ppxscal/embedding_network huggingface dataset:
Ant-based distributed topology control algorithms for mobile ad hoc networks
Authors: Chien-Chung Shen, Zhuochuan Huang, Chaiporn Jaikaeo
Venue: Wireless Networks (2005)

By adjusting the transmission power of mobile nodes, topology control aims to reduce wireless interference, reduce energy consumption, and increase effective network capacity, subject to connectivity constraints. In this paper, we introduce the Ant-Based Topology Control (ABTC) algorithm that adapts the biological metaphor of Swarm Intelligence to control topology of mobile ad hoc networks. ABTC is a distributed algorithm where each node asynchronously collects local information from nearby nodes, via sending and receiving ant packets, to determine its appropriate transmission power. The operations of ABTC do not require any geographical location, angle-of-arrival, topology, or routing information, and are scalable. In particular, ABTC attempts to minimize the maximum power used by any node in the network, or minimize the total power used by all of the nodes in the network. By adapting swarm intelligence as an adaptive search mechanism, ABTC converges quickly to a good power assignment with respect to minimization objectives, and adapts well to mobility. In addition, ABTC may achieve common power, or properly assign power to nodes with non-uniform distribution. Results from a thorough comparative simulation study demonstrate the effectiveness of ABTC for different mobility speed, various density, and diverse node distributions.
Robust Video-Based Surveillance by Integrating Target Detection with Tracking
Authors: Junxian Wang, George Bebis, Ronald Miller
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where detection and tracking are addressed simultaneously in a unified framework (i.e., detection results trigger tracking, and tracking re-enforces detections)to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other (i.e., "detect-then-track"), we feed the results of tracking back to the detection stage to adaptively optimize the threshold used in the detection stage and improve system robustness (i.e., "detect-and-track"). Specifically, the initial locations and representations of the targets are extracted by background subtraction. To model the background, we employ Support Vector Regression (SVR) along with an on-line learning scheme to update it efficiently over time. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and achieve a high shape matching confidence level. Feeding back the results of tracking to the detection stage restricts the range of threshold values, suppress false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing projection distortions. We have validated the proposed framework by detecting vehicles and pedestrians in traffic scenes using both visible and thermal video sequences. Experimental results and comparisons with framebased detection and kernel-based tracking methods illustrate the robustness of our approach.
Cluster Based Core Vector Machine
Authors: Asharaf S, M. Narasimha Murty, S. K. Shevade
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.
Vase or Face? A Neural Correlate of Shape-Selective Grouping Processes in the Human Brain
Authors: Uri Hasson, Talma Hendler, Dafna Ben Bashat, Rafael Malach
Venue: Journal of Cognitive Neuroscience (2001)

Recent neuroimaging studies have described a differential activation pattern associated with specific object images (e.g., face-related and building-related activation) in human occipito-temporal cortex. However, it is as yet unclear to what extent this selectivity is due to differences in the statistics of local object features present in the different object categories, and to what extent it reflects holistic grouping processes operating across the entire object image. To resolve this question it is essential to use images in which identical sets of local features elicit the perception of different object categories. The classic Rubin vase&ndash;face illusion provides an excellent experimental set to test this question. In the illusion, the same local contours lead to the perception of different objects (vase or face). Here we employed a modified Rubin vase&ndash;face illusion to explore to what extent the activation in face-related regions is attributable to the presence of local face features, or is due to a more holistic grouping process that involves the entire face figure. Biasing cues (gratings and color) were used to control the perceptual state of the observer. We found enhanced activation in face-related regions during the "face profile" perceptual state compared to the "vase" perceptual state. Control images ruled out the involvement of the biasing cues in the effect. Thus, object-selective activation in human face-related regions entails global grouping processes that go beyond the local processing of stimulus features.
Strongly efficient estimators for light-tailed sums
Authors: J. Blanchet, P. Glynn
Venue: Proceedings of the 1st international conference on Performance evaluation methodolgies and tools (2006)

