status, and attorney's rating. In reallife settings, this tool was used by beta-testers in the IBM IP department. Lawyers found it very useful in patent rating, specifically, in highlighting potentially valuable patents in a patent cluster. In this article, we describe the ranking techniques and system architecture of COA. We also present the results that validate its effectiveness.
Diversity Performance of Multimode Antennas in Directive Angle of Arrival Scenarios
Authors: Oliver Klemp, Hermann Eul
Venue: Wireless Personal Communications: An International Journal (2009)

The growing demand for wireless communications with increased spectral efficiency seeks for multi-element antenna techniques in personal communications to support high data rates by means of multiple-input multiple-output (MIMO) transmission schemes. Since the integration of multi-element antenna equipment at the mobile device needs to be well-aligned with the required form factors and aesthetical design issues, antenna array concepts with spatially decorrelated radiators are almost difficult to be implemented. Multimode antenna configurations operating with independent polarizations and different radiation patterns may substantially reduce size requirements since they do not necessarily require spatially separated phase centers. This article provides the theoretical framework for the analysis of the diversity efficiency of multimode antennas in directive angle-of-arrival scenarios. The analysis applies an idealized mathematical model of a multimode antenna based on the characterization of orthonormal, spherical harmonics. This mathematical foundation enables a detailed in-depth study of the impact of finite pattern correlation and branch power imbalances on the diversity efficiency of the multimode antenna. Based on the introduction of reliable performance metrics, the model enables a separate analysis of both effects. It serves as a guideline for the determination of the diversity efficiency when applying multimode antenna elements in mobile fading scenarios with defined angle-of-arrival scenarios.
Data mining approach for supply unbalance detection in induction motor
Authors: Abdülkadir Çakır, Hakan Çalış, Ecir U. Küçüksille
Venue: Expert Systems with Applications: An International Journal (2009)

This paper describes an approach for detection of the supply unbalance condition in induction motors by using data mining process. Simulation results have shown that a good indicator of the fault is the amplitude of the second harmonic of the supply frequency component (2f) in the signal obtained by the differences in supply current zero crossing instants. In the study, linear regression (LR), pace regression (PR), sequential minimal optimization (SMO), M5 model tree, M5'Rules, KStar, additive regression and back propagation neural network (BPNN) models are applied within the data mining process for determining the condition of the motor supply voltage. All data mining algorithms were applied using WEKA software. The best result for the determination of the fault related dominant parameter was obtained by using the M5P algorithm model.
Mining broad latent query aspects from search sessions
Authors: Xuanhui Wang, Deepayan Chakrabarti, Kunal Punera
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. A broad latent query aspect is a set of keywords that succinctly represents one particular sense, or one particular information need, that can aid users in reformulating such queries. We extract such broad latent aspects from query reformulations found in historical search session logs. We propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects the system can store, and the number of aspects that can be shown in response to any given query. We present algorithms to find a good set of aspects, and also to pick the best k aspects matching any query. Empirical results on real-world search engine logs show significant gains over a strong baseline that uses single-keyword reformulations: a gain of 14% and 23% in terms of human-judged accuracy and click-through data respectively, and around 20% in terms of consistency among aspects predicted for "similar" queries. This demonstrates both the importance of broad query aspects, and the efficacy of our algorithms for extracting them.
Improving optical character recognition through efficient multiple system alignment
Authors: William B. Lund, Eric K. Ringger
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

Individual optical character recognition (OCR) engines vary in the types of errors they commit in recognizing text, particularly poor quality text. By aligning the output of multiple OCR engines and taking advantage of the differences between them, the error rate based on the aligned lattice of recognized words is significantly lower than the individual OCR word error rates. This lattice error rate constitutes a lower bound among aligned alternatives from the OCR output. Results from a collection of poor quality mid-twentieth century typewritten documents demonstrate an average reduction of 55.0% in the error rate of the lattice of alternatives and a realized word error rate (WER) reduction of 35.8% in a dictionary-based selection process. As an important precursor, an innovative admissible heuristic for the A* algorithm is developed, which results in a significant reduction in state space exploration to identify all optimal alignments of the OCR text output, a necessary step toward the construction of the word hypothesis lattice. On average 0.0079% of the state space is explored to identify all optimal alignments of the documents.
Stateflow Diagrams in Circus
Authors: Ana Cavalcanti
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

The Matlab Simulink tool is widely used to construct and analyse control law diagrams. Many have worked on techniques to enhance analysis facilities, and previously, we have considered the complementary problem of proving correctness of implementations of diagrams. We use Circus, a refinement language that combines Z and CSP, and can capture both functional and behavioural aspects of diagrams and programs. We defined a Circus semantics for an extensive subset of discrete-time diagrams, and now extend it to cover Stateflow blocks, which are themselves defined by diagrams written in (a variant of) the statechart notation. We highlight the challenging features of the semantics of a diagram, describe how Circus models can be constructed, and discuss the formalisation of the Circus semantics as algebraic translation rules.