ppxscal/embedding_network huggingface dataset:
On Estimating Tight-Link Bandwidth Characteristics over Multi-Hop Paths
Authors: Seong-Ryong Kang, Amit Bhati, Dmitri Loguinov, Xiliang Liu
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

In this paper, we explore multi-hop bandwidth estimation assuming arbitrary cross-traffic at each node and develop a new probing method called Envelope that can asymptotically estimate not only the available bandwidth but also the raw capacity of the tight link. Envelope is based on a multi-link recursive extension of unbiased single-hop estimators proposed in the past (e.g., [14]) and a variation of the packet-cartouche technique [6]. Through extensive simulations, we evaluate Envelope in various network settings and cross-traffic conditions and find that it can measure tight-link bandwidth characteristics with accuracy that significantly surpasses that of the existing methods. We also find that Envelope can measure non-tight links in certain path and cross-traffic configurations.
Parietal rTMS Disrupts the Initiation but not the Execution of On-line Adjustments to a Perturbation of Object Size
Authors: Scott Glover, R. Chris Miall, Matthew F. S. Rushworth
Venue: Journal of Cognitive Neuroscience (2005)

Previous studies have implicated the human parietal lobes in the on-line guidance of action. However, no study to date has examined at what stage in the on-line adjustment process do the parietal lobes play their most critical role. Repetitive transcranial magnetic stimulation (rTMS ) was applied over the left intraparietal sulcus as participants reached to grasp a small or large illuminated cylinder. On some trials, the illumination could suddenly switch from the small to large cylinder, or vice-versa. Small&ndash;Large switches were associated with relatively early grip aperture adjustments, whereas Large&ndash;Small switches were associated with relatively late grip aperture adjustments. When rTMS was applied early in the movement, it disrupted on-line adjustments to Small&ndash;Large target switches, but not to Large&ndash;Small switches. Conversely, when rTMS was applied late in the movement, it disrupted adjustments to Large&ndash;Small target switches but not to Small&ndash;Large switches. The timing of the disruption by rTMS appeared linked to the initiation of the adjustment. It was concluded that the left parietal lobe plays a critical role in initiating an on-line adjustment to a change in target size, but not in executing that adjustment. The implications of these results for current views of on-line control are discussed.
Energy consumption prediction using sequential Monte Carlo methods
Authors: O. A. Alsayegh, O. A. Almatar
Venue: International Journal of Modelling and Simulation (2006)

In this paper, a new time-dependent model is proposed for the prediction of annual monthly energy consumption (EC). The aim is to furnish information that will lead to better analyses of future load demand rather than to enhance the forecasting accuracy rate. Conventional forecasting methods predict a future EC value and eglect information about its stochastic behaviour. Forecasting the behaviour of the EC is the main goal of this work. Recursive Bayesian filters by Monte Carlo simulations are utilized for predicting and analyzing the annual monthly EC change. The EC change posterior density function is represented by a set of random samples with associated weights; estimates are then computed based on these samples and weights. As the number of samples increases, this Monte Carlo characterization becomes an equivalent representation to the usual functional description of the posterior probability density function, which refiects the behaviour of the stochastic nature of EC change. The proposed technique was used to predict and analyze the annual-monthly EC in Kuwait. The EC data in Kuwait for the years 1992-2000 were used as a training set to predict the EC for the year 2001. The prediction performance was tested against an artificial neural network (ANN) and ARIMAX models. Furthermore, statistical EC analysis was performed using posterior density functions resulting from the proposed technique.
Web Robot Learning Powered by Bluetooth Communication System
Authors: Seref Sagiroglu, Nihat Yilmaz, M. Arif Wani
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

This paper presents a web robot web-robot learning powered by Bluetooth communication system. The web-robot system is used as the virtual robot laboratory integrating a number of disciplines in engineering. This virtual laboratory is a valuable teaching tool for engineering education used at any time and from any location through Internet. The mobile robot was controlled with robot server named as control center. The server can be connected to mobile robot via Bluetooth adapter. The mobile robot system focuses on vision sensing. Real time image processing techniques are realized by the web robot system. This system can also realize monitoring, tele-controlling, parameter adjusting and reprogramming through Internet exclusively with a standard Web browser without the need of any additional software.
Simplified workload characterization using unified prediction
Authors: K. Driesen
Venue: Proceedings of the 2000 IEEE International Symposium on Performance Analysis of Systems and Software (2000)

Quantitative workload characterization is essential to high performance computer architecture design. Unfortunately, quantitative results are typically hard to interpret, reproduce and compare, due to the staggering amount of detail inherent in modern architecture. Source language, compiler technology target ISA, and micro-architecture, intertwined with system aspects such as memory hierarchy and multitasking regime, all add to the complexity of workload characterization. We propose two simple metrics to characterize program execution: a footprint measures the "size" of a program, and a Unified Prediction profile shows its "complexity". These metrics are architecture-independent, and allow meaningful comparisons of program behavior at a numerical but abstract level. We believe they can provide direction to subsequent, more detailed and costly simulation efforts.
A Mutual Authentication Enabled Fair-Exchange and Anonymous E-Payment Protocol
Authors: Q. Zhang, K. Markantonakis, K. Mayes
Venue: Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (2006)

