ppxscal/embedding_network huggingface dataset:
Foundations of Computer Science, 2nd edition
Authors: Behrouz A. Forouzan, Firouz Mosharraf
Venue:  (2007)

Based on the ACM model curriculum guidelines, this easy-to-read and easy-to-navigate text covers all the fundamentals of computer science required for first year students embarking on a computing degree. Divided into five parts computer and data, computer hardware, computer software, data organization and with an introduction to some of the more advanced topics Foundations of Computer Science gives students a birds eye view of the subject. Each chapter includes key terms, summaries, review questions, multiple-choice questions, and exercises to enhance learning, while introducing tools such as UML, structure chart and pseudocode, which students will need in order to succeed in later courses. The text is also supported by numerous figures, examples, exercises, selected solutions and a test bank, all designed to ease and aid the learning process. Updated to cover the latest technologies and changes to course requirements, this second edition features new and updated coverage of: Artificial Intelligence, Computer Ethics and Crimes, Networking, LINUX, and Security.
Empirical analysis on chinese academic plagiarism
Authors: Yang Shen, Huijuan Fu, Zitao Liu, Pengpeng Liu, Qingchuan Fu
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

This poster, from angels of subjects, authors' social network, authors' combination, and students' plagiarism law, apply self-developed ROST Anti-plagiarism Software to check 3781 papers, do a survey among 450 students, quantitatively analyzed academic plagiarism conditions in China, and draw several conclusions.
Exploring the evolution of software quality with animated visualization
Authors: Guillaume Langelier, Houari Sahraoui, Pierre Poulin
Venue: Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (2008)

Assessing software quality and understanding how events in its evolution have lead to anomalies are two important steps toward reducing costs in software maintenance. Unfortunately, evaluation of large quantities of code over several versions is a task too time-consuming, if not overwhelming, to be applicable in general.
Complete search algorithms for model counting, inference, and optimization problems
Authors: Paul Beame, Henry Kautz
Venue:  (2008)

Although NP-complete problems including satisfiability testing (SAT) are traditionally thought as intractable, the efficiency of SAT solvers has been hugely improved over the past decade and a half, and modern SAT solvers are successfully used to solve many practical hard problems in hardware/software verification, planning, diagnosis, and other areas. Despite the popularity of SAT, #SAT, the problem of counting models of a formula, has been much less exploited. That is partly due to the #P hardness of model counting, which may remain hard even if SAT is in P. This thesis focuses on how to design a new sound, complete, and efficient #SAT algorithm, which is a novel integration of several major techniques including dynamic decomposition, component caching, clause learning, branching heuristics binary clause propagation and backtracking. Finally, it shows how to apply the resulting #SAT solver Cachet to (1) Bayesian inference via CNF encoding and weighted model counting, and (2) various optimization problems such as Most Probable Explanation (MPE), Maximum Satisfiability (MAX-SAT) and Maximum A Posteriori (MAP) by extending it with dynamic bounding. This thesis reports empirical results on a wide range of problem domains, compares our solver with other state-of-the-art solvers and concludes that our approach is often significantly better than or at least very competitive with others. The main contribution of this work is to present new efficient #SAT-based algorithms and show how they can be successfully applied to some hard AI problems that require exhaustive search.
A geometric approach for classification and comparison of structural variants
Authors: Suzanne Sindi, Elena Helman, Ali Bashir, Benjamin J. Raphael
Venue: Bioinformatics (2009)

Motivation: Structural variants, including duplications, insertions, deletions and inversions of large blocks of DNA sequence, are an important contributor to human genome variation. Measuring structural variants in a genome sequence is typically more challenging than measuring single nucleotide changes. Current approaches for structural variant identification, including paired-end DNA sequencing/mapping and array comparative genomic hybridization (aCGH), do not identify the boundaries of variants precisely. Consequently, most reported human structural variants are poorly defined and not readily compared across different studies and measurement techniques. Results: We introduce Geometric Analysis of Structural Variants (GASV), a geometric approach for identification, classification and comparison of structural variants. This approach represents the uncertainty in measurement of a structural variant as a polygon in the plane, and identifies measurements supporting the same variant by computing intersections of polygons. We derive a computational geometry algorithm to efficiently identify all such intersections. We apply GASV to sequencing data from nine individual human genomes and several cancer genomes. We obtain better localization of the boundaries of structural variants, distinguish genetic from putative somatic structural variants in cancer genomes, and integrate aCGH and paired-end sequencing measurements of structural variants. This work presents the first general framework for comparing structural variants across multiple samples and measurement techniques, and will be useful for studies of both genetic structural variants and somatic rearrangements in cancer. Availability: http://cs.brown.edu/people/braphael/software.html Contact: braphael@brown.edu
The Incompleteness of Planning with Volatile External Information
Authors: Tsz-Chiu Au, Dana Nau
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

