ppxscal/embedding_network huggingface dataset:
Relaxation Labeling for Selecting and Exploiting Efficiently Non-local Dependencies in Sequence Labeling
Authors: Guillaume Wisniewski, Patrick Gallinari
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

We consider the problem of sequence labeling and propose a two steps method which combines the scores of local classifiers with a relaxation labeling technique. This framework can account for sparse dynamically changing dependencies, which allows us to efficiently discover relevant non-local dependencies and exploit them. This is in contrast to existing models which incorporate only local relationships between neighboring nodes. Experimental results show that the proposed method gives promising results.
Blending e-learning and knowledge management for optimizing learning paths
Authors: Dumitru Dan Burdescu, Marian Cristian Mihaescu
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

Within any e-Learning environment there are many types of users, with different backgrounds and different goals. This paper addresses the problem of optimizing learner's learning path in order to achieve a certain goal. This goal regards acquisition of a certain level of knowledge regarding a discipline. The paper proposes a methodology for guiding the student such that he finally obtains needed knowledge level in minimum amount of time. Since evaluation is the activity that generates quality in teaching, the method is based on continuous testing and monitoring learners. Within this process, a learner's model is created and maintained. This model is consulted whenever needed for classification purposes of students.
Comparative Studies of Fuzzy Genetic Algorithms
Authors: Qing Li, Yixin Yin, Zhiliang Wang, Guangjun Liu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Many adaptive schemes for controlling the probabilities of crossover and mutation in genetic algorithms with fuzzy logic have been reported in recent years. However, there has not been known work on comparative studies of these algorithms. In this paper, several fuzzy genetic algorithms are briefly summarized first, and they are studied in comparison with each other under the same simulation conditions. The simulation results are analyzed in terms of search speed and search quality.
Is Graphics Recognition an Unidentified Scientific Object?
Authors: Karl Tombre
Venue: Graphics Recognition. Recent Advances and New Opportunities: 7th International Workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007. Selected Papers (2008)

This paper summarizes the presentation and discussions at the panel session held at the conclusion of the GREC'07 workshop. After making a short review of where the graphics recognition stands, we raise some questions (hopefully) of interest for the future of this community.
Plenary lecture IV: path-based modeling: a generalized framework for formulating combinatorial optimization problems as linear programs
Authors: Moustapha Diaby
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

The subject of combinatorial optimization spans several fields of study, and is applicable in a large number of practical contexts. The great difficulty involved in solving combinatorial optimization problems has to do, mostly, with their discrete nature. The general idea of most of the theoretical developments dealing with these problems over the past several decades has been to tackle this discreteness directly, by enforcing it through integrality requirement constraints. As a result of this "quest for integrality," the optimal resolution of these problems has typically required some enumeration of the solutions, and the goal of the theoretical developments in general has consisted of providing tools that can be used to craft "tailor-made" procedures that exploit, to the extent possible, the particular characteristics of the problem at hand. In this talk we will define the notion of "Path-Based Modeling" and discuss its use in formulating some of the well-known NP-Complete combinatorial optimization problems as linear programs. Unlike other approaches, the focus in this approach is on enforcing the convexity of a complete feasible solution with respect to the objects being modeled, rather than attempting to enforce the integrality of individual variables. The approach will be illustrated using the Set Partitioning Problem as an example. Some reflections on the computational complexity paradigm will also be discussed.
Developing an SSME Initiative for Instruction and Research at Morgan State University
Authors: Jakita O. Thomas, S. Keith Hargrove, Montressa Washington
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Service Science or Service Science, Management and Engineering (SSME) is a growing multi-disciplinary research and academic effort that integrates aspects of established fields like computer science, operations research, engineering, management sciences, business strategy, social and cognitive sciences, and legal sciences. More than 20 major research institutions in the US have adopted SSME as a discipline or program for educating students, but no Historically Black College or University (HBCU) has yet to fully develop and implement this new discipline. This paper will describe an initiative at a HBCU to promote the emerging discipline of Service Science, Management and Engineering (SSME) for instruction in the classroom, and a proposed research effort targeted in healthcare delivery systems. This initiative utilizes collaborative learning tools, case studies and research projects to reinforce SSME concepts. The HBCU is also taking the lead in creating an alliance of other HBCUs to promote the discipline of SSME.
Byzantine Fault Tolerant Coordination for Web Services Atomic Transactions
Authors: Wenbing Zhao
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

