ppxscal/embedding_network huggingface dataset:
Predictability of Web-Server Traffic Congestion
Authors: Yuliy Baryshnikov, Ed Coffman, Guillaume Pierre, Dan Rubenstein, Mark Squillante, Teddy Yimwadsana
Venue: Proceedings of the 10th International Workshop on Web Content Caching and Distribution (2005)

Large swings in the demand for content are commonplace within the Internet. When a traffic hotspot happens, however, there is a delay before measures such as heavy replication of content can be applied. This paper investigates the potential for predicting hotspots sufficiently far, albeit shortly, in advance, so that preventive action can be taken before the hotpot takes place. Performing accurate load predictions appears to be a daunting challenge at first glance, but this paper shows that, when applied to web-server page-request traffic, even elementary prediction techniques can have a surprising forecasting power. We first argue this predictability from principles, and then confirm it by the analysis of empirical data, which reveals that large server overloads can often be seen well in advance. This allows steps to be taken to reduce substantially the degradation of service quality.
A closed-form algorithm for covariance-constrained optimal estimation
Authors: Joseph D. Mook
Venue:  (2005)

This dissertation describes a new method which results in a closed-form solution for constrained optimal state estimation and system identification of discretely measured dynamic systems, and illustrates the method for simple examples. This post-experiment optimal state estimation method is especially appropriate in the presence of significant model error and/or significant measurement error. The new approach is robust in the presence of significant measurement noise and capable of estimating accurate states, by incorporating the known statistical characteristics of the noisy measurements as constraints. The determination of the optimal state estimates is derived from a minimization of a cost functional subject to differential equation constraints and statistical constraints of the noisy measurements. Estimation of the state and the dynamic model error are obtained as part of the solution of a jump discontinuous two-point boundary value problem associated with the algorithm. The resulting state estimates are continuous and optimal in a global sense. The dynamic model error terms to be identified are assumed unknown and may take any form (including nonlinear). The new method presented in this dissertation provides a way of obtaining a closed-form solution for determining optimal state estimation with incorporation of the average variance of the discrete state measurement as a constraint in the presence of significant error in the assumed (nominal) model. The weight matrix in the Minimum Model Error algorithm is eliminated since the constraints are incorporated in the problem and satisfied as part of the solution. This new constrained approach greatly improves computational speed and results in an exact analytical enforcement of the covariance constraint.
Adaptive Signal-Dependent Audio Watermarking Based on Human Auditory System and Neural Networks
Authors: Hung-Hsu Tsai, Ji-Shiung Cheng
Venue: Applied Intelligence (2005)

Based the characteristics of the human auditory system (HAS) and the techniques of neural networks, this work proposes an Adaptive Signal-Dependent Audio Watermarking (ASDAW) technique for protecting audio copyrights. First, a signal-dependent watermark for the ASDAW technique is generated by using the characteristics of the HAS (the temporal and frequency maskings). Next, the signal-dependent watermark is hidden in an original audio on the temporal domain. The ASDAW technique can make the signal-dependent audio watermark imperceptive (inaudible) because of the characteristics of the HAS. Moreover, an artificial neural network (ANN) is trained in the ASDAW technique so that the ASDAW technique can memorize the relationships between an original audio and the corresponding watermarked audio. Using the trained ANN (TANN), the ASDAW technique can extract the signal-dependent watermarks without the original audio. The extracted watermarks are then exploited in verifying legal duplications made of an audio during audio authentication. Consequently, the copyright forgery for audio can be suppressed greatly. Furthermore, experimental results illustrate that the ASDAW technique significantly possesses memorized, adaptive, and robust capabilities, making it immune against common audio manipulations and pirate attacks for counterfeiting audio copyrights.
Probabilistic aspects of wireless ad hoc networks
Authors: Peng-Jun Wan
Venue:  (2005)

A randomly-deployed large-scale wireless ad hoc network is naturally represented by an r-disk graph over a random point set. For a monotonically increasing topology property, the smallest transmission radius such that the induced r-disk graph has this property is called the critical transmission radius. Instead of giving deterministic algorithms, we offer a systematic probabilistic study on the critical transmission radius. In the random geometric graphs, the critical transmission radii for monotonic-increasing properties have sharp thresholds. Therefore, by assigning transmission radius lager than the threshold, the wireless network almost surely has the corresponding property. We have conducted probabilistic studies on the critical transmission radii for k-connectivity, k-coverage, Gabriel graph, and greedy forward routing. All these critical transmission radii can be explicitly given in terms of the number of nodes.
Enriching Reverse Engineering with Semantic Clustering
Authors: Adrian Kuhn, Stéphane Ducasse, Tudor Girba
Venue: Proceedings of the 12th Working Conference on Reverse Engineering (2005)

