ppxscal/embedding_network huggingface dataset:
Revealing relationships in search engine results
Authors: Cláudio Max. Zaina, M. Cecília C. Baranauskas
Venue: Proceedings of the 2005 Latin American conference on Human-computer interaction (2005)

The amount of information available in the Internet is so vast that finding the desired information in such an unstructured repository easily becomes a tedious task. Graphical cluster-based representations of results from search engines shift the user's mental load from slower thought-intensive processes of reading information got from linear lists of results to faster perceptual processes such as pattern recognition in a visual display. In this paper we investigate the subject by presenting the design proposal of a new system that uses concepts of Information Visualization to help the user of Internet search engines to identify the group of documents that are related to the needed information by examining or browsing documents in the group. Some results of preliminary usability tests for the system are also provided shedding some light to the subject.
Reinforcement Temporal Difference Learning Scheme for Dynamic Energy Management in Embedded Systems
Authors: Lakshmi Prabha Viswanathan, Elwin Chandra Monie
Venue: Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design (2006)

Dynamic power management is a technique to reduce power consumption of electronic systems by selectively shutting down idle components. In this paper a novel and non-trivial enhancement of conventional reinforcement learning is adopted to predict the optimal policy out of the existing DPM policies. Reinforcement learning is a computational approach to understanding and automating goal-directed learning and decision-making. The effectiveness of this approach is demonstrated by an event driven simulator which is designed using JAVA for power-manageable embedded devices. Results of the experiments conducted in this regard establish that the proposed DPM scheme enhances power savings considerably.
Similarity-driven Sequence Classification Based on Support Vector Machines
Authors: Hansheng Lei, Venu Govindaraju
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

A novel sequence classification method is proposed in the context of Support Vector Machines (SVM). This method is driven by an intuitive similarity measure, namely ER2, which directly tells the similarity of two sequences (1- or multi-dimensional). If sequence X is very similar to Y (for instance, the similarity by ER2 is above 90%), it is safe to assign X to the same class as Y . ER2 is plugged into standard SVM to speed up the decision-making of multi-class classification. The immediate application of the method is in the adaptive on-line handwriting recognition, where handwritten characters are represented by 2D sequences of X-,Y-coordinates. Experiments on the benchmark database UNIPEN show that the classification driven by ER2 can be about three times faster than standard SVM while the classification accuracy is enhanced or comparable.
Pitch-Dependent Identification of Musical Instrument Sounds
Authors: Tetsuro Kitahara, Masataka Goto, Hiroshi G. Okuno
Venue: Applied Intelligence (2005)

This paper describes a musical instrument identification method that takes into consideration the pitch dependency of timbres of musical instruments. The difficulty in musical instrument identification resides in the pitch dependency of musical instrument sounds, that is, acoustic features of most musical instruments vary according to the pitch (fundamental frequency, F0). To cope with this difficulty, we propose an F0-dependent multivariate normal distribution, where each element of the mean vector is represented by a function of F0. Our method first extracts 129 features (e.g., the spectral centroid, the gradient of the straight line approximating the power envelope) from a musical instrument sound and then reduces the dimensionality of the feature space into 18 dimension. In the 18-dimensional feature space, it calculates an F0-dependent mean function and an F0-normalized covariance, and finally applies the Bayes decision rule. Experimental results of identifying 6,247 solo tones of 19 musical instruments shows that the proposed method improved the recognition rate from 75.73% to 79.73%.
Agreement-Based Workload and Resource Management
Authors: Sergio Andreozzi, Tiziana Ferrari, Elisabetta Ronchieri, Salvatore Monforte
Venue: Proceedings of the First International Conference on e-Science and Grid Computing (2005)

Resource-sharing is one of the Grid intrinsic properties enabling efficient usage of services by clients from many Virtual Organizations. In such an environment, the provisioning of Quality of Service requires mechanisms for the control of the concurrent access to resources and services, as well as protocols for the establishment of Service Level Agreements between users and service providers. Nevertheless, while the availability of guarantees is important to allow a more flexible usage of the distributed infrastructure, most of the Grids today only offer best-effort services and do not support Service Level Agreement signalling. In this paper, we present the architecture and the prototype implementation of a novel resource-independent set of workload and resource management services supporting discovery, Service Level Agreement signalling and monitoring, and adopting the functional division into agreement and service provider layers currently under definition by the Grid Resource Allocation and Agreement Protocol Working Group of the Global Grid Forum.
Using Component Business Modeling to Facilitate Business Enterprise Architecture and Business Services at the US Department of Defense
Authors: David Flaxer, Anil Nigam, John Vergo
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2005)

