ppxscal/embedding_network huggingface dataset:
Research of Intelligent Decision Support System Based on Neural Networks
Authors: Kai Li, Zhonghua Xu, Baoqin Wang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Although the decision support systems (DSS) theory and application has already taken significant progress, the development of DSS is strongly challenged for the bottleneck of existing serial characters store and process mechanism. The neural network is an ultra large-scale, time dynamic system with highly nonlinear. Its most important features are paralleled distributive treatment with large scale, high robust and learning and memory function. The research of intelligent decision support system combining neural networks have a highly theoretical and practical value, and becomes one of decision support system's research fronts. The paper discusses the problems in DSS, illustrates the mechanism of parallel process using neural networks, proposes the research frame of intelligent decision support system combined with neural networks, concretely describes the implementation thoughts and methods of key problems, and analyses the basic assignment and method of all levels.
A Supporting Architecture for Generic Service Integration in IMS Learning Design
Authors: Luis Fuente Valentin, Yongwu Miao, Abelardo Pardo, Carlos Delgado Kloos
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Learning Design offers the possibility of capturing the process, activities, user organization and resources used in a learning experience. But a wider set of scenarios appear when generic services are considered. Integrating such services in a Unit of Learning is difficult due to the lack of a defined bi-directional protocol for information exchange. In this paper the Generic Service Integration paradigm is presented. It extends the Learning Design specification to use generic services, first at the design stage of a Unit of Learning, and then at the deployment and run times. The framework allows for bi-directional exchange of information between a Unit of Learning and a service. The consequences of the approach are that services can be configured to suit the needs of activities in a learning environment, and a Unit of Learning may adapt its behavior based on the events that took place in any of the used services.
XSelMark: A Micro-benchmark for Selectivity Estimation Approaches of XML Queries
Authors: Sherif Sakr
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

Estimating the sizes of query results and intermediate results is a crucial part of any effective query optimization process. Due to several reasons, the selectivity estimation problem in the XML domain is more complicated than that in the relational domain. Several research efforts have proposed selectivity estimation approaches in the XML domain. Lacking of a suitable benchmark was one of the main reasons which prevented a real assessment and comparison between the approaches to be conducted. In this paper we propose a selectivity estimation benchmark for XML queries, XSelMark. It consists of a set of 25 queries organized into seven groups and covers the main aspects of selectivity estimation of XML queries. These queries have been designed with respect to an XML document instance of a popular benchmark for XML data management, XMark. In addition, we suggest some criteria of assessing the capability and quality of XML queries selectivity estimation approaches. Finally, we use the proposed benchmark to assess the capabilities of the-state-of-the-art of the selectivity estimation approaches.
Softening Splits in Decision Trees Using Simulated Annealing
Authors: Jakub Dvořák, Petr Savický
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

Predictions computed by a classification tree are usually constant on axis-parallel hyperrectangles corresponding to the leaves and have strict jumps on their boundaries. Frequently a better approximation may be expected, if the prediction function of the original tree is replaced by a continuous approximation. The approximation is constructed using the same training data on which the original tree was grown and the structure of the tree is preserved.The current paper uses the model of trees with soft splits suggested by Quinlan and implemented in C4.5, however, the training algorithm is substantially different. The method uses simulated annealing, so it is quite computationally expensive. However, numerical test with data derived from an experiment in particle physics shows that besides the expected better approximation of the training data, also smaller generalization error is achieved.
Applications of the Moving Average of nth -Order Difference Algorithm for Time Series Prediction
Authors: Yang Lan, Daniel Neagu
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Currently, as a typical problem in data mining, Times Series Analysis and Prediction are facing continuously more applications on a wide variety of domains. Huge data collections are generated or updated from science, military, financial and environmental applications. Prediction of the future trends based on previous and existing values is of a high importance and various machine learning algorithms have been proposed. In this paper we discuss results of a new approach based on the moving average of the n th -order difference of limited range margin series terms. Based on our original approach, a new algorithm has been developed: performances on measurement records of sunspots for more than 200 years are reported and discussed. Finally, Artificial Neural Networks (ANN) are added for improving the precision of prediction by addressing the error of prediction in the initial approach.
Achieving Effective Multi-term Queries for Fast DHT Information Retrieval
Authors: Quanqing Xu, Heng Tao Shen, Yafei Dai, Bin Cui, Xiaofang Zhou
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

