ppxscal/embedding_network huggingface dataset:
Long-Term Electricity Demand Forecasting Using Relevance Vector Learning Mechanism
Authors: Zhi-Gang Du, Lin Niu, Jian-Guo Zhao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In electric power system, long term peak load forecasting plays an important role in terms of policy planning and budget allocation. The planning of power system expansion project starts with the forecasting of anticipated load requirement. Accurate forecasting method can be helpful in developing power supply strategy and development plan, especially for developing countries where the demand is increased with dynamic and high growth rate. This paper proposes a peak load forecasting model using relevance vector machine (RVM), which is based on a probabilistic Bayesian learning framework with an appropriate prior that results in a sparse representation. The most compelling feature of the RVM is, while capable of generalization performance comparable to an equivalent support vector machine (SVM), that it typically utilizes dramatically fewer kernel functions. The proposed method has been tested on a practical power system, and the result indicates the effectiveness of such forecasting model.
Using Ontologies and Relatedness Metrics for Semantic Document Analysis on the Web
Authors: Antonio Picariello, Antonio M. Rinaldi
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

The problem of defining new methodologies for Knowledge Representation has a great influence on information technology and, from a general point of view, on cognitive sciences. In the last years some new approach strictly related to the above matter has been proposed and some of them are based on ontologies to reduce conceptual or terminological mess and to have a common view of the same information. We here propose an ontological model to represent information and we implement it in a content based information system for scoring documents w.r.t. a given topic an ad hoc metric; we use the Web as our context of interest.
To Tutor the Tutor: Adaptive Domain Support for Peer Tutoring
Authors: Erin Walker, Nikol Rummel, Kenneth R. Koedinger
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

The effectiveness of intelligent tutoring systems at increasing learning might be improved if the systems were combined with collaborative activities that encouraged conceptual elaboration. We extended the Cognitive Tutor Algebra, an intelligent tutoring system for high-school mathematics, with a peer tutoring activity that was designed to encourage interaction, reflection, and accountability. Two types of domain support were provided: adaptive support, which used the intelligent tutor domain models to provide feedback to the peer tutor, and fixed support, which simply consisted of answers to the problems. We compared the two peer tutoring conditions (adaptive or fixed support) to individual use of the cognitive tutor (without peer-tutoring activities). Even though students in the individual condition solved more problems during instruction, we did not find significant differences between the individual and collaborative conditions on learning. However, we found a correlation between tutee impasses and tutor learning.
Memetic Algorithms for Feature Selection on Microarray Data
Authors: Zexuan Zhu, Yew-Soon Ong
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, we present two novel memetic algorithms (MAs) for gene selection. Both are synergies of Genetic Algorithm (wrapper methods) and local search methods (filter methods) under a memetic framework. In particular, the first MA is a Wrapper-Filter Feature Selection Algorithm (WFFSA) fine-tunes the population of genetic algorithm (GA) solutions by adding or deleting features based on univariate feature filter ranking method. The second MA approach, Markov Blanket-Embedded Genetic Algorithm (MBEGA), fine-tunes the population of solutions by adding relevant features, removing redundant and/or irrelevant features using Markov blanket. Our empirical studies on synthetic and real world microarray dataset suggest that both memetic approaches select more suitable gene subset than the basic GA and at the same time outperforms GA in terms of classification predictions. While the classification accuracies between WFFSA and MBEGA are not significantly statistically different on most of the datasets considered, MBEGA is observed to converge to more compact gene subsets than WFFSA.
Predicting Defects in Software Using Grammar-Guided Genetic Programming
Authors: Athanasios Tsakonas, Georgios Dounias
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules.
A new application of ELECTRE III and revised Simos' procedure for group material selection under weighting uncertainty
Authors: A. Shanian, A. S. Milani, C. Carson, R. C. Abeyaratne
Venue: Knowledge-Based Systems (2008)

