ppxscal/embedding_network huggingface dataset:
Ontological Foundations for Scholarly Debate Mapping Technology
Authors: Neil Benn, Simon Buckingham Shum, John Domingue, Clara Mancini
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

Mapping scholarly debates is an important genre of what can be called Knowledge Domain Analytics (KDA) technology --i.e. technology which combines both quantitative and qualitative methods of analysing specialist knowledge domains. However, current KDA technology research has emerged from diverse traditions and thus lacks a common conceptual foundation. This paper reports on the design of a KDA ontology that aims to provide this foundation. The paper then describes the argumentation extensions to the ontology for supporting scholarly debate mapping as a special form of KDA and demonstrates its expressive capabilities using a case study debate.
New Technologies, Mobility and Security, 1st edition
Authors: Houda Labiod, Mohamad Badra
Venue:  (2007)

NTMS2007 is the first IFIP International Conference on New Technologies, Mobility and Security thatwas held from May 2 to May 4, 2007 in Paris, France. NTMS'2007 aims at fostering advances in the areas of New Technologies, Wireless Networks, Mobile Computing, Ad hoc and Ambient Networks, QoS, Network Security and E-commerce, to mention a few, and provides a dynamic forum for researchers, students and professionals to present their state-of-the-art research and development in these interesting areas. The eventwas combined with tutorial sessions and workshops. Tutorials preceded the main program, aiming at the dissemination of mature knowledge and technology advances in the field. One Workshop immediately followed the main conference, offering the opportunity for a more focused exchange of ideas and presentation of on-going research relevant to selected topics.
A structural model for quality requirements regarding Electronic Health Records - State of the art and first concepts
Authors: Alexander Hoerbst, Elske Ammenwerth
Venue: Proceedings of the 2009 ICSE Workshop on Software Engineering in Health Care (2009)

Objective. Quality assurance is a major task with regard to the implementation of Electronic Health Records (EHR). Currently there are only a few approaches explicitly dealing with the quality of EHR services. The objective of this paper is to introduce a new model-based approach to structure and describe quality requirements of EHRs.
Higher Order Neurodynamics of Associative Memory for Sequential Patterns
Authors: Hiromi Miyajima, Noritaka Shigei, Shuji Yatsuki
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper describes higher order neurodynamics of associative memory for sequential patterns using a statistical method. First, the statistical analysis of direct correlations between the cross talk noise terms for higher order neural networks is made. Further, it is shown that storage capacities for k = 1, 2 and 3 dimensional cases are 0.263n, $0.207\binom{n}{2}$ and $0.180\binom{n}{3}$, respectively, where n is the number of neurons and $\binom{n}{k}$ means the combination of k from n. The result for the one dimensional case is in fairly general agreement with Meir's result, 0.269n, obtained by the replica theory.
Integrated I-cache Way Predictor and Branch Target Buffer to Reduce Energy Consumption
Authors: Weiyu Tang, Alexander Veidenbaum, Alexandru Nicolau, Rajesh Gupta
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

In this paper, we present a Branch Target Buffer (BTB) design for energy savings in set-associative instruction caches. We extend the functionality of a BTB by caching way predictions in addition to branch target addresses. Way prediction and branch target prediction are done in parallel. Instruction cache energy savings are achieved by accessing one cache way if the way prediction for a fetch is available. To increase the number of way predictions for higher energy savings, we modify the BTB management policy to allocate entries for non-branch instructions. Furthermore, we propose to partition a BTB into ways for branch instructions and ways for non-branch instructions to reduce the BTB energy as well.We evaluate the effectiveness of our BTB design and management policies with SPEC95 benchmarks. The best BTB configuration shows a 74% energy savings on average in a 4-way set-associative instruction cache and the performance degradation is only 0.1 . When the instruction cache energy and the BTB energy are considered together, the average energy-delay product reduction is 65%.
JAPELAS2: Japanese Polite Expressions Learning Assisting System in Ubiquitous Environments
Authors: Chengjiu Yin, Hiroaki Ogata, Yuqin Liu, Yoneo Yano
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This paper proposes a PDA-based language learning system for Japanese polite expressions. To support the foreigners learning Japanese polite expressions, we had implemented a PDA-based system, called Japanese polite expressions learning assisting system (JAPELAS) [11]. JAPELAS is a one-to-one system, and it works without any input of the context information. Based on the JAPELAS, now we propose architecture of new one-to-many system called JAPELAS2, where the learner can interact with many persons in the same situation. Learners can use JAPELAS2 to review the history records that they have learned.
Rational Agents under ASP in Games theory
Authors: Fernando Zacarías Flores, Dionicio Zacarías Flores, José Arrazola Ramírez, Rosalba Cuapa Canto
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

