ppxscal/embedding_network huggingface dataset:
A MDP Solution for Mobile Agents Displacement
Authors: Salah El Falou, Francois Bourdon
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

The majority of Internet applications requires interaction between various entities through the network, in order to exchange data and to distribute tasks. The client/server model where exchanges are given by distant interactions is the most used model. It has the disadvantage of increasing network traffic by exchanging intermediary information. In this paper we show that in some cases it is better to send the code to the server and to work locally. We study two models of communication (the client/server and the mobile agent) and propose a hybrid one, where agent uses the two models and chooses the best one according to the characteristic of the network. Interaction between different application entities is formulated by a probabilistic model. The Markov decision processes (MDPs) are used to calculate the optimal policy for agent displacement. This policy is applied by the agent in order to decrease network traffic.
Image annotations by combining multiple evidence & wordNet
Authors: Yohan Jin, Latifur Khan, Lei Wang, Mamoun Awad
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
Using Dominators to Extract Observable Protocol Contexts
Authors: Mahadevan Subramaniam, Jiangfan Shi
Venue: Proceedings of the Third IEEE International Conference on Software Engineering and Formal Methods (2005)

While verifying complex protocols, it is often fruitful to consider all protocol contexts in which an interesting set of transitions may appear. The contexts are represented as yet another protocol called observable protocol that may be further analyzed. An efficient approach based on static analysis to compute an over-approximated protocol that includes all the runs of an observable protocol is described. The approach uses dominator relations over state and message dependency graphs. An over-approximation of transitions that occur with an interesting transition in any run are produced, from which a transition relation of the overapproximated protocol is automatically generated. To facilitate systematic state space exploration of the over approximated protocol, it is shown how a series of underapproximations can be generated by identifying parallelism among the transitions using dominators. The effectiveness of the proposed approach is illustrated by model checking several examples including several coherence protocols.
DECIDE: a scheme for decentralized identity escrow
Authors: Noburou Taniguchi, Koji Chida, Osamu Shionoiri, Atsushi Kanai
Venue: Proceedings of the 2005 workshop on Digital identity management (2005)

Anonymity is one of the most controversial issues related to the Internet. It greatly encourages misdeeds such as piracy, defamation, and fraud. However, anonymity is important to protect the privacy of individuals and to support the freedom of speech. Identity escrow is a potential solution to achieving a balance between anonymity and identity. The basic idea is to utilize a two-tiered identity: the first-tier identity carries the minimal amount of information for normal use, and the second-tier identity bears precise information regarding the individual to be disclosed when necessary. In this paper, we introduce a decentralized identity escrow scheme called DECIDE. It has three identity tiers: anonymity, pseudonymity, and identity, and it has a high decentralization capability. We also present an analysis on three service models - online discussion community, whistleblowing, school networking - to explore the public acceptance of the proposed scheme.
Towards Software Quality Economics for Defect-Detection Techniques
Authors: Stefan Wagner
Venue: Proceedings of the 29th Annual IEEE/NASA on Software Engineering Workshop (2005)

There are various ways to evaluate defect-detection techniques. However, for a comprehensive evaluation the only possibility is to reduce all influencing factors to costs. There are already some models and metrics for the cost of quality that can be used in that context. The existing metrics for the effectiveness and efficiency of defect-detection techniques and experiences with them are combined with cost metrics to allow a more fine-grained estimation of costs and a comprehensive evaluation of defect-detection techniques. The current model is most suitable for directly comparing concrete applications of different techniques.
Context-sensitive web search
Authors: Jeffrey D. Ullman
Venue:  (2005)

