ppxscal/embedding_network huggingface dataset:
Histopathology Image Classification Using Bag of Features and Kernel Functions
Authors: Juan C. Caicedo, Angel Cruz, Fabio A. Gonzalez
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

Image representation is an important issue for medical image analysis, classification and retrieval. Recently, the bag of features approach has been proposed to classify natural scenes, using an analogy in which visual features are to images as words are to text documents. This process involves feature detection and description, construction of a visual vocabulary and image representation building through visual-word occurrence analysis. This paper presents an evaluation of different representations obtained from the bag of features approach to classify histopathology images. The obtained image descriptors are processed using appropriate kernel functions for Support Vector Machines classifiers. This evaluation includes extensive experimentation of different strategies, and analyses the impact of each configuration in the classification result.
Mining meaning from Wikipedia
Authors: Olena Medelyan, David Milne, Catherine Legg, Ian H. Witten
Venue: International Journal of Human-Computer Studies (2009)

Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.
A New Parallel Algorithm for Analog Circuit Simulation
Authors: Jiafang Wang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

Parallel simulation is an efficient strategy to accelerate the simulation process for the analog circuit designs with increasing size. In this paper, low communication and coarse grain parallelization are concerned to achieve good performance on network of workstations. First, to split differential/algebraic system presenting the electronic circuit into sub-blocks, we present an efficient partitioning technique to produce sub-blocks with few interconnections. Second, for minimizing communication between the partitions, a set of evaluation factors are defined and a new static load balancing algorithm is proposed. At last, a practical circuit is taken to demonstrate the speedup of the parallel algorithm.
On Stateless Multicounter Machines
Authors: Ömer Eğecioğlu, Oscar H. Ibarra
Venue: Proceedings of the 5th Conference on Computability in Europe: Mathematical Theory and Computational Practice (2009)

We investigate the computing power of stateless multi-counter machines with reversal-bounded counters. Such a machine has m-counters and it operates on a one-way input delimited by left and right end markers. The move of the machine depends only on the symbol under the input head and the signs of the counters (zero or positive). At each step, every counter can be incremented by 1, decremented by 1 (if it is positive), or left unchanged. An input string is accepted if, when the input head is started on the left end marker with all counters zero, the machine eventually reaches the configuration where the input head is on the right end marker with all the counters again zero. Moreover, for a specified k, no counter makes more than k-reversals (i.e., alternations between increasing mode and decreasing mode) on any computation, accepting or not. We mainly focus our attention on deterministic realtime (the input head moves right at every step) machines. We show hierarchies of computing power with respect to the number of counters and reversals. It turns out that the analysis of these machines gives rise to rather interesting combinatorial questions.
Superstabilizing, fault-containing distributed combinatorial optimization
Authors: Adrian Petcu, Boi Faltings
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

Self stabilization in distributed systems is the ability of a system to respond to transient failures by eventually reaching a legal state, and maintaining it afterwards. This makes such systems particularly interesting because they can tolerate faults, and are able to cope with dynamic environments. We propose the first self stabilizing mechanism for multiagent combinatorial optimization, which works on general networks and stabilizes in a state corresponding to the optimal solution of the optimization problem. Our algorithm is based on dynamic programming, and requires a linear number of messages to find the optimal solution in the absence of faults. We show how our algorithm can be made super-stabilizing, in the sense that while transiting from one stable state to the next, our system preserves the assignments from the previous optimal state, until the new optimal solution is found. We offer equal bounds for the stabilization and the superstabilization time. Furthermore, we describe a general scheme for fault containment and fast response time upon low impact failures. Multiple, isolated failures are handled effectively. To show the merits of our approach we report on experiments with practically sized distributed meeting scheduling problems in a multiagent system.
Self Protection through Collaboration Using D-CAF: A Distributed Context-Aware Firewall
Authors: Cristián Varas, Thomas Hirsch
Venue: Proceedings of the 2009 Third International Conference on Emerging Security Information, Systems and Technologies (2009)

