ppxscal/embedding_network huggingface dataset:
Convergence of Web and TV Broadcast Data for Adaptive Content Access and Navigation
Authors: Pieter Bellekens, Kees Sluijs, Lora Aroyo, Geert-Jan Houben
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

iFanzy is a personalized TV guide application aiming at offering users television content in a personalized and context-sensitive way. It consists of a client-server system with multiple clients and devices such that the user can ubiquitously use TV set-top box, mobile phone and Web-based applications to select and receive personalised TV content. TV content and background data from various heterogeneous sources is integrated to provide a transparent knowledge structure, which allows the user to navigate and browse the vast content sets nowadays available. Semantic Web techniques are applied for enriching and aligning Web data and (live) broadcast content. The resulting RDF/OWL knowledge structure is the basis for iFanzy's main functionality, like semantic search of the broadcast content and execution of context-sensitive recommendations.
A High-Level Implementation of a System for Automated Reasoning with Default Rules (System Description)
Authors: Christoph Beierle, Gabriele Kern-Isberner, Nicole Koch
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

An overview of a system modelling an intelligent agent being able to reason by using default rules is given. The semantics of the qualitative default rules is defined via ordinal conditional functions which model the epistemic state of the agent, providing her with the basic equipment to perform different knowledge management and belief revision tasks. Using the concept of Abstract State Machines, the fully operational system was developed in AsmL, allowing for a high-level implementation that minimizes the gap between the mathematical specification of the underlying concepts and the executable code in the implemented system.
Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series
Authors: Kyriakos Kentzoglanakis, Matthew Poole, Carl Adams
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

In this paper, we address the problem of reverse-engineering a gene regulatory network from gene expression time series. We approach the problem by implementing an ant system to generate candidate network structures. The quality of a candidate structure is evaluated using a particle swarm optimization algorithm that tunes the parameters of the corresponding model, by minimizing the error between the actual time series and the trained model's output. We extend this approach by incorporating domain-specific heuristics to the ant system, as a mechanism that has the potential to bias the pheromone amplification effect towards biologically plausible relationships. We apply the method to a subset of genes from a real world data set and report on the results.
Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems
Authors: Yu Ding, Eunshin Byon, Chiwoo Park, Jiong Tang, Yi Lu, Xin Wang
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this multi-university collaborative research, we will develop a framework for the dynamic data-driven fault diagnosis of wind turbines which aims at making the wind energy a competitive alternative in the energy market. This new methodology is fundamentally different from the current practice whose performance is limited due to the non-dynamic and non-robust nature in the modeling approaches and in the data collection and processing strategies. The new methodology consists of robust data pre-processing modules, interrelated, multi-level models that describe different details of the system behaviors, and a dynamic strategy that allows for measurements to be adaptively taken according to specific physical conditions and the associated risk level. This paper summarizes the latest progresses in the research.
Politeness and alignment in dialogues with a virtual guide
Authors: Markus de Jong, Mariët Theune, Dennis Hofs
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Language alignment is something that happens automatically in dialogues between human speakers. The ability to align is expected to increase the believability of virtual dialogue agents. In this paper we extend the notion of alignment to affective language use, describing a model for dynamically adapting the linguistic style of a virtual agent to the level of politeness and formality detected in the user's utterances. The model has been implemented in the Virtual Guide, an embodied conversational agent giving directions in a virtual environment. Evaluation shows that our formality model needs improvement, but that the politeness tactics used by the Guide are mostly interpreted as intended, and that the alignment to the user's language is noticeable.
SVM-Based Face Recognition Using Genetic Search for Frequency-Feature Subset Selection
Authors: Aouatif Amine, Mohammed Rziza, Driss Aboutajdine
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

The goal of this paper is to study if there is a dependency between a selected feature vector at each generation of the genetic algorithm and the resulting fitness. In order to see the relation between these parameters, we first use Discrete Cosine Transforms (DCT) to transform each image as a feature vector (i.e., Frequency Feature Subset (FFS)). A Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain DCT coefficients that do not seem to encode important information about recognition task. When using SVM, two problems are confronted: how to choose the optimal input feature subset for SVM, and how to set the best kernel parameters. Therefore, obtaining the optimal feature subset and SVM parameters must occur simultaneously. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem.
Automatic Control of Video Surveillance Camera Sabotage
Authors: P. Gil-Jiménez, R. López-Sastre, P. Siegmann, J. Acevedo-Rodríguez, S. Maldonado-Bascón
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

