ppxscal/embedding_network huggingface dataset:
Decision-Making Based on Case Similarity with Alterable Weights
Authors: Chuanhua Zeng, Yang Xu, Weicheng Xie
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

In the light of the Case-Based Decision Theory (CBDT), this paper proposes that the attributes' weights corresponding to different acts should also be different. Based on this idea, the concept of Case Similarity with Alterable Weights is put forward, a decision-making method introduced, and optimization model for weights setting presented. Meanwhile, this method is applied to the case of examining vehicles' technical state under accident, and it proves effective.
A Novel Algorithm for Identifying Corresponding Attributes in Heterogeneous Databases
Authors: Bao-hua Qiang, Ling Chen, Jian-qing Xi, Zhong-fu Wu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

Identifying corresponding attributes is an important issue to realize data sharing and interoperability in heterogeneous databases. The main approaches at present adopted predefined rules to evaluate the similarity of attributes by comparing all attributes, which have some limitations. So two-phase-checking algorithm based on BP neural network was presented in this paper to realize attributes matching. The experimental results show our proposed approach can improve the system performance, the precision and recall of attributes matching obviously.
P2P authority analysis for social communities
Authors: Josiane Xavier Parreira, Sebastian Michel, Matthias Bender, Tom Crecelius, Gerhard Weikum
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

PageRank-style authority analyses of Web graphs are of great importance for Web mining. Such authority analyses also apply to hot "Web 2.0" applications that exhibit a natural graph structure, such as social networks (e.g., MySpace, Facebook) or tagging communities (e.g., Flickr, Del.icio.us). Finding the most trustworthy or most important authorities in such a community is a pressing need, given the huge scale and also the anonymity of social networks. Computing global authority measures in a Peer-to-Peer (P2P) collaboration of autonomous peers is a hot research topic, in particular because of the incomplete local knowledge of the peers, which typically only know about (arbitrarily overlapping) sub-graphs of the complete graph. We demonstrate a self-organizing P2P collaboration that, based on the local sub-graphs, efficiently computes global authority scores. In hand with the loosely-coupled spirit of a P2P system, the computation is carried out in a completely asynchronous manner without any central knowledge or coordinating instance. We demonstrate the applicability of authority analyses to large-scale distributed systems.
Sparse Sampling Action Values Initialized by a Compact Representation Technique
Authors: Celeny F. Alves, Esther L. Colombini, Carlos H. C. Ribeiro
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

Most of the techniques proposed for problems involving mobile robots are specified in terms of optimal control of Markov Decision Processes (MDPs). However, the state space dimension explosion makes such tabular MDP-based solutions unfeasible. As an alternative to this, a planning technique based on sparse sampling (SSA) of simulated in- stances of a MDP model has been suggested. Because the execution time of this algorithm is exponential on the level of an exploration tree and on the number of samplings to be generated, this paper proposes a technique where leaves null-values in the SSA algorithm are substitute by meaning- ful values, acquired from any of the following approaches: 1) a simple environment reward distribution; 2) a standard Reinforcement Learning algorithm, and 3) a compact rep- resentation on a coarse state discretization for generating initial estimates of the action values. The experiments car- ried out showed that such information-based variants of SSA lead quickly to better results than the original tech- nique.
A Novel Edge Detection Technique with Orientation-Based Similarity and Immunological Principles
Authors: Boping Shi, Jingsong He
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

In order to avoid the shortage of intensity threshold, we propose an edge detection approach based on an instinct behavior orientation-based similarity of edge(OBS), which is absolutely independent from intensity gradient. We first form series of overlapping curves by using OBS and then identify the pixels in the majority rule. A system designed by using some immunological principles is the executor of this rule. Experimental results, which verity the usefulness of OBS and immunological principles, are given.
A New Model Based Hybrid Particle Swarm Algorithm for Multi-objective Optimization
Authors: Jingxuan Wei, Yuping Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

