ppxscal/embedding_network huggingface dataset:
Data Provenance Support in Relational Databases for Stored Procedures
Authors: Winly Jurnawan, Uwe Röhm
Venue: Database Systems for Advanced Applications: DASFAA 2009 International Workshops: BenchmarX, MCIS, WDPP, PPDA, MBC, PhD, Brisbane, Australia, April 20 - 23, 2009 (2009)

The increasing amounts of data produced by automated scientific instruments require scalable data management platforms for storing, transforming and analyzing scientific data. At the same time, it is paramount for scientific applications to keep track of the provenance information for quality control purposes and to be able to re-trace workflow steps. Relational database systems are designed to efficiently manage and analyze large data volumes, and modern extensible database systems can also host complex data transformations as stored procedures. However, the relational model does not naturally support data provenance or lineage tracking. In this paper, we focus on providing data provenance management in relational databases for stored procedures. Our approach, called PSP, leverages the XML capabilities of SQL:2003 to keep track of the lineage of the data that has been processed by any stored procedure in a relational database as part of a scientific workflow. We show how this approach can be implemented in a state-of-the-art DBMS and discuss how the captured provenance data can be efficiently queried and analyzed.
Children's Choice of Games: The Influence of Prosocial Tendency and Education-Level
Authors: Vivian Hseuh-Hua Chen, Weirong Lin, Chiew Woon Ng, Su Li Chai, Angeline Cheok Khoo, Henry Been-Lirn Duh
Venue: Proceedings of the 8th International Conference on Entertainment Computing (2009)

This study employed the uses and gratifications approach to examine children's choice of gaming genres. The measure of prosocial behavioral tendency was used as an approximation of a child's offline gratification, and this was related to the exposure to three different genres of games (violent, aggressive and prosocial). The influence of education level was also taken into consideration. Data was compiled and analyzed from a survey conducted on Singaporean schoolchildren (N = 2,640). Overall results supported the supplementary model of gratification seeking behavior. Children with higher prosocial scores spent significantly less time playing violent and aggressive games, whereas children of a higher education level spent more time playing games of all genres. The results are presented and discussed.
Techniques and Tools for Local Search Landscape Visualization and Analysis
Authors: Franco Mascia, Mauro Brunato
Venue: Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics (2009)

Because of their high dimensionality, combinatorial optimization problems are often difficult to analyze, and the researcher's intuition is insufficient to grasp the relevant features. In this paper we present and discuss a set of techniques for the visualization of search landscapes aimed at supporting the researcher's intuition on the behavior of a Stochastic Local Search algorithm applied to a combinatorial optimization problem.We discuss scalability issues posed by the size of the problems and by the number of potential solutions, and propose approximate techniques to overcome them. Examples generated with an application (available for academic use) are presented to highlight the advantages of the proposed approach.
Segmentation of Printed Urdu Scripts Using Structural Features
Authors: Hamna Malik, Muhammad Abuzar Fahiem
Venue: Proceedings of the 2009 Second International Conference in Visualisation (2009)

Character segmentation forms the basis for optical character recognition. In this paper, we have proposed a character segmentation approach for printed Urdu script. Urdu is cursive by nature and its script is written from right to left. Both these factors make the segmentation more difficult and require special attention. Our approach is based on structural features and we have overcome different problems like over segmentation and under segmentation, present in previous approaches. We have achieved an accuracy rate of 99.4% which is better than others. The approach may be very useful for developing an optical character recognition system for Urdu language.
Geostatistical Analysis of Spatial and Temporal Variations of Groundwater Depth in Shule River
Authors: Hua Zhang, Cheng Wang, Yi Shanzhen, Qing Jiang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

In this paper, spatial and temporal analysis of groundwater depth fluctuations monthly of monitored data of 51 monitoring wells during 6 years was carried out in semi-arid and arid area. Geostatistics has been applied to study the spatial and temporal structure of groundwater depth fluctuation. Results showed that groundwater level drop and groundwater level fluctuations have spatial and temporal structure according to nugget effects. Ordinary and universal kriging methods with cross-validation were used to evaluate the accuracy of the chosen variograms in estimation of the groundwater depth drop and fluctuations were underestimated by about 10%. whether or not estimation of groundwater depth drop and groundwater depth fluctuations could be performed with low uncertainty in both space and time scales according to spatial and temporal structure. Moreover, the results showed that kriging is a beneficial and capable tool for estimating the trend of groundwater depth fluctuations.
A robust and extensible exemplar-based model of thematic fit
Authors: Bram Vandekerckhove, Dominiek Sandra, Walter Daelemans
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

