ppxscal/embedding_network huggingface dataset:
A Living Lab for Ambient Assisted Living in the Municipality of Schwechat
Authors: P. Panek, W. L. Zagler
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This paper describes the Living Lab for Ambient Assisted Living (AAL) technologies and services in the city of Schwechat. Local authorities, social service providers, elderly persons, carers, research entities and companies have started to cooperate closely as full partners in this Living Lab in order to invent, discuss, explore, implement, and evaluate innovative technologies to support the independent living of senior citizens. This approach allows focusing on the actual needs of the future users by involving them right from the beginning. The paper gives an overview on the approach of the Living Lab, it describes some pilot projects currently being carried out and discusses the preliminary experiences gathered since 2006. The first findings in the Living Lab are promising, especially the feedback from the user community is very positive.
Minimizing the Impact of Change on User Productivity
Authors: Ratvinder Singh Grewal, Barbara Targonski, Quoc Hao Mach
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

In order to stay abreast of technological advancements in the business world, companies must weigh the benefits of adopting new technology with the setbacks associated with new implementation and training. Understanding the interactions between the end users and new technology is crucial for a successful transition from the present day technology to future technology. Users are required to learn new technology quickly, retaining knowledge of old technology while transferring this knowledge base to the new technology. Understanding of good design and interpersonal user characteristics as well as use of mental models can have a significant impact on maintaining user expert level when the change is introduced. Ability to maintain high expertise among users and use knowledge transfer will ultimately result in diminishing the time it takes to reach an expert level and translate to minimizing costs related to learning.
Convex Polyhedral Enclosures of Interval-Based Hierarchical Object Representations
Authors: Eva Dyllong
Venue: Reliable Implementation of Real Number Algorithms: Theory and Practice: International Seminar Dagstuhl Castle, Germany, January 8-13, 2006 Revised Papers (2008)

In this paper, we discuss approaches to constructing convex polyhedral enclosures of interval-based hierarchical structures. Hierarchical object representations are the data structures most frequently used for reconstructing real scenes. This object modelling does not depend on the nature of a real solid but only on the chosen maximum level of the hierarchical structure. This is a useful property for objects with complex shapes that are difficult to describe via exact mathematical formulas. We focus on reliable object modeling using an interval-based octree data structure. To obtain a convex polyhedral enclosure of an octree, we seek feasible ways to limit the number of considered points. For this purpose, we use the concept of extreme vertices of the tree nodes. Accurate algorithms for constructing the convex hull of these vertices yield a convex polyhedron as an adaptive and reliable object enclosure at each level of the tree.
Adaptation of Personality-Based Decision Making to Crowd Behavior Simulations
Authors: Fatemeh Alavizadeh, Caro Lucas, Behzad Moshiri
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

Simulation of crowd behaviors in emergency contexts is a critical issue concerning human lives. However, lack of human behavioral data in most of current simulation tools made them unrealistic. This is partially because of instability in the theoretical issues due to complexity of human behavior, and partially because of the gap between theories and what that can be implemented in agent environments. This research is an effort to investigate a system for realistic and intelligent crowd simulations based on personality, age, and gender of individuals. The model demonstrates some emergent behaviors, such as competitive pushing, queuing, and herding.
Industry Speaker, Energy Panelists Assail Government Regulation
Authors: J. Schallan
Venue: Computer (1978)

After briefly sketching his company's reliance on computers in oil and gas production and exploration, Standard Oil of California Chairman Harold J. Haynes attacked government regulation of the energy industry. "A phased return to the principles of a free competitive market is not only compatible with our tradition of economic freedom," the NCC industry lucheon speaker asserted "it is also the most effective way of getting the job done-in energy or in any other type of industrial enterprise." 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
Recommending Background Information and Related Content in Web 2.0 Portals
Authors: Andreas Nauerz, Birgitta König-Ries, Martin Welsch
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

