ppxscal/embedding_network huggingface dataset:
A general, abstract model of incremental dialogue processing
Authors: David Schlangen, Gabriel Skantze
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

We present a general model and conceptual framework for specifying architectures for incremental processing in dialogue systems, in particular with respect to the topology of the network of modules that make up the system, the way information flows through this network, how information increments are 'packaged', and how these increments are processed by the modules. This model enables the precise specification of incremental systems and hence facilitates detailed comparisons between systems, as well as giving guidance on designing new systems.
Overview of the INEX 2008 Ad Hoc Track
Authors: Jaap Kamps, Shlomo Geva, Andrew Trotman, Alan Woodley, Marijn Koolen
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)

This paper gives an overview of the INEX 2008 Ad Hoc Track. The main goals of the Ad Hoc Track were two-fold. The first goal was to investigate the value of the internal document structure (as provided by the XML mark-up) for retrieving relevant information. This is a continuation of INEX 2007 and, for this reason, the retrieval results are liberalized to arbitrary passages and measures were chosen to fairly compare systems retrieving elements, ranges of elements, and arbitrary passages. The second goal was to compare focused retrieval to article retrieval more directly than in earlier years. For this reason, standard document retrieval rankings have been derived from all runs, and evaluated with standard measures. In addition, a set of queries targeting Wikipedia have been derived from a proxy log, and the runs are also evaluated against the clicked Wikipedia pages. The INEX 2008 Ad Hoc Track featured three tasks: For the Focused Task a ranked-list of non-overlapping results (elements or passages) was needed. For the Relevant in Context Task non-overlapping results (elements or passages) were returned grouped by the article from which they came. For the Best in Context Task a single starting point (element start tag or passage start) for each article was needed. We discuss the results for the three tasks, and examine the relative effectiveness of element and passage retrieval. This is examined in the context of content only (CO, or Keyword) search as well as content and structure (CAS, or structured) search. Finally, we look at the ability of focused retrieval techniques to rank articles, using standard document retrieval techniques, both against the judged topics as well as against queries and clicks from a proxy log.
Microarray Gene Expression Analysis Using Combinatorial Fusion
Authors: Cameron McMunn-Coffran, Christina Schweikert, D. Frank Hsu
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

Microarray technology is a popular and informative technique widely used in experimental molecular biology, which can produce quantitative expression measurements for thousands of genes in a single cellular mRNA sample. Analysis methods for determining significant genes are essential to extracting information from the multitude of data generated from a single microarray experiment. Raw gene expression measurements alone, most often, do not indicate significant genes for the given condition. While analysis methods are abundant, there is a need for enhanced performance when attempting to identify significant genes from such experiments. Additionally, the ability to more accurately predict informative genes from cross-laboratory and/or cross-experiment data can certainly aid in disease detection. We propose the application of Combinatorial Fusion Analysis (CFA) in order to enhance and expedite the identification of significant genes in a cross-experiment analysis. Previous methods to identify significant genes applied SAM to analyze the data sets and then took the intersection of top ranked genes. In this paper, we used CFA to combine the scoring functions of two data sets produced by SAM. Moreover, both score and rank combinations are used. Both combinations can achieve better results than the previous approach of taking the intersection. In addition, by using the Rank-Score Characteristic function as a diversity measure, we are able to show that rank combination performed better than score combination. CFA can robustly identify significant genes from multiple microarray data sets so that experimental biology researchers can efficiently perform the next phase of analysis on a smaller subset of genes.
Real-Time Pedestrian Detection Based on Improved Gaussian Mixture Model
Authors: Juan Li, Chunfu Shao, Wangtu Xu, Chunjiao Dong
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Applying image processing technologies to pedestrian detection has been a hot research topic in Intelligent Transportation Systems (ITS). However, the existing video-based algorithms to extract background image may suffer their inefficiency in detecting slow or static pedestrians. To fill the gap, an improved Gaussian Mixture Model (GMM) for pedestrian detection is proposed in this paper. Three novel components have been incorporate into the traditional model. Firstly, the phase of graph segmentation is added before conventional parameters updating. Secondly, a mergence time adjustment scheme is employed to prevent foreground from merging into background. Thirdly, the notion of average weight is introduced as a secondary judgment criterion of foreground segmentation. To show the performance of the proposed method, this algorithm is applied into the real videos for pedestrian detection. The results show the accuracy and adaptability of this proposed method are over standard GMM.
Developing a serious game to evaluate and train group decision making skills
Authors: Conor Linehan, Shaun Lawson, Mark Doughty, Ben Kirman
Venue: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era (2009)

