ppxscal/embedding_network huggingface dataset:
Anonymizing healthcare data: a case study on the blood transfusion service
Authors: Noman Mohammed, Benjamin C.M. Fung, Patrick C.K. Hung, Cheuk-kwong Lee
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public hospitals, and identify the major challenges that make traditional data anonymization methods not applicable. Furthermore, we propose a new privacy model called LKC-privacy, together with an anonymization algorithm, to meet the privacy and information requirements in this BTS case. Experiments on the real-life data demonstrate that our anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large datasets.
Optimal sensors network layout using evolutionary algorithms
Authors: Corina Rotar, Mircea RÎsteiu, Ioan Ileana, C-Tin Hutanu
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

Wireless Sensor Networks are widely used to monitor the areas. In many WSN applications it is important to achieve a good coverage of the observed area while the energetic efficiency and the lifetime of the network are high. The task of placing the sensor nodes while addressing these objectives is known as WSN layout problem Also, the characteristics and the total number of the sensors influence the final solution of the problem. A new evolutionary technique for multiobjective optimization called Guided Hyper-plane Evolutionary Algorithm (GHEA) has been recently proposed. The originality of the approach consists in the fact that the fitness assignment is realized by using a target hyperplane of the population. GHEA has been tested on consecrated benchmarks and proved to offer better results than consecrated techniques. As the optimal sensors network layout can be reformulated as a multiobjective optimization problem, the paper presents a possible use of GHEA technique for solving the considered problem.
Experimental demonstration and analysis of high performance and low 1/f noise Tri-gate MOSFETs by optimizing device structure
Authors: Weitao Cheng, Akinobu Teramoto, Tadahiro Ohmi
Venue: Microelectronic Engineering (2009)

In this paper, we experimentally investigate the performance of multi-gate MOSFETs (MUGFETs) using the advanced radical gate oxide and the accumulation-mode (AM) FD-SOI MOSFETs. Firstly, we experimentally demonstrate that the drain current in AM multi-gate MOSFET is improved about 1.3 times compared with conventional inversion-mode (IM) MOSFETs with the same gate oxide. Secondly, we indicate that 1/f noise levels in AM MUGFETs are obviously suppressed compared with the conventional IM MUGFETs. The advantages resulted from the AM device structure for MUGFETs are demonstrated in this experiment.
Verification of Prerequisite Relationship among Learning Objects
Authors: King Kang Tsoi
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

Many Intelligent Tutoring Systems (ITS) support adaptive navigation according to the prerequisite relationship between the learning materials. This research aims to propose a method so that a system can verify and update the prerequisite relationship. We assume that the prerequisite can improve the learning result or is necessary for the study of the learning object. Prerequisite relationship can be verified by means of the conditional dependence of the passing rate in a learning object with that of its prior learning object. As a result, new learning paths can be formed adaptively to suit for different groups of learners.
EigenTransfer: a unified framework for transfer learning
Authors: Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, Yong Yu
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

This paper proposes a general framework, called EigenTransfer, to tackle a variety of transfer learning problems, e.g. cross-domain learning, self-taught learning, etc. Our basic idea is to construct a graph to represent the target transfer learning task. By learning the spectra of a graph which represents a learning task, we obtain a set of eigenvectors that reflect the intrinsic structure of the task graph. These eigenvectors can be used as the new features which transfer the knowledge from auxiliary data to help classify target data. Given an arbitrary non-transfer learner (e.g. SVM) and a particular transfer learning task, EigenTransfer can produce a transfer learner accordingly for the target transfer learning task. We apply EigenTransfer on three different transfer learning tasks, cross-domain learning, cross-category learning and self-taught learning, to demonstrate its unifying ability, and show through experiments that EigenTransfer can greatly outperform several representative non-transfer learners.
Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning
Authors: Hai Zhuge
Venue: IEEE Transactions on Knowledge and Data Engineering (2009)

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.
Unifying biological image formats with HDF5
Authors: Matthew T. Dougherty, Michael J. Folk, Erez Zadok, Herbert J. Bernstein, Frances C. Bernstein, Kevin W. Eliceiri, Werner Benger, Christoph Best
Venue: Communications of the ACM (2009)