In many real-world planning environments, some of the information about the world is both external (the planner must request it from external information sources) and volatile (it changes before the planning process completes). In such environments, a planner faces two challenges: how to generate plans despite changes in the external information during planning, and how to guarantee that a plan returned by the planner will remain valid for some period of time after the planning ends. Previous works on planning with volatile information have addressed the first challenge, but not the second one. This paper provides a general model for planning with volatile external information in which the planner offers a guarantee of how long the solution will remain valid after it is returned, and an incompleteness theorem showing that there is no planner that can succeed in solving all solvable planning problems in which there is volatile external information.
Finite Time Stability of Cohen-Grossberg Neural Network with Time-Varying Delays
Authors: Dingguo Jiang
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

This paper considers the problem of finite time stability (FTS) of the Cohen-Grossberg neural networks with or without delay. Based on the Lyapunov function and linear matrix inequality (LMI) technique, some delay-dependent and delay-independent criterions are derived to guarantee finite-time stability. Finally, one example is given to demonstrate the validity of the proposed methodology and to show the differences between globally exponential stability and finite-time stability.
A neural network classifier with rough set-based feature selection to classify multiclass IC package products
Authors: Y. H. Hung
Venue: Advanced Engineering Informatics (2009)

The choice of packaging type is important to the process of researching and developing an integrated circuit (IC). Indeed, for an IC chip designer, the importance can be compared to an architect's choice of construction design. Since there are considerable variations in characteristics and in the types of products available, collecting information about packaging technologies and products can be difficult and time-consuming. Therefore, finding the means to provide packaging information to designers quickly and efficiently is necessary and important, as this will not only help designers accurately decide on design methods for an IC, but also significantly reduce processing risks. In this study, existing product information, such as the dimensions, characteristics and design and application criteria, of a product was analyzed. One of the biggest issues when data from multi-dimensional measurements are represented as a feature vector is that the feature space of the raw data often has very large dimensions. This study explores the use of rough set attribute reduction (RSAR) to reduce attributes of the IC package family dataset, and artificial neural networks, to construct an efficient IC package type classifier model. The experimental results show that the features produced by RSAR improve on generalization accuracy: the training and testing set classification accuracy rates were 96.9% and 98.2%, respectively.
The impact of general and specific performance and self-efficacy on learning with computer-based concept mapping
Authors: Jeng-Yi Tzeng
Venue: Computers in Human Behavior (2009)

When students learn a new and challenging task, for which they have very limited corresponding personal or vicarious experiences to refer, it is important to understand how their self-efficacy beliefs evolve during the course of sequential lessons; how they differ in the way their general learning performance and self-efficacy influence their ratings of task-specific self-efficacy and performance; and, how such differences may result in different learning outcomes and motivations for learning. By examining a group of 66 students engaged in learning to construct good concept maps with computer software, this study revealed several important findings on these questions. Students generally tended to initially overestimate their ability to successfully carry out the required task. They also varied in the way their general self-efficacy and performance were associated with their task-specific self-efficacy and performance. And, these differences did appear to be associated with different learning outcomes and motivations for learning. These results indicate that investigating individual differences in students' patterns of association between general and specific performance and their self-efficacy may lead to a better understanding of how students differ in their levels of motivation and outcomes when learning a new and challenging task.
Workshop on Using Emerging Parallel Architectures for Computational Science
Authors: Bertil Schmidt, Douglas Maskell
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