Towards the end of a design process designers may face a number of candidate materials with different attributes that are difficult to distinguish with the aid of available databases. In such situations material selection of sensitive components is perhaps one of the most challenging problems in the design of structural elements in some industries such as aerospace. The selection process is often realized as a team-work task to enhance reliability of the chosen material. During group decision making, however, separations in design preferences can be encountered. Furthermore, there may be uncertainties in each designer's mind with regards to expressing his/her preferences over design criteria. This paper using a revised Simos' method with the ELECTRE III optimization model is an attempt to provide a decision aid framework that account for both of these effects. A post-aggregation strategy with an overall rank loss function is proposed to arrive at candidate materials that show the most stable and the highest ranks in a list of given candidates. To show the applicability of the approach, a sample case study in the material selection of a thermal loaded conductor cover sheet is conducted and validated by an available database. It is also illustrated that, using ELECTRE III, a non-compensatory aspect of material selection can be assumed while attaining a reasonable sensitivity to weight fluctuations.
Research and Implementation of Hybrid Tracking Techniques in Augmented Museum Tour System
Authors: Hong Su, Bo Kang, Xiaocheng Tang
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

Augmented Museum Tour (AMT) system aims at providing a vivid and interactive approach by using Augmented Reality Techniques (ART) for museum wandering, but few effective tracking methods were taken into research previously. In this paper, we try to apply a hybrid tracking approach which integrates inertial (6-DOF) and vision-based technologies to promote the tracking performance for Augmented Museum Tour system, followed by analyzing the tracking problems and requirements for it. An AR experimental system based on VisTracker, a Vision-Inertial Self tracker is also designed. Experimental results and analysis demonstrate the system's effectiveness and its further application prospect in Augmented Museum Tour system.
Incremental Simulation and Simulation-on-Demand for Large-Scale Circuit Simulation by Using Selective Tracing Waveform Relaxation Algorithm
Authors: Chun-Jung Chen, Li-Ping Chou, Allen Y. Chang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

This paper considers an alternative strategy to speed up the circuit simulation. This strategy considers characteristics of simulator users' behaviors and involves two techniques, the incremental simulation and simulation-on-demand (SOD). These techniques are implemented in an "Interactive Simulation Environment" to dramatically accelerate the simulation speed in most practical cases. Also, a new model for incremental circuit simulation, the Incremental-Pseudo Circuit Method, and the method for SOD in relaxation-based algorithms are proposed. All presented methods have been implemented and really tested. Experimental results justify their excellent performance.
Estimating the availability of the interaction man: machine in control systems
Authors: Calin Ciufudean, Constantin Filote
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

When conducting an availability analysis we cannot verify an assigned probability, as it expresses the analyst's uncertain prior observation. We only review the background information used as the rationale for the assignment but, generally, it will not be possible to explicitly document all the transformation steps from this background information to the assigned probability. There are various algorithms that have been used to perform this task. This paper explores Markov chains techniques for searching a given problem probability for a particular target. The work in this paper has two parts. In the first part, a theoretical approach for determining the availability of interacting system man-machine is presented. In the second part an illustrative example of the presented approach is given. Results have shown that this approach allows the estimation of availability for complex systems, such as those involved in railway transport, without performing laborious calculus, e.g. large Markov chains.
Accelerating advanced MRI reconstructions on GPUs
Authors: S. S. Stone, J. P. Haldar, S. C. Tsao, W. -m. W. Hwu, B. P. Sutton, Z. -P. Liang
Venue: Journal of Parallel and Distributed Computing (2008)

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128^3 voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, for the data set studied in this article, the percent error exhibited by the advanced reconstruction is roughly three times lower than the percent error incurred by conventional reconstruction techniques.
Privacy-Aware Collaborative Access Control in Web-Based Social Networks
Authors: Barbara Carminati, Elena Ferrari
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

