ppxscal/embedding_network huggingface dataset:
A New Method for Web Service Selection Based on Hybrid QoS Criteria
Authors: Yanxiang He, Fei Li, Zhao Wu, Zhao Liang
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

In many cases, the values of qualitative QoS criteria are often imprecisely defined or acquired. Moreover, it is also not easy to accurately quantify the weight of each QoS criterion since human judgments including preference are often vague. In this paper, the decision making problem has been modeled as Hybrid Multiple Criteria Decision Making (FMCDM). The lingustic QoS criteria is transformed to fuzzy numbers, and then we use COWA operator to turn interval numbers and fuzzy numbers into precise numbers. A optimization method with adjustability is proposed to seek subjective weight, then objective weight based on improved entropy model and subjective weight are integrated into sythentic weight linearly, the candidate services are ranking according to the membership of gray incidence degree. Finally, a numerical example is given to illustrate the developed method.
Trust-Oriented Multi-visualization of Cooperation Context
Authors: Annie Guerriero, Sylvain Kubicki, Gilles Halin
Venue: Proceedings of the 2009 Second International Conference in Visualisation (2009)

The building construction activity is uncertain by nature because of its specificities (e.g. heterogeneous stakeholders, ephemeral teams, etc.) and particularly, because it corresponds to a particular mode of production "on site" and is submitted to variable conditions. So the construction management is essential to guarantee the correct progression of the construction activity. Moreover, trust is a central component in this type of activity to surmount the uncertainty. Therefore, we suggest making a connection between the coordination assistance tool and the notion of trust. This paper suggests representing trust in the correct progression of the activity in order to support the construction management and using this representation to guide user navigation in a multi-visualization environment. It will describe a methodology to identify trust information in a construction activity context, measure trust value and implement a trust-based prototype.
From Social Networks to Behavioral Networks in Recommender Systems
Authors: Ilham Esslimani, Armelle Brun, Anne Boyer
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique. However, classical CF systems use only direct links and common features to model relationships between users. This paper presents a new Collaborative Filtering approach (BNCF) based on a behavioral network that uses navigational patterns to model relationships between users and exploits social networks techniques, such as transitivity, to explore additional links throughout the behavioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. BNCF is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions. Indeed, BNCF highly improves the accuracy of predictions, especially in terms of HMAE.
Maintaining control while delegating trust: Integrity constraints in trust management
Authors: Sandro Etalle, William H. Winsborough
Venue: ACM Transactions on Information and System Security (TISSEC) (2009)

We introduce the use, monitoring, and enforcement of integrity constraints in trust management-style authorization systems. We consider what portions of the policy state must be monitored to detect violations of integrity constraints. Then, we address the fact that not all participants in a trust-management system can be trusted to assist in such monitoring, and show how many integrity constraints can be monitored in a conservative manner so that trusted participants detect and report if the system enters a policy state from which evolution in unmonitored portions of the policy could lead to a constraint violation.
A case study on the critical role of geometric regularity in machine learning
Authors: Jason Gauci, Kenneth O. Stanley
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

An important feature of many problem domains in machine learning is their geometry. For example, adjacency relationships, symmetries, and Cartesian coordinates are essential to any complete description of board games, visual recognition, or vehicle control. Yet many approaches to learning ignore such information in their representations, instead inputting flat parameter vectors with no indication of how those parameters are situated geometrically. This paper argues that such geometric information is critical to the ability of any machine learning approach to effectively generalize; even a small shift in the configuration of the task in space from what was experienced in training can go wholly unrecognized unless the algorithm is able to learn the regularities in decision-making across the problem geometry. To demonstrate the importance of learning from geometry, three variants of the same evolutionary learning algorithm (NeuroEvolution of Augmenting Topologies), whose representations vary in their capacity to encode geometry, are compared in checkers. The result is that the variant that can learn geometric regularities produces a significantly more general solution. The conclusion is that it is important to enable machine learning to detect and thereby learn from the geometry of its problems.
An Algorithm of Mining Exceptions in High Dimensional Data Cube
Authors: Youwei Ding, Kongfa Hu, Ling Chen
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

Existing algorithms of mining exceptions in high dimension data cube often require users to fix out one or more thresholds beforehand to judge whether a data point is an exception, and different thresholds will lead to different efficiency. To overcome this problem, we propose a new definition of exception, interval exception, which can help users find the exceptions in data cell fast and efficiently without any threshold users given. This method computes reference range of a point firstly according to regression parameters, and then considers whether it is an interval exception by comparing its absolute value of residual to reference range. Performance study shows that the method is practical and effective.
Statistical High-Level Synthesis under Process Variability
Authors: Yuan Xie, Yibo Chen
Venue: IEEE Design Test (2009)

Editor's note:CMOS process variability is a major challenge in deep-submicron SoC designs. The variations in transistor parameters are complicating both timing and power consumption prediction. This article surveys recent progress in the statistical high-level synthesis area.&#x2014;Philippe Coussy, Universit&#x00E9; de Bretagne-Sud
Audio Interpolation and Stitching Detection Based on Band-Partitioning Spectral Smoothness
Authors: Ding Qi, Xu Wang, Ping Xi-jian
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

