ppxscal/embedding_network huggingface dataset:
Blind channel equalization of multiple-input single-output FIR systems for chaotic digital communication systems
Authors: Gökçen Çetinel, Cabir Vural
Venue: Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science (2009)

Recently, various blind channel equalization techniques for chaotic communication systems have been developed. However, in most of these studies communication channel is assumed to be a single-input single-output (SISO) system. There is no study for multiple-input multiple-output (MIMO) unknown channel case. In this study, we propose an adaptive blind channel equalization algorithm for multiple-input single-output (MISO) chaotic communication systems in which the channel is modelled as a MIMO finite impulse response (FIR) system and the equalizer is designed as a MISO adaptive FIR filter The nonlinear predictability of a chaotic signal is exploited to derive the adaptive algorithm and the equalizer coefficients are updated by the proposed algorithm. An optimum fixed filter is designed by assuming that the channel is known. Since there do not exist a method for comparison, the proposed algorithm is compared to the optimum fixed filter. Simulations show that the method gives results very close to those of the fixed filter.
Automated Analysis of Reasoning and Argumentation Structures in Texts
Authors: Helmut Horacek
Venue: Proceeding of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference (2005)

In many application areas of intelligent systems, natural language communication is considered a major source for substantial progress, even for systems whose pure reasoning capabilities are exceptional. Unfortunately, it turns out to be extremely difficult to build adequate natural language processing facilities for the interaction with such systems. In this talk, I will expose some fundamental reasons for the difficulties associated with automatically analysing such inference-rich discourse, by elaborating discrepancies between effective human argumentation and efficient machine-based argumentative reasoning. On the human side, these discrepancies manifest themselves in several degrees of explicitness and levels of granularity, concise but ambiguous rhetorical signals, and varying conceptual perspectives, which all need to be related to uniform and fully explicit representations on the side of a machine. I will discuss approaches that aim at bridging these discrepancies to some degree, for both analysis and generation of argumentative texts. Issues addressed include disambiguation methods for discourse markers, identification of expected arguments, and dedicated content planning and structuring techniques. 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
Semantic enrichment of journal articles using chemical named entity recognition
Authors: Colin R. Batchelor, Peter T. Corbett
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

We describe the semantic enrichment of journal articles with chemical structures and biomedical ontology terms using Oscar, a program for chemical named entity recognition (NER). We describe how Oscar works and how it can been adapted for general NER. We discuss its implementation in a real publishing workflow and possible applications for enriched articles.
A grasp-knapsack hybrid for a nurse-scheduling problem
Authors: Melissa D. Goodman, Kathryn A. Dowsland, Jonathan M. Thompson
Venue: Journal of Heuristics (2009)

This paper is concerned with the application of a GRASP approach to a nurse-scheduling problem in which the objective is to optimise a set of preferences subject to a set of binding constraints. The balance between feasibility and optimality is a key issue. This is addressed by using a knapsack model to ensure that the solutions produced by the construction heuristic are easy to repair. Several construction heuristics and neighbourhoods are compared empirically. The best combination is further enhanced by a diversification strategy and a dynamic evaluation criterion. Tests show that it outperforms previously published approaches and finds optimal solutions quickly and consistently.
Dynamic situation modeling and reasoning under uncertainty
Authors: Prem Prakash Jayaraman, Arkady Zaslavsky, Jerker Delsing
Venue: Proceedings of the 2009 international conference on Pervasive services (2009)

Reasoning under uncertainty is a key challenge in context aware pervasive systems. In this paper we propose R-CS a situation based context reasoning model that employs ranking technique to rank and order context attributes. Using the proposed ranking technique and available context information, we compute dynamic situation spaces (a collection of contextual attributes that best represent a real world situation) We also propose and incorporate multilevel hierarchical contextual regions into R-CS that enables situation reasoning to be based on one or more dependent context attributes. We present a theoretical approach to compute importance and relevance of newly discovered context attributes which are not defined within the situation space definition by employing the approach of investigating similar neighboring situation spaces. R-CS builds on context spaces theory, a context model based on situation reasoning. We have implemented the proposed algorithms/approaches into R-CS and have validated them by evaluating against context spaces reasoning model.
On Importing Knowledge from Ontologies.
Authors: Alexander Borgida
Venue: Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization (2009)