In this paper, a hybrid PSO algorithm is proposed. The new algorithm uses a simulated annealing based weighted-sum method to perform local search. The local search mechanism prevents premature convergence, hence enhances the convergence ability to the true pareto front. Meanwhile the multi-objective optimization problem is converted into the constrained optimization problem. For the converted problem, a new selection strategy based on the constraint dominance principle is used to select the next swarm. This attempt integrates particle swarm and evolutionary algorithm together in order to take advantage of both algorithms and improve the quality of solutions. The computer simulations for four difficulty benchmark functions show that the new algorithm is able to find uniformly distributed pareto optimal solutions and is able to converge to the true pareto-optimal front.
A nanotechnology-based approach to data storage
Authors: E. Eleftheriou, P. Bächtold, G. Cherubini, A. Dholakia, C. Hagleitner, T. Loeliger, A. Pantazi, H. Pozidis, T. R. Albrecht, G. K. Binnig, M. Despont, U. Drechsler, U. Dürig, B. Gotsmann, D. Jubin, W. Häberle, M. A. Lantz, H. Rothuizen, R. Stutz, P. Vettiger, D. Wiesmann
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

Ultrahigh storage densities of up to 1 Tb/in2. or more can be achieved by using local-probe techniques to write, read back, and erase data in very thin polymer films. The thermomechanical scanning-probe-based data-storage concept, internally dubbed "millipede", combines ultrahigh density, small form factor, and high data rates. High data rates are achieved by parallel operation of large 2D arrays with thousands micro/nanomechanical cantilevers/tips that can be batch-fabricated by silicon surface-micromachining techniques. The inherent parallelism, the ultrahigh areal densities and the small form factor may open up new perspectives and opportunities for application in areas beyond those envisaged today.
A Knowledge-Based System for Designing Testable VLSI Chips
Authors: Magdy Abadir, Melvin Breuer
Venue: IEEE Design Test (1985)

The complexity of VLSI circuits has increased the need for design for testability (DFT). Numerous techniques for designingmore easily tested circuits have evolved over the years, with particular emphasis on built-in testing approaches. What hasnot evolved is a design methodology for evaluating and making choices among the numerous existing approaches. This articledescribes efforts to build a knowledge-based expert system for designing testable VLSI chips. A framework for a methodologyincorporating structural, behavioral, qualitative, and quantitative aspects of known DFT techniques is introduced. This methodologyprovides a designer with a systematic DFT synthesis approach. The process of partitioning a design into subcircuits for individualprocessing is described and a new concept?I-path?is used to transfer data from one place in the circult to another. Rulesfor applying testable design methodologies to circuit partitions and for evaluating the various solutions obtained are alsopresented. Finally, a case study using a prototype system is described.
Scenario-Based Requirements for Web Macro Tools
Authors: Christopher Scaffidi, Allen Cypher, Sebastian Elbaum, Andhy Koesnandar, Brad Myers
Venue: Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (2007)

Web macros automate interactions with web sites and related information systems. Though web macro recorders and players have grown in sophistication over the past decade, these tools cannot yet meet many needs of users in daily life. Based on observations of browser users, we have compiled ten scenarios describing tasks that users would benefit from automating. Our analysis of these scenarios yields specific requirements that web macro tools should support if those tools are to be applicable to these real-life tasks. Our set of requirements constitutes a benchmark for evaluating tools.
A New Representation of Sequential Machines for Determining All Incompatible Pairs
Authors: Sureshchander
Venue: IEEE Transactions on Computers (1974)

In this correspondence a new representation of a sequential machine (without output) is given which enables the determination of all incompatible pairs without any intermediate step(s). The method is programmable, is much faster, and requires less memory than existing methods.
Software Reuse through Building Blocks
Authors: M. Lenz
Venue: IEEE Software (1987)

Specification, design, and code can all be reused easily if handled as a building block. An IBM group recently developed this concept and applied it to systems programming - with success.
Gene expression modeling through positive boolean functions
Authors: Francesca Ruffino, Marco Muselli, Giorgio Valentini
Venue: International Journal of Approximate Reasoning (2008)

In the framework of gene expression data analysis, the selection of biologically relevant sets of genes and the discovery of new subclasses of diseases at bio-molecular level represent two significant problems. Unfortunately, in both cases the correct solution is usually unknown and the evaluation of the performance of gene selection and clustering methods is difficult and in many cases unfeasible. A natural approach to this complex issue consists in developing an artificial model for the generation of biologically plausible gene expression data, thus allowing to know in advance the set of relevant genes and the functional classes involved in the problem. In this work we propose a mathematical model, based on positive Boolean functions, for the generation of synthetic gene expression data. Despite its simplicity, this model is sufficiently rich to take account of the specific peculiarities of gene expression, including the biological variability, viewed as a sort of random source. As an applicative example, we also provide some data simulations and numerical experiments for the analysis of the performances of gene selection methods.
Vision paper: enabling privacy for the paranoids
Authors: G. Aggarwal, M. Bawa, P. Ganesan, H. Garcia-Molina, K. Kenthapadi, N. Mishra, R. Motwani, U. Srivastava, D. Thomas, J. Widom, Y. Xu
Venue: Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 (2004)

