ppxscal/embedding_network huggingface dataset:
Different Search Patterns and Similar Decision Outcomes: How Can Experts Agree in the Decisions They Make When Reading Digital Mammograms?
Authors: Claudia Mello-Thoms, Marie Ganott, Jules Sumkin, Christiane Hakim, Cynthia Britton, Luisa Wallace, Lara Hardesty
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Experts may agree in most decisions that they make when they read a case set of digital mammograms, but eye-position tracking studies suggest that they use very different visual search strategies to make such decisions. If indeed each expert uses a unique strategy, it may be very difficult to teach radiology trainees effective ways to search the background parenchyma. In this study, we examined how much agreement exists in the actual locations used by the experts in their decision making process when reading digital mammograms.
A survey of methods for image annotation
Authors: Allan Hanbury
Venue: Journal of Visual Languages and Computing (2008)

In order to evaluate automated image annotation and object recognition algorithms, ground truth in the form of a set of images correctly annotated with text describing each image is required. In this paper, three image annotation approaches are reviewed: free text annotation, keyword annotation and annotation based on ontologies. The practical aspects of image annotation are then considered. We discuss the creation of keyword vocabularies for use in automated image annotation evaluation. As direct manual annotation of images requires much time and effort, we also review various methods to make the creation of ground truth more efficient. An overview of annotated image datasets for computer vision research is provided.
Integration of Biological, Psychological, and Social Aspects in Agent-Based Simulation of a Violent Psychopath
Authors: Tibor Bosse, Charlotte Gerritsen, Jan Treur
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

In the analysis of criminal behaviour, a combination of biological, psychological and social aspects may be taken into account. Dynamical modelling methods developed in recent years often address these aspects separately. This paper contributes an agent-based modelling approach for behaviour of a certain criminal type, the violent psychopath, in which these aspects are integrated in one dynamical system. It is shown how within a certain social context, an interaction between biological factors and cognitive and emotional factors can lead to a crime committed when an opportunity is perceived.
Using a Triple Graph Grammar for State Machine Implementations
Authors: Michael Striewe
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

State machines can be comprehensively specified, simulated and validated at design time to get a formally founded skeleton for a software application. A direct implementation of state machines in Java source code can realize states as classes, transitions as methods and variables as well as pre-conditions and variable updates as auxilliary methods, invoking either arbitrary application methods to retrieve the current variable values or evaluating expressions [1]. Viewed from an abstract level, implementation and state machine are two models, sharing the same semantics. To maintain the modelled software systems, it is vitally important to perserve as much of this semantics as possible in the source code to be able to track back changes and errors [2,3]. In contrast to these considerations, current techniques of model-driven development use several unidirectional steps, starting from an abstract model and resulting in platfrom specific source code. 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
Combining Algorithms for Deciding Knowledge in Security Protocols
Authors: Mathilde Arnaud, Véronique Cortier, Stéphanie Delaune
Venue: Proceedings of the 6th international symposium on Frontiers of Combining Systems (2007)

In formal approaches, messages sent over a network are usually modeled by terms together with an equational theory, axiomatizing the properties of the cryptographic functions (encryption, exclusive or, ...). The analysis of cryptographic protocols requires a precise understanding of the attacker knowledge. Two standard notions are usually considered: deducibility and indistinguishability. Those notions are well-studied and several decidability results already exist to deal with a variety of equational theories. However most of the results are dedicated to specific equational theories.We show that decidability results can be easily combined for any disjoint equational theories: if the deducibility and indistinguishability relations are decidable for two disjoint theories, they are also decidable for their union. As an application, new decidability results can be obtained using this combination theorem.
Simulating human behaviors in agent societies
Authors: Alicia Ruvinsky, Michael N. Huhns
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

