ppxscal/embedding_network huggingface dataset:
A Quantitative Look at Software Licensing for IT Managers
Authors: Alan T. Schroeder Jr.
Venue: IT Professional (2003)

Negotiate better terms for your company by uncovering the primary hidden cost in licensing: paying for more usage than you need. 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
A distributed strict strong coloring algorithm for broadcast applications in ad hoc networks
Authors: Mohammed Haddad, Lyes Dekar, Hamamache Kheddouci
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

A challenging research area in computer networks is ad hoc networks. These are defined to be spontaneous and totally autonomous networks. Many communication protocols (like routing, resources sharing, ...) use broadcasting mechanism to diffuse or gather information. A strict strong coloring of a graph G is a vertex proper coloring of G such that each vertex of G is adjacent to at least one non empty color class. In this paper, we give a novel broadcasting approach based on a distributed algorithm for strict strong coloring.
Periodicity of Recurrent Neural Networks with Reaction-Diffusion and Dirichlet Boundary Conditions
Authors: Chaojin Fu, Chongjun Zhu, Boshan Chen
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

In this paper, a class of reaction-diffusion recurrent neural networks with time-varying delays and Dirichlet boundary conditions are considered by using an approach based on the delay differential inequality and the fixed-point theorem. Some sufficient conditions are obtained to guarantee that the reaction-diffusion recurrent neural networks have a periodic orbit and this periodic orbit is globally attractive. The results presented in this paper are the improvement and extension of the existed ones in some existing works.
Research on Refining the Distributed Supply Chain Procurement Plans Based on CRL
Authors: Xia Zhanguo, Guan Hongjie, Wang Ke
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

In this paper, we propose a method of Coordinated Reinforcement Learning (CRL) to refine the distributed transportation plans generated by a group of agents using Double Auction Market Mechanism (DAMM) in the context of supply chain procurement problem. Through analyzing the drawbacks of DAMM due to its strict market rules, we develop a refinement method based on reinforcement learning, namely CRL. Numerical results demonstrate that CRL effectively refines the distributed transportation plans generated by the DAMM.
Anti-Honeypot Technology
Authors: Neal Krawetz
Venue: IEEE Security and Privacy (2004)

Spammers continually scan the Internet for open proxy relays: by using these open relays, they can obscure their originating IP address and remain anonymous. However, when a spammer comes across a service on a honeypot, that honeypot can collect valuable information about the spammer's true identity and help unmask it. In response to the threat that honeypots pose to spammers, the first commercial anti-honeypot technology has surfaced: Send-Safe's Honeypot Hunter (www.send-safe.com) attempts to detect "safe" proxies for use with bulk-mailing tools.
A New SOM Initialization Algorithm for Nonvectorial Data
Authors: Antonino Fiannaca, Riccardo Rizzo, Alfonso Urso, Salvatore Gaglio
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms of quantization error.
Evolving Neural Network Using Genetic Simulated Annealing Algorithms for Multi-spectral Image Classification
Authors: Xiaoyang Fu, Chen Guo
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In this paper, an evolving neural network classifier using genetic simulated annealing algorithms (GSA) and its application to multi-spectral image classification is investigated. By means of GSA, the classifier presented is available to automatically evolve the appropriate architecture of neural network and find a near-optimal set of connection weights globally. Then, with Back-Propagation (BP) algorithm, the conformable connection weights for multi-spectral image classification can be found. The GSA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data effectively. The simulation results demonstrated that GSA-BP classifier possesses better performance on multi-spectral image classification. Its overall accuracy is improved by 4%~6% than conventional classifiers.
Breast Abnormality Detection Incorporating Breast Density Information Based on Independent Components Analysis
Authors: Styliani Petroudi, Nicoletta Nicolaou, Julius Georgiou, Michael Brady
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

This paper introduces an approach to breast abnormality classification which incorporates breast density information. Features are extracted by a novel technique based on Independent Component Analysis, which decomposes the selected images into sets of independent source regions and corresponding basis functions (weights). The coefficients which result from the source regions are used in turn to describe normality and abnormality. The method has been tested on the MIAS database and has high sensitivity.
Perfect Population Classification on Hapmap Data with a Small Number of SNPs
Authors: Nina Zhou, Lipo Wang
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

