ppxscal/embedding_network huggingface dataset:
Answer Set Programming with Constraints Using Lazy Grounding
Authors: Alessandro Palù, Agostino Dovier, Enrico Pontelli, Gianfranco Rossi
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

The paper describes a novel methodology to compute stable models in Answer Set Programming. The proposed approach relies on a bottom-up computation that does not require a preliminary grounding phase. The implementation of the framework can be completely realized within the framework of Constraint Logic Programming over finite domains. The use of a high level language for the implementation and the clean structure of the computation offer an ideal framework for the implementation of extensions of Answer Set Programming. In this work, we demonstrate how non-ground arithmetic constraints can be easily introduced in the computation model. The paper provides preliminary experimental results which confirm the potential for this approach.
Wetland Extraction in Sanjiang Plain Based on Self-Organized Feature Map Neural Network Clustering Model
Authors: Liu Hanli, Pei Tao, Zhou Chenghu
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

By analyzing spectral characteristics of MODIS remote sensing data in Sanjiang Plain, we extract the wetland in this area based on a set of multi-temporal and multi-spectral MODIS data. We first transform the selected data by a Minimum Noise Fraction (MNF) rotation and take the first two components of the transformed data as the experimental data. To improve the accuracy of classification of wetland, we use a Self-organization Feature Map (SOM) neural network model. SOM has a good performance in resisting noise and can be implemented with parallel processing technique. It is capable of keeping the topological structure of the original data. As a result, it may achieve a better classification compared with other clustering models. After clustering, we perform a discrete wavelet transform (DWT) to smooth the data and eliminate noise from the data. The result shows that the SOM model is effective and the clustering result has been improved.
WordNet::Similarity: measuring the relatedness of concepts
Authors: Ted Pedersen, Siddharth Patwardhan, Jason Michelizzi
Venue: Demonstration Papers at HLT-NAACL 2004 on XX (2004)

WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.
Temperature and humidity measurements in nasal cavity
Authors: Tsakiropoulou Evangelia, Konstantakos Vasileios, Leiacker Richard, Rettinger Gerhard, Lindemann Joerg
Venue: Proceedings of the 2009 IEEE International Workshop on Medical Measurements and Applications (2009)

This work presents a method for making temperature and humidity measurements within the nasal cavity. First, the need for obtaining such measurements and the necessity of implementing such a practical set up is pointed out. Then, a measuring scheme is presented with focused characteristics like accuracy, and fast response to the quantities being monitored.
Multimodal Biometrics Recognition by Dimensionality Reduction Method
Authors: Cheng Lu, Di Liu, Jisong Wang, Shuyan Wang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

Multimodal biometric system utilizes two or moreindividual modalities, e.g., face, ear, and fingerprint, toimprove the recognition accuracy of conventional unimodalmethods. We propose a new dimensionality reduction methodcalled Dimension Reduce Projection (DRP) in this paper. DRPcan not only preserve local information by capturing the intramodal geometry, but also extract between-class relevant structures for classification effectively. Experimental results show that our proposed method performs better than other algorithms including PCA, LDA and MFA.
Ontology-Based Service Composition Framework for Syndicating Building Intelligence
Authors: Wei-Tek Tsai, Yann-Hang Lee, Avi Wiezel, Xin Sun, Wu Li
Venue: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (2009)

Building Information Model (BIM) encompasses building geometry, spatial relationships, geographic information, and quantities and properties of building components; however, it does not include any information related to the functions that a building might require. On the other side, Service–Oriented computing emerged as a promising technology to enable fast, efficient software development. Even though features such as standard protocols, loosely coupled components, ontology-enabled dynamic discovery and composition of SOA seems a perfect fit for the building construction domain, the potential gain of applying SOA techniques has not really been discovered. In the paper, an ontology-based service composition framework for construction domain is proposed, and BIM is extended to include function requirements and service specifications. An example is shown to demonstrate the advantages over traditional techniques also.
Digital Watermarking Schemes Using Multi-resolution Curvelet and HVS Model
Authors: H. Y. Leung, L. M. Cheng, L. L. Cheng
Venue: Proceedings of the 8th International Workshop on Digital Watermarking (2009)