Distributed Hash Tables (DHTs) are well-suited for exact match look- ups using unique identifiers, but do not directly support multi-term queries. Related research of query expansion has shown that adding new terms to a query via ad hoc feedback improves the retrieval effectiveness of such query. In the paper, we propose an effective multi-term query processing algorithm for information retrieval in DHT systems. Given the significance of first term in a multi-term query, the query is sent to the peers containing the first term. To enhance the query effectiveness, we design two query expansion mechanisms and an implicit relevance feedback approach based on users' behaviors. Additionally, we record the query log and the expansion terms for each query which can accelerate the future queries and improve the query accuracy. Experimental results show that our query methods yield substantial improvements in retrieval effectiveness in the following three aspects: recall, precision at 10 standard recall levels and precision histograms.
Protecting AES Software Implementations on 32-Bit Processors Against Power Analysis
Authors: Stefan Tillich, Christoph Herbst, Stefan Mangard
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

The Advanced Encryption Standard is used in many embedded devices to provide security. In the last years, several researchers have proposed to enhance general-purpose processors with custom instructions to increase the efficiency of cryptographic algorithms. In this work we have evaluated the impact of such instruction set extensions on the implementation security of AES. We have compared several AES implementation options which incorporate state-of-the-art software countermeasures against power-analysis attacks--with and without the use of instruction set extensions. For both scenarios we provide a thorough analysis for different countermeasures with regard to security, performance, and memory. We have found that even a moderate level of protection requires a considerable overhead both in terms of speed and memory. The instruction set extensions, which have been solely designed to increase performance, help to reduce this overhead, but it still remains high. An implementation with proper protection through software countermeasures is only feasible in a setting where the need for resistance against power analysis outweighs the need for performance.
The effect of computer-based authentic assignments on learners' foreign language abilities and intrinsic motivation
Authors: Terence C. Ahern
Venue:  (2007)

The present research study investigated the influence of computer-based authentic assignments on learners' foreign language abilities and intrinsic motivation to learn the language. The intervention designed was a WebQuest in Spanish. Four research questions guided the study: Research Question 1: Do computer-based authentic assignments have any effect on learners' foreign language reading comprehension?, Research Question 2: Do computer-based authentic assignments have any effect on learners' writing outcomes?, Research Question 3: Do computer-based authentic assignments have any effect on learners' speaking outcomes?, and Research Question 4: Do computer-based authentic assignments have any effect on learners' intrinsic motivation to learn the target language? The study included a quantitative research method with a small portion of qualitative data analysis. The population of this study consisted of intermediate Spanish learners at college level. For this investigation the participants were students who enroll in two sections of Intermediate Spanish (Span 204) in the Department of Foreign Languages at a Mid-Atlantic University during the 2006 Fall semester. One section had 25 participants and the other 23 with a total of 48 participants for the entire study (n=48). Five instruments were used to collect a different data set for this study: A demographic survey, the Spanish Reading Comprehension Assessment (SRCA), the Language Learning Motivational Questionnaire (LLMQ), and two rubrics designed to evaluate learners' writing and speaking outcomes. The findings indicate that computer-based authentic assignments did not affect learners' reading abilities, writing and speaking outcomes, and intrinsic motivation to learn the language between the groups. However, rejection to collaborative learning at college level, refusal to speak in a foreign language without proper written notes, and acquisition of new knowledge emerged from the qualitative analysis as aspects that influenced the learning experience. Additionally, the WebQuest intervention was perceived as a useful and helpful component that helped to develop the project.
The Effect of Emotional Speech on a Smart-Home Application
Authors: Theodoros Kostoulas, Iosif Mporas, Todor Ganchev, Nikos Fakotakis
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

The present work studies the effect of emotional speech on a smart-home application. Specifically, we evaluate the recognition performance of the automatic speech recognition component of a smart-home dialogue system for various categories of emotional speech. The experimental results reveal that word recognition rate for emotional speech varies significantly across different emotion categories.
Using Machine-Learning Methods for Musical Style Modeling
Authors: Shlomo Dubnov, Gerard Assayag, Olivier Lartillot, Gill Bejerano
Venue: Computer (2003)