Access control over resources shared by social network users is today receiving growing attention due to the widespread use of social networks not only for recreational but also for business purposes. In a social network, access control is mainly regulated by the relationships established by social network users. An important issue is therefore to devise privacy-aware access control mechanisms able to perform a controlled sharing of resources by, at the same time, satisfying privacy requirements of social network users wrt their relationships. In this paper, we propose a solution to this problem, which enforces access control through a collaboration of selected nodes in the network. The use of cryptographic and digital signature techniques ensures that relationship privacy is guaranteed during the collaborative process. In the paper, besides giving the protocols to enforce collaborative access control we discuss their robustness against the main security threats.
Learning Environment for Improving Critical Thinking Skills Based on New Synthesis Theory
Authors: Hiroki Satake, Haruna Marumoto, Kazuhisa Seta, Motohide Umano, Mitsuru Ikeda
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Critical thinking, thinking about one's own thinking processes, is a kind of meta-cognition. For advance of the quality of medical care, nurses must perform critical thinking processes to clarify the evidence of one's own decision-making explicitly and to validate it. To train critical thinking skills, we adopt a new synthesis theory as a base of our learning scheme. In this paper we propose a new synthesis theory based learning system for developing critical thinking skills in a nursing domain. Furthermore, we illustrate a scenario of a learner's system utilization to explain what functionaries are embedded in our system to encourage Critical Thinking learning.
Neural Network Based BCI by Using Orthogonal Components of Multi-channel Brain Waves and Generalization
Authors: Kenji Nakayama, Hiroki Horita, Akihiro Hirano
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

FFT and Multilayer neural networks (MLNN) have been applied to `Brain Computer Interface' (BCI). In this paper, in order to extract features of mental tasks, individual feature of brain waves of each channel is emphasized. Since the brain wave in some interval can be regarded as a vector, Gram-Schmidt orthogonalization is applied for this purpose. There exists degree of freedom in the channel order to be orthogonalized. Effect of the channel order on classification accuracy is investigated. Next, two channel orders are used for generating the MLNN input data. Two kinds of methods using a single NN and double NNs are examined. Furthermore, a generalization method, adding small random numbers to the MLNN input data, is applied. Simulations are carried out by using the brain waves, available from the Colorado State University website. By using the orthogonal components, a correct classification rate P c can be improved from 70% to 78%, an incorrect classification rate P e can be suppressed from 10% to 8%. As a result, a rate R c = P c /(P c + P e ) can be improved from 0.875 to 0.907. When two different channel orders are used, P e can be drastically suppressed from 10% to 2%, and R c can be improved up to 0.973. The generalization method is useful especially for using a sigle channel order. P c can be increased up to 84~88% and P e can be suppressed down to 2~4%, resulting in R c = 0.957~0.977.
A Statistical Motion Model Based on Biomechanical Simulations for Data Fusion during Image-Guided Prostate Interventions
Authors: Yipeng Hu, Dominic Morgan, Hashim Uddin Ahmed, Doug Pendsé, Mahua Sahu, Clare Allen, Mark Emberton, David Hawkes, Dean Barratt
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

A method is described for generating a patient-specific, statistical motion model (SMM) of the prostate gland. Finite element analysis (FEA) is used to simulate the motion of the gland using an ultrasound-based 3D FE model over a range of plausible boundary conditions and soft-tissue properties. By applying principal component analysis to the displacements of the FE mesh node points inside the gland, the simulated deformations are then used as training data to construct the SMM. The SMM is used to both predict the displacement field over the whole gland and constrain a deformable surface registration algorithm, given only a small number of target points on the surface of the deformed gland. Using 3D transrectal ultrasound images of the prostates of five patients, acquired before and after imposing a physical deformation, to evaluate the accuracy of predicted landmark displacements, the mean target registration error was found to be less than 1.9mm.
A Study on the Use of Stemming for Monolingual Ad-Hoc Portuguese Information Retrieval
Authors: Viviane Moreira Orengo, Luciana S. Buriol, Alexandre Ramos Coelho
Venue: Evaluation of Multilingual and Multi-modal Information Retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006, Revised Selected Papers (2007)

For UFRGS's first participation in CLEF our goal was to compare the performance of heavier and lighter stemming strategies using the Portuguese data collections for monolingual Ad-hoc retrieval. The results show that the safest strategy was to use the lighter alternative (reducing plural forms only). On a query-by-query analysis, full stemming achieved the highest improvement but also the biggest decrease in performance when compared to no stemming. In addition, statistical tests showed that the only significant improvement in terms of mean average precision, precision at ten and number of relevant retrieved was achieved by our lighter stemmer.
Secure Session Management Mechanism in VoIP Service
Authors: Insu Kim, Keecheon Kim
Venue: Proceedings of the SSDSN, UPWN, WISH, SGC, ParDMCom, HiPCoMB, and IST-AWSN international workshops held at ISPA 2007 on Frontiers of High Performance Computing and Networking (2007)