Keeping network services in the Internet available overtime is not an easy task. Sudden changes in usage volumes are common, not least due to Flash Crowds and Denial of Service attacks. Given the difficulty to discern malicious users from regular customers, administrators have little chance to mitigate without compromising availability or security. The presented Distributed Context-Aware Firewall (D-CAF) architecture, avails itself of the specialized knowledge of the protected services to minimize the impact.The protected services participate in in a valuation process,forwarding a per-user value/cost ratio information to the D-CAF. When a traffic overload occurs, the firewall selectively limits the access to resources of the protected system based on the aggregated reports. The semantic simplicity of the report lends itself to propagation and collaboration between several D-CAF instances. In this paper we discuss the approach, architecture and first testing results.
Research of Automatic Control System Based on 6K Controller Welding Robot
Authors: Gao Mingliang, Huo Limin, Xing Yazhou, Yi Jinggang, Tong Leiguang
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

After analyzing the peculiarity of automatic control system in the all position welding robot at home and abroad, contrapose the need of steeliness cylinder shape central heating welding and fixing, contrive the welding technology which have portability and appease different situations, also offer the most at the best price with the automatic control system in the welding robot. The system take the 6K linearity multiple spindle controller as its controller, take the DC servo-motor and the stepper motor as its power element. It can control robotic arm of the welding gun to move, flex, swing, promote by interface circuit and software, it can appease demand that all position welding technology need, and thereby achieve automation of welding. It is justified by systemic test welding that weld joint molding is good and raise working efficiency.
Anytime classification for a pool of instances
Authors: Bei Hui, Ying Yang, Geoffrey I. Webb
Venue: Machine Learning (2009)

In many real-world applications of classification learning, such as credit card transaction vetting or classification embedded in sensor nodes, multiple instances simultaneously require classification under computational resource constraints such as limited time or limited battery capacity. In such a situation, available computational resources should be allocated across the instances in order to optimize the overall classification efficacy and efficiency. We propose a novel anytime classification framework, Scheduling Anytime Averaged Probabilistic Estimators (SAAPE), which is capable of classifying a pool of instances, delivering accurate results whenever interrupted and optimizing the collective classification performance. Following the practice of our previous anytime classification system AAPE, SAAPE runs a sequence of very efficient Bayesian probabilistic classifiers to classify each single instance. Furthermore, SAAPE implements seven alternative scheduling schemes to decide which instance gets available computational resources next such that a new classifier can be applied to refine its classification. We formally present each scheduling scheme's definition, rationale and time complexity. We conduct large-scale experiments using 60 benchmark data sets and diversified statistical tests to evaluate SAAPE's performance on zero-one loss classification as well as on probability estimation. We analyze each scheduling scheme's advantages and disadvantages according to both theoretical understandings and empirical observations. Consequently we identify effective scheduling schemes that enable SAAPE to accomplish accurate anytime classification for a pool of instances.
Follow My Finger Navigation
Authors: Rami Ajaj, Frédéric Vernier, Christian Jacquemin
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

This paper presents a novel interaction technique called Follow my Finger (FmF) for navigation in 3D virtual environments using a 2D interactive view on a table-top device. FmF consists in moving a camera icon that represents the 2D subjective position and orientation of a viewpoint in the 3D world. Planar, tactile, and direct manipulation of the camera icon facilitates navigation in the 3D environment. From the user's perspective the camera icon follows her/his finger trajectory to interactively modify the horizontal location, inclination, and orientation of the 3D point of view.
A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation
Authors: Takao Kobayashi, Ikuko Shimizu
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

We have proposed a fast learning and classification method by using distributed representation of vectors. In this paper, first, we shows that our method provides faster and better performance than 1-NN method by introducing a definition of a similarity concerned with LSH scheme. Next we compare our method with the Naive Bayes with respect to the number of dimensions of features. While the Naive Bayes requires a considerably large dimensional feature space, our method achieves higher performance even where the number of dimensions of a feature space of our method is much smaller than that of Naive Bayes. We explain our method by formalizing as a linear classifier in a very high dimensional space and show it is a special case of Naive Bayes model. Experimental results show that our method provides superior classification rates with small time complexity of learning and classification and is applicable to large data set.
Block-Matched Based Scalable Video Coding
Authors: Yang Yang, Yu-Hua Peng, Yun-Xia Liu, Li-Xin Tang
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