In this paper, a robust non-blind watermarking scheme based on Curvelet transform is proposed. This work extends the work proposed by Leung [1] to increase the quality of watermarked image. The proposed algorithm modifies the watermark extracting rule and adds a Human Visual System (HVS model). The experimental results demonstrate that the proposed algorithm can provide great robustness against most image processing methods.
Study on the Influence of Signal Countdown Device on Traffic Safety of Intersections
Authors: Keping Li, Sheng Dong, Jian Sun, Xuan Yu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

This paper applies the principle of vehicle dynamics and traffic conflict theory to analyze the driver behaviors and speed of next two vehicles with green lights. Based on the analysis, we propose the smallest calculation formula of green change interval which ensure vehicles cross intersection safely. Combining with traffic psychology, we study the impact of signal countdown device to green change interval which is the key security factor of intersection and analyze the safety efficiency of signal countdown device. At last, the conclusions are given.
eBusiness & eCommerce: Managing the Digital Value Chain, 1st edition
Authors: Andreas Meier, Henrik Stormer
Venue:  (2009)

This textbook focuses on the members of the digital value chain for eBusiness and eCommerce and dedicates a separate chapter to eachof them: eProductsand eServices, eProcurement, eMarketing, eContracting, eDistribution, ePayment, as well as eCustomer relationship management. In addition to business models and business webs, digital procurement and marketing processes such as electronic negotiation processes, security questions with digital signatures, as well as electronic supplier relationship management and customer relationship management are also addressed.The topics are described using explicit procedures and descriptive examples of their application. A case study describing the gradual creation of an electronic webshop for DVDs is developed over the course of the book. The book is intendedforstudents of economicsat universities and technical colleges; it is also suitable for executives, project leaders, and company experts who deal with the digital value chain.
Social Adaptation of ERP Software: Tagging UI Elements
Authors: Marcus Nitsche, Martin Christof Kindsmüller, Udo Arend, Michael Herczeg
Venue: Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009 (2009)

In this paper we present a newly designed annotation and collaboration component, which has been prototypically implemented on top of an ERP (Enterprise Resource Planning) system. Standard ERP software is often described as being inflexible in respect to personal needs of a single user or a user group. To cope with this problem, theories, and principles from classical CSCW research and design were combined with recent developments in Social Software and what is now often summarized as Web 2.0. Our component is inspired by Web 2.0 principles like user generated content, information sharing, and harnessing network effects. As the central paradigm we applied social tagging based on folksonomies as e.g. used in del.ico.us, Flickr, and YouTube. In addition best practices from research on online community building were used to design a social annotating component for ERP systems.
Knowledge Representation in Difficult Medical Diagnosis
Authors: Ana Aguilera, Alberto Subero
Venue: Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects (2009)

This article is based on medical knowledge produced thought collaborative problem solving by a group of experts, in the field of medical diagnosis. In this work, we propose a representation format for a medical case base into a traditional RDBMS representation, which is queryable using standard SQL. We are concerned in difficult medical cases which imply a solution in several steps with several expert solvers (medical specialists). Some queries on this case base are proposed. A case base was implemented and validated in real time with experts on the real scenarios.
Generated narratives for computer-aided language teaching
Authors: Michael Levison, Greg Lessard
Venue: Proceedings of the Workshop on eLearning for Computational Linguistics and Computational Linguistics for eLearning (2004)

VINCI is a Natural Language Generation environment designed for use in computer-aided second language instruction. It dynamically generates multiple parallel trees representing an initial text, questions on this text, and expected answers, and either orthographic or phonetic output. Analyses of a learner's answers to questions are used to diagnose comprehension and language skills and to adaptively control subsequent generation. The paper traces stages in the generation of short texts in English and French, and discusses issues of architecture, textual enrichment, and planning.
Model minimization, regression, and propositional STRIPS planning
Authors: Robert Givan, Thomas Dean
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