The single nucleotide polymorphisms (SNPs) are believed to determine human differences and, to some degree, provide biomedical researchers a possibility of predicting risks of some diseases and explaining patients' different responses to drug regimens. With the availability of millions of SNPs in the Hapmap Project, although large amount of information about SNPs is available, the tremendous size also causes a major challenge for research on SNPs. Inspired from the recent research work on population classification by Park et al (2006), we attempt to find as few SNPs as possible from the original nearly 4 millions SNPs to classify the 3 populations in the Hapmap genotype data. In this paper, we propose to first use a modified t-test measure to rank SNPs, and then combine the ranking result with a classifier, e.g., the support vector machine, to find the optimal SNP subset. Compared with Park et al's result, our proposed method is more efficient in ranking features and classifying the three populations, i.e., we obtained perfect classification using only 11 SNPs in comparison with 82 SNPs used by Park et al.
Evolutionary Public-Key Cryptographic Circuits
Authors: Nadia Nedjah, Luiza Macedo Mourelle
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this paper, we evolve digital circuits for public-key cryptosystems. The methodology used is based on genetic programming. The evolutionary process attempts to minimise the hardware area required to implement modular multiplication and exponentiation. It does so while it also attempts to optimise the encryption and decryption time, by considering multiple objectives. We show that the evolved designs are shielded against side-channel leakage. We compare our results against existing and well-known designs, which were produced by human designers.
A Self-stabilizing Algorithm with Tight Bounds for Mutual Exclusion on a Ring
Authors: Viacheslav Chernoy, Mordechai Shalom, Shmuel Zaks
Venue: Proceedings of the 22nd international symposium on Distributed Computing (2008)

In [Dij74] Dijkstra introduced the notion of self-stabilizing algorithms and presented, among others, an algorithm with three states for the problem of mutual exclusion on a ring of processors. In this work we present a new three state self-stabilizing algorithm for mutual exclusion, with a tight bound of $\frac{5}{6} n^2 + O(n)$ for the worst case complexity, which is the number of moves of the algorithm until it stabilizes. This bound is better than lower bounds of other algorithms, including Dijkstra's. Using similar techniques we improve the analysis of the upper bound for Dijkstra's algorithm and show a bound of $3\frac{13}{18} n^2 + O(n)$.
One-Class SVM Based Segmentation for SAR Image
Authors: Jianjun Yan, Jianrong Zheng
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Image segmentation is of great importance in the field of image processing. A wide variety of approaches have been proposed for image segmentation. However, SAR image segmentation poses a difficult challenge owing to the high levels of speckle noise. In this paper, we proposed a SAR image segmentation method based on one-class support vector machines (SVM) to solve this problem. One-class SVM and two-class SVM for segmentation is discussed. One-class way is a kind of unsupervised learning, and one-class SVM based segmentation method reduces greatly human interactions, while yielding good segmentation results compared to two-class SVM based segmentation method. The segmentation results based on SVM are also compared to threshold method and adaptive threshold method. Experimental results demonstrate that the proposed method works well for image segmentation while reducing the speckle noise.
Scale Expanding and Efficiency Improvement of Commercial Banks of China
Authors: Liu Zhongsheng, Li Dong
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Operating efficiency of commercial banks is essential to keeping financial basis of the country. Analyzing effect based on Principal Components Analysis show that during 1999 to 2003, improving of efficiency of stock banks mainly depended on expanding of banks’ scale. But on improving of technical and profit-making efficiency, the stock banks lagged behind the state-owned banks. Moderate competition is a fundamental approach to growing of efficiency especially profit-making of Chinese banks. So, building a system which may promote moderate competition between banks is necessary.
Discrete sets with minimal moment of inertia
Authors: S. Brlek, G. Labelle, A. Lacasse
Venue: Theoretical Computer Science (2008)

We analyze the moment of inertia I(S), relative to the center of gravity, of finite plane lattice sets S. We classify these sets according to their roundness: a set S is rounder than a set T if I(S)
Improved Way Prediction Policy for Low-Energy Instruction Caches
Authors: Zhou Hongwei, Zhang Chengyi, Zhang Mingxuan
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

