ppxscal/embedding_network huggingface dataset:
Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks
Authors: T. Kathirvalavakumar, S. Jeyaseeli Subavathi
Venue: Neurocomputing (2009)

The major drawbacks of backpropagation algorithm are local minima and slow convergence. This paper presents an efficient technique ANMBP for training single hidden layer neural network to improve convergence speed and to escape from local minima. The algorithm is based on modified backpropagation algorithm in neighborhood based neural network by replacing fixed learning parameters with adaptive learning parameters. The developed learning algorithm is applied to several problems. In all the problems, the proposed algorithm outperform well.
Solving Non-linear Polynomial Arithmetic via SAT Modulo Linear Arithmetic
Authors: Cristina Borralleras, Salvador Lucas, Rafael Navarro-Marset, Enric Rodríguez-Carbonell, Albert Rubio
Venue: Proceedings of the 22nd International Conference on Automated Deduction (2009)

Polynomial constraint-solving plays a prominent role in several areas of engineering and software verification. In particular, polynomial constraint solving has a long and successful history in the development of tools for proving termination of programs. Well-known and very efficient techniques, like SAT algorithms and tools, have been recently proposed and used for implementing polynomial constraint solving algorithms through appropriate encodings. However, powerful techniques like the ones provided by the SMT (SAT modulo theories) approach for linear arithmetic constraints (over the rationals) are underexplored to date. In this paper we show that the use of these techniques for developing polynomial constraint solvers outperforms the best existing solvers and provides a new and powerful approach for implementing better and more general solvers for termination provers.
Some aspects of the morphological processing of Bulgarian
Authors: Milena Slavcheva
Venue: Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages (2003)

This paper demonstrates the modelling of morphological knowledge in Bulgarian and applications of the created data sets in an integrated framework for production and manipulation of language resources. The production scenario is exemplified by the Bulgarian verb as the morphologically richest and most problematic part-of-speech category. The definition of the set of morphosyntactic specifications for verbs in the lexicon is described. The application of the tagset in the automatic morphological analysis of text corpora is accounted for. A Type Model of Bulgarian verbs handling the attachment of short pronominal elements to verbs, is presented.
The Research of Salt Cavern 3D Modeling Technology
Authors: Tao Zhiyong, Ma Zhenhe
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

Salt cavern provides a tremendous and safe underground space for the storage of those substances which are insoluble by salt. The cavity of the cavern is used to store petroleum and natural gas so that ensure the supply security of energy. This method has been used more and more extensive in the world wide. In the process of building cavern, the shape measurement is often needed to provide data for controlling the cavern shape, so as to ensure the cavern to form the best physical shape. In this paper, the author takes Germanic advanced cavern measurement technology as an example, to probe into the data modeling technology of cavern measurement, and analyzes the existing three-dimensional modeling technology, on this basis, puts forward an new 3D modeling method named "space nearest point method".
On semi-supervised learning of Gaussian mixture models for phonetic classification
Authors: Jui-Ting Huang, Mark Hasegawa-Johnson
Venue: Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (2009)

This paper investigates semi-supervised learning of Gaussian mixture models using an unified objective function taking both labeled and unlabeled data into account. Two methods are compared in this work - the hybrid discriminative/generative method and the purely generative method. They differ in the criterion type on labeled data; the hybrid method uses the class posterior probabilities and the purely generative method uses the data likelihood. We conducted experiments on the TIMIT database and a standard synthetic data set from UCI Machine Learning repository. The results show that the two methods behave similarly in various conditions. For both methods, unlabeled data improve training on models of higher complexity in which the supervised method performs poorly. In addition, there is a trend that more unlabeled data results in more improvement in classification accuracy over the supervised model. We also provided experimental observations on the relative weights of labeled and unlabeled parts of the training objective and suggested a critical value which could be useful for selecting a good weighing factor.
A Multimodal Composite Transportation Network Model and Topological Relationship Building Algorithm
Authors: Lin Yang, Bo Wan
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

The urban transportation has transformed to the multimodal and mutual-supported system, in order to express the multimodal and integrative transportation network more accurately, satisfy the needs of multimodal network modeling for both public and private departments better, and implement the transport planning and accessibility analysis more efficiently, this paper deals with the issue of integrating transportation network of different modes to a seamless and integrated one, and discusses the network model and algorithms to achieve it. This paper designed a multimodal composite transport network model and put forward an efficient topology building algorithm to automatically create connected relationship inside mode and between modes. An experimental work of bi-modal network building has been done using the street network and subway network of several cities in China, which shown the algorithm can build multimodal model exactly, and verified the efficiency of the algorithm and the feasibility of the network model. This paves the way to further implement network analysis and network application in multimodal network.
A new problem for rule following
Authors: Mark Hogarth
Venue: Natural Computing: an international journal (2009)

