ppxscal/embedding_network huggingface dataset:
A Discrete Wavelet Transform Based State-Codebook Search Algorithm for Vector Quantization
Authors: Chin-Chen Chang, Yung-Chen Chou, Jau-Ji Shen
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

Side match vector quantization method collects the possible codewords by using partial pixels of neighboring blocks. Furthermore, the state codebook design provides a way to improve compression ratio. In the complex content image, side match vector quantization method has a lower compression performance. The proposed method utilizes the property of discrete wavelet transform to save the computation cost for finding a suitable codeword. Moreover, a well designed novel state codebook by observing the characteristics of images in order to improve the compression ratio. In the test of smooth images, the visual quality of decompressed image by using our proposed method has approximated to the full search vector quantization. Moreover, the compression ratio for complex content images has a better performance than full search vector quantization and side match vector quantization.
Evolutionary Takagi-Sugeno Fuzzy Modelling for MR Damper
Authors: Haiping Du, Nong Zhang
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

This paper presents an approach for learning the Takagi-Sugeno (T-S) fuzzy model by Genetic Algorithm (GA). In this approach, the fuzzy rule structure is encoded by binary code in the chromosome in which the position of 1 indicates the selected rules and the sum of 1 indicates the number of rules. The membership function (MF) parameters (centres and bases) are evolved by GA in combining with the pseudo-inversion algorithm for obtaining the consequent parameters. The sum of squared error (SSE) between the true output and the T-S model prediction is used as objective function. Then, this approach is applied to the modelling of dynamic behaviour of a magneto-rheological (MR) damper which shows highly nonlinear characteristics due to hysteretic phenomenon. It is shown by the validation test that the developed T-S fuzzy model can represent the dynamic behaviour of the MR damper satisfactorily.
Sampling from large graphs
Authors: Jure Leskovec, Christos Faloutsos
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e.g., the diameter), to get estimates for the large graph. The deeper, underlying question is subtle: how do we measure success?.We answer the above questions, and test our answers by thorough experiments on several, diverse datasets, spanning thousands nodes and edges. We consider several sampling methods, propose novel methods to check the goodness of sampling, and develop a set of scaling laws that describe relations between the properties of the original and the sample.In addition to the theoretical contributions, the practical conclusions from our work are: Sampling strategies based on edge selection do not perform well; simple uniform random node selection performs surprisingly well. Overall, best performing methods are the ones based on random-walks and "forest fire"; they match very accurately both static as well as evolutionary graph patterns, with sample sizes down to about 15% of the original graph.
Approximation algorithms for scheduling real-time jobs with multiple feasible intervals
Authors: Jian-Jia Chen, Jun Wu, Chi-Sheng Shih
Venue: Real-Time Systems (2006)

Time-critical jobs in many real-time applications have multiple feasible intervals. Such a job is constrained to execute from start to completion in one of its feasible intervals. A job fails if the job remains incomplete at the end of the last feasible interval. Earlier works developed an optimal off-line algorithm to schedule all the jobs in a given job set and on-line heuristics to schedule the jobs in a best effort manner. This paper is concerned with how to find a schedule in which the number of jobs completed in one of their feasible intervals is maximized. We show that the maximization problem is $${\cal N}{\cal P}$$ -hard for both non-preemptible and preemptible jobs. This paper develops two approximation algorithms for non-preemptible and preemptible jobs. When jobs are non-preemptible, Algorithm Least Earliest Completion Time First (LECF) is shown to have a 2-approximation factor; when jobs are preemptible, Algorithm Least Execution Time First (LEF) is proved being a 3-approximation algorithm. We show that our analysis for the two algorithms are tight. We also evaluate our algorithms by extensive simulations. Simulation results show that Algorithms LECF and LEF not only guarantee the approximation factors but also outperform other multiple feasible interval scheduling algorithms in average.
Nogood based asynchronous distributed optimization (ADOPT ng)
Authors: Marius C. Silaghi, Makoto Yokoo
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