This paper presents a new, exemplar-based model of thematic fit. In contrast to previous models, it does not approximate thematic fit as argument plausibility or 'fit with verb selectional preferences', but directly as semantic role plausibility for a verb-argument pair, through similarity-based generalization from previously seen verb-argument pairs. This makes the model very robust for data sparsity. We argue that the model is easily extensible to a model of semantic role ambiguity resolution during online sentence comprehension. The model is evaluated on human semantic role plausibility judgments. Its predictions correlate significantly with the human judgments. It rivals two state-of-the-art models of thematic fit and exceeds their performance on previously unseen or low-frequency items.
KA-CAPTCHA: an opportunity for knowledge acquisition on the web
Authors: Bruno Norberto Da Silva, Ana Cristina, Bicharra Garcia
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Any Web user is a potential knowledge contributor, but it remains a challenge to make them devote their time contributing to some purpose. In order to align individual with social interests, we selected the CAPTCHA Web resource protection application to embed knowledge elicitation within the users' main task of accessing a Web resource. Consequently, unlike previous knowledge acquisition approaches, no extra effort is expected from users since they are already willing to use a CAPTCHA to perform some particular task. We present an application where we extract pictorial knowledge from Web users, and experiments suggest that our approach enables knowledge acquisition while still satisfying CAPTCHA's security requirements.
A Comparative Study of Outlier Detection Algorithms
Authors: Charlie Isaksson, Margaret H. Dunham
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

Data Mining is the process of extracting interesting information from large sets of data. Outliers are defined as events that occur very infrequently. Detecting outliers before they escalate with potentially catastrophic consequences is very important for various real life applications such as in the field of fraud detection, network robustness analysis, and intrusion detection. This paper presents a comprehensive analysis of three outlier detection methods Extensible Markov Model (EMM), Local Outlier Factor (LOF) and LCS-Mine, where algorithm analysis shows the time complexity analysis and outlier detection accuracy. The experiments conducted with Ozone level Detection, IR video trajectories, and 1999 and 2000 DARPA DDoS datasets demonstrate that EMM outperforms both LOF and LSC-Mine in both time and outlier detection accuracy.
Application of Learning Mechanism in Agent-Based Automatic Negotiation Technology
Authors: Wang Zhaoming
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Business negotiation is a key technique for the development of electronic commerce. In order to improve trade efficiency and reduce trade cost, it is necessary to realize automatic negotiation or half-automatic negotiation during the electronic trade, and the agent technique may achieve these functions. In the multi-agent systems (MAS), the theory of learning games is one of important methods for agent negotiation. During negotiation between Buyer side and seller side, the trade which is whether successful or not depends on their trade strategy model to a great extent. Therefore, in this paper, we use the learning game to discuss the negotiation of e-business, and an agent-based negotiation model is presented by using a new learning algorithm. We specify the agents how to do learning efficiency based on the change of opponents’ strategies and negotiation setting, then readjust their optimal strategy in order to maximize their payoff. In last, a correct trade strategies model is proven through an actual example.
Task Scheduling of Real-time Systems on Multi-Core Architectures
Authors: Pengliu Tan
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

This paper proposes a hybrid scheduling method for real-time system on homogeneous multi-core architecture. To make the best of the available parallelism in these systems, first an application is partitioned into some parallel tasks as much as possible. Then the parallel tasks are dispatched to different cores, so as to execute in parallel. In each core, real-time applications can run concurrently with non-real-time applications. The scheduling architecture uses a two-level scheduling scheme. At the top level, a sporadic server is assigned to each scheduling policy. Each sporadic server is used to schedule the dispatched tasks according to its scheduling policy. At the bottom level, a rate-monotonic OS scheduler is adopted to schedule the top level sporadic servers.
Automatic Segmentation of Brain Structures Using Geometric Moment Invariants and Artificial Neural Networks
Authors: Mostafa Jabarouti Moghaddam, Hamid Soltanian-Zadeh
Venue: Proceedings of the 21st International Conference on Information Processing in Medical Imaging (2009)