In this paper, we present the mechanisms needed for Byzantine fault tolerant coordination of Web services atomic transactions. The mechanisms have been incorporated into an open-source framework implementing the standard Web services atomic transactions specification. The core services of the framework, namely, the activation service, the registration service, the completion service, and the distributed commit service, are replicated and protected with our Byzantine fault tolerance mechanisms. Such a framework can be useful for many transactional Web services that require high degree of security and dependability.
A framework for checking proofs naturally
Authors: Masahiko Sato
Venue: Journal of Intelligent Information Systems (2008)

We propose a logical framework, called Natural Framework (NF), which supports formal reasoning about computation and logic (CAL) on a computer. NF is based on a theory of Judgments and Derivations. NF is designed by observing how working mathematical theories are created and developed. Our observation is that the notions of judgments and derivations are the two fundamental notions used in any mathematical activity. We have therefore developed a theory of judgments and derivations and designed a framework in which the theory provides a uniform and common play ground on which various mathematical theories can be defined as derivation games and can be played, namely, can write and check proofs. NF is equipped with a higher-order intuitionistic logic and derivations (proofs) are described following Gentzen's natural deduction style. NF is part of an interactive computer environment CAL and it is also referred to as NF/CAL. CAL is written in Emacs Lisp and it is run within a special buffer of the Emacs editor. CAL consists of user interface, a general purpose parser and a checker for checking proofs of NF derivation games. NF/CAL system has been successfully used as an education system for teaching CAL for undergraduate students for about 8 years. We will give an overview of the NF/CAL system both from theoretical and practical sides.
On the Minimisation of Acyclic Models
Authors: Pepijn Crouzen, Holger Hermanns, Lijun Zhang
Venue: Proceedings of the 19th international conference on Concurrency Theory (2008)

This paper presents a novel algorithm to compute weak bisimulation quotients for finite acyclic models. It is developed in the setting of interactive Markov chains, a model overarching both labelled transition systems and continuous-time Markov chains. This model has lately been used to give an acyclic compositional semantics to dynamic fault trees, a reliability modelling formalism.While the theoretical complexity does not change substantially, the algorithm performs very well in practice, almost linear in the size of the input model. We use a number of case studies to show that it is vastly more efficient than the standard bisimulation minimisation algorithms. In particular we show the effectiveness in the analysis of dynamic fault trees.
D-book: a mobile social networking application for delay tolerant networks
Authors: Russell J. Clark, Evan Zasoski, Jon Olson, Mostafa Ammar, Ellen Zegura
Venue: Proceedings of the third ACM workshop on Challenged networks (2008)

We describe a mobile application for social networking that demonstrates the features of DTN#, a .Net-based implementation of the DTN bundle specification and testbed for research in challenged networks. The application, dubbed D-Book, includes the ability for users to create, modify, and share profiles much like the popular web-based social networking services.
Understanding Promise Theory Using Rewriting Logic
Authors: Kyrre Begnum, Mark Burgess
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

We describe our effort to evaluate the syntax and constraints of Promise Theory using a tool developed in the Maude framework. Through the development of a Maude module we are able to do searches and queries in all possible syntactically valid Promise expressions of an arbitrary initial state of participants and constraints. We use this approach to improve the set of grammatical rules that is already available.
An Authentication Based Source Address Spoofing Prevention Method Deployed in IPv6 Edge Network
Authors: Lizhong Xie, Jun Bi, Jianpin Wu
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In today's Internet routing architecture, the router doesn't validate the correctness of the source address carried in the packet, nor keep the state information when forwarding the packet. Thus the DDoS attacks with spoofed IP source address can cause security problems. In this paper, we aim to prevent the attackers from attacking somewhere outside the IPv6 edge network with forged source address in the fine granularity. The proposed methods include source address authentication by using session key and hash digest algorithm, and replay attack prevention by combining the sequence number method and the timestamp method. This paper presents the algorithm design and evaluates its feasibility and correctness by simulation experiments.
Geospatial Modelling of Urban Security: A Novel Approach with Virtual 3D City Models
Authors: Markus Wolff, Hartmut Asche
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

