ppxscal/embedding_network huggingface dataset:
Automatic Eye-Level Height System for Face and Iris Recognition Systems
Authors: Ramzi Abiantun, Marios Savvides, Pradeep K. Khosla
Venue: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies (2005)

In this paper we present a fully automated mobile camera device platform that will automatically adjust to the eye-levels of the persons in front or approaching the system. This system serves as a front-end to aid the face recognition and iris recognition systems during both the enrollment and verification of different people of variable heights. Currently most systems are positioned at fixed heights and require subjects who are very tall or short to adjust themselves for enrollment. In some cases this requires the enrollment system operator (or officer) to adjust the camera system accordingly to the user's height. This leads to much failure to acquire errors. The current U.S. VISIT system, Immigration officers manually move the camera goose-necks to adjust to the height of the visitors for photographing. The system presented in this paper can fully automate this process by automatically adjusting the height of the camera (or other biometric device such as Iris acquisition systems) to provide a good enrollment/verification photo image for matching using the automated face detection to drive the biometric sensors to the appropriate height level. The need for such a system can help the results obtained by the Independent Testing of Iris Recognition Technology (ITIRT) [6] and face recognition systems that can lead to minimal user co-operation.
The mining of fuzzy gradient in the case of materialized cube
Authors: Yubao Liu, Jian Yin
Venue: Proceedings of the The Fifth International Conference on Computer and Information Technology (2005)

The mining of cube gradient is a new research problem in the data mining community. It is an extension of the traditional association rules mining in the data cube and its purpose is to mine the changes of measures in a multidimensional space. However, previous works explore for cube gradients with raw number, and the semantics of the changes of cube gradients, such as high or low, are not clear and those are not easy to be understood for the decision makers. In this paper, the fuzzy gradient mining problem with fuzzy constraint sets is presented. The mining algorithm, MFG, for the problem in the case of materialized cube is presented. In MFG, the condensed cube technology is used to reduce the searching space of the mining algorithm. The performance tests based on the synthesized data sets show the mining algorithm is effective and scalable with the fuzzy constraint sets.
E-democracy, E-Contestation and the Monitorial Citizen
Authors: Jeroen Hoven
Venue: Ethics and Information Technology (2005)

It is argued that Pettit's conception of "contestatory democracy" is superior to deliberative, direct and epistemic democracy. The strong and weak points of these conceptions are discussed drawing upon the work of a.o Bruce Bimber. It is further argued that `contestation' and `information' are highly relevant notions in thinking about, just, viable and sustainable design for E-democracy.
Online Auto-Calibration in Man-Made Worlds
Authors: Roman Pflugfelder, Horst Bischof
Venue: Proceedings of the Digital Image Computing on Techniques and Applications (2005)

This paper proposes a method to auto-calibrate a static camera in man-made worlds. The calibration is done with vanishing points which are estimated with line segments. Instead of a single image, the method uses constantly acquired images from a camera. This online behavior of the method has two advantages: A continuous and adaptive estimation of the vanishing points over thousands of images suppresses the amount of noise in their positions. Newly detected line segments can further improve the estimated positions of vanishing points. This method is of practical importance, because simple installation and maintenance of cameras in video surveillance is a competitive advantage. Furthermore, applications like tracking can profit, because knowledge about the scene geometry can be a potential clue. The method was evaluated in a broad range of indoor and outdoor environments. The relative error between the internal camera parameter estimates and the true values was on average 5%.
Usage-Based PageRank for Web Personalization
Authors: Magdalini Eirinaki, Michalis Vazirgiannis
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this paper we present UPR, a novel personalization algorithm which combines usage data and link analysis techniques for ranking and recommending web pages to the end user. Using the web site's structure and its usage data we produce personalized navigational graph synopses (prNG) to be used for applying UPR and produce personalized recommendations. Experimental results show that the accuracy of the recommendations is superior to pure usage-based approaches.
The Art of Nurturing Citizen Scientists through Mixed Reality
Authors: Christopher Stapleton, Eileen Smith, Charles E. Hughes
Venue: Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality (2005)