An implementation of the called game “ONE” with imperfect information and chance outcomes based on rational agents is deployed.
Sparse Gaussian graphical models with unknown block structure
Authors: Benjamin M. Marlin, Kevin P. Murphy
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization methods to find the penalized maximum likelihood estimate. This is similar to performing MAP estimation with a prior that prefers sparse graphs. In this paper, we use the stochastic block model as a prior. This prefer graphs that are blockwise sparse, but unlike previous work, it does not require that the blocks or groups be specified a priori. The resulting problem is no longer convex, but we devise an efficient variational Bayes algorithm to solve it. We show that our method has better test set likelihood on two different datasets (motion capture and gene expression) compared to independent L1, and can match the performance of group L1 using manually created groups.
Software CompanionThe MEXAR2 Support to Space Mission Planners
Authors: Amedeo Cesta, Gabriella Cortellessa, Simone Fratini, Angelo Oddi, Nicola Policella
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

This paper describes a fielded AI system in daily use at the European Space Agency (ESA-ESOC) since February 2005. The tool, named MEXAR2, provides continuous support to human mission planners in synthesizing plans for downlinking on-board memory data from the MARS EXPRESS spacecraft to Earth. The introduction of the tool in the mission planning workflow significantly decreased the time spent in producing plans. Moreover MEXAR2 improves the quality of the produced plans thus guaranteeing a strong reliability in data return enabling a more intensive science activity on board. The introduction of MEXAR2 has modified the role of the human mission planners who can now evaluate and compare different solutions rather than dedicating their time exclusively to computing single solutions (a tedious and repetitive task which does not capitalize on the mission planners' decision-making expertise). These characteristics have effectively made MEXAR2 a fundamental work companion for the human mission planners.
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Authors: Soon Ae Chun, Rodrigo Sandoval, Priscilla Regan
Venue: ACM International Conference Proceeding Series; Vol. 390 (2009)

It is our pleasure to welcome you to dg.o 2009, the Tenth Annual International Conference on Digital Government Research. The dg.o is unique in its ability to bring together computer and social science researchers, government officials and representatives of industry to investigate how information and communication technologies can be used to make government information, processes and services better, more convenient, and secure, as well as available to all. The dg.o conference is an international conference attracting participants from around the world. For the second year in a row, we are pleased to hold the conference in a country other than US. Dg.o 2008 was held in Montr&eacute;al, Canada. and we are appreciative of the warm welcome from people and officials in Puebla, Mexico this year. We are extremely fortunate to have two keynote speakers, Beth Noveck and Janice Nall, who have used cutting-edge technology for innovation in public institutions. Their efforts facilitate democratic processes via citizen participation in the patent review process and disease control. The challenges and solutions they pose give us insights into where efforts may be most needed. We are also honored to have a sponsored lunch with a speech from the governor of Guerrero State, Mexico, Zeferino Torreblanca, and we are equally pleased to have a distinguished keynote panel featuring leading Mexican e-government experts, Andres Hoffman, Rodolfo Torres, and Le&oacute;n David P&eacute;rez. This year's conference includes thirty five research papers, policy/management papers and student research papers, three of which will be presented in a new Spanish session. This is the first effort to include a language other than English in dg.o. In addition, we have prepared seventeen demos and posters, five panels, one workshop and five tutorials.
Smoothed particle filtering for dynamic Bayesian networks
Authors: Theodore Charitos
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Particle filtering (PF) for dynamic Bayesian networks (DBNs) with discrete-state spaces includes a resampling step which concentrates samples according to their relative weight in regions of interest of the state-space. We propose a more systematic approach than resampling based on regularisation (smoothing) of the empirical distribution associated with the samples, using the kernel method. We show in our experiments that the smoothed particle filtering (SPF) leads to more accurate estimates than the PF.
Efficient dependency tracking for relevant events in concurrent systems
Authors: Anurag Agarwal, Vijay K. Garg
Venue: Distributed Computing (2007)

