ppxscal/embedding_network huggingface dataset:
Toward an understanding of bug fix patterns
Authors: Kai Pan, Sunghun Kim, E. James Whitehead,  Jr.
Venue: Empirical Software Engineering (2009)

Twenty-seven automatically extractable bug fix patterns are defined using the syntax components and context of the source code involved in bug fix changes. Bug fix patterns are extracted from the configuration management repositories of seven open source projects, all written in Java (Eclipse, Columba, JEdit, Scarab, ArgoUML, Lucene, and MegaMek). Defined bug fix patterns cover 45.7% to 63.3% of the total bug fix hunk pairs in these projects. The frequency of occurrence of each bug fix pattern is computed across all projects. The most common individual patterns are MC-DAP (method call with different actual parameter values) at 14.9---25.5%, IF-CC (change in if conditional) at 5.6---18.6%, and AS-CE (change of assignment expression) at 6.0---14.2%. A correlation analysis on the extracted pattern instances on the seven projects shows that six have very similar bug fix pattern frequencies. Analysis of if conditional bug fix sub-patterns shows a trend towards increasing conditional complexity in if conditional fixes. Analysis of five developers in the Eclipse projects shows overall consistency with project-level bug fix pattern frequencies, as well as distinct variations among developers in their rates of producing various bug patterns. Overall, data in the paper suggest that developers have difficulty with specific code situations at surprisingly consistent rates. There appear to be broad mechanisms causing the injection of bugs that are largely independent of the type of software being produced.
Mining constraint-based patterns using automatic relaxation
Authors: Arnaud Soulet, Bruno Crémilleux
Venue: Intelligent Data Analysis (2009)

Constraint-based mining is an active field of research which is a necessary step to achieve interactive and successful KDD processes. The limitations of the task lies in languages being limited to describe the mined patterns and the ability to express varied constraints. In practice, current approaches focus on a language and the most generic frameworks mine individually or simultaneously a monotone and an anti-monotone constraints. In this paper, we propose a generic framework dealing with any partially ordered language and a large set of constraints. We prove that this set of constraints called primitive-based constraints not only is a superclass of both kinds of monotone ones and their boolean combinations but also other classes such as convertible and succinct constraints. We show that the primitive-based constraints can be efficiently mined thanks to a relaxation method based on virtual patterns which summarize the specificities of the search space. Indeed, this approach automatically deduces pruning conditions having suitable monotone properties and thus these conditions can be pushed into usual constraint mining algorithms. We study the optimal relaxations. Finally, we provide an experimental illustration of the efficiency of our proposal by experimenting it on several contexts.
Collusion-resistant anonymous data collection method
Authors: Mafruz Zaman Ashrafi, See Kiong Ng
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

The availability and the accuracy of the data dictate the success of a data mining application. Increasingly, there is a need to resort to on-line data collection to address the problem of data availability. However, participants in on-line data collection applications are naturally distrustful of the data collector as well as their peer respondents, resulting in inaccurate data collected as the respondents refuse to provide truthful data in fear of collusion attacks. The current anonymity-preserving solutions for on-line data collection are unable to adequately resist such attacks in a scalable fashion. In this paper, we present an efficient anonymous data collection protocol for a malicious environment such as the Internet. The protocol employs cryptographic and random shuffling techniques to preserve participants' anonymity. The proposed method is collusion-resistant and guarantees that an attacker will be unable to breach an honest participant's anonymity unless she controls all N-1 participants. In addition, our method is efficient and achieved 15-42% communication overhead reduction in comparison to the prior state-of-the-art methods.
Maximum likelihood linear programming data fusion for speaker recognition
Authors: Enric Monte-Moreno, Mohamed Chetouani, Marcos Faundez-Zanuy, Jordi Sole-Casals
Venue: Speech Communication (2009)

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on different feature extraction techniques. Our experimental results assessed the robustness of the system in front changes on time (different sessions) and robustness in front of changes of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationally with the number of scores to be fusioned as the simplex method for linear programming.
Multi-criterion decision making by artificial intelligence techniques
Authors: Jerzy Balicki
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

