ppxscal/embedding_network huggingface dataset:
Comparison of Pilot Recovery and Response Times in Two Types of Cockpits
Authors: Vishal Hiremath, Robert W. Proctor, Richard O. Fanjoy, Robert G. Feyen, John P. Young
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

According to general aviation manufacturers, all aircraft rolling off the assembly line are or will be equipped with next-generation electronic flight instrument cockpits, called `glass' cockpits. Because most pilots were trained with older analog displays, it becomes imperative to find out what human factors issues the pilots will encounter when they transition to glass displays. A comparative study was carried out in a general aviation aircraft simulator between instrumentation of the type used in conventional and glass cockpits for recovery from unusual attitudes. Glass displays showed longer recovery time than round-dial displays. Low-time pilots judged analog displays as more usable than glass displays. Suggestions are made to design a hybrid display of round dial and vertical tapes as well as examine unusual attitude training methods more closely.
History Guided Low-Cost Change Detection in Streams
Authors: Weiyun Huang, Edward Omiecinski, Leo Mark, Minh Quoc Nguyen
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

Change detection in continuous data streams is very useful in today's computing environment. However, high computation overhead prevents many data mining algorithms from being used for online monitoring. We propose a history-guided low-cost change detection method based on the "s-monitor" approach. The "s-monitor" approach monitors the stream with simple models ("s-monitors") which can reflect changes of complicated models. By interleaving frequent s-monitor checks and infrequent complicated model checks, we can keep a close eye on the stream without heavy computation overhead.The selection of s-monitors is critical for successful change detection. History can often provide insights to select appropriate s-monitors and monitor the streams. We demonstrate this method using subspace cluster monitoring for log data and frequent item set monitoring for retail data. Our experiments show that this approach can catch more changes in a more timely manner with lower cost than traditional approaches.The same approach can be applied to different models in various applications, such as monitoring live weather data, stock market fluctuations and network traffic streams.
Research on Improving BPR Efficiency: Strengthening Core Competitive Power Form a New Perspective
Authors: Liu Xun
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

It is well known that adroit BRP is absolutely necessary for enterprises, so how to attain adroit BRP becomes an important content. Firstly, This article introduces research background of BRP; then, this article analyses BRP structure based on force-field model, analyzing the factors that counteract positive change of enterprises, epurating the factors that promote the improvement of enterprises; lastly , the writer put forward lots of tactics and measures to conquer negative and resisting factors, at the same time ,the writer bring forward some suggestions to strengthen positive and stimulative factors. This study can play an important role in constituting strategies and improving core competitive power of enterprises.
Enhancing Mobile Recommender Systems with Activity Inference
Authors: Kurt Partridge, Bob Price
Venue: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH (2009)

Today's mobile leisure guide systems give their users unprecedented help in finding places of interest. However, the process still requires significant user interaction, for example to specify preferences and navigate lists. While interaction is effective for obtaining desired results, learning the interaction pattern can be an obstacle for new users, and performing it can slow down experienced users. This paper describes how to infer a user's high-level activity automatically to improve recommendations. Activity is determined by interpreting a combination of current sensor data, models generated from historical sensor data, and priors from a large time-use study. We present an initial user study that shows an increase in prediction accuracy from 62% to over 77%, and discuss the challenges of integrating activity representations into a user model.
Voice and Data Traffic Analysis in IEEE 802.11 DCF Infrastructure WLANs
Authors: P. Raptis, V. Vitsas, P. Chatzimisios, K. Paparrizos
Venue: Proceedings of the 2009 Second International Conference on Advances in Mesh Networks (2009)

This paper presents a novel mathematical model for evaluating the performance of infrastructure Wireless Local Area Networks (WLANs) carrying voice and data traffic flows. We evaluate the performance of the access point (AP) flows and of the stations that employ IEEE 802.11 Distributed Coordination Function (DCF) as contention mechanism to access the channel. Metrics as throughput, average delay, delay jitter and packet loss rate are considered in order to evaluate the performance of an infrastructure WLAN assuming ideal channel conditions. The paper proposes a bursting feature to the AP to avoid bottle-neck and to increase the capacity of voice stations. The proposed model, which is validated by simulation results, considers the Basic Access scheme but can be easily applied to the RTS/CTS scheme.
A Development of Information System for Disaster Victims with Autonomous Wireless Network
Authors: Yuichi Takahashi, Daiji Kobayashi, Sakae Yamamoto
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

