ppxscal/embedding_network huggingface dataset:
Getting Started with Sketch Tools
Authors: Beryl Plimmer, Tracy Hammond
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Diagrams are an important, if not pivotal, part in both education and design. In an effort to understand abstract concepts, students play an active role in their education by specifying visual and abstract concepts in hand-sketched diagrams. While students are understanding abstract concepts through hand-drawn diagrams, designers are creating those abstract concepts. Just as the act of hand-drawing a diagram (as opposed to using a mouse-and-palette CAD tool) better engages the student in the learning process, the act of hand-drawing a diagram also improves the design process by freeing the designer of constraints that may otherwise impede creativity and innovation.
Evolution of Valence Systems in an Unstable Environment
Authors: Matthijs Snel, Gillian M. Hayes
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

We compare the performance of drive- versus perception-based motivational systems in an unstable environment. We investigate the hypothesis that valence systems (systems that evaluate positive and negative nature of events) that are based on internal physiology will have an advantage over systems that are based purely on external sensory input. Results show that inclusion of internal drive levels in valence system input significantly improves performance. Furthermore, a valence system based purely on internal drives outperforms a system that is additionally based on perceptual input. We provide arguments for why this is so and relate our architecture to brain areas involved in animal learning.
Mining Natural Language Programming Directives with Class-Oriented Bayesian Networks
Authors: Manolis Maragoudakis, Nikolaos Cosmas, Aristogiannis Garbis
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Learning a programming language is a painstaking process, as it requires knowledge of its syntax, apart from knowing the basic process of representing logical sequences to programming stages. This fact deteriorates the coding process and expels most users from programming. Particularly for novice users or persons with vision problems, learning of how to program and tracing the syntax errors could be improved dramatically by using the most natural of all interfaces, i.e. natural language. Towards this orientation, we suggest a wider framework for allowing programming using natural language. The framework can be easily extended to support different object-oriented programming languages such as C, C++, Visual Basic or Java. Our suggested model is named "Language Oriented Basic" and it concerns an intelligent interface that supports code creation, modification and control in Visual Basic. Users can use simple-structured Greek sentences in natural language and the system can output the corresponding syntactic tree. When users declare end of input, the system transforms the syntactic trees to source code. Throughout the whole interaction process, users can check the under-development code in order to verify its correspondence to their expectations. Due to the fact that using natural language can cause a great degree of ambiguity, Bayesian networks and learning from examples have been utilized as an attempt to reason on the most probable programming representation, given a natural language input sentence. In order to enhance the classifier, we propose a novel variation of Bayesian networks that favor the classification process. Experimental results have depicted precision and recall measures in a range of 73% and 70% respectively.
Finding Small Holes
Authors: Jeff Erickson
Venue: Proceedings of the 10th international workshop on Algorithms and Data Structures (2007)

Numerous applications call for the detection of small topological features in various spaces; examples include simplification of surfaces reconstructed from point clouds, efficient algorithms for graphs embedded on surfaces, coverage analysis for ad-hoc/sensor networks, and topological analysis of high-dimensional data. This talk is a survey algorithms for one of the simplest problems of this type: finding the shortest cycle in a given topological space that cannot be continuously contracted to a point. Spaces of interest include polygons with holes, combinatorial surfaces, piecewise-linear 2-manifolds, Rips-Vietoris complexes, and general simplicial complexes. Almost no optimal algorithms are known, even in settings where the problem has a straightforward polynomial-time solution; consequently, the talk will include several open problems. No prior knowledge of topology will be assumed. 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
Robust Watermark Algorithm Based on the Wavelet Moment Modulation and Neural Network Detection
Authors: Dianhong Wang, Dongming Li, Jun Yan
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Moment-domain based watermark can resist geometric attacks but can not be detected blindly. The purpose of this paper is to outline the state of the research of wavelet moment modulation-based watermark and to propose a neural network detection algorithm towards it. With regard to the later we first analyze the computation of the wavelet moment and inverse wavelet moment. Then we focus on watermark added with template embedding and detection based on neural network. Results of the experiments revealed that our watermark detection algorithm is more robust comparing with conventional wavelet-based algorithm. In addition, it detects the watermark blindly.
Performance analysis of single channel blind source separation
Authors: Bin Gao, W. L. Woo, S. S. Dlay
Venue: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (2008)