The current paper describes the building of a serious game designed to teach group decision making skills to a unique audience; people who co-ordinate responses to real-world emergencies such as floods, fires, volcanoes and chemical spills. Eighteen participants were recruited and videotaped while playing a paper prototype of the game. Players' actions within the game were analysed in terms of whether the challenges that are present in real world decision-making environments are also present in the game-world decision making environment. It appears that the defining characteristics of group decision making behaviour, especially the mistakes, are evident in groups that play our game. In addition, the round-based game structure allows a tutor the opportunity to deliver in-depth qualitative feedback without interrupting game play. Thus, the game design should prove to be a valid environment in which to train, practice and evaluate the decision making behaviour of groups and function as a valuable and engaging part of a group decision making skills training course.
Shrinking exponential language models
Authors: Stanley F. Chen
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

In (Chen, 2009), we show that for a variety of language models belonging to the exponential family, the test set cross-entropy of a model can be accurately predicted from its training set cross-entropy and its parameter values. In this work, we show how this relationship can be used to motivate two heuristics for "shrinking" the size of a language model to improve its performance. We use the first heuristic to develop a novel class-based language model that outperforms a baseline word trigram model by 28% in perplexity and 1.9% absolute in speech recognition word-error rate on Wall Street Journal data. We use the second heuristic to motivate a regularized version of minimum discrimination information models and show that this method outperforms other techniques for domain adaptation.
The water falls but the waterfall does not fall: New perspectives on objects, processes and events
Authors: Antony Galton, Riichiro Mizoguchi
Venue: Applied Ontology (2009)

We challenge the widespread presumption that matter and objects are ontologically prior to processes and events, and also the less widespread but increasingly popular view that processes and events are ontologically prior to matter and objects. Instead we advance a third view according to which each of these pairs of categories is ontologically dependent on the other. In particular, taking a cue from an ontology of devices, we identify the object as an interface between those processes which are internal to it and those which are external to it and which it may be said to enact, thereby linking objects intrinsically to the processes in which they are involved as well as providing a more powerful determinant of object identity than more traditional, non-dynamic criteria based on demarcation from the environment. The internal processes are themselves external processes in relation to the components of the object which enact them, leading to a potentially open-ended recursive decomposition of both objects and processes in a complex web of mutual interdependency. We also discuss how matter is related to objects, and processes to events, bringing the four categories together in a diagram which clarifies the relations between them - often considered problematic - and establishes a framework for a highly general top-level ontology.
Cooperative Signalling and Its Application in a Power-Controlled MAC Protocol
Authors: Minghao Cui, Violet R. Syrotiuk
Venue: Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks (2009)

In wireless networks utilizing half-duplex transceivers, a mechanism is required to inform a sender of the outcome of its transmission. Traditionally, such feedback comes from the receiver. This approach cannot distinguish between a failure due to a collision at the receiver and a failure due to the receiver moving out of range. To address this problem we propose cooperative signalling, where nodes that overhear the transmission provide feedback to the sender in addition to the feedback from the receiver. Cooperating nodes also provide information to the receiver, allowing it to distinguish between failures which, in turn, enables the sender to respond to each outcome differently. We apply cooperative signalling in a carrier sense multiple access (CSMA) protocol using transmission power control. In simulation, CSMA/PC achieves substantial improvements in throughput per unit energy compared to IEEE 802.11 in both static and mobile ad hoc networks.
Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion
Authors: M. Antón-Rodríguez, F. J. Díaz-Pernas, J. F. Díez-Higuera, M. Martínez-Zarzuela, D. González-Ortega, D. Boto-Giralda
Venue: Neurocomputing (2009)

