ppxscal/embedding_network huggingface dataset:
Coalition structure generation: dynamic programming meets anytime optimization
Authors: Talal Rahwan, Nicholas R. Jennings
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Coalition structure generation involves partitioning a set of agents into exhaustive and disjoint coalitions so as to maximize the social welfare. What makes this such a challenging problem is that the number of possible solutions grows exponentially as the number of agents increases. To date, two main approaches have been developed to solve this problem, each with its own strengths and weaknesses. The state of the art in the first approach is the Improved Dynamic Programming (IDP) algorithm, due to Rahwan and Jennings, that is guaranteed to find an optimal solution in O(3n), but which cannot generate a solution until it has completed its entire execution. The state of the art in the second approach is an anytime algorithm called IP, due to Rahwan et aI., that provides worst-case guarantees on the quality of the best solution found so far, but which is O(nn). In this paper, we develop a novel algorithm that combines both IDP and IP, resulting in a hybrid performance that exploits the strength of both algorithms and, at the same, avoids their main weaknesses. Our approach is also significantly faster (e.g. given 25 agents, it takes only 28% of the time required by IP, and 0.3% of the time required by IDP).
How to Steer Characters in Group Games
Authors: I. Abiyasa Suhardi, Jörn Loviscach
Venue: Transactions on Edutainment II (2009)

Video games played by several persons using the same screen require specific user interfaces. In this work, we look at options to let players steer a number of on-screen characters. To this end, we have built a prototype system that employs a number of Nintendo Wii Remote devices for input. We have devised three types of control that are based on different sensors available in these devices. In addition, we propose a mode to enable teams of players to collaboratively control a single character. This paper closes with a preliminary evaluation based on a prototypical game.
Emerging Information Infrastructures: Cooperation in Disasters
Authors: Mikael Asplund, Simin Nadjm-Tehrani, Johan Sigholm
Venue: Critical Information Infrastructure Security: Third International Workshop, CRITIS 2008, Rome, Italy, October13-15, 2008. Revised Papers (2009)

Disasters are characterised by their devastating effect on human lives and the society's ability to function. Unfortunately, rescue operations and the possibility to re-establish a working society after such events is often hampered by the lack of functioning communication infrastructures. This paper describes the challenges ahead in creating new communication networks to support post-disaster operations, and sets them in the context of the current issues in protection of critical infrastructures. The analysis reveals that while there are some common concerns there are also fundamental differences. The paper serves as an overview of some promising research directions and pointers to existing works in these areas.
BioNLP
Authors: K. Bretonnel Cohen
Venue: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts on XX (2007)

This tutorial will provide general natural language processing specialists with an introduction to the field of "BioNLP"---natural language processing in the fields of medicine and biology. This field has long roots in the history of natural language processing, but has been an absolutely burgeoning area of interest in recent years. The past few years have been characterized by an unusual mixing of bioinformatics and NLP specialists at the conferences of both communities: ACL or NAACL has now hosted workshops on BioNLP every year since 2002, with excellent attendance numbers, and bioinformatics and medical informatics meetings have featured NLP papers, sessions, and SIG meetings since the late 1990s. Recent MUC-like and TREC-sponsored shared tasks have had some unusual results, and the implications of these findings should make for an interesting tutorial for the general NLP specialist.
Integration Testing from Structured First-Order Specifications via Deduction Modulo
Authors: Delphine Longuet, Marc Aiguier
Venue: Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing (2009)

Testing from first-order specifications has mainly been studied for flat specifications, that are specifications of a single software module. However, the specifications of large software systems are generally built out of small specifications of individual modules, by enriching their union. The aim of integration testing is to test the composition of modules assuming that they have previously been verified, i.e. assuming their correctness. One of the main method for the selection of test cases from first-order specifications, called axiom unfolding, is based on a proof search for the different instances of the property to be tested, thus allowing the coverage of this property. The idea here is to use deduction modulo as a proof system for structured first-order specifications in the context of integration testing, so as to take advantage of the knowledge of the correctness of the individual modules.
Rapid mixing of Gibbs sampling on graphs that are sparse on average
Authors: Elchanan Mossel, Allan Sly
Venue: Random Structures Algorithms (2009)