VoIP (Voice-over-IP) technology has come of age and is quickly gaining momentum on Broadband networks. VoIP packetizes phone calls through the same routes used by network and Internet traffic and is consequently prone to the same cyber threats that plague data networks today. These include denial - of service attacks, worms, viruses, and hacker exploitation. In addition to these traditional network security and availability concerns, there are also a plethora of new VoIP protocols that have yet to undergo detailed security analysis and scrutiny. The new challenges of VoIP security are session hijacking, abnormal termination, call flooding and spam. History has shown that many other advances and trends in information technology typically outpace the corresponding realistic security requirements that are often tackled only after these technologies have been widely adopted and deployed. In this paper, we describe the technology protects networks against the known VoIP threats, and propose security mechanism for VoIP session management.
The Many Roles of Speaker Classification in Speaker Verification and Identification
Authors: Judith Markowitz
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

Speaker classification is a fundamental component of speaker identification and verification (SIV) technologies. This paper provides and overview of the many guises that classification takes within SIV systems.
Can We Build an Automatic Program Verifier? Invariant Proofs and Other Challenges
Authors: Myla Archer
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)

This paper reviews some common knowledge about establishing correctness of programs and the current status of program specification and verification. While doing so, it identifies several challenges related to the grand challenge of building a verifying compiler. The paper argues that invariants are central to establishing correctness of programs and that thus, a major part of an automatic program verifier must be automated support for verifying invariants, a significant problem in itself. The paper discusses where the invariants come from, what can be involved in establishing that they hold, and the extent to which the process of finding and proving invariants can be automated. The paper also discusses several of the related challenges identified, argues that addressing them would make the significance to global program behavior of feedback from a verifying compiler clearer, and recommends that many of them should be included within the scope of the grand challenge.
Online recruiting: The effects of organizational familiarity, website usability, and website attractiveness on viewers' impressions of organizations
Authors: Phillip W. Braddy, Adam W. Meade, Christina M. Kroustalis
Venue: Computers in Human Behavior (2008)

Previous research on Internet recruitment has made the implicit assumption that recruitment websites influence viewers' opinions of recruiting organizations. This study tested this assumption using a pretest/posttest design. Findings revealed that participants' organizational favorability, image as employer, and organizational attractiveness perceptions were affected by their viewing of organizational recruitment websites. Greater increases in favorable organizational evaluations from the pretest measures to the posttest measures occurred with organizations maintaining websites that were easy to navigate and/or that were appealing. Contrary to predictions made by signaling theory, recruitment websites had similar effects on the organizational impressions of all individuals, regardless of their familiarity with the organizations maintaining the recruitment websites that they viewed.
Argumentation-based agents for eProcurement
Authors: Paul-Amaury Matt, Francesca Toni, Thanassis Stournaras, Dimitris Dimitrelos
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track (2008)

Procurement is the complete process of obtaining goods and services -- from preparation and processing of a requisition through to receipt and approval of the invoice for payment. The support for eProcurement is important for realising agent-based eBusiness applications. This paper proposes the use of argumentation-based agents to support the selection of suppliers for goods and services within the negotiation phase of procurement. Argumentation is used to compare candidate suppliers and identify the one that best meets the buyer's business-specific needs. The use of argumentation-based technology presents important advantages over traditional procurement methods such as competitive bidding, direct negotiation or single-source acquisition in that it can cope with qualitative uncertainty and preferences as well as the construction of contracts. We apply the method to the industrial procurement of an eOrdering system.
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
Authors: Dervis Karaboga, Bahriye Akay, Celal Ozturk
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

Training an artificial neural network is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms has some drawbacks such as getting stuck in local minima and computational complexity. Therefore, evolutionary algorithms are employed to train neural networks to overcome these issues. In this work, Artificial Bee Colony (ABC) Algorithm which has good exploration and exploitation capabilities in searching optimal weight set is used in training neural networks.