ppxscal/embedding_network huggingface dataset:
Using expectation maximization to find likely assignments for solving CSP's
Authors: Eric I. Hsu, Matthew Kitching, Fahiem Bacchus, Sheila A. Mcllraith
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

We present a new probabilistic framework for finding likely variable assignments in difficult constraint satisfaction problems. Finding such assignments is key to efficient search, but practical efforts have largely been limited to random guessing and heuristically designed weighting systems. In contrast, we derive a new version of Belief Propagation (BP) using the method of Expectation Maximization (EM). This allows us to differentiate between variables that are strongly biased toward particular values and those that are largely extraneous. Using EM also eliminates the threat of non-convergence associated with regular BP. Theoretically, the derivation exhibits appealing primal/dual semantics. Empirically, it produces an "EMBP"-based heuristic for solving constraint satisfaction problems, as illustrated with respect to the Quasigroup with Holes domain. EMBP outperforms existing techniques for guiding variable and value ordering during backtracking search on this problem.
A Qualitative Evaluation of Implementing Virtual Prototyping in Construction
Authors: Stephen C. W. Kong, Heng Li
Venue: Proceedings of the 2009 Second International Conference in Visualisation (2009)

The use of 3D computer-aided design (CAD) models to support construction project planning has been increasing in the previous year. 3D CAD models reveal more planning ideas by visually showing the construction site environment in different stages of the construction process. Using 3D CAD models together with scheduling software to prepare construction plan can identify errors in process sequence and spatial arrangement, which is vital to the success of a construction project. A number of 4D (3D plus time) CAD tools has been developed and utilized in different construction projects due to the awareness of their importance. Virtual prototyping extends the idea of 4D CAD by integrating more features for simulating real construction process. Virtual prototyping originates from the manufacturing industry where production of products such as cars and airplanes are virtually simulated in computer before they are built in the factory. Virtual prototyping integrates 3D CAD, simulation engine, analysis tools (like structural analysis and collision detection), and knowledgebase to streamline the whole product design and production process. In this paper, we present the application of a virtual prototyping software which has been used in a few construction projects in Hong Kong to support construction project planning. Specifically, the paper presents an implementation of virtual prototyping in a residential building project in Hong Kong. The applicability, difficulties and benefits of construction virtual prototyping are examined based on this project.
Freebase: a shared database of structured general human knowledge
Authors: Kurt Bollacker, Robert Cook, Patrick Tufts
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.
Developing a Usable Mobile Flight Case Learning System in Air Traffic Control Miscommunications
Authors: Kuo-Wei Su, Keh-Yeu Lee, Po-Hsin Huang, I-Tsun Chen
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains (2009)

Aviation's highest priority is safety. The primary risk to safety derives not from automated systems but from human factors, most notably pilots and air traffic control. We present results from development of a flight case learning system designed and aviation regulation retrieve system to operate on mobile phones used by pilots and air traffic controllers. Our system takes advantage of key ontology concepts, human-centered design strategies, and appropriate small-screen interface design protocols. A questionnaire to assess user interaction satisfaction (QUIS) was deployed for subsequent usability testing and to verify acceptance of, and satisfaction with the system. Twelve students participated in the questionnaire-based evaluation of subjective satisfaction. In addition, two flight experts served on a review panel for domain knowledge verification and acceptance of the interface design. Our results confirm that MFCLS is a suitably designed mobile learning system that can accelerate self-learning for both pilots and controllers.
A new class of spatial concentration measures
Authors: Giuseppe Arbia, Gianfranco Piras
Venue: Computational Statistics Data Analysis (2009)

In this paper we propose a new class of spatial concentration measures to verify hypotheses on the spatial concentration of empirical phenomena. Our proposed measures incorporate the ideas of both a-spatial concentration and spatial autocorrelation. We discuss many issues related to the approximate sampling theory and we suggest a Monte Carlo test for the presence of significant spatial concentration. A simulation experiment studying the relationship of the new measure to different spatial patterns and to different levels of a-spatial variability is also reported. A discussion related to irregular space is included.
Data Mining in Financial Markets
Authors: Longbing Cao
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

