ppxscal/embedding_network huggingface dataset:
An Optimized Parallel Decision Tree Model Based on Rough Set Theory
Authors: Xiaowang Ye, Zhijing Liu
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 an optimized parallel decision tree model based on rough set theory, first the model divides global database into subsets, then using the intuitive classification ability of decision tree to learn the rules in each subset, at last merge each subset's rule set to obtain the global rule set. In this model, with the uncertain information analysis method of rough set, the author presents a massive data segmentation method and using the Weighted Mean Roughness as a decision tree method for attribute selection. This parallel data mining model with the best segmentation algorithm based on rough set can be well used in dealing with massive database
Design Automation: Lessons of the Past, Challenges for the Future
Authors: John Mayo
Venue: IEEE Computer Graphics and Applications (1983)

Design automation, born to manage the complexity of a dozen or so electronic elements, now handles hundreds of thousands with ease. But its greatest challenges lie ahead. 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
The Georgia WebBSIT: an innovative collaborative online baccalaureate degree in information technology
Authors: Kam Fui Lau, Han Reichgelt, Vickie Booth, Larry Booth, Art Gowan
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

The Georgia Web Bachelor of Science in Information Technology (Georgia WebBSIT) is a fully online baccalaureate degree program in Information Technology, jointly developed and offered by five institutions within the University System of Georgia (USG). Each of the five participating institutions offers a traditional on-campus program in Information Technology. WebBSIT students enroll in one of the participating institutions, and take courses offered by that institution, even if the instructor for that particular course may be a faculty member elsewhere. The program enrolled its first students in August 2004 and saw its first graduates in May 2008. The WebBSIT program is a multi-institutional program that is delivered fully online and in an asynchronous fashion. It combines general education courses, which students can complete through the UGS eCore program, with a set of IT courses. In this paper, we describe the genesis of the program and its current state, including its impact to this point, its curricular structure and rationale, and the make-up of the current student body. We also describe some of the obstacles we had to overcome, both in terms of administration of the program, attracting students and in delivery.
Probabilistic Approach to Service Commitment in Service-Oriented Systems
Authors: Hadi Bannazadeh, Alberto Leon-Garcia
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

In this paper, we consider the problem of committing service instances to the applications in a service-oriented environment. We propose a heuristic algorithm which is able to analytically compute the over-commitment and application failure probabilities, and consequently, commit required service instances to each application instance while controlling these probabilities. Moreover, this proposed algorithm is suitable for general distribution functions for the services execution times and applications interarrival times. We also formulate a linear programming problem for maximizing the overall system reward, and through simulations and performance comparisons we show that the proposed mechanisms significantly improve the system's overall performance while achieving the target level for the over-commitment and application failure probabilities.
Describing and Generating Web-Based Affective Human-Agent Interaction
Authors: Xia Mao, Zheng Li, Haiyan Bao
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

In this paper, we discuss our research on the multi-modal interaction markup language (MIML) which serves as a core for integrating various components, including lifelike agent ActiveX controller, facial expression recognition ActiveX controller and speech emotion recognition ActiveX controller, to describe and generate web-based affective human-agent interaction. Emotional control on lifelike agents provided by MIML makes the human-agent interaction even more intelligent. With the MIML and components we designed, web-based affective interaction can be described and generated easily.
Resolution of Singularities and Stochastic Complexity of Complete Bipartite Graph-Type Spin Model in Bayesian Estimation
Authors: Miki Aoyagi, Sumio Watanabe
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In this paper, we obtain the main term of the average stochastic complexity for certain complete bipartite graph-type spin models in Bayesian estimation. We study the Kullback function of the spin model by using a new method of eigenvalue analysis first and use a recursive blowing up process for obtaining the maximum pole of the zeta function which is defined by using the Kullback function. The papers [1,2] showed that the maximum pole of the zeta function gives the main term of the average stochastic complexity of the hierarchical learning model.
Some Experiments on Clustering Similar Sentences of Texts in Portuguese
Authors: Eloize Rossi Seno, Maria Das Nunes
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

