ppxscal/embedding_network huggingface dataset:
News Video Story Segmentation Based on Topic Caption Text and Audio Information
Authors: Zhao Yaqin, Zhou Xianzhong, Chen Huiming
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

This paper presents a novel scheme of news video story segmentation with the combination of topic caption frame and audio information. First of all, a new method of topic caption frame detection is proposed in which the topic caption frame is detected by features extraction from frame differences, the topic caption lasting time and times of caption transition in the same shot. Afterwards, the silence clip between two continuous news story boundaries is detected according to short-time average energy and short-time average zero-crossing rate. Moreover, one news video is segmented into a series of news story clips on the basis of topic caption text and silence clip. The experimental results showed that the proposed method could effectively detect topic caption text and news story clip boundaries for news video.
Object-oriented Case Representation and Its Application in IDS
Authors: Qian Quan, Zhang Rui, Che Hong-Yi
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Case representation is a key issue in case-based reasoning (CBR). This paper introduces a novel object-oriented model to represent cases and applies this technology to intrusion detection system (IDS) in order to solve the over-sensitive alarm problem which remains in most of the commercial IDSs. In this model we try to represent the complex case structural information through class hierarchy. The new method not only overcomes limitations that traditional attribute-value case representation can not represent complex cases, but also combines domain knowledge and case representation through constraint rules. In the aspect of case similarity, we divide it into two categories: the intra- class similarity and the inter-class similarity, which shows more accurate and convenient. The object-oriented case representation has been used in our own IDS product and it plays a quite efficient role in mitigating over-sensitive alarm problems.
Regulation Model of Environmental Impact in Expressway Construction Based on Jieke Theory
Authors: Jia Jinxiu, Han Sen
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

In accordance with present situation about environment impact of expressway construction and deficiency in environmental protection measures of expressway construction, jieke theory was used to analyze the jieke sectors of eco-environment and their roles. Basic concept and structure of eco-environment system was elaborated, and its application prospect in environment protection of expressway construction was analyzed. At last, regulation model of environmental impact in expressway construction was put forward. By the model, eco-environment impacts can be dynamically adjusted in the process of expressway construction. Further, on the precondition of ensuring natural expressway construction, environmental impacts were quantitatively reduced to the minimum extent. It provides researchers with a new thought and method about eco-environmental impact of expressway construction.
Various amenability properties of the L1-algebra of polynomial hypergroups and applications
Authors: R. Lasser
Venue: Journal of Computational and Applied Mathematics (2009)

We investigate amenability, weak amenability and @a"t-amenability of the L^1-algebra of polynomial hypergroups, and derive from these properties some applications for the corresponding orthogonal polynomials.
Simulation on Steering Stability of 4WS Tractor Semi-trailer
Authors: Sheng Chang, Hong-guo Xu, Hong-fei Liu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

Based on the analysis of the steering performance, the objective of this paper is to present evidence for the tractor adopted the four-wheel steering (4WS) which can improve the handling and stability of tractor semi-trailer. The simplified single track model with 3 DOF for 4WS tractor semi-trailer has been constructed for dynamic simulation through time-domain response on the two states of small angle turning and straight driving. The analysis and simulations with MATLAB show that this strategy can keep the vehicle yaw rate in a small scale. The comparisons are done with the front-wheel steering (FWS) and the 4WS for vehicle combination, the results verify that the 4WS has good effect on improving the high-speed stability and low-speed maneuverability of tractor semi-trailer.
Better binarization for the CKY parsing
Authors: Xinying Song, Shilin Ding, Chin-Yew Lin
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

We present a study on how grammar binarization empirically affects the efficiency of the CKY parsing. We argue that binarizations affect parsing efficiency primarily by affecting the number of incomplete constituents generated, and the effectiveness of binarization also depends on the nature of the input. We propose a novel binarization method utilizing rich information learnt from training corpus. Experimental results not only show that different binarizations have great impacts on parsing efficiency, but also confirm that our learnt binarization outperforms other existing methods. Furthermore we show that it is feasible to combine existing parsing speed-up techniques with our binarization to achieve even better performance.
Factorial validity of problematic Internet use scales
Authors: Ronnie Jia, Heather H. Jia
Venue: Computers in Human Behavior (2009)