One of the main characteristics of a video surveillance system is its reliability. To this end, it is needed that the images captured by the videocameras are an accurate representation of the scene. Unfortunately, some activities can make the proper operation of the cameras fail, distorting in some way the images which are going to be processed. When these activities are voluntary, they are usually called sabotage, which include partial o total occlusion of the lens, image defocus or change of the field of view.In this paper, we will analyze the different kinds of sabotage that could be done to a video surveillance system, and some algorithms to detect these inconveniences will be developed. The experimental results show good performance in the detection of sabotage situations, while keeping a very low false alarm probability.
From AO Methodologies to MAS Infrastructures: The SODA Case Study
Authors: Ambra Molesini, Enrico Denti, Andrea Omicini
Venue: Engineering Societies in the Agents World VIII: 8th International Workshop, ESAW 2007, Athens, Greece, October 22-24, 2007, Revised Selected Papers (2008)

In the last years, research on agent-oriented (AO) methodologies and multi-agent system (MAS) infrastructures has developed along two opposite paths: while AO methodologies have essentially undergone a top-down evolution pushed by contributions from heterogeneous fields like human sciences, MAS infrastructures have mostly followed a bottom-up path growing from existing and widespread (typically object-oriented) technologies. This dichotomy has produced a conceptual gap between the proposed AO methodologies and the agent infrastructures actually available, as well as a technical gap in the MAS engineering practice, where methodologies are often built ad hoc out of MAS infrastructures, languages and tools.This paper proposes a new method for filling the gap between methodologies and infrastructures based on the definition and study of the meta-models of both AO methodologies and MAS infrastructures. By allowing structural representation of abstractions to be captured along with their mutual relations, meta-models make it possible to map design-time abstractions from AO methodologies upon run-time abstractions from MAS technologies, thus promoting a more coherent and effective practice in MAS engineering. In order to validate our method, we take an AO methodology --- SODA --- and show how it can be mapped upon three different MAS infrastructures using meta-models as mapping guidelines.
Behavioural Modeling by Clustering Based on Utility Measures
Authors: Philip Hoelgaard, Ángel Valle, Fernando Corbacho
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

This paper presents a new framework for behavioural modelling that allows to unravel the key drivers that direct specific cognitive behaviours. In order to do so, a novel framework for clustering based on utility measures is presented that allows to understand the different behaviours that different groups of people may have, and allows the creation of profiles that are relevant with respect to the utility measure. The proposed method is not contrary to other clustering methods but rather builds on the functionality of 'basic' clustering algorithms. A common aim of clustering consists of partitioning a set of patterns into different subsets of patterns which have homogeneous characteristics. In this paper we suggest a more ambitious goal that additionally tries to maximize a utility measure. The paper also describes the results obtained when the method is used to analyze human behaviour in the area of customer intelligence. Specifically, the paper analyzes human behaviour with respect to different socio-demographic and economic indicators and allows to uncover the underlying characteristics that may explain the observed cognitive behaviour.
Stability of Coordination Requires Mutuality of Interaction in a Model of Embodied Agents
Authors: Tom Froese, Ezequiel A. Paolo
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

We used an evolutionary robotics methodology to generate pairs of simulated agents capable of reliably establishing and maintaining a coordination pattern under noisy conditions. Unlike previous related work, agents were only evolved for this ability and not for their capacity to discriminate social contingency (i.e., a live responsive partner) from non-contingent engagements (i.e., a recording). However, when they were made to interact with a recording of their partner made during a successful previous interaction, the coordination pattern could not be established. An analysis of the system's underlying dynamics revealed (i) that stability of the coordination pattern requires ongoing mutuality of interaction, and (ii) that the interaction process is not only constituted by, but also constitutive of, individual behavior. We suggest that this stability of coordination is a general property of a certain class of interactively coupled dynamical systems, and conclude that psychological explanations of an individual's sensitivity to social contingency need to take into account the role of the interaction process.
CARP: Context-Aware Resource Provisioning for Multimedia over 4G Wireless Networks
Authors: Navrati Saxena, Abhishek Roy, Jitae Shin
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this paper a context-aware resource provisioning framework is developed for multi-system, heterogeneous 4G wireless networks. The framework envisions that each individual sub-network is fairly independent, and uses Bayesian learning scheme for capturing user's mobility profiles. The concept of Asymptotic Equipartition Property (AEP) helps to predict the most likely path-segments, that the mobile user (MU) is going to follow in near future, with very good accuracy. This helps in proactive resource management along the MU's future paths and locations providing the optimal resource (e.g., bandwidth) reservation. Simulation results on a synthetic wireless traces corroborate this high prediction success and demonstrate sufficient improvement in delay, blocking and throughput of ongoing wireless multimedia sessions.
Portable Patient Information Integration System for Patient Safety Improvement
Authors: Tu-Bin Chu, Ming-Hui Jin, Li-Xin Xu, Jong-Tsong Chiang, Cheng-Yan Kao
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)