Identifying similar text passages plays an important role in many applications in NLP, such as paraphrase generation, automatic summarization, etc. This paper presents some experiments on detecting and clustering similar sentences of texts in Brazilian Portuguese. We propose an evalution framework based on an incremental and unsupervised clustering method which is combined with statistical similarity metrics to measure the semantic distance between sentences. Experiments show that this method is robust even to treat small data sets. It has achieved 86% and 93% of F-measure and Purity, respectively, and 0.037 of Entropy for the best case.
Verified Software: Theories, Tools, Experiments Vision of a Grand Challenge Project
Authors: Tony Hoare, Jay Misra
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

The ideal of correct software has long been the goal of research in Computer Science. We now have a good theoretical understanding of how to describe what programs do, how they do it, and why they work.This understanding has already been applied to the design, development and manual verification of simple programs of moderate size that are used in critical applications.Automatic verification could greatly extend the benefits of this technology.This paper argues that the time is ripe to embark on an international Grand Challenge project to construct a program verifier that would use logical proof to give an automatic check of the correctness of programs submitted to it.Prototypes for the program verifier will be based on a sound and complete theory of programming; they will be supported by a range of program construction and analysis tools; and the entire toolset will be evaluated and evolve by experimental application to a large and widely representative sample of useful computer programs.The project will provide the scientific basis of a solution for many of the problems of programming error that afflict all builders and users of software today.This paper starts with an optimistic vision of a possible long-term future of reliable programming. It argues that scientific research will play an essential role in reaching these long-term goals. It suggests that their achievement should be accelerated by a major international research initiative, modelled on a Grand Challenge, with specific measurable goals. The suggested measure is one million lines of verified code.By definition, this consists of executable programs, together with their specifications, designs, assertions, etc., and together with a machine-checked proof that the programs are consistent with this documentation. We anticipate that the project would last more than ten years, consume over one thousand person-years of skilled scientific effort, drawn from all over the world.Each country will contribute only a proportion of the effort, but all the benefits will be shared by all.The paper concludes with suggestions for exploratory pilot projects to launch the initiative and with a call to volunteers to take the first steps in the project immediately after this conference.A possible first step will be to revise and improve this paper as a generally agreed report of goals and methods of the scientists who wish to engage in the project.
Generating Embodied Descriptions Tailored to User Preferences
Authors: Mary Ellen Foster
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

We describe two user studies designed to measure the impact of using the characteristic displays of a speaker expressing different user-preference evaluations to select the head and eye behaviour of an animated talking head. In the first study, human judges were reliably able to identify positive and negative evaluations based only on the motions of the talking head. In the second study, subjects generally preferred positive displays to accompany positive sentences and negative displays to accompany negative ones, and showed a particular dislike for negative facial displays accompanying positive sentences.
Using Architecture Analysis to Evolve Complex Industrial Systems
Authors: Tommy Kettu, Eckhard Kruse, Magnus Larsson, Goran Mustapic
Venue: Architecting Dependable Systems V (2008)

ABB is a large industrial company with a broad product portfolio that contains products that can be categorized as highly complex industrial systems. Software embedded in complex industrial systems must support rigid system dependability requirements. It is not only a challenge to design and implement these systems as dependable, but it is also difficult to maintain this important property over time. There are several factors that make software evolution a challenging task, such as: size of the software base is measured in order of MLOC, products are long-lived and extended to support new requirements over time longer than 10 years. Because of personnel turnover important knowledge is lost from time to time, and the only artifact that is really up-to-date is the implementation itself. Therefore, to obtain an up-to-date view of the system and prevent expensive mistakes during system evolution, it is beneficial to find practical ways to obtain an up-to-date view on an architectural level without having to read thousands of lines of source code. These activities should be seen as an important contribution for preventing the introduction of faults into software systems since they contribute to improve and maintain the overall system dependability. This experience paper provides practical advices on how to reconstruct the architecture of existing systems by combining the use of tools and the existing knowledge within the organization. The paper is based on experiences from two cases in different sub domains within industrial automation.
New Radial Basis Function Neural Network Training for Nonlinear and Nonstationary Signals
Authors: Seng Kah Phooi, Ang L. M
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper deals with the problem of adaptation of radial basis function neural networks (RBF NN). A new RBF NN supervised training algorithm is proposed. This method possesses the distinctive properties of Lyapunov Theory-based Adaptive Filtering (LAF) in [1]-[2]. The method is different from many RBF NN training using gradient search methods. A new Lyapunov function of the error between the desired output and the RBF NN output is first defined. The output asymptotically converges to the desired output by designing the adaptation law in Lyapunov sense. Error convergence analysis in this paper has proven that the design of the new RBF NN training algorithm is independent of statistic properties of input and output signals. The new adaptation law has better tracking capability compared with the tracking performance of LAF in [1]-[2]. The performance of the proposed technique is illustrated through the adaptive prediction of nonlinear and nonstationary speech signals.
Discovering Novel Multistage Attack Strategies
Authors: Zhitang Li, Aifang Zhang, Dong Li, Li Wang
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

