ppxscal/embedding_network huggingface dataset:
The Application of "Project-oriented" Teaching Mode in Computer Course of Advanced Vocational Education
Authors: Ming Xue, Changjun Zhu
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

Based on the concept oftransitionfromtheoryteaching to practice teaching in advancedvocationaleducationsystems, this paperproposesthe “Project-oriented” teaching mode, and gives insights to theconcept together with the teaching approach of it. To better illustrate themode of basic project settings and the implementation process, theteachingpractice of courseis introduced asan example. Finally, the main innovations of this mode as well as the success implementations of importantpointsof thismode are also discussed. It isproved that developing the “Project-oriented” teaching mode courses plays an important role in the computer coursesteaching of advanced vocational education.
Semantic Annotation of City Transportation Information Dialogues Using CRF Method
Authors: Agnieszka Mykowiecka, Jakub Waszczuk
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

The article presents results of an experiment consisting in automatic concept annotation of the transliterated spontaneous human-human dialogues in the city transportation domain. The data source was a corpus of dialogues collected at a Warsaw call center and annotated with about 200 concepts' types. The machine learning technique we used is the linear-chain Conditional Random Fields (CRF) sequence labeling approach. The model based on word lemmas in a window of length 5 gave results of concept recognition with an F-measure equal to 0.85.
Decidable Analysis for a Class of Cryptographic Group Protocols with Unbounded Lists
Authors: Najah Chridi, Mathieu Turuani, Michael Rusinowitch
Venue: Proceedings of the 2009 22nd IEEE Computer Security Foundations Symposium (2009)

Cryptographic protocols are crucial for securing electronic transactions. The confidence in these protocols can be increased by the formal analysis of their security properties. Although many works have been dedicated to standard protocols like Needham-Schroeder very few address the more challenging class of group protocols. We have introduced in previous work a synchronous model for group protocols, that generalizes standardprotocol models by permittingunbounded lists insidemessages. This approach also applies to analyzing Web Services manipulating sequences of items.In this model we propose now a decision procedure for the sub-class of Well-Tagged protocols with Autonomous Keys.
JBIG for Printer Pipelines: A Compression Test
Authors: Daniele Ravì, Tony Meccio, Giuseppe Messina, Mirko Guarnera
Venue: Computational Color Imaging: Second International Workshop, CCIW 2009, Saint-Etienne, France, March 26-27, 2009. Revised Selected Papers (2009)

The proposed paper describes a compression test analysis of JBIG standard algorithm. The aim of such work is to proof the effectiveness of this standard for images acquired through scanners and processed into a printer pipeline. The main issue of printer pipelines is the necessity to use a memory buffer to store scanned images for multiple prints. This work demonstrates that for very large scales the buffer can be fixed using medium compression case, using multiple scans in case of uncommon random patterns.
A natural, tiered and executable UIDL for 3D user interfaces based on Concept-Oriented Design
Authors: Chadwick A. Wingrave, Joseph J. Laviola,  Jr., Doug A. Bowman
Venue: ACM Transactions on Computer-Human Interaction (TOCHI) (2009)

3D User Interface (3DUI) design and development requires practitioners (designers and developers) to represent their ideas in representations designed for machine execution rather than natural representations, hampering development of effective 3DUIs. As such, Concept-Oriented Design (COD) was created as a theory of software development for both natural and executable design and development. Instantiated in the toolkit Chasm, Chasm is a natural, tiered, executable User Interface Description Language (UIDL) for 3DUIs resulting in improved understandability, as well as reduced complexity and reuse. Chasm's utility is shown through evaluations by domain experts, case studies of long-term use, and an analysis of spaces.
Test Case Generation Method for BPEL-Based Testing
Authors: Wenli Dong
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

This paper describes a framework for the design of a test tool that could generate test cases automatically based on given BPEL specifications. The key problems that need to be addressed are how to transform the BPEL specifications into a HPN, and how to design a script language to describe the test case generation that according to the characteristics of BPEL. A BPEL Specification Analyzer and a Test Script Language are presented. A tool called BPEL-based Testing Automatic has been designed and partially implemented. BTA will take a user-defined test case template and the set of test data generated to produce the executable test cases.
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
Authors: Chad Hogg, Héctor Muñoz-Avila, Ugur Kuter
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

