ppxscal/embedding_network huggingface dataset:
Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design, 1st edition
Authors: Diomidis Spinellis, Georgios Gousios
Venue:  (2009)

What are the ingredients of robust, elegant, flexible, and maintainable software architecture? Beautiful Architecture answers this question through a collection of intriguing essays from more than a dozen of today's leading software designers and architects. In each essay, contributors present a notable software architecture, and analyze what makes it innovative and ideal for its purpose. Some of the engineers in this book reveal how they developed a specific project, including decisions they faced and tradeoffs they made. Others take a step back to investigate how certain architectural aspects have influenced computing as a whole. With this book, you'll discover: How Facebook's architecture is the basis for a data-centric application ecosystem The effect of Xen's well-designed architecture on the way operating systems evolve How community processes within the KDE project help software architectures evolve from rough sketches to beautiful systems How creeping featurism has helped GNU Emacs gain unanticipated functionality The magic behind the Jikes RVM self-optimizable, self-hosting runtime Design choices and building blocks that made Tandem the choice platform in high-availability environments for over two decades Differences and similarities between object-oriented and functional architectural views How architectures can affect the software's evolution and the developers' engagement Go behind the scenes to learn what it takes to design elegant software architecture, and how it can shape the way you approach your own projects, with Beautiful Architecture.
Bayesian Network-Based Service Context Recognition Model
Authors: Xiuquan Qiao, Xiaofeng Li
Venue: International Journal of Distributed Sensor Networks (2009)

Service context refers to the environmental information which influences the service execution. The processing of service context information is the foundation of the future intelligent telecommunication services in the ubiquitous convergent network. As the complexity and variability of the real world, there exists a large number of uncertain service context information, such as the imprecise information collected by sensors, data noise, and inaccurate location by different location technologies. Hence, it is crucial to recognize the correct service context environment based on unreliable context information. In recent years, context computing and context-awareness have become a major topic of research in an ubiquitous computing field. Ontology technology is often used to support context modeling and reasoning. And the probability theory, especially the Bayesian network, is adopted to deal with uncertainty. However, much of the existing relevant research work mainly concentrated on the modeling and reasoning of context information. But the way to construct and evolve the service context recognition model supporting uncertain reasoning effectively and systematically has not involved. In addition, most of the relative work is only the qualitative analysis, and lacks the quantitative performance analysis and experimental verification. What is more, the research on how to apply context-awareness technology to the telecommunication field and support the intelligence and individualization of the service is scarce. In this article, we combine context-awareness technology with telecommunication service network, and discuss the mechanism of service intelligence in ubiquitous convergent network environment. Then a construction method of the service context recognition model based on the Bayesian Network is put forward. The process of constructing the Bayesian network-based service context recognition model is mainly divided into the following steps: First, according to different service requirements and scenarios, the specific context problem domain should be specified; second, constructing the service context recognition model, which includes the determination of the topology structure and the node probability distribution of Bayesian network; third, when the preliminary Bayesian network-based service context recognition model is constructed, it could be initially applied to the practical system to do the clustering or causality analysis; and last, when the model cannot satisfy the need, the model update should be performed based on the feedback information of the evaluation step and the newly-generated plentiful sample data. This approach has been applied to intelligent access adaptation processing of call service in the office environment. The experimental results show that this approach can strengthen the intelligence of service and compensate for restrictions of certain reasoning.
Research works on cluster computing and storage area network
Authors: Masato Oguchi
Venue: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication (2009)

Cluster Computing has attracted attention since the middle of 1990's. It has an advantage of outstanding scalability and cost/performance ratio, compared with the traditional parallel computers. We have constructed a prototype of large-scale PC cluster, making use of commoditized technologies in those days. Cluster Computing has become a mainstream of high-performance computing shortly after, especially for data-intensive applications. Once the technique of constructing clusters has been established, the back-end of clusters, namely storage, has become a next essential research theme. When the number of nodes increases in clusters, the management cost of storage at each node becomes extremely high. Thus the storage area network (SAN) has been introduced in order to consolidate the back-end of clusters. Currently, Fibre Channel (FC) SAN is dominant for the consolidation of high-performance servers that have cluster configuration. While FC-SAN is still growing, IP-SAN based on commoditized IP technologies is expected to be significant. We have investigated various techniques in terms of Cluster Computing and SAN until now. Some of those research topics are introduced in this paper.
Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents
Authors: Elif Derya íbeyli
Venue: Expert Systems with Applications: An International Journal (2009)

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.
Non-temporal Mutliple Silhouettes in Hidden Markov Model for View Independent Posture Recognition
Authors: Yunli Lee, Keechul Jung
Venue: Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 01 (2009)

This paper introduces a non-temporal multiple silhouettes in Hidden Markov Model (HMM) for offering view independent human posture recognition. The multiple silhouettes are used to reduce the ambiguity problem of posture recognition. A simple feature extraction of the 2D shape contour based histogram is used for image encoding and K-Means algorithm is applied for clustering and code-wording of eight simple postures from multiple views. Therefore, 3D volume reconstruction is not required, in return helps to reduce the complexity of modeling and computational power of feature extraction. HMM is trained to obtain view independent recognition model using multiple silhouettes. A combination of non-temporal multiple silhouettes, code-wording and HMM methods in this proposed approach make it possible to recognize human posture in view independent. The experimental results demonstrate the effectiveness of non-temporal multiple silhouettes in HMM for recognizing posture.
Steganalysis of Multi Bit Plane Image Steganography
Authors: Johann Barbier, Kichenakoumar Mayoura
Venue: Proceedings of the 6th International Workshop on Digital Watermarking (2008)

In this paper, we present an efficient specific steganalysis scheme for multi bit plane image steganography. This algorithm proposed by Nguyen, Yoon and Lee at IWDW'06 is applied to uncompressed images and embeds data into several bit planes of the canonical gray coding. It is designed to be robust against RS steganalysis and pixel difference histogram analysis. We show how to adapt RS analysis into a local analysis to design an efficient detector against multi bit plane image steganography. This method takes advantages from both the counter-measures introduced by the authors and the power of RS analysis approach. Finally, we designed a specific classifier to detect images embedded with Nguyen's algorithm. This detector has high detection rates, even for low embedding rates.
Receptivity of India's small and medium-sized enterprises to information system adoption
Authors: Milind K. Sharma
Venue: Enterprise Information Systems (2009)

This study surveys the receptivity of India's small and medium-sized enterprises (SMEs) to the adoption of information systems (IS). Utilising a portion of Rogers' model of innovation diffusion as the framework, and treating IS as a form of new innovation, this study analyses factors affecting IS diffusion. A multiple regression analysis is carried out, with the five attributes of innovation highlighted by Rogers, to determine the attributes that affect the willingness to adopt. Out of the five factors affecting the adoption of IS by SMEs, only relative advantage, compatibility and trialability appear significant, with the overall regression explaining around 39% of willingness to adopt.
Thermography based breast cancer analysis using statistical features and fuzzy classification
Authors: Gerald Schaefer, Michal Závišek, Tomoharu Nakashima
Venue: Pattern Recognition (2009)

Medical thermography has proved to be useful in various medical applications including the detection of breast cancer where it is able to identify the local temperature increase caused by the high metabolic activity of cancer cells. It has been shown to be particularly well suited for picking up tumours in their early stages or tumours in dense tissue and outperforms other modalities such as mammography for these cases. In this paper we perform breast cancer analysis based on thermography, using a series of statistical features extracted from the thermograms quantifying the bilateral differences between left and right breast areas, coupled with a fuzzy rule-based classification system for diagnosis. Experimental results on a large dataset of nearly 150 cases confirm the efficacy of our approach that provides a classification accuracy of about 80%.
A service-oriented system for distributed data querying and integration on Grids
Authors: Carmela Comito, Anastasios Gounaris, Rizos Sakellariou, Domenico Talia
Venue: Future Generation Computer Systems (2009)

Data Grids rely on the coordinated sharing of and interaction across multiple autonomous database management systems. They provide transparent access to heterogeneous and autonomous data resources stored on Grid nodes. Data sharing tools for Grids must include both distributed query processing and data integration functionality. This paper presents the implementation of a data sharing system that (i) is tailored to data Grids, (ii) supports well established and widely spread relational DBMSs, and (iii) adopts a hybrid architecture by relying on a peer model for query reformulation to retrieve semantically equivalent expressions, and on a wrapper-mediator integration model for accessing and querying distributed data sources. The system builds upon the infrastructure provided by the OGSA-DQP distributed query processor and the XMAP query reformulation algorithm. The paper discusses the implementation methodology, and presents empirical evaluation results.
Predictive Control Strategy of Hydraulic Turbine Turning System Based on BGNN Neural Network
Authors: Yijian Liu, Yanjun Fang
Venue: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence (2008)

A model predictive control (MPC) strategy based on a novel Bayesian-Gaussian neural network (BGNN) model was proposed for the controller design of hydraulic turbine in this paper. The BGNN was used to learn the nonlinear dynamic model of controlled hydraulic turbine on-line as the predictive model for the design of MPC controller. Experiments show that the proposed nonlinear MPC strategy based on BGNN performs much better than the conventional PID controller.
A Multidisciplinary Framework For Resilence To Disasters And Disruptions
Authors: Scott Jackson
Venue: Journal of Integrated Design Process Science (2007)

System resilience is the ability of organizational, hardware and software systems to mitigate the severity and likelihood of failures or losses, to adapt to changing conditions, and to respond appropriately after the fact. Challenger, Columbia, Chernobyl and Bhopal are examples of such failures. System resilience goes beyond traditional disciplines, such as reliability and system safety to achieve its goal. System resilience requires that technical and managerial processes be executed as a unified whole. System resilience employs the systems approach at product and infrastructure levels. The infrastructure system includes such nodes as the developer, the customer, the user, the maintainer, and the operator. System resilience requires that systems engineering principles be practiced across organizational boundaries and to a greater level of detail than is common in today's world. System resilience incorporates the principles of organizational psychology to develop beneficial paradigms within all nodes of the infrastructure. The combination of capabilities, culture and infrastructure forms the basic framework of system resilience. A key aspect of catastrophes is emergence, that is, characteristics of a system that cannot be predicted from the characteristics of the components. Sometimes emergence results from the negative interaction among two or more elements of the system, which, when acting alone, perform benignly. While positive, or desirable, emergence is widely studied, negative, or undesirable, emergence is of interest in the study of resilience. Prediction of emergence and design of adaptive systems that will survive emergent events is a major challenge of resilience. Adaptability, agility and robustness are the characteristics of a system that allow it to survive a disruption. Although the principles of adaptability, agility and robustness have been formulated and shown to reflect the characteristics of systems that have survived disruption, the actual implementation of them lie largely in the heuristics of systems architecting.
Electric contacts analysis
Authors: Štepán Rusňák, Jana Jiřičková
Venue: Proceedings of the 5th conference on Applied electromagnetics, wireless and optical communications (2007)

The article describes problems of stationary contacts and description of the discharge inception processes, which are related to the function of stationary contact. These are e.g. problems so - called contacts sparking, it is study in light of interfering fields generation. The theory is in connection with questions of electromagnetic compatibility. The measurements on the heterogeneous structure of the surface layers in the electrical contact bring many interesting results.
An Efficient PTAS for Two-Strategy Anonymous Games
Authors: Constantinos Daskalakis
Venue: Proceedings of the 4th International Workshop on Internet and Network Economics (2008)

We present a novel polynomial time approximation scheme for two-strategy anonymous games, in which the players&rsquo; utility functions, although potentially different, do not differentiate among the identities of the other players. Our algorithm computes an &epsilon;-approximate Nash equilibrium of an n-player 2-strategy anonymous game in time $\text{poly}(n) \cdot (1/\epsilon)^{O(1/\epsilon^2)}$, which significantly improves upon the running time $n^{O(1/\epsilon^2)}$ required by the algorithm of Daskalakis Papadimitriou, 2007. The improved running time is based on a new structural understanding of approximate Nash equilibria: We show that, for any &epsilon;, there exists an &epsilon;-approximate Nash equilibrium in which either only O(1/&epsilon; 3) players randomize, or all players who randomize use the same mixed strategy. To show this result we employ tools from the literature on Stein&rsquo;s Method.
Harnessing the power of simulation in the project management/decision support aspects of the construction industry
Authors: Gunnar Lucko, Perakath C. Benjamin, Michael G. Madden
Venue: Proceedings of the 40th Conference on Winter Simulation (2008)