In times of huge disaster such as earthquake, information needs increase among victims and rescuers. Social ferment rises within afflicted area and the damage is spread, if the needs of information are not satisfied. In this research we developed an information system for disaster victims as a distributed autonomous system using a wireless network. This system consists of many sub systems. These sub systems are robust for collecting disaster information because they are small and simple. An authorized user can register information using one of the sub systems that is working correctly. Asynchronously, they search another sub system via wireless network, and then they communicate to each other in order to exchange information they have. As a result, the information will be shared within a wide area by those processes like a bucket brigade.
POGGI: Puzzle-Based Online Games on Grid Infrastructures
Authors: Alexandru Iosup
Venue: Proceedings of the 15th International Euro-Par Conference on Parallel Processing (2009)

Massively Multiplayer Online Games (MMOGs) currently entertain millions of players daily. To keep these players online and generate revenue, MMOGs are currently relying on manually generated content such as logical challenges (puzzles). Under increased demands for personalized content from a growing community, it has become attractive to generate personalized puzzle game content automatically. In this work we investigate the automated puzzle game content generation for MMOGs on grid infrastructures. First, we characterize the requirements of this novel grid application. With long-term real traces taken from a popular MMOG we show that hundreds of thousands of players are simultaneously online during peak periods, which makes content generation a large-scale compute-intensive problem. Second, we design the POGGI architecture to support this type of application. We assess the performance of our reference implementation in a real environment by running over 200,000 tasks in a pool of over 1,600 nodes, and demonstrate that POGGI can generate commercial-quality content efficiently and robustly.
Application of Immune Genetic Neural Network in Pump-jack Fault Diagnosis
Authors: Weijian Ren, Dan Liu
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

A new RBF neural network is presented to overcome the shortcoming that the training process of RBF neural network is slow in the paper. The immune genetic algorithm is combined with the RBF neural network to optimize the center of the RBF network, improve the definition method of affinity degree, and introduce adjusting factor based on density. Thus the learning efficiency and approximation precision are improved, and the number of constructing the centers of hide layer of the network is dispensable. The algorithm was successful in fault diagnosis of pump-jack.
Parameterizing Singularities of Positive Integral Index
Authors: M. Nieser, K. Polthier
Venue: Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII (2009)

Classical surface parameterization algorithms often place singularities in order to enhance the quality of the resulting parameter map. Unfortunately, singularities of positive integral index (as the north pole of a sphere) were not handled since they cannot be described with piecewise linear parameter functions on a triangle mesh. Preprocessing is needed to adapt the mesh connectivity. We present an extension to the QuadCover parameterization algorithm [1], which allows to handle those singularities.A singularity of positive integral index can be resolved using bilinear parameter functions on quadrilateral elements. This generalization of piecewise linear functions for quadrilaterals enriches the space of parameterizations. The resulting parameter map can be visualized by textures using a rendering system which supports quadrilateral elements, or it can be used for remeshing into a quad mesh.
A Novel Component-Based Model and Ranking Strategy in Constrained Evolutionary Optimization
Authors: Yu Wu, Yuanxiang Li, Xing Xu
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

