ppxscal/embedding_network huggingface dataset:
Computing observation vectors for max-fault min-cardinality diagnoses
Authors: Alexander Feldman, Gregory Provan, Arjan Van Gemund
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

Model-Based Diagnosis (MBD) typically focuses on diagnoses, minimal under some minimality criterion, e.g., the minimal-cardinality set of faulty components that explain an observation α. However, for different α there may be minimal-cardinality diagnoses of differing cardinalities, and several applications (such as test pattern generation and benchmark model analysis) need to identify the α leading to the max-cardinality diagnosis amongst them. We denote this problem as a Max-Fault Min-Cardinality (MFMC) problem. This paper considers the generation of observations that lead to MFMC diagnoses. We present a near-optimal, stochastic algorithm, called MIRANDA (Max-fault mIn-caRdinAlity observatioN Deduction Algorithm), that computes MFMC observations. Compared to optimal, deterministic approaches such as ATPG, the algorithm has very low cost, allowing us to generate observations corresponding to high-cardinality faults. Experiments show that MIRANDA delivers optimal results on the 74XXX circuits, as well as good MFMC cardinality estimates on the larger ISCAS85 circuits.
Preprocessing Circuit Design in Feeble Optical Pulse Detection System Based on Programmable Controlling
Authors: Liang Yi-tao, Fan Chao, He Lian-lian, Chang Hua, Gao Yu
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01 (2009)

In this study, we present an integration scheme of preprocessing circuit for detecting optical pulse based FPGA-tech. Using the photoelectric detection technique, the scheme includes a the photodiode detector (PDD), which can convert an optical pulse to a small current signal; and a FPGA produces code to control MUXes for different channel, which changes circuit parameters to adapt different detection situation. Design issues are addressed. Testing circuits are implemented. Simulation wave displayed on the oscillograph and the data of the system acquiring are presented. And the feasibility of the scheme is confirmed by the data analyse. Presently, the scheme has been used in a lightning detection system.
Towards standardized distributed multitouch interaction
Authors: Florian Klompmaker, Christian Reimann
Venue: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era (2009)

We introduce an extension of an existing interaction framework that allows a grouping of input data in multitouch (MT) setups to "hands", "users" and "pens". The data of users and pens is analyzed by an interaction wrapper that informs registered applications about applied interaction techniques.
Specialization of the Cell SPE for Media Applications
Authors: Cor Meenderinck, Ben Juurlink
Venue: Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (2009)

There is a clear trend towards multi-cores to meet the performance requirements of emerging and future applications. A different way to scale performance is, however, to specialize the cores for specific application domains. This option is especially attractive for low-cost embedded systems where less silicon area directly translates to less cost. We propose architectural enhancements to specialize the Cell SPE for video decoding. Specifically, based on deficiencies we observed in the H.264 kernels, we propose a handful of application-specific instructions to improve performance. The speedups achieved are between 1.84 and 2.37.
The Hermitian Positive Definite Solution of Matrix Equations and Its Application
Authors: Hongkui Li, Daojin Song
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

In image processing, we must solve a system of linear equations. We know the solving of the System can be transformed to the solving of the equations. In this paper,we study the Hermitian positive definite solutions of the nonlinear matrix equation. We discuss the relation between andby the eigenvalue and eigenvector of and respectively, and the property of numerical range of, construct an iterative method for obtaining positive definite solutions of the equation at last.
Data Mining for Software Engineering
Authors: Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu
Venue: Computer (2009)

To improve software productivity and quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks. However, mining SE data poses several challenges. The authors present various algorithms to effectively mine sequences, graphs, and text from such data.
A New Approach to Non-termination Analysis of Logic Programs
Authors: Dean Voets, Danny Schreye
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

In this paper, we present a new approach to non-termination analysis of logic programs, based on moded SLDNF-resolution. Moded SLDNF-resolution is a symbolic execution for moded goals, developed for termination prediction. To prove non-termination, we use a complete loop checker to create a finite symbolic derivation tree of a logic program for a moded query. Then, we check if this derivation tree contains an infinite loop, using a new non-termination condition. We implemented this approach and tested it on the benchmark from the Termination Competition of 2007. The results are very satisfactory: our tool is able to prove non-termination and construct non-terminating queries for all non-terminating benchmark programs, and thus, significantly improves on the results of the only other non-termination analyzer, NTI.
AR gone wild: two approaches to using augmented reality learning games in Zoos
Authors: Judy Perry, Eric Klopfer, Marleigh Norton, Dan Sutch, Richard Sandford, Keri Facer
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

Participants in Augmented Reality (AR) games are equipped with location-aware handheld computers, allowing players to physically move throughout a real world location while simultaneously triggering virtual information based on their physical location. Researchers are only beginning to understand how to leverage the pedagogical strengths of location-based AR games. This paper includes case studies for two separate research projects: Researchers from MIT will present "Zoo Scene Investigators: Challenges of Designing a Mystery Themed AR Game for Students Ages 10-14 in a U.S. Zoo". Researchers from Futurelab will present "Crafting mediascapes for a Zoo Setting using Create-A-Scape with Singaporean Primary School Students".
An Efficient Fusion Algorithm on Conflicting Evidence
Authors: Jinyuan Yang, Xinhan Huang, Min Wang
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