Modern Web 2.0 Portals have become highly collaborative participation platforms. Users do not only retrieve information, they even contribute content. Due to the large number of different users contributing, Web 2.0 sites grow quickly and, most often, in a more uncoordinated way than centrally controlled sites. Finding relevant information can hence become a tedious task. We will demonstrate a solution allowing for the in-place, in-context recommendation of background information with respect to a certain term or topic and for the recommendation of related content being available in the system. Our solution is based on the extraction of enriched units of information which we either gain automatically via unstructured data analysis or by analyzing user-applied annotations. Our main concepts have been embedded and evaluated within IBM's WebSphere Portal.
Closing the Sensory-Motor Loop on Dopamine Signalled Reinforcement Learning
Authors: Paul Chorley, Anil K. Seth
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

It has been shown recently that dopamine signalled modulation of spike timing-dependent synaptic plasticity (DA-STDP) can enable reinforcement learning of delayed stimulus-reward associations when both stimulus and reward are delivered at precisely timed intervals. Here, we test whether a similar model can support learning in an embodied context, in which timing of both sensory input and delivery of reward depend on the agent's behaviour. We show that effective reinforcement learning is indeed possible, but only when stimuli are gated so as to occur as near-synchronous patterns of neural activity and when neuroanatomical constraints are imposed which predispose agents to exploratative behaviours. Extinction of learned responses in this model is subsequently shown to result from agent-environment interactions and not directly from any specific neural mechanism.
Modeling the magnetic interactions between paramagnetic beads in magnetorheological fluids
Authors: Eric E. Keaveny, Martin R. Maxey
Venue: Journal of Computational Physics (2008)

In this study, we develop and compare new and existing methods for computing the magnetic interactions between paramagnetic particles in magnetorheological (MR) fluids. The commonly employed point-dipole methods are outlined and the inter-particle magnetic forces, given by these representations, are compared with exact values. An alternative finite-dipole model, where the magnetization of a particle is represented as a distribution of current density, is described and the associated computational effort is shown to scale as O(N). As the dipole moments and forces given by this model depend on the length scale of the current distribution, a sensitivity analysis is performed to reveal a proper choice of this length scale. While the dipole models give a good estimation of the far-field interactions, as two particles come into contact, higher order multipoles are needed to properly resolve their interaction. We present the exact two-body calculation and describe a procedure to include the higher multipoles arising in a pairwise interaction into a dipole model. This inclusion procedure can be integrated with any dipole or higher-multipole calculation. Results from relevant three-body problems are compared to exact solutions to provide information as to how well the inclusion procedure performs in simulations of self-assembly and estimating the yield strength of structures.
On the Study of Negotiation Strategies
Authors: Leila Amgoud, Souhila Kaci
Venue: Agent Communication II: International Workshops on Agent Communication, AC 2005 and AC 2006, Utrecht, Netherlands, July 25, 2005, and Hakodate, Japan, May 9, 2006, Selected and Revised Papers (2006)

The basic idea behind a negotiation is that the agents make offers that they judge "good" and respond to the offers made to them until a compromise is reached. The choice of the offer to propose at a given step in a negotiation dialogue is a strategic matter. In most works on negotiation dialogues, the agents are supposed to be rational, and thus propose and accept only the offers which satisfy all their goals. This strategy is very restrictive since in everyday life, it is difficult to find an offer which satisfies all the agent's goals.The aim of this paper is to propose less restrictive strategies than the one used in the literature. Those strategies are based not only on the goals and beliefs of the agents but also on their rejections. A three-layered setting is proposed. The properties of each strategy are given as well as a comparative study between these strategies.
Detection of Packet Forwarding Misbehavior in Mobile Ad-Hoc Networks
Authors: Oscar F. Gonzalez, Michael Howarth, George Pavlou
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

Mobile Ad Hoc networks (MANETs) are susceptible to having their effective operation compromised by a variety of security attacks. Nodes may misbehave either because they are malicious and deliberately wish to disrupt the network, or because they are selfish and wish to conserve their own limited resources such as power, or for other reasons. In this paper, we present a mechanism that enables the detection of nodes that exhibit packet forwarding misbehavior. We present evaluation results that demonstrate the operation of our algorithm in mobile ad hoc environments and show that it effectively detects nodes that drop a significant fraction of packets.
Grid Resource Aggregation Integrated P2P Mode
Authors: Zenggang Xiong, Yang Yang, Xuemin Zhang, Ming Zeng
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

