ppxscal/embedding_network huggingface dataset:
Dealing with Self-occlusion in Region Based Motion Capture by Means of Internal Regions
Authors: Christian Schmaltz, Bodo Rosenhahn, Thomas Brox, Joachim Weickert, Lennart Wietzke, Gerald Sommer
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

Self-occlusion is a common problem in silhouette based motion capture, which often results in ambiguous pose configurations. In most works this is compensated by a priori knowledge about the motion or the scene, or by the use of multiple cameras. Here we suggest to overcome this problem by splitting the surface model of the object and tracking the silhouette of each part rather than the whole object. The splitting can be done automatically by comparing the appearance of the different parts with the Jensen-Shannon divergence. Tracking is then achieved by maximizing the appearance differences of all involved parts and the background simultaneously via gradient descent. We demonstrate the improvements with tracking results from simulated and real world scenes.
Design for Debate in Robotics: Questioning Paradigms in a Techno-social Discourse
Authors: Wouter Reeskamp, Katrien Ploegmakers
Venue: Proceedings of the 6th international conference on Entertainment Computing (2007)

'Design for Debate' is an emerging approach in the domain of design. This paper proposes a more pragmatic application. A workshop was developed and conducted with experts in the field of social robotics. Concepts for affective robots were visualized to provoke out-of-the-box thinking. During eight workshops, robotic professionals questioned their paradigms.
Prosodic Phrases and Semantic Accents in Speech Corpus for Czech TTS Synthesis
Authors: Jan Romportl
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

We describe a statistical method for assignment of prosodic phrases and semantic accents in read speech data. The method is based on statistical evaluation of listening test data by a maximum-likelihood approach with parameters estimated by an EM algorithm. We also present linguistically relevant quantitative results about the prosodic phrase and semantic accent distribution in 250 Czech sentences. 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
Providing multilayered context for building smart home
Authors: Kwang-Won Koh, Chang-Ik Choi, Kyung-Lang Park, Shin-Young Lim, Shin-Dug Kim
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Ubiquitous computing supports context-aware applications under smart environment. Currently, generality, reusability, and simple query mechanism of the context are issued to realize the context-awareness. In this work, a well-structured architecture to support context-awareness, namely context engine, is designed. For this goal, multilayered contexts which consist of raw context, general context, application specific context, and inferred context, are defined to provide the generality of the context engine, and several components are composed to support the context-aware applications efficiently. The evaluation shows the generality of the proposed context engine is quite applicable to the smart home environment.
Relation of nursing students' motivational state and learning effort to motivational appeal and learning outcomes in the use of an interactive computer-based multimedia program
Authors: James D. Lehman
Venue:  (2007)

This exploratory study investigated relationships between learners' motivational state, the amount of learning effort expended, the perceived motivational appeal, and the learning outcomes in a computer-based learning (CBL) program designed with embedded motivational strategies based on Keller's Attention, Relevance, Confidence and Satisfaction (ARCS) model of motivational design. Motivational state was measured by subscales of the Motivated Strategies for Learning Questionnaire (MSLQ); learning effort was measured by the total time in the program, the number of learning activities completed, and total number of instructional pages completed; perceived motivational appeal of the instructional program was measured by the Instructional Materials Motivation Survey (IMMS), and learning outcomes were measured by posttest and simulation. Sixty-five junior nursing students participated. A measure of learning effort, total time in the program, correlated positively with the attention and relevance variables of motivational appeal well as with the posttest measure of learning outcome. Motivational appeal of the program had a positive correlation with total time spent in the program. Motivational state was found to correlate only with the motivational appeal of the program. The total time spent in the program was the variable found most predictive of the score on the posttest. Very few studies in nursing and healthcare have sought to address the impact of motivation in the design and effectiveness of computer-based learning. Participants were highly positive about using the format of computer-based instruction with over 90% indicating they would like to learn other basic nursing skills in this format. Technology has long been embraced by nursing faculty who seek to create authentic learning experiences prior to actual hands-on experience in a clinical setting. This study provided support for the use of the ARCS model as a framework for the design of motivational strategies in a computer-based learning program.
A New Feature Selection Method for Improving the Precision of Diagnosing Abnormal Protein Sequences by Support Vector Machine and Vectorization Method
Authors: Eun-Mi Kim, Jong-Cheol Jeong, Ho-Young Pae, Bae-Ho Lee
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

Pattern recognition and classification problems are most popular issue in machine learning, and it seem that they meet their second golden age with bioinformatics. However, the dataset of bioinformatics has several distinctive characteristics compared to the data set in classical pattern recognition and classification research area. One of the most difficulties using this theory in bioinformatics is that raw data of DNA or protein sequences cannot be directly used as input data for machine learning because every sequence has different length of its own code sequences. Therefore, this paper introduces one of the methods to overcome this difficulty, and also argues that the capability of generalization in this method is very poor as showing simple experiments. Finally, this paper suggests different approach to select the fixed number of effective features by using Support Vector Machine, and noise whitening method. This paper also defines the criteria of this suggested method and shows that this method improves the precision of diagnosing abnormal protein sequences with experiment of classifying ovarian cancer data set.
A Novel Hybrid GMM/SVM Architecture for Protein Secondary Structure Prediction
Authors: Emad Bahrami Samani, M. Mehdi Homayounpour, Hong Gu
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