There exists a number of multidimensional measurement scales for problematic Internet use (PIU) with varying factor structures. This study reviews the factor analytic techniques used to develop these measures and discusses their implications for the factorial validity, particularly discriminant validity, of these PIU scales. To further illustrate these points, we reformulate the four-factor Online Cognition Scale into a more parsimonious two-factor measure (i.e., dependency and distraction) and demonstrate its factorial validity as well as robustness across student and working adult samples. Contributions of this research are discussed.
Image-to-Physical Registration for Image-Guided Interventions Using 3-D Ultrasound and an Ultrasound Imaging Model
Authors: Andrew P. King, Ying-Liang Ma, Cheng Yao, Christian Jansen, Reza Razavi, Kawal S. Rhode, Graeme P. Penney
Venue: Proceedings of the 21st International Conference on Information Processing in Medical Imaging (2009)

We present a technique for automatic intensity-based image-to-physical registration of a 3-D segmentation for image-guided interventions. The registration aligns the segmentation with tracked and calibrated 3-D ultrasound (US) images of the target region. The technique uses a probabilistic framework and explicitly incorporates a model of the US image acquisition process. The rigid body registration parameters are varied to maximise the likelihood that the real US image(s) were formed using the US imaging model from the probe transducer position. The proposed technique is validated on images segmented from cardiac magnetic resonance imaging (MRI) data and 3-D US images acquired from 3 volunteers and 1 patient. We show that the accuracy of the algorithm is 2.6-4.2mm and the capture range is 9-18mm. The proposed technique has the potential to provide accurate image-to-physical registrations for a range of image guidance applications.
Multi-dimensional Moderation in Online Communities: Experiences with Three Norwegian Sites
Authors: Gheorghita Ghinea, Bendik Bygstad, Christoph Schmitz
Venue: Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009 (2009)

Online-communities and user contribution of content have become widespread over the last years. This has triggered new and innovative web concepts, and perhaps also changed the power balance in the society. Many large corporations have embraced this way of creating content to their sites, which has raised concerns regarding abusive content. Previous research has identified two main different types of moderation; one where the users have most of the control as in Wikipedia, and the other where the owners control everything. The media industry, in particular, are reluctant to loose the control of their content by using the member-maintained approach even if it has proven to cost less and be more efficient.This research proposes to merge these two moderation types through a concept called multidimensional moderation. To test this concept, two prototype solutions have been implemented and tested in large-scale discussion groups. The results from this study show that a combination of owner and user moderation may enhance the moderation process.
ART-Based Neural Networks for Multi-label Classification
Authors: Elena P. Sapozhnikova
Venue: Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII (2009)

Multi-label classification is an active and rapidly developing research area of data analysis. It becomes increasingly important in such fields as gene function prediction, text classification or web mining. This task corresponds to classification of instances labeled by multiple classes rather than just one. Traditionally, it was solved by learning independent binary classifiers for each class and combining their outputs to obtain multi-label predictions. Alternatively, a classifier can be directly trained to predict a label set of an unknown size for each unseen instance. Recently, several direct multi-label machine learning algorithms have been proposed. This paper presents a novel approach based on ART (Adaptive Resonance Theory) neural networks. The Fuzzy ARTMAP and ARAM algorithms were modified in order to improve their multi-label classification performance and were evaluated on benchmark datasets. Comparison of experimental results with the results of other multi-label classifiers shows the effectiveness of the proposed approach. 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 Investigation into Audiovisual Speech Correlation in Reverberant Noisy Environments
Authors: Simone Cifani, Andrew Abel, Amir Hussain, Stefano Squartini, Francesco Piazza
Venue: Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions: COST Action 2102 International Conference Prague, Czech Republic, October 15-18, 2008 Revised Selected and Invited Papers (2009)

As evidence of a link between the various human communication production domains has become more prominent in the last decade, the field of multimodal speech processing has undergone significant expansion. Many different specialised processing methods have been developed to attempt to analyze and utilize the complex relationship between multimodal data streams. This work uses information extracted from an audiovisual corpus to investigate and assess the correlation between audio and visual features in speech. A number of different feature extraction techniques are assessed, with the intention of identifying the visual technique that maximizes the audiovisual correlation. Additionally, this paper aims to demonstrate that a noisy and reverberant audio environment reduces the degree of audiovisual correlation, and that the application of a beamformer remedies this. Experimental results, carried out in a synthetic scenario, confirm the positive impact of beamforming not only for improving the audio-visual correlation but also in a complete audio-visual speech enhancement scheme. Thus, this work inevitably highlights an important aspect for the development of future promising bimodal speech enhancement systems.
Technique of Large-scale Image Set Construction Based on Web Image Searching Engine
Authors: Ran Li, Weiguang Xu, Jianjiang Lu, Yafei Zhang, Zining Lu
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Large-scale image training set is the precondition of large numbers of various images' semantic annotation. However, due to the absence of image content, state-of-the-art text-based web image searching engine's results can't serve as image set directly. In this paper, we propose a novel framework for large-scale image set construction, which is based on re-ranking current text-based web image searching engine’s results. For a particular concept to be included in future image set, a genetic feature selection algorithm is performed to obtain optimal features and relevant optimal weights based on the results of web image searching engine and users' relevance feedback. With the optimal feature set and optimal weights, the distance between image in original searching results and positive or negative instances users provided is considered to be the main factor of rank score. After re-ranking thousands of concepts and obtaining numbers of images ranked on top for each concept, large-scale image set can be constructed.
Gene expression reliability estimation through cluster-based analysis
Authors: Luca Sterpone, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano
Venue: Proceedings of the 2009 IEEE International Workshop on Medical Measurements and Applications (2009)