This chapter focuses on the notion of importing terms from an ontology. Rather than proposing a specific formalism for this task and proving theorems about its properties, it starts by surveying a sample of lesser known papers on this topic in both the AI and Database literature. Based on this, it then derives a list of desirable properties for the notion of "importing a set of identifiers from an exporting to an importing knowledge base", and provides a framework for alternate formal definitions of this notion. Some of the more novel aspects are the idea of modifying the exporting ontology before the subset of axioms to be imported is determined, and limiting the use of imported terms. The chapter concludes with a review of the concept of "expressive power", and how it might apply to importing mechanisms.
Collaborative Support Model for Group Learners among Distance Teaching Assistant
Authors: Ryo Takaoka, Shinichi Iwasa, Ayumi Kodama, Toshio Okamoto
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

The final aim of this study is to develop collaborative support protocols among pedagogical agents to support collaborative group learning. As the first step of this research, we have designed a learning support model that includes a teacher who gives a lesson in a school and distance teaching assistants (Distance TAs) who asynchronously support group learners, who are engaged in problem-solving activities in a learning support environment. In this environment, there are three tools: a Web work tool to record and save group or individual work, a dialog pattern-based Web bulletin board tool to support remote group learning, and a meeting bulletin board tool to allow the Distance TAs to discuss support strategies and tactics. In this support environment, we performed a practice lesson, in which high school students designed a sightseeing plan in a group. Thirty-nine high school students as learners and 27 university students as Distance TAs participated in this practice. From its results, we extracted some cases of collaborative support involving Distance TAs in the learning and support process. Moreover, we propose a collaborative support model among distance teaching assistants in supporting collaborative group learning.
A high fidelity ungrounded torque feedback device: The iTorqU 2.0
Authors: Kyle N. Winfree, Jamie Gewirtz, Thomas Mather, Jonathan Fiene, Katherine J. Kuchenbecker
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This paper discusses the design and operation of the iTorqU 2.0, an ungrounded, handheld torque feedback device for haptic applications. Based upon the gyroscopic effect, the iTorqU 2.0 uses a metal flywheel inside of a two-axis actuated gimbal to create directional torques that are applied to the user's hand. The coupling of angular velocity and angular momentum creates a torque that is orthogonal to the two input angular velocities, giving the user the impression that their hand is being twisted in free air. Following a review of prior work in the field of ungrounded torque feedback devices, we first present our preliminary prototype, the iTorqU 1.0. Building on empirical observations and user feedback from a public demonstration, we revised and augmented this design to create the iTorqU 2.0. This paper covers the major mechanical, electrical, and controls design considerations that went into creating the iTorqU 2.0, along with an analysis of its torque output capabilities.
Parallax photography: creating 3D cinematic effects from stills
Authors: Ke Colin Zheng, Alex Colburn, Aseem Agarwala, Maneesh Agrawala, David Salesin, Brian Curless, Michael F. Cohen
Venue: Proceedings of Graphics Interface 2009 (2009)

We present an approach to convert a small portion of a light field with extracted depth information into a cinematic effect with simulated, smooth camera motion that exhibits a sense of 3D parallax. We develop a taxonomy of the cinematic conventions of these effects, distilled from observations of documentary film footage and organized by the number of subjects of interest in the scene. We present an automatic, content-aware approach to apply these cinematic conventions to an input light field. A face detector identifies subjects of interest. We then optimize for a camera path that conforms to a cinematic convention, maximizes apparent parallax, and avoids missing information in the input. We describe a GPU-accelerated, temporally coherent rendering algorithm that allows users to create more complex camera moves interactively, while experimenting with effects such as focal length, depth of field, and selective, depth-based desaturation or brightening. We evaluate and demonstrate our approach on a wide variety of scenes and present a user study that compares our 3D cinematic effects to their 2D counterparts.
A hybrid finite difference scheme for a class of singularly perturbed delay differential equations
Authors: Zhongdi Cen
Venue: Neural, Parallel Scientific Computations (2008)

In this paper a class of singularly perturbed delay differential equations are examined. A hybrid difference scheme with an appropriate piecewise uniform Shishkin-type mesh is derived. We prove that the method is almost second-order convergent in the maximum norm, independently of the small parameter. Numerical experiments support these theoretical results and indicate that the estimates are sharp.
Constraint Programming and Mixed Integer Linear Programming for Rescheduling Trains under Disrupted Operations
Authors: Rodrigo Acuna-Agost, Philippe Michelon, Dominique Feillet, Serigne Gueye
Venue: Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2009)