The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the operation of two main components: a segmentation component composed of the colour opponent system (COS) and the chromatic segmentation system (CSS); and a recognition component formed by an ARTMAP-based neural network with scale and orientation-invariance properties. Segmentation is achieved by perceptual contour extraction and diffusion processes on the colour opponent channels based on the human psychophysical theory of colour perception. This colour regions enhancement along with their local textural features constitutes the recognition pattern to be sent to the supervised neural classifier. The CSS accomplishes the colour region enhancement through a multiple scale loop of oriented filters and competition-cooperation mechanisms. Afterwards, the neural architecture performs an attentive recognition of the scene using those oriented filters responses and the chromatic diffusions. Some comparative tests with other models are included in order to prove the recognition capabilities of this neural architecture and how the use of colour information encourages the texture classification and the accuracy of the boundary detection.
A Novel Differential Energy Video Watermarking Based on Watson Visual Model
Authors: Tanfeng Sun, Xinghao Jiang, Shusen Shi, Zhigao Lin, Guanglei Fu
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

This paper proposes a novel differential energy watermarking based on the Watson visual model, which inserts robust watermark into video streaming according to the differential energy theory. This algorithm can control the watermark's embedding intensity of sub-low AC coefficients in the video streaming adaptively based on the Watson visual model. And it also can choose the region to embed watermark according to the relationship between the energy adjustable threshold and their differential energy. So watermark not only meets the non-visual perception, but also has the better robustness. Experiments show that this algorithm has strong robustness and security against the usual video attacks such as noise, filter and frame attack etc with low complexity of energy computation. This algorithm will be applied to the multimedia copyrights protection.
Residual variance estimation in machine learning
Authors: Elia Liitiäinen, Michel Verleysen, Francesco Corona, Amaury Lendasse
Venue: Neurocomputing (2009)

The problem of residual variance estimation consists of estimating the best possible generalization error obtainable by any model based on a finite sample of data. Even though it is a natural generalization of linear correlation, residual variance estimation in its general form has attracted relatively little attention in machine learning. In this paper, we examine four different residual variance estimators and analyze their properties both theoretically and experimentally to understand better their applicability in machine learning problems. The theoretical treatment differs from previous work by being based on a general formulation of the problem covering also heteroscedastic noise in contrary to previous work, which concentrates on homoscedastic and additive noise. In the second part of the paper, we demonstrate practical applications in input and model structure selection. The experimental results show that using residual variance estimators in these tasks gives good results often with a reduced computational complexity, while the nearest neighbor estimators are simple and easy to implement.
Optimization of Connection Design for Cable Connector in Underwater Monitoring System
Authors: Rui Zhao, Chunjie Qiao, Yueke Wang
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

This paper introduces the research on connection design and optimization for cable connector which is applied to the underwater monitoring system. Design method which is based on time-sharing working of cable and backup of cable conductors will increase reliability of power supply and communication. However, it is complicated to choose optimal connection design with increasing of cable conductors. Answers to questions like: “How many connection designs satisfying the requirement are there?”, “How reliability of connection design is computed?” and “How complexity of connection design is computed?” become very important. So a connection model is proposed. It includes all connection designs between cable conductors and function contacts by a structure matrix, of which the method for computing reliability and complexity is offered. Illustrative example of connection design and optimization for the connector with 6 cable conductors is presented.
The Road Safety Prediction Model Based on the Fuzzy Linear Regression
Authors: Chen Bin, Luo Yong
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

In recent years the number of death and injury in the traffic accident is continue increasing year by year in China, which brings a great lost for social economy. In this paper we mainly studied on the data of traffic accident and follow volume in a certain section of road. And based on the theory of fuzzy mathematics and linear regulation to construct the fuzzy linear regression prediction model, and make an evaluation for the model. Finally, making a fuzzy predict for the accident number in 2010, and provide the scientific evidence for the road security reconstruction.
Diagnosis Service for Software Component and Its Application to a Heterogeneous Sensor Data Management System
Authors: Thi-Quynh Bui, Oum-El-Kheir Aktouf, Michel Dang, Levent Gurgen, Claudia Roncancio
Venue: Proceedings of the 2009 Second International Conference on Dependability (2009)