This work proposes an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs) using a new kind of nogoods, namely valued nogoods. The proposed technique is an extension of the asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning, leading to important speed-up. Valued nogoods are an extension of classic nogoods that associates each nogood with a threshold and optionally with a set of references to culprit constraints.DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages, as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). Among the mentioned techniques, DPOP performs very well for the chosen metric (requiring a number of such sequential messages linear in the number of agents), but in general has exponential space requirements. DisAO and ADOPT have the advantage of needing only polynomial space. ADOPT has the property of maintaining the initial distribution of the problem. ADOPT needs a preprocessing step consisting of computing a depth first search (DFS) tree on the agent graph. We show that valued nogoods reduce the practical importance/need of this preprocessing since independent subproblems are now dynamically detected and exploited.
A CoS-enabled Request Scheduling Policy
Authors: Yubin Yang, Yazhe Tang, Ming Zhao, Lei Yao
Venue: Proceedings of the 10th IEEE on International Enterprise Distributed Object Computing Conference Workshops (2006)

As a lightweight SLA between service providers and consumers, class of service (CoS) can avoid the runtime overhead brought by multiple negotiation of service agreements. This is especially important in grid environment where lots of transient services exist. We bring CoS into grid environment and propose a QoS-enabled framework named CQEF for grid service. In this paper, we present a CoSenabled request scheduling policy for CoS deployment in CQEF. According to this policy, incoming requests can be identified and classified based on the CoS information attached to them and subsequently be assigned to different waiting queues with different priorities. Besides, queuing scheduling algorithm is used to achieve the CoS enabling purpose. A prototype has been developed over the Globus platform. The test results show that, by using this policy, service providers are able to offer differentiated services for different CoS and QoS of premium CoSs will be guaranteed preferentially.
A formal approach based on UML and B for the specification and development of database applications
Authors: Amel Mammar, Régine Laleau
Venue: Automated Software Engineering (2006)

This article describes a formal approach to specify and develop database applications. This approach consists of two complementary phases. In the first phase, B specifications are automatically generated from UML class, state and collaboration diagrams describing the data and the transactions of the system we are developing. In the second phase, these specifications are successively refined until they become close enough to a relational implementation. The tool supporting this approach is implemented as an extension of the Rational Rose tool to develop and visualize graphical (UML) and formal (B) notations in a single environment.
A Scene Change Detection Scheme Using Local x^2-Test on Telematics
Authors: Seong-Yoon Shin, Sheng Guo-Rui, Ki-Hong Park
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

This paper presents a method that allows for detection of all rapid and gradual scene changes. The method features a combination of the current color histogram and the local c2-test. For the purpose of this paper, the c2-test scheme outperforming existing histogram-based algorithms was transformed, and a local c2-test in which weights were applied in accordance with the degree of brightness was used to increase detection efficiency in the segmentation of color values. This Method allows for analysis and segmentation of complex time-varying images in the most general and standardized manner possible. Experiments were performed to compare the proposed local x^2-test method with the current x^2-test method.
A Neuro-Fuzzy-Based Agent System with Data Distribution among the Agents for Classification Tasks
Authors: Laura Emmanuella O. Santana, Anne M. P. Canuto, Marjory C. C. Abreu
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems. As a result of this, the NeurAge system was proposed, which is a Neuro-based multi-agent system. This system has presented good results in some conventional (centralized) and distributed classification tasks. In this paper, instead of neural networks, the NeurAge agents will be composed of neuro-fuzzy networks. The main aim of this paper is to investigate the benefits of using fuzzy concepts in a neural-based multi-agent system in which data are distributed among its agents.
Virtual and augmented reality as spatial ability training tools
Authors: Andreas Dünser, Karin Steinbügl, Hannes Kaufmann, Judith Glück
Venue: Proceedings of the 7th ACM SIGCHI New Zealand chapter's international conference on Computer-human interaction: design centered HCI (2006)

Virtual reality (VR) and augmented reality (AR -- overlaying virtual objects onto the real world) offer interesting and wide spread possibilities to study different components of human behaviour and cognitive processes. One aspect of human cognition that has been frequently studied using VR technology is spatial ability. Research ranges from training studies that investigate whether and/or how spatial ability can be improved by using these new technologies to studies that focus on specific aspects of spatial ability for which VR is an efficient investigational tool. In this paper we first review studies that used VR technologies to study different aspects of spatial ability. Then results and findings will be presented from one of the first large-scale studies (215 students) that investigated the potential of an AR application to train spatial ability.
Anomaly Based Web Phishing Page Detection
Authors: Ying Pan, Xuhua Ding
Venue: Proceedings of the 22nd Annual Computer Security Applications Conference (2006)