Component Business Modeling (CBM) is an aggregation of models, methods and techniques that are designed to organize, understand, evaluate, and ultimately, transform an enterprise. The decomposition of an enterprise into well bounded and discrete business components enables a straightforward understanding of a complex enterprise and facilitates the realization of business intent by information technology. This paper examines the use of CBM within the government sector. At more than twice the size of the world's largest commercial enterprise, the Business Management Modernization Program (BMMP) of the Department of Defense (DoD) is undertaking a dramatic and complex business transformation. Our team proposed the use of CBM to address their objectives. We will first discuss how business components (referred to as business capabilities by the DoD) is used to leverage the enterprise architecture, to analyze business transformation opportunities, and to identify business services. We will then describe how the results of CBM can be used to refine a service oriented view of the business.
Text Detection in Color Scene Images based on Unsupervised Clustering of Multi-channel Wavelet Features
Authors: Tomoyuki Saoi, Hideaki Goto, Hiroaki Kobayashi
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

Texts in natural scenes provide us with much useful information. In order to use such information automatically, it is necessary to make computers detect text regions in the images. Gllavata et al. proposed a method based on unsupervised classification of high frequency wavelet coefficients for text detection in video frames [1]. Although the method is very accurate, it does not work so well with some color images, since it lacks the ability of discriminating color difference. This paper proposes an enhanced version of the method. We develop a new unsupervised clustering technique for the classification of multi-channel wavelet features to deal with color images. Experimental results show that the new method yields better results for color scene images.
Intelligent Code Analyzer for Online Course Management System
Authors: Jong-Yih Kuo, Louisa Chu
Venue: Proceedings of the Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (2005)

Online course management system (OCMS) mainly aids various events in online instructing, including testing, course discussion, assignment submission, and assignment grading. This essay is mainly designed basing on the study of completed OCMS of the past. Online assignment submission is prone to easy plagiarism, infecting the learning process of the students and interfering with their studies. In the past, using human power to inspect for plagiarism is very time-consuming. This research then is focused on allowing programming courses to employ procedures such as code standardization, textual analysis, structural analysis, and variable analysis, to evaluate and compare programming codes. We provide an intelligent agent as a daemon to analyze the program code for OCMS. Textually, we use Document Fingerprinting Algorithm as a basis for text comparison; structurally, we utilize formal algebraic expression and dynamic control structure tree (DCS tree) to rebuild and evaluate the program structure; variable-wise, we not only record relevant information for each variable, but also analyze the programming structure where the variables are positioned. By applying a similarity measuring method, we output a similarity value for each program in the three aspects mentioned above. This research implements a convenient user interface that can be applied independently for assignment analyzation. Moreover, we have designed a set of Application Programming Interface (API) that could be embedded into online course management systems.
A Simulation Model of Self-organising Evolvability in Software Systems
Authors: Stephen Cook, Rachel Harrison Harrison, Paul Wernick
Venue: Proceedings of the IEEE International Workshop on Software Evolvability (2005)

The evolvability of a software artifact is its capacity for producing heritable or reusable variants; the inverse quality is the artifact's inertia or resistance to evolutionary change. Evolvability in software systems may arise from engineering and/or self-organising processes. We describe our 'Conditional Growth' simulation model of software evolution and show how it can be used to investigate evolvability from a self-organisation perspective. The model is derived from the Bak-Sneppen family of 'self-organised criticality' simulations. It shows good qualitative agreement with Lehman's 'laws of software evolution' and reproduces phenomena that have been observed empirically. The model suggests interesting predictions about the dynamics of evolvability and implies that much of the observed variability in software evolution can be accounted for by comparatively simple self-organising processes.
Architecture for Multi-Stage Network Attack Traceback
Authors: W. Timothy Strayer, Christine E. Jones, Beverly I. Schwartz
Venue: Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary (2005)

