ppxscal/embedding_network huggingface dataset:
Research on Affective State Recognition in E-Learning System by Using Neural Network
Authors: Egui Zhu, Qizhen Liu, Xiaoshuang Xu, Tinan Lei
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Aiming at current E-Learning system was not able to estimate the affective state of the users , an intelligent affective state recognizer that used facial expression as input was proposed and implemented in the paper. The recognizer system consists of two neural network classifiers and a fuzzy logic facial analysis module. The recognizer system had been used in an E-Learning system to recognize the emotion state. The results manifested that the recognizer system was effective. However, other bio-signals such as heartbeat, skin resistance and voice tone could also be used for affective state recognition.
Distinguishing the Communicative Functions of Gestures
Authors: Kristiina Jokinen, Costanza Navarretta, Patrizia Paggio
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

This paper deals with the results of a machine learning experiment conducted on annotated gesture data from two case studies (Danish and Estonian). The data concern mainly facial displays, that are annotated with attributes relating to shape and dynamics, as well as communicative function. The results of the experiments show that the granularity of the attributes used seems appropriate for the task of distinguishing the desired communicative functions. This is a promising result in view of a future automation of the annotation task.
M-CLANN: Multi-class Concept Lattice-Based Artificial Neural Network for Supervised Classification
Authors: Engelbert Mephu Nguifo, Norbert Tsopzé, Gilbert Tindo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Multi-layer neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. However defining its architecture is a difficult task, and might make their usage very complicated. To solve this problem, a rule-based model, KBANN, was previously introduced making use of an apriori knowledge to build the network architecture. Neithertheless this apriori knowledge is not always available when dealing with real world applications. Other methods presented in the literature propose to find directly the neural network architecture by incrementally adding new hidden neurons (or layers) to the existing network, network which initially has no hidden layer. Recently, a novel neural network approach CLANN based on concept lattices was proposed with the advantage to be suitable for finding the architecture of the neural network when the apriori knowledge is not available. However CLANN is limited to application with only two-class data, which is not often the case in practice. In this paper we propose a novel approach M-CLANN in order to treat multi-class data. Carried out experiments showed the soundness and efficiency of our approach on different UCI datasets compared to standard machine learning systems. It also comes out that M-CLANN model considerably improved CLANN model when dealing with two-class data.
K-Means Initialization Methods for Improving Clustering by Simulated Annealing
Authors: Gabriela Trazzi Perim, Estefhan Dazzi Wandekokem, Flávio Miguel Varejão
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

Clustering is defined as the task of dividing a data set such that elements within each subset are similar between themselves and are dissimilar to elements belonging to other subsets. This problem can be understood as an optimization problem that looks for the best configuration of the clusters among all possible configurations. K-means is the most popular approximate algorithm applied to the clustering problem, but it is very sensitive to the start solution and can get stuck in local optima. Metaheuristics can also be used to solve the problem. Nevertheless, the direct application of metaheuristics to the clustering problem seems to be effective only on small data sets. This work suggests the use of methods for finding initial solutions to the K-means algorithm in order to initialize Simulated Annealing and search solutions near the global optima.
Managing stakeholders around inter-organizational systems: A diagnostic approach
Authors: Albert Boonstra, Jan de Vries
Venue: The Journal of Strategic Information Systems (2008)

Inter-organizational systems (IOS) are generally used in a context of many interested parties. If these parties are not identified and if their power and interests related to the IOS are not explored and taken into consideration, implementation is likely to be disappointing and troublesome. This paper presents a diagnostic approach to the identification of stakeholders around IOS and the assessment of their power and interests with respect to these IOS. This approach is illustrated by means of an in-depth case study. This case study indicates that focusing on power and interest relationships may help make the critical success factors in the implementation and improvement of inter-organizational systems more explicit. The framework explicitly stimulates users to repeat their analysis of the players and their power and interests in order to acquire new 'pictures' of the playing field. Given the various positions, these modified pictures then have to be interpreted and used to make new plans of action.
Imitative Reinforcement Learning for Soccer Playing Robots
Authors: Tobias Latzke, Sven Behnke, Maren Bennewitz
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