Wittgenstein saw a problem with the idea that `rule following' is a transparent process. Here I present an additional problem, based on recent ideas about non-Turing computing. I show that even the simplest algorithm--Frege's successor function, i.e. counting--cannot by itself determine the `output'. Specification of a computing machine is also required.
Minimum rank error language modeling
Authors: Jen-Tzung Chien, Meng-Sung Wu
Venue: IEEE Transactions on Audio, Speech, and Language Processing (2009)

Statistical language modeling has been successfully developed for speech recognition and information retrieval. The minimum classification error (MCE) training was undertaken to enhance speech recognition performance by minimizing the word error rate. This paper presents a new minimum rank error (MRE) algorithm for n-gram language model training. Rather than speech recognition, the proposed language models are estimated for information retrieval by considering the metric of average precision. However, the maximization of average precision is closely linked to minimizing the rank error or optimizing the order of the ranked documents. Accordingly, this paper calculates the rank error loss function from the misordering pairs of relevant and irrelevant documents in the rank list. The Bayes risk due to the expected rank loss is minimized to develop the Bayesian retrieval rule for ad-hoc information retrieval. Consequently, the discriminative training of language model is performed by integrating discrimination information from individual relevant documents relative to their corresponding irrelevant documents. Experimental results on TREC collections indicate that the proposed MRE language model improves the order of relevant documents, and degrades that of irrelevant documents. The MRE method achieves significantly higher average precision for test queries than the maximum likelihood and the MCE retrieval models.
The Design and Implementation of a Pseudo Random Number Generation Algorithm
Authors: Zuohu Liu, Minghe Huang, Shaojun Zhu
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

This paper analyzes the generation of pseudorandom number theory, gives a brief description of the pseudorandom number generation mechanisms in SQL Server 2000,and then proposes a new method to generate pseudo random number in database system. This method converts a GUID code into a random plastic number through hash function,generates a random sequence composed by 0 and 1 according to the parity of the plastic number, and then take further operation to the 0-1 random sequence, in order to enhance its randomness, and ultimately generates the required random number according to the 0-1 sequence. At the end, tests the randomness of the 0-1 sequence with a run test.
ARMOR security for Los Angeles international airport
Authors: James Pita, Manish Jain, Fernando Ordóñez, Christopher Portway, Milind Tambe, Craig Western, Praveen Paruchuri, Sarit Kraus
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Security at major locations of economic or political importance is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or monitoring, e.g. they can plan an attack avoiding existing patrols. Hence, randomized patrolling or monitoring is important, but randomization must provide distinct weights to different actions based on their complex costs and benefits. To this end, this demonstration showcases a promising transition of the latest in multi-agent algorithms into a deployed application. In particular, it exhibits a software assistant agent called ARMOR (Assistant for Randomized Monitoring over Routes) that casts this patrolling/monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weight the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features: (i) It uses the fastest known solver for Bayesian Stackelberg games called DOBSS, where the dominant mixed strategies enable randomization; (ii) Its mixed-initiative based interface allows users to occasionally adjust or override the automated schedule based on their local constraints. ARMOR has been successfully deployed since August 2007 at the Los Angeles International Airport (LAX) to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals.
A nonparametric test for the equality of counting processes with panel count data
Authors: N. Balakrishnan, Xingqiu Zhao
Venue: Computational Statistics Data Analysis (2010)

This paper considers the problem of nonparametric comparison of counting processes with panel count data, which arise naturally when recurrent events are considered. For the problem considered, we construct a new nonparametric test statistic based on the nonparametric maximum likelihood estimator of the mean function of the counting processes over observation times. The asymptotic distribution of the proposed statistic is derived and its finite-sample property is examined through Monte Carlo simulations. The simulation results show that the proposed method is good for practical use and also more powerful than the existing nonparametric tests based on the nonparametric maximum pseudo-likelihood estimator. A set of panel count data from a floating gallstone study is analyzed and presented as an illustrative example.
A high abstraction, high accuracy power estimation model for networks-on-chip
Authors: Luciano Ost, Guilherme Guindani, Leandro Soares Indrusiak, Cezar Reinbrecht, Thiago Raupp, Fernando Moraes
Venue: Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes (2009)