In this paper, the motion estimation technique used in video compression is extended to intra-frame coding of scalable video coding, for which we called it the Block-Matching based image compression. Using a small amount of sampled data in the spatial domain, an approximate image of the original image is reconstructed. Then each block of the original image is obtained by searching the approximate image. The best match is selected with the minimal prediction error. This gives motion vector and the residual coefficients are obtained. Finally the sampled data, residual coefficients and block position are coded as the output bit-stream. Experimental work has been done based on H.264 to show the validity of proposed algorithm.
Email Reply Prediction: A Machine Learning Approach
Authors: Taiwo Ayodele, Shikun Zhou, Rinat Khusainov
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work.
Spanish adverbial frozen expressions
Authors: Dolors Català, Jorge Baptista
Venue: Proceedings of the Workshop on a Broader Perspective on Multiword Expressions (2007)

This paper presents an electronic dictionary of Spanish adverbial frozen expressions. It focuses on their formal description in view of natural language processing and presents an experiment on the automatic application of this data to real texts using finite-state techniques. The paper makes an assessment of the advantages and limitations of this method for the identification of these multiword units in texts.
Verb subcategorization frequency differences between business-news and balanced corpora: the role of verb sense
Authors: Douglas Roland, Daniel Jurafsky, Lise Menn, Susanne Gahl, Elizabeth Elder, Chris Riddoch
Venue: Proceedings of the Workshop on Comparing Corpora (2000)

We explore the differences in verb subcategorization frequencies across several corpora in an effort to obtain stable cross corpus subcategorization probabilities for use in norming psychological experiments. For the 64 single sense verbs we looked at, subcategorization preferences were remarkably stable between British and American corpora, and between balanced corpora and financial news corpora. Of the verbs that did show differences, these differences were generally found between the balanced corpora and the financial news data. We show that all or nearly all of these shifts in subcategorization are realised via (often subtle) word sense differences. This is an interesting observation in itself, and also suggests that stable cross corpus subcategorization frequencies may be found when verb sense is adequately controlled.
Using GIS to Develop an Efficient Spatio-temporal Task Allocation Algorithm to Human Groups in an Entirely Dynamic Environment Case Study: Earthquake Rescue Teams
Authors: Ali Reza Vafaeinezhad, Ali Asghar Alesheikh, Majid Hamrah, Reza Nourjou, Rouzbeh Shad
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part I (2009)

Using GIS, the present paper aims at modeling task allocation to human groups in a dynamic and spatio-temporal form. To do this, a novel method inspired by Market Based Procedure is proposed. Governing factors such as space, time, tiredness of the persons, importance, priority and the difficulty of the work and environmental dynamism, the functions referred to cost, reward and profit are considered in developing the model. By holding auctions and profits proposed by each of the bidders, the tasks are dedicated to those who yield the most profit to the group. On this basis, in a group consisting of several different tasks, it can be determined who, when and where should do what activity in order to increase the efficiency and effectiveness of the group. The proposed model was evaluated in ArcGIS software by simulation of the tasks of two groups of life-detectors and rubble-removers of earthquake rescue teams. In this way, in addition to confirming the efficiency of the suggested model, a new and spatio-temporal method is presented for the management of earthquake rescue teams in a fully dynamic environment.
Incremental parsing with parallel multiple context-free grammars
Authors: Krasimir Angelov
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

Parallel Multiple Context-Free Grammar (PMCFG) is an extension of context-free grammar for which the recognition problem is still solvable in polynomial time. We describe a new parsing algorithm that has the advantage to be incremental and to support PMCFG directly rather than the weaker MCFG formalism. The algorithm is also top-down which allows it to be used for grammar based word prediction.
Coefficient of Variation and Its Application to Strength Prediction of Adhesively Bonded Joints
Authors: Xiaocong He
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