In this paper, the spectral characteristics of uninterpolated and interpolated signals are analyzed, and a new audio spectral measure, Band-Partitioning Spectral Smoothness (BPSS) is proposed. For interpolated signals, the spectral smoothness in the high frequency band produced by interpolation is much smaller than in the other frequency band. And then the signal spectrum is partitioned into several sub-bands uniformly and BPSS is used to denote the spectral smoothness of each sub-band. A comprehensive strategy to detect audio interpolation and stitching by BPSS is presented. The proposed method can determine whether an audio signal has been interpolated or stitched, furthermore the original sampling rate of the interpolated signal can be estimated. Experiment results prove its high accuracy. This method has low computational
Learning voting trees
Authors: Ariel D. Procaccia, Aviv Zohar, Yoni Peleg, Jeffrey S. Rosenschein
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.
Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe---Based Neural Network Computing
Authors: Kazunari Ozasa, Masashi Aono, Mizuo Maeda, Masahiko Hara
Venue: Proceedings of the 8th International Conference on Unconventional Computation (2009)

In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.
Topology Optimization of a Three-Dimension Compliant Microgripper Using Multi-objective Compliance
Authors: Mahmoud Helal, Liguo Chen, Lining Sun, Tao Chen
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

Micro and nano positioning stages are critically important to both the mechanical and electronic industries. Gripping micro objects are required for a wide range of important applications such as the assembly of micro-parts to obtain miniature systems or component assembly in electronics packages. An effective mechanical micro and nano manipulator should possess the ability to grasp objects of different shapes steadily with high positioning accuracy. A microgripper compliant mechanism is one of the key elements in micro-robotics and micro-assembly technologies for handling micro-objects without damage. Compliant mechanisms can play an important role in the design of micro- devices for micro-electro-mechanical systems (MEMS) applications. Compliant mechanisms are flexible structures, which can generate the desired motions by undergoing elastic deformation instead of through rigid linkage/joints as in rigid body mechanisms. The topology optimization methods search for an ideal material distribution of a structure. In this paper, topology optimization method is applied for designing a three- dimension compliant microgripper mechanism. The optimal topology configuration of the compliant microgripper mechanism is demonstrated. This compliant mechanism can be used to handle a wide range of micro and nano objects up to 400μm.
Judgemental Minimal and Maximal Rules Learning and Its Application
Authors: Li Guo-qing, Chen Jun-jie
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

This paper conducts attribute reduction for training set using Rough Set theory, and then obtains the decision tree rules by use of decision tree algorithm. Afterwards, two criteria on rules screening are proposed in accordance with the concept of the rule information quantity and rule credibility, and the two criteria are applied to minimal and maximal rules learning method, which forms the Judgemental Minimal and Maximal Rule Learning. Have the algorithm using to decision tree rules simplification, which can narrow the scope of simplification and ensure consistency of coverage of the rules, and the total number of rules are also be reduced.
Improving data reduction for 3D shape preserving
Authors: Sultan Aljahdali, E. A. Zanaty, Narayan Debnath
Venue: Journal of Computational Methods in Sciences and Engineering (2009)

A novel 3D shape preserving data reduction technique for managing the amount of data acquired by laser scanning is presented that overcomes the shortcomings of existing filter-based methods. The technique is based on a discrete Gaussian image of the scanned points which is obtained by estimating surface normals and projecting them into a Gaussian sphere. The discrete Gaussian image is then used to partition the points into cells. In each cell, a reference point and its neighbours are used to determine the cell representative point and all other points are removed. The performance of the proposed method is illustrated using a range of point clouds scanned from typical engineering surfaces.
Mention detection crossing the language barrier
Authors: Imed Zitouni, Radu Florian
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

While significant effort has been put into annotating linguistic resources for several languages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagating information (specifically mention detection information) into such low resource languages from richer ones. Experiments run on three language pairs (Arabic-English, Chinese-English, and Spanish-English) show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance using various degrees of linguistic information in a statistical framework, results show that propagated features from English help improve the source-language system performance even when used in conjunction with all feature types built from the source language. The experiments also show that using propagated features in conjunction with lexically-derived features only (as can be obtained directly from a mention annotated corpus) yields similar performance to using feature types derived from many linguistic resources.
The Evaluation Model of Knowledge Management Based on Information Entropy and RBF Neural Network (IE-RBF)
Authors: Yu Chen
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

Knowledge management is a complex systems engineering, so the evaluation of knowledge management is of non-linear characteristics. Neural network with the ability of adaptive learning is an excellent tool to deal with the issue of non-linear. This paper analyzed the essence of knowledge and knowledge management. We proposed an evaluation model of knowledge management based on the theory of information entropy and RBF neural network. After reduction of the indices system with information entropy to reduce, we would evaluate the knowledge management with RBF neural network. After empirical research with MATLAB7.0, it is had been proved that the method is validity and practicality. And then it did not only overcome the traditional methods’ shortcoming which is too subjective, but also avoided complex process of the traditional evaluation method.
How gender composition influences individual knowledge elaboration in CSCL
Authors: Ning Ding
Venue: Proceedings of the 9th international conference on Computer supported collaborative learning - Volume 1 (2009)