Nowadays, medical centers are working on setting a safe medicating environment by using information technology. This study tried to improve patients' safety environment, communications among medical workers and patients, medicating quality, and medical process efficiency through a wireless handheld RFID patient safety enhancement system for medical personnel. The proposed portable patient information integration system combines Radio Frequency Identification (RFID) technology, Wireless Network, Personal Digital Assistant (PDA) and Front-Monitoring System. Through the help of the proposed system, the medical workers can identify patients by non contact identification and get medical record immediately. Meanwhile the proposed system can record the history of the interaction between medical personnel and patients. It can also send alarm to the corresponding medical workers when reporting a high risk testing result and give medicine safety suggestion. The proposed system can improve the correctness and instantaneousness of patients' medical information and hence provide a safe environment for patients, and the Wan Fang Hospital is now verifying the performance of this system for improving the patients'safety.
A Testbed for Neural-Network Models Capable of Integrating Information in Time
Authors: Stefano Zappacosta, Stefano Nolfi, Gianluca Baldassarre
Venue: Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (2007)

This paper presents a set of techniques that allow generating a class of testbeds that can be used to test recurrent neural networks' capabilities of integrating information in time. In particular, the testbeds allow evaluating the capability of such models, and possibly other architectures and algorithms, of (a) categorizing different time series, (b) anticipating future signal levels on the basis of past ones, and (c) functioning robustly with respect to noise and other systematic random variations of the temporal and spatial properties of the input time series. The paper also presents a number of analysis tools that can be used to understand the functioning and organization of the dynamical internal representations that recurrent neural networks develop to acquire the aforementioned capabilities, including periodicity, repetitions, spikes, and levels and rates of change of input signals. The utility of the proposed testbeds is illustrated by testing and studying the capacity of Elman neural networks to predict and categorize different signals in two exemplary tasks.
Second Generation Quad-Core Intel Xeon Processors Bring 45 nm Technology and a New Level of Performance to HPC Applications
Authors: Paweł Gepner, David L. Fraser, Michał F. Kowalik
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

The second generation of Quad-Core Intel® Xeon® processors was launched on November 12th 2007. In this paper we take a look at what the new 45 nm based Quad-Core Intel Xeon Processor brings to high performance computing. We compare an Intel Xeon 5300 series based system with a server utilizing his successor the Intel Xeon 5400. We measure both CPU generations operating in dual socket platforms in typical HPC benchmark scenario using some common HPC benchmarks. The results presented clearly show that the new Intel Xeon processor 5400 family provides significant performance advantage on typical HPC workloads and would therefore be seen to be an appropriate choice for many of HPC installations.
Computerized Detection and Classification of Malignant and Benign Microcalcifications on Full Field Digital Mammograms
Authors: Lubomir Hadjiiski, Peter Filev, Heang-Ping Chan, Jun Ge, Berkman Sahiner, Mark A. Helvie, Marilyn A. Roubidoux
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