Attacks can originate from anywhere in the network but there is little the network can tell operators about where the attacker is located. Packet traceback techniques have been proposed to find the source of one or more IP packets, but some attackers use multiple remote login sessions, or stepping stones, to increase obfuscation. IP packet traceback can only find the source of one of the several connections in the stepping stone connection chain. Stealthy Tracing Attackers Research Light TracE (STARLITE) is a customization and significant extension to BBN's Source Path Isolation Engine (SPIE.) The goal of STARLITE was to construct a prototype to integrate single packet traceback with stepping stone detection. The resulting prototype traces a packet to an ingress router, and then discovers if the flow of that packet is related to a flow in another connection. A successful correlation can then be continued until an ultimate source is located.
Context-Based Segmentation of Image Sequences
Authors: Jacob Goldberger, Hayit Greenspan
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data.
A semiautomatic task-oriented programming system for sensor-controlled robotised small-batch and one-off manufacturing
Authors: Per Cederberg, Magnus Olsson, Gunnar Bolmsjö
Venue: Robotica (2005)

A task-oriented system structure has been developed. In normal industrial robot programming, the path is created and the process is based on the path. Here a process-focused method is proposed, where a task can be split in sub-tasks, one for each part of the process with similar process-characteristics. By carefully encapsulating the information needed to execute a sub-task, this component can be re-used whenever the actual sub-task occurs. Applications using system design do not change between simulation and actual shop floor runs and the system allows a mix of real- and simulated components during simulation and run-time.
Standards?
Authors: Diana Patterson
Venue: ACM SIGDOC Asterisk Journal of Computer Documentation (1976)

I have this fear of standards, I'd like to talk about. They say talking about your fears helps, so, please bear with me. 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
High Performance MTCMOS Technique for Leakage Reduction in Hybrid SOI-Epitaxial Technologies with Enhanced-Mobility PFET Header
Authors: Koushik K. Das, Shih-Hsien Lo, Ching-Te Chuang
Venue: Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design (2006)

Power management has become a key constraint in the design of modern digital VLSI chips. Moreover, with minimum transistor dimensions reaching 100-nm and below, traditional scaling has slowed down. The ITRS roadmap has indicated that device mobility enhancement would be necessary to maintain the generational performance improvement in the sub-100nmVLSI era. This paper presents a comprehensive analysis of the popular low-leakage power MTCMOS circuit technique in various emerging technologies with enhanced-mobility PFETs.
Semantic Analysis of Matrix Structures
Authors: Alan P. Sexton, Volker Sorge
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

The automatic interpretation of mathematical text structures has received only limited attention so far. While there has been work on the particularities of the character recognition of two dimensional formulas such as matrices in texts, very little has been done on exploiting their additional semantics in order to make them available for further processing. We present an algorithm that provides the interface between the recognition of matrix expressions and their concrete interpretation as mathematical objects. Given an underspecified matrix representation as they commonly appear in textbooks, containing ellipses and fill symbols, our algorithm extracts the semantic information contained. Matrices are interpreted as a collection of regions that can be interpolated with a particular term structure. The effectiveness of our algorithm can be demonstrated by its ability to derive concrete instances for the processed matrix representations in a symbolic computation system.
On Optimization of Sensor Selection for Aircraft Gas Turbine Engines
Authors: Ramgopal Mushini, Dan Simon
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

Many science and management problems can be formulated as global optimization problems. Conventional optimization methods that make use of derivatives and gradients are not, in general, able to locate or identify the global optimum. Sometimes these problems can be solved using exact methods like brute force. Unfortunately these methods become computationally intractable because of multidimensional search spaces. Hence the application of heuristics for a class of problems that incorporates knowledge about the problem helps solve optimization problems. Optimization of sensor selection using heuristics can lead to significant improvements in the controllability and observability of a dynamic system. The aim of this research is to investigate optimal or alternate measurement sets for the problem of aircraft gas turbine engine health parameter estimation. The performance metric is defined as a function of the steady state error covariance and the cost of the selected sensors.
Scheduling and memory requirements analysis with AADL
Authors: F. Singhoff, J. Legrand, L. Nana, L. Marcé
Venue: ACM SIGAda Ada Letters (2005)

This article describes an Ada set of packages which allows designers to perform resource requirements analysis of AADL specifications. This set of Ada packages is part of Cheddar, an Ada framework that we are developing at the University of Brest [22].The framework provides tools to check if AADL threads will meet their deadline at execution time. Some new AADL properties are proposed to model and analyze dependent AADL thread sets. It also provides some tools to perform memory requirements analysis on AADL specifications.
Combining Generative and Discriminative Learning for Face Recognition
Authors: Shaokang Chen, Brian C. Lovell, Ting Shan
Venue: Proceedings of the Digital Image Computing on Techniques and Applications (2005)