The biosciences need an image format capable of high performance and long-term maintenance. Is HDF5 the answer?
Bilateral Two-Dimensional Locality Preserving Projections with Its Application to Face Recognition
Authors: Xiao-Guo Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

In this paper, we propose a novel algorithm for face feature extraction, namely the bilateral two-dimensional locality preserving projections (B2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experiments on ORL and PIE face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm.
Handwritten Arabic words recognition using multi layer perceptron and Zernik moments
Authors: I. El-Fegh, Zakaria Suliman Zubi, Ali A. Elrowayati, Faraj A. El-Mouadib
Venue: Proceedings of the 10th WSEAS international conference on evolutionary computing (2009)

This paper presents the application of Multi Layer Perceptron (MLP) Artificial Neural Network to classification of handwritten Arabic words. Zernik Moments are used as a feature vector for each word. An efficient way to select the most suitable order of Zernik moments is also presented. The MLP is trained in a supervised fashion using the Back Propagation learning algorithm. Having being trained, the MLP is tested on different set of handwritten Arabic words that has never been seen by the MLP. Several experiments are performed to select the best MLP structure. Experimental results have shown that with the presented structure and the order of the Zernik Moments more than 87% of correct recognition was obtained.
Algorithms and Complexity of Automata Synthesis by Asynhcronous Orchestration With Applications to Web Services Composition
Authors: Philippe Balbiani, Fahima Cheikh, Guillaume Feuillade
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Composition of services is necessary for realizing complex tasks on the Web. It has been characterized either as a plan synthesis problem or as a software synthesis problem: given a goal and a set of Web services, generate a composition of the Web services that satisfies the goal. We propose algorithms for performing automated Web service composition. We also examine the composition of services from the perspective of computational complexity.
Knowledge Work and Learning at the Workplace-Key Issues and Solutions
Authors: Alexander Karapidis, Till Becker, Volker Zimmermann
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

In an economy in which knowledge becomes more and more important as the main resource of economical success, workers, teams and companies need to develop the necessary competencies to be able to participate in a work life that is mainly based on knowledge productivity. In this paper we first outline knowledge work and its key issue areas. Second, we stress the necessity to combine work processes with training processes in the context of knowledge work. In the third part of this paper, we outline one approach developed by a consortium of research institutes and companies to overcome the gap between knowledge requested and content provided by a tool-based service to improve the skills of employees at the workplace.
Design of an image classification system for jaguar (Panthera onca) and puma (puma concolor) track recognition
Authors: Carlos A. Martínez-Ortiz, O. Eric Ramírez Bravo, Carlos A. López González
Venue: Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry (2009)

Jaguar (P. onca) tracks can help in different studies on big felid research. However, tracks can be confused with those of pumas (P. concolor) making it difficult to determine priority areas. There has been some attempts to obtain a formula to differentiate between the two species however this can be time consuming and requires some experience. We propose the use of a computer based image processing system in order to facilitate studies with these species. A set of 44 footprints from jaguar (23) and puma (21) was obtained in Sonora. These footprints were used to generate a program based in a Generalized Linear Model (GLM) that uses two shape descriptors (a boundary based orientability measure and boundary length) to classify the footprints. From the existing data set, the GLM was able to classify the footprints with an accuracy of 75%. A larger data set is expected to increase the classification accuracy. Additional shape descriptors may be added for further improvement. This will provide a method of easy access to carry further studies at a really low cost.
A Cheminformatics Approach for Zeolite Framework Determination
Authors: Shujiang Yang, Mohammed Lach-Hab, Iosif I. Vaisman, Estela Blaisten-Barojas
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Knowledge of the framework topology of zeolites is essential for multiple applications. Framework type determination relying on the combined information of coordination sequences and vertex symbols is appropriate for crystals with no defects. In this work we present an alternative machine learning model to classify zeolite crystals according to their framework types. The model is based on an eighteen-dimensional feature vector generated from the crystallographic data of zeolite crystals that contains topological, physical-chemical and statistical descriptors. Trained with sufficient known data, this model predicts the framework types of unknown zeolite crystals within 1-2 % error and shows to be better suited when dealing with real zeolite crystals, all of which always have geometrical defects even when the structure is resolved by crystallography.
Introduction to Interactive Document Search with Multi-Class Diverse Density
Authors: Chalita Hiransoog, Lucien Murray-Pitts
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

An interactive document search is introduced. It is a document search that allows a user to report to the search engine their opinion on how relevant each document is. The search engine then uses this information to narrow down the list of documents given to a user as a result of the search. It is proposed that there are three types of keywords: general keywords; hidden keywords; and negative keywords, that the search engine needs to uncover. The problem is then redefined as Multi-Class Multiple-Instance learning problem where the Multi-Class Diverse Density method developed as part of this research was used to find the solution.
Bowling online: social networking and social capital within the organization
Authors: Charles Steinfield, Joan M. DiMicco, Nicole B. Ellison, Cliff Lampe
Venue: Proceedings of the fourth international conference on Communities and technologies (2009)

Within an organizational setting, social capital facilitates knowledge management processes in that it enables individuals to locate useful information, draw on resources and make contributions to the network. This paper explores the relationship between various dimensions of organizational social capital and the use of an internal social network site (SNS). We hypothesize that the use of a SNS contributes to social capital within the organization in that SNS users are able to maintain larger networks of heterogeneous contacts. Additionally, the affordances of the site support social interaction between users, thus helping individuals maintain existing relationships and deepen developing ones. We find that bonding relationships, sense of corporate citizenship, interest in connecting globally, and access to new people and expertise are all associated with greater intensity of use of the social network site.
A geometrical method in solving overdetermined systems
Authors: Mona-Mihaela Doroftei
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

An overdetermined system is analyzed using a reducing method to a representation of gradient systems in locally of finite type Lie algebras with a supplementary hypothesis on the involution of fields.
On a synergetic architecture for cognitive adaptive behavior of future communication systems
Authors: Apostolos Kousaridas, Nancy Alonistioti
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

The future area of communication systems is considered as a representative example of a complex adaptive organization, where several elements, with various computational capabilities and network resources, are interconnected. This evolution renders imperative the need for adaptable and scalable systems that operate in unpredictable environments, having self-management features and the ability to handle complexity. The scope of this paper is to describe a coherent architectural framework in order to support adaptive and cognitive behavior of future communication systems, forming synergies from the most microscopic up to the most macroscopic level. Cognitive mechanisms embedded at all scales of a communication system will enable its autonomous hypostasis, facilitating also its self-organization. The synergetic architecture is modeled using autonomic element, dynamical hierarchies, and self-similarity concepts.
Variable density moving boundary model for CO2diffusion into liquid n-decane
Authors: Damelys Zabala, Aura L. López De Ramos
Venue: Proceedings of the 2nd conference on European computing conference (2008)

In previous works [1,2], carbon dioxide diffusion into n-decane inside cylindrical and square capillary tubes was modeled. Two different models were done for each tube and the convective model for the square tube depended on the results of the cylindrical one. For those models, the liquid phase density was always considered constant and its value was adjusted from the experimental data of gas-liquid interface position. This approach was done using the diffusivities obtained by correlations which modify the infinite dilution diffusion coefficient using a thermodynamical factor. Now, the liquid phase density is considered variable on time with perfect mixing inside the phase and an effective diffusivity can be determined. This effective diffusivity involves the molecular and convective contributions to the global mass transfer. Both interface displacements (inside cilyndrical and square tubes) can be modeled using the same model without dependency between their results. Also, the liquid phase density vs time is obtained for each displacement. The terms inside the finite difference matrix for the liquid phase are not constant, because they depend on the solute concentration and on the liquid density. The numerical solution was partially implicit because this matrix was evaluated for the liquid density value at the previous time (j) and the concentration terms were evaluated at the present time (j+1), using the iterative algorithm developed in [1]. Results showed that the model results, adjusted to the experimental interface position values, predicted effective diffusivities which are variable on time. The effective diffusivity is higher for the square capillary tube, as it was expected for the convective effect of the liquid filaments in the tube corners. Simulation time (60 min) in this model is considerably higher than simulation time in the previous constant density models (6 to 10 min).
Proceedings of the 7th international conference on Mobile systems, applications, and services
Venue: International Conference On Mobile Systems, Applications And Services (2009)

We welcome you to the 7th ACM Annual International Conference on Mobile Systems, Applications and Services -- MobiSys'09. This year's conference program continues the MobiSys tradition of providing a premiere forum for publication of papers that take a broad systems perspective of mobile computing. Papers appearing in this year's conference cover a range of topics from sensor and wireless systems to applications and services. Continuing the tradition of past conferences, this year's conference is preceded by a day of workshops and a PhD forum. Bruce Tuch is delivering the keynote speech this year, addressing the business and technology successes and failures that lead to product acceptance. The demonstration and poster chair, Rajesh Krishna Balan, has put together an exciting session that exhibits the best of the current work in our field. MobiSys'09 received 128 submissions, up 4% from the previous two years. The papers were reviewed in two rounds. Each paper received at least three reviews during the first round. Based on these reviews, 63 papers were selected to be reviewed in the second round, with each paper receiving two additional reviews. All told, our diligent reviewers produced 714 reviews, with the vast majority of these coming from PC members. We discussed 51 papers at an all-day PC meeting, which followed the HotMobile workshop in Santa Cruz, CA. At the meeting, 26 papers were accepted to appear in the conference program. All accepted papers were shepherded by a PC member.
Optimal alignment of multiple sequence alignments
Authors: John D. Kececioglu
Venue:  (2008)

An essential tool in biology is the alignment of multiple sequences. Biologists use multiple sequence alignments for tasks such as predicting protein structure and function, reconstructing phylogenetic trees, and finding motifs. Constructing high-quality multiple alignments is computationally hard, both in theory and in practice, and is typically done using heuristic methods. The majority of state-of-the-art multiple alignment programs employ a form and polish strategy, where in the construction phase, an initial multiple alignment is formed by progressively merging smaller alignments, starting with single sequences. Then in a local-search phase, the resulting alignment is polished by repeatedly splitting it into smaller alignments and re-merging. This merging of alignments, the basic computational problem in the construction and local-search phases of the best multiple alignment heuristics, is called the Aligning Alignments Problem. Under the sum-of-pairs objective for scoring multiple alignments, this problem may seem to be a simple extension of two-sequence alignment. It is proven here, however, that with affine gap costs (which are recognized as necessary to get biologically-informative alignments) the problem is NP-complete when gaps are counted exactly. Interestingly, this form of multiple alignment is polynomial-time solvable when we relax the exact count, showing that exact gap counts themselves are inherently hard in multiple sequence alignment. Unlike general multiple alignment however, we show that Aligning Alignments with affine gap costs and exact counts is tractable in practice, by demonstrating an effective algorithm and a fast implementation. Our software AlignAlign is both time- and space-efficient on biological data. Computational experiments on biological data show instances derived from standard benchmark suites can be optimally aligned with surprising efficiency, and experiments on simulated data show the time and space both scale well.
Automatic computation of the complete root classification for a parametric polynomial
Authors: Songxin Liang, David J. Jeffrey
Venue: Journal of Symbolic Computation (2009)

An improved algorithm, together with its implementation, is presented for the automatic computation of the complete root classification of a real parametric polynomial. The algorithm offers improved efficiency and a new test for non-realizable conditions. The improvement lies in the direct use of 'sign lists', obtained from the discriminant sequence, rather than 'revised sign lists'. It is shown that the discriminant sequences, upon which the sign lists are based, are closely related both to Sturm-Habicht sequences and to subresultant sequences. Thus calculations based on any of these quantities are essentially equivalent. One particular application of complete root classifications is the determination of the conditions for the positive definiteness of a polynomial, and here the new algorithm is applied to a class of sparse polynomials. It is seen that the number of conditions for positive definiteness remains surprisingly small in these cases.