P3P [23, 24] is a set of standards that allow corporations to declare their privacy policies. Hippocratic Databases [6] have been proposed to implement such policies within a corporation's datastore. From an end-user individual's point of view, both of these rest on an uncomfortable philosophy of trusting corporations to protect his/her privacy. Recent history chronicles several episodes when such trust has been willingly or accidentally violated by corporations facing bankruptcy courts, civil subpoenas or lucrative mergers. We contend that data management solutions for information privacy must restore controls in the individual's hands. We suggest that enabling such control will require a radical re-think on modeling, release, and management of personal data.
Choosing t-Norms and t-Conorms for Fuzzy Controllers
Authors: Khurshid Ahmad, Andrea Mesiarova-Zemankova
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

The choice of t-norms and the dual t-conorms should, in principle, play a key role in the operation of a fuzzy control system, particularly the output of the system. The t-norms are expressed differently and appear to have different prop- erties. We show analytically, using a zero-order Takagi- Sugeno controller the regions where the choice of a t-norm is important. We then simulated the behaviour of a Cartpole as a Mamdani-controller and computed the controller's out- put for different choices of t-norms and t-conorms.
An Optimal Model Based on Queueing Theory for the Problem of Inspection Station Location in a Traffic Network
Authors: Yinzhen Li, Ruichun He, Yaohuang Guo
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

Determining the appropriate locations for inspection stations in a traffic network is a special kind of flow in- terception problem. It has very important applications in the fields of traffic management and police defense man- agement. It is of great significance to research this prob- lem. Based on the analysis of the characteristics of traf- fic flow, and on the assumption that the interarrival times of the cars follow Poisson distribution and the service time follows negative exponential distribution, this paper intends to research that with the case of the constraints of the ex- pected car queue length, the expected car waiting time, the server busy rate and inspection station capacity taken into account, and based on queueing theory, a non-linear inte- ger programming model for the problem of inspection sta- tion location is formulated. Finally, a numerical example is given.
Perceptual coding of audio signals using adaptive time-frequency transform
Authors: Karthikeyan Umapathy, Sridhar Krishnan
Venue: EURASIP Journal on Audio, Speech, and Music Processing (2007)

Wide band digital audio signals have a very high data-rate associated with them due to their complex nature and demand for high-quality reproduction. Although recent technological advancements have significantly reduced the cost of bandwidth and miniaturized storage facilities, the rapid increase in the volume of digital audio content constantly compels the need for better compression algorithms. Over the years various perceptually lossless compression techniques have been introduced, and transform-based compression techniques have made a significant impact in recent years. In this paper, we propose one such transform-based compression technique, where the joint time-frequency (TF) properties of the nonstationary nature of the audio signals were exploited in creating a compact energy representation of the signal in fewer coefficients. The decomposition coefficients were processed and perceptually filtered to retain only the relevant coefficients. Perceptual filtering (psychoacoustics) was applied in a novel way by analyzing and performing TF specific psychoacoustics experiments. An added advantage of the proposed technique is that, due to its signal adaptive nature, it does not need predetermined segmentation of audio signals for processing. Eight stereo audio signal samples of different varieties were used in the study. Subjective (mean opinion score--MOS) listening tests were performed and the subjective difference grades (SDG) were used to compare the performance of the proposed coder with MP3, AAC, and HE-AAC encoders. Compression ratios in the range of 8 to 40 were achieved by the proposed technique with subjective difference grades (SDG) ranging from -0.53 to -2.27.
One-Sided Fuzzy SVM Based on Sphere for Imbalanced Data Sets Learning
Authors: Hui Han, Binghuan Mao, Hairong Lv, Qing Zhuo, Wenyuan Wang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

Learning from imbalanced data sets presents a new challenge to machine learning community, as traditional algorithms are biased to the majority classes and produce poor detection rate of the minority classes. This paper presents a one-sided fuzzy support vector machine algorithm based on sphere to improve the classification performance of the minority class. Firstly, the approach obtains the minimal hyper sphere of the majority class; secondly, it uses the center and radius of the hyper sphere to give the fuzzy member- ship of the majority instances, and thus effectively reduces the influence of majority noises and redundant instances in the classification process. Experiments show that our new approach improves not only the classification performance of the minority class more effectively, but also the classification performance of the whole data set comparing with other methods.
Special Feature: Studying the Oceans from Space
Authors: W. Myers
Venue: Computer (1978)

