ppxscal/embedding_network huggingface dataset:
An Expected Utility Approach to Active Feature-Value Acquisition
Authors: Prem Melville, Maytal Saar-Tsechansky, Foster Provost, Raymond Mooney
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

In many classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is often prohibitively expensive or unnecessary, while acquiring a random subset of feature values may not be most effective. The goal of active feature-value acquisition is to incrementally select feature values that are most cost-effective for improving the model's accuracy. We present an approach that acquires feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost. Experimental results demonstrate that our approach consistently reduces the cost of producing a model of a desired accuracy compared to random feature acquisitions.
PCA vs. Automatically Pruned Wavelet-Packet PCA for Illumination Tolerant Face Recognition
Authors: Ramamurthy Bhagavatula, Marios Savvides
Venue: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies (2005)

Facial recognition/verification [1] is a continuing and growing area of research in the field of biometrics. One of the first approaches to this challenge was Principal Component Analysis (PCA) [2, 3]. Typically PCA is performed in the original spatial domain. However, PCA has a high sensitivity to illumination effects in the original spatial domain. We propose that by using Wavelet Packet Decomposition [4] to create localized space-frequency subspaces of the original data, we can perform PCA in these subspaces which can generalize better across illumination variations. We report results on the CMU PIE database [5] by comparing reconstruction error in the original spatial domain to that of the reconstruction error in the spatial subspaces (keeping same number of eigenvectors). It is seen that the total reconstruction error of the space-frequency subspaces is smaller than that of the original space and the automatically pruned Wavelet packet PCA produced better face recognition performance across illumination.
Individual displacements for linear probing hashing with different insertion policies
Authors: Svante Janson
Venue: ACM Transactions on Algorithms (TALG) (2005)

We study the distribution of the individual displacements in hashing with linear probing for three different versions: First Come, Last Come and Robin Hood. Asymptotic distributions and their moments are found when the the size of the hash table tends to infinity with the proportion of occupied cells converging to some &alpha;, 0 < &alpha; < 1. (In the case of Last Come, the results are more complicated and less complete than in the other cases.)We also show, using the diagonal Poisson transform studied by Poblete, Viola and Munro, that exact expressions for finite m and n can be obtained from the limits as m,n &rarr; &infin;.We end with some results, conjectures and questions about the shape of the limit distributions. These have some relevance for computer applications.
D&eacute;finition et repr&eacute;sentation du contexte pour des agents sensibles au contexte
Authors: Oana Bucur, Philippe Beaune, Olivier Boissier
Venue: Proceedings of the 2nd French-speaking conference on Mobility and ubiquity computing (2005)

Developing context-aware applications needs facilities for recognizing and representing context, reasoning on it and adapting to it accordingly. In what concerns context representation, the newest and most challenging representation is the ontological one. The problem is that current ontologies for context are quite simplistic and do not provide a standard for representing complex context attributes. In this paper, we propose a context definition and representation used to construct a context-based agent architecture. The representation we propose combines the generality provided by ontologies with the complexity inspired by the object oriented models. The goal of the proposed representation is to support the deployment of context-aware agents able to learn how to recognize and adapt to the context of their decisions.
Implicit Meshes for Surface Reconstruction
Authors: Slobodan Ilic, Pascal Fua
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

Deformable 3D models can be represented either as traditional explicit surfaces, such as triangulated meshes, or as implicit surfaces. Explicit surfaces are widely accepted because they are simple to deform and render, but fitting them involves minimizing a nondifferentiable distance function. By contrast, implicit surfaces allow fitting by minimizing a differentiable algebraic distance, but are harder to meaningfully deform and render. Here, we propose a method that combines the strength of both approaches. It relies on a technique that can turn a completely arbitrary triangulated mesh, such as one taken from the Web, into an implicit surface that closely approximates it and can deform in tandem with it. This allows both automated algorithms to take advantage of the attractive properties of implicit surfaces for fitting purposes and people to use standard deformation tools they feel comfortable for interaction and animation purposes. We demonstrate the applicability of our technique to modeling the human upper-body, including face, neck, shoulders, and ears, from noisy stereo and silhouette data.
Minimizing Security Risks in Ubicomp Systems
Authors: Jason I. Hong
Venue: Computer (2005)