In this paper, a multi-way way prediction policy (MWWP) with a two-port Way Predictor (TPWP) is proposed for reducing the dynamic and leakage energy in multi-way set associative drowsy I-Cache without dramatic loss of performance. One port of TPWP is used for predicting the matching way in current set, only the predicted way and not all the ways is accessed to reduce the dynamic energy. The other is used for predicting the matching way in subsequent set, only the cache line in predicted way is pre-woken up from the drowsy mode to reduce the leakage energy. Different with the traditional way prediction policy, the MWWP has the lower performance overhead by selecting multiple ways speculatively for each access to improve way prediction hit ratio (WPHR). The simulation and estimation results show that, in a 4-way set-associative drowsy I-Cache, with 0.98% and 0.4% performance overhead respectively, our proposed 2-way and 3-way way prediction policy with TPWP can reduce 59% and 47% of energy in I-Cache, and save the 6.1% and 5.4% of the whole processor energy. The EDP is improved by 4.5% and 4.1% on average.
Statistically Optimal Averaging for Image Restoration and Optical Flow Estimation
Authors: Kai Krajsek, Rudolf Mester, Hanno Scharr
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

In this paper we introduce a Bayesian best linear unbiased estimator (Bayesian BLUE) and apply it to generate optimal averaging filters. Linear filtering of signals is a basic operation frequently used in low level vision. In many applications, filter selection is ad hoc without proper theoretical justification. For example input signals are often convolved with Gaussian filter masks, i.e masks that are constructed from truncated and normalized Gaussian functions, in order to reduce the signal noise. In this contribution, statistical estimation theory is explored to derive statical optimal filter masks from first principles. Their shape and size are fully determined by the signal and noise characteristics. Adaption of the estimation theoretical point of view not only allows to learn optimal filter masks but also to estimate the variance of the estimate. The statistically learned filter masks are validated experimentally on image reconstruction and optical flow estimation. In these experiments our approach outperforms comparable approaches based on ad hoc assumptions on signal and noise or even do not relate their method at all to the signal at hand.
Price competition with elastic traffic
Authors: Asuman Ozdaglar
Venue: Networks (2008)

In this paper, we present a combined study of price competition and traffic control in a congested network. We study a model in which service providers own the routes in a network and set prices to maximize their profits, while users choose the amount of flow to send and the routing of the flow according to Wardrop's principle. When utility functions of users are concave and have concave first derivatives, we characterize a tight bound of 2-3 on efficiency in pure strategy equilibria of the price competition game. We obtain the same bound under the assumption that there is no fixed latency cost, i.e., the latency of a link at zero flow is equal to zero. These bounds are tight even when the numbers of routes and service providers are arbitrarily large. &copy; 2008 Wiley Periodicals, Inc. NETWORKS, 2008
Hierarchical Adaptive Clustering
Authors: Gabriela Şerban, Alina Câmpan
Venue: Informatica (2008)

This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was established by applying the hierarchical agglomerative clustering algorithm (HACA) (Han and Kamber, 2001) before the feature set changed. We aim to reach the result more efficiently than running HACA again from scratch on the feature-extended object set. Experiments testing the method's efficiency and a practical distributed systems problem in which the HAC method can be efficiently used (the problem of adaptive horizontal fragmentation in object oriented databases) are also reported.
Human Lips as Emerging Biometrics Modality
Authors: Michał Choraś
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In many cases human identification biometrics systems are motivated by real-life criminal and forensic applications. One of the most interesting emerging method of human identification, which originates from the criminal and forensic practice, is human lips recognition. In this paper we consider lips' shape and color features in order to determine human identity. In our lip biometric system geometrical parameters, Zernike moments as well as color features are calculated.
Speaker Characteristics
Authors: Tanja Schultz
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

In this chapter, we give a brief introduction to speech-driven applications in order to motivate why it is desirable to automatically recognize particular speaker characteristics from speech. Starting from these applications, we derive what kind of characteristics might be useful. After categorizing relevant speaker characteristics, we describe in more detail language, accent, dialect, idiolect, and sociolect. Next, we briefly summarize classification approaches to illustrate how these characteristics can be recognized automatically, and conclude with a practical example of a system implementation that performs well on the classification of various speaker characteristics.
Grid-Supported Simulation of Vapour-Liquid Equilibria with GridSFEA
Authors: I. L. Muntean, E. Elts, M. Buchholz, H. -J. Bungartz
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