Propositional STRIPS planning problems can be viewed as finite state automata (FSAs) represented in a factored form. Automaton minimization is a well-known technique for reducing the size of an explicit FSA. Recent work in computer-aided verification on model checking has extended this technique to provide automaton minimization algorithms for factored FSAs. In this paper, we consider the relationship between STRIPS problem-solving techniques such as regression and the recently developed automaton minimization techniques for factored FSAs. We show that regression computes a partial and approximate minimized form of the FSA corresponding to the STRIPS problem. We then define a systematic form of regression which computes a partial but exact minimized form of the associated FSA. We also relate minimization to methods for performing reachability analysis to detect irrelevant fluents. Finally, we show that exact computation of the minimized automaton is NP-complete under the assumption that this automaton is polynomial in size.
Test bed design for detect and avoid of ultra wideband devices
Authors: Gianmarco Baldini, Janie Baños, Xiaochen Chen
Venue: Proceedings of the 2009 ACM workshop on Cognitive radio networks (2009)

Ultra Wideband (UWB) radio regulations have been debated for long time because of the potential interference that can be caused by UWB radios to other radio systems with which they share the spectrum. A mitigation technique for UWB devices called Detect and Avoid (DAA) has been proposed as a potential solution to decrease the risk of interference. Radio regulators have proposed the adoption of DAA in certain regulatory domains and for specific radio frequency bands. DAA can be considered a simple form of cognitive radio, where UWB devices sense the presence of the primary user and use the spectrum only if it is deemed empty. DAA testing has distinctive technical challenges including the detection of very low emission power levels, which require very sensitive test equipment. The paper presents the design of a test bed to verify the correct implementation of DAA mitigation techniques by UWB devices. The paper describes the testing challenges and related test bed design solutions for both radiated and conducted test environments.
Pose tracking from natural features on mobile phones
Authors: Daniel Wagner, Gerhard Reitmayr, Alessandro Mulloni, Tom Drummond, Dieter Schmalstieg
Venue: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality (2008)

In this paper we present two techniques for natural feature tracking in real-time on mobile phones. We achieve interactive frame rates of up to 20Hz for natural feature tracking from textured planar targets on current-generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. We present evaluations on robustness and performance on various devices and finally discuss their appropriateness for Augmented Reality applications.
Task-Based Second Language Learning Game System
Authors: Jun'Ichi Hoshino, Tetsuya Saito, Shiratori Kazuto
Venue: Proceedings of the 8th International Conference on Entertainment Computing (2009)

In traditional English learning as a second language, learners rarely have the opportunity to practice oral communication, so the acquisition of oral proficiency is a slow process. In this paper, we propose a task-based second language learning game system. The task-based learning method enables learners to obtain communicative skills through the practice of particular "missions" using voice and gesture communications with life-size 3D game character.
Computational tools for the analysis of spatial patterns of gene expression in Common Lisp
Authors: Cyrus Harmon
Venue: Proceedings of the 2007 International Lisp Conference (2007)

Scientific and technological advances in biotechnology have led to the determination of the sequence of the genomes of a growing number of organisms. Tools such as DNA microarrays offer the ability to determine the levels of expression of substantially all of the genes in a given genome in a single experiment, yet are limited to providing the levels of expression across an ensemble of cells, such as a particular organ or the entire organism. In order to understand the developmental roles of these genes, it is highly desirable not just to know if a given gene expressed, but in which tissues and at which times a given gene is expressed. Using high-throughput laboratory techniques we have constructed an atlas of spatial patterns of gene expression in Drosophila melanogaster imaginal discs. Building such an atlas requires a diverse array of computational techniques and, ideally, the atlas would be presented to the users not just in a static, browsable form, but in a computable form, along with tools to analyze the data contained in the atlas. To this end, we have developed a number of algorithms, tools and libraries, implemented in Common Lisp, to address problems such as matrix and image data representation, computer vision tasks, knowledge representation of the genes in the genome and maps of their global and spatial patterns of gene expression, and to query and analyze these data sets.
A New Data Mining Algorithm Based on Improved Neural Network
Authors: Niugai Fang, Jing Wang, Qingyu Sun
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

The application of neural networks in the data mining has become wider and wider. Neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. We focus on the data mining process based on neural network. In this paper the data mining based on adaptive neural network is researched in detail, the key technology and ways to achieve the data mining based on neural networks are also researched.
Agile Career Development: Lessons and Approaches from IBM, 1st edition
Authors: Mary Ann Bopp, Diana A. Bing, Sheila Forte-Trammell
Venue:  (2009)