Let (Sn : n &ge; 0) be a mean zero random walk (rw) with light-tailed increments. One of the most fundamental problems in rare-event simulation involves computing P (Sn > n&beta;) for &beta; > 0 when n is large. It is well known that the optimal exponential tilting (OET), although logarithmically efficient, is not strongly efficient (the squared coefficient of variation of the estimator grows at rate n1/2). Our analysis of the zero-variance change-of-measure provides useful insights into why OET is not strongly efficient. In particular, the iid nature of OET induces an overshoot over the boundary n&beta; that is too big and causes the coefficient of variation to grow as [EQUATION]. We study techniques used to provide a state-dependent change-of-measure that yields a strongly efficient estimator. The application of our state-dependent algorithm to the Gaussian case reveals the fine structure of the zero-variance change-of-measure. We see how (Sn : n &ge; 0) transitions from a rw under OET to a process that looks "mean reverting" aroung &beta;n, indicating that less bias is required as the process approaches the boundary &beta;n.
Ambidextrous coping strategies in globally distributed software development projects
Authors: Gwanhoo Lee, William DeLone, J. Alberto Espinosa
Venue: Communications of the ACM (2006)

Strategies for enhancing flexibility and rigor.
A comparison of calculated NMR shielding probes
Authors: Ned H. Martin, David M. Loveless, Dustin C. Wade
Venue: Journal of Molecular Graphics (2004)

In a strong magnetic field, covalently bonded hydrogen nuclei located over the plane of an anisotropic π bond-containing functional group experience magnetic shielding (or deshielding) that results from the combined effect of the magnetic anisotropy of the functional group and other nearby covalent bonds plus other intramolecular shielding effects. These effects can now be calculated with reasonable accuracy using ab initio methods. We have investigated several computational probes of the magnetic shielding surface near anisotropic functional groups and compared the results to previous reports of experimental observations in example structures. GIAO-HF in Gaussian 03 was employed to calculate isotropic shielding values and to predict the net NMR shielding increment for several computational probes: methane, diatomic hydrogen, a hydrogen atom, a helium atom, or a ghost atom, each held in various positions over simple test molecules (ethene, ethyne, benzene and HCN) that contain the functional groups studied. Also, the effect of performing single point calculations versus constrained geometry-optimized calculations was examined. In addition, the effect of the angle of the orientation of the probe molecule (in the case of CH4 and H2) relative to the π bond in the test molecule was studied. Finally, the atomic charges in the molecular probes (CH4 and H2) were computed to investigate the nature of the interaction of the probe with the test molecule. The optimal, most economical computational results were obtained using single point calculations of a diatomic hydrogen probe oriented perpendicular to the surface (or axis) of the test molecule.
Multi-Agent Coordination and Cooperation through Classical Planning
Authors: Yannis Dimopoulos, Pavlos Moraitis
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Multi-agent planning is a fundamental problem in multi-agent systems that has acquired a variety of meanings in the relative literature. In this paper we focus on a setting where multiple agents with complementary capabilities cooperate in order to generate non-conflicting plans that achieve their respective goals. We study two situations. In the first, the agents are able to achieve their subgoals by themselves, but they need to find a coordinated course of action that avoids harmful interactions. In the second situation, some agents may ask the assistance of others in order to achieve their goals. We formalize the two problems and present algorithms for their solution. These algorithms are based on an underlying classical planner which is used by the agents to generate their individual plans, but also to find plans that are consistent with those of the other agents. The procedures generate optimal plans under the plan length criterion. The central role that has been given to the classical planning algorithm, can be seen as an attempt to establish a stronger link between classical and multi-agent planning.
Condition Adaptation in Synchronous Consensus
Authors: Taisuke Izumi, Toshimitsu Masuzawa
Venue: IEEE Transactions on Computers (2006)