Gene expression is the fundamental control of the structure and functions of the cellular versatility and adaptability of any organisms. The measurement of gene expressions is performed on images generated by optical inspection of microarray devices which allow the simultaneous analysis of thousands of genes. The images produced by these devices are used to calculate the expression levels of mRNA in order to draw diagnostic information related to human disease. The quality measures are mandatory in genes classification and in the decision-making diagnostic. However, microarrays are characterized by imperfections due to sample contaminations, scratches, precipitation or imperfect gridding and spot detection. The automatic and efficient quality measurement of microarray is needed in order to discriminate faulty gene expression levels. In this paper we present a new method for estimate the quality degree and the data's reliability of a microarray analysis. The efficiency of the proposed approach in terms of genes expression classification has been demonstrated through a clustering supervised analysis performed on a set of three different histological samples related to the Lymphoma's cancer disease.
Virtual Arc consistency for weighted CSP
Authors: M. Cooper, S. De Givry, M. Sanchez, T. Schiex, M. Zytnicki
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Optimizing a combination of local cost functions on discrete variables is a central problem in many formalisms such as in probabilistic networks, maximum satisfiability, weighted CSP or factor graphs. Recent results have shown that maintaining a form of local consistency in a Branch and Bound search provides bounds that are strong enough to solve many practical instances. In this paper, we introduce Virtual Arc Consistency (VAC) which iteratively identifies and applies sequences of cost propagation over rational costs that are guaranteed to transform a WCSP in another WCSP with an improved constant cost. Although not as strong as Optimal Soft Arc Consistency, VAC is faster and powerful enough to solve submodular problems. Maintaining VAC inside branch and bound leads to important improvements in efficiency on large difficult problems and allowed us to close two famous frequency assignment problem instances.
Slicing through the Scientific Literature
Authors: Christopher J. Baker, Patrick Lambrix, Jonas Laurila Bergman, Rajaraman Kanagasabai, Wee Tiong Ang
Venue: Proceedings of the 6th International Workshop on Data Integration in the Life Sciences (2009)

Success in the life sciences depends on access to information in knowlegde bases and literature. Finding and extracting the relevant information depends on a user's domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant queries in response to keyword input from the user. Queries are presented in natural language, translated to formal query syntax and issued to a knowledge base of scientific literature, documents or aligned document segments. We describe the components of the system and exemplify using real-world examples.
Optimality and duality for nonsmooth multiobjective programming problems with V-r-invexity
Authors: Tadeusz Antczak
Venue: Journal of Global Optimization (2009)

In the paper, we consider a class of nonsmooth multiobjective programming problems in which involved functions are locally Lipschitz. A new concept of invexity for locally Lipschitz vector-valued functions is introduced, called V-r-invexity. The generalized Karush---Kuhn---Tuker necessary and sufficient optimality conditions are established and duality theorems are derived for nonsmooth multiobjective programming problems involving V-r-invex functions (with respect to the same function &eta;).
A Massively Parallel Coprocessor for Convolutional Neural Networks
Authors: Murugan Sankaradas, Venkata Jakkula, Srihari Cadambi, Srimat Chakradhar, Igor Durdanovic, Eric Cosatto, Hans Peter Graf
Venue: Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (2009)