As deployed Grids increase from tens to thousands of nodes, Peer-to-Peer(P2P) technologies and protocols can be used to implement scalable services and application in Grid environments. This paper firstly proposes a Grid resource management model integrated P2P mode. Then, in order to allow every user to extract aggregated resources from Grid environments integrated P2P mode, a resource aggregation approach is presented, which is used the multi-agent technology and ant algorithm. Through simulations, we have confirmed that the proposed resource aggregation method can enhance the performance of resource aggregation in Grid environments integrated P2P mode.
Ontology-Based Information Extraction and Reasoning for Business Intelligence Applications
Authors: Thierry Declerck, Christian Federmann, Bernd Kiefer, Hans-Ulrich Krieger
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

In this demo we present the actual state of development of ontology-based information extraction in real world applications, as they are defined in the context of the MUSING European R D project dealing with Business Intelligence applications. We present in some details the actual state of ontology development, including a time and domain ontologies, for guiding information extraction onto an ontology population task. We then show how the information is stored in the MUSING knowledge repository and how reasoning can act on this repository for generating new knowledge and also applications specific statistical models for supporting decision procedures.
Games for Temporal Logics on Trees
Authors: Z. Ésik, Sz. Iván
Venue: Proceedings of the 13th international conference on Implementation and Applications of Automata (2008)

We associate a temporal logic XTL$({\mathcal L})$ with each class $\mathcal L$ of (regular) tree languages and provide both an algebraic and a game-theoretic characterization of the expressive power of the logic XTL($\mathcal L$).
Using multi-agent potential fields in real-time strategy games
Authors: Johan Hagelbäck, Stefan J. Johansson
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

Bots for Real Time Strategy (Rts) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a Multi-agent Potential Field based bot architecture that is evaluated in a real time strategy game setting and compare it, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. Although our solution did not reach the performance standards of traditional Rts bots in the test, we see great unexploited benefits in using multi-agent potential field based solutions in Rts games.
History of Eighteen Symposia
Authors: Fred J. Gruenberger
Venue: IEEE Annals of the History of Computing (1980)

Editor's Note: We are publishing the following history of eighteen symposia organized by Fred Gruenberger and Paul Armer to give our readers further insight into the flavor of the early days of computing in Southern California.
Solving jigsaw puzzles using image features
Authors: Ture R. Nielsen, Peter Drewsen, Klaus Hansen
Venue: Pattern Recognition Letters (2008)

In this article, we describe a method for automatic solving of the jigsaw puzzle problem based on using image features instead of the shape of the pieces. The image features are used for obtaining an accurate measure for edge similarity to be used in a new edge matching algorithm. The algorithm is used in a general puzzle solving method which is based on a greedy algorithm previously proved successful. We have been able to solve computer generated puzzles of 320 pieces as well as a real puzzle of 54 pieces by exclusively using image information. Additionally, we investigate a new scalable algorithm which exploits the divide and conquer paradigm to reduce the combinatorially complex problem by classifying the puzzle pieces and comparing pieces drawn from the same group. The paper includes a brief preliminary investigation of some image features used in the classification.
Reliability score based multimodal fusion for biometric person authentication
Authors: Girija Chetty, Michael Wagner
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