Blind source separation is an advanced statistical tool that has found widespread use in many signal processing applications. The main idea to single channel blind source separation is based on exploiting the inherent time structure of sources known as basis filters in time domain that encode the sources in a statistically efficient manner. This paper is focused on the analysis of separation results by exploiting one channel recording of real time speech and music signals.
A Web Database for Computer-Aided Detection and Diagnosis of Medical Images
Authors: Dave Tahmoush, Hanan Samet
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Building effective Computer-Aided Detection and Diagnosis (CAD) systems involves the combination of running experiments, image markup, security, analysis, evaluation, feature extraction, and feature combination in order to capture and evaluate medical images effectively. This requires the involvement of a large community of experts across several fields. We have created a CAD development system which integrates the community together without requiring disclosure of sensitive details. The system design enables researchers to upload their feature sets and quickly compare the effectiveness of their methods against other stored feature sets. Additionally, research into the techniques used by radiologists is possible through double-blind radiologist comparisons based on their annotations and feature markups. This research archive contains the essential technologies of secure transmission and storage, textual and feature searches, spatial searches, annotation searching, filtering of result sets, feature creation, and bulk loading of features, while creating a repository and testbed for the community.
The Extreme Energy Ratio Criterion for EEG Feature Extraction
Authors: Shiliang Sun
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Energy is an important feature for electroencephalogram (EEG) signal classification in brain computer interfaces (BCIs). It is not only physiologically rational but also empirically effective. This paper proposes extreme energy ratio (EER), a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EER criterion, and hence EER can as well be regarded as a feature extractor for distilling energy. The paper derives the solutions which optimize the EER criterion, shows the theoretical equivalence of EER to the existing method of common spatial patterns (CSP), and gives the computational savings EER makes in comparison with CSP. Two paradigms extending EER from binary classification to multi-class classification are also provided.
Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements
Authors: Alexandre R. Mesquita, João P. Hespanha, Karl Åström
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

We consider the problem of seeking the maximum of a scalar signal using a swarm of autonomous vehicles equipped with sensors that can take point measurements of the signal. Vehicles are not able to measure their current position or to communicate with each other. Our approach induces the vehicles to perform a biased random walk inspired by bacterial chemotaxis and controlled by a stochastic hybrid automaton. With such a controller, it is shown that the positions of the vehicles evolve towards a probability density that is a specified function of the spatial profile of the measured signal, granting higher vehicle densities near the signal maxima.
Interference Aware Construction of Multi- and Convergecast Trees in Wireless Sensor Networks
Authors: Tomas Johansson, Evgeny Osipov, Lenka Carr-Motyčkovà
Venue: Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking (2008)

In this paper we consider a problem of building a forwarding tree for multicast and convergecast traffic in short-range wireless sensor networks. Interference awareness and energy efficiency are the major design objectives for WSN protocols in order to maximize the network lifetime. The existing multicast algorithms aim at constructing low-energy cost trees. Adding interference-awareness, however, leads to increased throughput and further reduces the energy consumption by avoiding unnecessary retransmissions due to interference-induced packet losses. We propose a Localized Area-Spanning Tree (LAST) protocol for wireless short-range sensor networks. Unlike previous similar protocols, the LAST protocol reaches all the nodes in a given geographical area, rather than only specific individual nodes. When creating the tree, the protocol jointly optimizes the energy cost and the interference imposed by the structure.
Approximate private information retrieval
Authors: André Madeira
Venue: Proceedings of the 2nd SIGMOD PhD workshop on Innovative database research (2008)

We present efficient protocols for the 1-out-of-n single-server Computationally-Private Information Retrieval (CPIR) problem for l-bit strings. In particular, our results achieve simultaneously polylogarithmic extra storage, communication, and Client and Server local computational complexities, improving the best current bounds. Most designs for CPIR exploit the communication vs. computation trade-off, whereas ours exploit instead trade-offs between accuracy and the metrics mentioned above. The work is of a theoretical nature. In a nutshell, our polylogarithmic claims show that it is possible to substantially reduce the mentioned complexity measures at the expense of accuracy of the results. The main indirect (practical) implication of this work is to show that CPIR is actually viable for very large databases. We also present some open questions and future directions.
Military uses of nanotechnology&mdash;Too much complexity for international security?
Authors: Jürgen Altmann
Venue: Complexity (2008)

