ppxscal/embedding_network huggingface dataset:
A criterion-based multilayer access control approach for multimedia applications and the implementation considerations
Authors: Leon Pan, Chang N. Zhang
Venue: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) (2008)

In this article, a novel criterion-based multilayer access control (CBMAC) approach is presented to enhance existing access control models such as Role-Based, Mandatory, and Discretionary Access Control models to support multilayer (multilevel) access control. The proposed approach is based on a set of predefined security criteria which are extracted from authorization rules. The security attributes of objects and users are specified by security criterion expressions (serving as locks) and the elements (serving as keys) of security criterion subsets respectively. An object embedded with a number of security criterion expressions becomes a secure object while a user associated with a security criterion subset is called a secure user. The multilayer access control is achieved by evaluating the embedded security criterion expressions (actuating locks) by the elements (keys) in a user's security criterion subset. The paper also provides the details of integrating the proposed approach with existing access control models and presents the implementation considerations of Criterion-Based Role-Based Multilayer Access Control, the integration of CBMAC and Role-Based Access Control.
Visualization of GML Map Using 3-Layer POI on Mobile Device
Authors: Eun-Ha Song, Laurence T. Yang, Young-Sik Jeong
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

GIS can only be applied to certain areas by storing format, and it is subordinate to a system when displaying geographic information data. It is therefore inevitable for GIS to use GML that supports efficient usage of various geographic information data and interoperability for integration and sharing. The paper constructs VisualGML that translates currently-used geographic information such as DXF(Drawing Exchange Format), DWG(DraWinG), or SHP(Shapefile) into GML format to visualize. In order to provide a flexibility of a mobile device, VisualGML constructs integrated map preprocess module which filters geographic information data according to its tag and properties. VisualGML also provides two major GIS services for user and administrator, it can be enable visualizing location search this is applied with 3-Layer POI structure for user. For administrator, it has trace monitoring visualization through moving information of mobile devices.
Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face
Authors: Nojun Kwak, Sang-Il Choi, Chong-Ho Choi
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper, we propose a new feature extraction method for regression problems. It is a modified version of linear discriminant analysis (LDA) which is a very successful feature extraction method for classification problems. In the proposed method, the between class and the within class scatter matrices in LDA are modified so that they fit in regression problems. The samples with small differences in the target values are used to constitute the within class scatter matrix while the ones with large differences in the target values are used for the between class scatter matrix. We have applied the proposed method in estimating the head pose and compared the performance with the conventional feature extraction methods.
An Integration of Statistical Deformable Model and Finite Element Method for Bone-Related Soft Tissue Prediction in Orthognathic Surgery Planning
Authors: Qizhen He, Jun Feng, Horace H. Ip, James Xia, Xianbin Cao
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

In this paper, we propose a novel statistical deformable model for bone-related soft-tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Model(FEM) and Statistical Deformable Model(SDM) to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based surgery simulation for sample generation and SDM for soft tissue prediction, we are able to capture the prior knowledge of bone-related soft-tissue deformation for different surgical plans. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimization. Our experiments have shown that Br-SDM is able to give comparable soft-tissue prediction accuracy with respect to conventional FEM-based prediction while only requires 10% of its computational cost.
Creating Contract Templates for Car Insurance Using Multi-agent Based Text Understanding and Clustering
Authors: Igor Minakov, George Rzevski, Petr Skobelev, Simon Volman
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

The paper discusses the problem of automated processing and classification of unstructured text and proposes a new approach based on the multi-agent technology. The proposed method was successfully used to develop a system for a large UK insurance company capable of analysing and classifying 25000 documents related to car insurance, as well as creating template contract documents. The paper describes the system, presents testing results and discusses perspectives.
Readers' Remarks
Venue: IEEE MultiMedia (2004)

This new department, "Reader's Remarks," provides a forum for our community torespond not only to the content of this magazine, but also to what's transpiring in themultimedia community at large. What visions do you have for a multimedia future? As peoplewho actually do multimedia, how should we maintain or expand our focus as a magazine? Wewould love to hear from you! Contact Editor-in-Chief Forouzan Golshani atgolshani@cs.wright.edu. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Derivation of program models for web application systems using meta-models
Authors: Takakazu Kaneko, Nobuoki Mano
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