Complex urban structures are exposed to a variety of security risks. Especially concerning man-made hazards, cities can be considered as particularly vulnerable. Due to their high concentration of population, technical and social infrastructure, as well as their importance in politics, culture, economy and finance, metropolitan areas, in particular, can be considered as vulnerable environments. Since not every part of an urban area is exposed to the same level of potential security threats, it can be assumed that this level differs regionally within a metropolis. Based on methods of geoinformation science, this paper presents an innovative approach to identify particularly vulnerable urban regions. Using the 3D city model of the German capital Berlin as an example, the potential of such models for mapping, analysis and assessment of different threat levels in urban environments is demonstrated. This geovisual and analytical potential of 3D city models can be instrumental for decision makers working in security agencies for both threat assessment and intuitive cartographic communication of spatial phenomena related to urban security issues.
A tunable high-performance architecture for enhancement of stream video captured under non-uniform lighting conditions
Authors: Ming Z. Zhang, Ming-Jung Seow, Li Tao, Vijayan K. Asari
Venue: Microprocessors Microsystems (2008)

A novel architecture for performing hue-saturation-value (HSV) domain enhancement of digital color images captured under non-uniform lighting conditions is proposed in this paper for video streaming applications. The approach promotes log-domain computation to eliminate all multiplications, divisions and exponentiations utilizing the compact high-speed logarithmic estimation modules. An optimized quadrant symmetric architecture is incorporated into the design of homomorphic filter for the enhancement of intensity value. Efficient modules are also presented for conversion between RGB and HSV color spaces with tunable H and S components in HSV for more flexible color rendering. The design is able to bring out details hidden in shadow regions of the image and preserve the bright parts with adjustable vividness and color shift for improvement of visual quality while maintaining its consistency. It is capable of producing 187.86 million outputs per second (MOPs) on Xilinx's Virtex II XC2V2000-4ff896 field programmable gate array (FPGA) at a clock frequency of 187.86MHz. It can process over 179.1 (1024x1024) frames per second, which is very suitable for high definition videos, and consumes approximately 70.7% and 76.8% less hardware resource with 127% and 280% performance boost when compared to the designs with machine learning algorithm in [M.Z. Zhang, M.J. Seow, V.K. Asari, A high performance architecture for color image enhancement using a machine learning approach, International Journal of Computational Intelligence Research - Special Issue on Advances in Neural Networks 2(1) (2006) 40-47], and with separated dynamic and contrast enhancements in [H.T. Ngo, M.Z. Zhang, L. Tao, V.K. Asari, Design of a high performance architecture for real-time enhancement of video stream captured in extremely low lighting environment, International Journal of Embedded Systems: Special Issue on Media and Stream Processing, in press], respectively. This approach also provide 83.4 times performance gain with more consistent fidelity in the results compared to some DSP based implementations (256x256 frame size) [G.D. Hines, Z. Rahman, D.J. Jobson, G.A. Woodell, DSP implementation of the retinex image enhancement algorithm, visual information processing XIII, in: Proceedings of the SPIE, vol. 5438, 2004, pp. 13-24; G.D. Hines, Z. Rahman, D.J. Jobson, G.A. Woodell, Single-scale retinex using digital signal processors, in: Proceedings of the Global Signal Processing Conference, September 2004, pp. 1-6] under the reflectance-illuminance category of image enhancement models.
Utilization of the fluctuationlessness theorem in the evaluation of certain operator matrix representations for optimally controlled simple quantum harmonic oscillator
Authors: Muzaffer Ayvaz, Metin Demiralp
Venue: Proceedings of the 1st WSEAS International Conference on Multivariate Analysis and its Application in Science and Engineering (2008)

