ppxscal/embedding_network huggingface dataset:
Sharing Gaze Control in a Robotic System
Authors: Daniel Hernandez, Jorge Cabrera, Angel Naranjo, Antonio Dominguez, Josep Isern
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

The use of the vision in humans is a source of inspiration for many research works in robotics. Attention mechanisms has received much of this effort, however, aspects such as gaze control and modular composition of vision capabilities have been much less analyzed. This paper describes the architecture of an active vision system that has been conceived to ease the concurrent utilization of the system by several visual tasks. We describe in detail the functional architecture of the system and provide several solutions to the problem of sharing the visual attention when several visual tasks need to be interleaved. The system's design hides this complexity to client processes that can be designed as if they were exclusive users of the visual system. Besides, software engineering principles for design and integration, often forgotten in this kind of developments, have been considered. Some preliminary results on a real robotic platform are also provided.
Safe Q-Learning on Complete History Spaces
Authors: Stephan Timmer, Martin Riedmiller
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

In this article, we present an idea for solving deterministic partially observable markov decision processes (POMDPs) based on a history space containing sequences of past observations and actions. A novel and sound technique for learning a Q-function on history spaces is developed and discussed. We analyze certain conditions under which a history based approach is able to learn policies comparable to the optimal solution on belief states. The algorithm presented is model-free and can be combined with any method learning history spaces. We also present a procedure able to learn history spaces especially suited for our Q-learning algorithm.
Normalising Brain PET Images
Authors: Elia Ferrando Juliá, Daniel Ruiz Fernández, Antonio Soriano Payá
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

PET is a nuclear medical examination which constructs a three-dimensional image of metabolism inside the body; in this article in particular, images are taken from the brain. The high complexity inherent to the interpretation of the brain images makes that any help is important to the specialists in order to accurate the diagnostic. In order to reach reliable and good images, a normalization process is suggested in this paper, consisting of centring the brain in the three-dimensional image, scaling it according to a template brain and, finally, rotating the brain according to the inclination of the template. For not reducing the quality of the information the application works with PET image format and radioactivity measures instead of translate to an ordinary colour image.
Image-Adaptive Spread Transform Dither Modulation Using Human Visual Model
Authors: Xinshan Zhu
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

This paper presents a new approach on image-adaptive spread-transform dither modulation (STDM). The approach is performed in the discrete cosine transform (DCT) domain, and modifies the original STDM in such a way that the spread vector is weighted by a set of just noticeable differences (JND's) derived from Watson's model before it is added to the cover work. An adaptive quantization step size is next determined according to the following two constraints: 1) the covered work is perceptually acceptable, which is measured by a global perceptual distance; 2) the covered work is within the detection region. We derive the strategy on the choice of the quantization step. Further, an effective solution is proposed to deal with the amplitude scaling attack, where the scaled quantization step is produced using an extracted signal in proportion to the amplitudes of the cover work. Experimental results demonstrate that the proposed approach achieves the improved robustness and fidelity.
A Novel PSO-DE-Based Hybrid Algorithm for Global Optimization
Authors: Ben Niu, Li Li
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

This paper presents a new hybrid global optimization algorithm PSODE combining particle swarm optimization (PSO) with differential evolution (DE). PSODE is a type of parallel algorithm, in which PSO and DE are executed in parallel to enhance the population with frequent information sharing. To demonstrate the effectiveness of the proposed algorithm, four benchmark functions are performed, and the performance of the proposed algorithm is compared to PSO and DE to demonstrate its superiority.
Clustering with Interactive Feedback
Authors: Maria-Florina Balcan, Avrim Blum
Venue: Proceedings of the 19th international conference on Algorithmic Learning Theory (2008)

In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback. We introduce a query-based model in which users can provide feedback to a clustering algorithm in a natural way via split and merge requests. We then analyze the "clusterability" of different concept classes in this framework -- the ability to cluster correctly with a bounded number of requests under only the assumption that each cluster can be described by a concept in the class -- and provide efficient algorithms as well as information-theoretic upper and lower bounds.
Study on the Fast Matching Location Algorithm Based on Feature Points for Wire Bonding
Authors: Fanzhi Kong, Yizhong Wang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