In monitoring anomalous network activities, intrusion detection systems tend to generate a large amount of alerts, which greatly increase the workload of post-detection analysis and decision-making. A system to detect the ongoing attacks and predict the upcoming next step of a multistage attack in alert streams by using known attack patterns can effectively solve this problem. The complete, correct and up to date pattern rule of various network attack activities plays an important role in such a system. An approach based on sequential pattern mining technique to discover multistage attack activity patterns is efficient to reduce the labor to construct pattern rules. But in a dynamic network environment where novel attack strategies appear continuously, the novel approach that we propose to use incremental mining algorithm shows better capability to detect recently appeared attack. In order to improve the correctness of results and shorten the running time of the mining algorithms, the directed graph is presented to restrict the scope of data queried in mining phase, which is especially useful in incremental mining. Finally, we remove the unexpected results from mining by computing probabilistic score between successive steps in a multistage attack pattern. A series of experiments show the validity of the methods in this paper.
High-Level Theories
Authors: Jacques Carette, William M. Farmer
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

We introduce high-level theories in analogy with high-level programming languages. The basic point is that even though one can define many theories via simple, low-level axiomatizations, that is neither an effective nor a comfortable way to work with such theories. We present an approach which is closer to what users of mathematics employ, while still being based on formal structures.
ICS: An Interactive Classification System
Authors: Yan Zhao, Yiyu Yao, Mingwu Yan
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

Interactive data mining focuses on efficient and effective human-computer interactions for data analysis purposes. An interactive system is an integration of a human user and a computer machine. ICS, an interactive classification system, is implemented to demonstrate the power of interactive data mining. The interaction is mutually beneficial to users and machines. This article describes the architecture of ICS, and introduces the main features of ICS in the entire data mining process.
Interlingua for French and German Topological Prepositions
Authors: Djelloul Aroui, Mustapha Kamel Rahmouni
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