We have been making researches on system design method for web application systems using models in knowledge base. Web systems are comprised of hetero-genius domains: user-interface, communication system, and database system. In this paper we propose a method using meta-models to derive Java source codes. This method, first, extracts program model skeletons by using knowledge about implementation patterns. Then, it derives concrete program models by using basic functional units. This method can be applied for problems over a wide range: from communication application in pure Java to communication using sessions in WEB system.
Boundary Refining Aiming at Speech Synthesis Applications
Authors: Monique V. Nicodem, Sandra G. Kafka, Rui Seara,  Jr., Rui Seara
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

In concatenative synthesis, speech is produced by joining segments automatically selected among units contained in a previously segmented database. The synthetic speech resulting from such a technique is often improved when accurate segmentation tools are considered. The performance of these tools is often enhanced by a hybrid approach resulting from the association of an HMM modeling with a boundary refining process. Such a refining has been carried out sucessfully by using techniques based on neural networks. This paper presents a set of networks that outperform other topologies discussed in the literature. These networks are trained by performing a clusterization of the training set taking into consideration phonetic transitions with similarities to each other.
Emotionally based multi-agent e-commerce platform
Authors: Nicolae Jascanu
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: doctoral mentoring program (2008)

Experiencing and expressing emotions are integral parts of our life. In the retail area of commerce, emotion plays a fundamental role. When we choose or buy a commodity, our choice has a profound emotional dimension. We are social beings, we are influenced by others opinions, we have our opinions and we like to bargain for everything. To capture the magic behind negotiation, a first step will be to build a model that incorporates human emotions and community emotional opinions. Using dimensional models of emotion, the agent is able to capture and interpret such knowledge. The thesis proposes a novel design for an e-commerce system based on three agents: customer, supplier and community. The thesis settles the ground for emotional negotiations and the role of emotion in bilateral, multi-issue negotiations in retail markets.
Secure Two-Party Computation of Squared Euclidean Distances in the Presence of Malicious Adversaries
Authors: Marc Mouffron, Frederic Rousseau, Huafei Zhu
Venue: Information Security and Cryptology: Third SKLOIS Conference, Inscrypt 2007, Xining, China, August 31 - September 5, 2007, Revised Selected Papers (2007)

Squared Euclidean Distance metric that uses the same equation as the Euclidean distance metric, but does not take the square root (thus clustering with the Squared Euclidean Distance metric is faster than clustering with the regular Euclidean Distance) is an efficient tool for clustering databases. Since there appears to be no previous implementation of secure Squared Euclidean Distance protocols in the malicious model, this paper studies two-party computation of Squared Euclidean Distance protocols in the presence of malicious adversaries based on state-of-the art homomorphic cryptographic primitives without using Yao-style circuit. The security of our protocol is analyzed by comparing what an adversary can do in the a real protocol execution to what it can do in an ideal scenario. We show that the proposed scheme is provably secure against malicious adversary assuming that the underlying homomorphic commitment is statistically hiding and computationally binding and the homomorphic encryption scheme is semantically secure in the common reference string model.
A Hybrid ARCH-M and BP Neural Network Model For GSCI Futures Price Forecasting
Authors: Wen Bo, Wang Shouyang, K. K. Lai
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1), CGARCH(1,1), EGARCH(1,1) and ARIMA-ANN models on the RMSE, MAPE, Theil IC evaluation criteria.
Bankinter's online broker model: the success formula
Authors: Luis Ferruz, Francisco Javier Rivas
Venue: International Journal of Electronic Finance (2008)

This paper aims to study the different models of implementing the internet in financial brokerage, especially in trying to identify the main factors that explain the success of Bankinter in this area of business. Bankinter's position, with respect to the other financial entities in the Spanish market, would have implied that others would have taken the leadership in Spanish online brokerage. It was completely different and today, Bankinter is one of the world references in this area. In fact, it is clear that Bankinter's model is one of complete integration of the internet with the rest of their channels.
Particle Swarm Optimization for Object Recognition in Computer Vision
Authors: Hugo A. Perlin, Heitor S. Lopes, Tânia Mezzadri Centeno
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Particle Swarm Optimization (PSO) is an evolutionary computation technique frequently used for optimization tasks. This work aims at applying PSO for recognizing specific patterns in complex images. Experiments were done with gray level and color images, with and without noise. PSO was able to find predefined reference images, submitted to translation, rotation, scaling, occlusion, noise and change in the viewpoint in the landscape image. Several experiments were done to evaluate the performance of PSO. Results show that the proposed method is robust and very promising for real-world applications.
Kernel-Based Text Classification on Statistical Manifold
Authors: Shibin Zhou, Shidong Feng, Yushu Liu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