The condition-based approach is one of the sophisticated methods used to overcome several impossibility results in the distributed consensus problem (e.g., impossibility of fault tolerance in asynchronous consensus or time complexity lower bounds in synchronous consensus). It introduces conditions on input vectors to specify subsets of all possible input vectors to consensus algorithms and condition-based algorithms can circumvent the impossibility if actual input vectors satisfy a particular condition. In this paper, we present a new condition-based paradigm for synchronous consensus. We introduce the new concept of adaptation on the time complexity of condition-based algorithms and present the adaptive condition-based approach to synchronous consensus. In our approach, all possible input vectors are classified into hierarchical conditions according to their difficulty called the legality level. The execution time of adaptive condition-based algorithms depends on the legality level of input vectors. We propose two adaptive condition-based algorithms for synchronous consensus. The first algorithm requires that the majority of processes be correct, and terminates within \min\{f + 2, t+ 1\} - l rounds if l
A framework to assess the factors affecting success or failure of the implementation of government-supported regional e-marketplaces for SMEs
Authors: Denise E. Gengatharen, Craig Standing
Venue: European Journal of Information Systems (2005)

Regional electronic marketplaces (REMs) have been used to promote e-commerce uptake by small and medium enterprises (SMEs). However, the community and regional economic development motives underpinning REM creation can cause complexities. While REM development and management can be informed by existing theoretical frameworks, there is no comprehensive framework that can be used to examine the factors affecting the success or failure of government-supported SME-REMs. The objective of this paper is to construct such a framework that can be used to examine these factors. The literature on e-marketplaces and IT/IS/e-commerce adoption by SMEs is examined to determine the appropriateness of existing theoretical frameworks, the key constructs of which are synthesised to form an integrated theoretical framework. The value of the framework is confirmed by content analysis of published case studies and empirical results from in-depth case studies of two SME-REMs. The most significant factors affecting success or failure of government-supported SME-REMs are as follows: SME-owner innovativeness; REM ownership structure and governance that engender trust and build critical mass by including SMEs in REM development and management; matching REM focus and structure with regional profile by leveraging community ties and existing business relationships; adopting a staged approach to REM development; and ensuring REM benefits are understood by SMEs.
Classification of Mammograms Using Decision Trees
Authors: L. Vibha, G M. Harshavardhan, K Pranaw, P Deepa Shenoy, K R. Venugopal, L M. Patnaik
Venue: Proceedings of the 10th International Database Engineering and Applications Symposium (2006)

Mammography is a medical imaging technique that combines, low-dose radiation and high-contrast, highresolution film for examination of the breast and screening for breast cancer. This paper proposes a Random Forest Decision Classifier (RFDC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., Normal, Cancerous and Benign. Experimental results show that this method performs well with the classification accuracy reaching nearly 90% in comparison with the already existing algorithms.
Voluntary Selection of Task Sets Revealed by Functional Magnetic Resonance Imaging
Authors: Birte U. Forstmann, Marcel Brass, Iring Koch, D. Yves Von Cramon
Venue: Journal of Cognitive Neuroscience (2006)

In everyday life, we have to selectively adapt our behavior to different situations and tasks. In cognitive psychology, such adaptive behavior can be investigated with the task-switching paradigm. However, in contrast to everyday life, in experiments participants are unequivocally told which task to perform. The present functional magnetic resonance imaging (fMRI) study was set out to investigate processes that are relevant when participants can decide by their own which task to perform. The number of tasks to choose from was varied between a forced condition (no choice) and two voluntary selection conditions (two or three choices). We expected to find prolonged reaction times as well as higher activations within the midcingulate cortex for the choice conditions compared to the no-choice condition. The fMRI results revealed a significant activation difference for the choice conditions versus the no-choice condition. For the choice contrast, activation was found in the rostral cingulate zone (RCZ) as well as the superior parietal lobule and the posterior part of the intraparietal sulcus. These activations revealed no selection-specific difference between three and two choices. Finally, a post hoc analysis showed that the activation in the RCZ is not associated with higher task-dependent response conflict when participants can select a task set. Taken together, these findings indicate that distinct brain areas are involved in the voluntary selection of abstract task set information.
Energy-Efficient Cache Coherence for Embedded Multi-Processor Systems through Application-Driven Snoop Filtering
Authors: Alokika Dash, Peter Petrov
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

