ppxscal/embedding_network huggingface dataset:
A Strategic Model for Measuring Agility with Fuzzy Logic
Authors: Gholamreza Khoshsima
Venue: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence (2008)

Agility is the set of capabilities and competencies that an organization needs to thrive and prosper in a continuously changing and unpredictable business environment. This paper, based upon our studies in Iran's Manufacturing Industry, presents a model for organizational agility and then suggests a knowledge-based methodology for measuring agility. For being agile, every organization needs a number of capabilities (e.g. responsiveness) and competencies (e.g., knowledge management (KM) and flexibility), these capabilities and competencies are broken into some dimensions and finally the resulting dimensions to some indices. For measuring agility we use "IF {Fuzzy antecedents} THEN {Fuzzy consequent}" rules. This system uses expert knowledge and includes implementation of fuzzy logic methods and terminology.
An operating system kernel mechanism for the poly-processor system PPS-R
Authors: Makoto Amamiya, Naohisa Takahashi, Yutaka Ogawa, Kenji Koyama
Venue: Proceedings of the May 19-22, 1980, national computer conference (1980)

Conventional functionally centralized systems have several problems such as cost-performance ratio, reliability, availability, system software productivity, and expandability, for changing the system size and service grade in time sharing systems (TSS).
Telling my baby's stories: family communication and narrative practices of young mothers living abroad
Authors: Nazean Jomhari, Victor M. Gonzalez, Sri H. Kurniawan
Venue: Proceedings of the 20th Australasian Conference on Computer-Human Interaction: Designing for Habitus and Habitat (2008)

This study investigates the motivations of using rich-media forms of computer-mediated-communication (CMC) tools among young mothers living abroad to maintain ties with their geographically-separated families and friends. Our research involves sixteen Malaysian young mothers living in the United Kingdom, most of them dealing with the challenges of juggling work or studies and taking care of their babies. We investigate the digital records and family communication practices as well as motivations that underlie the use of CMC to create baby stories and narratives. Our results highlight the value of technologies to share and have access to the quotidian experiences at each side of the globe and to the baby's life. We discuss the implications of these results for the designing of future rich-media types of CMCs to capture narratives.
Fuzzy compact operators and topological degree theory
Authors: I. Sadeqi, M. Salehi
Venue: Fuzzy Sets and Systems (2009)

In this paper we first prove that the definition of fuzzy norm continuity and fuzzy boundedness of linear operators are equivalent. We show that the continuity assumption in the definition of compact linear operator is not necessary. We also point out that there is a gap in the proof of a theorem of Xiao and Zhu and we give a corrected version of the theorem such that all results based on the revised theorem remain true. Furthermore, we define a fuzzy product norm on the Cartesian product of two fuzzy normed spaces and prove a multiplicative property for the Leray-Schauder topological degree.
On the Suboptimality of Linear Lossy Codes
Authors: Shigeaki Kuzuoka
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

This letter reveals that linear lossy codes cannot attain the rate-distortion function in general, even if the source is binary i.i.d. and the distortion is measured by the Hamming distortion measure.
Expansive competitive learning for kernel vector quantization
Authors: Davide Bacciu, Antonina Starita
Venue: Pattern Recognition Letters (2009)

In this paper we present a necessary and sufficient condition for global optimality of unsupervised Learning Vector Quantization (LVQ) in kernel space. In particular, we generalize the results presented for expansive and competitive learning for vector quantization in Euclidean space, to the general case of a kernel-based distance metric. Based on this result, we present a novel kernel LVQ algorithm with an update rule consisting of two terms: the former regulates the force of attraction between the synaptic weight vectors and the inputs; the latter, regulates the repulsion between the weights and the center of gravity of the dataset. We show how this algorithm pursues global optimality of the quantization error by means of the repulsion mechanism. Simulation results are provided to show the performance of the model on common image quantization tasks: in particular, the algorithm is shown to have a superior performance with respect to recently published quantization models such as Enhanced LBG [Patane, G., Russo, M., 2001. The enhanced LBG algorithm. Neural Networks 14 (9), 1219-1237] and Adaptive Incremental LBG [Shen, F., Hasegawa, O., 2006. An adaptive incremental LBG for vector quantization. Neural Networks 19 (5), 694-704].
Influence on transmission characteristics of power line communication when using surge protective devices
Authors: Hitoshi Kijima, Kenji Takato, Hiroshi Iwao
Venue: Proceedings of the 5th conference on Applied electromagnetics, wireless and optical communications (2007)