The problem of secondary structure prediction can be formulated as a pattern classification problem and methods from statistics and machine learning are suitable. This paper proposes a new combination approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) by typical sample extraction based on a UBM/GMM system for SVM in protein secondary structure prediction. Our hybrid model achieved a good performance of three-state overall per residue accuracy Q 3 = 77.6% which is comparable to the best techniques available.
Simple & Private Cricket Emergency Call System for Location-Based Service under U-911 in an Indoor Environment
Authors: Jae-Bong Yoo, BumJung Yoo, SangYoon Kim, Young-Woong Ko, Chan-Young Park
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

As home network technologies are improving, sensors collecting environmental information such as temperature, humidity, and illumination in a building or a house are evolving. One of the many available home network technologies is fire detection. Many sensors have been developed for fire detection using CCTV cameras or fiber-optical sensors. We focus on finding information to locate the person calling the fire station who is in dangerous situation, especially in an indoor environment. We designed and implemented the Cricket Emergency Call System for Location-based Service to increase survival rates by reducing rescue times at the scene of a fire.
An Access Control Scheme for Protecting Personal Data
Authors: Wilfred Villegas, Bader Ali, Muthucumaru Maheswaran
Venue: Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust (2008)

We present a personal data access control (PDAC) scheme inspired by protection schemes used in communities for sharing valuable commodities. We assume PDAC users are members of an online social network such as facebook.com. PDAC computes a “trusted distance” measure between users that is composed of the hop distance on the social network and an affine distance derived from experiential data. The trusted distance classifies users into three zones: acceptance, attestation, and rejection. User requests falling in the acceptance zone are accepted immediately while the requests in the rejection zone are rejected outright. Requests in the attestation zone need additional authorization to gain access. PDAC also tracks reposts to minimize the spread of data beyond the limits set by the data originator. PDAC was implemented on a social network emulator to demonstrate its viability. The performance of certain PDAC functions were examined using simulations driven by portions of social graphs obtained from myspace.com.
Solving Prize-Collecting Traveling Salesman Problem with Time Windows by Chaotic Neural Network
Authors: Yanyan Zhang, Lixin Tang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper presents an artificial neural network algorithm for prize-collecting traveling salesman problem with time windows, which is often encountered when scheduling color-coating coils in cold rolling production or slabs in hot rolling mill. The objective is to find a subset sequence from all cities such that the sum of traveling cost and penalty cost of city unvisited is minimized. To deal with this problem, we construct mathematical model and the corresponding network formulation. Chaotic neurodynamic is introduced and designed to obtain the solution of the problem, and the workload reduction strategy is proposed to speed up the solving procedure. To verify the efficiency of the proposed method, we compare it with ordinary Hopfield neural network by performing experiment on the problem instances randomly generated. The results clearly indicate that the proposed method is effective and efficient for given size of problems with respect to solution quality and computation time.
Wireless sensor network modeling using modified recurrent neural network: application to fault detection
Authors: Azzam Issam Moustapha
Venue:  (2008)

Wireless Sensor Networks (WSNs) consist of a large number of sensors, which in turn have their own dynamics. They interact with each other and the base station, which controls the network. In multi-hop wireless sensor networks, information hops from one node to another and finally to the network gateway or base station. Dynamic Recurrent Neural Networks (RNNs) consist of a set of dynamic nodes that provide internal feedback to their own inputs. They can be used to simulate and model dynamic systems such as a network of sensors. In this dissertation, a dynamic model of wireless sensor networks and its application to sensor node fault detection are presented. RNNs are used to model a sensor node, the node's dynamics, and the interconnections with other sensor network nodes. A neural network modeling approach is used for sensor node identification and fault detection in WSNs. The input to the neural network is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type neural network. The input to the neural network (NN) and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme. The simulation with comparison to the Kalman filtering technique was carried out on a network with 15 sensor nodes. A fault such as drift was introduced and successfully detected with the modified recurrent neural net model with no early false alarm that could have resulted when using the Kalman filtering approach. In this dissertation, we also present the real-time implementation of a neural network-based fault detection for WSNs. The method is implemented on a TinyOS operating system. A collection tree network is formed, and multi-hoping data is sent to the base station root. Nodes take environmental measurements every N seconds while neighboring nodes overhear the measurement as it is being forwarded to the base station for recording it. After nodes complete M and receive/store M measurements from each neighboring node, recurrent neural networks are used to model the sensor node, the node's dynamics, and the interconnections with neighboring nodes. The physical measurement is compared to the predicted value and to a given threshold of error to determine a sensor fault. The process of neural network training can be repeated indefinitely to maintain self-aware network fault detection. By simply overhearing network traffic, this implementation uses no extra bandwidth or radio broadcast power. The only costs of the approach are the battery power required to power the receiver for overhearing packets and the processor time to train the RNN.
Operon Prediction Using Neural Network Based on Multiple Information of Log-Likelihoods
Authors: Wei Du, Yan Wang, Shuqin Wang, Xiumei Wang, Fangxun Sun, Chen Zhang, Chunguang Zhou, Chengquan Hu, Yanchun Liang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Operon represents a basic organizational unit in microbial genomes. Operon prediction is an important step to study genic transcriptional and regulatory mechanism in microbial genomes. This paper predicted operons in the Escherichia coli K12 genome using neural network based on four types of genomic log-likelihood data. First this method estimated the log-likelihood values for intergenic distances, COG gene functions, conserved gene pairs and phylogenetic profiles, and then used these information by a generalized regression neural network to discriminate pairs of genes within operons (WO pairs) or transcription unit borders (TUB pairs). We test the method on E. coli K12 and find that it can obtain average sensitivity, specificity and accuracy at 85.9%, 89.2% and 87.9% respectively, which indicates that the proposed method has a powerful capability for operon prediction.
Agent-Based Inter-Organizational Workflow Management System
Authors: Paulo Leitão, João Mendes
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