The ongoing global financial recession has dramatically affected public confidence and market development. An example is the market manipulation schemes hidden in capital markets, which have caused losses in billions of dollars, dramatically damaging public confidence and contributing to the global financial and credit crisis. While most investors lost during market falls, for instance, sophisticated speculators can manipulate markets to make money by illegally using a variety of maneuvering techniques such as wash sales. With financial globalization, manipulators are becoming increasingly imaginative and professional, employing creative tactics such as using many nominee accounts at different broker-dealers. However, regulators currently are short on effective technology to promptly identify abnormal trading behavior related to complex manipulation schemes. As a result, shareholders are complaining that too few market manipulators were being caught. In this talk, I will discuss issues related to this topic, present case studies and lessons learned in identifying abnormal trading behavior in capital markets. I will discuss the use of data mining techniques in this area such as activity mining, combined mining, adaptive mining and domain-driven data mining. 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
Constructive Adaptive User Interfaces Based on Brain Waves
Authors: Masayuki Numao, Takayuki Nishikawa, Toshihito Sugimoto, Satoshi Kurihara, Roberto Legaspi
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques (2009)

We demonstrate a method to locate relations and constraints between a music score and its impressions, by which we show that machine learning techniques may provide a powerful tool for composing music and analyzing human feelings. We examine its generality by modifying some arrangements to provide the subjects with a specified impression. This demonstration introduces some user interfaces, which are capable of predicting feelings and creating new objects based on seed structures, such as spectrums and their transition for sounds that have been extracted and are perceived as favorable by the test subject. This paper proposes to define knowledge components for the seed structure.
Planning referential acts for animated presentation agents
Authors: Elisabeth André, Thomas Rist
Venue: Referring Phenomena in a Multimedia Context and their Computational Treatment (1997)

Computer-based presentation systems enable the realization of effective and dynamic presentation styles that incorporate multiple media. In particular, they allow for the emulation of conversational styles known from personal human-human communication. In this paper, we argue that life-like characters are an effective means of encoding references to world objects in a presentation. We present a two-phase approach which first generates high-level referential acts and then transforms them into fine-grained animation sequences.
Subgroup Discovery for Weight Learning in Breast Cancer Diagnosis
Authors: Beatriz López, Víctor Barrera, Joaquim Meléndez, Carles Pous, Joan Brunet, Judith Sanz
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

In the recent years, there is an increasing interest of the use of case-based reasoning (CBR) in medicine. CBR is an approach to problem solving that is able to use specific knowledge of previous experiences. However, the efficiency of CBR strongly depends on the similarity metrics used to recover past experiences. In such metrics, the role of attribute weights is critical. In this paper we propose a methodology that use subgroup discovery methods to learn the relevance of the attributes. The methodology is applied to a Breast Cancer dataset obtaining significant improvements. ...
UMND2: SenseClusters applied to the sense induction task of Senseval-4
Authors: Ted Pedersen
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

SenseClusters is a freely--available open--source system that served as the University of Minnesota, Duluth entry in the Senseval-4 sense induction task. For this task SenseClusters was configured to construct representations of the instances to be clustered using the centroid of word cooccurrence vectors that replace the words in an instance. These instances are then clustered using k--means where the number of clusters is discovered automatically using the Adapted Gap Statistic. In these experiments SenseClusters did not use any information outside of the raw untagged text that was to be clustered, and no tuning of the system was performed using external corpora.
Mining Safety Signals in Spontaneous Reports Database Using Concept Analysis
Authors: Mohamed Rouane-Hacene, Yannick Toussaint, Petko Valtchev
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