Nanotechnology, converging with other advanced technologies, will bring benefits and risks. Particular dangers can arise from military uses. Weapons and other systems that are autonomous and-or small, cheap, and numerous will greatly increase complexity. Proceeding from criteria of preventive arms control, several potential military applications should be limited preventively. Here the USA plays a key role. In the future verification of compliance would have to be very intrusive, with inspection rights and criminal prosecution as within states. Would this be compatible with nation states maintaining armed forces for their security? Or does mastering the complexity brought about by the new technologies require changes in the international system? &copy; 2008 Wiley Periodicals, Inc. Complexity, 2008
A method for efficiently computing the number of codewords of fixed weights in linear codes
Authors: Iliya Bouyukliev, Valentin Bakoev
Venue: Discrete Applied Mathematics (2008)

The problem of computing the number of codewords of weights not exceeding a given integer in linear codes over a finite field is considered. An efficient method for solving this problem is proposed and discussed in detail. It builds and uses a sequence of different generator matrices, as many as possible, so that the identity matrix takes disjoint places in them. The efficiency of the method is achieved by optimizations in three main directions: (1) the number of the generated codewords, (2) the check whether a given codeword is generated more than once, and (3) the operations for generating and computing these codewords. Since the considered problem generalizes the well-known problems ''Weight Distribution'' and ''Minimum Distance'', their efficient solutions are considered as applications of the algorithms from the method.
Inference of Stochastic Finite-State Transducers Using N-Gram Mixtures
Authors: Vicente Alabau, Francisco Casacuberta, Enrique Vidal, Alfons Juan
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Statistical pattern recognition has proved to be an interesting framework for machine translation, and stochastic finite-state transducers are adequate models in many language processing areas such as speech translation, computer-assisted translations, etc. The well-known n-gram language models are widely used in this framework for machine translation. One of the application of these n-gram models is to infer stochastic finite-state transducers. However, only simple dependencies can be modelled, but many translations require to take into account strong context and style dependencies. Mixtures of parametric models allow to increase the description power of the statistical models by modelling subclasses of objects. In this work, we propose the use of n-gram mixtures in GIATI, a procedure to infer stochastic finite-state transducers. N-gram mixtures are expected to model topics or writing styles. We present experimental results showing that translation performance can be improved if enough training data is available.
A New Key Exchange Scheme Based on Chebyshev Polynomials
Authors: Dahu Wang, Haizhu Yang, Fashan Yu, Xudong Wang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Kocarev gave the first cryptosystems based on the semi-group property of Chebyshev polynomials, which seemed excellent but actually insecure. Due to the inherent periodicity of trigonometric function, an attack can easily get plaintext given ciphertext. In this paper, we extend Chebyshev polynomials from real number to finite fields to avoid the attack and present the corresponding key exchange scheme, which is secure and practical.
Coordination of staffing and pricing decisions in a service firm
Authors: Doǧan A. Serel, Erdal Erel
Venue: Applied Stochastic Models in Business and Industry (2008)

Customer demand is sensitive to the price paid for the service in many service environments. Using queueing theory framework, we develop profit maximization models for jointly determining the price and the staffing level in a service company. The models include constraints on the average waiting time and the blocking probability. We show convexity of the single-variable subproblem under certain plausible assumptions on the demand and staffing cost functions. Using numerical examples, we investigate the sensitivity of the price and the staffing level to changes in the marginal service cost and the user-specified constraint on the congestion measure. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Fault tolerant mechanism design
Authors: Ryan Porter, Amir Ronen, Yoav Shoham, Moshe Tennenholtz
Venue: Artificial Intelligence (2008)

We introduce the notion of fault tolerant mechanism design, which extends the standard game theoretic framework of mechanism design to allow for uncertainty about execution. Specifically, we define the problem of task allocation in which the private information of the agents is not only their costs of attempting the tasks but also their probabilities of failure. For several different instances of this setting we present both, positive results in the form of mechanisms that are incentive compatible, individually rational, and efficient, and negative results in the form of impossibility theorems.
Research on Learners' Personality Mining Based on Improved Decision Tree Algorithm
Authors: Qiang Yang, Jianli Wang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