In order to benefit from grid computing, software applications in CSE often need to be substantially modified or rewritten to a large extent. To reduce the required grid know-how and effort the computational scientist (end user and software developer) needs for this task, we developed a framework for engineering simulations in grid environments (GridSFEA). This paper presents two novel features of GridSFEA: the integrated support for parameter investigations and the controlled execution of long-running simulations in grids. They allow the grid enabling of CSE applications with minimal or even without changes of their source code. Furthermore, the overhead for working in grid environments introduced by our approach, compared to working on classical HPC platforms, is very low. We provide two examples of using GridSFEA for performing vapour-liquid equilibria (VLE) simulations using Molecular Dynamics and Monte Carlo methods. To develop VLE models, parameter investigations are carried out. Large VLE scenarios are computed over a long time, to create test cases for the development of HPC software.
Exploring Factors That Make Online Interactive Games Successful: A Heuristic Approach
Authors: Yin-Leng Theng, Agnes Kit-Ying Ho, Esther Shir-Wei Wee
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

Game design is challenging and expensive. It is challenging in that it is difficult to create games that are easy to learn as well as enjoyable to play. It is also expensive as only a small number of games, having spent millions in creating the games, eventually becomes successfully in the market yearly. While technologies have been improving swiftly, game design guidelines have evolved slowly, and developers have to rely on limited theoretical findings, experiences and foreknowledge to create innovative and successful games. Using a theoretical model underlying game heuristics, this paper describes a study to investigate factors that might make online interactive games successful. The findings identified that usability and aesthetic appeal of the game interface are the two most important factors governing gamers' decisions to purchase a particular game. The paper concludes with implications for the design of online interactive games.
A Space Efficient Solution to the Frequent String Mining Problem for Many Databases
Authors: Adrian Kügel, Enno Ohlebusch
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

In the frequent string mining problem, one is given m databases ${\cal D}_1,...,{\cal D}_m$ of strings and searches for strings that fulfill certain frequency constraints. The constraints consist of m pairs of thresholds $(\mathit{minf}_1,\mathit{maxf}_1),$ $...,(\mathit{minf}_m,\mathit{maxf}_m)$ and one wants to find all strings ¿ that satisfy $\mathit{minf}_i \le \mathit{freq}(\phi, {\cal D}_i) \le \mathit{maxf}_i$ for all i with 1 ≤ i ≤ m, where $\mathit{freq}(\phi,\mathcal{D}_i) = |\{ \psi \in \mathcal{D}_i : \phi \mbox{ is a substring of } \psi \}|$.Fischer et al. [2] presented an algorithm that solves the frequent string mining problem in linear time under the assumption that the number of databases is treated as a constant. The space consumption of this algorithm, however, is proportional to the total size of all databases. We improve this algorithm in such a way that its space consumption is proportional to the size of the largest database, and it takes linear time regardless of the number of databases. Also, our algorithm is more flexible in the sense that one of several databases can be replaced without having to recalculate everything, that is, intermediate data can be stored on file and be reused.
A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection
Authors: Hristo N. Djidjev
Venue: Algorithms and Models for the Web-Graph: Fourth International Workshop, WAW 2006, Banff, Canada, November 30 - December 1, 2006. Revised Papers (2007)

One of the most useful measures of cluster quality is the modularity of the partition, which measures the difference between the number of the edges joining vertices from the same cluster and the expected number of such edges in a random (unstructured) graph. In this paper we show that the problem of finding a partition maximizing the modularity of a given graph G can be reduced to a minimum weighted cut problem on a complete graph with the same vertices as G. We then show that the resulted minimum cut problem can be efficiently solved with existing software for graph partitioning and that our algorithm finds clusterings of a better quality and much faster than the existing clustering algorithms.
A framework for analyzing and mitigating the vulnerabilities of complex systems via attack and protection trees
Authors: Richard A. Raines
Venue:  (2007)

In recent years, attack trees have been developed to describe processes by which malicious users attempt to exploit or break complex systems. Attack trees offer a method of decomposing, visualizing, and determining the cost or likelihood of attacks. Attack trees by themselves do not provide enough decision support to system defenders. The defenders need methods to determine which protections to implement and where to place them in the system to mitigate the vulnerabilities found. This research develops the concept of using protection trees to offer a detailed risk analysis for the protection of a system. In addition to developing protection trees, this research improves the existing concept of attack trees as well as developing rule sets for the manipulation of metrics used in the security of complex systems. This research specifically develops the framework for using an attack and protection tree methodology to analyze the security of complex systems. To accomplish this, the structure of attack trees is extended and modified to create the concept of protection trees. To validate the effectiveness of the methodology, the Schematic Protection Model (SPM) is used. The SPM is extended and applied to verify that a system protected using the attack and protection tree methodology is safe. To demonstrate the general usefulness of this novel methodology, it is used to analyze the security of several varied domains including computer networks, online banking, homeland security, and mobile ad hoc networks.