Gibbs sampling also known as Glauber dynamics is a popular technique for sampling high dimensional distributions defined on graphs. Of special interest is the behavior of Gibbs sampling on the Erd&odblac;s-R&eacute;nyi random graph G(n,d-n), where each edge is chosen independently with probability d-n and d is fixed. While the average degree in G(n,d-n) is d(1 - o(1)), it contains many nodes of degree of order log n-log log n. The existence of nodes of almost logarithmic degrees implies that for many natural distributions defined on G(n,p) such as uniform coloring (with a constant number of colors) or the Ising model at any fixed inverse temperature &beta;, the mixing time of Gibbs sampling is at least n1+&Omega;(1-log log n). Recall that the Ising model with inverse temperature &beta; defined on a graph G = (V,E) is the distribution over &lcub;&plusmn;&rcub;Vgiven by $ P(\sigma) = {1 \over Z} \exp (\beta \Sigma_{(v,u)\varepsilon E}\ \sigma(v)\sigma(u)) $. High degree nodes pose a technical challenge in proving polynomial time mixing of the dynamics for many models including the Ising model and coloring. Almost all known sufficient conditions in terms of &beta; or number of colors needed for rapid mixing of Gibbs samplers are stated in terms of the maximum degree of the underlying graph. In this work, we show that for every d < &infin; and the Ising model defined on G (n, d-n), there exists a &beta;d > 0, such that for all &beta; < &beta;d with probability going to 1 as n &rarr;&infin;, the mixing time of the dynamics on G (n, d-n) is polynomial in n. Our results are the first polynomial time mixing results proven for a natural model on G (n, d-n) for d > 1 where the parameters of the model do not depend on n. They also provide a rare example where one can prove a polynomial time mixing of Gibbs sampler in a situation where the actual mixing time is slower than npolylog(n). Our proof exploits in novel ways the local tree like structure of Erd&odblac;s-R&eacute;nyi random graphs, comparison and block dynamics arguments and a recent result of Weitz. Our results extend to much more general families of graphs which are sparse in some average sense and to much more general interactions. In particular, they apply to any graph for which every vertex v of the graph has a neighborhood N(v) of radius O(log n) in which the induced sub-graph is a tree union at most O(log n) edges and where for each simple path in N(v) the sum of the vertex degrees along the path is O(log n). Moreover, our result apply also in the case of arbitrary external fields and provide the first FPRAS for sampling the Ising distribution in this case. We finally present a non Markov Chain algorithm for sampling the distribution which is effective for a wider range of parameters. In particular, for G(n, d-n) it applies for all external fields and &beta; < &beta;d, where d tanh(&beta;d) = 1 is the critical point for decay of correlation for the Ising model on G(n, d-n). &copy; 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2009
On the Reversibility of Parallel Insertion, and Its Relation to Comma Codes
Authors: Bo Cui, Lila Kari, Shinnosuke Seki
Venue: Proceedings of the 3rd International Conference on Algebraic Informatics (2009)

This paper studies conditions under which the operation of parallel insertion can be reversed by parallel deletion, i.e., when does the equality $(L_1 \Leftarrow L_2) \Rightarrow L_2 = L_1$ hold for languages L 1 and L 2. We obtain a complete characterization of the solutions in the special case when both languages involved are singleton words. We also define comma codes, a family of codes with the property that, if L 2 is a comma code, then the above equation holds for any language $L_1 \subseteq {\it \Sigma}^*$. Lastly, we generalize the notion of comma codes to that of comma intercodes of index m. Besides several properties, we prove that the families of comma intercodes of index m form an infinite proper inclusion hierarchy, the first element which is a subset of the family of infix codes, and the last element of which is a subset of the family of bifix codes.
Objective parametric model selection procedures from a Bayesian nonparametric perspective
Authors: Eduardo Gutiérrez-Peña, Raúl Rueda, Alberto Contreras-Cristán
Venue: Computational Statistics Data Analysis (2009)

In this paper we propose objective Bayes procedures for model selection. To this end, we follow Gutierrez-Pena and Walker [Gutierrez-Pena, E., Walker, S.G., 2005. Statistical decision problems and Bayesian nonparametric methods. International Statistical Review 73, 309-330], who view traditional parametric procedures as statistical decision problems where the uncertainty on the unknown model generating the observations is modelled nonparametrically. In contrast with some of the competing methods, our proposals are not affected by the lack of propriety of the prior distribution. We compare the proposed procedures with other objective methods through a simple yet challenging example. Finally, we present a simulation study and introduce a 'mosaic plot' which is useful to summarise the output of our simulations.
Network Administrator Street Smarts: A Real World Guide to CompTIA Network+ Skills, 2nd edition
Authors: Toby Skandier
Venue:  (2009)