An Electronic Institution is a computational framework that provides a set of services supporting the lifecycle of Virtual Organizations. In a virtual organization, different business partners cooperate in order to achieve a common goal (the established contract), being the coordination of the corresponding inter-organizational workflow an important issue. This paper describes an inter-organizational workflow management architecture, based in multi-agent systems principles, aiming to orchestrate the contract execution and to obtain feedback from the shop floor level. Aiming to validate its correctness and applicability, a special focus is devoted to its implementation, using the JADE agent development framework, and its operation under different scenarios.
The Mitkov Algorithm for Anaphora Resolution in Portuguese
Authors: Amanda Rocha Chaves, Lucia Helena Rino
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

This paper reports on the use of the Mitkov´s algorithm for resolution of third person pronouns in texts written in Brazilian Portuguese. A system for anaphora resolution was built that embeds most of the Mitkov's features. Some of his resolution factors were directly incorporated into the system; others had to be slightly modified for language adequacy. The resulting approach was intrinsically evaluated on hand-annotated corpora. It was also compared to Lappin Leass's algorithm, also customized to pronoun resolution in Portuguese. Success rate was the only evaluation measure used.
Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones
Authors: Ivan Tanev, Kikuo Yuta
Venue: Information Sciences: an International Journal (2008)

We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behavior of a team of predator agents in the predator-prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.
A Scalable and Oblivious Atomicity Assertion
Authors: Rachid Guerraoui, Marko Vukolić
Venue: Proceedings of the 19th international conference on Concurrency Theory (2008)

This paper presents SOAR: the first oblivious atomicity assertion with polynomial complexity. SOAR enables to check atomicity of a single-writer multi-reader register implementation. The basic idea underlying the low overhead induced by SOAR lies in greedily checking, in a backward manner, specific points of an execution where register operations could be linearized, rather than exploring all possible precedence relations among these.We illustrate the use of SOAR by implementing it in +CAL. The performance of the resulting automatic verification outperforms comparable approaches by more than an order of magnitude already in executions with only 6 read/write operations. This difference increases to 3-4 orders of magnitude in the "negative" scenario, i.e., when checking some non-atomic execution, with only 5 operations. For example, checking atomicity of every possible execution of a single-writer single-reader (SWSR) register with at most 2 write and 3 read operations with the state of the art oblivious assertion takes more than 58 hours to complete, whereas SOAR takes just 9 seconds.
Auto-structure of Presynaptic Activity Defines Postsynaptic Firing Statistics and Can Modulate STDP-Based Structure Formation and Learning
Authors: Gordon Pipa, Raul Vicente, Alexander Tikhonov
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

In this paper we study the impact of presynaptic activity that is deviating from Poissonian firing onto the postsynaptic firing of a conductance based integrate and fire neuron. We first show that the compound activity of a large group of neurons, e.g. presynaptic cells, cannot be described by a Poisson process in general. Then we demonstrate that the auto-structure of the presynaptic drive has strong impact onto the auto-structure of the postsynaptic spike-train. And finally, we discuss the potential impact of non-Poissonian presynaptic activity on the structure formation in recurrent networks based on Spike Timing Dependent Plasticity (STDP).
PittPatt Face Detection and Tracking for the CLEAR 2007 Evaluation
Authors: Michael C. Nechyba, Louis Brandy, Henry Schneiderman
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

This paper describes Pittsburgh Pattern Recognition's participation in the face detection and tracking tasks for the CLEAR 2007 evaluation. Since CLEAR 2006, we have made substantial progress in optimizing our algorithms for speed, achieving better than real-time processing performance for a speed-up of more than 500× over the past two years. At the same time, we have maintained the high level of accuracy of our algorithm. In this paper, we first give a system overview, briefly explaining the three main stages of processing: (1) frame-based face detection; (2) motion-based tracking; and (3) track filtering. Second, we report our results, both in terms of accuracy and speed, over the CHIL and VACE test data sets. Finally, we offer some analysis on both speed and accuracy performance
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Authors: Cheng-Lung Huang, Jian-Fan Dun
Venue: Applied Soft Computing (2008)

This study proposed a novel PSO-SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO-SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy.