ppxscal/embedding_network huggingface dataset:
A soft computing approach for data mining based query processing using rough sets and genetic algorithms
Authors: K. G. Srinivasa, K. R. Venugopal, L. M. Patnaik
Venue: International Journal of Hybrid Intelligent Systems (2008)

The optimization of queries is critical in database management systems and the complexity involved in finding optimal solutions has led to the development of heuristic approaches. Answering data mining query involves a random search over large databases. Due to the enormity of the data set involved, model simplification is necessary for quick answering of data mining queries. In this paper, we propose a hybrid model using rough sets and genetic algorithms for fast and efficient query answering. Rough sets are used to classify and summarize the datasets, whereas genetic algorithms are used for answering association related queries and feedback for adaptive classification. Here, we consider three types of queries, i.e., select, aggregate and classification based data mining queries. Summary tables that are built using rough sets and analytical model of attributes are used to speed up select queries. Mining associations, building concept hierarchies and reinforcement of reducts are achieved through genetic algorithms. The experiments are conducted on three real-life data sets, which include KDD 99 Cup data, Forest Cover-type data and Iris data. The performance of the proposed algorithm is analyzed for both execution time and classification accuracy and the results obtained are good.
Comments-oriented document summarization: understanding documents with readers' feedback
Authors: Meishan Hu, Aixin Sun, Ee-Peng Lim
Venue: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)

Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multi-relation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.
Myocardial Ischemia Detection by Pulse Signal Features and Fuzzy Clustering
Authors: Kang-Ming Chang, Zhi-Zhong Lin, Shing-Hong Liu, Chu-Chang Tyan
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02 (2008)

The aim of this study is to propose a new diagnosing algorithm, for myocardial ischemia, based on the newly developed non-invasive measurement, three-axis sphygmography, and further processed by modified fuzzy c-means algorithms. The three-axis sphygmography measures pressure pulse waveform (PPW), and the PPW are analyzed by its harmonic components in the frequency domain. The first three harmonics of the Fourier series of PPW are significantly different between myocardial ischemia patients and normal subjects. The harmonic of the Fourier series of PPW are feed into a modified fuzzy c-means algorithm, and developed PPW criteria to identify myocardial ischemia with sensitivity 69% and specificity 100%
Transcending from Virtual Reality into Tele-Immersive Technologies and Applications: A Perspective
Authors: Ramesh Singh, Anubhav Kumar Singh
Venue: Ubiquity (2008)

The goal of this paper is to investigate the basic principles of a tele-immersive system, its main components, development history, demonstrations and potential uses as one of today's most challenging technologies which takes all our knowledge of virtual reality, networks, haptics etc to the limits and beyond. Ultimately the paper presents a future research and development roadmap for this technology. Tele-immersion involves combining teleconferencing, tele-presence and virtual reality to allow individuals to manipulate a shared environment, converse, interact and even see each other. But the ultimate objective of tele-immersion is not solely to reproduce a real face-to-face meeting in detail, but also to provide the interface for collaborators, world-wide, to work together in a shared virtual environment, even though they may be miles apart physically. Tele-immersion stands at the crossroad of researches related to virtual reality and networking, computer visualization and user interfaces and can be said to unify all of these research areas.
Symmetric Active/Active Replication for Dependent Services
Authors: Christian Engelmann, Stephen L. Scott, Chokchai Leangsuksun, Xubin He
Venue: Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (2008)

During the last several years, we have established the symmetric active/active replication model for service-level high availability and implemented several proof-of-concept prototypes. One major deficiency of our model is its inability to deal with dependent services, since its original architecture is based on the client-service model. This paper extends our model to dependent services using its already existing mechanisms and features. The presented concept is based on the idea that a service may also be a client of another service, and multiple services may be clients of each other. A high-level abstraction is used to illustrate dependencies between clients and services, and to decompose dependencies between services into respective client-service dependencies. This abstraction may be used for providing high availability in distributed computing systems with complex service-oriented architectures.
Neural network Othello competition
Authors: Simon M. Lucas
Venue: ACM SIGEVOlution (2007)

IEEE WCCI 2008 also saw the latest instalment of the Neural Network Othello competition. The aim of this is to find the best performing neural network (or more generally, any form of value function) for playing Othello. The mode of operation is as follows. Entrants submit their trained architectures to the competition web server, which evaluates them against a standard heuristic player, and immediately updates a league table with the performance of the submitted player. The leading entry by each entrant is then played off in a round robin league against the other leading entries to determine the competition winner. The competition server has been running for over two years now, and has received well over 1,000 entries. The IEEE CEC 2006 entry was won by an MLP (with a single hidden layer), submitted by Kyung-Joong Kim and Sung-Bae Cho of Yonsei University, Seoul, South Korea. In the league table below this entry is referred to as CEC 2006 Champ.
Relative widths of smooth functions determined by fractional order derivatives
Authors: Liu Yongping, Yang Lianhong
Venue: Journal of Complexity (2008)

For two subsets W and V of a normed space X. The relative Kolmogorov n-width of W relative to V in X is defined byK"n(W,V)"X@?infL"nsupf@?Winfg@?V@?L"n@?f-g@?"X,where the infimum is taken over all n-dimensional subspaces L"n of X. For @a@?R"+, define W"p^@a(1=1-1q and the case p=1,23-1q, we obtain their weak asymptotic results. In addition, we also obtain the weak asymptotic result of W"p^@a relative to W"p^@a in the space L"p(T) for 0
On levels in arrangements of curves, iii: further improvements
Authors: Timothy M. Chan
Venue: Proceedings of the twenty-fourth annual symposium on Computational geometry (2008)

We revisit the problem of bounding the combinatorial complexity of the k-level in a two-dimensional arrangement of n curves. We give a number of small improvements over the results from the author's previous paper (FOCS'03). For example: For pseudo-parabolas, we obtain an upper bound of O(n3/2 log n), which improves the previous bound of O(n3/2 log2 n). For 3-intersecting curves, we obtain an upper bound of O(n2-1/(3+√7))=O(n1.823), the first improvement over the previous bound of O(n11/6)=O(n1.834). For s-intersecting curves or curve segments with s ≥ 3, we obtain an upper bound of O(n2--1/2s--δs) if s is odd, and O(n2--1/2(s--1)--δs) if s is even, for some constant δs > 0. For pseudo-segments, we obtain an upper bound of O(n4/3 log1/3--δ n) for some constant δ > 0. the previous bound was O(n4/3 log 2/3 n). For s-intersecting curve segments such that all but B pairs intersect at most once, we obtain an upper bound of O((n4/3 + n1+δB1/3--δ) log1/3--δ n + B) for some constant δ > 0. We also observe that better concrete bounds for k-levels for constant values of n could in principle lead to better asymptotic bounds for arbitrary n.
Can great research be taught?: independent research with cross-disciplinary thinking and broader impact
Authors: Nick Feamster, Alexander Gray
Venue: Proceedings of the 39th SIGCSE technical symposium on Computer science education (2008)

This paper describes a course we have developed for preparing new Ph.D. students in computer science for a career in research. The course is intended to teach the skills needed for research and independent work, prepare students psychologically and socially for years lying before them, and help them find a good Ph.D. topic by providing principles and examples. In this course, we emphasize and encourage impact through cross-disciplinary research and broader societal outreach. To our knowledge, the course represents a first-of-its-kind systematic introduction to a graduate research career. This paper describes our high-level goals for this curricular initiative, the structure of the course (including lecture components and assignments), and the challenges we faced in developing this course. As we continue to develop this course, which is now in its second year, we hope it will serve as a model "introduction of Ph.D. research" course for other computer science departments.
Dependencies revisited for improving data quality
Authors: Wenfei Fan
Venue: Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2008)

Dependency theory is almost as old as relational databases themselves, and has traditionally been used to improve the quality of schema, among other things. Recently there has been renewed interest in dependencies for improving the quality of data. The increasing demand for data quality technology has also motivated revisions of classical dependencies, to capture more inconsistencies in real-life data, and to match, repair and query the inconsistent data. This paper aims to provide an overview of recent advances in revising classical dependencies for improving data quality.
Configurable memory protection by aspects
Authors: Daniel Lohmann, Jochen Streicher, Wanja Hofer, Olaf Spinczyk, Wolfgang Schröder-Preikschat
Venue: Proceedings of the 4th workshop on Programming languages and operating systems (2007)

We describe the implementation of memory protection by means of aspect-oriented programming (AOP) in CiAO, an AUTOSAR-like family of embedded operating systems. The use of AOP was originally motivated by the fact that memory protection is a cross-cutting policy, which, furthermore, has to be configurable at build-time in AUTOSAR. We learned, however, that besides switching between full protection and no protection, an AOP-based approach also makes it easy to apply completely different models of protection. For the domain of statically configured embedded systems, where certain failure scenarios can often be excluded by means of code analysis or even probability, this facilitates tailored and light-weight "pay-as-you-use" protection strategies.
Constructive state-affine transformation of a class of nonlinear systems
Authors: V. López-Morales, A. Glumineau, L. E. Ramos-Velasco
Venue: Proceedings of the 5th WSEAS International Conference on Applied Mathematics (2004)

Constructive coordinate transformation problem of nonlinear systems into a particular state-affine systems and the analysis of their equivalence, is here tackled. The proposed method is based on the analysis of the Input/Output Differential Equation structure of these nonlinear systems. The polynomial structure of their corresponding state-affine system is then explicitly given and new necessary and sufficient conditions for their tranformation (i.e. time-varying linearization) are obtained in a constructive way.
Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach
Authors: Lee J. Lancashire, Robert C. Rees, Graham R. Ball
Venue: Artificial Intelligence in Medicine (2008)

Objective: The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature. Methods: Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer. Results: Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether metastasis to the axillary lymph node had occurred. A number of these genes had been previously reported to have a role in cancer. Significantly fewer genes were used compared to other previous studies. The models using the optimal gene subsets were internally validated using an extensive random sample cross-validation procedure and externally validated using a follow up dataset from a different cohort of patients on a newer array chip containing the same and additional probe sets. Here, the models retained high accuracies, emphasising the potential power of this approach in analysing complex systems. These findings show how the proposed method allows for the rapid analysis and subsequent detailed interrogation of gene expression signatures to provide a further understanding of the underlying molecular mechanisms that could be important in determining novel prognostic markers associated with cancer.
Nonparametric hypothesis testing with small type I or type II error probabilities
Authors: M. S. Ermakov
Venue: Problems of Information Transmission (2008)

For the problem of signal detection in Gaussian white noise, we obtain lower bounds for the asymptotics of moderate deviation probabilities of type I and type II errors. These asymptotics are attained on tests of the ¿ 2 type. Using these lower bounds, we find lower bounds for nonparametric confidence estimation in the moderate deviation zone.
A Performance Study of Secure Data Mining on the Cell Processor
Authors: Hong Wang, Hiroyuki Takizawa, Hiroaki Kobayashi
Venue: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (2008)

This paper examines the potential of the Cell processor as a platform for secure data mining on the future volunteer computing systems. Volunteer computing platforms have the potential to provide massive computing power. However, privacy and security concerns prevent using volunteer computing for data mining of sensitive data. The Cell processor comes with a hardware security feature. The secure volunteer data mining can be achieved by using this hardware security feature. In this paper, we present a general security scheme for the volunteer computing, and a secure parallelized K-Means clustering algorithm for the Cell processor. We also evaluate the performance of the algorithm on the Cell secure system simulator. Evaluation results indicate that the proposed secure data clustering outperforms a non-secure clustering algorithm on the general purpose CPU, but incurs a huge performance overhead introduced by the decryption process of the Cell security features.
Matrix norms and their sensitivity to noise a computational study
Authors: M. O. Abdalla
Venue: Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation (2005)

Most of the objective (cost) functions in optimization techniques utilize norms especially when dealing with signals, vectors, or matrices. In this work, three norms were studied, namely matrix One norm, Infinity norm, and Frobenius (Euclidean or Two) norm. The effect of noise on these matrix norms was studied with the aid of a generalized eigen equation. Basic analysis of the effect of noise on matrix norms is provided, which is also complimented with a computer simulated results. It turns out that the Frobenius norm is the least sensitive norm to noise.
Mapping pipeline skeletons onto heterogeneous platforms
Authors: Anne Benoit, Yves Robert
Venue: Journal of Parallel and Distributed Computing (2008)

Mapping applications onto parallel platforms is a challenging problem, that becomes even more difficult when platforms are heterogeneous - nowadays a standard assumption. A high-level approach to parallel programming not only eases the application developer's task, but it also provides additional information which can help realize an efficient mapping of the application. In this paper, we discuss the mapping of pipeline skeletons onto different types of platforms: Fully Homogeneous platforms with identical processors and interconnection links; Communication Homogeneous platforms, with identical links but different-speed processors; and finally, Fully Heterogeneous platforms. We assume that a pipeline stage must be mapped on a single processor, and we establish new theoretical complexity results for different mapping policies: a mapping can be required to be one-to-one (a processor is assigned at most one stage), or interval-based (a processor is assigned an interval of consecutive stages), or fully general. In particular, we show that determining the optimal interval-based mapping is NP-hard for Communication Homogeneous platforms, and this result assesses the complexity of the well-known chains-to-chains problem for different-speed processors. We provide several efficient polynomial heuristics for the most important policy/platform combination, namely interval-based mappings on Communication Homogeneous platforms. These heuristics are compared to the optimal result provided by the formulation of the problem in terms of the solution of an integer linear program, for small problem instances.
Privacy Preserving Support Vector Machines in Wireless Sensor Networks
Authors: Dong Seong Kim, Muhammad Anwarul Azim, Jong Sou Park
Venue: Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (2008)

It is important to achieve energy efficient data mining in Wireless Sensor Networks (WSN) while preserving privacy of data. In this paper, we present a privacy preserving data mining based on Support Vector Machines (SVM). We review the previous approach in privacy preserving data mining in distributed system. And we also review energy efficient data mining in WSN. We then propose an energy efficient privacy preserving data mining in WSN. We use SVM because it has been shown best classification accuracy and sparse data presentation using support vectors. We show security analysis and energy estimation of our proposed approach.
Synthesis and Traceability of Scenario-Based Executable Models
Authors: Ankit Goel, Abhik Roychoudhury
Venue: Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (2006)

Message Sequence Charts (MSCs) or Sequence Diagrams are one of the behavioral diagram types in the Unified Modeling Language or UML. In system requirements modeling, MSCs are conventionally used for describing possible system scenarios. In the recent past, there have been concerted attempts to develop executable system modeling languages directly based on MSCs --- Live Sequence Charts, Triggered Message Sequence Charts and Interacting Process Classes, to name a few. In this paper, we study the problem of model synthesis in these languages --- how to translate informal requirements into formal models. We also discuss (a) test generation from these formal models, and (b) how the generated tests can be traced back to the informal requirements.
Mitigating Malicious Code
Authors: Ken Dunham, Gold Honors
Venue: Information Systems Security (2007)

Mitigation of malicious code is increasingly complicated by multi-staged and mutli-variant attacks taking place daily on the Internet today. It is now common for computers to be infected for long periods of time, with malicious browser help objects, rootkits, and similar stealth codes. Identification and removal from a computer can be especially difficult. In some cases, the only reasonable effort may be to completely wipe and reinstall an image of the system, known to be free of malicious code. Manual mitigation of malicious code is a sophisticated process of threat identification, research, mitigation, and monitoring to properly remove all threat components related to an attack.
A reliable method for cell phenotype image classification
Authors: Loris Nanni, Alessandra Lumini
Venue: Artificial Intelligence in Medicine (2008)

Objective: Image-based approaches have proven to be of great utility in the automated cell phenotype classification, it is very important to develop a method that efficiently quantifies, distinguishes and classifies sub-cellular images. Methods and materials: In this work, the invariant locally binary patterns (LBP) are applied, for the first time, to the classification of protein sub-cellular localization images. They are tested on three image datasets (available for download), in conjunction with support vector machines (SVMs) and random subspace ensembles of neural networks. Our method based on invariant LBP provides higher accuracy than other well-known methods for feature extraction; moreover, our method does not require to (direct) crop the cells for the classification. Results and conclusion: The experimental results show that the random subspace ensemble of neural networks outperforms the SVM in this problem. The proposed approach based on the solely LBP features gives accuracies of 85%, 93.9% and 88.4% on the 2D HeLa dataset, LOCATE endogenous and transfected datasets, respectively, and in combination with other state-of-the-art methods for the cell phenotype image classification we obtain a classification accuracy of 94.2%, 98.4% and 96.5%.