Supercharge Performance by Linking Employee-Driven Career Development with Business Goals How do you make career development work for both the employee and the business? IBM has done it by tightly linking employee-driven career development programs with corporate goals. In Agile Career Development, three of IBMs leading HR innovators show how IBM has accomplished this by illustrating various lessons and approaches that can be applied to other organizations as well. This book is for every HR professional, learning or training manager, executive, strategist, and any other business leader who wants to create a high performing organization. In the 21st century, there will be an increasing competitive need for any company to operate as a globally integrated enterprise that can effectively develop and then tap the skills and capabilities of its workforce anywhere in the world. In IBM, we have worked to enable a workforce that is adaptive, flexible, and capable of responding to changes in the marketplace and the needs of our clients. Agile Career Development shows how focusing on career development opportunities and guidance for employees is a key factor in our business strategy and a major source of value for IBM employees. This book can be used as a guide to any organization that is seeking to find practical ways to develop the talent of its workforce. J. Randall MacDonald, Senior Vice President, IBM Human Resources This book highlights tried and true best practices developed at a company known the world over for active dedication to their workforce. Mary Ann, Diana, and Sheila have captured the key issues that will enhance and streamline your career development program and, subsequently, increase employee engagement, retention, and productivity. I particularly like their practical, real-life understanding of the barriers to most career development programs and the manageable framework to bring career growth to life. They also teach us how to make a business case for career developmentcritical in creating the foundation for a sustainable program. This includes a good blend of benefits both for the individual employee and the organization as a whole. I only wish I had this book available to me years ago when I was managing a career development program! Jim Kirkpatrick, Ph.D., author of Implementing the Four Levels of Transferring Learning to Behavior
Data Collection with Multiple Sinks in Wireless Sensor Networks
Authors: Sixia Chen, Matthew Coolbeth, Hieu Dinh, Yoo-Ah Kim, Bing Wang
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

In this paper, we consider Multiple-Sink Data Collection Problem in wireless sensor networks, where a large amount of data from sensor nodes need to be transmitted to one of multiple sinks. We design an approximation algorithm to minimize the latency of data collection schedule and show that it gives a constant-factor performance guarantee. We also present a heuristic algorithm based on breadth first search for this problem. Using simulation, we evaluate the performance of these two algorithms, and show that the approximation algorithm outperforms the heuristic up to 60%.
An Improved ID-Based Multi-Proxy Multi-Signcryption Scheme
Authors: Zhou Xiaoyan, Wu Yan, Du Weifeng, Gao Yan
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

In 2007, Sunder Lal and Tej Singh gave an ID-based multi-proxy multi-signcryption scheme. By analysis, we discover that Sunder Lal and Tej Singh's scheme wrongly used two types of undefined bilinear pairing calculations, which caused that the calculation of Lal and Tej Singh's scheme is unfeasible. Against the error, we give an improved scheme. To avoid of using these undefined calculations and make it feasible, the improved scheme takes use of a more Hash function. At last, the security of the improved scheme is analyzed.
Two approaches to aspect assignment in an English-Polish machine translation system
Authors: Anna Kupść
Venue: Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT (2003)

The paper presents two approaches to aspect assignment in a knowledge-based English-Polish machine translation (MT) system. The first method uses a set of heuristic rules based on interlingua (IR) representation provided by the system, whereas the other employs machine learning techniques. Both methods have similar performance and obtain high accuracy of over 88% on test data. The crucial difference, however, is the development effort: the machine learning technique is fully automatic, whereas heuristic rules are derived manually.
Experiencing the Improbable: Rare Events and Organizational Learning
Authors: Joseph Lampel, Jamal Shamsie, Zur Shapira
Venue: Organization Science (2009)

Organizations that go through rare and unusual events, whether they are costly or beneficial, face the challenge of interpreting and learning from these experiences. Although research suggests that organizations respond to this challenge in a variety of ways, we lack a framework for comparing and analyzing how organizational learning is affected by rare events. This paper develops such a framework. We begin by first outlining two views of rare events. The first view defines rare events as probability estimates, usually calculated from the frequency of the event. The second view defines rare events as opportunities for unique sensemaking based on the enacted salience of specific features of the rare events. We next use these definitions to explore how rare events trigger learning, and then examine the kind of learning processes that are triggered by rare events. We conclude with a discussion of promising areas of research on learning from rare events.