As increasing numbers of processors and agents pervade the human environment, societies comprising both humans and agents will emerge. Presently, it is unknown how a person might fair in such mixed societies. For the societies to operate effectively and efficiently, it is important for the humans and agents to recognize and understand each other's behavior. This paper provides an initial step in that understanding via two contributions: (1) we provide models, within a limited domain, for agents that behave like humans and (2) we present the results of simulated interactions between the human-like agents and a variety of purely rational agents. Our models for the behaviors of people are based on recent sociological research by Simpson and Willer [10] that explores humans' cooperative prosocial behavior, a conceivably non-rational process. Modeling human behaviors presents a means of exploring and understanding motivations, consequences, and resolutions to human-agent interactions. We aspire to exploit measured human behavior in order to observe its ramifications in an agent world, and to motivate development of human-agent societies. Our results show that, although there are pitfalls to which humans are vulnerable, there exist niches for human prosperity in a rational agent world.
Ontology-based information extraction and integration from heterogeneous data sources
Authors: Paul Buitelaar, Philipp Cimiano, Anette Frank, Matthias Hartung, Stefania Racioppa
Venue: International Journal of Human-Computer Studies (2008)

In this paper we present the design, implementation and evaluation of SOBA, a system for ontology-based information extraction from heterogeneous data resources, including plain text, tables and image captions. SOBA is capable of processing structured information, text and image captions to extract information and integrate it into a coherent knowledge base. To establish coherence, SOBA interlinks the information extracted from different sources and detects duplicate information. The knowledge base produced by SOBA can then be used to query for information contained in the different sources in an integrated and seamless manner. Overall, this allows for advanced retrieval functionality by which questions can be answered precisely. A further distinguishing feature of the SOBA system is that it straightforwardly integrates deep and shallow natural language processing to increase robustness and accuracy. We discuss the implementation and application of the SOBA system within the SmartWeb multimodal dialog system. In addition, we present a thorough evaluation of the different components of the system. However, an end-to-end evaluation of the whole SmartWeb system is out of the scope of this paper and has been presented elsewhere by the SmartWeb consortium.
Towards Efficiency in Cloth Simulation
Authors: Fernando Birra, Manuel Santos
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

Cloth simulation is an extremely expensive task. Realistic cloth models coupled with stable numerical integration demand all the processing power we can spend. Although implicit integration schemes allow us to use large time steps, the exponential time complexity limits the number of particles that are reasonable to use in any simulation. In this paper, we present a technique that simulates cloth surfaces with adaptive level of detail without degrading the surface physical properties or the stability of the simulation. The underlying mesh operations are derived from subdivision surfaces, but rules are adapted to prevent the numerical simulation from diverging. We also demonstrate how the model constants can be specified in a resolution independent way.While the use of DLOD effectively allows the formation of folds and wrinkles, the final appearance can still be far from smooth. To further increase the realism we can use a GPU post processing algorithm to smooth the final rendered surface.
A New Motion Detection Algorithm Based on Snake and Mean Shift
Authors: Yulan Liu, Silong Peng
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

Active contour model and mean shift are both motion detection algorithms. Each of them has its own merits and shortcomings. An active contour tends to be tracked by noise points and results in a false boundary. A mean shift vector always points to the edge area when the start point is around the object With initial curves given near the objects in each image automatically, we presented a new motion detection algorithm which used the internal energy of active contour to keep a curve continuous and smooth, and also used the mean shift vector to track the curve to the real object boundary step by step, with an iterative process. Experimental results showed that this algorithm can improve the segmenting results greatly in noisy videos.
Compressed XTR
Authors: Masaaki Shirase, Dong-Guk Han, Yasushi Hibino, Ho Won Kim, Tsuyoshi Takagi
Venue: Proceedings of the 5th international conference on Applied Cryptography and Network Security (2007)

XTR public key system was introduced at Crypto 2000, which is based on a method to present elements of a subgroup of a multiplicative group of a finite field. Its application in cryptographic protocols leads to substantial savings both in communication and computational overhead without compromising security. It was shown how the use of finite extension fields and subgroups can be combined in such a way that the number of bits to be exchanged is reduced by a factor 3.In this paper we show how to more compress the communication overhead. The compressed XTR leads to a factor 6 reduction in the representation size compared to the traditional representation and achieves as twice compactness as XTR. The computational overhead of it is a little worse than that of XTR, however the compressed XTR requires only about additional 6% computational effort. If finding 4-th roots of unity is pre-computed, then the computational overhead is only 1% compared to that of original XTR. Furthermore, the required size of public key data of it reduces about 26% from that of XTR.
From Wrapping to Knowledge: Domain Ontology Learning from Deep Web
Authors: Zhiming Cui, Pengpeng Zhao, Wei Fang, Chao Lin
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The next generation of the Web, called Semantic Web, has to improve the Web with semantic page annotations to enable knowledge-level querying and searches. However, manual construction of these ontologies is a time consuming and difficult task. In this paper, we describe an automatic extraction method that learns domain ontologies for semantic web from deep web. Our approach first learns a base ontology from deep web query interfaces, then grows the current ontology by probing the sources and discovering additional concepts and instances from the result pages. We have evaluated our approach in several real-world domains. Preliminary results indicate that the proposed extraction method discovers concepts and instances with high accuracy.
ISiS: An Intention-Oriented Model to Help Teachers in Learning Scenarios Design
Authors: Jean-Philippe Pernin, Valérie Emin, Viviane Guéraud
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