Maintaining local caches coherent in bus-based multiprocessor systems results in significantly elevated power consumption, as the bus snooping protocols require local cache lookups for each memory reference placed on the common bus. Such a conservative approach is warranted in general-purpose systems, where no prior knowledge regarding the communication structure between threads or processes is available. In such a general-purpose context the assumption is that each memory request is potentially a reference to a shared memory region, which may result in cache inconsistency, if no correcting activities are undertaken. The approach we propose exploits the fact that in embedded systems, important knowledge is available to the system designers regarding communication activities between tasks allocated to the different processor nodes. We demonstrate how the snoop-related cache probing activity can be drastically reduced by identifying in a deterministic way all the shared memory regions and the communication patterns between the processor nodes. Cache snoop activity is enabled only for the fraction of the bus transactions, which refer to locations belonging to known shared memory regions for each processor node; for the remaining larger part of memory references known to be of no relation to the given processor node, snoop probings in the local cache are completely disabled, thus saving a large amount of power. The experiments which we have performed on a number of important applications demonstrate the effectiveness of the proposed approach.
On-line identification of echo-path impulse responses by Haar-wavelet-based adaptive filter
Authors: M. Doroslovacki, H. Fan
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Long-distance telephone communications of good quality require absence of echoes. The physical characteristics of an echo path are time varying and the impulse responses measured at different times can differ from each other. This means that an echo canceller must track the changes. Using a small number of coefficients in Haar-wavelet-based models we can efficiently identify echo paths which have certain typical impulse response shapes. The modeling error energy obtained is low (less than 2%). A simple wavelet-based LMS adaptive filter can be used for on-line estimation of the coefficients. A low number of time-consuming computations is obtained per input sample due to the usage of Haar wavelets. This number is less than the ones obtained by the FIR of DFT domain based modeling.
SLOM: a new measure for local spatial outliers
Authors: Sanjay Chawla, Pei Sun
Venue: Knowledge and Information Systems (2006)

We propose a measure, spatial local outlier measure (SLOM), which captures the local behaviour of datum in their spatial neighbourhood. With the help of SLOM, we are able to discern local spatial outliers that are usually missed by global techniques, like "three standard deviations away from the mean". Furthermore, the measure takes into account the local stability around a data point and suppresses the reporting of outliers in highly unstable areas, where data are too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets that show that our approach is novel and scalable to large datasets.
User interface layout with ordinal and linear constraints
Authors: Christof Lutteroth, Gerald Weber
Venue: Proceedings of the 7th Australasian User interface conference - Volume 50 (2006)

User interfaces as well as documents use tabular layout mechanisms. The HTML table construct and the GridBag layout in Java are typical examples. There are, however, shortcomings of these mechanisms that become obvious with more advanced content like semi-structured data or object-oriented models. We present a generalized table construct that solves these shortcomings and generalizes tabular layouts to a foundation of 2D layout. We describe an algorithm for specifying and rendering user interfaces -- and 2D documents in general -- using simple but expressive mathematical properties. In particular, the new tabular layout is described by ordinal and linear constraints. The ordinal information makes it possible to describe the general structure of a table and merge multiple cells into rectangular areas. The linear constraints allow it to map particular points of the table to particular coordinates or specify the size of areas in an absolute or relative manner. The resulting layout engine is easy to use and can render 2D information in real-time.
A neural network based information granulation approach to shorten the cellular phone test process
Authors: Chao-Ton Su, Long-Sheng Chen, Tai-Lin Chiang
Venue: Computers in Industry (2006)

In the cellular phone OEM/ODM industry, reducing test time and cost are crucial due to fierce competition, short product life cycle, and a low margin environment. Among the inspection processes, the radio frequency (RF) function test process requires more operation time than any other. Hence, manufacturers need an effective method to reduce the RF test items so that the inspection time can be reduced while maintaining the quality of the RF function test. However, traditional feature selection methods such as neural networks and genetic algorithm lead to a high level of Type II error in the situation of imbalanced data where the amount of good products is far greater than the defective products. In this study, we propose a neural network based information granulation approach to reduce the RF test items for the finished goods inspection process of a cellular phone. Implementation results show that the RF test items were significantly reduced, and that the inspection accuracy remains very close to that of the original testing process. In addition, the Type II errors decreased as well.
Efficient and decentralized PageRank approximation in a peer-to-peer web search network
Authors: Josiane Xavier Parreira, Debora Donato, Sebastian Michel, Gerhard Weikum
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user's thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation.This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation.