ppxscal/embedding_network huggingface dataset:
Semi-random subspace method for face recognition
Authors: Yulian Zhu, Jun Liu, Songcan Chen
Venue: Image and Vision Computing (2009)

The small sample size (SSS) and the sensitivity to variations such as illumination, expression and occlusion are two challenging problems in face recognition. In this paper, we propose a novel method, called semi-random subspace (Semi-RS), to simultaneously address the two problems. Different from the traditional random subspace method (RSM) which samples features from the whole pattern feature set in a completely random way, the proposed Semi-RS randomly samples features on each local region (or a sub-image) partitioned from the original face image. More specifically, we first divide a face image into several sub-images in a deterministic way, then construct a set of base classifiers on different randomly sampled feature sets from each sub-image set, and finally combine all base classifiers for the final decision. Experimental results on five face databases (AR, Extended YALE, FERET, Yale and ORL) show that the proposed Semi-RS method is effective, relatively robust to illumination and occlusion, etc., and also suitable to slight variations in pose angle and the scenario of one training sample per person. In addition, kappa-error diagram, which is used to analyze the diversity of algorithm, reveals that Semi-RS constructs more diverse base classifiers than other methods, and also explains why Semi-RS can yield better performance than RSM and V-SpPCA.
Genetically Engineered Architecture - Design Exploration with Evolutionary Computation
Authors: Peter von Buelow
Venue:  (2007)

This book explores design tools based on evolutionary computation (EC), oriented primarily toward conceptual design of architectural and civil engineering structures. EC tools are well suited for exploration in a way which promotes creative design. The multiplicity of solutions generated by EC techniques is less likely to cause design fixation, and so promote a more thorough exploration of possible solutions. The use of such tools also allows the designer greater latitude in exploring design criteria, such as aesthetics, by utilizing an interactive human-computer interface. This book begins with a survey of techniques that have been used in early phases of architectural design, and establishes a set of successful attributes, which are then discussed in the context of EC techniques. Finally, a specific implementation developed by the author is described. Several examples are given in the area of architectural engineering, and comparisons are made with results obtained with more conventional optimization tools. This book is especially useful for designers interested in new methods for generating and exploring structural form, and is accessible to non-programmers in either field. 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
Automated three-dimensional face authentication & recognition
Authors: Myungsoo Bae
Venue:  (2008)

The needs of security have been increasing therefore organizations have been searching for more secure authentication methods. Face based method is less intrusive and easy to use. The main problems in 2D face recognition are caused by different poses and/or different ambient illumination conditions. These problems can be solved using the 3D face image. This dissertation presents a fully automated 3D face authentication (verification) and recognition (identification) method. The major contributions of this dissertation are: (a) the method can handle data with different facial expressions including hair, upper body, clothing, etc.; (b) development of a robust feature extraction method; and, (c) development of weighted features for discrimination. The input to the face recognition system is a triangular mesh and it outputs a matching rate against a gallery. This method includes both surface and curve based features that are automatically extracted from a given face data. From the experiment, it is concluded that combining the weighted features including profile curves and surfaces gives better authentication and recognition rate than any single feature or combination of weighted features in any feature class.
Testing for jumps in the EGARCH process
Authors: Xiuhong Shi, Masahito Kobayashi
Venue: Mathematics and Computers in Simulation (2009)

This paper considers testing for jumps in the exponential GARCH (EGARCH) models with Gaussian and Student-t innovations. The Wald and log likelihood ratio tests contain a nuisance parameter unidentified under the null hypothesis of no jumps, and hence are unavailable for this problem, because jump probability and variance of jumps in the test statistic cannot be estimated under the null hypothesis of no jumps. It is shown that the nuisance parameter is cancelled out in the Lagrange multiplier (LM) test statistic, and hence that the test is nuisance parameter-free. The one-sided test is also proposed using the nonnegative constraint on jump variance. The actual size and power of the tests are examined in a Monte Carlo experiment. The test is applied to daily returns of S&P 500 as an illustrative example.
Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum
Authors: Rasa Jurgelenaite, Tjeerd M. H. Dijkstra, Clemens H. M. Kocken, Tom Heskes
Venue: Bioinformatics (2009)

Motivation: To date, there is little knowledge about one of the processes fundamental to the biology of Plasmodium falciparum, gene regulation including transcriptional control. We use noisy threshold models to identify regulatory sequence elements explaining membership to a gene expression cluster where each cluster consists of genes active during the part of the developmental cycle inside a red blood cell. Our approach is both able to capture the combinatorial nature of gene regulation and to incorporate uncertainty about the functionality of putative regulatory sequence elements. Results: We find a characteristic pattern where the most common motifs tend to be absent upstream of genes active in the first half of the cycle and present upstream of genes active in the second half. We find no evidence that motif's score, orientation, location and multiplicity improves prediction of gene expression. Through comparative genome analysis, we find a list of potential transcription factors and their associated motifs. Contact: r.jurgelenaite@cmbi.ru.nl Supplementary information: Supplementary data are available at Bioinformatics online.
A granular computing framework for self-organizing maps
Authors: Joseph P. Herbert, JingTao Yao
Venue: Neurocomputing (2009)

When using granular computing for problem solving, one can focus on a specific level of understanding without looking at unwanted details of subsequent (more precise) levels. We present a granular computing framework for growing hierarchical self-organizing maps. This approach is ideal since the maps are arranged in a hierarchical manner and each is a complete abstraction of a pattern within data. The framework allows us to precisely define the connections between map levels. Formulating a neuron as a granule, the actions of granule construction and decomposition correspond to the growth and absorption of neurons in the previous model. In addition, we investigate the effects of updating granules with new information on both coarser and finer granules that have a derived relationship. Called bidirectional update propagation, the method ensures pattern consistency among data abstractions. An algorithm for the construction, decomposition, and updating of the granule-based self-organizing map is introduced. With examples, we demonstrate the effectiveness of this framework for abstracting patterns on many levels.
Knowledge management for context-aware, policy-based ubiquitous computing systems
Authors: John Strassner, Declan O'Sullivan
Venue: Proceedings of the 6th international workshop on Managing ubiquitous communications and services (2009)

Ubiquitous computing systems depend on a distributed intelligence that relates the context of the task being performed to the available system resources and services. Currently, this is hampered by the problems inherent in heterogeneous devices and technologies used to manage network resources and services, and the associated lack of a network language lingua franca that all systems can use and understand. This paper describes a new approach for representing, using and managing knowledge for ubiquitous computing systems, based on a novel combination of extracting knowledge from models and ontologies to form such a lingua franca. An extensible context model is used to select applicable policies to govern system behaviour; as context changes, policies change, which in turn causes system behaviour to change accordingly.
CloneDetective - A workbench for clone detection research
Authors: Elmar Juergens, Florian Deissenboeck, Benjamin Hummel
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

The area of clone detection has considerably evolved over the last decade, leading to approaches with better results, but at the same time using more elaborate algorithms and tool chains. In our opinion a level has been reached, where the initial investment required to setup a clone detection tool chain and the code infrastructure required for experimenting with new heuristics and algorithms seriously hampers the exploration of novel solutions or specific case studies. As a solution, this paper presents CloneDetective, an open source framework and tool chain for clone detection, which is especially geared towards configurability and extendability and thus supports the preparation and conduction of clone detection research.
On xml query processing
Authors: Wen-Chi Hou
Venue:  (2008)

While XML (Extensible Markup Language) has been widely accepted as the standard for data storage, exchange, and integration over the Internet, The XML query processing becomes an interesting and challenging research topic in XML database research because of the rapidly emerging applications in XML query analysis and optimization. Finding all occurrences of the given query pattern is the core operation of XML query processing, which typically involves the tree-structured navigation and pattern matching. While currently proposed query processing algorithm, such as TwigStack, iTwigList and TJFast, provides an effective way to locate and retrieve the query matches, its time and space efficiency still needs more enhancement. The potentially large size of the intermediate results and the cumbersome path merging phase, which are two major deficiencies among the current algorithms, slow down the query estimation process. This kind of situation is further exacerbated in the online applications that require the evaluation process to be reported timely and flexibly. Our research improves XML query processing on two main aspects. First, we proposed a novel single-phase holistic query matching method, which elegantly avoids the generation and processing of intermediate results in traditional two-phase based algorithms. Without generating individual path matches as intermediate results, our method is able to avoid the storage and output/input of the individual path matches, and totally eliminate the potentially time-consuming merging operation. Experimental results demonstrate the applicability and advantages of our approach. On the other hand, we enhance the XML query processors with the ability to process queries progressively or incrementally and report partial or estimated results and progress of query evaluation continually. The methodology lays its foundation on sampling. We shed light on how effective samples can be drawn from semi-structured XML data, as opposed to flat-table relational data. Several innovative sampling schemes on XML data are designed. The progress of query processing is also reported continually. The proposed methodology advances XML query processing to the next level - being more flexible, responsive, user-informed, and user-controllable, to meet emerging needs and future challenges.
On the selection of equity securities: An expert systems methodology and an application on the Athens Stock Exchange
Authors: P. Xidonas, E. Ergazakis, K. Ergazakis, K. Metaxiotis, D. Askounis, G. Mavrotas, J. Psarras
Venue: Expert Systems with Applications: An International Journal (2009)

In this article we present an expert systems methodology for supporting decisions that concern the selection of equities, on the basis of financial analysis. The proposed methodology is employed for selecting the attractive equities, through the evaluation of the overall corporate performance of the corresponding firms. The crucial importance issue of the industry/sectoral accounting singularities was strongly taken into account. An elaborate review of coherent research studies is also provided. Finally, the validity of the proposed methodology is tested through a large scale application on the Athens Stock Exchange.
A novel feature-based approach to Chinese entity relation extraction
Authors: Wenjie Li, Peng Zhang, Furu Wei, Yuexian Hou, Qin Lu
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Relation extraction is the task of finding semantic relations between two entities from text. In this paper, we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set.
Control of cell planning with fuzzy logic in GSM system
Authors: Ali Hakan Isik, Erkan Afacan
Venue: Proceedings of the 8th Wseas international conference on Telecommunications and informatics (2009)

In a GSM system, where coverage areas are divided into cells in planning phase, the division method and its parameters have a great importance. This study explains GSM system and cell planning process, and gives information about the features of fuzzy logic algorithm. Fuzzy logic solves problems better than other control algorithms in which initial parameters are needed to be assigned by the designer, which have variables that aren't defined well and change with time and where system expert's knowledge and experience in design process have great importance. Cell planning has same important problems that aren't defined well, change with time. These important problems of cell planning are the cell radius, base station number and power, TRU (transceiver unit) number, N (number of cells per cluster), cost, CIR (channel interference ratio), Ds (distance between two cells which use same frequency at the same time) are found for urban, suburban and rural areas by using omni or three sector antennas. GSM cell planning is simulated by using fuzzy logic algorithm, a computer program is written to determine what kind of configuration changes have to be made so as to minimize the number of base station, and hence the total system cost, and also to maintain the requirements of the maximum traffic demand. In addition, input and output data can be defined through the interface which is created in C++ Builder programming language.
Application of Artificial Intelligence Technique in Distributed Generation System
Authors: Guoqing Weng, Youbing Zhang, Yi Hu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper gives a brief description of current situation of distributed generation system, and points out that microgrid can run in two kinds of operation modes. The key problems which need to be cautiously considered exist in each operation mode are summarized, and advanced artificial intelligence techniques are adopted to solve those problems as effective tools. The application situation and research status of multi-agent system, artificial neural network, genetic algorithm, fuzzy logic in distributed generation system home and abroad are summarized detailedly. The existing problems and solving thoughts of each artificial intelligence technique are analyzed, and developing directions of artificial intelligence applications in distributed generation system in the future are also prospected.
Combinatorial Optimization: Theory and Algorithms, 4th edition
Authors: Bernhard Korte, Jens Vygen
Venue: Algorithms And Combinatorics (2007)

This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It has arisen as the basis of several courses on combinatorial optimization and more special topics at graduate level. It contains complete but concise proofs, also for many deep results, some of which did not appear in a textbook before. Many very recent topics are covered as well, and many references are provided. Thus this book represents the state of the art of combinatorial optimization. This fourth edition is again significantly extended, most notably with new material on linear programming, the network simplex algorithm, and the max-cut problem. Many further additions and updates are included as well. From the reviews of the previous editions: "This book on combinatorial optimization is a beautiful example of the ideal textbook." Operations Research Letters 33 (2005), p.216-217 "The second edition (with corrections and many updates) of this very recommendable book documents the relevant knowledge on combinatorial optimization and records those problems and algorithms that define this discipline today. To read this is very stimulating for all the researchers, practitioners, and students interested in combinatorial optimization." OR News 19 (2003), p.42 "... has become a standard textbook in the field." Zentralblatt MATH 1099.90054
Promoting collaboration in a computer-supported medical learning environment
Authors: Elisa Boff, Cecília Flores, Ana Respício, Rosa Vicari
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