In this paper, a practical electronic-payment (epayment) protocol is presented for the purchase of digital products over the Internet. The proposed protocol realizes the true fair-exchange through a process of mutual authentication between the customer and the merchant, by which each party ensures that the item (product or payment) he/she is about to receive from the other party is valid and correct, whilst maintaining customer and merchant anonymity.
Fostering innovation implementation: findings about supporting scale from GLOBE
Authors: Barry J. Fishman, William R. Penuel, Ryoko Yamaguchi
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

This study explores factors related to successful implementation of the GLOBE science education program through an examination of outcomes related to professional development (PD), technical support, and resource provision. We analyzed results from a survey of 454 teachers served by 28 different PD providers using a 2-level hierarchical linear model. Results support findings from earlier studies about the importance of teachers' perceptions of coherence with their own and their district goals for student learning and the value of active learning strategies. At the same time, our models indicate that the correlates of effective PD vary for different outcomes, and that for program implementation equipment and technical support are critical factors. The results provide guidance for researchers and innovation developers seeking to better understand the conditions which foster implementation, scalability, and sustainability.
Parietal Lobe Lesions Disrupt Saccadic Remapping of Inhibitory Location Tagging
Authors: Ayelet Sapir, Amy Hayes, Avishai Henik, Shai Danziger, Robert Rafal
Venue: Journal of Cognitive Neuroscience (2004)

Maintaining a coherent percept of the visual scene while eye position continuously changes requires that saccades be accompanied by remapping of the visual environment. We studied saccadic remapping in patients with unilateral lesions in the intraparietal sulcus and healthy controls, using inhibition of return (IOR)&mdash;an inhibitory tag that enables efficient visual search. In healthy controls, IOR was found at both retinal and environmental locations of the cue, indicating that the inhibitory tag had been remapped into environmental coor dinates. In contrast, right parietal patients demonstrated IOR only at the retinal location of the cue, indicating that the intraparietal sulcus is involved in remapping of the environment after eye movements to afford a stable, environmentally based reference frame. Note that patients did not show environmental IOR in either visual field. These results also suggest that this region may be the neural substrate for encoding inhibitory spatial tags in an environmentally based reference frame.
Function Call Optimization in Behavioral Synthesis
Authors: Yuko Hara, Hiroyuki Tomiyama, Shinya Honda, Hiroaki Takada
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

Behavioral synthesis, which automatically synthesizes an RTL circuit from a sequential program, is one of promising technologies to improve the design productivity. However, behavioral synthesis has not become popular yet in industry since the quality of generated circuits is not satisfactory, especially in the synthesis from the large programs with a number of functions. This paper proposes a method to optimize function calls in behavioral synthesis. We formulate the optimization problem using integer linear programming. Our experimental results show that our method reduces the circuit area by 44.6%, compared with a traditional method.
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
Authors: Tao Li, Chris Ding
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

The nonnegative matrix factorization (NMF) has been shown recently to be useful for clustering and various extensions and variations of NMF have been proposed recently. Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences. In this paper we aim to provide a comprehensive study on matrix factorization for clustering. In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them. Experiments are also conducted to empirically evaluate and compare various factorization methods. In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering. We expect our study would provide good insights on matrix factorization research for clustering.
An LQP Method for Pseudomonotone Variational Inequalities
Authors: Abdellah Bnouhachem
Venue: Journal of Global Optimization (2006)

In this paper, we proposed a modified Logarithmic-Quadratic Proximal (LQP) method [Auslender et al.: Comput. Optim. Appl. 12, 31---40 (1999)] for solving variational inequalities problems. We solved the problem approximately, with constructive accuracy criterion. We show that the method is globally convergence under that the operator is pseudomonotone which is weaker than the monotonicity and the solution set is nonempty. Some preliminary computational results are given.
From evidence to explanations: engaging undergraduate geology students in inquiry
Authors: Xornam S. Apedoe
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

This paper reports the synthesis of six case studies of students' engagement in inquiry-based learning activities in an undergraduate geology course. Details of how students engaged in the scientific inquiry practices of formulating explanations from evidence, and communicating and justifying proposed explanations are presented. Data for this study included classroom observations and archival data (i.e., student work samples) collected during a semester-long investigation. The results suggest that students were able to appropriate these inquiry practices with varying degrees of success, however, it was not without its challenges. A detailed discussion of the ways in which students were successful, and where they had challenges engaging in these inquiry practices is presented, with the goal of helping guide practitioners and researchers in creating learning environments that can take full advantage of the pedagogical strategy of inquiry-based learning.
Combining Log-Spectral Domain Compensation with MVA Feature Post-Processing for Robust Speech Recognition
Authors: Jianjun Lei, Jian Wang, Jun Guo, Gang Liu, Haifeng Shen
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

