ppxscal/embedding_network huggingface dataset:
Function-based visualization and haptic rendering in shared virtual spaces
Authors: Lei Wei, Alexei Sourin, Olga Sourina
Venue: The Visual Computer: International Journal of Computer Graphics (2008)

We seek to further expand the collaborative potential of shared virtual spaces by using haptic force-feedback. We propose how to define tangible physical properties of the objects, together with their geometry and appearance, by using mathematical functions. We illustrate this concept by developing software which allows us to touch and feel surfaces of VRML and X3D objects, convert them to solid objects, as well as create any other solid objects using the function-based extension of VRML and&#x00a0;X3D. We define geometry, appearance and tangible physical properties of the solid objects by implicit, explicit and parametric functions straight in the VRML/X3D code or in loadable libraries. Since the function-defined models are small in size, it is possible to perform their collaborative interactive modifications with concurrent synchronous visualization at each client computer with any required level of detail. We illustrate the proposed models with several application examples.
Classical Program Extraction in the Calculus of Constructions
Authors: Alexandre Miquel
Venue: Proceedings of the 21st international workshop and the 16th Annual Conference of the EACSL on Computer Science Logic (2007)

We show how to extract classical programs expressed in Krivine ¿ c -calculus from proof-terms built in a proof-irrelevant and classical version of the calculus of constructions with universes. For that, we extend Krivine's realisability model of classical second-order arithmetic to the calculus of constructions with universes using a structure of ¿-set which is reminiscent of ¿-sets, and show that our realisability model validates Peirce's law and proof-irrelevance. Finally, we extend the extraction scheme to a primitive data-type of natural numbers in a way which preserves the whole compatibility with the classical realisability interpretation of second-order arithmetic. 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
Learning MultiLinguistic Knowledge for Opinion Analysis
Authors: Ruifeng Xu, Kam-Fai Wong, Qin Lu, Yunqing Xia
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Most existing opinion analysis techniques used word-level sentiment knowledge but lack the learning capacity on the behaviors of context-dependent opinion words. Meanwhile, the use of collocation-level sentiment knowledge is not well studied. This paper presents an opinion analysis system, namely OA, which incorporates the word-level and collocation-level sentiment knowledge. Based on the observation on the NTCIR-6 opinion training corpus, some word-level and collocation-level linguistic clues for opinion analysis are discovered. Learning techniques are developed to learn the features corresponding to these discovered clues. These features are in turn incorporated into a classifier based on support vector machine to identify opinionated sentences and determine their polarities from running text. Evaluations on NTCIR-6 opinion testing dataset show that OA achieved promising overall performance.
Amazons Discover Monte-Carlo
Authors: Richard J. Lorentz
Venue: Proceedings of the 6th international conference on Computers and Games (2008)

Monte-Carlo algorithms and their UCT-like successors have recently shown remarkable promise for Go-playing programs. We apply some of these same algorithms to an Amazons-playing program. Our experiments suggest that a pure MC/UCT type program for playing Amazons has little promise, but by using strong evaluation functions we are able to create a hybrid MC/UCT program that is superior to both the basic MC/UCT program and the conventional minimax-based programs. The MC/UCT program is able to beat Invader, a strong minimax program, over 80% of the time at tournament time controls.
Robust line detection methods
Authors: Costin-Anton Boiangiu, Bogdan Raducanu
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

Line Detection is an important problem that arises in automatic content conversion systems (among other domains). Such a system is used to generate digital documents from hard copies of books, newspapers, etc. Given a black and white image, usually obtained from a scanning device, our task is to identify groups of pixels which form horizontal or vertical lines. There are various techniques used to solve this problem. In this paper we analyze a common approach (using the Hough transform), introduce a new method and compare the results.
Learning in decentralized systems: a nonparametric approach
Authors: Michael I. Jordan
Venue:  (2007)

Rapid advances in information technology result in increased deployment of decentralized decision-making systems embedded within large-scale infrastructure consisting of data collection and processing devices. In such a system, each statistical decision is performed on the basis of limited amount of data due to constraints given by the decentralized system. For instance, the constraints maybe imposed by limits in energy source, communication bandwidth, computation or time budget. A fundamental research problem arised in decentralized systems involves the development methods that takes into account not only the statistical accuracy of decision-making procedures, but also the constraints imposed by the system limits. It is this general problem that drives the focus of this thesis. In particular, we focus on the development and analysis of statistical learning methods for decentralized decision-making by employing a nonparametric approach. The nonparametric approach imposes very little a priori assumption on the data; such flexibility allows it to be applicable to a wide range of applications. Coupled with tools from convex analysis and empirical process theory we develop computationally efficient algorithms and analyze their statistical 1 behavior both theoretically and empirically. Our specific contributions include the following. We develop a novel kernel-based algorithm for centralized detection and estimation in the ad hoc sensor networks through the challenging task of sensor mote localization. Next, we develop and analyze a nonparametric decentralized detection algorithm using the methodology of convex surrogate loss functions and marginalized kernels. The analysis of this algorithm leads to an in-depth study of the correspondence between the class of surrogate loss functions widely used in statistical machine learning and the class of divergence functionals widely used in information theory. This correspondence allows us to provide an interesting decision-theoretic justification to a given choice of divergence functionals, which often arise from asymptotic analysis. In addition, this correspondence also motivates the development and analysis of a novel M-estimation procedure for estimating divergence functionals and the likelihood ratio. Finally, we also investigate a sequential setting of the decentralized detection algorithm, and settle an open question regarding the characterization of optimal decision rules in such a setting.
The Composition of Event-B Models
Authors: Michael Poppleton
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

The transition from classical B [2] to the Event-B language and method [3] has seen the removal of some forms of model structuring and composition, with the intention of reinventing them in future. This work contributes to that reinvention. Inspired by a proposed method for state-based decomposition and refinement [5] of an Event-B model, we propose a familiar parallel event composition (over disjoint state variable lists), and the less familiar event fusion (over intersecting state variable lists). A brief motivation is provided for these and other forms of composition of models, in terms of feature-based modelling. We show that model consistency is preserved under such compositions. More significantly we show that model composition preserves refinement.
Dissociation between decoding and reasoning about mental states in patients with theory of mind reasoning impairments
Authors: Progress Njomboro, Shoumitro Deb, Glyn W. Humphreys
Venue: Journal of Cognitive Neuroscience (2008)

Theory of mind (ToM) reasoning may involve a multiplicity of processes, including an initial stage, where cues relevant for social processes are detected and decoded, and a mentalizing stage, where the decoded information is used to reason about mental states. Here we report that the processing of lower-order facial cues relevant to social judgments can be relatively spared in patients with impaired ToM reasoning. We discuss the implications for understanding the mechanisms underlying social judgments in brain-lesioned patients.
Multimedia content transformation: fragmentation, enrichment, and adaptation
Authors: Pablo Cesar, Dick C.A. Bulterman, Jack Jansen, Maria da Graca Pimentel, Simone Barbosa
Venue: Proceeding of the eighth ACM symposium on Document engineering (2008)

This working session will be an interactive discussion about multimedia content transformation. The basic assumption is that content transformation activities should be provided as non-destructive operations. The final goal of the panel is to gather researchers within the community interested in manipulating multimedia content for providing rich user experiences. The organizers of the panel will moderate and shape the discussion; nevertheless, position papers from the participants are expected.
GPU accelerated pathfinding
Authors: Avi Bleiweiss
Venue: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware (2008)

In the past few years the graphics programmable processor (GPU) has evolved into an increasingly convincing computational resource for non graphics applications. The GPU is especially well suited to address problem sets expressed as data parallel computation with the same program executed on many data elements concurrently. In pursuing a scalable navigation planning approach for many thousands of agents in crowded game scenes, developers became more attracted to decomposable movement algorithms that lend to explicit parallelism. Pathfinding is one key computational intelligence action in games that is typified by intense search over sparse graph data structures. This paper describes an efficient GPU implementation of parallel global pathfinding using the CUDA programming environment, and demonstrates GPU performance scale advantage in executing an inherently irregular and divergent algorithm.
A Vision for Platform Security
Authors: Ernie Brickell
Venue: Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems (2008)

Intel has recently produced several new capabilities to enhance security on the platform that have been released or will be released in the near future. In this presentation I will give a review of these capabilities and discuss their benefit to the security of the platform.
Distributed mining of censored production rules in data streams: an evolutionary approach
Authors: Saroj Saroj, K. K. Bharadwaj
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