We propose an automatic method for the segmentation of the brain structures in three dimensional (3D) Magnetic Resonance Images (MRI). The proposed method consists of two stages. In the first stage, we represent the shape of the structure using Geometric Moment Invariants (GMIs) in 8 scales. For each scale, an Artificial Neural Network (ANN) is designed to approximate the signed distance function of a desired structure. The GMIs along with the voxel intensities and coordinates are used as the input features of the ANN and the signed distance function as its output. In the second stage, we combine the outputs of the ANNs of the first stage and design another ANN to classify the image voxels into two classes, inside or outside of the structure. We introduce a fast method for moment calculations. The proposed method is applied to the segmentation of caudate, putamen, and thalamus in MRI where it has outperformed other methods in the literature.
PrDB: Managing Large-Scale Correlated Probabilistic Databases (Abstract)
Authors: Amol Deshpande
Venue: Proceedings of the 3rd International Conference on Scalable Uncertainty Management (2009)

Increasing numbers of real-world application domains are generating data that is inherently noisy, incomplete, and probabilistic in nature. Statistical inference and probabilistic modeling often introduce another layer of uncertainty on top of that. Examples of such data include measurement data collected by sensor networks, observation data in the context of social networks, scientific and biomedical data, and data collected by various online cyber-sources. Over the last few years, numerous approaches have been proposed, and several systems built, to integrate uncertainty into databases. However, these approaches typically make simplistic and restrictive assumptions concerning the types of uncertainties that can be represented. Most importantly, they often make highly restrictive independence assumptions, and cannot easily model rich correlations among the tuples or attribute values. Furthermore, they typically lack support for specifying uncertainties at different levels of abstractions, needed to handle large-scale uncertain datasets.
An Approach for Sensitive Binary File Protection
Authors: Gu Junkai, Ji Weiyong
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01 (2009)

When a host computer is attacked, files in the host computer are more or less unauthorizedly modified so as to achieve attacker's goal. Based on trusted computing technology, we present a trusted computer based approach for sensitive binary files protection. Different from traditional methods, our approach does not based on "condition-code", "intrusion-rule" and so on. It is checksum-based: each time before a sensitive binary file’s running, the kernel captures its running request first. And then the kernel calculates and compares its current hash, for example, SHA-1, against the expected one stored in the Trusted Platform Module (TPM) in advance. If the comparison passes, the running request granted. Otherwise denied and the maliciously tampered file will be automatically recovered.
Mario, Luigi and Dave: the effect of language on the social structure of a bilingual online mobile game
Authors: Ben Kirman, Shaun Lawson, Conor Linehan
Venue: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era (2009)

In this paper, we explore the structure of a social community built in an online game that was released in two languages, specifically examining the behaviours of players involved in inter-lingual interaction. This asynchronous social game was released simultaneously in Italian and English. The player base was seeded with English and Italian players but allowed to grow organically without restriction. Despite the built-in segregation by language, we found that the entire player-base formed into a single social network and developed strategies for overcoming the challenges faced by a multi-lingual game community. Using Network Analysis, we break down the community in the game based on language and play style. We demonstrate that the behaviour of both English and Italian players was equivalent, and that play style had no effect on the likelihood of players deliberately engaging in inter-lingual communication. In the context of the strategies used by the players in our experiment, we discuss game design patterns that provide incentives for users to behave more socially and how to create tools to enable the players to cross the lingual and cultural barriers in online games.
A survey of learning-based techniques of email spam filtering
Authors: Enrico Blanzieri, Anton Bryl
Venue: Artificial Intelligence Review (2008)

Email spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. In this paper we give an overview of the state of the art of machine learning applications for spam filtering, and of the ways of evaluation and comparison of different filtering methods. We also provide a brief description of other branches of anti-spam protection and discuss the use of various approaches in commercial and non-commercial anti-spam software solutions.
The Decision-making Method on the Probability Decision-making Space Based on SPA Probability Lattice Ordered Structure
Authors: Zhang Chunying, Liu Baoxiang, Zhou Ruiying
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01 (2009)

In decision-making process, as the uncertain decision-making state is influenced by the probability of events, and the method of set pair analysis is connected to the probability expression system, the SPA (Set Pair Analysis) relation function probability lattice ordered structure on the probability decision making space (omega, A, P) is defined; and based on which the decision-making method on the probability decision-making space is given. The validity is validated by an example and a new decision-making method is proposed for the lattice ordered decision making on the probability decision-making space(omega, A, P) .
Semantic-Oriented Metadata Management Model in Semantic Grid
Authors: Huang Ying, Luo Xiangang, Guo Mingqiang, Liu Yong
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