In this paper, we present a new scheme combining environment compensation with feature postprocessing to improve the robustness of speech recognition systems. The environment compensation is implemented in the log-spectral domain and the environment model is approximated by Statistical Linear Approximation (SLA). The MVA feature postprocessing is used to deal with the residual mismatch between compensated noisy speech and clean speech. We have evaluated recognition performance under noisy environments using NOISEX-92 database and recorded speech signals in continuous speech recognition task. Experimental results show that our approach exhibits considerable improvements in the degraded environment.
Rules for an Ontology-based Approach to Adaptation
Authors: Thanh Tran, Philipp Cimiano, Anupriya Ankolekar
Venue: Proceedings of the First International Workshop on Semantic Media Adaptation and Personalization (2006)

Adaptation addresses the complexity and overload caused by an increasing amount of available resources by the identification and presentation of relevant resources. Drawbacks of existing approaches to this end include the limited degree of customization, difficulties in the acquisition of model information and the lack of control and transparency of the system's adaptive behavior. This paper proposes an approach which addresses these drawbacks to perform meaningful and effective adaptation by the application of semantic web technologies. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. The focus of this paper lies in the presentation of adaptation rules.
Color Subspaces as Photometric Invariants
Authors: Todd Zickler, Satya P. Mallick, David J. Kriegman, Peter N. Belhumeur
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image 'features' that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe two such invariants" one invariant to specular reflections, and the other invariant to both specular reflections and diffuse shading" that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from subspaces of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, material-based segmentation, and motion estimation.
Architectural support for operating system-driven CMP cache management
Authors: Nauman Rafique, Won-Taek Lim, Mithuna Thottethodi
Venue: Proceedings of the 15th international conference on Parallel architectures and compilation techniques (2006)

The role of the operating system (OS) in managing shared resources such as CPU time, memory, peripherals, and even energy is well motivated and understood [23]. Unfortunately, one key resource—lower-level shared cache in chip multi-processors—is commonly managed purely in hardware by rudimentary replacement policies such as least-recentlyused (LRU). The rigid nature of the hardware cache management policy poses a serious problem since there is no single best cache management policy across all sharing scenarios. For example, the cache management policy for a scenario where applications from a single organization are running under "best effort" performance expectation is likely to be different from the policy for a scenario where applications from competing business entities (say, at a third party data center) are running under a minimum service level expectation. When it comes to managing shared caches, there is an inherent tension between flexibility and performance. On one hand, managing the shared cache in the OS offers immense policy flexibility since it may be implemented in software. Unfortunately, it is prohibitively expensive in terms of performance for the OS to be involved in managing temporally fine-grain events such as cache allocation. On the other hand, sophisticated hardware-only cache management techniques to achieve fair sharing or throughput maximization have been proposed. But they offer no policy flexibility.This paper addresses this problem by designing architectural support for OS to efficiently manage shared caches with a wide variety of policies. Our scheme consists of a hardware cache quota management mechanism, an OS interface and a set of OS level quota orchestration policies. The hardware mechanism guarantees that OS-specified quotas are enforced in shared caches, thus eliminating the need for (and the performance penalty of) temporally fine-grained OS intervention. The OS retains policy flexibility since it can tune the quotas during regularly scheduled OS interventions. We demonstrate that our scheme can support a wide range of policies including policies that provide (a) passive performance differentiation, (b) reactive fairness by miss-rate equalization and (c) reactive performance differentiation.
Fuzzy Adaptive Agent for Supply Chain Management
Authors: Sio-Fan Lou, Yain-Whar Si
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Negotiating with suppliers and with customers is a key part of supply chain management. However, with recent technological advances, the mechanisms available to carry out such activities have become increasingly sophisticated, and the environment in which these activities take place has become highly dynamic. In this paper, we describe the strategy of a Fuzzy Logic based supply chain management agent which is designed to adapt to the market environment. The agent has competed in the 2006 Trading Agent Competition and has achieved good result.
Towards User-Defined Performance Monitoring of Distributed Java Applications
Authors: Wlodzimierz Funika, Piotr Godowski, Piotr Pegiel, Marian Bubak
Venue: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (2006)

This paper presents a new approach to the issues of performance monitoring and visualization of distributed applications using the J-OCM monitoring system. We are focusing on the building of a GUI measurement-oriented tool with a flexible functionality, Java Measurement Tool (JMT) on top of J-OCM which provides an OMIS-based monitoring interface. The tool addresses the issues of user-defined metrics and user-friendly visualization of distributed Java applications. The paper introduces the functionality, design, and status of JMT.
Finding centric local outliers in categorical/numerical spaces
Authors: Jeffrey Xu Yu, Weining Qian, Hongjun Lu, Aoying Zhou
Venue: Knowledge and Information Systems (2006)

Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets.
Continuous nearest neighbor monitoring in road networks
Authors: Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as fluctuations of edge weights. The first one maintains the query results by processing only updates that may invalidate the current NN sets. The second method follows the shared execution paradigm to reduce the processing time. In particular, it groups together the queries that fall in the path between two consecutive intersections in the network, and produces their results by monitoring the NN sets of these intersections. We experimentally verify the applicability of the proposed techniques to continuous monitoring of large data and query sets.