Develop the skills you need in the real world Hit the ground running with the street-smart training you'll find in this practical book. Using a "year in the life" approach, it gives you an inside look at network administration, with key information organized around the actual day-to-day tasks, scenarios, and challenges you'll face in the field. This valuable training tool is loaded with hands-on, step-by-step exercises covering all phases of network administration, including: Designing a network Implementing and configuring networks Maintaining and securing networks Troubleshooting a network An invaluable study tool This no-nonsense book also covers the common tasks that CompTIA expects all of its Network+ candidates to know how to perform. So whether you're preparing for certification or seeking practical skills to break into the field, you'll find the instruction you need, including: Designing and drawing an internetwork Identifying devices and components Configuring wireless components Planning for static and dynamic routing Setting up hubs, switches, and routers Maintaining security with encryption, scans, and more Troubleshooting your network The Street Smarts series is designed to help current or aspiring IT professionals put their certification to work for them. Full of practical, real world scenarios, each book features actual tasks from the field and then offers step-by-step exercises that teach the skills necessary to complete those tasks. And because the exercises are based upon exam objectives from leading technology certifications, each Street Smarts book can be used as a lab manual for certification prep.
Noun and Keyword Detection of Korean in Ubiquitous Environment
Authors: Seong-Yoon Shin, Oh-Hyung Kang, Sang-Joon Park, Jong-Chan Lee, Seong-Bae Pyo, Yang-Won Rhee
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part I (2009)

In a language, noun and keyword extraction is a key element in a ubiquitous environment. When it comes to processing Korean language information, however, there are still a lot of problems with noun and keyword extraction. This paper proposes an effective noun extraction method that considers noun emergence features. The proposed method can be effectively used in areas like information retrieval where large volumes of documents and data need to be processed in a fast manner. In this paper, a category-based keyword construction method is also presented that uses an unsupervised learning technique to ensure high volumes of queries are automatically classified. Our experimental results show that the proposed method outperformed both the supervised learning-based X2 method known to excel in keyword extraction and the DF method, in terms of classification precision.
Learning CPG sensory feedback with policy gradient for biped locomotion for a full-body humanoid
Authors: Gen Endo, Jun Morimoto, Takamitsu Matsubara, Jun Nakanishi, Gordon Cheng
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

This paper describes a learning framework for a central pattern generator based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve biped walking with a 3D hardware humanoid, and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feed-back controller can be acquired within a thousand trials by numerical simulations and the obtained controller in numerical simulation achieves stable walking with a physical robot in the real world. Numerical simulations and hardware experiments evaluated walking velocity and stability. Furthermore, we present the possibility of an additional online learning using a hardware robot to improve the controller within 200 iterations.
Application of Elman Neural Network to Width Spread Prediction in Medium Plate Mill
Authors: Lingqi Meng, Haoliang Wu, Jianxun Wang, Mingjie Lei
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Based on the fact that the Elman model of neural network can well approach any nonlinear continuous function and has ability to reflect dynamic features of the systems, a method of width spread to medium plate mill prediction by Elman model was present. Through the width prediction by Elman model and BP model, the results show that the convergence and accuracy of prediction by the Elman model was higher than that by the BP network model.
Fuzzy Logic in Exploring Data Effects: A Way to Unveil Uncertainty in EEG Feedback
Authors: Fang Zheng, Bin Hu, Li Liu, Tingshao Zhu, Yongchang Li, Yanbin Qi
Venue: Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009 (2009)

To unveil effects of data sets with uncertainty, we develop a method applying fuzzy logic to determine data weights in fuzzy inference. Preferable adjustments of initial weight assignment shall be obtained by comparison of assumptions' truth grade values with practical effectiveness evaluation. We apply this method in the process of implying patients' depressive mood for the user case study of developing antidepressant multimedia therapy and evaluate its veracity. According to users' feedback, iterative application of this method may leads to further understanding of EEG data's effects in user context.
Unsupervised induction of modern standard Arabic verb classes
Authors: Neal Snider, Mona Diab
Venue: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX (2006)

We exploit the resources in the Arabic Treebank (ATB) for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). Verbs are clustered into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. The results of the clustering experiments are compared with a gold standard set of classes, which is approximated by using the noisy English translations provided in the ATB to create Levin-like classes for MSA. The quality of the clusters is found to be sensitive to the inclusion of information about lexical heads of the constituents in the syntactic frames, as well as parameters of the clustering algorithm. The best set of parameters yields an F&beta;=1 score of 0.501, compared to a random baseline with an F&beta;=1 score of 0.37.
Protein Identification from Tandem Mass Spectra with Probabilistic Language Modeling
Authors: Yiming Yang, Abhay Harpale, Subramaniam Ganapathy
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