We describe HTN-MAKER, an algorithm for learning hierarchical planning knowledge in the form of decomposition methods for Hierarchical Task Networks (HTNs). HTN-MAKER takes as input the initial states from a set of classical planning problems in a planning domain and solutions to those problems, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic information in order to determine which portions of the input plans accomplish a particular task and constructs HTN methods based on those analyses. Our theoretical results show that HTN-MAKER is sound and complete. We also present a formalism for a class of planning problems that are more expressive than classical planning. These planning problems can be represented as HTN planning problems. We show that the methods learned by HTN-MAKER enable an HTN planner to solve those problems. Our experiments confirm the theoretical results and demonstrate convergence in three well-known planning domains toward a set of HTN methods that can be used to solve nearly any problem expressible as a classical planning problem in that domain, relative to a set of goals.
JEDI: Adaptive Stochastic Estimation for Joint Enhancement and Despeckling of Images for SAR
Authors: Wen Zhang, Alexander Wong, David A. Clausi
Venue: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision (2009)

Synthetic aperture radar (SAR) images are degraded by a form of multiplicative noise known as speckle. Current methods for despeckling are limited in that they either do not perform enough noise attenuation, or do not adequately preserve or enhance image detail. We propose a novel adaptive stochastic method for joint enhancement and despecking of images (JEDI) for SAR. The proposed method utilizes an adaptive importance sampling scheme based on local statistics to generate random samples while reducing estimation variance. A Monte Carlo estimate is computed based on the generated samples, wherein the samples are aggregated to form a despeckled and detail-enhanced result. The advantage of JEDI is the ability to efficiently take advantage of information redundancy in speckled images to reduce the effects of speckle while simultaneously enhancing detail visualization. Testing with both simulated and real speckled images shows that JEDI typically outperforms popular despeckling algorithms such as Frost filtering, anisotropic diffusion, median filtering, Gamma-MAP and GenLik in terms of quantitative and qualitative visual quality. On average, JEDI provides a 2-15% improvement in PSNR and a 5-14% improvement in image quality index measures over the tested methods.
An empirical study on multiple LVCSR model combination by machine learning
Authors: Takehito Utsuro, Yasuhiro Kodama, Tomohiro Watanabe, Hiromitsu Nishizaki, Seiichi Nakagawa
Venue: Proceedings of HLT-NAACL 2004: Short Papers on XX (2004)

This paper proposes to apply machine learning techniques to the task of combining outputs of multiple LVCSR models. The proposed technique has advantages over that by voting schemes such as ROVER, especially when the majority of participating models are not reliable. In this machine learning framework, as features of machine learning, information such as the model IDs which output the hypothesized word are useful for improving the word recognition rate. Experimental results show that the combination results achieve a relative word error reduction of up to 39% against the best performing single model and that of up to 23% against ROVER. We further empirically show that it performs better when LVCSR models to be combined are chosen so as to cover as many correctly recognized words as possible, rather than choosing models in descending order of their word correct rates.
Multiple Description Scalable Coding for Video Transmission over Unreliable Networks
Authors: Roya Choupani, Stephan Wong, Mehmet R. Tolun
Venue: Proceedings of the 9th International Workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation (2009)

Developing real time multimedia applications for best effort networks such as the Internet requires prohibitions against jitter delay and frame loss. This problem is further complicated in wireless networks as the rate of frame corruption or loss is higher in wireless networks while they generally have lower data rates compared to wired networks. On the other hand, variations of the bandwidth and the receiving device characteristics require data rate adaptation capability of the coding method. Multiple Description Coding (MDC) methods are used to solve the jitter delay and frame loss problems by making the transmitted data more error resilient, however, this results in reduced data rate because of the added overhead. MDC methods do not address the bandwidth variation and receiver characteristics differences. In this paper a new method based on integrating MDC and the scalable video coding extension of H.264 standard is proposed. Our method can handle both jitter delay and frame loss, and data rate adaptation problems. Our method utilizes motion compensating scheme and, therefore, is compatible with the current video coding standards such as MPEG-4 and H.264. Based on the simulated network conditions, our method shows promising results and we have achieved up to 36dB for average Y-PSNR.
Expression of Moral Emotions in Cooperating Agents
Authors: Celso M. Melo, Liang Zheng, Jonathan Gratch
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

