ppxscal/embedding_network huggingface dataset:
Using an institutional repository for personal digital collections of retired faculty members
Authors: Sarah Kim
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

In this poster, I address practical issues related to using IRs for personal digital collections of retired faculty members.
Algorithmic techniques for nanometer vlsi design and manufacturing closure
Authors: Jiang Hu
Venue:  (2008)

As Very Large Scale Integration (VLSI) technology moves to the nanoscale regime, design and manufacturing closure becomes very difficult to achieve due to increasing chip and power density. Imperfections due to process, voltage and temperature variations aggravate the problem. Uncertainty in electrical characteristic of individual device and wire may cause significant performance deviations or even functional failures. These impose tremendous challenges to the continuation of Moore's law as well as the growth of semiconductor industry. Efforts are needed in both deterministic design stage and variation-aware design stage. This research proposes various innovative algorithms to address both stages for obtaining a design with high frequency, low power and high robustness. For deterministic optimizations, new buffer insertion and gate sizing techniques are proposed. For variation-aware optimizations, new lithography-driven and post-silicon tuning-driven design techniques are proposed. For buffer insertion, a new slew buffering formulation is presented and is proved to be NP-hard. Despite this, a highly efficient algorithm which runs > 90&times; faster than the best alternatives is proposed. The algorithm is also extended to handle continuous buffer locations and blockages. For gate sizing, a new algorithm is proposed to handle discrete gate library in contrast to unrealistic continuous gate library assumed by most existing algorithms. Our approach is a continuous solution guided dynamic programming approach, which integrates the high solution quality of dynamic programming with the short runtime of rounding continuous solution. For lithography-driven optimization, the problem of cell placement considering manufacturability is studied. Three algorithms are proposed to handle cell flipping and relocation. They are based on dynamic programming and graph theoretic approaches, and can provide different tradeoff between variation reduction and wire-length increase. For post-silicon tuning-driven optimization, the problem of unified adaptivity optimization on logical and clock signal tuning is studied, which enables us to significantly save resources. The new algorithm is based on a novel linear programming formulation which is solved by an advanced robust linear programming technique. The continuous solution is then discretized using binary search accelerated dynamic programming, batch based optimization, and Latin Hypercube sampling based fast simulation.
Detection and compression of moving objects based on new panoramic image modeling
Authors: Donggyu Sim, Yongmin Kim
Venue: Image and Vision Computing (2009)

In this paper, a parametric video coding method based on new panoramic modeling is proposed for panning cameras. An input video frame from a panning camera is decomposed into a background image, rectangular moving object regions, and a residual image. Each area is then coded separately. In coding the background, we employ a panoramic model that can account for several image formation processes, such as perspective projection, lens distortion, vignetting and illumination effects. Moving objects are detected, and their minimum bounding rectangular regions are coded using a JPEG-2000 coder. The reconstruction error using only the estimated background and the moving objects is computed, and the residual image is separately encoded for image quality enhancement and rate control. We evaluated the effectiveness of the proposed algorithm using several indoor and outdoor sequences and found that the peak signal-to-noise ratio (PSNR) improved by 1.3~4.4dB compared to that of JPEG-2000.
Smallest Bipartite Bridge-Connectivity Augmentation
Authors: Pei-Chi Huang, Hsin-Wen Wei, Wan-Chen Lu, Wei-Kuan Shih, Tsan-sheng Hsu
Venue: Algorithmica (2009)

This paper addresses two augmentation problems related to bipartite graphs. The first, a fundamental graph-theoretical problem, is how to add a set of edges with the smallest possible cardinality so that the resulting graph is 2-edge-connected, i.e., bridge-connected, and still bipartite. The second problem, which arises naturally from research on the security of statistical data, is how to add edges so that the resulting graph is simple and does not contain any bridges. In both cases, after adding edges, the graph can be either a simple graph or, if necessary, a multi-graph. Our approach then determines whether or not such an augmentation is possible. We propose a number of simple linear-time algorithms to solve both problems. Given the well-known bridge-block data structure for an input graph, the algorithms run in O(log&#x2009;n) parallel time on an EREW PRAM using a linear number of processors, where n is the number of vertices in the input graph. We note that there is already a polynomial time algorithm that solves the first augmentation problem related to graphs with a given general partition constraint in O(n(m+nlog&#x2009;n)log&#x2009;n) time, where m is the number of distinct edges in the input graph. We are unaware of any results for the second problem.
FERRY: database-supported program execution
Authors: Torsten Grust, Manuel Mayr, Jan Rittinger, Tom Schreiber
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