This paper addresses collaborative learning in the medical domain. In particular, it focuses on the evaluation of a component specially devised to promote collaborative learning using AMPLIA. AMPLIA is an intelligent multi-agent environment to support diagnostic reasoning and the modeling of diagnostic hypotheses in domains with complex, and uncertain knowledge, such as the medical domain. Recently, AMPLIA has been extended with a new component providing support in workgroup formation. Workgroups are proposed based on individual aspects of the students, such as learning style, performance, affective state, personality traits, and also on group aspects, such as acceptance and social skills. The paper also presents and discusses the results of an experiment evaluating the performance of workgroups composed according to suggestions provided by the system.
What is optimal scheduling in wireless networks?
Authors: Anna Pantelidou, Anthony Ephremides
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

We consider a wireless network consisting of multiple transmitters with multicast traffic destined for a set of receivers. We are interested in the problem of joint scheduling and rate control under two performance objectives; the objective of maximizing the total sum throughput of the network and of being proportionally fair with respect to the received rate at each receiver. We first consider static wireless networks, and then extend our analysis for the more general and more realistic case of time-varying networks. We finally verify our analytical results through a set of simulations.
Cluster-based retrieval from a language modeling perspective
Authors: W. Bruce Croft
Venue:  (2008)

The standard approach to document retrieval is to assume that the relevance of documents could be assessed independently. The fact that a document is relevant does not contribute to predicting the relevance of a closely-related document. Cluster-based retrieval, on the other hand, assumes that the probability of relevance of a document should depend on the relevance of other similar documents to the same query. The goal is to find the best group of documents. The most common approach to cluster-based retrieval, which was proposed in the 1970s, is to retrieve one or more clusters in their entirety to a query. Research in this area has suggested that "optimal" clusters exist that, if retrieved, would yield very large improvements in effectiveness relative to document retrieval. However, no real retrieval strategy has achieved this result. Except for precision-oriented searches on very small data sets, document retrieval is found to be generally more effective. There has been a resurgence of research in cluster-based retrieval in the past few years including our own efforts in this area. The general approach is to use clusters as a form of document smoothing. Studies have shown that clusters can indeed improve retrieval performance automatically on modern test collections and the language modeling framework is an effective probabilistic retrieval framework for studying this type of problems.This thesis revisits the problem of retrieving the best group of documents, from the language-modeling perspective. We study both cluster smoothing and cluster retrieval. We analyze the advantages and disadvantages of a range of representation techniques, derive features that characterize good document clusters, and develop new probabilistic representations that capture the identified features. An extensive empirical evaluation is provided for various techniques proposed in this work. We find that whether good document clusters could be successfully identified or utilized by an IR system largely depends on how they are represented. Both the CBDM model for cluster smoothing and the geometric mean representation for cluster retrieval are shown to be effective approaches for cluster-based retrieval.
Evolution Differential Systems with Impulse and Delay
Authors: Yan Li
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, we discuss a class of impulsive evolution neutral functional differential inclusions with infinite delay. The existence of mild solutions of these evolution neutral differential systems is obtained by using the new nonlinear alternatives of Leray-Schauder type.
Multi-channel marketing: an experiment on leading citizens to online public services
Authors: Marije L. Teerling, Willem Pieterson
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

Governments have a variety of channels at their disposal to help them interact with their citizens. Having realized that citizens still prefer the traditional channels, governments are now focusing on ways to lead them to the web. Previously, we have shown that citizens prefer the use of soft instruments, such as communication. Communication instruments are characterized by the transfer of information from government to citizens, for example, by means of mass media or a letter. This study focuses on how a communication instrument -- in this case a personal letter -- can be used to lead citizens to the digital channel. We also show the effects of the instrument both in terms of channel usage and citizen perceptions. Based on a field experiment, carried out in cooperation with a Dutch national government agency, we show that unobtrusive channel marketing can be effectively used to increase usage of government e-services, without negatively affecting citizens' current level of satisfaction with service delivery.