This paper presents an interdisciplinary investigation of statistical information retrieval (IR) techniques for protein identification from tandem mass spectra, a challenging problem in proteomic data analysis. We formulate the task as an IR problem, by constructing a "query vector" whose elements are system-predicted peptides with confidence scores based on spectrum analysis of the input sample, and by defining the vector space of "documents" with protein profiles, each of which is constructed based on the theoretical spectrum of a protein. This formulation establishes a new connection from the protein identification problem to a probabilistic language modeling approach as well as the vector space models in IR, and enables us to compare fundamental differences in the IR models and common approaches in protein identification. Our experiments on benchmark spectrometry query sets and large protein databases demonstrate that the IR models significantly outperform well-established methods in protein identification, by enhancing precision in high-recall regions in particular, where the conventional approaches are weak.
Team Meetings within Clinical Domains --- Exploring the Use of Routines and Technical Support for Communication
Authors: Kristina Groth, Ann Lantz, Eva-Lotta Sallnäs, Oscar Frykholm, Anders Green
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

Today, it is common that a team of clinicians, from different disciplines, instead of one single doctor, care for a patient. This is especially true when it concerns more complicated diseases in highly specialised health care. Going from one doctor to a team of doctors raises new dimensions/problems/issues when deciding about the diagnosis and how to treat the patient. Instead of one person deciding, based on the information given from others, a group of people need to agree on a decision. How do the participants during such decision meetings argue for their experience and skill? What kind of technologies are available and how do they support the communication in the meeting? Måseide (2006), for example, focuses on how different forms of evidence influence and regulate the judgements and decisions of medical practitioners during such meetings. Groth et al. (2008), for example, focuses on the technology used during such meetings, with a focus on audio, video, and images.
Analyzing usage in a large coursecasting service: issues & strategies
Authors: Owen G. McGrath
Venue: Proceedings of the ACM SIGUCCS fall conference on User services conference (2009)

Coursecasting, the video and audio recording of lectures and events for live or on-demand playback, has become a significant IT service at many institutions of higher education. By making available on the internet collections of full-length digitized recordings, college and university information technology groups find themselves supporting large and varied distributed audiences--from local students to faraway elearners. Managing the up-to-date production and on-demand delivery of this kind of open content requires a technical infrastructure for capturing, publishing, and archiving that can adapt quickly to changing workload demands. Adapting quickly, moreover, requires special attention to understanding usage of the time-based content: the different kinds of users, where and how they access content, and shifting patterns of media access. Those who carry out usage analytics for open educational resource services involving internet-based video and audio face special challenges. This paper explores approaches to monitoring and analyzing audio and video content usage across two related but distinct dimensions-server workload and user behavior-in a large scale, open content coursecasting service.
An Automatic Translation Evaluation System Based on Semantic Similarities and Fuzzy Neartude
Authors: Ke Xiao-hua
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

Most current translation assignments and tests are evaluated in manual work instead of machine, as automatic translation assessment has been considered to be very difficult. In this paper, a system was proposed to evaluate the quality of translation based on semantic similarities and fuzzy neartude. The experiment results are promising and indicate that the system can identify the semantic characteristics and yield scores that correlate with human judgments of translation quality. Our future work is to improve the evaluation strategies and introduce more linguistic resources.
Self-indexed Text Compression Using Straight-Line Programs
Authors: Francisco Claude, Gonzalo Navarro
Venue: Proceedings of the 34th International Symposium on Mathematical Foundations of Computer Science 2009 (2009)

Straight-line programs (SLPs) offer powerful text compression by representing a text T[1,u] in terms of a restricted context-free grammar of n rules, so that T can be recovered in O(u) time. However, the problem of operating the grammar in compressed form has not been studied much. We present a grammar representation whose size is of the same order of that of a plain SLP representation, and can answer other queries apart from expanding nonterminals. This can be of independent interest. We then extend it to achieve the first grammar representation able of extracting text substrings, and of searching the text for patterns, in time o(n). We also give byproducts on representing binary relations.
Adaptive Higher Order Neural Networks
Authors: Shuxiang Xu, Ling Chen
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

This paper introduces an adaptive Higher Order Neural Network (HONN) and applies it in data mining such as Australian credit card assessment. The proposed adaptive HONN model offers significant advantages over conventional Artificial Neural Network (ANN) models such as more accurate predictions and the ability in handling incomplete/missing values in a dataset. A new approach for determining the best number of hidden neurons is also proposed.