The aim of the study is to explore the gender difference in learning achievement and knowledge elaboration process in CSCL. A sample of ninety-six secondary school students, aged 16, participated in the two-week experiment. Students were randomly paired to solve six problems about Newtonian mechanics. Their pre- and post-test performances and online interactions were analyzed. We found that female students' learning performance and knowledge elaboration process were sensitive to their partner gender, but that's not the case for male students. Besides, due to a divergent knowledge elaboration process, mixed-gender dyads run the risk of disadvantaging female students in CSCL.
Automated Analysis of Eye-Tracking Data for the Evaluation of Driver Information Systems According to ISO/TS 15007-2: 2001
Authors: Christian Lange, Martin Wohlfarter, Heiner Bubb
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends (2009)

First of all, the most important content of the ISO/TS 15007-2:2001 standard for performing eye tracking experiments will be described. The following text includes a detailed description of how eye/gaze experiments using the Dikablis eye tracking system are conducted according to the above mentioned standard and how recorded statistical evaluations can be automated and visualized.
Research on the Methods of the Measurement of Printing Pressure in Offset Press
Authors: Shuqin Wu, Yiming Wang, Chengwen Chai, Jianjun Bai
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

Pressure is one of the important parameters that decide the print quality in offset printing. A precise measurement method is needed at present. According to the working principle about the films with micro-encapsulated sensor, a new method for measuring the printing pressure was put forward in this paper. A detection experiment of impression force was carried out, which studies on the contact area between the impression cylinder and the blanker cylinder of an offset sheeted printing machine. At the same time, the experimental results were analyzed. The results show that the uniformity of the pressure distribution in the contact area mainly depends on the nature of the blanket, the substrate and other materials. In the contact area, along the direction of the cylinder axis, the pressure distribution is in accordance with the theory.
Rule filtering by pattern for efficient hierarchical translation
Authors: Gonzalo Iglesias, Adrià de Gispert, Eduardo R. Banga, William Byrne
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory usage through modifications to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in translation. Rules are put into syntactic classes based on the number of non-terminals and the pattern, and various filtering strategies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST Arabic-to-English evaluation task.
Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media
Authors: Suzy Cutts, Philip Davies, David Newell, Nick Rowe
Venue: Proceedings of the 2009 First International Conference on Advances in Multimedia (2009)

Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain.
Reinforcing Recommendation Using Implicit Negative Feedback
Authors: Danielle H. Lee, Peter Brusilovsky
Venue: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH (2009)

Recommender systems have explored a range of implicit feedback approaches to capture users' current interests and preferences without intervention of users' work. However, current research focuses mostly on implicit positive feedback. Implicit negative feedback is still a challenge because users mainly target information they want. There have been few studies assessing the value of negative implicit feedback. In this paper, we explore a specific approach to employ implicit negative feedback and assess whether it can be used to improve recommendation quality.
Understanding Multi-touch Manipulation for Surface Computing
Authors: Chris North, Tim Dwyer, Bongshin Lee, Danyel Fisher, Petra Isenberg, George Robertson, Kori Inkpen
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

Two-handed, multi-touch surface computing provides a scope for interactions that are closer analogues to physical interactions than classical windowed interfaces. The design of natural and intuitive gestures is a difficult problem as we do not know how users will approach a new multi-touch interface and which gestures they will attempt to use. In this paper we study whether familiarity with other environments influences how users approach interaction with a multi-touch surface computer as well as how efficiently those users complete a simple task. Inspired by the need for object manipulation in information visualization applications, we asked users to carry out an object sorting task on a physical table, on a tabletop display, and on a desktop computer with a mouse. To compare users' gestures we produced a vocabulary of manipulation techniques that users apply in the physical world and we compare this vocabulary to the set of gestures that users attempted on the surface without training. We find that users who start with the physical model finish the task faster when they move over to using the surface than users who start with the mouse.
Fast and Accurate Prediction of the Destination of Moving Objects
Authors: Austin Parker, V. S. Subrahmanian, John Grant
Venue: Proceedings of the 3rd International Conference on Scalable Uncertainty Management (2009)

Companies and organizations that track moving objects are interested in predicting the intended destination of these moving objects. We develop a formal model for destination prediction problems where the agent (Predictor) predicting a destination may not know anything about the route planning mechanism used by another agent (Target) nor does the agent have historical information about the target's past movements nor do the observations about the agent have to be complete (there may be gaps when the target was not seen). We develop axioms that any destination probability function should satisfy and then provide a broad family of such functions guaranteed to satisfy the axioms. We experimentally compare our work with an existing method for destination prediction using Hidden Semi-Markov Models (HSMMs). We found our algorithms to be faster than the existing method. Considering prediction accuracy we found that, when the Predictor knows the route planning algorithm the target is using, the HSMM method is better, but without this assumption our algorithm is better.