This paper reviews the history of construction simulation systems in light of their graphical representation of complex scenarios. The simulation of construction operations has been a growing field of research over the last several decades. Since the introduction of the first simulation system, which was based on the activity cycle diagram paradigm of modeling, numerous additional tools have been introduced, each building on and expanding the modeling and analytical capabilities of previous approaches. However, despite such rich body of knowledge, which by now is expanding into areas such as visualization, animation, and virtual reality applications for construction project management, the beneficial application of simulation in practice has been marginal. This paper describes both historical and practical reasons for this situation and presents an ontology-based approach that can harness existing information in construction project management, especially the scheduling function, and has the potential to significantly improve its operational planning and optimization.
Taming Modal Impredicativity: Superlazy Reduction
Authors: Ugo Dal Lago, Luca Roversi, Luca Vercelli
Venue: Proceedings of the 2009 International Symposium on Logical Foundations of Computer Science (2009)

Pure, or type-free, Linear Logic proof nets are Turing complete once cut-elimination is considered as computation. We introduce modal impredicativity as a new form of impredicativity causing cut-elimination to be problematic from a complexity point of view. Modal impredicativity occurs when, during reduction, the conclusion of a residual of a box b interacts with a node that belongs to the proof net inside another residual of b. Technically speaking, superlazy reduction is a new notion of reduction that allows to control modal impredicativity. More specifically, superlazy reduction replicates a box only when all its copies are opened. This makes the overall cost of reducing a proof net finite and predictable. Specifically, superlazy reduction applied to any pure proof nets takes primitive recursive time. Moreover, any primitive recursive function can be computed by a pure proof net via superlazy reduction.
Pivoted table index for querying product-property-value information
Authors: Hyunja Lee, Junho Shim
Venue: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication (2009)

The query for triple information on product -attribute (property)-value is one of the most frequent queries in e-commerce. Storing the triple, schema vertically is effective for avoidance of sparse data and schema evolution. However, conventional horizontal schema often shows better query performance when properties are queried as groups clustered by product. Therefore, we propose vertical schema as a primary table structure for the triple information in RDBMS and a pivoted table index created from the basic vertical table. This index is beneficial to performance of the frequent pattern query on the group properties associated with each product class.
Bit-Depth Scalable Video Coding Based on H.264/AVC
Authors: Sangseok Park, K.R. Rao
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

A bit-depth scalability is proposed in an adaptive way based on modified inter-layer predictions of the spatial scalability. A simple prediction for high dynamic range (HDR) sequences is implemented to reduce the redundancy of the residual signals between the base layer which contains low dynamic range (LDR) sequences and the enhancement layer which contains HDR sequences by using scaling and offset values.
Structural group auditing of a UMLS semantic type's extent
Authors: Yan Chen, Huanying (Helen) Gu, Yehoshua Perl, James Geller, Michael Halper
Venue: Journal of Biomedical Informatics (2009)

Each UMLS concept is assigned one or more of the semantic types (STs) from the Semantic Network. Due to the size and complexity of the UMLS, errors are unavoidable. We present two auditing methodologies for groups of semantically similar concepts. The straightforward procedure starts with the extent of an ST, which is the group of all concepts assigned this ST. We divide the extent into groups of concepts that have been assigned exactly the same set of STs. An algorithm finds subgroups of suspicious concepts. The human auditor is presented with these subgroups, which purportedly exhibit the same semantics, and thus she will notice different concepts with wrong or missing ST assignments. The dynamic procedure detects concepts which become suspicious in the course of the auditing process. Both procedures are applied to two semantic types. The results are compared with a comprehensive manual audit and show a very high error recall with a much higher precision.
Social Norm Emergence in Virtual Agent Societies
Authors: Bastin Tony Savarimuthu, Maryam Purvis, Martin Purvis, Stephen Cranefield
Venue: Declarative Agent Languages and Technologies VI: 6th International Workshop, DALT 2008, Estoril, Portugal, May 12, 2008, Revised Selected and Invited Papers (2008)

The advent of virtual environments such as SecondLife call for a distributed approach for norm emergence and spreading. In open virtual environments, monitoring various interacting agents (avatars), using a centralized authority might be computationally expensive. The number of possible states and actions of an agent could be huge. An approach for sustaining order and smoother functioning of these environments can be facilitated through norms. Agents can generate norms based on interactions. In particular, those social norms that incur certain cost to an individual agent but benefit the whole society are more interesting than those benefit both the agent and the society. The problem is that the selfish agents might not be willing to share the norm adherence cost. In this work, we experiment with notion proposed by Axelrod that social norms are best at preventing small defections where the cost of enforcement is low. We also study how common knowledge can be used to facilitate the overall benefit of the society. We believe our work can be used to facilitate norm emergence in virtual online societies.