Component-based software development paradigm enables the construction of complex applications by assembling existing self-contained components. The cost of the software development process can then be sharply reduced. Current software systems are becoming more distributed and operate in highly dynamic environments. Therefore, dependability of component-based applications is an important research issue. Our contribution consists of a diagnosis service that enhances dependability of component-based applications. This diagnosis service provides the ability of detection and location of faulty components during runtime. In this paper, we describe the application of the proposed diagnosis service to an industrial case study that consists of a heterogeneous sensor data management system.
An internet based learning tool in orthopaedic surgery: Preliminary experiences and results
Authors: Musa Citak, Afshin Calafi, Daniel Kendoff, Thomas Kupka, Carl Haasper, Marianne Behrends, Christian Krettek, Herbert K. Matthies, Tobias Hüfner
Venue: Technology and Health Care (2009)

Today universities can offer a variety of educational resources to their students through the internet. These may include lecture notes, PowerPoint presentations, or even an entire recording of a lecture in video format. At the Hannover Medical School (Hannover, Germany), the Trauma Surgery Department in collaboration with the Institute of Medical Informatics has developed an alternative method of "E-learning". We created a web-based multimedia resource center for Trauma Surgery using the Content-Management-System (CMS) Schoolbook application, which was initially developed by the Institute of Medical Informatics. The so called "Trauma Surgery Schoolbook" was first adopted in October of 2005 and has since been used and evaluated by medical students at our institution. The evaluation results for the academic year 2005/06 are reported in this paper. The majority of students enrolled in the Trauma Surgery rotation utilized the Schoolbook, which they regarded as a helpful and effective study tool. Our students embraced the possibility of being able to prepare for lectures and use the Schoolbook for independent home studying purposes. Over time, there was a steady increase in the utilization of the Schoolbook by the students from 67% in the first trimester to 93% in the third trimester. The majority of the surveyed students (79.6%) found the Schoolbook to be constructive and helpful. 8.1% did not have any opinion, and only 12.3% found it to not be helpful. The instructors also found this web-based training program to be both constructive and practical, and were able to utilize its multimedia components to complement their lectures. Overall, our experience with this computer-aided learning program demonstrated that web-based technologies can improve the quality of medical education, benefiting both the students and the instructors.
On Intelligent Forewarning & Decision-Supporting System for the Management of Road Transportation Enterprises
Authors: Zeng Chuanhua, Yang Wei, Li Qingsong
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

A new notion in scientific enterprise management is to forewarn enterprises of their operation problems, and help them take appropriate measures to avoid potential harm. Based on the operating characteristics of road transportation enterprises, an intelligent forewarning & decision-supporting system for the management of transportation enterprises is introduced and studied in this paper. It elaborates on its structure, indexes, means, forewarning and decision knowledge base, and assisting decisions corresponding to forewarnings of different layers. The system, through analyzing the operating data of transportation enterprises and employing relevant knowledge, forewarns enterprises of risks and provides advices to the decision-makers.
Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks
Authors: Haibo Zhang, Hong Shen
Venue: IEEE Transactions on Parallel and Distributed Systems (2009)

Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime.
Learning hidden curved exponential family models to infer face-to-face interaction networks from situated speech data
Authors: Danny Wyatt, Tanzeem Choudhury, Jeff Bilmes
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

In this paper, we present a novel probabilistic framework for recovering global, latent social network structure from local, noisy observations. We extend curved exponential random graph models to include two types of variables: hidden variables that capture the structure of the network and observational variables that capture the behavior between actors in the network. We develop a novel combination of informative and intuitive conversational (local) and structural (global) features to specify our model. The model learns, in an unsupervised manner, the relationship between observable behavior and hidden social structure while simultaneously learning properties of the latent structure itself. We present empirical results on both synthetic data and a real world dataset of face-to-face conversations collected from 24 individuals using wearable sensors over the course of 6 months.