This study deals with an application of the method of the coefficient of variation in strength prediction of structural adhesive joints. Defined as the ratio of the standard deviation to the mean, the coefficient of variation may be used in both the reliability-based design of adhesively bonded joints and in the evaluation of existing products. In this study, the concept and definition of the coefficient of variation are stated. The procedure of the use of coefficient of variation for approximate calculations of statistical parameters is then presented and compared with the classical Taylor expansion method. This is illustrated with a numerical example.
The Research of Worms in P2P Networks
Authors: Xie Chunyan, Yin Zhiyu
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

With the rapid development of the Internet, P2P technology has changed the traditional concept of Internet; this technology has become the dominant flow and greatly satisfied the needs of the users. At the same time, P2P worm has become one of the serious threats. This article include characteristics of P2P worms, classification, propagation model, detection and defense mechanism, also analyze some advantages and disadvantages of simulation software. At last this article discusses the direction for future research.
LCC-SRN: LCC's SRN system for SemEval 2007 task 4
Authors: Adriana Badulescu, Munirathnam Srikanth
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

This document provides a description of the Language Computer Corporation (LCC) SRN System that participated in the SemEval 2007 Semantic Relation between Nominals task. The system combines the outputs of different binary and multi-class classifiers build using machine learning algorithms like Decision Trees, Semantic Scattering, Iterative Semantic Specialization, and Support Vector Machines.
What is answer set programming?
Authors: Vladimir Lifschitz
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Answer set programming (ASP) is a form of declarative programming oriented towards difficult search problems. As an outgrowth of research on the use of nonmonotonic reasoning in knowledge representation, it is particularly useful in knowledge-intensive applications. ASP programs consist of rules that look like Prolog rules, but the computational mechanisms used in ASP are different: they are based on the ideas that have led to the creation of fast satisfiability solvers for propositional logic.
Utility based Q-learning to facilitate cooperation in Prisoner's Dilemma games
Authors: Koichi Moriyama
Venue: Web Intelligence and Agent Systems (2009)

This work deals with Q-learning in a multiagent environment. There are many multiagent Q-learning methods, and most of them aim to converge to a Nash equilibrium, which is not desirable in games like the Prisoner's Dilemma (PD). However, normal Q-learning agents that use a stochastic method in choosing actions to avoid local optima may yield mutual cooperation in a PD game. Although such mutual cooperation usually occurs singly, it can be facilitated if the Q-function of cooperation becomes larger than that of defection after the cooperation. This work derives a theorem on how many consecutive repetitions of mutual cooperation are needed to make the Q-function of cooperation larger than that of defection. In addition, from the perspective of the author's previous works that discriminate utilities from rewards and use utilities for learning in PD games, this work also derives a corollary on how much utility is necessary to make the Q-function larger by one-shot mutual cooperation.
Probabilistic Methods for Link-Based Classification at INEX 2008
Authors: Luis M. Campos, Juan M. Fernández-Luna, Juan F. Huete, Alfonso E. Romero
Venue: Advances in Focused Retrieval: 7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2008. Revised and Selected Papers (2009)

In this paper we propose a new method for link-based classification using Bayesian networks. It can be used in combination with any content only probabilistic classsifier, so it can be useful in combination with several different classifiers. We also report the results obtained of its application to the XML Document Mining Track of INEX'08.
A Probabilistic PTAS for Shortest Common Superstring
Authors: Kai Plociennik
Venue: Proceedings of the 34th International Symposium on Mathematical Foundations of Computer Science 2009 (2009)

We consider approximation algorithms for the shortest common superstring problem (SCS). It is well-known that there is a constant f > 1 such that there is no efficient approximation algorithm for SCS achieving a factor of at most f in the worst case, unless P = NP. We study SCS on random inputs and present an approximation scheme that achieves, for every ¿> 0, a 1 + ¿-approximation in expected polynomial time. This result applies not only if the letters are chosen independently at random, but also to the more realistic mixing model, which allows dependencies among the letters of the random strings. Our result is based on a sharp tail bound on the optimal compression, which improves a previous result by Frieze and Szpankowski. 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