Modern society requires the public to have an increased knowledge of science beyond the basics, with average people needing to understand concepts in science, technology, engineering and mathematics that rapidly evolve throughout their lifetimes. Our future depends upon closing the gaps between citizen and scientist to create a "Citizen Scientist." Mixed Reality innovations provide the magic to spark a lifetime of learning for community learning centers by continuously providing new content and reinventing the free-choice learning experience that helps citizens make informed choices in an increasingly complex world. This paper explores a Mixed Reality experiential learning landscape that expands our ability to provide dynamic content structures for venues to engage the user's physical environment and interactive imagination by incorporating the conventions of story, play and game employed in competing leisure time activities. This is intended to reverse the standard decline of attendance by teenagers and young adults.
Online Character Recognition Based on Elastic Matching and Quadratic Discrimination
Authors: Hiroto Mitoma, Seiichi Uchida, Hiroaki Sakoe
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

We try to link elastic matching with a statistical discrimination framework to overcome the ovefitting problem which often degrades the performance of elastic matchingbased online character recognizers. In the proposed technique, elastic matching is used just as an extractor of a feature vector representing the dzfference between input and reference patterns. Then quadratic discrimination is performed under the assumption that the feature vector is governed by a Gaussian distribution. The result of a recognition experiment on UNIPEN database (Train-ROlN07, l a ) showed that the proposed technique can attain a high recognition rate (97.95%) and outperforms a recent elastic matching-based recognizer.
Arabic Handwriting Recognition Using Baseline Dependant Features and Hidden Markov Modeling
Authors: Ramy El-Hajj, Laurence Likforman-Sulem, Chafic Mokbel
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In this paper we describe a 1D HMM off-line handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline- dependant features.
A model for the correction for guessing on multiple-choice tests
Authors: E. A. Hansen, F. L. Schmidt, J. C. Hansen
Venue: ACM SIGSOC Bulletin (1975)

Multiple-choice tests are used widely because of obvious practical advantages, but inherent in them is the problem of dealing with the effects of guessing. The only widely used model for predicting effects of guessing is based on the primary assumption that, the probability of making a correct response on each test item is either 1.0 or the reciprocal of the number of available choices. The assumption that the subject either makes the correct response on each trial or guesses blindly is clearly too restrictive. Nunnally in [1] relates that because in most educational tests there is some opportunity for "narrowing" alternatives before guessing the standard correction for guessing tends to be an underestimate of the actual effects of guessing. The model proposed in this paper attempts to correct for this.
Data management research at Technische Universit&auml;t Darmstadt
Authors: A. Buchmann, M. Cilia
Venue: ACM SIGMOD Record (2005)

The Databases and Distributed Systems Group at Technische Universit&auml;t Darmstadt is devoted to research in the areas of data management middleware and reactive, event-based systems. Special emphasis is placed on handling the flow of data and events in a variety of environments: publish/subscribe mechanisms, information dissemination and integration, ubiquitous computing, peer-to-peer infrastructures, and a variety of sensor-based systems ranging from passive RFID infrastructures to active wireless sensor networks. A special concern is placed on non-functional aspects of the middleware, such as performance, scalability and security, where members of our group are involved in the definition of the SPEC family of benchmarks for J2EE (SPECjAppServer200x) and JMS.
Four-Valued Memory Circuit Designed by Multiple-Peak MOS-NDR Devices and Circuits
Authors: Dong-Shong Liang, Kwang-Jow Gan, Long-Xian Su, Chi-Pin Chen, Chung-Chih Hsiao, Cher Shiung Tsai, Yaw-Hwang Chen, Shih-Yu Wang, Shun-Huo Kuo, Feng-Chang Chiang
Venue: Proceedings of the Fifth International Workshop on System-on-Chip for Real-Time Applications (2005)

This paper describes the design of a four-valued memory cell based on a three-peak MOS-NDR circuit. We connect three MOS-NDR devices in parallel that can create a three-peak current-voltage curve by suitably arranging the parameters. Due to its folding IV characteristics, multiple -peak NDR device is a very promising device for multiple -valued logic application. This memory cell structure can be easily extended to implement more states in a memory circuit.
Binary rule encoding schemes: a study using the compact classifier system
Authors: Xavier Llorà, Kumara Sastry, David E. Goldberg
Venue: Proceedings of the 2005 workshops on Genetic and evolutionary computation (2005)