The Workshop on Using Emerging Parallel Architectures for Computational Science, held in conjunction with ICCS 2009, provides a forum for exploring the capabilities of emerging parallel architectures such as GPUs, FPGAs, Cell B.E., and multi-cores to accelerate computational science applications.
Integrating information sources for recommender systems
Authors: Silvana Aciar, Josefina López Herrera, Josep Lluis de la Rosa
Venue: Proceeding of the 2005 conference on Artificial Intelligence Research and Development (2005)

This paper presents a Multi-agent System and a Methodology to select and to integrate heterogenous and distributed information sources to make recommendations. A set of intrinsic characteristics has been defined. These characteristics allow having a description of the information contained in the sources to select the most relevant information sources. Ontologies are used to integrate the information from the selected sources. And a case study confirms our proposal.
A new matrix test for randomness
Authors: Otokar Gro&#x0161;ek, Milan Vojvoda, R&#x00f3;bert Krchnav&#x00fd;
Venue: Computing (2009)

Let be one of the two multiplicative semigroups: M × M Boolean matrices, or the semigroup of M × M matrices over the field GF(2). Then for any matrix there exist two unique smallest numbers, namely the index and period k, d, such that A k = A k+d . This fact allows us to form a new statistical test for randomness which we call the Semigroup Matrix Test. In this paper, we present details and results of our experiments for this test. We use Boolean matrices for M = 2, . . . , 5, and matrices over GF(2) of the size M = 2, . . . , 6. We also compare the results with the results obtained by the well-known Binary Matrix Rank Test.
A context-aware ubiquitous learning environment for conducting complex science experiments
Authors: Gwo-Jen Hwang, Tzu-Chi Yang, Chin-Chung Tsai, Stephen J. H. Yang
Venue: Computers Education (2009)

Context-aware ubiquitous learning (u-learning) is an innovative approach that integrates wireless, mobile, and context-awareness technologies to detect the situation of learners in the real world and provide adaptive support or guidance accordingly. In this paper, a context-aware u-learning environment is developed for guiding inexperienced researchers to practice single-crystal X-ray diffraction operations. Experimental results showed that the benefits of this innovative approach are that it is ''systematic'', ''authentic'', and ''economical'', which implies the potential of applying it to complex science experiments, such as physics, chemistry or biotechnology experiments, for graduate and PhD students in colleges, or research workers in research institutes.
Topology Dynamics in a P2PTV Network
Authors: Siyu Tang, Yue Lu, Javier Martín Hernández, Fernando Kuipers, Piet Mieghem
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

In recent years, a number of commercial peer-to-peer TV (P2PTV) applications have been launched. Yet, their mechanisms and characteristics are unknown. In this paper, we study SopCast, a typical proprietary P2PTV system. Treating SopCast as a black box, we perform a set of experiments that are suitable to analyze SopCast in depth. We attempt to disclose the SopCast protocol. The dynamic nature of the SopCast overlay, in terms of node degree, is also addressed in this paper. Our approaches in analyzing the SopCast mechanism and characterizing its topological properties reveal important design insights in SopCast, and may help to better understand similar P2PTV systems.
The case for prevention-based, host-resident defenses in the modern PCS network
Authors: Charles Payne,  Jr., Richard C. O'Brien, J. Thomas Haigh
Venue: Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies (2009)

The process control system (PCS) owner can no longer rely on a physical air gap and custom hardware to protect her network from attack. Demand for greater visibility into PCS operations, coupled with greater use of commodity hardware, now exposes the PCS network to the same threats facing other networks. To address these threats, we argue for the deployment of prevention-based, host-resident, network layer devices, coupled with scalable, service-based management, that will not only protect PCS communications but will also support higher level reasoning about PCS trustworthiness. We explain why the modern PCS network is particularly well-suited for this approach, and we highlight where our own research supports this claim.
COMPASS: A Community-driven Parallelization Advisor for Sequential Software
Authors: Simha Sethumadhavan, Nipun Arora, Ravindra Babu Ganapathi, John Demme, Gail E. Kaiser
Venue: Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering (2009)