DSmT would resolve the issue of evidence portfolios when the high conflict of evidences appeared. But the computation oversized more easily because more focus elements were additional in the DSmT rules. And the fusion result was worse than that of DST when the low conflict situation was occurred. In order to efficiently combine high conflicting evidences, an improved fusion algorithm on conflict evidence is proposed. The method uses conflict mass as a basis for judgment in conflict situations. DST rules are adopted when conflict mass is lower. And DSmT fusion algorithm was adopted while opposition. During the switch between DST and DSmT, a mutual belief degree and support degree of the evidence is calculated firstly. Then the weight is obtained. Finally the conflict mass redistributed to every focus element weightily. Compared with the exist methods, this new algorithm has the advantage of better performance and more rational.
Indirectly heated micro-electrothermal actuator with a monolithically integrated displacement sensor
Authors: Kongying Xie, Yongjun Lai
Venue: Microsystem Technologies (2009)

In this article, an indirectly heated micro-electrothermal actuator is presented. The actuator has a capacitive sensor monolithically integrated to provide in vivo sensing of the actuator&#x2019;s displacement. The actuator and sensor are fabricated using the PolyMUMPs process. Electrothermal and thermal-mechanical models of the actuator have been developed. Simulated displacement versus input current reasonably agrees with tested results. The integrated capacitive sensor readout is an indicator for the displacement.
Predicting investment behavior: An augmented reinforcement learning model
Authors: Tetsuya Shimokawa, Kyoko Suzuki, Tadanobu Misawa, Yoshitaka Okano
Venue: Neurocomputing (2009)

The goal of this paper is to augment the ordinal temporal-difference type (TD-type) reinforcement learning model in order to detect the most suitable learning model of the human decision-making process in financial investment tasks. The simplicity and robustness of the TD-type learning model is fascinating. However, the available evidence and our observation suggest the necessity of introducing the nonlinear effect in learning and the possibility that additional factors might play important roles in the investment decision-making process. To extend the ordinal TD-type learning model, we adopt a three-layered perceptron as the basis function and the hierarchical Bayesian method to calibrate the parameter values. The result of the predictive test suggests that the augmented TD-type learning model constructed in this paper can evade the overfitting and can predict people's investment behavior well as compared to other familiar learning models.
Adaptive XML Tree Classification on Evolving Data Streams
Authors: Albert Bifet, Ricard Gavaldà
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

We propose a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.
Towards Intelligent Computer Assisted Educational Role-Play
Authors: Mei Yii Lim, Ruth Aylett, Sibylle Enz, Michael Kriegel, Natalie Vannini, Lynne Hall, Susan Jones
Venue: Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development (2009)

This paper investigates how graphically displayed intelligent virtual actors, mobile devices and innovative interaction modalities can support and enhance educational role-play as well as deepen the sense of engagement and presence in participants to produce more successful learning. The discussion will be presented using a showcase from the eCIRCUS project, ORIENT, an application combining virtual and real life role-play for social and emotional learning.
Catalysing the Development of a Conference Workspace
Authors: Jukka Huhtamäki, Ossi Nykänen, Jaakko Salonen
Venue: Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009 (2009)

Modern Web introduces new means to support conference information retrieval and, moreover, social tools that enable conference delegates to actively contribute to a conference. Simultaneously, existing case studies show that collecting explicitly represented conference data is possible in real-life settings. From these grounds, methods and tools of component-based information visualisation can be used to process conference data, firstly, in order to create visualisations of the conference data, and, secondly, to produce data for populating interactive conference tools. We claim that when coupled with usage tracking and analysis, the automated creation of visualisations and population data can catalyse the iterative development of a conference workspace. In this article, we present case OPAALS 2008 where formal conference data was successfully harvested and utilised to partly automate the creation workflow of the conference workspace. Moreover, we introduce the methods that were used to gain insight on the dynamics of the workspace usage.
Web Site Description Based on Genres and Web Design Patterns
Authors: Milos Kudelka, Vaclav Snasel, Zdenek Horak, Ajith Abraham
Venue: Proceedings of the 2009 International Workshop on Social Informatics (2009)

In our paper we are looking for such Web site descriptionthat can be used for simple visualization and reasoningabout Web sites similarity from the views of users, developersand owners. For this description we use objects closely related toWeb genres and Web design patterns. We detect these objects onsingle page using our own Pattrio method. Web site descriptionthen comes from the frequency of these objects on individualpages. An experiment proving usability of our approach andpossible direction for further research is also included.
An Introduction to Biodiversity Informatics
Authors: Indra Neil Sarkar
Venue:  (2009)