As distributed data streams are gaining importance in a growing number of applications, the centralized approach for data mining is inappropriate for the distributed and ubiquitous data mining environments. The conventional mining algorithms for data streams depend on computationally expensive update procedures to incorporate the changing patterns in the streaming data. Moreover, the most common knowledge structure learnt, in knowledge discovery, is standard Production Rules (PRs) in the form: If P Then D. However, PRs ignore exceptions as noise. These are not efficient for approximate reasoning and unable to exhibit variable precision logic in the reasoning process due to rigidity in their structure. This paper proposes an evolutionary approach for distributed mining of CPRs using cumulative learning scheme. Local classifiers consisting of PRs and CPRs are generated for the data streams at distributed sites and then a meta-classifier is produced by combining the local classifiers. A Censored Production Rule (CPR) is an extension of PR and is of the form, If P Then D Unless C, where C is the censor representing exception condition. 'If P Then D' part of a CPR holds frequently and the censor C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight. Thus 'If P Then D' part of the CPR expresses important information while the 'Unless C' part acts only as a switch that changes the polarity of D to ~D, whenever a censor evaluates true A Genetic Algorithm is designed with a fixed length chromosome encoding that allows variable length rules. Appropriate genetic operators are suggested for the specific encoding and a fitness function incorporating the constraints of CPRs is formulated. Experimental results are presented to demonstrate the effectivity of CPR for cumulative learning in data streams.
Parallel iterator for parallelising object oriented applications
Authors: Nasser Giacaman, Oliver Sinnen
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

With the advent of multi-core processors, desktop application developers must finally face parallel computing and its challenges. A large portion of the computational load in a program rests within iterative computations. In object-oriented languages these are commonly handled using iterators which are inadequate for parallel programming. This paper presents a powerful parallel iterator concept for object-oriented programmers to use for the parallel traversal of a collection of elements. The parallel iterator allows the structure of the program to remain unchanged, it may be used with any collection type (even those inherently sequential) and it supports several scheduling schemes which may even be decided dynamically at run-time. Along with the ease of use, the results reveal negligible overhead and the expected inherent speedup.
Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors
Authors: Li Liu, Wenxin Liu, David A. Cartes
Venue: Engineering Applications of Artificial Intelligence (2008)

The high-performance application of high-power permanent magnet synchronous motors (PMSM) is increasing. PMSM models with accurate parameters are significant for precise control system designs. Acquisition of these parameters during motor operation is a challenging task due to the inherent nonlinearity of motor dynamics. This paper proposes an intelligent model parameter identification method using particle swarm optimization (PSO). PSO, an intelligent computational method based on stochastic search, is shown to be a versatile and efficient tool for this complicated engineering problem. Through both simulation and experiment, this paper verifies the effectiveness of the proposed method in identification of PMSM model parameters. Specifically, stator resistance and load torque disturbance are identified in this PMSM application. Though PMSM is presented, the method is generally applicable to other types of electrical motors, as well as other dynamic systems with nonlinear model structure.
Generalized Dynamic Fuzzy Neural Network-Based Tracking Control of Robot Manipulators
Authors: Qiguang Zhu, Hongrui Wang, Jinzhuang Xiao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A robust adaptive control based on generalized dynamic fuzzy neural network (GD-FNN) is presented for robot manipulators. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. Being use of radial basis function neural network (RBFNN) the learning speed is very fast. The asymptotic stability of the control system is established using Lyapunov theorem. Simulations are given for a two-link robot in the end of paper, and validated the control arithmetic.
Correlation-Aware Object Placement for Multi-Object Operations
Authors: Ming Zhong, Kai Shen, Joel Seiferas
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

A multi-object operation incurs communication or synchronization overhead when the requested objects are distributed over different nodes. The object pair correlations (the probability for a pair of objects to be requested together in an operation) are often highly skewed and yet stable over time for real-world distributed applications. Thus, placing strongly correlated objects on the same node (subject to node space constraint) tends to reduce communication overhead for multi-object operations. This paper studies the optimization of correlation-aware data placement. First, we formalize a restricted form of the problem as a variant of the classic Quadratic Assignment problem and we show that it is NP-hard. Based on a linear programming relaxation, we then propose a polynomial-time approximation algorithm that finds an object placement with communication overhead at most two times that of the optimal placement. We further show that the computation cost can be reduced by limiting the optimization scope to a relatively small number of most important objects. We quantitatively evaluate our approach on keyword index placement for full-text search engines using real traces of 3.7 million web pages and 6.8 million search queries. Compared to the correlation-oblivious random object placement, our approach achieves 37–86% communication overhead reduction on a range of optimization scopes and system sizes. The communication reduction is 30–78% compared to a correlation-aware greedy approach.
Analysis of IEEE 802.11e Line Topology Scenarios in the Presence of Hidden Nodes
Authors: Katarzyna Kosek, Marek Natkaniec, Andrzej R. Pach
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