Many anti-phishing schemes have recently been proposed in literature. Despite all those efforts, the threat of phishing attacks is not mitigated. One of the main reasons is that phishing attackers have the adaptability to change their tactics with little cost. In this paper, we propose a novel approach, which is independent of any specific phishing implementation. Our idea is to examine the anomalies in web pages, in particular, the discrepancy between a web site's identity and its structural features and HTTP transactions. It demands neither user expertise nor prior knowledge of the website. The evasion of our phishing detection entails high cost to the adversary. As shown by the experiments, our phishing detector functions with low miss rate and low false-positive rate.
Scaffolding learner motivation through a virtual peer
Authors: Yanghee Kim, Eric R. Hamilton, Jinjie Zheng, Amy L. Baylor
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

It is well-documented that advanced technology can scaffold learning and cognitive development and also that human-peer models in classrooms positively influence learner motivation, in particular, self-efficacy in specific tasks. This paper describes three exploratory studies that investigated the potential of virtual peers (VP, animated digital characters designed as peer-like) to scaffold college students' motivation towards lesson-planning tasks. The studies examined how differing characteristics of the VPs motivated learners, as measured by learners' task-related self-efficacy beliefs and learner interest in the task and in working with the VPs. Study 1 examined the competency and interaction type of a VP; Study 2 examined the gender and emotional expression of VPs; and Study 3 examined the gender and emotional response of VPs. Results indicated the close relationships between learner motivation and VP characteristics. These findings support for the instructional utility of the virtual peers as motivational scaffolds.
XML Schema and Object-relational Modeling of Multimedia Digital Libraries
Authors: Muhammad Umer Arshad, Muhammad Naeem Ayyaz
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

Digital Libraries (DLs) are repositories for large collection of digitized data. Traditionally, these libraries have been used to manage structured text documents with embedded images. In future, these libraries will have to handle multimedia data types as well, such as video, audio, images, graphics, and animations etc. Multimedia digital libraries (MMDLs) will emerge as a standard for information management. They will store structured media information in the form of multimedia documents (MMDs). One major problem in implementing these libraries is data integration, since different multimedia data types use different data models and structures to represent and store the information. We use (a) XML Schema and MPEG-7 content description standards for structural and content-basedmodeling of MMDs and (b) the object-relational (OR) data model as a unified approach for representation, storage, and retrieval of structured MMDs. The OR model makes it possible to pose semantic as well as content-based queries to the MMDLs thus enabling access and retrieval of any type of information contained in the constituent MMDs.
A Reinforcement Learning Approach for Learning Coordination Rules in Production Networks
Authors: Wilhelm Dangelmaier, Tobias Rust, Andre Doring, Benjamin Klopper
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

In production networks companies need fast reactions due to changes of supply and demand. To realize such a change management in an effective way the involved companies have to synchronize their quantities and capacities collaboratively. For these purposes the multiagent system MASCOPP was developed at the Heinz Nixdorf Institute, which tries to eliminate conflicts in a production network, based on changes of plans, through bilateral communication between the involved companies. Human experts have to configure the system by creating coordination rules to solve the conflicts. In this paper we introduce a machine learning concept to learn these coordination rules objectively by a reinforcement learning approach.
Scalable and Robust Aggregation Techniques for Extracting Statistical Information in Sensor Networks
Authors: Hongbo Jiang, Shudong Jin
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

Wireless sensor networks have stringent constraints on system resources and data aggregation techniques are critically important. However, accurate data aggregation is difficult due to the variation of sensor readings and due to the frequent communication failures. To address these difficulties, we propose a scalable and robust data aggregation algorithm. The novelty of our work includes two aspects. First, our algorithm exploits the mixture model and the Expectation Maximization (EM) algorithm for parameter estimation. Hence, it captures the effects of aggregation over different scales while keeping the communication cost low. Second, our algorithm exploits loss-tolerant multi-path routing schemes. Hence, it obtains accurate statistical information even in the presence of high link and node failure rates. We demonstrate that our techniques reduce communication cost while retaining the precious statistical information otherwise neglected by other aggregation techniques. Our evaluation shows the proposed techniques are robust against link and node failures, and perform consistently well.
Input Selection Using Binary Particle Swarm Optimization
Authors: Thavit Amonchanchaigul, Worapoj Kreesuradej
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