As the Web continues to grow and encompass broader and more diverse sources of information, providing effective search facilities to users becomes an increasingly challenging problem. To help users deal with the deluge of Web-accessible information, we propose a search system which makes use of context to improve search results in a scalable way. By context, we mean any sources of information, in addition to any search query, that provide clues about the user's true information need. For instance, a user's bookmarks and search history can be considered a part of the search context. We consider two types of context-based search. The first type of functionality we consider is similarity search. In this case, as the user is browsing Web pages, URLs for pages similar to the current page are retrieved and displayed in a side panel. No query is explicitly issued; context alone (i.e., the page currently being viewed) is used to provide the user with useful related information. The second type of functionality involves taking search context into account when ranking results to standard search queries. Web search differs from traditional information retrieval tasks in several major ways, making effective context-sensitive Web search challenging. First, scalability is of critical importance. With billions of publicly accessible documents, the Web is much larger than traditional datasets. Similarly, with millions of search queries issued each day, the query load is much higher than for traditional information retrieval systems. Second, there are no guarantees on the quality of Web pages, with Web-authors taking an adversarial, rather than cooperative, approach in attempts to inflate the rankings of their pages. Third, there is a significant amount of metadata embodied in the link structure corresponding to the hyperlinks between Web pages that can be exploited during the retrieval process. In this thesis, we design a search system, using the Stanford WebBase platform, that exploits the link structure of the Web to provide scalable, context-sensitive search.
Gabor Feature Extraction for Character Recognition: Comparison with Gradient Feature
Authors: Cheng-Lin Liu, Masashi Koga, Hiromichi Fujisawa
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

Gabor filter feature has been applied to character recognition but was not compared with the best direction feature: gradient feature. In this paper, we propose a principled method for implementing Gabor filters for character feature extraction and compare the recognition performances of Gabor feature and gradient feature on three databases. The results show that Gabor filters with low orientation sensitivity and broad frequency band favor recognition accuracy. The Gabor feature performs comparably or better than the gradient feature on two of the three databases, but is inferior on the rest one.
Simultaneous Column Minimization-Encoding Approach for Serial Decomposition
Authors: Shruti Patil, V. Muthukumar
Venue: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (2005)

In this paper we present an approach that combines the process of column encoding and input-output encoding. Traditionally, the process of column minimization is represented as a set covering or clique partitioning or graph coloring algorithms. These algorithms are well known NP-hard problems. The column minimization problem is followed by the input-output encoding problem which is represented as a weighted graph assignment problem which is again a well known NP-hard problem. The combined column minimization and input-output encoding algorithm reduces the complexity by representing both the above problems as a single weighted graph encoding problem. The proposed algorithm has been implemented and evaluated with standard benchmarks.
A Framework to Percussive Educational Systems
Authors: Suzana Mesquita De Borba Maranhao, Matheus Cabral De Araujo Gois, Erika Pessoa Araujo, Giordano Ribeiro de Eulalio Cabral
Venue: Proceedings of the Seventh IEEE International Symposium on Multimedia (2005)

Computers have been used to assist users to learn music concepts and acquire musical skills as soon as they were introduced in people's life. However, even within a specific class of application, such as the Instrument Performance Systems (IPS), that tries to teach intuitively how to play a musical instrument, there is no attempt to provide reusable components. This paper proposes an original framework architecture whose purpose is to support the development of percussive IPS. The framework was validated by an instantiation on the alfaia, a sort of tambour used in Brazilian folklore.
An Adaptive Fault Tolerance for Situation-Aware Ubiquitous Computing
Authors: Eung-Nam KO
Venue: Proceedings of the Third ACIS Int'l Conference on Software Engineering Research, Management and Applications (2005)

Since ubiquitous applications need situation-aware middleware services and computing environment (e.g., resources) keeps changing as the applications change, it is challenging to detect errors and recover them in order to provide seamless services and avoid a single point of failure. This paper proposes an Adaptive Fault Tolerance (AFT) algorithm in situation-aware middleware framework and presents its simulation model of AFT-based agents.
A Hierarchy Navigation Framework: Supporting Scalable Interactive Exploration over Large Databases
Authors: Nishant K. Mehta, Elke A. Rundensteiner, Matthew O. Ward
Venue: Proceedings of the 9th International Database Engineering & Application Symposium (2005)

Modern computer applications from business decision support to scientific data analysis use visualization techniques. However, visual exploration tools do not scale well for large data sets due to screen clutter. Visualization tools have thus been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive brushes. We now investigate how best to couple such a near real-time responsive visualization tool with database support. For this, we have developed a tree labeling method, called MinMax tree, that allows the movement of the on-line recursive processing of visual user interactions on hierarchical data sets into an off-line precomputation step. Using MinMax tree we map the recursive processing at the interface level to two dimensional range queries that can be answered efficiently using spatial indexes. We also employ caching and prefetching at the client side to cope with the real-time response requirements. The techniques have been incorporated into XmdvTool, a free software package for multi-variate data visualization and exploration. Our experimental results show 70% to 80% reduction in response time latency even with limited system resources.
Estimating flow distributions from sampled flow statistics
Authors: Nick Duffield, Carsten Lund, Mikkel Thorup
Venue: IEEE/ACM Transactions on Networking (TON) (2005)