Three high-speed data streams from a new ocean-monitoring satellite are computer-processed on the ground into meaningful geophysical and meteorological information. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
An Efficient Artificial Immune Network with Elite-Learning
Authors: Zhonghua Li, Yunong Zhang, Hong-Zhou Tan
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

This paper proposed an efficient artificial immune network (EaiNet) for function optimization with the guide of spirit of particle swarm optimization (PSO). On the one hand, this algorithm absorbs the learning mechanism of PSO, i.e., the elite learning that each individual is capable of learning from the best in the social population. The introduction of the elite learning quickens the convergence speed of EaiNet. On the other hand, EaiNet has self-learning capability, especially when it is stick in the local optima, which will result in finer global optima. Compared to the conventional artificial immune network (aiNet), EaiNet proposed in this paper has better solution quality and faster convergence speed, which indicates that EaiNet is an effective optimization method.
Integrating information for on demand computing
Authors: Nelson Mendonça Mattos
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

Information integration provides a competitive advantage to businesses and is fundamental to on demand computing. It is strategic area of investment by software companies today whose goal is to provide a unified view of the data regardless of differences in data format, data location and access interfaces, dynamically manage data placement to match availability, currency and performance requirements, and provide autonomic features that reduce the burden on IT staffs for managing complex data architectures. This paper describes the motivation for integrating information for on demand computing, explains its requirements, and illustrates its value through usage scenarios. As shown in the paper, there is still a tremendous amount of research, engineering, and development work needed to make the full information integration vision a reality and it is expected that software companies will continue to heavily invest in aggressively pursing the information integration vision.
Dealing with Sporadic Strangers, or the (Un)Suitability of Trust for Mobile P2P Security
Authors: Esther Palomar, Juan M. E. Tapiador, Julio C. Hernandez-Castro, Arturo Ribagorda
Venue: Proceedings of the 18th International Conference on Database and Expert Systems Applications (2007)

A number of factors, such as the increasing popularity of wireless networks, the opportunities offered by 3G services, and the rapid proliferation of mobile devices, have stimulated a general trend towards extending Peer-to-Peer (P2P) characteristics to wireless environments. As a result, the P2P paradigm has begun to migrate to pervasive computing scenarios. So far, research in this domain has led to some middleware models working over Mobile Ad Hoc Networks (MANETs), which are now viewed as a Mobile-P2P (M-P2P) networks. However, the highly dynamic, decentralized and self-organizing nature of MANETs does not fit well with many approaches developed in the P2P world. In particular, it does not seem easy to deploy security solutions for M-P2P due, among other reasons, to the inherent limitations of the peers' devices and their potentially very sporadic interaction with other peers. Under these assumptions, a significant challenge is how to establish a decentralized trust management system. In this paper, we first summarize the advances in M-P2P security services and point out potential future applications based on opportunistic interactions. Then, we discuss several kind of threats and attacks, and finally analyze the (un)suitability of employing trust-based systems in some of these environments.
Telemeter: Network Distance Prediction Base on Network Topology
Authors: Lin Cong, Yang Chen, Xiaohui Shi, Ye Wang, Weiyu Wu, Beixing Deng, Xing Li
Venue: Proceedings of the Sixth International Conference on Grid and Cooperative Computing (2007)

Coordinates-based distance prediction algorithms can improve the performance of many Internet applications, especially in the peer-to-peer architecture and overlay construction. But some recent researches indicate that the performance gain via distance prediction based on coordinates, rather disappointed, can be significantly worse than the improvement obtained by direct measurement algorithm, and the inaccuracy of coordinates-based distance prediction would degrade the performance of applications evidently. In this paper, we propose a novel distance prediction algorithm base on network topology information, Telemeter, which uses the topology and routing information from CERNET2's ISP, instead of coordinates mechanism. Telemeter can predict the distance accurately between any pair of nodes from CERNET2 without any end-to-end measurement overhead. We evaluate the performance of Telemeter from P2P-related application's perspective systematically, and compare it with three representative coordinates-based distance prediction algorithms (Triangulated Heuristic, GNP, and IDES). We demonstrate that Telemeter overcomes some drawback shared among coordinates-based distance prediction algorithms, and bridge the significant performance gap between measurement-based algorithm and these algorithms.