Power line communication (PLC) has been available since October 2006 in Japan. Because no investigation has been done on transmission characteristics of PLC when using surge protective devices (SPDs), we examined PLC transmission performances using SPDs such as varistors and gas discharge tubes (GDTs) or a combination of the two. From our experiments we found that the transmission rate degraded when using varistors because of their low impedance values in the PLC frequency range. We also found that when installing an SPD on a PLC circuit, using only a GDT or connecting a GDT with a varistor in series is necessary.
Analyzing the evolutionary stability of the vendor-managed inventory supply chains
Authors: Haisheng Yu, Amy Z. Zeng, Lindu Zhao
Venue: Computers and Industrial Engineering (2009)

One of the widely used strategies for achieving system integration in the arena of supply chain management is the vendor-managed inventory (VMI) approach. Although there exists a large amount of literature that examines various aspects of VMI, little looks at how this model evolves as the implementation progresses and matures. Therefore, this paper shows how to analyze the intrinsic evolutionary mechanism of the VMI supply chains by applying the evolutionary game theories. It is found that during the early stage of the VMI implementation, the upstream supplier will have some profit loss; however, as the transaction quantity increases in the long run, which will eventually benefit the entire chain, it is necessary for the downstream buyer to share profit with the upstream supplier to cover the supplier's initial loss in order to exploit and sustain the benefits of the VMI. Additionally, the impact factors for VMI to become an evolutionary stable strategy are examined. All the results identify the conditions under which the VMI model is favorable over the traditional chain structure and shed lights on when and why collaboration is critical for a successful, long-term implementation of VMI.
A survey on common practice in designing audio in the user interface
Authors: Christopher Frauenberger, Tony Stockman, Marie-Luce Bourguet
Venue: Proceedings of the 21st British CHI Group Annual Conference on HCI 2007: People and Computers XXI: HCI...but not as we know it - Volume 1 (2007)

The current practice of designing the auditory mode in the user interface is poorly understood. In this survey, we aim at revealing the common understanding of the role of audio in human-computer interaction and how designers approach design tasks involving audio. We investigate which guidelines and principles participants use in their designs and which guidance is needed to improve the quality of auditory design. The responses are analysed and interpreted by quantitative and qualitative methods. The 86 participants enabled us to draw a relatively accurate picture of how the field is perceived and helped to identify problems in the design of efficient audio in the user interface. The results of the survey are subsequently developed into requirements for a methodological design framework, with the aim to provide easily accessible guidance for designers to integrate audio in the user interface.
A hybrid method based on artificial immune system and k-NN algorithm for better prediction of protein cellular localization sites
Authors: Ibrahim Turkoglu, E. Didem Kaymaz
Venue: Applied Soft Computing (2009)

The use of machine learning tools in biological data analysis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system based k-nearest neighbors algorithm with and without cross-validation in a class of imbalanced problems from bioinformatics field. Furthermore, we used an unsupervised artificial immune system algorithm for reduction training data dimension and k-nearest neighbors algorithm for classification purpose. The conducted experiments showed the effectiveness of the proposed schema. By selecting the E. coli database, we could compare our classification accuracy with other methods which were presented in the literature. The proposed hybrid system produced much more accurate results than the Horton and Nakai's proposal [P. Horton, K. Nakai, A probabilistic classification system for predicting the cellular localization sites of proteins, in: Proceedings of the 4th International Conference on Intelligent Systems for Molecular Biology, AAAI Press, St. Louis, 1996, pp. 109-115; P. Horton, K. Nakai, Better prediction of protein cellular localization sites with the k-nearest neighbors classifier, in: Proceedings of Intelligent Systems in Molecular Biology, Halkidiki, Greece, 1997, pp. 368-383]. Besides the accuracy improvement, one of the important aspects of the proposed methodology is the complexity. As the artificial immune system provided data reduction, the training complexity of the proposed system is considerably low against the k-nearest neighbors classifier.
An efficient parameters estimation method for automatic patch-based texture synthesis
Authors: Jakrapong Narkdej, Pizzanu Kanongchaiyos
Venue: Proceedings of the 6th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa (2009)

Patch-based texture synthesis is a method for synthesizing bigger texture from smaller sample patch by patch. This method requires two user defined parameters including patch size and boundary zone which cannot directly evaluated. To obtain optimal parameters, we can analyze texture using Markov Random Field, but it is too expensive to be used with large textures. This paper introduces more efficient method to find optimal parameters. Firstly, we use graph-based image segmentation to extract segments from the sample. Secondly, we choose main feature to be preserved in result. Finally, we calculate optimal parameters based on size and repetition of the segments. Our technique reduces time used to determine the parameters compared to former method and can be used with wide range of textures.
Agent-Based Infrastructure for Data and Transaction Management in Mobile Heterogeneous Environment
Authors: Machigar Ongtang, Ali R. Hurson, Yu Jiao
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 03 (2009)

Mobile technology and advances in databases have allowed users to access and manipulate data across multiple heterogeneous and autonomous data sources via wireless connection, namely mobile multidatabase system. Transaction management in this environment is non-trivial due to heterogeneity and autonomy of the participating data sources, frequent disconnections, and technological constraints of the access devices. Agent-based Transaction Management scheme for Mobile Multidatabase (AT3M) systems uses autonomous agents to enable a fully distributed transaction management, accommodates users' mobility, allows parallel execution of the global subtransactions, and responds to some of the limitations of the technology. The framework is augmented to support user connectivity and mobility in internetworking environment and to provide QoS-based services. The performance of the proposed AT3M is simulated and evaluated. The simulation study shows that the proposed scheme outperforms the V-locking and the Pre-Serialization schemes, as advanced in the literature.
All-Optical Computing and All-Optical Networks are Dead
Authors: Charles Beeler, Craig Partridge
Venue: Queue (2009)

Anxiously awaiting the arrival of all-optical computing? Don't hold your breath.
A fast scalable automaton-matching accelerator for embedded content processors
Authors: Kuo-Kun Tseng, Yuan-Cheng Lai, Ying-Dar Lin, Tsern-Huei Lee
Venue: ACM Transactions on Embedded Computing Systems (TECS) (2009)

Home and office network gateways often employ a cost-effective embedded network processor to handle their network services. Such network gateways have received strong demand for applications dealing with intrusion detection, keyword blocking, antivirus and antispam. Accordingly, we were motivated to propose an appropriate fast scalable automaton-matching (FSAM) hardware to accelerate the embedded network processors. Although automaton matching algorithms are robust with deterministic matching time, there is still plenty of room for improving their average-case performance. FSAM employs novel prehash and root-index techniques to accelerate the matching for the nonroot states and the root state, respectively, in automation based hardware. The prehash approach uses some hashing functions to pretest the input substring for the nonroot states while the root-index approach handles multiple bytes in one single matching for the root state. Also, FSAM is applied in a prevalent automaton algorithm, Aho-Corasick (AC), which is often used in many content-filtering applications. When implemented in FPGA, FSAM can perform at the rate of 11.1Gbps with the pattern set of 32,634 bytes, demonstrating that our proposed approach can use a small logic circuit to achieve a competitive performance, although a larger memory is used. Furthermore, the amount of patterns in FSAM is not limited by the amount of internal circuits and memories. If the high-speed external memories are employed, FSAM can support up to 21,302 patterns while maintaining similar high performance.
VisualMOQL: The DISIMA Visual Query Language
Authors: Vincent Oria, M. Tamer Ozsu, Bing Xu, L. Irene Cheng, Paul J. Iglinski
Venue: Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2 (1999)