This paper presents ISiS model (Intentions, Strategies, interactional Situations), a conceptual framework elaborated to structure the design of learning scenarios by teachers-designers and to favour sharing and reuse practices. The framework is based on an intention-oriented approach and proposes to identify intentional, strategic, tactical and operational dimensions in a learning scenario. This paper gives an overview of research context, presents the model and how it has been elaborated in collaboration with teachers.
Symbolic Computation Software Composability
Authors: Sebastian Freundt, Peter Horn, Alexander Konovalov, Steve Linton, Dan Roozemond
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

We present three examples of the composition of Computer Algebra Systems to illustrate the progress on a composability infrastructure as part of the SCIEnce (Symbolic Computation Infrastructure for Europe) project. One of the major results of the project so far is an OpenMath based protocol called SCSCP (Symbolic Computation Software Composability Protocol). SCSCP enables the various software packages for example to exchange mathematical objects, request calculations, and store and retrieve remote objects, either locally or accross the internet. The three examples show the current state of the GAP, KANT, and MuPAD software packages, and give a demonstration of exposing Macaulay using a newly developed framework.
Fast Quadruple Precision Arithmetic Library on Parallel Computer SR11000/J2
Authors: Takahiro Nagai, Hitoshi Yoshida, Hisayasu Kuroda, Yasumasa Kanada
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

In this paper, the fast quadruple precision arithmetic of four kinds of basic operations and multiply-add operations are introduced. The proposed methods provide a maximum speed-up factor of 5 times to gcc 4.1.1 with POWER 5+ processor used on parallel computer SR11000/J2. We also developed the fast quadruple precision vector library optimized on POWER 5 architecture. Quadruple precision numbers, which is 128 bit long double data type, are emulated with a pair of 64 bit double data type on POWER 5+ prosessor used on SR11000/J2 with Hitachi Optimizing Compiler and gcc 4.1.1. To avoid rounding errors in computing quadruple precision arithmetic operations, emulation needs high computational cost. The proposed methods focus on optimizing the number of registers and instruction latency.
Using Collaborative Models to Adaptively Predict Visitor Locations in Museums
Authors: Fabian Bohnert, Ingrid Zukerman, Shlomo Berkovsky, Timothy Baldwin, Liz Sonenberg
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

The vast amounts of information presented in museums can be overwhelming to a visitor, whose receptivity and time are typically limited. Hence, s/he might have difficulties selecting interesting exhibits to view within the available time. Mobile, context-aware guides offer the opportunity to improve a visitor's experience by recommending exhibits of interest, and personalising the delivered content. The first step in this recommendation process is the accurate prediction of a visitor's activities and preferences. In this paper, we present two adaptive collaborative models for predicting a visitor's next locations in a museum, and an ensemble model that combines their predictions. Our experimental results from a study using a small dataset of museum visits are encouraging, with the ensemble model yielding the best performance overall.
A theoretical framework on the perception of web-based self-directed learning environment
Authors: Rong-Jyue Fang, Yung-Sheng Chang, Chien-Chung Lin, Hua-Lin Tsai, Chi-Jen Lee, Pofen Wang, Dai-Hua Li
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

The purpose of this study is to develop the theoretical framework on the perception of web-based self-directed learning environment. The research objectives are to identify the structure categories, dimensions, and aspects for the perception of the environment. An extensive literature review was conducted as the research method. The results include 3 system categories, 5 dimensions, and 13 aspects in the framework. The 3 system categories are system environment, interactive relationship, and personal development. The 5 dimensions are internet resources, web-based functions, peer interaction, learning content type, and self-directed learning. The study also presents the 13 aspects of the structure in the system model. The findings and recommendations can be provided for further related studies and teaching references.
Tegola tiered mesh network testbed in rural Scotland
Authors: Giacomo Bernardi, Peter Buneman, Mahesh K. Marina
Venue: Proceedings of the 2008 ACM workshop on Wireless networks and systems for developing regions (2008)