Nowadays, multi-layer feed forward networks are often used for modeling complex relationships between the data sets. And if we can choose only the important data from the training sets, it will make the networks less size and can save more time. Because we realize in this point, this paper provides procedure of feature selection to train the neural networks using binary particle swarm optimization. It also introduces the suitable function for the binary particle swarm optimization technique by changing concept in part of member value adjustment function for each particle.
A Logarithmic-Quadratic Proximal Prediction-Correction Method for Structured Monotone Variational Inequalities
Authors: Bing-Sheng He, Ya Xu, Xiao-Ming Yuan
Venue: Computational Optimization and Applications (2006)

Inspired by the Logarithmic-Quadratic Proximal (LQP) method for variational inequalities, we present a prediction-correction method for structured monotone variational inequalities. Each iteration of the new method consists of a prediction and a correction. Both the predictor and the corrector are obtained easily with tiny computational load. In particular, the LQP system that appears in the prediction is approximately solved under significantly relaxed inexactness restriction. Global convergence of the new method is proved under mild assumptions. In addition, we present a self-adaptive version of the new method that leads to easier implementations. Preliminary numerical experiments for traffic equilibrium problems indicate that the new method is effectively applicable in practice.
Semantic Similarity Measurement of Chinese Financial News Titles Based on Event Frame Extracting
Authors: Ching-Hao Mao, Ta-Wei Hung, Jan-Ming Ho, Hahn-Ming Lee
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

The Chinese financial news titles has only few words so that it is hard for measuring the similarity between titles if compare all their keywords only. In this study, we proposed a method of semantic similarity measurement for Chinese financial news titles based on constructing the Event Frame structure as the template of a Chinese financial news title. It concerns the relation between the basic meanings of two news titles for similarity measurement. In addition, a semantic similarity function is used to integrate both the relation of Event Frames of the financial news titles and the relation between the keywords of these titles. In this matter, the proposed method can differentiate the Chinese financial news that mention the same event from all other Chinese financial news by the Event Frame, since it concerns the relation between the basic meanings of two news titles and reduces the comparing time. The result of this approach shows that the Event Frame extracting has high precision and the provided semantic similarity measurement can emphasize the relation between the connotations of two news titles.
An Efficient Heuristic for Discovering Multiple Ill-Defined Attributes in Datasets
Authors: Sylvain Halle
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

The accuracy of the rules produced by a concept learning system can be hindered by the presence of errors in the data, such as "ill-defined" attributes that are too general or too specific for the concept to learn. In this paper, we devise a method that uses the Boolean differences computed by a program called Newton to identify multiple ill-defined attributes in a dataset in a single pass. The method is based on a compound heuristic that assigns a real-valued rank to each possible hypothesis based on its key characteristics. We show by extensive empirical testing on randomly generated classifiers that the hypothesis with the highest rank is the correct one with an observed probability quickly converging to 100%. Moreover, the monotonicity of the function enables us to use it as a rough estimator of its own likelihood.
Why complicate things?: introducing programming in high school using Python
Authors: Linda Grandell, Mia Peltomäki, Ralph-Johan Back, Tapio Salakoski
Venue: Proceedings of the 8th Austalian conference on Computing education - Volume 52 (2006)

Deciding what to teach novices about programming and what programming language to use is a common topic for debate. Should an industry relevant programming language be taught, or should a language designed for teaching novices be used? Typically, these questions are raised at university level, but in this paper we address them from a high school perspective.We present a case study with a twofold goal: (1) examining how programming can be introduced at high school level, and (2) evaluating how suitable the programming language Python is to support both teachers and learners in this process. During the school year 2004/2005, an introductory programming course was given to four student groups in two different high schools. The students enjoyed programming and learnt to think in terms of re-use and interfaces. In addition, we found that many features of Python facilitated both teaching and learning (for instance, a simple and flexible syntax, immediate feedback, easy-to-use modules and strict requirements on proper indentation).Our findings support results from previous studies in that students have difficulties in dealing with abstract concepts - even though the syntax for implementing these is simple. In addition, compared to university students, high school students are young and have necessarily not yet developed the writing skills required for producing proper documentation. The course was designed to be well suited for high school students in general, but still all participants were boys. Since high schools should provide all-round learning to all students, we, as do all computer science teachers, face the challenge of making programming more appealing to girls.