Several binary rule encoding schemes have been proposed for Pittsburgh-style classifier systems. This paper focus on the analysis of how rule encoding may bias the scalability of learning maximally general and accurate rules by classifier systems. The theoretical analysis of maximally general and accurate rules using two different binary rule encoding schemes showed some theoretical results with clear implications to the scalability of any genetic-based machine learning system that uses the studied encoding schemes. Such results are clearly relevant since one of the binary representations studied is widely used on Pittsburgh-style classifier systems, and shows an exponential shrink of the useful rules available as the problem size increases.
Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking
Authors: Mohammad El-Hajj, Osmar R. Zaiane, Paul Nalos
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Mining for frequent itemsets can generate an overwhelming number of patterns, often exceeding the size of the original transactional database. One way to deal with this issue is to set filters and interestingness measures. Others advocate the use of constraints to apply to the patterns, either on the form of the patterns or on descriptors of the items in the patterns. However, typically the filtering of patterns based on these constraints is done as a post-processing phase. Filtering the patterns post-mining adds a significant overhead, still suffers from the sheer size of the pattern set and loses the opportunity to exploit those constraints. In this paper we propose an approach that allows the efficientmining of frequent itemsets patterns, while pushing simultaneously both monotone and anti-monotone constraints during and at different strategic stages of the mining process. Our implementation shows a significant improvement when considering the constraints early and a better performance over Dualminer which also considers both types of constraints.
Be real! XCS with continuous-valued inputs
Authors: Hai H. Dam, Hussein A. Abbass, Chris Lokan
Venue: Proceedings of the 2005 workshops on Genetic and evolutionary computation (2005)

XCS is widely accepted as one of the most reliable Michigan-style learning classifier system (LCS) for data mining. In order to handle real-valued inputs effectively, the traditional ternary representation has been replaced by the interval-based representation and the modified XCS has shown to work well. Existing interval-based representations still suffer from a few drawbacks which this paper address. In this paper, we propose an alternative approach called the Min-Percentage representation which produces comparable results to other methods in the literature with the extra advantage of overcoming the drawbacks in these methods.
Player identification in soccer videos
Authors: Marco Bertini, Alberto Del Bimbo, Walter Nunziati
Venue: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (2005)

A method for the identification of players in soccer videos is presented. The proposed approach exploits the inherent multiple media structure of soccer videos to perform people identification without relying on face recognition. Instead, faces are detected in close-up shots, and then the filmed player is recognized by means of recognition of the number depicted on the frontal part of its jersey, or by detection and interpretation of superimposed closed caption. Players not identified by this process are then assigned to one of the labeled faces by means of a face similarity measure. We present results obtained from soccer videos of the last European Championship for national teams, held in Portugal in June 2004.
Reinforcement learning with Gaussian processes
Authors: Yaakov Engel, Shie Mannor, Ron Meir
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framework by addressing two pressing issues, which were not adequately treated in the original GPTD paper (Engel et al., 2003). The first is the issue of stochasticity in the state transitions, and the second is concerned with action selection and policy improvement. We present a new generative model for the value function, deduced from its relation with the discounted return. We derive a corresponding on-line algorithm for learning the posterior moments of the value Gaussian process. We also present a SARSA based extension of GPTD, termed GPSARSA, that allows the selection of actions and the gradual improvement of policies without requiring a world-model.
A rank-by-feature framework for interactive exploration of multidimensional data
Authors: Jinwook Seo, Ben Shneiderman
Venue: Information Visualization (2005)

Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems often are a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. We offer a set of principles and a novel rank-by-feature framework that could enable users to better understand distributions in one (1D) or two dimensions (2D), and then discover relationships, clusters, gaps, outliers, and other features. Users of our framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods users can systematically examine the most important 1D and 2D axis-parallel projections. We summarize our Graphics, Ranking, and Interaction for Discovery (GRID) principles as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. We implemented the rank-by-feature framework in the Hierarchical Clustering Explorer, but the same data exploration principles could enable users to organize their discovery process so as to produce more thorough analyses and extract deeper insights in any multidimensional data application, such as spreadsheets, statistical packages, or information visualization tools.
A 0.65V, 1.9mW CMOS Low-Noise Amplifier at 5GHz
Authors: Yanjie Wang, M. Zamin Khan, Kris Iniewski
Venue: Proceedings of the Fifth International Workshop on System-on-Chip for Real-Time Applications (2005)