In the text literature, a variety of useful kernel methods have been developed by many researchers. However, embedding text data into Euclidean space is the key characteristic of common kernels-based text categorization. In this paper, we focus on representation text vectors as points on Riemann manifold and use kernels to integrate discriminative and generative model. And then, we present diffuse kernel based on Dirichlet Compound Multinomial manifold (DCM manifold) which is a space about Dirichlet Compound Multinomial model combining inverse document frequency and information gain. More specifically, as demonstrated by our experimental results on various real-world text datasets, we show that the kernel based on this DCM manifold is more desirable than Euclidean space for text categorization. And our kernel method provides much better computational accuracy than some current state-of-the-art methods.
Wearable and Ubiquitous Computing
Authors: Tom Martin
Venue: IEEE Pervasive Computing (2003)

This issue's column continues its coverage of innovative courses in pervasive computing. Tom Martin of Virginia Tech describes a course on wearable and ubiquitous computing that he developed and has taught twice. He describes the course's scope, assignments and grading, and design projects and his experiences with them.
Evaluation of Models for the Recognition of Hadwritten Digits in Medical Forms
Authors: Willian Zalewski, Huei Diana Lee, Adewole M. Caetano, Ana C. Lorena, André G. Maletzke, João José Fagundes, Cláudio Saddy, Rodrigues Coy, Feng Chung Wu
Venue: Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology (2008)

Medicine has benefited widely from the use of computational techniques, which are often employed in the analysis of data generated in medical clinics. Among the computational techniques used in these analyses are those from Knowledge Discovery in Databases (KDD). In order to apply KDD techniques in the analysis of clinical data, it is often necessary to map them into an adequate structured format. This paper presents an extension in a methodology to map medical forms into structured datasets, in which a sub-system for handwritten digit recognition is added to the overall mapping system.
Benevolence and Effectiveness: Persuasive Technology's Spillover Effects in Retail Settings
Authors: Martha G. Russell
Venue: Proceedings of the 3rd international conference on Persuasive Technology (2008)

Results point to perceived store benevolence and staff effectiveness as important spillover effects in the use of persuasive technologies in the retail setting. The comparison of customer exit interviews, conducted at control stores and at test stores, before and after the introduction of video displays playing advertising and promotion messages, documented that customers noticed the displays, recalled the content and were influenced toward the desired effect. Additionally, significant attitudinal differences related to customer perception of staff and stores were also noted. Product marketers, brand managers, retailers, and academicians can draw actionable insights for the use of persuasive technologies by understanding the impact of persuasive technologies on relational exchanges.
Combining User Profiles and Situation Contexts for Spontaneous Service Provision in Smart Assistive Environments
Authors: Weijun Qin, Daqing Zhang, Yuanchun Shi, Kejun Du
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

Wireless hotspots are permeating our workplace, home and public places bringing interesting services and spontaneous connectivity to mobile users. Since mobile users will be immersed in thousands of different kinds of pervasive services, it's of paramount importance to provide appropriate services to the right person in the right form. Existing approaches offer a rather simplistic solution to the problem and seldom consider the multi-dimensional contextual information. In this paper we present a service provision mechanism which can enable effective service provision based on semantic similarity measure with the combination of user profiles and situation context in WLAN enabled environment. We illustrate our ideas through a scenario of service discovery and provision in public places (e.g. shopping mall) where a disabled person can select appropriate services with the constraints of his mobile device.
Performance Enhancement of RBF Networks in Classification by Removing Outliers in the Training Phase
Authors: Hieu Trung Huynh, Nguyen H. Vo, Minh-Tuan T. Hoang, Yonggwan Won
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