Constructing a musical theory from examples presents an intellectual challenge that could foster a range of new creative applications. Thus, the authors sought to apply machine-learning methods to the problem of musical style modeling. Their work has produced examples of musical generation and applications to a computer-aided composition system. Using statistical and information-theoretic tools that analyze musical pieces, they seek to capture some of the regularity apparent in the composition process. The resulting models can be used for inference and prediction, and to generate new works that imitate the great masters' styles.
Nondestructive Variety Discrimination of Fragrant Mushrooms Based on Vis/NIR Spectral Analysis
Authors: Haiqing Yang, Yong He
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

The potential of artificial neural network (ANN) as a way for the nondestructive variety discrimination of fragrant mushrooms was evaluated. First, the visual and near infrared spectral data were analyzed by principal components analysis (PCA) for space clustering. The principal components (PCs) containing the main information of original spectra were picked out as the inputs of BP-ANN with three layers. The accumulative credibility of the first three PCs reaches 94.37%. The 3-D scores plot shows good space clustering of the samples. In the test, total 195 samples were examined, in which 150 samples were selected randomly for model-building and other 45 for model-prediction. With the prediction rate over 91%, the results indicate that the new hybrid model combing PCA with BP-ANN is reliable and practicable so that it could serve as an approach for machine recognition of various fragrant mushrooms.
Belief operations for motivated BDI agents
Authors: Patrick Krümpelmann, Matthias Thimm, Manuela Ritterskamp, Gabriele Kern-Isberner
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

The beliefs of an agent reflecting her subjective view of the world constitute one of the main components of a BDI agent. In order to incorporate new information coming from other agents, or to adjust to changes in the environment, the agent has to carry out belief change operations while taking metalogical information on time and reliabilities into account. In this paper, we describe a framework for belief operations within a BDI agent, sketching the interactions of beliefs with desires and intentions, respectively. Furthermore, we illustrate how motivations and know-how come into play in our agent model of this framework. We focus on the presentation of a complex setting for belief change that makes use of techniques both from merging and update, and provides a BDI agent with advanced reasoning capabilities. Extended logic programs under the answer set semantics will serve as the basic knowledge representation formalism.
Analysing the 2D, 3D and Web User Interface Navigation Structures of Normal Users and Users with Mild Intellectual Disabilities
Authors: Rita Mátrai, Zsolt T. Kosztyán, Cecília Sik-Lányi
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Design questions of home pages are examined by numerous visual search experiments as well. However, in the majority of experiments only one target object has to be searched. On web pages and on 2D and 3D scenes often more objects, more information have to be discovered. Spatial placement of objects has influential role on reaction times, how soon an object will be found. In our work more objects had to be found on home pages and on skill-improving game programs by normal users and users with mild intellectual disabilities. We investigated the time and the sequence of finding targets in function of their location on the screen and their properties (e.g. size). We worked out a method for analysing navigation routes and discovering differences between the target groups, which can be used in further investigations as well.
Modeling and Building Software Product Lines with Pure: :Variants
Authors: Danilo Beuche
Venue: Proceedings of the 2008 12th International Software Product Line Conference (2008)

The demonstration will show how pure::variants supports Software Product Line Development.
Generic Transformation to Strongly Unforgeable Signatures
Authors: Qiong Huang, Duncan S. Wong, Yiming Zhao
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

Recently, there are several generic transformation techniques proposed for converting unforgeable signature schemes (the message in the forgery has not been signed yet) into strongly unforgeable ones (the message in the forgery could have been signed previously). Most of the techniques are based on trapdoor hash functions and all of them require adding supplementary components onto the original key pair of the signature scheme. In this paper, we propose a new generic transformation which converts any unforgeable signature scheme into a strongly unforgeable one, and also keeps the key pair of the signature scheme unchanged. Our technique is based on strong one-time signature schemes. We show that they can be constructed efficiently from any one-time signature scheme that is based on one-way functions. The performance of our technique also compares favorably with that of those trapdoor-hash-function-based ones. In addition, this new generic transformation can also be used for attaining strongly unforgeable signature schemes in other cryptographic settings which include certificateless signature, identity-based signature, and several others. To the best of our knowledge, similar extent of versatility is not known to be supported by any of those comparable techniques. Finally and of independent interest, we show that our generic transformation technique can be modified to an on-line/off-line signature scheme, which possesses a very efficient signing process.
Computerised Guidelines Implementation: Obtaining Feedback for Revision of Guidelines, Clinical Data Model and Data Flow
Authors: S. Panzarasa, S. Quaglini, A. Cavallini, S. Marcheselli, M. Stefanelli, G. Micieli
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