Decision making for complex systems is based on multi-criterion-optimization. A decision making support can be applied to find the Pareto solutions. Multi-criterion tabu programming is a new paradigm for that task. Similarly to rules applied in the genetic programming, tabu programming solves problems by using a tabu algorithm that modifies some computer programs. We consider the multi-criterion problem of task assignment, where both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the distributed system is maximized. Furthermore, there are constraints for the performance of the distributed systems and the probability that all tasks meet their deadlines. What is more, constraints related to memory limits and computer locations are imposed on the feasible task assignment.
Robotic Companions for Smart Space Interactions
Authors: Duckki Lee, Tatsuya Yamazaki, Sumi Helal
Venue: IEEE Pervasive Computing (2009)

In the possible world of robotic companions, robots are adopted as butlers in our homes, as baby watchers, as friends and in general as life companions. But robots haven't gained majored acceptance. They're not in every home or even in a tiny fraction of homes. However, bold new research is taking shape with early commercial products already hitting the market place. Robots could soon be the "chief appliance" in your home, or the "new interface technology" for smart spaces. This piece reports on the emerging social robots technology and their use as interfaces to smart spaces such as smart homes. A case study on the usability of such robots, conducted in the Ubiquitous Home, in NICT, Japan, is briefly presented.
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
Authors: Sebastian Nowozin, Stefanie Jegelka
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We propose a new method to quantify the solution stability of a large class of combinatorial optimization problems arising in machine learning. As practical example we apply the method to correlation clustering, clustering aggregation, modularity clustering, and relative performance significance clustering. Our method is extensively motivated by the idea of linear programming relaxations. We prove that when a relaxation is used to solve the original clustering problem, then the solution stability calculated by our method is conservative, that is, it never overestimates the solution stability of the true, unrelaxed problem. We also demonstrate how our method can be used to compute the entire path of optimal solutions as the optimization problem is increasingly perturbed. Experimentally, our method is shown to perform well on a number of benchmark problems.
Variational problems in weighted sobolev spaces with applications to computational fluid dynamics
Authors: Rouben Rostamian
Venue:  (2008)

We study variational problems in weighted Sobolev spaces on bounded domains with angular points. The specific forms of these variational formulations are motivated by, and applied to, a finite element scheme for the time-dependent Navier-Stokes equations. Specifically, we introduce new variational formulations for the Poisson and Helmholtz problems in what would be a weighted counterpart of H2 &cap; H10 , show the existence and uniqueness of solutions, and establish the relationship with the traditional H1 formulations. We formulate a conforming C 1 finite element method for solving these variational problems and prove its convergence. We apply our methods to the time-dependent Navier-Stokes equations on non-convex polygonal domains by adapting an iterative algorithm due to Liu, Liu, and Pego. The well-posedness of the modified algorithm follows from our analysis of the Poisson and Helmholtz problems. Numerical results for several benchmark problems are provided.
Packet Loss Concealment Algorithm for VoIP Transmission in Unreliable Networks
Authors: Artur Janicki, Bartłomiej Księżak
Venue: Proceeding of the 2008 conference on New Trends in Multimedia and Network Information Systems (2008)

In this chapter, the authors propose an algorithm for packet loss concealment (PLC) in transmission over IP-based networks with high packet loss rate. The algorithm is a sender-receiver-based extension of ANSI T1.521a Annex B PLC standard for G.711 voice codec. It consists in adding to a transmitted packet redundant parameters describing speech signal in another packet. Efficiency of the proposed algorithm was verified using subjective Absolute Category Rating (ACR) method and objective PESQ algorithm, and compared with original ANSI T1.521a Annex B standard. The intelligibility of speech was assessed using Semantically Unpredictable Sentences (SUS) tests. For high packet loss rates, all assessment methods proved superiority of the proposed algorithm over the original ANSI standard. The ACR tests showed that the proposed method can maintain speech quality above 3 in MOS scale even for packet loss rates of 20%-25%.
Effects of Cognitive Load in Acquisition of Assembly Skills
Authors: Akira Urao, Kazuhisa Miwa
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Recently, instructional videos are widely being used in educational settings. Many researches have proposed the construction of video-based learning environments that reflect Cognitive Load Theory. In this study, we examined the effects of cognitive load induced by the speed of video playing in an interactive video-based learning environment. The experimental results showed that increasing or decreasing cognitive load affected the learners' selection of problem solving strategies, but did not provide learning effects. The results also indicated that learners showed adaptive behaviors for adjusting cognitive load depending on learning situations.
An acoustical study of high speed train transits
Authors: J. Quartieri, A. Troisi, C. Guarnaccia, T. L. L. Lenza, P. D'Agostino, S. D'Ambrosio, G. Iannone
Venue: WSEAS TRANSACTIONS on SYSTEMS (2009)