Understanding a software system by just analyzing the structure of the system reveals only half of the picture, since the structure tells us only how the code is working but not what the code is about. What the code is about can be found in the semantics of the source code: names of identifiers, comments etc. In this paper, we analyze how these terms are spread over the source artifacts using Latent Semantic Indexing, an information retrieval technique. We use the assumption that parts of the system that use similar terms are related. We cluster artifacts that use similar terms, and we reveal the most relevant terms for the computed clusters. Our approach works at the level of the source code which makes it language independent. Nevertheless, we correlated the semantics with structural information and we applied it at different levels of abstraction (e.g. classes, methods). We applied our approach on three large case studies and we report the results we obtained.
Multi-class protein fold recognition using adaptive codes
Authors: Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds. Our method learns relative weights between one-vs-all classifiers and encodes information about the protein structural hierarchy for multi-class prediction. Our code weighting approach significantly improves on the standard one-vs-all method for the fold recognition problem. In order to compare against widely used methods in protein sequence analysis, we also test nearest neighbor approaches based on the PSI-BLAST algorithm. Our code weight learning algorithm strongly outperforms these PSI-BLAST methods on every structure recognition problem we consider.
Incentives for P2P Fair Resource Sharing
Authors: Emmanuelle Anceaume, Maria Gradinariu, Aina Ravoaja
Venue: Proceedings of the Fifth IEEE International Conference on Peer-to-Peer Computing (2005)

We consider the problem of Fair Resource Sharing to optimize the performance of resource sharing in peer to peer systems. Resource sharing systems currently face rational peers which may exhibit a variety of strategies including : no participation, also referred as free-riding, and greedy behavior. The first aspect has been extensively studied in the late years, while the second one has not received much attention. The broad class of proposed solutions focus on designing incentives to reward cooperative peers. The side effect of these incentives is twofold : the system load is not balanced and the resource potential of the system is not fully exploited. The P2P fair resource sharing aims at both balancing the load and maximizing the use of system resources. The contribution of our work is twofold. First, we specify the P2P Fair Resource Sharing problem and propose a mechanism to solve it in large scale dynamic networks with rational users. Our mechanism is composed of a novel incentive (i.e. fair cooperation) and an algorithmic part encapsulated in a middleware layer. Second, we propose an architecture for our mechanism middleware layer including four distributed services that bring together several research area : aggregation, semantic group membership and tracking. Finally, we implement our mechanism using a peer-to-peer unstructured model and evaluate it through simulations.
A Lecture Allocation Support System for Elective Subjects-Based Curricula
Authors: Tokuro Matsuo, Takayuki Fujimoto
Venue: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (2005)

This paper proposes a lecture allocation support system based on users¿ utilities in elective subjects. Elective subjects systems are employed in many universities and colleges and students select subjects based on their interests and preferences. Generally, educational institutions (universities) determine members of elective subjects based on only one simple rule, such as the order of grade, random selection, and so on. Current determination systems have strong limitations in terms of reflecting users multiple preferences. We propose a new determination algorithm and a user support system based on users multi attribute preferences. We employ the multi-attribute utility theory to show as formula and calculate users multiple preferences. Furthermore, our system takes the manager¿s intention into consideration. When users preferences are not reflected in algorithm-based determination, a coordination agent decides lecture allocation cooperatively. Advantages of our system are mainly as follows. Lectures are allocated effectively, that is reflected users¿ multiple preferences. Managers can operate the class organization based on their intentions.
Determining Optimal Filters for Binarization of Degraded Grayscale Characters Using Genetic Algorithms
Authors: Yusuke Ojima, Satoshi Kirigaya, Toru Wakahara
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