Moral emotions have been argued to play a central role in the emergence of cooperation in human-human interactions. This work describes an experiment which tests whether this insight carries to virtual human-human interactions. In particular, the paper describes a repeated-measures experiment where subjects play the iterated prisoner's dilemma with two versions of the virtual human: (a) neutral, which is the control condition; (b) moral, which is identical to the control condition except that the virtual human expresses gratitude, distress, remorse, reproach and anger through the face according to the action history of the game. Our results indicate that subjects cooperate more with the virtual human in the moral condition and that they perceive it to be more human-like. We discuss the relevance these results have for building agents which are successful in cooperating with humans.
Graph-based word clustering using a web search engine
Authors: Yutaka Matsuo, Takeshi Sakaki, Kôki Uchiyama, Mitsuru Ishizuka
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised algorithm for word clustering based on a word similarity measure by web counts. Each pair of words is queried to a search engine, which produces a co-occurrence matrix. By calculating the similarity of words, a word co-occurrence graph is obtained. A new kind of graph clustering algorithm called Newman clustering is applied for efficiently identifying word clusters. Evaluations are made on two sets of word groups derived from a web directory and WordNet.
Providing predictions on distributed HMMs with privacy
Authors: Sahin Renckes, Huseyin Polat, Yusuf Oysal
Venue: Artificial Intelligence Review (2007)

As forecasting is increasingly becoming important, hidden Markov models (HMMs) are widely used for prediction in many applications such as finance, marketing, bioinformatics, speech recognition, and so on. After creating an HMM, the model owner can start providing predictions. When the model is owned by one party, predictions can be easily provided. However, it becomes a challenge when the model is horizontally or vertically distributed between various parties, even competing companies. The parties want to integrate the split models they own for better forecasting purposes. Due to privacy, financial, and legal reasons; however, they do not want to share their models. We investigate how such parties produce predictions on the distributed model without violating their privacy. We then analyze our proposed schemes in terms of accuracy, privacy, and performance; and finally present our findings.
Selecting the "right" number of senses based on clustering criterion functions
Authors: Ted Pedersen, Anagha Kulkarni
Venue: Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations (2006)

This paper describes an unsupervised knowledge-lean methodology for automatically determining the number of senses in which an ambiguous word is used in a large corpus. It is based on the use of global criterion functions that assess the quality of a clustering solution.
Continuous Monitoring of Spatial Queries in Wireless Broadcast Environments
Authors: Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias
Venue: IEEE Transactions on Mobile Computing (2009)

Wireless data broadcast is a promising technique for information dissemination that leverages the computational capabilities of the mobile devices in order to enhance the scalability of the system. Under this environment, the data are continuously broadcast by the server, interleaved with some indexing information for query processing. Clients may then tune in the broadcast channel and process their queries locally without contacting the server. Previous work on spatial query processing for wireless broadcast systems has only considered snapshot queries over static data. In this paper, we propose an air indexing framework that 1) outperforms the existing (i.e., snapshot) techniques in terms of energy consumption while achieving low access latency and 2) constitutes the first method supporting efficient processing of continuous spatial queries over moving objects.
Design of K-Means Clustering Algorithm Based on Distance Concentration
Authors: Tao Liu, Guiping Dai, Li Zhang, Zhijie Wang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

Using the immune recognizing principle, the data object to cluster was denoted as the antigens set, and the clustering center was the antibodies set. The clustering was the process to obtain the best antibodies to catch the antigens by producing the antibodies and recognizing the antigens unceasingly. The distance concentration and the affinity, between antibody and antigen, and between antibody and antibody, were defined about the K-means clustering; the antibody reproduction function was proposed. The antibody cloning algorithm was presented. The experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed.
The web page as a WYSIWYG end-user customizable database-backed information management application
Authors: David R. Karger, Scott Ostler, Ryan Lee
Venue: Proceedings of the 22nd annual ACM symposium on User interface software and technology (2009)