Recent years brought a new angle of view to the problems involving matrix representations of, especially, functions. It is the utilization of the fuctuation free matrix representation of those functions, where the matrix representation of the function is taken as the image of the matrix representation of the independent variable under the concerned function as a first approximation. The quality of the approximation becomes better as the fluctuations are suppressed by changing the subspace dimension under consideration. In this study, we discuss the quantum optimal control problem of simple harmonic oscillator as a first step towards the construction of possibly an efficient new tool. Here, the expectation value based optimal control equations are formulated and the all agents of the problem are taken linear for simplicity. The resultant linear system of ordinary differantial equations have exact solutions. By using these solutions and the fluctuationlessness approximation, it is possible to evaluate the matrix representation of an analytic function of the position and momentum operators.
An Area-Efficient Hardware Implementation for Real-Time Window-Based Image Filtering
Authors: M. H. Seyed Javadi, H. Rafi, S.Tabatabaei, A. T. Haghighat
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Real-time image processing is used in a wide range of vision applications in recent years. Whereas these processing require very high speed and computational power, hardware implementation is a good choice for achieving high performance. In this paper a new low capacity and parallel architecture based on a special memory management and arithmetic unit is proposed for real-time spatial image processing. The architecture is implemented on FPGA at a 50 MHz clock frequency and a processing time of 5 ms for 3 × 3 generic window-based operations on 512 × 512 gray-scale images. Experimental results show that the proposed architecture outperforms the existing architectures in the area utilization aspect.
Application of Fuzzy Ontology Framework to Information Retrieval for SCM
Authors: Jun Zhai, Qinglian Wang, Miao Lv
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Information retrieval is the important work of Supply Chain Management (SCM) on the Semantic Web. Ontology-based semantic retrieval is a hotspot of current research. In order to achieve fuzzy semantic retrieval, this paper applies a fuzzy ontology framework to information retrieval system in SCM. The framework includes three parts: concepts, properties of concepts and values of properties, in which property’s value can be either standard data type or linguistic values of fuzzy concepts. The semantic query expansion is constructed by order relation, equivalence relation and inclusion relation between fuzzy concepts defined in fuzzy linguistic variable ontologies. The application to retrieve customer and product information in supply chain shows that the framework can overcome the localization of other fuzzy ontology models, and this research facilitates the semantic retrieval of information through fuzzy concepts on the Semantic Web.
An Incremental Chinese Text Classification Algorithm Based on Quick Clustering
Authors: Houfeng Ma, Xinghua Fan, Ji Chen
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Most conventional incremental learning algorithms perform incremental learning by selecting only one optimized text sample each time, which neither considers the relationship between texts in the unlabeled text set, nor improves incremental learning efficiency. In addition, because of the shortage of the classifier’s information storage, the selected optimized text is easily classified incorrectly. And the consequence of selecting wrong labeled text will reduce incremental learning precision. For overcoming these problems mentioned above, a new incremental learning algorithm based on quick clustering is proposed in this paper. On the one hand, it improves incremental learning efficiency by clustering all similar texts in unlabeled text set. All texts which are the centers of text clusters are selected as a representative text set. Then the incremental learning process is to choose texts in the representative text set under the 0-1 loss rate. On the other hand, for improving incremental learning precision, a new method for choosing reasonable learning sequence is proposed, which not only strengthen the positive impact of the more mature data on classification but also weaken the negative impact of the noisy data. The experimental results show that the classification efficiency and precision are both increased by using the algorithm.
Supervision and diagnosis of joint actions in multi-agent plans
Authors: Roberto Micalizio, Pietro Torasso
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

The paper formalizes a distributed approach to the problem of supervising the execution of a multi-agent plan where (possibly joint) actions are executed concurrently by a team of cooperating agents in a partially observable environment. The notions of plan and agent diagnosis are introduced and discussed.
Missing Data Imputation in Time Series by Evolutionary Algorithms
Authors: Juan C. Figueroa García, Dusko Kalenatic, Cesar Amilcar Lopez Bello
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

This paper presents a proposal based in an Evolutionary algorithm for imputing missing observations in Time Series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean and variance, is presented.All methodological aspects of the genetic structure are presented. An extended explanation of the design of the Fitness Function is provided. Four application examples are provided and solved by the proposed method.
Parallel support vector machines for multi-category classification of large scale data
Authors: Chaoyang Zhang
Venue:  (2007)

Support Vector Machines (SVM) is a classification algorithm based on statistical learning theory, which has been receiving wide attention for classification problems, because of its accuracy and generalization property. There are many SVM tools available for classification, which perform well for binary classification, but multi-class classification increases the complexity of the problem and makes the computation more expensive or even prohibitive for larger datasets. In this work we propose a method to parallelize the classification of multi-class data based on Sequential Minimal Optimization (SMO) SVM algorithm using parallel computing techniques. This parallel implementation allows us to use high performance computing resources to perform multi-class classification in an efficient way. The SMO algorithm breaks down the classification problem into the smallest Quadratic Programming (QP) problems avoiding expensive numerical optimization. In this implementation the SMO algorithm is used to perform multi-class classification by building a series of binary classifiers and then use them to perform one-versus-one multi-class classification. The implementation was tested on many publicly available datasets. The variable size of the data for each class results in load balancing problems, which is solved by preprocessing the data set and scheduling the subtasks based on the size of the subtasks to improve the throughput. The load balancing issue is addressed by developing different mapping schemes to distribute the tasks to the parallel nodes. The parallel algorithm developed minimizes the communication between the nodes, by reducing the data transferred between the processes. Therefore the parallel algorithms developed can be studied in both shared and distributed memory parallel systems. SVMs can be used for classification of Gene expression data, which helps to analyze and understand many aspects of gene functions and irregularities. SVMs are also used in face recognition, text categorization, credit card processing, etc.