Mobile devices and wireless networks are making existing security models obsolete.
Applying Systems Engineering to the Enterprise
Authors: Jack Ring
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

When treated as a system an enterprise can benefit from the principles, metaphors, archetypes, practices, and tools of systems engineering. Enterprise effectiveness and sustainability can be improved considerably relative to typical enterprises. This paper highlights how systems engineering can be applied to initializing and evolving a successful, sustainable enterprise whether the enterprise is in the public or private sector, profit or non-profit, small or large, and growing or not. Not surprisingly, the most challenging kind of enterprise to initialize and evolve is the kind that accomplishes systems engineering.
Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing
Authors: Andreas Krause, Matthias Ihmig, Edward Rankin, Derek Leong, Smriti Gupta, Daniel Siewiorek, Asim Smailagic, Michael Deisher, Uttam Sengupta
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the wearable system's energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data collected from the eWatch sensing and notification platform. We improve power consumption techniques by providing competitive classification performance even in the low frequency region of 1-10 Hz and for the highly erratic wrist based sensing location. Furthermore, we propose and analyze a collection of selective sampling strategies in order to reduce the number of required sensor readings and the computation cycles even further. Our results indicate that optimized sampling schemes can increase the deployment lifetime of a wearable computing platform by a factor of four without a significant loss in prediction accuracy.
Cooperation of Neighboring PEs in Clustered Architectures
Authors: Yukinori Sato, Ken-ichi Suzuki, Tadao Nakamura
Venue: Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing (2005)

Clustered architectures which intend to process data within a localized PE are one of the approaches to increase the performance under the difficulties of the wire delay problems. The performance of clustered architectures depends on the amount of parallel execution of instructions and the amount of inter-PE communication to synchronize dependent instructions. In this paper, we propose an arrangement of PEs cooperating with the adjacent PEs by means of adding communication structures between the adjacent PEs in order to relax the inter-PE communication and workload imbalance in an effective manner. We evaluate the proposed configurations and compare them with the existing one so far considered. The results show that the proposed adjacent forwarding network configuration with the instruction steering scheme that concerns both the register fanout and available free register can achieve higher instructions per clock (IPC) with the small number of registers per PE than the other configurations.
Distributed Algorithmic Mechanism Design for Scheduling on Unrelated Machines
Authors: Thomas E. Carroll, Daniel Grosu
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

In classical mechanism design setting the outcome of the mechanism is computed by a trusted central party. In this paper we consider distributed implementations in which the outcome is computed by the agents themselves. We propose a distributed mechanism for solving the problem of scheduling on unrelated machines. This mechanism, called Distributed MinWork (DMW) is a distributed implementation of theMinWork mechanism proposed by Nisan and Ronen [12]. DMW is resilient to agents deviating from the protocol since the cheaters are detected and eliminated from further participation. We show that DMW is truthful, since it preserves the incentives and allocation policies from MinWork. Finally, we show that DMW has polynomial communication and computation costs.
Space-efficient evaluation of hypergeometric series
Authors: Howard Cheng, Barry Gergel, Ethan Kim, Eugene Zima
Venue: ACM SIGSAM Bulletin (2005)

Many important constants, such as e and Ap&eacute;ry's constant &zeta;(3), can be approximated by a truncated hypergeometric series. The evaluation of such series to high precision has traditionally been done by binary splitting followed by fixed-point division. However, the numerator and the denominator computed by binary splitting usually contain a very large common factor. In this paper, we apply standard computer algebra techniques including modular computation and rational reconstruction to overcome the shortcomings of the binary splitting method. The space complexity of our algorithm is the same as a bound on the size of the reduced numerator and denominator of the series approximation. Moreover, if the predicted bound is small, the time complexity is better than the standard binary splitting approach. Our approach allows a series to be evaluated to a higher precision without additional memory. We show that when our algorithm is applied to compute &zeta;(3), the memory requirement is significantly reduced compared to the binary splitting approach.
Framework for evaluation of networked mobile games
Authors: Leo Petrak, Olaf Landsiedel, Klaus Wehrle
Venue: Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games (2005)