The problem of rescheduling trains under disrupted operations has received more attention in the last years because of the increasing demand in railway transportation. Railway networks are more and more saturated and the probability that delayed trains affect others is large. Therefore, it is very important to react after incidents by recalculating new arrival/departure times and reassigning tracks/platforms with the aim of minimizing the effect of propagation of incidents.
Computing For Beginners - The Next Step
Authors: Lynn Manning
Venue:  (2007)

This is my second book and is literally the 'Next Step' on from my first book "Computing for Beginners - the basics explained in Plain English". This book has been written in the same easy to follow format, with clear illustrations and detailed explanations to accompany each action. This is an easy to follow manual for the beginner who is now ready to venture further. This book covers Digital Photography (including transferring and viewing your photos, deleting unwanted photos, emailing and printing your photos); Music (including transferring music to your computer from CDs, making your own CDs, downloading music from the internet, iPods,MP3 players); Ebay (including how to register, search for items, buy, sell and what to look out for), Word-Processing (including inserting pictures into your work, resizing and moving pictures, Columns, Borders, WordArt, Envelopes and Labels) and Backing up your work.Whether you are using Windows XP or Windows Vista, this is the book for you as there are separate chapters for both XP and Vista users. Similarly there are two chapters relating to Word Processing, one for users of Word 2003 and earlier versions, and the other for users of the new version, Word 2007. 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
Numerical Learning Method for Process Neural Network
Authors: Tianshu Wu, Kunqing Xie, Guojie Song, Xingui He
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

Process neural network (PNN) dealing with process inputs is widely used. Currently, the learning method of PNN is mainly based on base functions expansion. However, selecting base functions and their parameters is much difficult, and moreover, the corresponding learning method is time consuming due to integral with numbers of base functions. A numerical learning method (NL) for PNN was proposed in this study. It represented PNN's inputs and weights functions in numerical forms and trained the network in a numerical way so that NL avoided the selections of base functions and their parameters. Experiments showed that NL based PNN was more accurate and had lower computation complexity.
Asymmetry-aware link layer services in wireless sensor networks
Authors: Junzhao Du, Weisong Shi, Kewei Sha
Venue: Journal of Embedded Computing (2009)

Recent studies in wireless sensor networks (WSNs) have observed that the irregular link quality is a common phenomenon, rather than an anomaly. The irregular link quality, especially link asymmetry, has significant impacts on the design of WSN protocols. In this paper, we propose two asymmetry-aware link layer services, including the neighborhood link quality service (NLQS) and the link relay service (LRS). The novelty of the NLQS service is taking the link asymmetry into consideration, providing timeliness link quality and distinguishing the inbound and outbound neighbors with the support of LRS, which builds a relay framework to alleviate the effects of link asymmetry. To demonstrate the proposed link layer service, we design and implement two example applications, building the shortest-hop routing path (SHRT) and the best path reliability routing path (BRRT), on the TinyOS platform. We found that the performance of two example applications is improved substantially. More than 40% of nodes identify more outbound neighbors and the percentage of increased outbound neighbors is between 14% and 100%. In SHRT, more than 15% of nodes reduce hops of the routing path and the percentage of reduced hops is between 14% and 100%. In BRRT, more than 16% of nodes improve the path reliability of the routing path and the percentage of the improved path reliability is between 2% to 50%.
Artificial neural networks for automated year-round temperature prediction
Authors: Brian A. Smith, Gerrit Hoogenboom, Ronald W. McClendon
Venue: Computers and Electronics in Agriculture (2009)

Crops and livestock in most of the southeastern United States are susceptible to potential losses due to extreme cold and heat. However, given suitable warning, agricultural and horticultural producers can mitigate the damage of extreme temperature events. To provide such a warning, air temperature prediction models are needed at horizons ranging from 1 to 12h. The goal of this project was to explore the application of artificial neural networks (ANNs) for the prediction of air temperature during the entire year based on near real-time data. Ward-style ANNs were developed using detailed weather data collected by the Georgia Automated Environmental Monitoring Network (AEMN). The ANNs were able to provide predictions throughout the year, with a mean absolute error (MAE) of the year-round models that was less during the winter months than the MAE of the models resulting from the application of previously developed winter-specific models. The prediction MAE for a year-round evaluation set ranged from 0.516^oC at the one-hour horizon to 1.873^oC at the twelve-hour horizon. A detailed graphical analysis of MAE by time-of-year and time-of-day was also performed. A tendency to over-predict temperatures during summer afternoons was associated with localized cloud cover during that period. The inclusion of rainfall as input to the model was also shown to improve prediction accuracy. In addition, two simple ensemble techniques were explored and neither parallel nor series aggregation was found to reduce prediction errors. When simulated over two extreme temperature events, the models were capable of rapidly adjusting predictions on the basis of new information. The final models were applied to prediction horizons of 1-12h and deployed on the website of the Georgia AEMN (www.GeorgiaWeather.net) for use as a general, year-round decision support tool.
Dependency Management in Smart Homes
Authors: Daniel Retkowitz, Sven Kulle
Venue: Proceedings of the 9th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (2009)