In pharmacovigilance, linking the adverse reactions by patients to drugs they took is a key activity typically based on the analysis of patient reports. Yet generating potentially interesting pairs (drug, reaction) from a record database is a complex task, especially when many drugs are involved. To limit the generation effort, we exploit the frequently occurring patterns in the database and form association rules on top of them. Moreover, only rules of minimal premise are considered as output by concept analysis tools, which are then filtered through standard measures for statistical significance. We illustrate the process on a small database of anti-hiv drugs involved in the haart therapy while larger-scope validation within the database of the French Medicines Agency is also reported.
A Systematic Study of the Work that Web Mining does in the Technology Opportunities Analysis
Authors: Gu Chengjian, Huang Lucheng
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

We put the technology opportunity analysis (TOA) on a very important position during the commercialization of emerging technologies, and find all necessary information from a variety of sources for the accurate analysis. However, the complex and redundancy of the data make the work extremely difficulty, thanks to the advent of web mining (WM), which gives us a window in this area to allow the management of science and technology (S&T) becoming moreconvenient. In this paper, we use the Keyword-based MA as the analysis vector doing the research on the role of WM on the TOA, and attempt to give the focus of future research in the final.
Probabilistic Satellite Image Fusion
Authors: Farid Flitti, Mohammed Bennamoun, Du Huynh, Amine Bermak, Christophe Collet
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

Remote sensing satellite images play an important role in many applications such as environment and agriculture lands monitoring. In such images the scene is usually observed with different modalities, e.g. wavelengths. Image Fusion is an important analysis tool that summarizes the available information in a unique composite image. This paper proposes a new transform domain image fusion (IF) algorithm based on a hierarchical vector hidden Markov model (HHMM) and the mixture of probabilistic principal component analysers. Results on real Landsat images, quantified subjectively and using objective measures, are very satisfactory. 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
Application of Classification Association Rule Mining for Mammalian Mesenchymal Stem Cell Differentiation
Authors: Weiqi Wang, Yanbo J. Wang, René Bañares-Alcántara, Zhanfeng Cui, Frans Coenen
Venue: Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects (2009)

In this paper, data mining is used to analyze the differentiation of mammalian Mesenchymal Stem Cells (MSCs). A database comprising the key parameters which, we believe, influence the destiny of mammalian MSCs has been constructed. This paper introduces Classification Association Rule Mining (CARM) as a data mining technique in the domain of tissue engineering and initiates a new promising research field. The experimental results show that the proposed approach performs well with respect to the accuracy of (classification) prediction. Moreover, it was found that some rules mined from the constructed MSC database are meaningful and useful.
User Modeling of Parallel Workloads: Predicting and Optimizing the Use of Parallel Computers
Authors: David Talby
Venue:  (2009)

The goal of workload modeling is to predict a computer?s workload well enough to design it correctly. A poor model will lead to degraded performance and user satisfaction. Analyzing logs from multiple real parallel computers uncovers several statistical features ? locality of sampling, daily cycles, weekly cycles, self similarity and flurries ? that are missing from current workload models. Their practical importance is demonstrated by two new kinds of scheduling algorithms ? adaptive scheduling and shortest job backfill first scheduling ? which achieve an average 10% bottom- line performance gain and 35% stability gain on the production workloads. The second part of this book presents a user based workload model. It identifies four stable user types and five stable session types. It then deduces model parameters, the distributions of the arrival and activity patterns for both users and sessions, their dependencies and temporal structure. The methodology and statistical toolset is explained to make it easier to reuse in other domains. The insights and practical advice provided will be of use to anyone building or operating a large-scale parallel computer. 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
Implementing Reliable Data Structures for MPI Services in High Component Count Systems
Authors: Justin M. Wozniak, Bryan Jacobs, Robert Latham, Sam Lang, Seung Woo Son, Robert Ross
Venue: Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2009)