In this paper we describe a module that allows to collect (a) motivations for non-compliance to guidelines, (b) motivations for poor data entry into the electronic patient record, and (c) comments on medical aspects of guideline recommendations, on their formalisation into computerised rules, and on the guideline integration into the computerised clinical chart. We organised a well-structured taxonomy of non-compliance motivations in such a way that the main hierarchical levels correspond to different medical or technical roles suitable for feedback managing. We analysed about 400 consecutive cases of patients with ischemic stroke. About 40 non-compliances, as well as several incomplete data forms have been identified and motivated.
Boosted Bayesian network classifiers
Authors: Yushi Jing, Vladimir Pavlović, James M. Rehg
Venue: Machine Learning (2008)

The use of Bayesian networks for classification problems has received a significant amount of recent attention. Although computationally efficient, the standard maximum likelihood learning method tends to be suboptimal due to the mismatch between its optimization criteria (data likelihood) and the actual goal of classification (label prediction accuracy). Recent approaches to optimizing classification performance during parameter or structure learning show promise, but lack the favorable computational properties of maximum likelihood learning. In this paper we present boosted Bayesian network classifiers, a framework to combine discriminative data-weighting with generative training of intermediate models. We show that boosted Bayesian network classifiers encompass the basic generative models in isolation, but improve their classification performance when the model structure is suboptimal. We also demonstrate that structure learning is beneficial in the construction of boosted Bayesian network classifiers. On a large suite of benchmark data-sets, this approach outperforms generative graphical models such as naive Bayes and TAN in classification accuracy. Boosted Bayesian network classifiers have comparable or better performance in comparison to other discriminatively trained graphical models including ELR and BNC. Furthermore, boosted Bayesian networks require significantly less training time than the ELR and BNC algorithms.
Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry
Authors: Adrian Sandu, Emil M. Constantinescu, Gregory R. Carmichael, Tianfeng Chai, John H. Seinfeld, Dacian Dăescu
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicability. In this paper we discuss practical aspects of nonlinear ensemble Kalman data assimilation applied to atmospheric chemical transport models. We highlight the challenges encountered in this approach such as filter divergence and spurious corrections, and propose solutions to overcome them, such as background covariance inflation and filter localization. The predictability is further improved by including model parameters in the assimilation process. Results for a large scale simulation of air pollution in North-East United States illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.
The technology acceptance model with online learning for the principals in elementary schools and junior high schools
Authors: Rong-Jyue Fang, Chung-Ping Lee, Yao-ming Chu, I-Hui Liu, Hua-Lin Tsai, Hsiao-Chiang Yang
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

With the rapid development of information technology and network infrastructure construction, the online learning system has been changed from traditional face-to-face classroom to speedy information technology. On the past decades, few of researchers have constructed specifically for the principals' attitudes toward online learning. The purpose of this study was to develop a Technology Acceptance Model (TAM) for the principals in elementary schools and junior high schools. The TAM on this study was framed by six subscales: perceived ease of use, perceived useful planning, perceived useful learning, perceived useful contents, attitudes toward using online learning, and behavioral intentions to manage via online learning. This study would also explore the relationship between online learning and the principals' leadership. At the same time, the study introduced perceived useful planning, perceived useful learning, and perceived useful contents as new factors that reflected the principals' intrinsic belief in online learning acceptance.
Human Distress Sound Analysis and Characterization Using Advanced Classification Techniques
Authors: Charalampos Doukas, Ilias Maglogiannis
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

The analysis of sounds generated in close proximity of a subject can often indicate emergency events like falls, pain and other distress situations. This paper presents a system for collecting and analyzing sounds and speech expressions utilizing on-body sensors and advanced classification techniques for emergency events detection. A variety of popular classification and meta-classification algorithms have been evaluated and the corresponding results are presented.