Passive traffic measurement increasingly employs sampling at the packet level. Many high-end routers form flow statistics from a sampled substream of packets. Sampling controls the consumption of resources by the measurement operations. However, knowledge of the statistics of flows in the unsampled stream remains useful, for understanding both characteristics of source traffic, and consumption of resources in the network. This paper provides methods that use flow statistics formed from sampled packet stream to infer the frequencies of the number of packets per flow in the unsampled stream. A key task is to infer the properties of flows of original traffic that evaded sampling altogether. We achieve this through statistical inference, and by exploiting protocol level detail reported in flow records. We investigate the impact on our results of different versions of packet sampling.
Learning through Multimedia Interaction
Authors: Rajarathnam Chandramouli
Venue: IEEE MultiMedia (2006)

This book reviews discusses the many ways we can use multimedia to help students learn within the classroom setting.
Experimental comparison between bagging and Monte Carlo ensemble classification
Authors: Roberto Esposito, Lorenza Saitta
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this framework it is also possible to define a new ensemble classifier, whose accuracy probability distribution can be computed exactly. This paper has two goals: first, an experimental comparison between the theoretical predictions and experimental results; second, a systematic comparison between bagging and Monte Carlo ensemble classification.
Combined continuous/discrete system simulation languages: usefulness, experiences and future development
Authors: Francois E. Cellier
Venue: ACM SIGSIM Simulation Digest (1977)

The term "combined simulation" is not sufficiently well understood in the literature yet to mean one and only one specific methodology or problem class. One can find references where e.g., combined simulation is used as synonym for hybrid simulation. This term, therefore, requires some definition first to clarify how it is going to be used in this context - especially since the author tends to use it in a slightly different way than most people do.
Symbolic Interpretation of Legacy Assembly Language
Authors: Jacques Carette, Pulak Kumar Chowdhury
Venue: Proceedings of the 12th Working Conference on Reverse Engineering (2005)

We apply static analysis and symbolic interpretation techniques to reverse engineer the semantics of legacy assembler code. We examine the case of IBM-1800 programs in detail. From the documented operational semantics of the IBM-1800, we simultaneously obtain an emulator and a symbolic analysis program. Augmented with some control flow information, we can use the symbolic analysis to provide both complete and generic semantics for some interesting code sequences.
Issues in computational frameworks
Authors: Aad J. van der Steen
Venue: Concurrency and Computation: Practice Experience (2006)

In this paper we discuss issues that are common to computational frameworks for compute-intensive modeling and either need to be resolved or require design decisions to arrive at a more uniform behavior of such frameworks. Copyright &copy; 2005 John Wiley & Sons, Ltd.
Bayesian sparse sampling for on-line reward optimization
Authors: Tao Wang, Daniel Lizotte, Michael Bowling, Dale Schuurmans
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration versus exploitation tradeoff. Our approach combines sparse sampling with Bayesian exploration to achieve improved decision making while controlling computational cost. The idea is to grow a sparse lookahead tree, intelligently, by exploiting information in a Bayesian posterior---rather than enumerate action branches (standard sparse sampling) or compensate myopically (value of perfect information). The outcome is a flexible, practical technique for improving action selection in simple reinforcement learning scenarios.
Independent Component Analysis Segmentation Algorithm
Authors: Yan Chen, Graham Leedham
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

segmentation algorithm called the ICA (Independent Component Analysis) Segmentation Algorithm and compare it against other existing overlapping strokes segmentation algorithms. The ICA Segmentation algorithm converts the original touching or overlapping word components into a blind source matrix and then calculates the weighted value matrix before the values are re-evaluated using a fast ICA model. The readjusted weighted value matrix is applied to the blind source matrix to separate the word components. The algorithm has been evaluated on 30 'overlapped' document images from the CEDAR letter database and another 30 degraded historical document images, which containing many different kinds of overlapping and touching words in adjacent lines. Quantitative analysis of the results by measuring text recall, and qualitative assessment of processed document image quality is reported. The ICA Segmentation Algorithm is demonstrated to be effective at resolving the problem in varying forms of overlapping or touching text lines.
An aspect-based process container for BPEL
Authors: Anis Charfi, Mira Mezini
Venue: Proceedings of the 1st workshop on Aspect oriented middleware development (2005)