In this paper, we propose a robust multilevel fusion strategy involving cascaded fusion of hybrid multimodal fusion of audio-lip-face motion, correlation and depth features for biometric person authentication. The proposed approach combines the information from different audio-video based modules, namely: audio-lip motion module, audio-lip correlation module, 2D+3D motiondepth fusion module, and performs a hybrid cascaded fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio-lip motion, correlation and depth) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric person authentication task.
Predictive Performance of Clustered Feature-Weighting Case-Based Reasoning
Authors: Sung Ho Ha, Jong Sik Jin, Jeong Won Yang
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Because many factors are complexly involved in the production of semiconductors, semiconductor manufacturers can hardly manage yield precisely. We present a hybrid machine learning system, i.e., a clustered feature-weighting case-based reasoning, to detect high-yield or low-yield lots in semiconductor manufacturing. The system uses self-organizing map neural networks to identify similar patterns in the process parameters. The trained back-propagation neural networks determine feature weights of case-based reasoning. Based on the clustered feature-weighting case-based reasoning, the hybrid system predicts the yield level of a new manufacturing lot. To validate the effectiveness of our approach, we apply the hybrid system to real data of a semiconductor company.
An Analysis About the Asymptotic Convergence of Evolutionary Algorithms
Authors: Lixin Ding, Jinghu Yu
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general square matrices are given. Then, some useful properties of homogeneous Markov chains with finite states are investigated. Finally, the geometric convergence rates of the transition operators, which is determined by the revised spectral of the corresponding transition matrix of a Markov chain associated with the EA considered here, are estimated by combining the acquired results in this paper.
A Hybrid Rule Extraction Method Using Rough Sets and Neural Networks
Authors: Shufeng Wang, Gengfeng Wu, Jianguo Pan
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Rough sets and neural networks are two common techniques applied to rule extraction from data table. Integrating the advantages of two approaches, this paper presents a Hybrid Rule Extraction Method (HREM) using rough sets and neural networks. In the HREM, the rule extraction is mainly done based on rough sets, while neural networks are only served as a tool to reduce the decision table and filter its noises when the final knowledge (rule sets) is generated from the reduced decision table by rough sets. Therefore, the HREM avoids the difficult of extracting rules from a trained neural network and possesses the robustness which the rough sets based approaches are lacking. The effectiveness of HREM is verified by comparing the experiment results with the approaches of traditional rough sets and neural networks.
Literature Based Discovery Support System and Its Application to Disease Gene Identification
Authors: Dimitar Hristovski, Borut Peterlin, Sašo Džeroski, Janez Stare
Venue: Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (2007)

We present an interactive discovery support system, which for a given starting concept of interest, discovers new, potentially meaningful relations with other concepts that have not been published in the medical literature before. The known relations between the medical concepts come from the MEDLINE bibliographic database and the UMLS (Unified Medical Language System). We use association rules to mine for the relationships between medical concepts. Then we demonstrate a successful application of the system for predicting a gene candidate for a disease, a fact recently confirmed via the positional cloning approach. We conclude that the discovery support system we developed is a useful tool, complementary to the already existing bioinformatic tools in the field of human genetics. The system described in this chapter is available at http://www.mf.uni-lj.si/bitola/
Palladio --- Prediction of Performance Properties
Authors: Klaus Krogmann, Ralf Reussner
Venue: The Common Component Modeling Example: Comparing Software Component Models (2007)

Palladio is a component modelling approach with a focus on performance (i.e. response time, throughput, resource utilisation) analysis to enable early design-time evaluation of software architectures. It targets modelling business information systems. The Palladio approach includes a meta-model called "Palladio Component Model" for structural views, component behaviour specifications, resource environment, component allocation and the modelling of system usage and multiple analysis techniques ranging from process algebra analysis to discrete event simulation. Additionally, the Palladio approach is aligned with a development process model tailored for component-based software systems. Early design-time predictions avoid costly redesigns and reimplementation. Palladio enables software architects to analyse different architectural design alternatives supporting their design decisions with quantitative performance predictions, provided with the Palladio approach.
A Hidden Pattern Discovery and Meta-synthesis of Preference Adjustment in Group Decision-Making
Authors: Huizhang Shen, Jidi Zhao, Huanchen Wang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Two aspects of group decision-making (GDM) have received much attention since its appearance, one is the organizing process of GDM which emphasizes in particular on behavioral science and qualitative analysis, and the other is the weight allocation and meta-synthesis of group preference focusing on the quantificational computation. Despite the abundant researches, the existing solutions do not take into account the dynamic change of group members' decision-making preference in the GDM process. This paper considers the preference change in GDM as a dynamical procedure, researches into the important hidden pattern in GDM process, and puts forward a GDM meta-synthesis method based on Markov Chain to mine the hidden information with both qualitative analysis and quantificational computations.