An ultra low-voltage (0.65 V), 5 GHz low noise amplifier (LNA) has been designed, laid out and simulated using Spectre simulator in a standard TSMC 0.18um CMOS technology. The proposed LNA achieves better performance than conventional cascode topology and are confirmed by simulation results. The LNA provides a high gain of 20 dB, a noise figure of 1.4 dB, power dissipation of 1.9 mW from a 0.65 V power supply. To the best of author¿s knowledge this is the lowest voltage supply CMOS LNA design reported to date.
Modelling Incremental LearningWith The Batch SOM Training Method
Authors: Vicente O. Baez-Monroy, Simon O'Keefe
Venue: Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (2005)

Self-Organizing Maps are popular tools for data visualization and clustering. At the same time, due to the incorporation of new transactions, real-life databases change periodically. As a consequence of changes in our databases; our maps, which are derived from them, often become outdated and are therefore no longer usable for decision support. To tackle this problem, the application of incremental training methods has been suggested. The current incremental methods have been developed based on non-batch procedures. In this work, a batch-incremental-training algorithm for a self-organizing map is proposed. The results obtained are promising enough to affirm that the batch method might be considered for non-stationary environments.
Learning strategies for story comprehension: a reinforcement learning approach
Authors: Eugene Grois, David C. Wilkins
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

This paper describes the use of machine learning to improve the performance of natural language question answering systems. We present a model for improving story comprehension through inductive generalization and reinforcement learning, based on classified examples. In the process, the model selects the most relevant and useful pieces of lexical information to be used by the inference procedure. We compare our approach to three prior non-learning systems, and evaluate the conditions under which learning is effective. We demonstrate that a learning-based approach can improve upon "matching and extraction"-only techniques.
Towards a model for assessing workers' risks resulting from the implementation of information and communication systems and technologies: Research Articles
Authors: Ricardo Vyhmeister, Pedro R. Mondelo, Maribel Novella
Venue: Human Factors in Ergonomics Manufacturing (2006)

The authors investigate the effects of information and communication systems and technologies (ICST) on workers' health and safety. A multidisciplinary and convergent perspective is used to define a model that evaluates the risks associated with the implementation of any information system. The model incorporates the organizational, individual, and social elements that affect workers' risks. Special attention has been paid to the incorporation of ergonomic and organizational factors including culture, technological change, and informatics ethics. &copy; 2006 Wiley Periodicals, Inc. Hum Factors Man 16: 39&ndash;59, 2006.
Energy-Efficient Data Aggregation Hierarchy for Wireless Sensor Networks
Authors: Yuanzhu Peter Chen, Arthur L. Liestman, Jiangchuan Liu
Venue: Proceedings of the Second International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (2005)

A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, we first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS). Then, we generalize it to an aggregation hierarchy and extend EPAS to a Hierarchical Energy-Efficient Protocol for Aggregator Selection (hEPAS). We derive the optimal number of aggregators with generalized compression and power-consumption models, and present fully distributed algorithms for aggregator deployment. Simulation results show that our algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols, and are thus applicable to a broad spectrum of application environments.
A protocol for partitionable group membership service in mobile Internet: Research Articles
Authors: Guojun Wang_aff1n2, Jiannong Cao, Keith C. C. Chan
Venue: Wireless Communications Mobile Computing (2005)

The explosive growth of mobile and wireless communications has attracted interests in the integration of mobile and wireless networks with wired ones and the wired Internet in particular. Such an integrated mobile Internet environment has brought up many new applications and there is an increasing demand for enhanced services to help users do mobile collaborations, which requires the support for mobile group communications. In this paper, based on a multitier proxy-based mobile Internet architecture, we propose a novel scalable, reliable, and partitionable membership protocol for mobile group communications. The proxies manage local groups of mobile users and are dynamically organized into a ring-based hierarchy for global membership management. We describe algorithms and mechanisms for membership propagation and topology maintenance in the presence of dynamic membership, dynamic locations of mobile hosts, and dynamic networks. The proposed protocol can tolerate proxy failures, and network failures including network partitions and network merges. Theoretical analysis and simulation studies show that, comparing with existing tree-based protocols, the proposed protocol has comparable scalability and higher reliability. Copyright &copy; 2005 John Wiley & Sons, Ltd.