In this position paper we propose an evaluation framework for networked mobile gaming, consisting of user, group, communication, and environment models. Each of these components acts as a detailed representation of real-world characteristics. The combination of these models allows a realistic modeling of mobile gaming scenarios. The main contribution of our approaches is a new class of mobility models, which are purely based on strategies derived from real-world scenarios. This allows us to simulate the behavior and the movement of users and groups, and the communication aspects, and thus, it facilitates a creation of test beds for mobile applications. These simulated users interact, e.g. communicate, with each other and are able to move purposeful as well as to build groups spontaneously depending on the similarity of their goals or individual strategies. The paper presents an analytic discussion of the framework, its architecture, and the current state of its implementation.
Visualizing and discovering non-trivial patterns in large time series databases
Authors: Jessica Lin, Eamonn Keogh, Stefano Lonardi
Venue: Information Visualization (2005)

Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequently occurring patterns (motif discovery), surprising patterns (anomaly detection), and query by content. VizTree works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic data sets. Based on the tree structure, we further device a coefficient which measures the dissimilarity between any two time series. This coefficient is shown to be competitive with the well-known Euclidean distance.
A channel-aware adaptive control to the MAC protocol in rate adaptive wireless LANs: Research Articles
Authors: Yanfeng Zhu, Zhisheng Niu
Venue: Wireless Communications Mobile Computing (2005)

Due to the low efficiency of legacy IEEE 802.11 MAC protocol, it has been the main bottleneck of wireless local area networks (WLANs). In this paper, we focus on multi-rate WLANs and propose an adaptive transmission control scheme, which adapts the transmission probability according to the number of active stations, transmission rates, and channel conditions. At first, an analytical model is built in terms of the proposed scheme to explore the throughput of the network. Then, by means of the characteristics of practical system, a heuristic algorithm is developed to approach the maximum throughput on-line. Extensive numerical calculations and simulations based on NS-2 are implemented to evaluate the performance of the proposed algorithm and the impact of inaccurate estimation of parameters. The results show that our algorithm outperforms the existing adaptive algorithms in imperfect channel, and insensitive to inaccurate estimation of parameters. Copyright &copy; 2005 John Wiley & Sons, Ltd.
Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification
Authors: Arnaud Quirin, Jerzy Korczak, Martin V. Butz, David E. Goldberg
Venue: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (2005)

In this article, two learning classifier systems based on evolutionary techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classifi- cation system XCSR. The two algorithms are detailed and validated on hyperspectral data.
A repeated video clip identification system
Authors: Xianfeng Yang, Ping Xue, Qi Tian
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

Identifying short repeated video clips, such as news program logo, station logo, TV commercials, etc., from broadcasting video databases or streams is important for video content indexing, personalization as well as monitoring. In this demo system we present the following two functions: 1) automatically identifying variable length unknown repeated clips and known reference clips from video collections or steams; 2) visualize temporal distribution of repeated short video clips and analyze video structure based them. Experiment has been conducted on 12 hour CNN and ABC news videos, and excellent results have been achieved in both short repeated video clip identification and news video structure analysis.
An Application-Based Performance Characterization of the Columbia Supercluster
Authors: Rupak Biswas, M. Jahed Djomehri, Robert Hood, Haoqiang Jin, Cetin Kiris, Subhash Saini
Venue: Proceedings of the 2005 ACM/IEEE conference on Supercomputing (2005)

Columbia is a 10,240-processor supercluster consisting of 20 Altix nodes with 512 processors each, and currently ranked as one of the fastest computers in the world. In this paper, we present the performance characteristics of Columbia obtained on up to four computing nodes interconnected via the InfiniBand and/or NUMAlink4 communication fabrics. We evaluate floatingpoint performance, memory bandwidth, message passing communication speeds, and compilers using a subset of the HPC Challenge benchmarks, and some of the NAS Parallel Benchmarks including the multi-zone versions. We present detailed performance results for three scientific applications of interest to NASA, one from molecular dynamics, and two from computational fluid dynamics. Our results show that both the NUMAlink4 and In- finiBand interconnects hold promise for multi-node application scaling to at least 2048 processors.
Link Contexts in Classifier-Guided Topical Crawlers
Authors: Gautam Pant, Padmini Srinivasan
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