In this paper, the comparison between experimental acoustical data and predictive software simulation is performed. The area under investigation contains a high velocity railway and it is very interesting in terms of geometrical features and environmental conditions. In a recent paper, the experimental campaign has been presented and a preliminary model has been tested with that measurements. In this work, we continue this analysis approaching an acoustical predictive software, CadnaA by Datakustik, and checking its response with respect to the experimental data. The need of a precise modelling is underlined, since the results of acoustical simulations are strongly affected by the number of sources included and their sound power level. Particular emphasis is also given to the representation of results, focusing on the production of "easy to read" maps. In the last part of the paper, noise coming from high velocity trains has been modeled as noise coming from simple geometrical elements, such as points and segments. The comparison between these simulated values and the experimental data is performed and appears to be an interesting basis for forthcoming studies.
Using the bottom clause and mode declarations in FOL theory revision from examples
Authors: Ana Luísa Duboc, Aline Paes, Gerson Zaverucha
Venue: Machine Learning (2009)

Theory revision systems are designed to improve the accuracy of an initial theory, producing more accurate and comprehensible theories than purely inductive methods. Such systems search for points where examples are misclassified and modify them using revision operators. This includes trying to add antecedents to clauses usually following a top-down approach, considering all the literals of the knowledge base. Such an approach leads to a huge search space which dominates the cost of the revision process. ILP Mode Directed Inverse Entailment systems restrict the search for antecedents to the literals of the bottom clause. In this work the bottom clause and mode declarations are introduced in a first-order logic theory revision system aiming to improve the efficiency of the antecedent addition operation and, consequently, also of the whole revision process. Experimental results compared to revision system FORTE show that the revision process is on average 55 times faster, generating more comprehensible theories and still not significantly decreasing the accuracies obtained by the original revision process. Moreover, the results show that when the initial theory is approximately correct, it is more efficient to revise it than learn from scratch, obtaining significantly better accuracies. They also show that using the proposed theory revision system to induce theories from scratch is faster and generates more compact theories than when the theory is induced using a traditional ILP system, obtaining competitive accuracies.
Effective and efficient analysis of spatio-temporal data
Authors: Weili Wu
Venue:  (2008)

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human&rsquo;s ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables&rsquo; changing has tight relation with some severe weather situation that will happen very soon. This dissertation is composed of three parts: an introduction, some basic knowledges and relative works, and my own three contributions to the development of approaches for spatio-temporal data mining: DYSTAL algorithm, STARSI algorithm, and COSTCOP+ algorithm.
The ZooLib tuplebase: an open-source, scalable database architecture for learning sciences research
Authors: Chris Teplovs, Andrew Green, Marlene Scardamalia
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

We introduce a free, open-source, non-relational database architecture that is robust, mature, scalable and extensible. The absence of a fixed schema is particularly important to research in the learning sciences where a broad spectrum of research data may need to be stored. We highlight an alternative query mechanism that is suited to highly interactive applications.
Association rules mining including weak-support modes using novel measures
Authors: Jian Hu, Xiang-Yang Li
Venue: WSEAS Transactions on Computers (2009)