We demonstrate the language Ferry and its editing, compilation, and execution environment FerryDeck. Ferry's type system and operations match those of scripting or programming languages; its compiler has been designed to emit (bundles of) compliant and efficient SQL:1999 statements. Ferry acts as glue that permits a programming style in which developers access database tables using their programming language's own syntax and idioms -- the Ferry-expressible fragments of such programs may be executed by a relational database back-end, i.e., close to the data. The demonstrator FerryDeck implements compile-and-execute-as-you-type interactivity for Ferry and offers a variety of (graphical) hooks to explore and inspect this approach to database-supported program execution.
RankVisu: Mapping from the neighborhood network
Authors: S. Lespinats, B. Fertil, P. Villemain, J. Hérault
Venue: Neurocomputing (2009)

Most multidimensional scaling methods focus on the preservation of dissimilarities to map high dimensional items in a low-dimensional space. However, the mapping function usually does not consider the preservation of small dissimilarities as important, since the cost is small with respect to the preservation of large dissimilarities. As a consequence, an item's neighborhoods may be sacrificed for the benefit of the overall mapping. We have subsequently designed a mapping method devoted to the preservation of neighborhood ranks rather than their dissimilarities: RankVisu. A mapping of data is obtained in which neighborhood ranks are as close as possible according to the original space. A comparison with both metric and non-metric MDS highlights the pros (in particular, cluster enhancement) and cons of RankVisu.
MOVIH-IDS: A mobile-visualization hybrid intrusion detection system
Authors: Álvaro Herrero, Emilio Corchado, María A. Pellicer, Ajith Abraham
Venue: Neurocomputing (2009)

A novel hybrid artificial intelligent system for intrusion detection, called MObile-VIsualization Hybrid IDS (MOVIH-IDS), is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist intrusion detection system (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the simple network management protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.
Research and Development in Intelligent Systems XXIV: Proceedings of AI-2007, The Twenty-seventh SGAI International Conference on Innovative Techniques ... of Artificial Intelligence v. 24, 1st edition
Authors: Max Bramer, Frans Coenen, Miltos Petridis
Venue:  (2007)

The papers in this volume are the refereed technical papers presented at AI-2007, the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2007. They present new and innovative developments in the field, divided into sections on Constraint Satisfaction, AI Techniques, Data Mining and Machine Learning, Multi-Agent Systems, Data Mining and KnowledgeAcquisition and Management,followed by the textof short papers presented as posters at the conference. This is the twenty-fourth volume in the Research and Development series. The series is essential reading for those who wish to keep up to date with developments in this important field. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems XV.
Diagnosis of specification parametric faults in the FPAA: the RBF neural network approach
Authors: Damian Grzechca, Tomasz Golonek, Jerzy Rutkowski
Venue: Proceedings of the 2nd conference on European computing conference (2008)

This paper presents an idea of the parametric fault detection and location in the field programmable analog arrays (FPAA) with use of the time domain input excitation, and Radial Basis Function (RBF) Neural Network (NN) application. The aim of the work is to diagnose a parametric fault in the CAB based on the output time domain response. A group of selected features feed the input layer of the neural network and the output indicates state of the examined FPAA block. The paper gives brief information about fault diagnosis in the analog electronic circuit as well as practical implementation of proposed method. Exemplary circuit is investigated and results with conclusions are discussed.
An Agent-Based P2P System to Distributed Knowledge Management
Authors: Jiuyang Tang, Weiming Zhang, Weidong Xiao, Daquan Tang
Venue: Proceeding of the 2005 conference on Self-Organization and Autonomic Informatics (I) (2005)

The problem of distributed knowledge management (DKM) is eminent in the industry enterprises area and is receiving a lot of attention in the research community. Peer-to-Peer (P2P) has shown to be the suitable distributed computing paradigm to the high dynamism knowledge management environment. However, there are several technical challenges in building scalable P2P based DKM systems. One of them is the intelligent coordination, which is complicated by the deficiency of P2P in exchanging complex information and coping with semantic heterogeneity. To address this problem, an agent-based P2P architecture is proposed. Each agent-based peer (agent peer) creates and manages its own knowledge. Moreover, agent peers intelligently collaborate with each other to deal with knowledge management actions over the P2P network.
Probing biomolecular machines with graphics processors
Authors: James C. Phillips, John E. Stone
Venue: Communications of the ACM (2009)