During data collection and analysis there often exist outliers which affect final results. In this paper we address reducing effects of outliers in classification with Radial Basis Function (RBF) networks. A new approach called iterative RBF (iRBF) is proposed. In which training RBF networks is repeated if there exist outliers in the training set. Detection of outliers is performed by relying upon outputs of the RBF networks which correspond to applying the training set at the input units. Detected outliers have had to be eliminated before the training set is used in the next training time. In this approach we achieve a good performance in outlier rejection and classification with training sets existing outliers.
Heterogeneous Workflows in Scientific Workflow Systems
Authors: Vasa Curcin, Moustafa Ghanem, Patrick Wendel, Yike Guo
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Workflow systems are used to model a range of scientific and business applications, each requiring a different set of capabilities. We analyze how these heterogeneous approaches can be resolved, look at how existing workflow systems address this and present the solution in Discovery Net, which combines three levels of workflows, control, data and grid, at different levels of abstraction.
A Centralized Network Design Problem with Genetic Algorithm Approach
Authors: Gengui Zhou, Zhenyu Cao, Jian Cao, Zhiqing Meng
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

A centralized network is a network where all communication is to and from a single site. In the combinatorial optimization literature, this problem is formulated as the capacitated minimum spanning tree problem (CMST). Up to now there are still no effective algorithms to solve this problem. In this paper, we present a completely new approach by using the genetic algorithms (GAs). For the adaptation to the evolutionary process, we developed a tree-based genetic representation to code the candidate solution of the CMST problem. Numerical analysis shows the effectiveness of the proposed GA approach on the CMST problem.
Adoption of leadership mandated project management standards, methods and training in a global enterprise
Authors: Linda L. Grantham
Venue:  (2008)

This quantitative descriptive study investigated hindrances and enabling factors affecting adoption of mandated project management practices. The study addressed why project managers may resist adopting consistent project management standards, methods, and training despite leadership mandates, sustained corporate resource commitment, and a centralized project management office (PMO). Interviews with 35 project management professionals (executives, managers, and practitioners) focused on subjects&rsquo; opinions and perceptions of project management consistency, influence of a PMO, and adoption of mandated change. The data illustrate substantial discrepancies among the job roles represented in this study. While the reasons for these discrepancies are not entirely clear and require further study, data show that inadequate alignment exists among executive strategies, the operations view of managers, and the task orientation of practitioners. These disparate views concerning project management hinder adoption and absorb considerable energy from the organization. The study also investigated the impact of organizational culture. The data support several recommendations for organizations desiring consistent project management standards, methods and training. These include a leadership commitment to nurturing an organizational culture that is open to change, a project management office staffed with a rotating corps of expert practitioners, and organizational training to effectively manage change adoption. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia
Authors: Paul Kennedy, Simeon J. Simoff, Daniel R. Catchpoole, David B. Skillicorn, Franco Ubaudi, Ahmad Al-Oqaily
Venue: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (2008)

This chapter presents an integrative visual data mining approach towards biomedical data. This approach and supporting methodology are presented at a high level. They combine in a consistent manner a set of visualisation and data mining techniques that operate over an integrated data set of several diverse components, including medical (clinical) data, patient outcome and interview data, corresponding gene expression and SNP data, domain ontologies and health management data. The practical application of the methodology and the specific data mining techniques engaged are demonstrated on two case studies focused on the biological mechanisms of two different types of diseases: Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia, respectively. The common between the cases is the structure of the data sets.
An Architectural Style for Data-Driven Systems
Authors: Reza Mahjourian
Venue: Proceedings of the 10th international conference on Software Reuse: High Confidence Software Reuse in Large Systems (2008)

Data-driven systems and applications are specialized software solutions for acquisition, management, and presentation of information. These systems are usually developed using the same software tools, technologies, and processes used for creating any other type of software. Not only is this approach inefficient, but also it results in extreme redundancies due to the inherently repetitive nature of these applications. However, data-driven systems exhibit characteristics which can be exploited for extensive reuse across a single application or a family of applications. In this paper, we present XPage, an architectural style which is especially designed for building data-driven systems. We also provide several case studies from real-world deployments of XPage to help evaluate its efficiency and flexibility for developing real-world solutions.