In order to truly realize the personalized learning in which learners are looked as the main characters, we should focus on the characteristics of learners. In order to obtain the characteristics of learners, we should mine the characteristics of learners. In this paper we put forward a kind of data mining algorithm based on improved decision tree, and combined with the characteristics data of personalized learning, then we simple analyze the application of the decision tree algorithm in the learners' personalized mining.
The Shannon Portal Installation: Interaction Design for Public Places
Authors: Luigina Ciolfi, Mikael Fernstrom, Liam J. Bannon, Parag Deshpande, Paul Gallagher, Colm McGettrick, Nicola Quinn, Stephen Shirley
Venue: Computer (2007)

The portal dolmen project at Ireland's Shannon airport tackled the challenges of a public exhibition and revealed the importance of focusing on situated activities as well as the crucial need for incorporating physical and aesthetic concerns into the design.
A note on proofs with graphs
Authors: Renata de Freitas, Petrucio Viana
Venue: Science of Computer Programming (2008)

Curtis and Lowe (S. Curtis, G. Lowe, Proofs with graphs, Sci. Comput. Program. 26 (1996) 197-216) presented a graphical calculus to prove inclusions between relations, leaving completeness of the system as an open question. Here we recast their system, excluding complementation and union, by presenting a low complexity decision algorithm to decide its valid inclusions. Completeness follows as a corollary.
A Search Engine Accepting On-Line Updates
Authors: Mauricio Marin, Carolina Bonacic, Veronica Gil Costa, Carlos Gomez
Venue: Proceedings of the 13th European international conference on Parallel Processing (2007)

We describe and evaluate the performance of a parallel search engine that is able to cope efficiently with concurrent read/write operations. Read operations come in the usual form of queries submitted to the search engine and write ones come in the form of new documents added to the text collection in an on-line manner, namely the insertions are embedded into the main stream of user queries in an unpredictable arrival order but with query results respecting causality. The search engine is built upon distributed inverted files for which we propose generic strategies for load balance and concurrency control.
A Semantic web approach to handling soft constraints in virtual organisations
Authors: Alun Preece, Stuart Chalmers, Craig McKenzie, Jeff Z. Pan, Peter M. D. Gray
Venue: Electronic Commerce Research and Applications (2008)

In this paper we present a proposal for representing soft constraint satisfaction problems (CSPs) within the Semantic Web architecture. The proposal is motivated by the need for a service-providing agent in a virtual organisation to reason about its commitments as soft constraints. The three essential requirements addressed are: (1) the need to have constraints express commitments in terms of Semantic Web services, (2) the need to associate utility values with constraints, to reflect the relative importance of satisfying them, and (3) the need to make statements about which constraints are satisfied and violated by a given solution. The proposal builds upon previous work in defining a Semantic Web constraint interchange format (CIF), which itself builds on the proposed Semantic Web rule language (SWRL). The paper describes an ontology for representing soft CSPs and their solutions, allowing an agent's set of commitments to be expressed as a collection of soft constraints. The ontology is an open interchange format for soft CSPs, allowing commitment to be communicated and exchanged among the members of a virtual organisation.
Neural mechanisms underlying adaptive actions after slips
Authors: Josep Marco-Pallarés, Estela Camara, Thomas F. Münte, Antoni Rodríguez-Fornells
Venue: Journal of Cognitive Neuroscience (2008)

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory theta-beta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.
Modelling Semantic Relationships and Centrality to Facilitate Community Knowledge Sharing
Authors: Styliani Kleanthous, Vania Dimitrova
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

Some of today's most widely spread applications are social systems where people can form communities and share knowledge. However, knowledge sharing is not always effective and communities often do not sustain. Can user modelling approaches help to identify what support could be offered and how this would benefit the community? The paper presents algorithms for extracting a model of a closely-knit virtual community following processes identified as important for effective communities. The algorithms are applied to get an insight of a real virtual community and to identify what support may be needed to help the community function better as an entity.