GPU acceleration and other computer performance increases will offer critical benefits to biomedical science.
Message Authentication in Computationally Constrained Environments
Authors: Benjamin Arazi
Venue: IEEE Transactions on Mobile Computing (2009)

RFID and Wireless Sensor Networks exemplify computationally constrained environments, where the compact nature of the components cannot support complex computations or high communication overhead. On the other hand, such components should support security applications such as message integrity, authentication, and time stamping. The latter are efficiently implemented by Hash Message Authentication Codes (HMAC). As clearly stated in the literature, current approved implementations of HMAC require resources that cannot be supported in constrained components. An approach to implement a compact HMAC by the use of stream ciphering is presented in this paper.
Compute Unified Device Architecture Application Suitability
Authors: Wen-Mei Hwu, Christopher Rodrigues, Shane Ryoo, John Stratton
Venue: Computing in Science and Engineering (2009)

Graphics processing units (GPUs) can provide excellent speedups on some, but not all, general-purpose workloads. Using a set of computational GPU kernels as examples, the authors show how to adapt kernels to utilize the architectural features of a GeForce 8800 GPU and what finally limits the achievable performance.
Improved collision attack on hash function MD5
Authors: Jie Liang, Xue-Jia Lai
Venue: Journal of Computer Science and Technology (2007)

In this paper, we present a fast attack algorithm to find two-block collision of hash function MD5. The algorithm is based on the two-block collision differential path of MD5 that was presented by Wang et al. in the Conference EUROCRYPT 2005. We found that the derived conditions for the desired collision differential path were not sufficient to guarantee the path to hold and that some conditions could be modified to enlarge the collision set. By using technique of small range searching and omitting the computing steps to check the characteristics in the attack algorithm, we can speed up the attack of MD5 efficiently. Compared with the Advanced Message Modification technique presented by Wang et al., the small range searching technique can correct 4 more conditions for the first iteration differential and 3 more conditions for the second iteration differential, thus improving the probability and the complexity to find collisions. The whole attack on the MD5 can be accomplished within 5 hours using a PC with Pentium4 1.70GHz CPU.
The Influence of Psychological Safety and Confidence in Knowledge on Employee Knowledge Sharing
Authors: Enno Siemsen, Aleda V. Roth, Sridhar Balasubramanian, Gopesh Anand
Venue: Manufacturing Service Operations Management (2009)

This research empirically examines the influence of psychological safety on knowledge sharing among coworkers in manufacturing and service operations contexts. Reconciling conflicting findings in the literature, we demonstrate that whereas psychological safety is an important antecedent of knowledge sharing, the relationship between psychological safety and knowledge sharing is moderated by the level of confidence that employees have in what they know. The greater this confidence, the lesser is the importance of psychological safety in facilitating knowledge sharing. Linking this result to social network theory, we find that psychological safety increases with the frequency of communication among coworkers and that the confidence of employees in their knowledge is related to the codifiability of the knowledge involved. We further investigate direct and indirect antecedents of psychological safety. This research offers insights into actions that managers can take to enhance psychological safety and, consequently, motivate their employees to share knowledge.
Generalized von Neumann-Kakutani transformation and random-start scrambled Halton sequences
Authors: Giray Ökten
Venue: Journal of Complexity (2009)

It is a well-known fact that the Halton sequence exhibits poor uniformity in high dimensions. Starting with Braaten and Weller in 1979, several researchers introduced permutations to scramble the digits of the van der Corput sequences that make up the Halton sequence, in order to improve the uniformity of the Halton sequence. These sequences are called scrambled Halton, or generalized Halton sequences. Another significant result on the Halton sequence was the fact that it could be represented as the orbit of the von Neumann-Kakutani transformation, as observed by Lambert in 1982. In this paper, I will show that a scrambled Halton sequence can be represented as the orbit of an appropriately generalized von Neumann-Kakutani transformation. A practical implication of this result is that it gives a new family of randomized quasi-Monte Carlo sequences: random-start scrambled Halton sequences. This work generalizes random-start Halton sequences of Wang and Hickernell. Numerical results show that random-start scrambled Halton sequences can improve on the sample variance of random-start Halton sequences by factors as high as 7000.
Finite difference method and iterative method with parallelism for dispersive equations
Authors: Bin Zheng, Qinghua Feng
Venue: WSEAS Transactions on Mathematics (2009)