Due to the vast number of alternatives in the design space of NoC-based MPSoCs, fast and accurate performance evaluation approaches can result in earlier - and often better - design decisions. Important design metrics for mobile embedded systems include power dissipation and energy consumption. To speed-up the evaluation of such metrics, state-of-the-art research proposes abstract models of the NoC interconnect, employing, for example, TLM SystemC, analytical descriptions and graph descriptions. Power parameters used at higher abstraction models (e.g. TLM) frequently rely upon data generated at lower abstraction levels (e.g. RTL). This paper presents an abstract model of a NoC coupled with a power estimation model, aiming to provide accurate estimations early on the design flow. Despite being abstract, this model considers typical NoC communication behavior such as congestion and burst transmissions, leading to accurate results compared to industrial tools. A proof-of-concept implementation using the Ptolemy II framework demonstrates the strength of this approach, showing that it is possible to use abstract models to estimate power and energy without incurring excessive accuracy loss. Other benefits of abstract modeling are increased system observability and simplicity of design space exploration. System observability is demonstrated with a graphic tool enabling the visualization of the power dissipation at runtime.
Quantifying Information Leaks in Outbound Web Traffic
Authors: Kevin Borders, Atul Prakash
Venue: Proceedings of the 2009 30th IEEE Symposium on Security and Privacy (2009)

As the Internet grows and network bandwidth continues to increase, administrators are faced with the task of keeping confidential information from leaving their networks. Today’s network traffic is so voluminous that manual inspection would be unreasonably expensive. In response, researchers have created data loss prevention systems that check outgoing traffic for known confidential information. These systems stop naïve adversaries from leaking data, but are fundamentally unable to identify encrypted or obfuscated information leaks. What remains is a high-capacity pipe for tunneling data to the Internet. We present an approach for quantifying information leak capacity in network traffic. Instead of trying to detect the presence of sensitive data—an impossible task in the general case--our goal is to measure and constrain its maximum volume. We take advantage of the insight that most network traffic is repeated or determined by external information, such as protocol specifications or messages sent by a server. By filtering this data, we can isolate and quantify true information flowing from a computer. In this paper, we present measurement algorithms for the Hypertext Transfer Protocol (HTTP), the main protocol for web browsing. When applied to real web browsing traffic, the algorithms were able to discount 98.5% of measured bytes and effectively isolate information leaks.
Human-System Interface (HSI) Challenges in Nuclear Power Plant Control Rooms
Authors: Jo-Ling Chang, Huafei Liao, Liang Zeng
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)

This study uses factor analysis to examine 30 errors due to human-system interface (HSI) in nuclear power plant control rooms. The results are used to validate the factor structure and the Decision-Action Model developed in this paper. Ten U.S. commercial operating nuclear plants, total of 18 units, participated in this study at the time this paper was written. The result is a five-factor structure: Operations Uncertainties, Design Improvements, Misoperations, Equipment Control, and Human Factors Redesign. The completed Decision-Action Model provides current operating plants with suggested corrective actions for each type of potential HSI errors.
Humanities 2.0: documents, interpretation and intersubjectivity in the digital age
Authors: Aurelien Benel, Christophe Lejeune
Venue: International Journal of Web Based Communities (2009)

With their focus on documents, interpretation and intersubjectivity, Web 2.0 technologies have surprising analogies with philosophical hermeneutics, including the theory of text interpretation. Philosophical hermeneutics was generalised from Biblical hermeneutics by Dilthey in the 19th century and chosen as an alternative to positivism as a foundation for the epistemology and methodology of the humanities and social sciences. This article explores how Web 2.0 technologies might better meet the needs of social and human sciences than traditional information technologies that are historically bound with logical positivism. Illustrations are provided from archaeology and sociology, two social and human sciences which were early adopters of punched cards and computers.
Application of Unascertained Neural Networks to Financial Early Warning
Authors: Huawang Shi
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