In a concurrent system with N processes, vector clocks of size N are used for tracking dependencies between the events. Using vectors of size N leads to scalability problems. Moreover, association of components with processes makes vector clocks cumbersome and inefficient for systems with a dynamic number of processes. We present a class of logical clock algorithms, called chain clock, for tracking dependencies between relevant events based on generalizing a process to any chain in the computation poset. Chain clocks are generally able to track dependencies using fewer than N components and also adapt automatically to systems with dynamic number of processes. We compared the performance of Dynamic Chain Clock (DCC) with vector clock for multithreaded programs in Java. With 1% of total events being relevant events, DCC requires 10 times fewer components than vector clock and the timestamp traces are smaller by a factor of 100. For the same case, although DCC requires shared data structures, it is still 10 times faster than vector clock in our experiments. We also study the class of chain clocks which perform optimally for posets of small width and show that a single algorithm cannot perform optimally for posets of small width as well as large width.
Graph Neural Networks for Object Localization
Authors: Gabriele Monfardini, Vincenzo Di Massa, Franco Scarselli, Marco Gori
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Graph Neural Networks (GNNs) are a recently proposed connectionist model that extends previous neural methods to structured domains. GNNs can be applied on datasets that contain very general types of graphs and, under mild hypotheses, they have been proven to be universal approximators on graphical domains. Whereas most of the common approaches to graphs processing are based on a preliminary phase that maps each graph onto a simpler data type, like a vector or a sequence of reals, GNNs have the ability to directly process input graphs, thus embedding their connectivity into the processing scheme. In this paper, the main theoretical properties of GNNs are briefly reviewed and they are proposed as a tool for object localization. An experimentation has been carried out on the task of locating the face of a popular Walt Disney character in comic covers. In the dataset the character is shown in a number of different poses, often in cluttered backgrounds, and in high variety of colors. The proposed learning framework provides a way to deal with complex data arising from image segmentation process, without exploiting any prior knowledge on the dataset. The results are very encouraging, prove the viability of the method and the effectiveness of the structural representation of images.
Plenary lecture IX: unexpected high temperature chemical freezing during slow isochoric cooling
Authors: Radu D. Rugescu
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

An observation is presented regarding the chemical freezing during a slow calorimeter cooling, after the combustion of solid propellants with excess fuel elements into a vacuum. The residual combustion gas within the calorimeter, mainly consisting of CO2, CO, H2O, H2 and N2, although cooled down to the room temperature, presents a chemical composition that meets almost exactly the value encountered at a very high temperature, namely around 1674±36°K. It appears abnormal that, during the very slow cooling which follows the isochoric combustion, when any chemical freezing seems mostly improbable, such a freezing occurs however. With utmost regularity, the gases refuse completely to follow the equilibrium below this high 1700K limit. The fact is known at much lower temperatures for the isobaric combustion with oxygen in excess, still there are doubts that it had been previously observed for calorimeter products involving the entire water-gas reaction. Numerical simulation of combustion and nozzle expansion processes got a high accuracy level when this process was included in the computational codes. Recent temperature measurements confirm with good accuracy that this freezing temperature is well below the maximal combustion temperature within the calorimeter, but still very high. Detailed investigations are envisaged through the national grant proposal INTECH by a consortium of professional research teams.
Very Fast Best-Fit Circular and Elliptical Boundaries by Chord Data
Authors: D. Shane Barwick
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

Many machine vision tasks require objects to be delineated during image segmentation that have shapes that are well approximated by circles or ellipses. Due to their computational efficiency least-squares, algebraic methods are a popular choice for fitting an elliptic primitive to noisy image data when real-time processing is required. These methods, however, suffer from biased estimates and sensitivity to outlier data. In this paper a real-time, least-squares method is proposed that provides an indirect geometric fit based on the quadratic polynomial form of parallel chord lengths. The algorithm is shown to be more computationally efficient and more easily made robust to outlier data than algebraic methods. Experimental results also suggest that it provides estimates that suffer less from bias error.
The use of MUVE technology in teaching AI algorithms
Authors: David C. Moffat, Kathryn Trinder
Venue: ACM SIGCSE Bulletin (2009)

The Second-Life (SL) MUVE (Multi-User Virtual Environment) is not often used for teaching core curriculum in computing. We made a simulation set in SL to illustrate key AI algorithms, and deployed them in labs to help the students visualize them.
Dispersing hash functions
Authors: Rasmus Pagh
Venue: Random Structures Algorithms (2009)