This textbook is designed as a general introduction to the emerging discipline of biodiversity informatics. It is thus the first of its kind to describe the key components of how to deal with or use informatics techniques in order to manage biodiversity knowledge. Designed primarily for senior-level undergraduates and first- or second-year graduate students, this textbook provides a fundamental overview of the key concepts and techniques for managing biodiversity data. The textbook first starts by describing the management of information from database and natural language sources, then shifts to molecular data management, and finally suggests ways that biodiversity data can be used to inform other related disciplines. 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
Data structures for generalised arc consistency for extensional constraints
Authors: Ian P. Gent, Chris Jefferson, Ian Miguel, Peter Nightingale
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

Extensional (table) constraints are an important tool for attacking combinatorial problems with constraint programming. Recently there has been renewed interest in fast propagation algorithms for these constraints. We describe the use of two alternative data structures for maintaining generalised arc consistency on extensional constraints. The first, the Next-Difference list, is novel and has been developed with this application in mind. The second, the trie, is well known but its use in this context is novel. Empirical analyses demonstrate the efficiency of the resulting approaches, both in GAC-schema, and in the watched-literal table constraint in Minion.
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification
Authors: Weiwei Cheng, Eyke Hüllermeier
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.
Short communication: Grapham: Graphical models with adaptive random walk Metropolis algorithms
Authors: Matti Vihola
Venue: Computational Statistics Data Analysis (2010)

Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the observed acceptance probability. Different variants of the algorithms allow one, for example, to use these two algorithms together, employ delayed rejection and adjust several parameters of the algorithms. The implemented Metropolis-within-Gibbs update allows arbitrary sampling blocks. The software is written in C and uses a simple extension language Lua in configuration.
Using Lightweight Inference to Solve Lightweight Problems
Authors: Marc Denecker, Joost Vennekens
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

Traditionally, Logic Programming and related Non-monotonic Reasoning formalisms have mainly been applied to "hard" AI problems, such as planning, scheduling, constraint solving, belief revision, etc. In the real world of software engineering, however, these hard problems are vastly outnumbered by more mundane tasks. A significant part of software that is written today consists of applications that any reasonably experienced programmer could write in a couple of weeks, using whatever imperative language happens to be the industry standard du jour.
Motivated Reinforcement Learning: Curious Characters for Multiuser Games, 1st edition
Authors: Kathryn E. Merrick, Mary Lou Maher
Venue:  (2009)

Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment. This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems in particular multiuser, online games.
Photosensitized Oxidative Desulfurization of Thiophene by Riboflavin
Authors: Fa-Tang Li, Rui-Hong Liu
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Photosensitized oxidative desulfurization of thiophene in n-octane/water extraction system with riboflavin as photosensitizer and O2 in air as oxidant was studied. A 500W high pressure Hg lamp was used as the light source for irradiation and air was introduced by a gas pump for supplying O2. The desulfurization yield of thiophene was 85.4% for a 5h photoirradiation under the conditions with the air flow at 150 ml•min-1, water/oil ratio at 1:1 and riboflavin concentration at 30µmol•L-1. The mechanism of photosensitized oxidative of thiophene is 1O2 exited from 3O2 by the addition of riboflavin.Under above conditions, the photooxidation kinetics of thiophene with O2/riboflavin is first-order with rate constant of 0.3277 h-1 and half-time of 1.37 h.
SPREAD&mdash;exploiting chemical features that cause differential activity behavior
Authors: Josef Scheiber, Jeremy L. Jenkins, Andreas Bender, Mariusz Milik, Dmitri Mikhailov, Sai Chetan K. Sukuru, Ben Cornett, Steven Whitebread, Laszlo Urban, John W. Davies, Meir Glick
Venue: Statistical Analysis and Data Mining (2009)

We present a novel generic method to better understand the divergent activities of molecules that often occur in orthogonal assays. The newly developed simple prediction of activity differences (SPREAD) method directly aims to model and understand the differences compounds exhibit when tested in two or more assays. By transforming the activity values from the assays into meta-categories (specifically defined for datasets under scrutiny), statistical models can be trained directly on the qualitative differences between assays. This contributes heavily toward a tangible understanding of molecular assay selectivity. Although ensembles of models could be used alternatively to predict compounds that score highly in one assay and low in another, the advantage of the SPREAD approach is that the chemical features influencing assay differences are parsed out immediately as a consequence of training the model on the coincident assay differences. By training the model that describes the difference between two or more assays, molecular substructures that are responsible for assay selectivity can be parsed out. The method was validated by using four challenging datasets. Copyright &copy; 2009 Wiley Periodicals, Inc., Statistical Analysis and Data Mining 2: 115-122, 2009
Semanta Supporting E-mail Workflows in Business Processes
Authors: Simon Scerri, Brian Davis, Siegfried Handschuh
Venue: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (2009)

Semanta provides a simple interface for semantic email.It enables machines to support email users with correctlyinterpreting, handling and keeping track of action itemswithin email messages, visualizing email workflows, andextracting tasks and appointments from email messages.