This paper presents a component-based model with a novel ranking method (CMR) for constrained evolutionary optimization. In general, many constraint-handling techniques inevitably solve two important problems: (1) how to generate the feasible solutions, (2) how to direct the search to find the optimal feasible solution. For the first problem, this paper introduces a component-based model. The model is useful for exploiting valuable information from infeasible solutions and for transforming infeasible solutions into feasible ones. Furthermore, a new ranking strategy is designed for the second problem. The new algorithm is tested on several well-known benchmark functions, and the empirical results suggest that it continuously found the optimums in 30 runs and has better standard deviations for robustness and stability.
Regularity in PDA Games Revisited
Authors: Václav Brožek
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We study the regularity of sets of winning configurations (winning regions) in countable-state stochastic games played on transition graphs of pushdown automata (PDA games) with reachability objectives. Our main result is proving the regularity of winning regions for qualitative reachability objectives. This completes the classification partially given in a previous paper on this topic. We also improve the upper bounds on the regular representation in cases already solved. We further mention a problem which has also been studied recently: determining the value of a reachability game. Using our methods we prove the regularity of the set of configurations with value 1 and 0.
R-CAST: integrating team intelligence for human-centered teamwork
Authors: Xiaocong Fan, John Yen
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Developing human-centered agent architectures requires the integral consideration of architectural flexibility, teamwork adaptability, and context reasoning capability. With the integration of various forms of team intelligence including shared teamwork process and progress, dynamic context management and infomation dependency reasoning, and recognition-primed collaborative decision mechanism, R-CAST offers a flexible solution to developing cognitive aids for the support of human-centered teamwork in information and knowledge intensive domains. In this paper, we present the key features of R-CAST. As evidence of its applications in complex real-world problems, we give two experimental evaluations of R-CAST as teammates and decision aids of human Command and Control teams.
Fast reconstruction of aerodynamic shapes using evolutionary algorithms and virtual nash strategies in a CFD design environment
Authors: J. Periaux, D. S. Lee, L. F. Gonzalez, K. Srinivas
Venue: Journal of Computational and Applied Mathematics (2009)

This paper compares the performances of two different optimisation techniques for solving inverse problems; the first one deals with the Hierarchical Asynchronous Parallel Evolutionary Algorithms software (HAPEA) and the second is implemented with a game strategy named Nash-EA. The HAPEA software is based on a hierarchical topology and asynchronous parallel computation. The Nash-EA methodology is introduced as a distributed virtual game and consists of splitting the wing design variables-aerofoil sections-supervised by players optimising their own strategy. The HAPEA and Nash-EA software methodologies are applied to a single objective aerodynamic ONERA M6 wing reconstruction. Numerical results from the two approaches are compared in terms of the quality of model and computational expense and demonstrate the superiority of the distributed Nash-EA methodology in a parallel environment for a similar design quality.
An internet-based IP protection scheme for circuit designs using linear feedback shift register (LFSR)-based locking
Authors: Raju Halder, Parthasarathi Dasgupta, Saptarshi Naskar, Samar Sen Sarma
Venue: Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes (2009)

IP reuse is rapidly proliferating recent automated circuit design. It is facing serious challenges like forgery, theft and misappropriation of intellectual property (IP) of the design. Thus, protection of design IP is a matter of prime concern. In this paper, we propose a novel Internet-based scheme to tackle this problem. Input to the proposed scheme is a graph corresponding to a digital system design. Watermarking of the graph and its encryption are achieved using a new linear feedback shift register(LFSR)-based locking scheme. The proposed scheme makes unauthorized disclosure of valuable design almost infeasible, and can easily detect any alteration of the design file during transmission. It ensures authentication of the original designer as well as non-repudiation between the seller and the buyer. Empirical evidences on several benchmark problem sets are encouraging.
Partial pathfinding using map abstraction and refinement
Authors: Nathan Sturtevant, Michael Buro
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

Classical search algorithms such as A* or IDA* are useful for computing optimal solutions in a single pass, which can then be executed. But in many domains agents either do not have the time to compute complete plans before acting, or should not spend the time to do so, due to the dynamic nature of the environment. Extensions to A* such as LRTA* address this problem by gradually learning an exact heuristic function, but the learning process is quite slow. In this paper we introduce Partial-Refinement A* (PRA*), which can fully interleave planning and acting through path abstraction and refinement. We demonstrate the etfectiveness of PRA* in the domain of real-time strategy (RTS) games. In maps taken from popular RTS games. we show that PRA* is not only able to cleanly interleave planning and execution. but it is also able to do so with only minimal losses of optimality.
Foundations of Computational Intelligence Volume 3: Global Optimization, 1st edition
Authors: Ajith Abraham, Aboul-Ella Hassanien, Patrick Siarry, Andries Engelbrecht
Venue:  (2009)

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.
Application of constrained independent component analysis algorithms in electrocardiogram arrhythmias
Authors: Raul Llinares, Jorge Igual
Venue: Artificial Intelligence in Medicine (2009)