In this paper, we apply Reinforcement Learning (RL) to a real-world task. While complex problems have been solved by RL in simulated worlds, the costs of obtaining enough training examples often prohibits the use of plain RL in real-world scenarios. We propose three approaches to reduce training expenses for real-world RL. Firstly, we replace the random exploration of the huge search space, which plain RL uses, by guided exploration that imitates a teacher. Secondly, we use experiences not only once but store and reuse them later on when their value is easier to assess. Finally, we utilize function approximators in order to represent the experience in a way that balances between generalization and discrimination. We evaluate the performance of the combined extensions of plain RL using a humanoid robot in the RoboCup soccer domain. As we show in simulation and real-world experiments, our approach enables the robot to quickly learn fundamental soccer skills.
Harnessing Learner's Collective Intelligence: A Web2.0 Approach to E-Learning
Authors: Hicham Hage, Esma Aïmeur
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Today, learners have access to a wide range of sources where they regularly search for, find and use learning resources outside the scope of regular course material. Although E-learning systems offer a variety of tools and functionalities, there are no specific provisions for learners to easily store and share these valuable resources. In this work we present SHAREK, a Web2.0 inspired approach to allow learners to store and share their resources. Specifically, SHAREK combines Artificial Intelligence techniques, such as Recommendation Systems and Information Retrieval with Web2.0 technologies, including RSS and tagging to allow easy sharing of resources and knowledge. Finally, we report on the implementation and validation of SHAREK.
A simulator for the course of electromechanical control techniques
Authors: Hasan Erdal, Vepa Halliyev
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

This study describes a simulator as an educational tool for the course of Electromechanical Control Techniques (ECT). The course, ECT, and its counterparts with some other different names are available within the curricula of faculties and high schools, such as Technical Education Faculties in Turkey, Faculties of Engineering Technology in the USA and European Countries. Since single/three phase power system is generally used in the applications of this course, students are always faced with a high voltage and current. A simulator is developed to establish a safety study environment. In this environment, students easily implement their designs or assignments to get familiar with the real applications in the laboratory.
Design of Optimal Laser Fields to Control Vibrational Excitations in Carboxy-myoglobin
Authors: Harjinder Singh, Sitansh Sharma, Praveen Kumar, Jeremy N. Harvey, Gabriel G. Balint-Kurti
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

Optimal control theory is applied to obtain infrared laser pulses for the selective vibrational excitation of a two mathematical dimensional model of carboxy-myoglobin. Density functional theory is used to obtain the potential energy and dipole moment surfaces of the active site model. The Conjugate gradient method is employed to optimize the cost functional and to obtain the optimized laser pulses. Optimized laser fields are found which give virtually 100% excitation probability to preselected vibrational levels.
Weightless Neural Networks: Knowledge-Based Inference System
Authors: Teresa B. Ludermir, Marcilio C. P. de Souto, Wilson R. de Oliveira
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

A knowledge-based inference system for Weightless Neural Networks (WNNs) is described in this paper. With the use such a system, rules can be inserted and extracted into/from WNNs. The process of rule insertion and rule extraction in WNNs is often more natural than in other neural network models. The system proposed allows the understanding on how the neural networks reach the solution to a problem. A straightforward application of the system is the design of expert systems and data mining techniques.
Extremal Hosoya index and Merrifield-Simmons index of hexagonal spiders
Authors: W. C. Shiu
Venue: Discrete Applied Mathematics (2008)

For any graph G, let m(G) and i(G) be the numbers of matchings (i.e., the Hosoya index) and the number of independent sets (i.e., the Merrifield-Simmons index) of G, respectively. In this paper, we show that the linear hexagonal spider and zig-zag hexagonal spider attain the extremal values of Hosoya index and Merrifield-Simmons index, respectively.
Cholinergic enhancement eliminates modulation of neural activity by task difficulty in the prefrontal cortex during working memory
Authors: Maura L. Furey, Emiliano Ricciardi, Mark B. Schapiro, Stanley I. Rapoport, Pietro Pietrini
Venue: Journal of Cognitive Neuroscience (2008)