Artificial neural network (ANN) has outstanding characteristics in machine learning, fault, tolerant, parallel reasoning and processing nonlinear problem abilities. Unascertained system that imitates the human brain's thinking logical is a kind of mathematical tools used to deal with imprecise and uncertain knowledge. Integrating unascertained method with neural network technology, the reasoning process of network coding can be tracked, and the output of the network can be given a physical explanation. A unascertained neural network was set up. It can be compared with the fuzzy network, so that their own advantages and shortcomings can be found and further study can be made on the uncertainty network to improve the uncertainty network more complete. 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
Formation Control Numerical Simulations of Geometric Patterns for Unmanned Autonomous Vehicles with Swarm Dynamical Methodologies
Authors: Zhibin Xue, Jianchao Zeng
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

Formation of geometric shapes with autonomous unmanned mobile agents (which could be ground, undersea, aerial vehicles namely UAV/robots, mobile sensors etc.) is a particular type of the coordination problem. Meanwhile, this is due to broad range of application of multi-agent systems in a lot of project fields in recent years, the coordinated task of multi-agent is currently an important and challenging field of research. By use of the swarm dynamical model, based on artificial potential field (APF) function and Newton-Raphson iteration method, we analyze the special form of swarm aggregation routes of a particular predefined geometrical configuration that is defined by a set of desired inter-agent distance values in this paper. We also perform numerical simulations to examine the performance of the method, the results further indicated that the individual members living in group during the course of coordinative motion can realize the local collision-free the stabilization of particular predefined geometrical configuration formations.
Answer Set Programming: A Primer
Authors: Thomas Eiter, Giovambattista Ianni, Thomas Krennwallner
Venue: Reasoning Web. Semantic Technologies for Information Systems: 5th International Summer School 2009, Brixen-Bressanone, Italy, August 30 - September 4, 2009, Tutorial Lectures (2009)

Answer Set Programming (ASP) is a declarative problem solving paradigm, rooted in Logic Programming and Nonmonotonic Reasoning, which has been gaining increasing attention during the last years. This article is a gentle introduction to the subject; it starts with motivation and follows the historical development of the challenge of defining a semantics for logic programs with negation. It looks into positive programs over stratified programs to arbitrary programs, and then proceeds to extensions with two kinds of negation (named weak and strong negation), and disjunction in rule heads. The second part then considers the ASP paradigm itself, and describes the basic idea. It shows some programming techniques and briefly overviews Answer Set solvers. The third part is devoted to ASP in the context of the Semantic Web, presenting some formalisms and mentioning some applications in this area. The article concludes with issues of current and future ASP research.
Lightly supervised transliteration for machine translation
Authors: Amit Kirschenbaum, Shuly Wintner
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

We present a Hebrew to English transliteration method in the context of a machine translation system. Our method uses machine learning to determine which terms are to be transliterated rather than translated. The training corpus for this purpose includes only positive examples, acquired semi-automatically. Our classifier reduces more than 38% of the errors made by a baseline method. The identified terms are then transliterated. We present an SMT-based transliteration model trained with a parallel corpus extracted from Wikipedia using a fairly simple method which requires minimal knowledge. The correct result is produced in more than 76% of the cases, and in 92% of the instances it is one of the top-5 results. We also demonstrate a small improvement in the performance of a Hebrew-to-English MT system that uses our transliteration module.
Fuzzy-Based Multi-domain Collaborative Evaluation
Authors: Liu Zhaobin, Xu Xinwei, Qin Zheng
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03 (2009)

In order to improve the robustness and accuracy of multi-domain evaluation, in response to the subjectivity of the inter-domain trust, this paper presents a target-restriction-free collaborative evaluation and decision-making algorithm based on fuzzy partial ordering relation. It also presents a mathematical model for evaluation and decision-making in the environment of multi-domain. In conclusion, a case study is conducted to verify that the collaborative evaluation and decision-making has a very good performance in integration and balance.
Letters: MIMLRBF: RBF neural networks for multi-instance multi-label learning
Authors: Min-Ling Zhang, Zhi-Jian Wang
Venue: Neurocomputing (2009)

In multi-instance multi-label learning (MIML), each example is not only represented by multiple instances but also associated with multiple class labels. Several learning frameworks, such as the traditional supervised learning, can be regarded as degenerated versions of MIML. Therefore, an intuitive way to solve MIML problem is to identify its equivalence in its degenerated versions. However, this identification process would make useful information encoded in training examples get lost and thus impair the learning algorithm's performance. In this paper, RBF neural networks are adapted to learn from MIML examples. Connections between instances and labels are directly exploited in the process of first layer clustering and second layer optimization. The proposed method demonstrates superior performance on two real-world MIML tasks.