Optimal binarization of degraded grayscale characters is a crucial step to subsequent character recognition. This paper proposes a new, promising binalization technique of grayscale characters using genetic algorithms (GA) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. First, we classify degraded samples of grayscale characters into several categories. Then, in the learning stage, by selecting a training sample from each degradation category we apply GA to the combinatorial optimization problem of determining a sequence of filters that maximizes the fitness value between the filtered training sample and its target image ideally binarized by humans. Finally, in the testing stage, we apply the optimal sequence of filters thus obtained to remaining test samples for each degradation category. Experiments using the public ICDAR 2003 robust OCR dataset demonstrate promising results of binarization of grayscale characters against a wide variety of degradation causes.
Wearable Hand Activity Recognition for Event Summarization
Authors: W. W. Mayol, D. W. Murray
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

In this paper we develop a first step towards the recognition of hand activity by detecting objects subject to manipulation, and use the results to build a visual summary of events. The motivation is to extract information from hand activity without requiring that the wearer is explicit as in gesture-based interaction. Our method uses simple image measurements within a probabilistic framework and allows real-time implementation.
Multiple Active Camera Assignment for High Fidelity 3D Video
Authors: Sofiane Yous, Norimichi Ukita, Masatsugu Kidode
Venue: Proceedings of the Fourth IEEE International Conference on Computer Vision Systems (2006)

We are designing a self controlling active camera system for a 3D video of a moving object (mainly human body). We made up our system of cameras with long focal length lenses for high resolution input images. However, such cameras can get only partial views of the object. We present, in this paper, a multiple active (pan-tilt) camera assignment scheme. The goal is to assign each camera to a specific part of the moving object so as to allow the best visibility of the whole object. For each camera, we evaluate the visibility to the different regions of the object, corresponding to different camera orientations and with respect to the field of view of the camera in question. Thereafter, we assign each camera to one orientation in such a way to maximize the visibility to the whole object.
Integrating Creativity into Requirements Processes: Experiences with an Air Traffic Management System
Authors: Neil Maiden, Suzanne Robertson
Venue: Proceedings of the 13th IEEE International Conference on Requirements Engineering (2005)

Requirements engineering is a creative process in which stakeholders and designers work together to create ideas for new systems that are eventually expressed as requirements. This paper describes RESCUE, a scenario-driven requirements engineering process that includes workshops that integrate creativity techniques with different types of use case and system context modeling. It reports research in which RESCUE creativity workshops were used to discover stakeholder and system requirements for MSP, a future air traffic management system to enable the more effective, longer-term planning of European airspace use. The workshops were successful in that they provided new and important outputs for subsequent requirements processes. The paper describes the workshops structures and results, and answers 3 important research questions.
Review of "Analyzing Qualitative/Categorical Data: Log-Linear Models and Latent-Structure Analysis, by Leo A. Goodman", Abt Books, 1978
Venue: ACM SIGSIM Simulation Digest (1979)

Researchers and statisticians dealing with human-related phenomena often have found that the traditional methods for summarizing cross-tabulations have proven inadequate for answering many of the questions under investigation. In recent years powerful new statistical methods have become available for analyzing qualitative/categorical data. These techniques start where the traditional methods leave off, and they have proven useful in revealing relatively simple structures underlying seemingly complex analytical interrelationships. 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
On Increasing End-to-End Throughput in Wireless Ad Hoc Networks
Authors: Zongpeng Li, Baochun Li
Venue: Proceedings of the Second International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (2005)

One of the main characteristics of wireless ad hoc networks is their node-centric broadcast nature of communication, leading to interferences and spatial contention between adjacent wireless links. Due to such interferences, pessimistic concerns have been recently raised with respect to the decreasing network capacity in wireless ad hoc networks when the number of nodes scales to several orders of magnitude higher. In this paper, we argue that in all cases of end-to-end data communications including oneto- k unicast and multicast data dissemination as well as kto- one data aggregation - the maximum achievable endto- end data throughput (measured on the sources) heavily depends on the strategy of arranging the the topology of transmission between sources and destinations, as well as possible per-node operations such as coding. An optimal strategy achieves better end-to-end throughput than an arbitrary one. We present theoretical studies and critical insights with respect to how these strategies may be designed so that end-to-end throughput may be increased.
Development of a solenoid injected liquid monopropellant-powered actuator for self-powered robots
Authors: Bobby Shields
Venue:  (2005)