We define a family of functions F from a domain U to a range R to be dispersing if for every set S &sube; U of a certain size and random h &isin; F, the expected value of &mid;S&mid; &ndash; &mid;h[S]&mid; is not much larger than the expectation if h had been chosen at random from the set of all functions from U to R. We give near-optimal upper and lower bounds on the size of dispersing families and present several applications where using such a family can reduce the use of random bits compared to previous randomized algorithms. A close relationship between dispersing families and extractors is exhibited. This relationship provides good explicit constructions of dispersing hash functions for some parameters, but in general the explicit construction is left open. &copy; 2008 Wiley Periodicals, Inc. Random Struct. Alg., 2009 A preliminary version appeared in the proceedings of RANDOM 2000.
Power management techniques for conserving energy in multiple system components
Authors: Neven Abou Gazala
Venue:  (2008)

Energy consumption is a limiting constraint for both embedded and high performance systems. CPU-core, caches and memory contribute a large fraction of energy consumption in most computing systems. As a result, reducing the energy consumption in these components can significantly reduce the system's overall energy consumption. However, applying multiple independent power management policies in the system (one for each component) may interfere with each other and in some occasions increase the combined energy consumption. In my thesis, I present three power management techniques that target more than a single component in a system. The focus is on reducing the total energy consumption in processors, caches and memory combinations. First, I present a memory-aware processor power management using collaboration between the OS and the compiler. The technique objectives are: (1) finish the application execution before its deadline and (2) minimize the combined energy consumption in processor and memory. Second, I present an integrated-DVS technique for processor and on-chip cache power management in multi-voltage domain systems. Integrated-DVS co-ordinates power management decisions across voltage domains rather than being applied in isolation in each domain. Third, I present a Power-Aware Cached DRAM (PA-CDRAM) memory organization for reducing the energy consumption in DRAM memory and off-chip caches. PA-CDRAM exploits the high internal memory bandwidth by bringing the off-chip caches &ldquo;closer&rdquo; to the memory, which improves both the overall performance and energy consumption. The techniques in my dissertation highlight the importance of designing power management schemes that consider multiple components and their interactions (in terms of power and performance) in the system rather than applying multiple isolated power management policies. This study should lay the foundation for further research in the domain of integrated power management, where a single power manager controls many system components. 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
An integrated approach to summarization and adaptation using H.264/MPEG-4 SVC
Authors: Luis Herranz, José M. Martínez
Venue: Image Communication (2009)

The huge amount of multimedia content and the variety of terminals and networks make video summarization and video adaptation two key technologies to provide effective access and browsing. With scalable video coding, the adaptation of video to heterogeneous terminals and networks can be efficiently achieved using together a layered coding hierarchy and bitstream extraction. On the other hand, many video summarization techniques can be seen as a special case of structural adaptation. This paper describes how some of them can be modified and included in the adaptation framework of the scalable extension of H.264/AVC. The advantage of this approach is that summarization and adaptation are integrated into the same efficient framework. The utility of this approach is demonstrated with experimental results for the generation of storyboards and video skims, showing that the proposed framework can generate the adapted bitstream of the summary faster than a conventional transcoding approach.
A virtual control concept for state constrained optimal control problems
Authors: K. Krumbiegel, A. Rösch
Venue: Computational Optimization and Applications (2009)

A linear elliptic control problem with pointwise state constraints is considered. These constraints are given in the domain. In contrast to this, the control acts only at the boundary. We propose a general concept using virtual control in this paper. The virtual control is introduced in objective, state equation, and constraints. Moreover, additional control constraints for the virtual control are investigated. An error estimate for the regularization error is derived as main result of the paper. The theory is illustrated by numerical tests.
A supervised learning approach to automatic synonym identification based on distributional features
Authors: Masato Hagiwara
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop (2008)

Distributional similarity has been widely used to capture the semantic relatedness of words in many NLP tasks. However, various parameters such as similarity measures must be hand-tuned to make it work effectively. Instead, we propose a novel approach to synonym identification based on supervised learning and distributional features, which correspond to the commonality of individual context types shared by word pairs. Considering the integration with pattern-based features, we have built and compared five synonym classifiers. The evaluation experiment has shown a dramatic performance increase of over 120% on the F-1 measure basis, compared to the conventional similarity-based classification. On the other hand, the pattern-based features have appeared almost redundant.