Objectives: The extraction of the atrial activity in atrial fibrillation episodes is a must for clinical purposes. During atrial fibrillation arrhythmia, the independent atrial and ventricular signals are superposed in the electrocardiogram, fulfilling the independent component analysis (ICA) model. We propose three new algorithms that constrain the classical ICA solution to fit the spectral content of the atrial component. This constraint allows the statement of the problem in terms of semiblind source extraction instead of blind source separation (BSS), in the sense that we only recover one source and we exploit the prior information about the sources in the extraction process. Methods and materials: The methods used are extensions of classical BSS methods based on second and higher order statistics. We exploit the prior assumption about the sources in order to obtain the source extraction algorithms that are focused on the extraction of the atrial component. The material corresponds to 10 synthetic recordings in order to measure and compare the quality of the different algorithms and 66 real recordings coming from two different databases, one public database from Physionet and one database from the Clinical University Hospital, Valencia, Spain. Results: We have analyzed the performance of the three new algorithms and compared it with the performance of the traditional ICA algorithms. In the case of the synthetic data, it is possible to obtain the mean square error, so the comparison is easier. The new methods outperform the non-constrained versions in addition to simplifying the solution, since they do not need to recover all the components in order to estimate the atrial activity, i.e., the new methods are focused on the extraction of the atrial activity, so the extraction is stopped after the atrial signal is recovered. Conclusions: We have shown that the ICA only version of the algorithms can be improved and adapted to fulfill the prior information about the characteristics of the atrial activity. This modification allows us to obtain new algorithms that have the following advantages compared to ICA only based solutions: they exploit prior information during the extraction, not in the postprocessing identification of the atrial signal; they extract only the interesting clinical signal instead of all the components; they outperform the ICA only version of the algorithm, improving the estimation of the atrial signal.
The Atlantic College Case Study- Measuring Performance to Evaluate Building Simulation and Enhance Student Learning
Authors: J. A. M. Counsell, A. Gillard, J. R. Littlewood
Venue: Proceedings of the 2009 Second International Conference in Visualisation (2009)

This paper is based upon a case study. Gillard Associates completed the refurbishment of the first of three 1960’s student accommodation blocks at Atlantic College, Wales in late summer 2008. The refurbishment was predicted to possibly achieve Code for Sustainable Homes level4, through the use of a combination of Archicad BIM and Ecotect Building Performance simulation software.The Cardiff School of Art and Design (CSAD) have now acquired re-deployable instrumentation with the intent of monitoring the refurbished accommodation through a complete cycle of seasons, in comparison with one of the existing blocks, and with the predicted results from the simulation process.CSAD intend to build upon the model of the ‘Live Studio’ learning case study that incorporates real time data from the building management system sensors and weather station. We argue that there is a lack of useful seasonal case study data for practitioners to calibrate their simulations, and for learning by students.
Context-Aware Mobile Media and Social Networks
Authors: Jonna Häkkilä, Albrecht Schmidt, Jani Mäntyjärvi, Alireza Sahami, Panu Åkerman, Anind K. Dey
Venue: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (2009)

Context-awareness is one of the rising trends of future mobile technology, and due to advances in technology development, new application and service concepts are being developed and demonstrated in an ever-increasing manner. This workshop brings together researchers and practitioners working on humancomputer interaction (HCI) aspects of context-aware mobile technology and communities to present their insights and research on new concepts, interaction design for mobile context-awareness, usability challenges, collaborative context-aware services and applications for supporting communities, and other topics related to HCI with mobile context-aware technology.
Multi-classes Semi-supervised Learning on Riemannian Manifolds
Authors: Zhong-Qiu Zhao, Herve Glotin, Jun Gao, Xin-Dong Wu
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

A modified semi-supervised learning algorithm based on Riemannian manifolds is proposed, which extends the applicable field to the case of multi-classes problem. Furthermore, the modified version largely increases the learning speed, while attains the classification performance as satisfying as the original algorithm.
Study on the Improved BP Algorithm and Application
Authors: Yuemei Xu, Hong Zhang
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

First, the paper analyzes the standard BP Algorithm. Toward the limitation of the standard BP algorithm, an improved BP algorithm is obtained by modifying the action function, regulating the learning rate and choosing the initial weights. And then a recognition example about license plate recognition technology is given to prove the improved BP algorithm. From the exam results, the improved BP algorithm has some advantages in fast convergence speed, short recognition time and high recognition rate.