In order to increase the positioning accuracy and speed of PRS (pattern recognition system) of a wire bonder, a fast positioning method based on feature points was proposed and implemented in this paper. The feature points with translation and rotation invariance were extracted from both the eye point image and the test image with the SIFT (Scale Invariant Feature Transform) feature detector, and the rule of vector angle was used to extract the robust feature point pairs. Experimental results show that the algorithm can improve the speed and accuracy of the PRS and satisfy the requirement of automatic wire bonder.
On the Combination of Locally Optimal Pairwise Classifiers
Authors: Gero Szepannek, Bernd Bischl, Claus Weihs
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

If their assumptions are not met, classifiers may fail. In this paper, the possibility of combining classifiers in multi-class problems is investigated. Multi-class classification problems are split into two class problems. For each of the latter problems an optimal classifier is determined. The results of applying the optimal classifiers on the two class problems can be combined using the Pairwise Coupling algorithm by Hastie and Tibshirani (1998).In this paper exemplary situations are investigated where the respective assumptions of Naive Bayes or the classical Linear Discriminant Analysis (LDA, Fisher, 1936) fail. It is investigated at which degree of violations of the assumptions it may be advantageous to use single methods or a classifier combination by Pairwise Coupling.
Reliable Software Systems Design: Defect Prevention, Detection, and Containment
Authors: Gerard J. Holzmann, Rajeev Joshi
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

The grand challenge that is the focus of this conference targets the development of a practical methodology for software verification: a methodology that can help us to reduce the number of residual defects in software products. Reducing residual defects is of course not in itself the objective of this exercise; the true objective is to reduce the number of failures in the use of software products. Or in other words: the objective is the development of a methodology for "reliable software systems design."
Sizing Your System
Authors: George Neville-Neil
Venue: Queue (2008)

Dear KV, I'm working on a network server that gets into the situation you called livelock in a previous response to a letter (Queue May/June 2008). Our problem is that our system has only a fixed amount of memory to receive network data, but the system is frequently overwhelmed and can't make progress. When I ask our application engineers about how much data they expect, the only answer I get is "a lot," which isn't much help. How can I figure out how to size our systems appropriately?
A Survey of the High-Speed Self-learning Intrusion Detection Research Area
Authors: Anna Sperotto, Remco Meent
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

Intrusion detection for IP networks has been a research theme for a number of years already. One of the challenges is to keep up with the ever increasing Internet usage and network link speeds, as more and more data has to be scanned for intrusions. Another challenge is that it is hardly feasible to adapt the scanning configuration to new threats manually in a timely fashion, because of the possible rapid spread of new threats. This paper is the result of the first three months of a PhD research project in high speed, self-learning network intrusion detection systems. Here, we give an overview of the state of the art in this field, highlighting at the same time the major open issues.
Loan payment prediction using rough sets
Authors: Bahadtin Ruzgar, Nursel Selver Ruzgar
Venue: Proceedings of the American Conference on Applied Mathematics (2008)

In small business loans, there is always a risk for nonpayment or non-refunding of loans though very detailed examinations are made about the company. In this study, behaviors that increase the risk in loans or causing non-refunding are tried to be determined by using the rough set approach. This paper will demonstrate that rough sets model is applicable to a wide range of practical problems pertaining to loan payment prediction. Moreover, the results show that the rough sets model is a promising alternative to the conventional methods for financial prediction.
A Thin-Plate Spline Calibration Model For Fingerprint Sensor Interoperability
Authors: Arun Ross, Rohan Nadgir
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Biometric sensor interoperability refers to the ability of a system to compensate for the variability introduced in the biometric data of an individual due to the deployment of different sensors. Poor inter-sensor performance has been reported in different biometric domains including fingerprint, face, iris and speech. In the context of fingerprints, variations are observed in the acquired images due to differences in sensor resolution, scanning area, sensing technology, etc. which impact the feature set extracted from these images. The inability of a fingerprint matcher to compensate for these variations introduced in fingerprints acquired using different sensors results in inferior inter-sensor performance. In this work it is demonstrated that a simple non-linear calibration scheme, based on Thin Plate Splines (TPS), is sufficient to facilitate sensor interoperability in the context of fingerprints. In the proposed technique, the variation between the images acquired using two different sensors is modeled using non-linear distortions. The proposed calibration model is tested on the MSU dataset comprising of fingerprint images obtained using two different sensor technologies: an optical Digital Biometrics (DBI) sensor and a solid-state capacitive VERIDICOM (VERI) sensor. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by ~35% to ~40% at a False Accept Rate (FAR) of 0.01%.
Defining the insider threat
Authors: Matt Bishop, Carrie Gates
Venue: Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (2008)