In recent years, several WS-* specifications have been proposed to address the middleware requirements of web services such as security, reliable messaging, and transactions. On the other hand side, BPEL is the upcoming standard for composing existing web services into more complex ones. In this paper, we look at the middleware requirements of web service compositions and specifically those specified in BPEL. We argue that the WS-* specifications such as WS-Security and WS-Reliability do not address these requirements appropriately. We introduce a framework for providing middleware support in BPEL engines based on a process container, which intercepts the execution of BPEL processes and calls dedicated middleware services to plug in support for security, persistence, reliable messaging, etc. The container is implemented as a lightweight container using AO4BPEL aspects and the middleware requirements of the process are specified in a deployment descriptor.
Algorithms for precomputing constrained widest paths and multicast trees
Authors: Stavroula Siachalou, Leonidas Georgiadis
Venue: IEEE/ACM Transactions on Networking (TON) (2005)

We consider the problem of precomputing constrained widest paths and multicast trees in a communication network. Precomputing and storing of the relevant information minimizes the computational overhead required to determine an optimal path when a new connection request arrives. We evaluate algorithms that precompute paths with maximal bandwidth (widest paths), which in addition satisfy given end-to-end delay constraints. We analyze and compare both the worst case and average case performance of the algorithms. We also show how the precomputed paths can be used to provide computationally efficient solutions to the constrained widest multicast tree problem. In this problem, a multicast tree with maximal bandwidth (widest multicast tree) is sought, which in addition satisfies given end-to-end delay constraints for each path on the tree from the source to a multicast destination.
Intramolecular Structure of Proteins as Driven by Steiner Optimization Problems
Authors: R. P. Mondaini, N. V. Oliveira
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

In this work we intend to report on some results obtained by an analytical modelling of biomacromolecular structures as driven by the study of Steiner points and Steiner trees with an Euclidean definition of distance.
A quick GPSS quiz
Authors: Thomas J. Schriber
Venue: ACM SIGSIM Simulation Digest (1974)

The Problem Corner feature for this issue takes the form of a one-question quiz for those SIGSIM members who know GPSS. The quiz question is given immediately below. The answer to the quiz appears at the bottom of the page, upside down.
Dynamic and efficient key management for access hierarchies
Authors: Mikhail J. Atallah, Keith B. Frikken, Marina Blanton
Venue: Proceedings of the 12th ACM conference on Computer and communications security (2005)

The problem of key management in an access hierarchy has elicited much interest in the literature. The hierarchy is modeled as a set of partially ordered classes (represented as a directed graph), and a user who obtains access (i.e., a key) to a certain class can also obtain access to all descendant classes of her class through key derivation. Our solution to the above problem has the following properties: (i) only hash functions are used for a node to derive a descendant's key from its own key; (ii) the space complexity of the public information is the same as that of storing the hierarchy; (iii) the private information at a class consists of a single key associated with that class; (iv) updates (revocations, additions, etc.) are handled locally in the hierarchy; (v) the scheme is provably secure against collusion; and (vi) key derivation by a node of its descendant's key is bounded by the number of bit operations linear in the length of the path between the nodes. Whereas many previous schemes had some of these properties, ours is the first that satisfies all of them. Moreover, for trees (and other "recursively decomposable" hierarchies), we are the first to achieve a worst- and average-case number of bit operations for key derivation that is exponentially better than the depth of a balanced hierarchy (double-exponentially better if the hierarchy is unbalanced, i.e., "tall and skinny"); this is achieved with only a constant increase in the space for the hierarchy. We also show how with simple modifications our scheme can handle extensions proposed by Crampton of the standard hierarchies to "limited depth" and reverse inheritance [13]. The security of our scheme relies only on the use of pseudo-random functions.