Dido is an application (and application development environment) in a web page. It is a single web page containing rich structured data, an AJAXy interactive visualizer/editor for that data, and a "metaeditor" for WYSIWYG editing of the visualizer/editor. Historically, users have been limited to the data schemas, visualizations, and interactions offered by a small number of heavyweight applications. In contrast, Dido encourages and enables the end user to edit (not code) in his or her web browser a distinct ephemeral interaction "wrapper" for each data collection that is specifically suited to its intended use. Dido's active document metaphor has been explored before but we show how, given today's web infrastructure, it can be deployed in a small self-contained HTML document without touching a web client or server.
ECG monitoring. A software tool for deriving time and frequency parameters
Authors: Marcel Stanciu, Mihaela Albu, Anatolie Boev
Venue: Proceedings of the 2009 IEEE International Workshop on Medical Measurements and Applications (2009)

In this paper is presented a software application which performs dynamic analysis of ECG tachogram using time framing (data windows) and appropriate sliding steps, as chosen by investigator. The application provides a high degree of versatility and the paper presents the algorithms, some experimental results and their significance.
Rescaled-Range (R/S) Analysis of Time Series of Heavy Metal Pollution in Iron Sulfide Mining Area
Authors: Shuitai Xu, Yunzhang Rao, Jianping Pan, Lingyan Xiong
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Studies of heavy metal pollution in mining areas mainly focus on non-dynamic and investigative aspects, but the forecasting of heavy metal migration and conversion is seldom considered in the contexts of both time structure and space structure. In this paper, with the concept of rescaled-range (R/S) analysis of fractal theory, the data of heavy metal copper (Cu) pollution in a certain iron sulfide mining area from 2005 to 2007 is calculated and analyzed, the Hurst exponents of which are all larger than 0.5. The results show that the trend of heavy metal Cu pollution in the mining area is obviously relevant and of long duration. At the same time, the levels of duration in decreasing order are respectively in 0 meter, 1,000 meter and 9,000 meter. With R/S analysis, a new method is provided for long-term forecasting of the changing trend of heavy metal Cu pollution in mining areas.
An Adaptive Mechanism for Multipath Video Streaming over Video Distribution Network (VDN)
Authors: Majd Ghareeb, Cesar Viho, Adlen Ksentini
Venue: Proceedings of the 2009 First International Conference on Advances in Multimedia (2009)

In the objective of improving the video quality as perceived by end-users, multipath video streaming in Video Distribution Network (VDN) comes as a promising solution. In this paper, we present a new adaptive mechanism to maximize the overall video quality at the client. Overlay path selection is dynamically done based on available bandwidth estimation, while the Quality of Experience (QoE) is subjectively measured using Pseudo-Subjective Quality Assessment (PSQA) tool. Simulation results show that our proposed method can automatically adapt to the load variation on the different Internet paths, in a way that it guarantees a best perceived quality by end-users.
Potential-based shaping in model-based reinforcement learning
Authors: John Asmuth, Michael L. Littman, Robert Zinkov
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have high value, this approach can decrease experience complexity--the number of trials needed to find near-optimal behavior. An orthogonal way of decreasing experience complexity is to use a model-based learning approach, building and exploiting an explicit transition model. In this paper, we show how potential-based shaping can be redefined to work in the model-based setting to produce an algorithm that shares the benefits of both ideas.
Interval-estimation for Measuring Customer Equity
Authors: Manyu Huang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

As business moves from transaction-centric to customer-centric, customer equity and metrics are becoming a strategic focus in marketing practice. Using statistical method,this paper seeks to classify the customer categories, explore the change rules of the expenditure of the customer category, and develop the interval-estimation of customer equity. The estimation method may accurately predict customers’ future expenditure by classification. Since the interval-estimation may reflect customer equity to be a random variable, it offers high quality metrics as a reference to marketing decisions.
Efficient computation of entropy gradient for semi-supervised conditional random fields
Authors: Gideon S. Mann, Andrew McCallum
Venue: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers on XX (2007)

Entropy regularization is a straightforward and successful method of semi-supervised learning that augments the traditional conditional likelihood objective function with an additional term that aims to minimize the predicted label entropy on unlabeled data. It has previously been demonstrated to provide positive results in linear-chain CRFs, but the published method for calculating the entropy gradient requires significantly more computation than supervised CRF training. This paper presents a new derivation and dynamic program for calculating the entropy gradient that is significantly more efficient---having the same asymptotic time complexity as supervised CRF training. We also present efficient generalizations of this method for calculating the label entropy of all sub-sequences, which is useful for active learning, among other applications.