In this paper, based on the concept of domain decomposition and alternating group, we construct a class of Finite Difference method for fifth order dispersive equations, Stability Analysis for he method is given. Then we construct a new alternating group explicit iterative method. Both the two methods are suitable for parallel computation. Results of numerical experiments show the methods are effective in computing.
Sequential modeling of a low noise amplifier with neural networks and active learning
Authors: Dirk Gorissen, Luciano De Tommasi, Karel Crombecq, Tom Dhaene
Venue: Neural Computing and Applications (2009)

The use of global surrogate models has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, prototyping, visualization, and sensitivity analysis. Neural networks have been proven particularly useful in this respect due to their ability to model high dimensional, non-linear responses accurately. In this article, we present the results of an extensive study on the performance of neural networks as compared to other modeling techniques in the context of active learning. We investigate the scalability and accuracy in function of the number design variables and number of datapoints. The case study under consideration is a high dimensional, parametrized low noise amplifier RF circuit block.
Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks
Authors: Shih-Wei Lin, Yeou-Ren Shiue, Shih-Chi Chen, Hui-Miao Cheng
Venue: Expert Systems with Applications: An International Journal (2009)

The prediction of bank performance is an important issue. The bad performance of banks may first result in bankruptcy, which is expected to influence the economics of the country eventually. Since the early 1970s, many researchers had already made predictions on such issues. However, until recent years, most of them have used traditional statistics to build the prediction model. Because of the vigorous development of data mining techniques, many researchers have begun to apply those techniques to various fields, including performance prediction systems. However, data mining techniques have the problem of parameter settings. Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features, without reducing the classification accuracy rate. In order to evaluate the proposed approaches, dataset collected from Taiwanese commercial banks are used as source data. The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.
Low Power Complexity-Reduced ME and Interpolation Algorithms for H.264/AVC
Authors: Choong Jin Hyun, Myung H. Sunwoo
Venue: Journal of Signal Processing Systems (2009)

This paper presents efficient memory reuse and the modified normalized partial distortion search algorithms for motion estimation of H.264/Advanced Video Coding. In addition, this paper presents multiplication-free sub-pixel interpolation and adaptive sub-pixel interpolation algorithms. The proposed memory reuse algorithm utilizes the position similarity of the predicted motion vectors among neighboring sub-macro blocks and the modified normalized partial distortion search algorithm uses an adaptive search range and the dynamic threshold methods. It can reduce a large number of memory access and can save power consumption by sharing the search range of the current block. In addition, simplifying weights of a sub-pixel interpolation filter can eliminate multiplications and the adaptive sub-pixel interpolation can reduce the number of sub-pixel search points, which can reduce memory access and hardware complexity. Therefore, the proposed algorithms are quite suitable for low power video compression.
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data
Authors: Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert
Venue: Similarity-Based Clustering: Recent Developments and Biomedical Applications (2009)

Pearson correlation is one of the standards for comparisons in biomedical analyses, possessing yet unused potential. Substantial value is added by transferring Pearson correlation into the framework of adaptive similarity measures and by exploiting properties of the mathematical derivatives. This opens access to optimization-based data models applicable in tasks of attribute characterization, clustering, classification, and visualization. Modern high-throughput measuring equipment creates high demand for analysis of extensive biomedical data including spectra and high-resolution gel-electrophoretic images. In this study cDNA arrays are considered as data sources of interest. Recent computational methods are presented for the characterization and analysis of these huge-dimensional data sets.
Bayesian Methods for Artificial Intelligence and Machine Learning
Authors: Zoubin Ghahramani
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Bayesian methods provide a framework for representing and manipulating uncertainty, for learning from noisy data, and for making decisions that maximize expected utility----components which are important to both AI and Machine Learning. However, although Bayesian methods have become more popular in recent years, there remains a good degree of skepticism with respect to taking a fully Bayesian approach. This talk will introduce fundamental topics in Bayesian statistics as they apply to machine learning and AI, and address some misconceptions about Bayesian approaches. I will then discuss some current work on non-parametric Bayesian machine learning, particularly in the area of unsupervised learning.
Computer Science meets environmental science
Authors: Karen A. Frenkel
Venue: Communications of the ACM (2009)

Scientists share knowledge and seek collaborators at computational sustainability conference.