The present study focuses on the definition of an Interlingua for the French topological prepositions " dans(in) ", " sur(on) ", " à(at) " and their equivalent in German " in ", " auf ", " an " with a view to their use in French-German machine translation and second language acquisition systems. Both languages have a concept of preposition but with a different lexical use. In the same spatial situation, not always equivalent prepositions are used. The French and German prepositions can cover the description of a certain spatial situation, although its meanings can overlap themselves only partially. The choice of the appropriate preposition in a target language depends on the meaning of the topological preposition. With the second language acquisition of spatial prepositions mistakes frequently occur, because one preposition in French does not correspond necessarily to a same basic meaning of a preposition in German. Regarding the machine translation of spatial prepositions, their meanings in a target language can not be defined in the same lexical way. Therefore the machine translation and the second language acquisition of spatial expressions must be occur not directly on a language dependent, i.e. linguistic, but on a language independent level, i.e. conceptual level. The definition of an Interlingua assumes that the meaning of French and German topological prepositions can be expressed in common neutral concepts.
An Acoustic Framework for Detecting Fatigue in Speech Based Human-Computer-Interaction
Authors: Jarek Krajewski, Rainer Wieland, Anton Batliner
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This article describes a general framework for detecting accident-prone fatigue states based on prosody, articulation and speech quality related speech characteristics. The advantages of this real-time measurement approach are that obtaining speech data is non obtrusive, and free from sensor application and calibration efforts. The main part of the feature computation is the combination of frame level based speech features and high level contour descriptors resulting in over 8,500 features per speech sample. In general the measurement process follows the speech adapted steps of pattern recognition: (a) recording speech, (b) preprocessing (segmenting speech units of interest), (c) feature computation (using perceptual and signal processing related features, as e.g. fundamental frequency, intensity, pause patterns, formants, cepstral coefficients), (d) dimensionality reduction (filter and wrapper based feature subset selection, (un-)supervised feature transformation), (e) classification (e.g. SVM, K-NN classifier), and (f) evaluation (e.g. 10-fold cross validation). The validity of this approach is briefly discussed by summarizing the empirical results of a sleep deprivation study.
On the Normal Boundary Intersection Method for Generation of Efficient Front
Authors: Pradyumn Kumar Shukla
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper is concerned with the problem of finding a representative sample of Pareto-optimal points in multi-objective optimization. The Normal Boundary Intersection algorithm is a scalarization scheme for generating a set of evenly spaced Efficient solutions. A drawback of this algorithm is that Pareto-optimality of solutions is not guaranteed. The contributions of this paper are two-fold. First, it presents alternate formulation of this algorithms, such that (weak) Pareto-optimality of solutions is guaranteed. This improvement makes these algorithm theoretically equivalent to other classical algorithms (like weighted-sum or ¿-constraint methods), without losing its ability to generate a set of evenly spaced Efficient solutions. Second, an algorithm is presented so as to know beforehand about certain sub-problems whose solutions are not Pareto-optimal and thus not wasting computational effort to solve them. The relationship of the new algorithm with weighted-sum and goal programming method is also presented.
Optimum Beamforming Based on Second Order Cone Programming
Authors: Yang Han, Chaohuan Hou, Xiaochuan Ma
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

An approach based on second order cone programming (SOCP) is proposed to the optimum beamforming of non-uniform linear array (NULA). The approach designs the optimum beamforming according to control sidelobe and improves the problem that the Chebychev methods can only be used in the optimum beamforming of uniform linear array(ULA). The GUI(graphical user interfaces) of the MATLAB is applied to computer simulation program ,we can choose the value of the key parameter. The application of GUI make the result practical and efficient.
An Algorithm of Neural Network and Application to Data Processing in Concrete Engineering
Authors: Ji-Zong Wang, Xi-Juan Wang, Hong-Guang Ni
Venue: Informatica (2003)

It is a complex non-linear problem to predict mechanical properties of concrete. As a new approach, the artificial neural networks can extract rules from data, but have difficulties with convergence by the traditional algorithms. The authors defined a new convex function of the grand total error and deduced a global optimization back-propagation algorithm (GOBPA), which can solve the local minimum problem. For weights' adjustment and errors' computation of the neurons in various layers, a set of formulae are obtained by optimizing the grand total error function over a simple output space instead of a complicated weight space. Concrete strength simulated by neural networks accords with the data of the experiments on concrete, which demonstrates that this method is applicable to concrete properties' prediction meeting the required precision. Computation results show that GOBPA performs better than a linear regression analysis.
Computing the State of Specknets: Further Analysis of an Innate Immune-Inspired Model
Authors: Despina Davoudani, Emma Hart, Ben Paechter
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

Specknets consist of hundreds of miniature devices, which are each capable of processing data and communicating wirelessly across short distances. Such networks, with their great complexity, pose considerable challenges for engineers due to the unreliability and scarce resources of individual devices. Their limitations make it difficult to apply traditional engineering approaches. In this paper, we describe a model inspired by the dendritic cells of the innate immune system; often overlooked in artificial immune systems, dendritic cells possess a unique ability to scout the body environment and then present an integrated picture of the internal state of the body to the adaptive system. We adopt a model, inspired by this approach, to sense the state of a Specknet and provide experimental results to show that useful information can be gathered from the Specknet in order to determine local states. Experiments are conducted using realistic random topologies in a simulation environment, in a scenario which models sensing temperature changes.