Many diverse groups have studied the insider threat problem, including government organizations such as the Secret Service, federally-funded research organizations such as RAND and CERT, and university researchers. In addition, many industry participants are interested in the problem, such as those in the financial sector. However, despite this interest, no consistent definition of an insider has emerged.
WS-VLAM: A GT4 Based Workflow Management System
Authors: Adianto Wibisono, Dmitry Vasyunin, Vladimir Korkhov, Zhiming Zhao, Adam Belloum, Cees Laat, Pieter Adriaans, Bob Hertzberger
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Generic Grid middleware, e.g., Globus Toolkit 4 (GT4), provides basic services for scientific workflow management systems to discover, store and integrate workflow components. Using the state of the art Grid services can advance the functionality of workflow engine in orchestrating distributed Grid resources. In this paper, we present our work on migrating VLAM-G, a Grid workflow engine based on GT2 to GT4. We discuss how we use the rich set of services provided by GT4 in the new design to realize the user interactivity, interoperability and monitoring. The experiment results show that use cases from previous systems can be migrated seamlessly into the new architecture.
Optimal design of the linear delta robot for prescribed cuboid dexterous workspace based on performance chart
Authors: Qiaoling Yuan, Shiming Ji, Zhongfei Wang, Guan Wang, Yuehua Wan, Li Zhan
Venue: Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology (2008)

Determine an optimal set of design parameter of PR whose DW fits a prescribed workspace as closely as possible is an important and foremost design task before manufacturing. In this paper, an optimal design method of a linear Delta robot (LDR) to obtain the prescribed cuboid dexterous workspace (PCDW) is proposed. The optical algorithms are based on the concept of performance chart. The performance chart shows the relationship between a criterion and design parameters graphically and globally. The kinematic problem is analyzed in brief to determine the design parameters and their relation. Two algorithms are designed to determine the maximal inscribed rectangle of dexterous workspace in the O-xy plane and plot the performance chart. As an applying example, a design result of the LDR with a prescribed cuboid dexterous workspace is presented. The optical results shown that every corresponding maximal inscribed rectangle can be obtained for every given RATE by the algorithm and the error of RATE is less than 0.05. The method and the results of this paper are very useful for the design and comparison of the parallel robot.
Methodological Assistance for Integrating Data Quality Evaluations into Case-Based Reasoning Systems
Authors: Annett Bierer
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

Case-based reasoning systems are used in more and more problem-solving domains supporting the long-term reusing and storing of experience. The performance of these systems essentially depends on the quality of the experience items in their knowledge base, represented as data. Defects in the quality of these data may interfere with the system's performance. By means of inspection and review the data quality is measured, evaluated, assured and improved. To support these activities in a case-based reasoning system, data quality criteria and control processes are required. Previous work in the field of data quality in case-based reasoning remains at a comparatively coarse-grained level. Existing approaches mostly do not provide sufficient methodological assistance in defining fine-grained quality criteria or designing and implementing control processes for the measurement and evaluation of the data quality. Therefore this paper proposes two approaches for methodological assistance in developing data quality inspections and data quality management for case-based reasoning systems.
Exploiting Single-Usage for Effective Memory Management
Authors: Thomas Piquet, Olivier Rochecouste, André Seznec
Venue: Proceedings of the 12th Asia-Pacific conference on Advances in Computer Systems Architecture (2007)