Previously, we demonstrated that enhancing cholinergic activity during a working memory (WM) task improves performance and reduces blood flow in the right anterior middle/superior frontal cortex, an area known to be important for WM. The purpose of this study was to evaluate the interaction between WM task demands and cholinergic enhancement on neural responses in the prefrontal cortex. Regional cerebral blood flow (rCBF) was measured using H215O and positron emission tomography, as 10 young healthy volunteers performed a parametrically varied match-to-sample WM for faces task. For each item, a picture of a face was presented, followed by a delay (1, 6, 11, or 16 sec), then by the presentation of two faces. Subjects were instructed to identify which face they previously had seen. For control items, nonsense pictures were presented in the same spatial and temporal manner. All conditions were performed during an intravenous infusion of saline and physostigmine (1 mg/hr). Subjects were blind to the substance being infused. Reaction time increased significantly with WM delay, and physostigmine decreased reaction time across delay conditions. Significant task-related rCBF increases during saline infusion were seen in superior frontal, middle frontal, and inferior frontal regions, and the response magnitudes in the regions increased systematically with task difficulty. In all of these prefrontal regions, physostigmine administration significantly reduced rCBF during task, particularly at longer task delays, so that no correlation between task delay and rCBF was observed. In the ventral visual cortex, physostigmine increased rCBF at longer task delays in medial regions, and decreased rCBF over delay conditions in lateral cortical areas. These results indicate that, during cholinergic potentiation, brain activity in prefrontal regions is not modulated by increases in WM task demands, and lends further support to the hypothesis that cholinergic modulation enhances visual processing, making the task easier to perform, and thus, compensate for the need to recruit prefrontal cortical regions as task demands increase.
Emulating a Complex Instruction Set Computer with a Reduced Instruction Set Computer
Authors: Kevin McNeley, Veljko Milutinovic
Venue: IEEE Micro (1987)

GaAs now allows up to 30K transistors per chip. With such a limitation, can you build a 32-bit CISC on a single GaAs chip? Yes, if you build a reduced instruction set computer and emulate the 32-bit CISC on it.
On Calculating Multiplicative Inverses Modulo $2^{m}$
Authors: Ortal Arazi, Hairong Qi
Venue: IEEE Transactions on Computers (2008)

A need to calculate modular multiplicative inverses that involve a power of 2 arises in various number theory and cryptography applications. Known algorithms that perform these calculations involve a number of steps, linearly proportional to the number of characters in a given number. The character can be a single bit or a binary word of any standard size. This letter presents an efficient methodology for a character-based computation of modular multiplicative inverses, efficient at any given base, reducing these calculations to a logarithmic number of steps. Hence, the amount of calculations involved and the computational resources are dramatically decreased from an O(m) complexity to a O(logm) complexity, where m denotes the number of characters in the operand.
Computational Understanding and Modeling of Filling-In Process at the Blind Spot
Authors: Shunji Satoh, Shiro Usui
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

A visual model for filling-in at the blind spot is proposed. The general scheme of standard regularization theory is used to derive a visual model deductively. First, we indicate problems of the diffusion equation, which is frequently used for various kinds of perceptual completion. Then, we investigate the computational meaning of a neural property discovered by Matsumoto and Komatsu (J. Neurophysiology, vol. 93, pp. 2374---2387, 2005) and introduce second derivative quantities related to image geometry into a priori knowledge of missing images on the blind spot. Moreover, two different information pathways for filling-in (slow conductive paths of horizontal connections in V1, and fast feedforward/feedback paths via V2) are regarded as the neural embodiment of adiabatic approximation between V1 and V2 interaction. Numerical simulations show that the outputs of the proposed model for filling-in are consistent with a neurophysiological experimental result, and that the model is a powerful tool for digital image inpainting.
Metrics for sampling-based motion planning
Authors: Nancy M. Amato
Venue:  (2007)

A motion planner finds a sequence of potential motions for a robot to transit from an initial to a goal state. To deal with the intractability of this problem, a class of methods known as sampling-based planners build approximate representations of potential motions through random sampling. This selective random exploration of the space has produced many remarkable results, including solving many previously unsolved problems. Sampling-based planners usually represent the motions as a graph (e.g., the Probabilistic Roadmap Methods or PRMs), or as a tree (e.g., the Rapidly exploring Random Tree or RRT). Although many sampling-based planners have been proposed, we do not know how to select among them because their different sampling biases make their performance depend on the features of the planning space. Moreover, since a single problem can contain regions with vastly different features, there may not exist a simple exploration strategy that will perform well in every region. Unfortunately, we lack quantitative tools to analyze problem features and planners performance that would enable us to match planners to problems. We introduce novel metrics for the analysis of problem features and planner performance at multiple levels: node level, global level, and region level. At the node level, we evaluate how new samples improve coverage and connectivity of the evolving model. At the global level, we evaluate how new samples improve the structure of the model. At the region level, we identify groups or regions that share similar features. This is a set of general metrics that can be applied in both graph-based and tree-based planners. We show several applications for these tools to compare planners, to decide whether to stop planning or to switch strategies, and to adjust sampling in different regions of the problem.
Reformulation and sampling to solve a stochastic network interdiction problem
Authors: Udom Janjarassuk, Jeff Linderoth
Venue: Networks (2008)