In future smart homes functionality will be provided to the inhabitants by software services decoupled from the underlying hardware devices. While this will enhance flexibility and will allow to provide cross-functionalities across multiple devices it will also lead to resource conflicts. Future devices will provide basic functionalities which are used by separate higher level services. Each person will use a number of different services and each environment can be inhabited by multiple users at the same time. All respective services have to be executed based on a limited number of devices, which will result in resource conflicts. In this paper we describe how we extended our existing dependency management approach for smart home services with a mechanism for monitoring service bindings and handling access control based on priority groups.
The Dynamic Representation of Reality and of Ourself between Real and Virtual Worlds
Authors: Lukasz Piwek
Venue: Proceeding of the 2008 conference on Current Issues in Computing and Philosophy (2008)

There seems to be a difference in the way we interact with reality and a reality experienced while playing computer games. I will argue that one of the most important features that distinguishes external world (or Open Reality) from reality experienced while playing computer games (or Closed Reality) is the degree of complexity, that is, the richness of the stimuli and the number of options available. One of the main consequences of the lower complexity of Closed Reality is that playing computer games triggers different cognitive alterations in an effortless and automatic manner. The question I ask is what really changes in our cognitive processing when we play computer games. One of the answers is that there is a change in the agent's cognitive representation of reality. Additionally I will suggest that there seems to be a change in the cognitive self while playing avatar-based computer games. I will discuss the last point in the brief context of identity problem and possible psychological implications.
Computational aspects of the expected differential probability of 4-round AES and AES-like ciphers
Authors: Joan Daemen, Mario Lamberger, Norbert Pramstaller, Vincent Rijmen, Frederik Vercauteren
Venue: Computing (2009)

In this paper we study the security of the Advanced Encryption Standard (AES) and AES-like block ciphers against differential cryptanalysis. Differential cryptanalysis is one of the most powerful methods for analyzing the security of block ciphers. Even though no formal proofs for the security of AES against differential cryptanalysis have been provided to date, some attempts to compute the maximum expected differential probability (MEDP) for two and four rounds of AES have been presented recently. In this paper, we will improve upon existing approaches in order to derive better bounds on the EDP for two and four rounds of AES based on a slightly simplified S-box. More precisely, we are able to provide the complete distribution of the EDP for two rounds of this AES variant with five active S-boxes and methods to improve the estimates for the EDP in the case of six active S-boxes.
A Head-Tracker Based on the Lucas-Kanade Optical Flow Algorithm
Authors: Frank Loewenich, Frederic Maire
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

Technology is advancing at a rapid pace, automating many everyday chores in the process, changing the way we perform work and providing various forms of entertainment. Makers of technology, however, often do not consider the needs of the disabled in their design of products by, for example, providing some alternative means of interaction with their devices. The use of computers presents a challenge to many disabled users who are not able to see graphical user interfaces, use a mouse or keyboard or otherwise interact with standard computers. This paper introduces a head-tracker based on the use of a modified Lucas-Kanade optical-flow algorithm for tracking head movements, eliminating the need to locate and track specific facial features. The implementation presents an alternative to the traditional mouse input device.
Evolutionary Approach to Data Discretization for Rough Sets Theory
Authors: Jacek Czerniak
Venue: Fundamenta Informaticae (2009)

This article presents the LDGen method which is based on genetic algorithm. The author proposed evolutionary approach to the solution of the discretization problem for systems that induce rules on the basis of rough sets theory. The study describes details of the method with special focus on the crossing operator. The proposed approach concerns working with multidimensional samples. Thanks to application of the author's own method of for visualizing multidimensionality, i.e. so called Pipes of Samples, it was possible to visualize up to 360 dimensions, which is usually sufficient in case of problems the Rough Sets Theory deals with. Mutation and crossing methods were developed using this visualisation so that, for real numbers, it allowed to create individuals that describe one solution of the discretization. Hence the population is a set of many complete discretizations of all the attributes.