We present a massively parallel coprocessor for accelerating Convolutional Neural Networks (CNNs), a class of important machine learning algorithms. The coprocessor functional units, consisting of parallel 2D convolution primitives and programmable units performing sub-sampling and non-linear functions specific to CNNs, implement a “meta-operator” to which a CNN may be compiled to. The coprocessor is serviced by distributed off-chip memory banks with large data bandwidth. As a key feature, we use low precision data and further increase the effective memory bandwidth by packing multiple words in every memory operation, and leverage the algorithm’s simple data access patterns to use off-chip memory as a scratchpad for intermediate data, critical for CNNs. A CNN is mapped to the coprocessor hardware primitives with instructions to transfer data between the memory and coprocessor. We have implemented a prototype of the CNN coprocessor on an off-the-shelf PCI FPGA card with a single Xilinx Virtex5 LX330T FPGA and 4 DDR2 memory banks totaling 1GB. The coprocessor prototype can process at the rate of 3.4 billion multiply accumulates per second (GMACs) for CNN forward propagation, a speed that is 31x faster than a software implementation on a 2.2 GHz AMD Opteron processor. For a complete face recognition application with the CNN on the coprocessor and the rest of the image processing tasks on the host, the prototype is 6-10x faster, depending on the host-coprocessor bandwidth.
Enabling always-available input with muscle-computer interfaces
Authors: T. Scott Saponas, Desney S. Tan, Dan Morris, Ravin Balakrishnan, Jim Turner, James A. Landay
Venue: Proceedings of the 22nd annual ACM symposium on User interface software and technology (2009)

Previous work has demonstrated the viability of applying offline analysis to interpret forearm electromyography (EMG) and classify finger gestures on a physical surface. We extend those results to bring us closer to using muscle-computer interfaces for always-available input in real-world applications. We leverage existing taxonomies of natural human grips to develop a gesture set covering interaction in free space even when hands are busy with other objects. We present a system that classifies these gestures in real-time and we introduce a bi-manual paradigm that enables use in interactive systems. We report experimental results demonstrating four-finger classification accuracies averaging 79% for pinching, 85% while holding a travel mug, and 88% when carrying a weighted bag. We further show generalizability across different arm postures and explore the tradeoffs of providing real-time visual feedback.
Experimental Study on Secure Data Collection in Vehicular Sensor Networks
Authors: Harry Gao, Seth Utecht, Fengyuan Xu, Haodong Wang, Qun Li
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

In this paper, we show through a simple secure symmetric key based protocol design and its implementation the feasibility of secure data collection in a vehicular sensor networks. We demonstrate that the protocol works in a realistic setting by collecting the real trace data through real implementation. Some of the key considerations are efficiency, deployability and security. The protocol does not safeguard against some of the techniques an adversary could deploy, such as jamming and deliberate battery-draining.
One-dimensional discrete-time quantum walks on random environments
Authors: Norio Konno
Venue: Quantum Information Processing (2009)

We consider discrete-time nearest-neighbor quantum walks on random environments in one dimension. Using the method based on a path counting, we present both quenched and annealed weak limit theorems for the quantum walk.
PKU: combining supervised classifiers with features selection
Authors: Peng Jin, Danqing Zhu, Fuxin Li, Yunfang Wu
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

This paper presents the word sense disambiguation system of Peking University which was designed for the SemEval-2007 competition. The system participated in the Web track of task 11 "English Lexical Sample Task via English-Chinese Parallel Text". The system is a hybrid model by combining two supervised learning algorithms SVM and ME. And the method of entropy-based feature chosen was experimented. We obtained precision (and recall) of 81.5%.
Graph-cut-based anaphoricity determination for coreference resolution
Authors: Vincent Ng
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

Recent work has shown that explicitly identifying and filtering non-anaphoric mentions prior to coreference resolution can improve the performance of a coreference system. We present a novel approach to this task of anaphoricity determination based on graph cuts, and demonstrate its superiority to competing approaches by comparing their effectiveness in improving a learning-based coreference system on the ACE data sets.
A new variant of L-curve for Tikhonov regularization
Authors: Mansoor Rezghi, S. Mohammad Hosseini
Venue: Journal of Computational and Applied Mathematics (2009)

In this paper we introduce a new variant of L-curve to estimate the Tikhonov regularization parameter for the regularization of discrete ill-posed problems. This method uses the solution norm versus the regularization parameter. The numerical efficiency of this new method is also discussed by considering some test problems.
Technology and Challenges of M-Commerce
Authors: Chen Xin
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Shortly after the advent of digital mobile phone service, a new innovative way of doing business known as mobile commerce (M-commerce) was created. Mobile commerce has been a huge success in terms of individuals’ adoption in some markets like Japan, while, surprisingly, not as flourishing in others. This article reviews the technology behind M-commerce and the products and services currently available. It also examines the challenges of M-commerce.