In this paper an innovative simulation study of five IEEE 802.11e network configurations is presented. The conducted analysis is crucial for understanding how a theoretically simple and, most of all, popular line topology network can be degraded by the presence of hidden and exposed nodes. The discussion of the obtained results helps to understand how and why the behavior of IEEE 802.11e based line topologies changes when the number of nodes increases. Furthermore, the usefulness of the four-way handshake mechanism is argued. Finally, the need for a better MAC protocol is stressed and a number of novel conclusions about the IEEE 802.11e nature is provided.
Alternatives for scheduling virtual machines in real-time embedded systems
Authors: Robert Kaiser
Venue: Proceedings of the 1st workshop on Isolation and integration in embedded systems (2008)

Embedded systems have so far hardly been regarded as a viable target for virtualisation, although this would definitely make sense: Today, embedded systems are facing many of the same problems that once initiated the consolidation movement in the server world. It seems only logical to apply the technology that has worked so well for servers to embedded devices now. However, embedded systems also have a requirement which is new to virtualisation: They must support real-time applications. In this contribution, we concentrate on the problem of achieving temporal determinism with virtual machines. We will give an estimation of the impact that virtualisation has on the timely execution of programs and we suggest some approaches how -- taking into account the typical requirements of real-time programs -- better timing predictability can be achieved.
AI-RPG Toolkit: Towards A Deep Model Implementation for Improvisational Virtual Drama
Authors: Chung-Cheng Chiu, Edward Chao-Chun Kao, Paul Hsueh-Min Chang, Von-Wun Soo
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

The form of improvisational drama allows participants to have their own choices to influence the ongoing story, and each play results in a different ending. However, authoring such story contents requires ad hoc scripting, and static story structures lose ingenuity once users hacked through them. Our purpose is to develop a toolkit for: (1) fast authoring the story content, and (2) allow it for repeated plays yet retaining fresh interactive experience. While most similar applications have explicit, sophisticated story structures to ensure the number of possible interactions and endings in specific situations, we argue that characters should have enough background knowledge to make any improvisational choices. The more knowledge they have, the more sophisticated course of actions they may express. As a result, we take a deep-model approach to implement virtual agents, allowing them to deliberate and act with established knowledge in unexpected situations.
Behavioral Modelling and Composition of Object Slices Using Event Observation
Authors: Iulian Ober, Bernard Coulette, Younes Lakhrissi
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Some analysis and design methods for complex software systems lead to the specification of components (classes) by slices. It is the case of the use-case slicing technique proposed by Jacobson and Ng, and of view-based modelling proposed by Nassar et al. The composition of class slices is known from the literature to be closer to aspect composition than to traditional interface-based composition, but remains largely an open problem.In this paper we propose a set of constructs to support the behavioral specification and composition of class slices, based on the idea of non-intrusive event observation. This allows slices to be specified separately --- for example by different design teams --- and to be integrated later without changes. The proposal is made in the context of VxUML, a language which supports view-based and use-case-driven separation of concerns.
Performance Analysis of Cooperative Hopfield Networks for Stereo Matching
Authors: Wenhui Zhou, Zhiyu Xiang, Weikang Gu
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper proposes a dense stereo matching algorithm based on cooperative Hopfield networks. It uses two Hopfield networks with similar structure to solve energy minimization problem of stereo matching in parallel. Two strategies are applied to the performance analysis. One strategy considers each pixel as a neuron. The other is the Coarse-to-Fine strategy, which firstly divides the images into non-overlapping homogeneous regions, and each region is represented as super-pixel of the coarse images. After coarse estimation, a more refined estimation is implemented in pixel domain. Experiments indicate the method with the Coarse-to-Fine strategy has better performance and more rapid convergence speed, and less insensitive to initial conditions of the neural networks and the neuron update orders.