The widespread adoption of multicores has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this paper, we describe the research being conducted at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers while they reengineer their code for parallelism. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some similar code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ability to automatically seek, find and synthesize this wisdom into advice that is tailored to the task at hand, i.e., the code the user is considering parallelizing and the environment in which the optimized program is planned to execute. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization - widely, and on diverse hardware and software. By leveraging the “wisdom of crowds” model [30], which has been conjectured to scale exponentially and which has successfully worked for wikis, COMPASS aims to enable rapid propagation of knowledge about code parallelization in the context of the actual parallelization reengineering, and thus continue to extend the benefits of Moores law scaling to science and society.
Analyzing the use of a rubric-based grading tool
Authors: Tuukka Ahoniemi, Ville Karavirta
Venue: Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education (2009)

Over the years, a lot of research has focused on how to assess programming courses. For programming courses, semiautomatic assessment combining automatic and manual feedback has been shown to be a good solution. In this paper, we will focus on the manual assessment part and analyze the use of a rubrics-based grading tool on larger courses with multiple graders. Our results show that the use of such tools can support objective grading with high-quality feedback with reasonable time usage. Finally, we will give some pointers for teachers intending to adopt such tools on their courses.
Grid management support by means of collaborative learning agents
Authors: Wico Mulder, Ceriel Jacobs
Venue: Proceedings of the 6th international conference industry session on Grids meets autonomic computing (2009)

The complex and dynamic settings of grid environments lead to challenges on their operational maintenance. The growth of these environments in terms of size and usage requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. Communication across organizational domains and interoperability between existing monitoring tools is subject to improvement. In this paper we present an information system, based on collaborative agents, that supports system administrators in monitoring the grid. While observing log files, the agents learn patterns about job-traffic in their own local domain of the grid and share information to provide global or multi-domain overviews. The agents represent their knowledge in the form of deterministic finite automata (DFA). We discuss our collaborative learning mechanism and show the results of our experiments with data of two grid-sites. Our system generated job-traffic overviews that gave new insights in the performance of the grid environment.
Plenary lecture 12: a critical review of the mathematical robustness of genetic algorithms in optimization problems
Authors: Manoj K. Jha
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

Genetic Algorithms (GAs) have been shown to be particularly effective in many optimization problems due to their ability to search in a continuous space without getting trapped in local optima while exploiting the entire search space. Some recent research works however, show that the mathematical theory of GAs are not fully understood and its validity may be questioned. This may affect the quality and reliability of solutions obtained from the GA optimization process. In addition, sensitivity of critical parameters in GAs may also affect the quality of solution. If GAs are applied properly, similar solutions should be expected at each replication, regardless of where the search process starts. Some of the critical parameters affecting search performance include the number of genetic operators, the number of decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. In this presentation I provide a critical review of the mathematical foundation of GAs and also show a sensitivity analysis of the critical parameters that may affect the quality of solutions. Attention is directed specifically to the schema theory, which are considered to be the building blocks for GA operation. Using my previous collaborative work with Dr. David Lovell of the University of Maryland, College Park I show a hard upper bound on the number of matching schemata in binary and real coded GAs for a population of a given size and string length. A loose upper bound is commonly reported in the published literature, but does not take into account redundancies that are inevitable for certain values of the population size. In this ppresentation, this over-counting is rectified. A special case when the string length is small compared to the population size is shown to have a particularly elegant solution. In order to investigate the effects of critical parameters when adopting GAs I show a sensitivity analysis using previous collaborative work with Dr. Eungcheol Kim of the University of Incheon, South Korea, which shows that quality of solutions depend on their proximity of convergence from different starting points. Finally, we investigate the improvement in solution quality with the derived hard upper bound.
The Heuristic Algorithm Based on Learning-Competing Model and Its Application to Task Assignment Problem
Authors: Zhe Wang, Hongwei Wang, Xi Chen, Yi Jiang
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

A universal model-learning-competing one is established for solving task assignment problem in National Economy Mobilization(NEM), in which local search was focused in learning model and global search in competing. Thereafter the strong points of the two models were amalgamated in the algorithm. A team of parameters was used to coordinating the correlation of the two models. Applying learning-competing model to an actual case, using greed algorithm in learning model, genetic algorithm in competing model, the results obtained were in coincidence with the analysis.