The network interdiction problem involves interrupting an adversary's ability to maximize flow through a capacitated network by destroying portions of the network. A budget constraint limits the amount of the network that can be destroyed. In this article, we study a stochastic version of the network interdiction problem in which the successful destruction of an arc of the network is a Bernoulli random variable, and the objective is to minimize the maximum expected flow of the adversary. Using duality and linearization techniques, an equivalent deterministic mixed integer program is formulated. The structure of the reformulation allows for the application of decomposition techniques for its solution. Using a parallel algorithm designed to run on a distributed computing platform known as a computational grid, we give computational results showing the efficacy of a sampling-based approach to solve the problem. &copy; 2008 Wiley Periodicals, Inc. NETWORKS, 2008
A Fuzzy Hybrid Intelligent System for Human Semen Analysis
Authors: Esmeralda Ramos, Haydemar Núñez, Roberto Casañas
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

This paper describes a fuzzy hybrid system to support the human semen analysis, that integrates the case and rule based reasoning. The module of case based reasoning uses a recovery scheme in two phases: first, recovery rules are used to generate a spermatic classification; second, the k-nearest neighbor technique (KNN) with a fuzzy similarity measure, is used to determine the degrees of severity to each alteration. In order to complete the solution of the new case, a module of rule based reasoning gives suggestions and observations from variables that do no affect this classification. Experimental results obtained show that the system provides a good percentage of successes; therefore it could be used as an instrument to support the decision-making process that carries out the professionals of this domain.
Better wavelet packet tree structures for PAPR reduction in WOFDM systems
Authors: Volkan Kumbasar, Oğuz Kucur
Venue: Digital Signal Processing (2008)

This paper presents a proper way to reduce the peak-to-average power ratio (PAPR) in Daubechies (Db) wavelet-based OFDM (WOFDM) by searching better wavelet packet tree (BWPT) structures. These BWPT structures are obtained by using a brute force search algorithm. Numerical and simulation results also show that BWPT structures have lower PAPR values than conventional Mallat structures without any degradation in bit error rate (BER) performance for the same bandwidth occupancy. In addition, this PAPR reduction method does not introduce any additional complexity to WOFDM system since BWPT structures are obtained by selecting the proper ones from wavelet packet tree space.
A Complete Hardware Implementation of an Integrated Sound Localization and Classification System Based on Spiking Neural Networks
Authors: Mauricio Kugler, Kaname Iwasa, Victor Alberto Benso, Susumu Kuroyanagi, Akira Iwata
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Several applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. This paper proposes an integrated sound localization and classification system based on the human auditory system and a respective compact hardware implementation. The proposed models are based on spiking neurons, which are suitable for processing time series data, like sound signals, and can be easily implemented in hardware. The system uses two microphones, extracting the time difference between the two channels with a chain of coincidence detection spiking neurons. A spiking neural networks process the time-delay pattern, giving a single directional output. Simultaneously, an independent spiking neural network process the spectral information of on audio channel in order to classify the source. Experimental results show that a the proposed system could successfully locate and identify several sound sources in real time with high accuracy.
On the Formal Analysis of a Spatio-temporal Role-Based Access Control Model
Authors: Manachai Toahchoodee, Indrakshi Ray
Venue: Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security (2008)

With the growing use of wireless networks and mobile devices, we are moving towards an era where spatial and temporal information will be necessary for access control. The use of such information can be used for enhancing the security of an application, and it can also be exploited to launch attacks. For critical applications, a model for spatio-temporal-based access control is needed that increases the security of the application and ensures that the location information cannot be exploited to cause harm. Consequently, researchers have proposed various spatio-temporal access control models that are useful in pervasive computing applications. Such models typically have numerous different features to support the various application requirements. The different features of a spatio-temporal access control model may interact in subtle ways resulting in conflicts. We illustrate how the access control model can be formally analyzed to detect the presence of conflicts. We use Alloy, a formal language based on first-order logic, for the purpose of our analysis. Alloy is supported by a software infrastructure that allows automated analysis of models and has been used to verify industrial applications. The results obtained by analyzing the spatio-temporal access control model will enable the users of the model to make informed decisions.