Efficient memory management is crucial when designing high performance processors. Upon a miss, the conventional operation mode of a cache hierarchy is to retrieve the missing block from lower levels and to store it into all hierarchy levels. It is however difficult to assert that storing the block into intermediate levels will be really useful. In particular, this is unnecessary if a cache block is accessed only once before getting evicted - i.e. a single-usage block. This paper is typically concerned with reducing the number of single-usage blocks. Our observations reveal that single-usage blocks are significant at runtime and especially in the lowest cache level. We show that using an address-based prediction mechanism is sufficient to identify this phenomenon. Two schemes are examined to remove pollution caused by single-usage blocks: a bypass scheme and a cache replacement policy. Our results show that leveraging single-usage pollution is beneficial to memory-intensive applications running on superscalar and multi-core architectures.
Plenary lecture II: decision support systems, human centric/centered computing, and computing with words: a synergistic combination?
Authors: Janusz Kacprzyk
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

We start with a brief account of complex decision making problems, and advocate the use of modern approaches to real world decision making emphasizing the concept of a decision making process that involves more factors and aspects like: the use of explicit and tacit knowledge, intuition, individual habitual domains, non-trivial rationality, different paradigms, etc. We stress the need for computer based decision support systems that should exhibit some "intelligence" which is meant in an individual and collective perspective, and give an overview of main types of decision support systems. We present some new so-called computing paradigms that try to attain a synergy, and bridge the gap between the human user and computer systems that is mainly caused by the fact that natural language is the only fully natural means of communication and articulation for a human being but it is "strange" to the computer. We advocate the so-called: human centric computing, human centered computing, human computing, etc. that can help bridge this gap. Then, we present Zadeh's paradigm of computing with words (and perceptions) as a tool that may help bring computing closer to the human being by an explicit use of (quasi)natural language in many phases of computing, problem solving, etc. We indicate relations between the computing with words and human centric computing paradigms, and indicate - first - that the former can be viewed as an attempt at providing proper tools to implement the latter, and that both can play a crucial role in intelligent decision support systems. We show some implementations of using linguistic data summaries in a business context and show that they can be viewed as extremely human consistent data mining tools, notably for novice users.
Towards a Design Theory of Blended Learning Curriculum
Authors: Ronghuai Huang, Ding Ma, Haisen Zhang
Venue: Proceedings of the 1st international conference on Hybrid Learning and Education (2008)

The purpose of this article is to develop a design theory of blended learning curriculum in ways of establishing a model for designing such a curriculum and a model for designing an activity in a blended learning curriculum as well as demonstrating how these models can be utilized in a curriculum design. It first attempts to define what the essence of blended learning is by drawing on definitions of previous studies. Then, it goes on to identify the characteristics and rationales of blended learning. Finally, it exemplifies the devised BLC activity model, which is supported by the BLC design model and the BLC process model.
Making a Robot Dance to Music Using Chaotic Itinerancy in a Network of FitzHugh-Nagumo Neurons
Authors: Jean-Julien Aucouturier, Yuta Ogai, Takashi Ikegami
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

We propose a technique to make a robot execute free and solitary dance movements on music, in a manner which simulates the dynamic alternations between synchronisation and autonomy typically observed in human behaviour. In contrast with previous approaches, we preprogram neither the dance patterns nor their alternation, but rather build in basic dynamics in the robot, and let the behaviour emerge in a seemingly autonomous manner. The robot motor commands are generated in real-time by converting the output of a neural network processing a sequence of pulses corresponding to the beats of the music being danced to. The spiking behaviour of individual neurons is controlled by a biologically-inspired model (FitzHugh-Nagumo). Under appropriate parameters, the network generates chaotic itinerant behaviour among low-dimensional local attractors. A robot controlled this way exhibits a variety of motion styles, some being periodic and strongly coupled to the musical rhythm and others being more independent, as well as spontaneous jumps from one style of motion to the next. The resulting behaviour is completely deterministic (as the solution of a non-linear dynamical system), adaptive to the music being played, and believed to be an interesting compromise between synchronisation and autonomy.
Stochastic Local Search for the Optimal Winner Determination Problem in Combinatorial Auctions
Authors: Dalila Boughaci, Belaid Benhamou, Habiba Drias
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

In this paper, a stochastic local search (SLS) is studied for the optimal winner determination problem (WDP) in combinatorial auctions. Experiments on various realistic instances of the considered problem are performed to show and compare the effectiveness of our approach. The computational experiments show that the SLS provides competitive results and finds solutions of a higher quality than Tabu search and Casanova methods.