High performance computing systems continue to grow: currently deployed systems exceed 160,000 cores and systems exceeding 1,000,000 cores are planned. Without significant improvements in component reliability, partial system failure modes could become an unacceptably regular occurrence, limiting the usability of advanced computing infrastructures. In this work, we intend to ease the development of survivable systems and applications through the implementation of a reliable key/value data store based on a distributed hash table (DHT). Borrowing from techniques developed for unreliable wide-area systems, we implemented a distributed data service built with MPI [1] that enables user data structures to survive partial system failure. The service is based on a new implementation of the Kademlia [2] distributed hash table.
Ontology-Driven Decision Support in Dynamic Supply-Chains
Authors: Andrew Nelson, Dickens Nyabuti, John Collins, Wolfgang Ketter, Maria Gini
Venue: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (2009)

We describe an approach to building a highly configurable,semi-autonomous agent that can support users playing a variety of roles in a supply-chain trading environment. The agent's decision processes are composed of networks of simple services that are described using an OWL ontology. The ontology describes both the abstract data structures that are produced and consumed by individual services, as well as the business meaning of these data elements.This approach supports goal-directed composition of services in order to generate performance dashboards, as well as direct injection of user input at arbitrary points in the network.
Driving Style Identification Method Based on Speed Graph Analysis
Authors: Artur Rygula
Venue: Proceedings of the 2009 International Conference on Biometrics and Kansei Engineering (2009)

In the paper presented results of analyzing driver’s speed profile treated as a criterion of behavioral biometric. As an input data records registered on tachograph paper disk were used. Driving dynamic features such as speed and acceleration distribution in the specific time intervalswere determined and compared. Shown that driving style analysis can be also used for person psycho-physiological state recognition, especially for driver sleepiness and fatigue detection.
Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning
Authors: Leandro S. Rezende, Armando M. Leite Da Silva, Leonardo Mello Honório
Venue: Proceedings of the 8th International Conference on Artificial Immune Systems (2009)

Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.
2-Dimensional cellular automata approach for robot grid formations
Authors: Ross Mead, Jerry B. Weinberg
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

One potentially cost-effective approach to harvesting solar power from space is the use of thousands of individual robots moving in formation, each with a piece of solar panel attached, to form a solar panel array. In previous work, we demonstrated an algorithm that treats a group of robots as a 1-dimensional cellular automation, which is able to establish formations defined by a single mathematical function. We now extend the algorithm to establish grid formations.
The PDG-Mixture Model for Clustering
Authors: M. Julia Flores, José A. Gámez, Jens D. Nielsen
Venue: Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery (2009)

Within data mining, clustering can be considered the most important unsupervised learning problem which deals with finding a structure in a collection of unlabeled data. Generally, clustering refers to the process of organizing objects into groups whose members are similar. Among clustering approaches, those methods based on probabilistic models have been extensively developed, such as Naïve Bayes (NB) with a latent class (cluster identifier) found via an EM algorithm.Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by other commonly used models. In this paper we propose to use a mixture of PDG models in cluster discovery, and an algorithm for automatic induction of the mixture and the models is introduced.The proposed approach was experimentally evaluated on both synthetic and real-world databases, and the presentation of the results includes a comparison with related techniques. The comparison demonstrates competitive performance of the mixture of PDG models with respect to likelihood. Also, the mixture of PDG models have a tendency to use fewer models (clusters) to represent domains where other models use large amounts of clusters.
LED Backlight for Better Accuracy in Medical Imaging
Authors: Silvio Bonfiglio, Luigi Albani
Venue: Proceedings of the International Conference on Ergonomics and Health Aspects of Work with Computers: Held as Part of HCI International 2009 (2009)

In clinical tasks the display is often the natural interface between the medical system and the medical professionals and in the current image-centric healthcare the accuracy of the visualized images represents a key requirement; ideally no compromise would be acceptable. In the recent past LED backlights for liquid-crystal displays have been intensively investigated for their use in displays addressed to the mainstream markets (mobile and portable displays, computer displays and TV). Accordingly adapted, they could offer new opportunities also to the displays used in healthcare by allowing better accuracy and consistency of the medical images. In this respect they could make possible a new, important advance towards a better quality of care.In this paper we will describe a novel LED backlight solution suitable for medical imaging.