Many rural and remote communities around the world see themselves on the wrong side of the digital divide. In particular, there is evidence to suggest that there is a growing digital divide between urban and rural areas in terms of broadband Internet access with people living in rural areas having fewer choices and pay higher prices for slower speeds. This is true even in developed countries. Motivated by the above observations, there has been an increasing interest in deploying and researching low cost rural wireless networks with active community participation. This paper presents an overview of our efforts in this direction in deploying a rural WiFibased long distance mesh network testbed in the Scottish Highlands and Islands. We highlight the unique aspects of our testbed that differentiate it from other existing rural wireless testbeds. We also outline some of the research issues that are currently being investigated in this project.
Shape-Preserving Spline Interpolation for Specifying Bivariate Functions on Grids
Authors: S. Dodd, D. McAllister, J. Roulier
Venue: IEEE Computer Graphics and Applications (1983)

Compared with other methods, this technique for smooth surface interpolation over grid data reduces the number of extraneous local optima and inflection points of the surface.
Streaming Algorithms for k-Center Clustering with Outliers and with Anonymity
Authors: Richard Matthew Mccutchen, Samir Khuller
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

Clustering is a common problem in the analysis of large data sets. Streaming algorithms, which make a single pass over the data set using small working memory and produce a clustering comparable in cost to the optimal offline solution, are especially useful. We develop the first streaming algorithms achieving a constant-factor approximation to the cluster radius for two variations of the k-center clustering problem. We give a streaming (4 + ¿)-approximation algorithm using O(¿ ¿ 1 kz) memory for the problem with outliers, in which the clustering is allowed to drop up to z of the input points; previous work used a random sampling approach which yields only a bicriteria approximation. We also give a streaming (6 + ¿)-approximation algorithm using O(¿ ¿ 1 ln (¿ ¿ 1) k + k 2) memory for a variation motivated by anonymity considerations in which each cluster must contain at least a certain number of input points.
Sequential decision making with untrustworthy service providers
Authors: W. T. Luke Teacy, Georgios Chalkiadakis, Alex Rogers, Nicholas R. Jennings
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly interacting with others in such environments. We then present a computationally tractable Bayesian reinforcement learning algorithm to approximate that solution by taking into account the expected value of perfect information of an agent's actions. Our algorithm is shown to dramatically outperform all previous finalists of the international Agent Reputation and Trust (ART) competition, including the winner from both years the competition has been run.
Deterministic Coincidence Detection and Adaptation Via Delayed Inputs
Authors: Zhijun Yang, Alan Murray, Juan Huo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

A model of one integrate-and-firing (IF) neuron with two afferent excitatory synapses is studied analytically. This is to discuss the influence of different model parameters, i.e., synaptic efficacies, synaptic and membrane time constants, on the postsynaptic neuron activity. An activation window of the postsynaptic neuron, which is adjustable through spike-timing dependent synaptic adaptation rule, is shown to be associated with the coincidence level of the excitatory postsynaptic potentials (EPSPs) under several restrictions. This simplified model, which is intrinsically the deterministic coincidence detector, is hence capable of detecting the synchrony level between intercellular connections. A model based on the proposed coincidence detection is provided as an example to show its application on early vision processing.
Learning Walking Patterns for Kinematically Complex Robots Using Evolution Strategies
Authors: Malte Römmermann, Mark Edgington, Jan Hendrik Metzen, Jose Gea, Yohannes Kassahun, Frank Kirchner
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Manually developing walking patterns for kinematically complex robots can be a challenging and time-consuming task. In order to automate this design process, a learning system that generates, tests, and optimizes different walking patterns is needed, as well as the ability to accurately simulate a robot and its environment. In this work, we describe a learning system that uses the CMA-ES method from evolutionary computation to learn walking patterns for a complex legged robot. The robot's limbs are controlled using parametrized distorted sine waves, and the evolutionary algorithm optimizes the parameters of these waveforms, testing the walking patterns in a physical simulation. The best solutions evolved by this system has been transferred to and tested on a real robot, and has resulted in a gait that is superior to those previously designed by a human designer.