One of the problems in the development of a human-scale, self-powered robot is that of a suitable power supply. Conventional actuation technology (i.e. battery-powered servomotors) does not possess the energy and power density required to allow such robots to perform work for extended periods of time. This dissertation concerns the development of an actuation system that can provide mechanical power to a self-powered, human-scale robot that has much greater energy and power density than current state-of-the-art batteries and motors. This will allow the robot to operate for a much longer time. Specifically, a direct-injection, liquid monopropellant-powered actuation system is developed that utilizes the catalytic decomposition of hydrogen peroxide as a hot gas generator for powering pneumatic-type actuators. Pressurization of a pneumatic actuator is provided via solenoid injection valves, which control the flow of the monopropellant through a catalyst pack into each side of the actuator. Depressurization is provided by a three-way proportional spool valve, which can exhaust one of the two cylinder chambers. A supervisory controller coordinates the operation of the three system inputs so that the actuator can track a desired force or position trajectory. A prototype of the actuation system is described, and experimental data are presented that demonstrates good force and position tracking performance. Experimental results characterizing the energetic performance of the system demonstrate that the prototype provides an energetic figure of merit of an order of magnitude greater than that of battery-powered servomotors.
Process-oriented simulation languages
Authors: Tuncay Saydam
Venue: ACM SIGSIM Simulation Digest (1985)

This article presents a brief general discussion of simulation languages followed by a more detailed description of some the important important features of process-oriented languages. Event oriented languages and some of their properties are briefly covered. Essential elements of process-oriented languages are described and desired features are elaborated. A need for a powerful, complex, yet easy to use process-oriented simulation language is emphasized.
A New Temporal CSP Framework Handling Composite Variables and Activity Constraints
Authors: Malek Mouhoub, Amrudee Sukpan
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

A well known approach to managing the numeric and the symbolic aspects of time is to view them as Constraint Satisfaction Problems (CSPs). Our aim is to extend the temporal CSP formalism in order to include activity constraints and composite variables. Indeed, in many real life applications the set of variables involved by the temporal constraint problem to solve is not known in advance. More precisely, while some temporal variables (called events) are available in the initial problem, others are added dynamically to the problem during the resolution process via activity constraints and composite variables. Activity constraints allow some variables to be activated (added to the problem) when activity conditions are true. Composite variables are defined on finite domains of events. We propose in this paper two methods based respectively on constraint propagation and stochastic local search (SLS) for solving temporal constraint problems with activity constraints and composite variables. We call these problems Conditional and Composite Temporal Constraint Satisfaction Problems (CCTCSPs). Experimental study we conducted on randomly generated CCTCSPs demonstrates the efficiency of our exact method based on constraint propagation in the case of middle constrained and over constrained problems while the SLS based method is the technique of choice for under constrained problems and also in case we want to trade search time for the quality of the solution returned (number of solved constraints).
Analysis and Modification of ASK Mobile Security Protocol
Authors: Il-Gon Kim, Hyun-Seok Kim, Ji-Yeon Lee, Jin-Young Choi
Venue: Proceedings of the Second IEEE International Workshop on Mobile Commerce and Services (2005)

Generally, security protocols have been designed and verified using informal techniques. In the result, it is now well recognized that many security protocols which were previously proposed have found to be vulnerable later on. In this paper, we model and verify of the ASK protocol, which is a complicated mobile security protocol. After showing the vulnerability of the ASK protocol using formal verification approach, we propose a modification, and then show that the new protocol is secure against replay attack.
Handwritten Character Recognition Using Gradient Feature and Quadratic Classifier with Multiple Discrimination Schemes
Authors: Hailong Liu, Xiaoqing Ding
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In the research of statistical approach for handwritten character recognition, directional element feature (DEF) and modified quadratic discriminant function (MQDF) have been extremely successful and widely used in practical applications. In this paper, we apply several state-of-the-art techniques of handwritten character recognition on this baseline system to improve the recognition accuracy. In feature extraction stage, gradient feature is extracted to replace DEF, which provides higher resolution on both magnitude and angle of the directional strokes in character image. In classification stage, the performance of MQDF classifier is enhanced by multiple discrimination schemes, including minimum classification error (MCE) training on the classifier parameters and modified distance representation for similar characters discrimination. All these techniques we use lead to improvement on the character recognition rate. The performance of the improved recognition system has been evaluated by both handwritten digit recognition and handwritten Chinese character recognition experiments, in which very promising results are achieved.