Face recognition is a very complex classification problem and most existing methods are classified into two categories: generative classifiers and discriminative classifiers.Generative classifiers are optimized for description and representation which is not optimal for classification. Discriminative classifiers may achieve less asymptotic errors but are inefficient to train and may overfit to training data. In this paper, we present a hybrid learning algorithm that combines both generative learning and discriminative learning to find a trade-off between these two approaches. Experiments on Asian Face Database show a reduction in classification error rate for our hybrid learning method.
CASLA through a social constructivist perspective: WebQuest in project-driven language learning
Authors: Vassiliki Simina, Marie-josée Hamel
Venue: ReCALL (2005)

The basic tenet of constructivism is that learners construct their knowledge on their own by associating new with prior information. The significance of the learner&rsquo;s interaction with his/her social and physical environment is here of great importance; the learner is at the center of the learning process while the tutor is seen as a facilitator, a guide. Considering the paradigm shift in education and language learning, the assumptions of the constructivist philosophy encourage the use of computers in second language acquisition. Computer technology is capable of providing the context for collaboration and social interaction in which learners will construct the knowledge of the target language on their own by being engaged in meaningful activities. Moreover, computers allow learners to interact not only with the learning materials but also with other people. The combination of the social and individual aspect is best expressed by social constructivism. Placing language learning in a socio-cognitive context, we will approach second language acquisition from a social constructivist perspective and indicate the value of such an approach for the design and evaluation of Computer Applications in Second Language Acquisition (CASLA). Firstly, an overview of constructivism as a theory of learning is required in order to make clear the basic assumptions of the constructivist theory. Secondly, the focus is placed on social constructivism which is examined in relation to second language acquisition. This in tandem exploration will lead us to provide a framework which integrates all four language skills in a general theoretical framework of social interaction and shows how social constructivism can promote second language acquisition. Finally, one type of on-line application such as WebQuest, which is best developed in project-driven language learning, will be provided as a potential example of good practice in approaching Computer Applications in Second Language Learning through a social constructivist perspective.
Intrusion Detection in RBAC-administered Databases
Authors: Elisa Bertino, Ashish Kamra, Evimaria Terzi, Athena Vakali
Venue: Proceedings of the 21st Annual Computer Security Applications Conference (2005)

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance security and privacy. An important component of any strong security solution is represented by intrusion detection (ID) systems, able to detect anomalous behavior by applications and users. To date, however, there have been very few ID mechanisms specifically tailored to database systems. In this paper, we propose such a mechanism. The approach we propose to ID is based on mining database traces stored in log files. The result of the mining process is used to form user profiles that can model normal behavior and identify intruders. An additional feature of our approach is that we couple our mechanism with Role Based Access Control (RBAC). Under a RBAC system permissions are associated with roles, usually grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals that while holding a specific role, have a behavior different from the normal behavior of the role. An important advantage of providing an ID mechanism specifi- cally tailored to databases is that it can also be used to protect against insider threats. Furthermore, the use of roles makes our approach usable even for databases with large user population. Our preliminary experimental evaluation on both real and synthetic database traces show that our methods work well in practical situations.
Enforcing in-order packet delivery in system area networks with adaptive routing
Authors: Michihiro Koibuchi, Juan C. Martinez, Jose Flich, Antonio Robles, Pedro Lopez, Jose Duato
Venue: Journal of Parallel and Distributed Computing (2005)

Adaptive routing, which dynamically selects the route of packets, has been widely studied for interconnection networks in massively parallel computers and system area networks. Although adaptive routing has the advantage of providing high bandwidth, it may deliver packets out-of-order, which some message passing libraries do not accept. In this paper, we propose two mechanisms called (1) FIFO transmission and (2) couple limitation to guarantee in-order packet delivery in adaptive routing. Both of them limit packet injection at source hosts. The FIFO transmission completely avoids packet sorting at destination hosts, while the couple limitation uses a few buffers to sort packets at destination hosts. Evaluation results show that the FIFO transmission and the couple limitation achieve a similar throughput to that of a method equipped with huge (infinite) buffers enough to store all out-of-order packets at destination hosts under both synthetic traffic and NAS Parallel Benchmarks. nchmarks.