In association rules mining application, some rules can provide a lot of useful knowledge for us, though these rules have the lower Support, called weak-support mode in this paper. However, in existing Support-Confidence framework, the rules with lower Support will be lost. Thus, this paper puts forward a new association rules mining technique, which sets up the lower support threshold value to ensure the weak-support rules to be mined and applies Csupport measure to recognise weak-support mode. Then, a new measure, called as N-confidence, is used to restrict mining size in generation frequent sets, which can strain away the weak-support rules without correlation. Furthermore, this paper puts forward a new interesting measure to distinguish from the association rules interesting degree. In order to enhance mining efficiency, a novel algorithm, namely FT-Miner, is presented to discover association rules in a forest by using two new data structures, including UFP-Tree and FP-Forest. The experimentation shows that the algorithm not only mines useful and weak-support rules, but also has better capability than classical association rules mining algorithms.
A logical characterization of iterated admissibility
Authors: Joseph Y. Halpern, Rafael Pass
Venue: Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge (2009)

Brandenburger, Friedenberg, and Keisler provide an epistemic characterization of iterated admissibility (i.e., iterated deletion of weakly dominated strategies) where uncertainty is represented using LPSs (lexicographic probability sequences). Their characterization holds in a rich structure called a complete structure, where all types are possible. Here, a logical characterization of iterated admissibility is given that involves only standard probability and holds in all structures, not just complete structures. Roughly speaking, our characterization shows that iterated admissibility captures the intuition that "all the agent knows" is that agents satisfy the appropriate rationality assumptions.
Information fusion for computer security: State of the art and open issues
Authors: Igino Corona, Giorgio Giacinto, Claudio Mazzariello, Fabio Roli, Carlo Sansone
Venue: Information Fusion (2009)

In this paper, we critically review the issue of information fusion for computer security, both in terms of problem formulation and in terms of state-of-the-art solutions. We also analyze main strengths and weaknesses of currently used approaches and propose some research issues that should be investigated in the future.
Reasoning about actions under uncertainty: A possibilistic approach
Authors: Juan Carlos Nieves, Mauricio Osorio, Ulises Cortés, Francisco Caballero, Antonio López-Navidad
Venue: Proceeding of the 2007 conference on Artificial Intelligence Research and Development (2007)

In this paper, we present an action language which is called APoss in order to perform reasoning about actions under uncertainty. This language is based on a possibilistc logic programming semantics which is able to deal with reasoning under uncertain information. In order to illustrate the language, we use a medical scenario.
Technical communique: Adaptive robust control of uncertain nonlinear systems with nonlinear delayed state perturbations
Authors: Hansheng Wu
Venue: Automatica (Journal of IFAC) (2009)

The problem of robust stabilization of uncertain nonlinear dynamical systems with multiple time delays is considered. In the paper, the upper bound of the nonlinearity and uncertainty, including delayed states, is assumed to be a linear function of some parameters which are still assumed to be unknown. Here, we do not require that the nonlinear terms including delayed states are linear norm-bounded in the states. An improved adaptation law with @s-modification is employed to estimate the unknown parameters, and a class of memoryless adaptive robust state feedback controllers is proposed. It is also shown that the proposed adaptive robust controllers can guarantee the uniform asymptotic stability of uncertain nonlinear time-delay systems. Finally, as a numerical example, an uncertain time-delay ecosystem with two competing species is given to demonstrate the validity of the results.
Credibility measure of fuzzy sets and applications
Authors: Xiang Li, Dan Ralescu
Venue: International Journal of Advanced Intelligence Paradigms (2009)

In order to measure a fuzzy event, credibility measure is proposed as a non-additive set function. In this paper, this concept is extended to fuzzy sets. First, a mean measure is defined by Lebesgue integral and some properties are investigated such as the monotonicity theorem, self-duality theorem and subadditivity theorem. Furthermore, by using Sugeno integral, an equilibrium measure is proposed and studied. Finally, these concepts are applied to optimisation problems, and the mean measure maximisation model and equilibrium measure maximisation model are proposed.
Analysis of Parliamentary Election Results and Socio-Economic Situation Using Self-Organizing Map
Authors: Pyry Niemelä, Timo Honkela
Venue: Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (2009)

The complex phenomena of political science are typically studied using qualitative approach, potentially supported by hypothesis-driven statistical analysis of some numerical data. In this article, we present a complementary method based on data mining and specifically on the use of the self-organizing map. The idea in data mining is to explore the given data without predetermined hypotheses. As a case study, we explore the relationship between parliamentary election results and socio-economic situation in Finland between 1954 and 2003.