The purpose of the study is to develop an automated system for detecting microcalcifications within a predefined region of interest (ROI), and classifying the clusters as malignant and benign on full-filled digital mammograms (FFDM). Our system consists of two stages. In the first stage, a detection program is used to detect cluster candidates within the ROI. A rule-based identification method is designed to differentiate the true and false clusters. In the second stage, morphological and texture features are extracted from the selected clusters and a classifier is trained to classify malignant and benign clusters. In this study, a data set of 247 ROIs (63 malignant and 184 benign) containing biopsy-pro-ven calcification clusters were used. An MQSA radiologist identified 117 corresponding clusters on the CC and MLO pairs of mammograms. Leave-one-case-out resampling was used for feature selection and classification. Two MQSA radiologists evaluated the two view pairs. The detection program correctly detected 100% (247/247) of the clusters of interest with 0.14 (35/247) FPs/ROI. The identification program correctly selected 99.2% (245/247) of the index clusters. In the classification stage an average of 4 features was selected from the training subsets. The most frequently selected features included 3 morphological and 1 texture features. The classifier achieved a test Az of 0.73 for classifying the 247 clusters as malignant or benign. For the 117 pairs of matched CC and MLO views the test Az was 0.77. The partial area index above a sensitivity of 0.9, Az(0.9), was 0.21. In comparison, the two experienced MQSA radiologists achieved Az of 0.76 and 0.73, respectively, for the 117 CC and MLO view pairs. The partial area index Az(0.9) was 0.27 and 0.12, respectively. Our classification system can detect the microcalcifications within the specified ROI on mammogram with high sensitivity and satisfactory specificity, and classify them with an accuracy comparable to that of an experienced radiologist.
Shortest Path Queries in Polygonal Domains
Authors: Hua Guo, Anil Maheshwari, Jörg-Rüdiger Sack
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

We consider shortest path queries in a polygonal domain P having n vertices and h holes. A skeleton graph is a subgraph of a Voronoi diagram of P. Our novel algorithm utilizes a reduced skeleton graph of P to compute a tessellation of P. It builds a data structure of size O(n 2) in O(n 2logn) time to support distance queries for any pair of query points in P in O(hlogn) time.
Empowering women through ict-based business initiatives: An overview of best practices in e-commerce/e-retailing projects
Authors: Sylvia Maier, Usha Nair-Reichert
Venue: Information Technologies and International Development (2008)

Information and communication technologies (ICTs) have been increasingly promoted as a key solution for comprehensive development, poverty eradication and the empowerment of historically disadvantaged groups, such as women and minorities in the Global South. ICT-based business initiatives, and e-commerce projects in particular, have been hailed as “potential goldmines” for women's empowerment. However, research and experience show that to be successful, projects must balance the need to overcome structural barriers to women's advancement with sensitivity to the limited space within which many women in the Global South navigate. In this paper, we review literature on ICT and empowerment of women, drawing upon several e-commerce/e-retailing projects as case studies to identify a set of best practices that underlie a successful project: 1) government and institutional support, 2) societal involvement, 3) training and empowerment, 4) expansion of market access, and 5) managerial best practices. We anticipate that the insights generated by this study will be useful both for purposes of effective program development and policy design.
Meta-adaptation: neurons that change their mode
Authors: Dominic Palmer-Brown, Miao Kang, Sin Wee Lee
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

This paper will explore the integration of learning modes into a single neural network structure in which layers of neurons and even individual neurons adopt different modes. There are several reasons to explore modal learning in neural networks. One motivation is to overcome the inherent limitations of any given mode (for example some modes memorise specific features, others average across features, and both approaches may be relevant according to the circumstances); another is inspiration from neuroscience, cognitive science and human learning, where it is impossible to build a serious model without consideration of multiple modes; and a third reason is non-stationary input data, or time-variant learning objectives, where the required mode is a function of time. Several modal learning ideas will be presented: The Snap-Drift Neural Network (SDNN) which toggles its learning between two modes, either unsupervised or guided by performance feedback (reinforcement); a general approach to swapping between several learning modes in real-time; and an adaptive function neural network (ADFUNN), in which adaptation applies simultaneously to both the weights and the individual neuron activation functions.
A Character Input System Using Tooth-Touch Sound for Disabled People
Authors: Koichi Kuzume
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This paper presents the realization of a character input system for disabled people using tooth-touch sound. The proposed system had several advantages, including low price, ease of handling, and reliability. First, we analyzed the characteristics of the tooth-touch sound, obtained using a bone conduction microphone. We utilized the tooth-touch sound as a control signal for the input device. For practical purposes, we developed a novel method for eliminating the voice signal and white noise. Finally, we designed a device using the FPGA (Field Programmable Gate Array) in practice and applied it to the character input system to confirm its usefulness.
Machine Learning Techniques for Decision Support in Anesthesia
Authors: Olivier Caelen, Gianluca Bontempi, Luc Barvais
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The growing availability of measurement devices in the operating room enables the collection of a huge amount of data about the state of the patient and the doctors' practice during a surgical operation. This paper explores the possibilities of generating, from these data, decision support rules in order to support the daily anesthesia procedures. In particular, we focus on machine learning techniques to design a decision support tool. The preliminary tests in a simulation setting are promising and show the role of computational intelligence techniques in extracting useful information for anesthesiologists.