In the field of spatial information sharing, with different cognitive styles of the real world, people will focus on different aspects when they observe and describe the same geographical object. It will lead to semantic heterogeneous. The semantic grid gives an ideal solution to this issue. This paper discusses the necessity of semantic-oriented metadata management in semantic grid firstly. Existing ontology-based spatial information sharing systems almost assume a centralized global ontology to access spatial information. In WAN, the hypothesis will not realize. Focus on the status of spatial information sharing, from the view of actual needs, this paper proposed a semantic-oriented metadata management model which can be extended. The model combines the features of ontology and encodes metadata semantic information and related managements elements of distributed, heterogeneous grid node computing pools. It has three layers, the grid node computing pool layer, the metadata management layer and the metadata recording layer. The most important part is the semantic metadata mapping service. The service transforms ontology metadata concepts between domain ontology metadata base and common ontology metadata knowledge base so as to all users of each field can access the spatial information from every grid node computing pool which has registered in metadata management layer and ensure that all operations are based on semantic. The carrier of semantic information--ontology plays a leading role in all operations. It guarantees the users’ semantic can be transform to every aspect; avoids semantic distortion and realized semantic-oriented information integration and interoperability. At last, one example provides a detailed way on how to implement the semantic metadata mapping service in this model. In conclusion, the adoption of the model makes each grid node computing pool in grid only need safeguarding the information resources itself. By the collaboration, all grid nodes can discover, understand and use entire distributed, heterogeneous and constantly changing information resources in grid. It solves the differences in metadata definition and standards among different units and enables users to access spatial information systems freely from the impact of semantic differences.
The impact of multimedia effect on science learning: Evidence from eye movements
Authors: Hsiao-Ching She, Yi-Zen Chen
Venue: Computers Education (2009)

This study examined how middle school students constructed their understanding of the mitosis and meiosis processes at a molecular level through multimedia learning materials presented in different interaction and sensory modality modes. A two (interaction modes: animation/simulation) by two (sensory modality modes: narration/on-screen text) factorial design was employed. The dependent variables included subjects' pre-test, post-test, and retention-test scores, showing their understanding of mitosis and meiosis process at molecular level, as well as data of subjects' eye-movement behavior. Results showed the group that received animation with narration allocated a greater amount of visual attention (number of fixations, total inspection time, and mean fixation duration) than the group that received animation with on-screen text, in both pictorial area and area of interest, which is consistent with students' immediate and long-term retained learning of the processes of mitosis and meiosis. The group that received simulation with on-screen text allocated a greater amount of visual attention than the group that received simulation with narration, consistent with students' immediate and retained learning. The group that received simulation with on-screen text also allocated a greater amount of visual attention than the group that received animation with on-screen text, consistent with students' immediate and retained learning. This study adds empirical evidence of a direct correlation between the length of eye fixation behavior and the depth of learning. Moreover, it provides insight into the multimedia effect on students' cognitive process through the use of eye fixation behavior evidence.
PAES: Policy-Based Authority Evaluation Scheme
Authors: Enrico Scalavino, Vaibhav Gowadia, Emil C. Lupu
Venue: Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII (2009)

Enterprise Rights Management (ERM) systems aim to protect disseminated data even after it has been sent to remote locations. Existing systems are based on common components, have similar functionalities and often have two shortcomings: a centralised architecture and a lack of concern for the trust and privacy of data recipients. To access the data, recipients must present their credentials to a policy evaluation authority, which they cannot choose and may not trust. Furthermore, recipients may be unable to access the data if their connection is intermittent or if they are off-line. To address these limitations, we propose PAES: a Policy-based Authority Evaluation Scheme, which combines data protection with a distributed policy evaluation protocol. The result allows us to implement the sticky policies paradigm in combination with trust management techniques. This permits distributing policy evaluation over a flexible set of authorities, simultaneously increasing the resilience of policy enforcement.
From Laplacian Eigenmaps to Kernel Locality Preserving Projections: Equivalence or Improvement?
Authors: Peng Jia, Junsong Yin, Zongtan Zhou, Dewen Hu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

Kernel Locality Preserving Projections (KLPP) and Laplacian eigenmaps (LE) are often taken as two different kinds of approaches in the application of nonlinear dimensionality reduction, but they are more closely related actually than expected. In this paper, KLPP is proved theoretically to solve exactly the same constrained minimization problem as LE. However, the application of KLPP is sensitive to the selections of kernel type and parameters, whereas LE is more efficient and straightforward. Unfolding results on different datasets of the two approaches are presented, together with the comparison of the computation time between KLPP and LE. In our experiments, the actual running time of LE is shorter than that of KLPP, though the time complexity of the two algorithms is comparable. The conclusion of this paper is a beneficial supplement to the nonlinear dimensionality reduction methods system and can be generalized to other algorithms.