Context of a hyperlink or link context is defined as the terms that appear in the text around a hyperlink within a Web page. Link contexts have been applied to a variety of Web information retrieval and categorization tasks. Topical or focused Web crawlers have a special reliance on link contexts. These crawlers automatically navigate the hyperlinked structure of the Web while using link contexts to predict the benefit of following the corresponding hyperlinks with respect to some initiating topic or theme. Using topical crawlers that are guided by a Support Vector Machine, we investigate the effects of various definitions of link contexts on the crawling performance. We find that a crawler that exploits words both in the immediate vicinity of a hyperlink as well as the entire parent page performs significantly better than a crawler that depends on just one of those cues. Also, we find that a crawler that uses the tag tree hierarchy within Web pages provides effective coverage. We analyze our results along various dimensions such as link context quality, topic difficulty, length of crawl, training data, and topic domain. The study was done using multiple crawls over 100 topics covering millions of pages allowing us to derive statistically strong results.
Have the cake and eat it too - Infusing usability into text-password based authentication systems
Authors: Sundararaman Jeyaraman, Umut Topkara
Venue: Proceedings of the 21st Annual Computer Security Applications Conference (2005)

Text-password based authentication schemes are a popular means of authenticating users in computer systems. Standard security practices that were intended to make passwords more difficult to crack, such as requiring users to have passwords that "look random" (high entropy), have made password systems less usable and paradoxically, less secure. In this work, we address the need for enhancing the usability of existing text-password systems without necessitating any modifications to the existing password authentication infrastructure. We propose, develop and evaluate a system that automatically generates memorable mnemonics for a given password based on a text-corpus. Initial experimental results suggest that automatic mnemonic generation is a promising technique for making text-password systems more usable. Our system was able to generate mnemonics for 80.5% of six-character passwords and 62.7% of sevencharacter passwords containing lower-case characters (az), even when the text-corpus size is extremely small (1000 sentences).
Augmenting naive Bayes for ranking
Authors: Harry Zhang, Liangxiao Jiang, Jiang Su
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is more desirable. Unfortunately, naive Bayes has been found to produce poor probability estimates. Numerous techniques have been proposed to extend naive Bayes for better classification accuracy, of which selective Bayesian classifiers (SBC) (Langley & Sage, 1994), tree-augmented naive Bayes (TAN) (Friedman et al., 1997), NBTree (Kohavi, 1996), boosted naive Bayes (Elkan, 1997), and AODE (Webb et al., 2005) achieve remarkable improvement over naive Bayes in terms of classification accuracy. An interesting question is: Do these techniques also produce accurate ranking? In this paper, we first conduct a systematic experimental study on their efficacy for ranking. Then, we propose a new approach to augmenting naive Bayes for generating accurate ranking, called hidden naive Bayes (HNB). In an HNB, a hidden parent is created for each attribute to represent the influences from all other attributes, and thus a more accurate ranking is expected. HNB inherits the structural simplicity of naive Bayes and can be easily learned without structure learning. Our experiments show that HNB outperforms naive Bayes, SBC, boosted naive Bayes, NBTree, and TAN significantly, and performs slightly better than AODE in ranking.
Non-Preemptive Robustness under Reduced System Load
Authors: Aloysius K. Mok, Wing-Chi Poon
Venue: Proceedings of the 26th IEEE International Real-Time Systems Symposium (2005)

Unlike preemptive scheduling policies, non-preemptive real-time scheduling policies can exhibit anomalies even for the single-processor case. In particular, a task set that is schedulable by a non-preemptive scheduler may become unschedulable when the utilization of the task set decreases relative to the CPU speed, e.g., when a faster CPU is used to run the same task set. In this paper, we define the notion of robustness to capture the essence of the scheduling anomaly on real-time system performance. We shall show that it is difficult to test for robustness in general but there are sufficient conditions for guaranteeing robustness.
Fuzzy-Dynamic Matrix pH Control for Treatment of Dye Wastewater Plant
Authors: Z. Zeybek, M. Alpbaz
Venue: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (2005)

This paper presents an experimental application of Fuzzy-Dynamic Matrix Control (FDMC) to study the coagulation process of wastewater treatment in a dye plant. Also this study includes a series of tests in which an FDMC was used for pH control. The performance results of the FDMC controller are compared with the results obtained by using conventional PID algorithm. Experimental results show that this technique is adequate for the output tracking performance.