Efforts have been made to design query languages with proper semantics to facilitate query optimization and processing in existing database systems. The majority of multimedia database systems are built on top of object or object-relational database systems with the underlying query facilities inherited. The DISIMA system is being built on top of a commercial OODBMS and we have chosen to extend the standard object-oriented query language OQL with some multimedia functionalities. The resulting language is called MOQL. This paper presents VisualMOQL, a visual query language implementing the image component of MOQL.
The Future of Reputation: Gossip, Rumor, and Privacy on the Internet
Authors: Daniel J. Solove
Venue:  (2008)

Teeming with chatrooms, online discussion groups, and blogs, the Internet offers previously unimagined opportunities for personal expression and communication. But theres a dark side to the story. A trail of information fragments about us is forever preserved on the Internet, instantly available in a Google search. A permanent chronicle of our private livesoften of dubious reliability and sometimes totally falsewill follow us wherever we go, accessible to friends, strangers, dates, employers, neighbors, relatives, and anyone else who cares to look. This engrossing book, brimming with amazing examples of gossip, slander, and rumor on the Internet, explores the profound implications of the online collision between free speech and privacy.Daniel Solove, an authority on information privacy law, offers a fascinating account of how the Internet is transforming gossip, the way we shame others, and our ability to protect our own reputations. Focusing on blogs, Internet communities, cybermobs, and other current trends, he shows that, ironically, the unconstrained flow of information on the Internet may impede opportunities for self-development and freedom. Long-standing notions of privacy need review, the author contends: unless we establish a balancebetweenprivacy and free speech, we may discover that the freedom of the Internet makes us less free.
Dynamic inference control in privacy preference enforcement
Authors: Xiangdong An, Dawn Jutla, Nick Cercone
Venue: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services (2006)

In pervasive (ubiquitous) environments, context-aware agents are used to obtain, understand, and share local contexts with each other so that the environments could be integrated seamlessly. Context sharing among agents should be made privacy-conscious. Privacy preferences are generally specified to regulate the exchange of the contexts, where who have rights under what conditions to have what contexts are designated. However, released contexts could be used to infer those unreleased. In particular, different contexts released could endanger the security of different contexts unreleased. The existing privacy preference specification platforms do not have a mechanism to prevent inference. To date, there have been very few inference control mechanisms specifically tailored to context management in pervasive (ubiquitous) environments. A Bayesian network based mechanism has been proposed to prevent privacy-sensitive contexts from being inferred from those to be released. Nevertheless, contexts in pervasive (ubiquitous) environments could change from time to time and are history dependent. In this paper, we propose to use dynamic Bayesian networks to track the most updated beliefs of the adversaries about the dynamic domains in order to evaluate which contexts in the domains could be released safely in various situations.
On Small Sample Prediction of Financial Crisis
Authors: Sankha Pallab Saha
Venue: Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition (2009)

Prediction of financial crisis is a challenging problem in financial research. On the basis of the information provided by financial statements, companies are usually classified into two groups, e.g., the groups of solvent and insolvent companies. Linear discriminant analysis (LDA), logistic regression and artificial neural network (ANN) are the most common statistical tools used for this classification. LDA and logistic regression separate the two groups using a hyperplane, and they provide good lower dimensional view of class separability. However, these methods are not robust against outliers and they also get affected by deviations from underlying model assumptions. Moreover, if the number of observations is small compared to the dimension of the measurement vector, these classical methods may lead to poor classification. On the contrary, ANN is more flexible and does not make any assumption about the population structure. But, it separates the competing populations using a complex surface. So, we sacrifice the lower dimensional view and the interpretability of the result, which are often the major concern in financial analysis. In this article, we propose to use a semiparametric method which preserves the interpretability and the lower dimensional view of class separability, but at the same time it is robust against outliers and capable to work well in high dimension and low sample size set up. We use two real life financial data sets to show the utility of this semiparametric method.
A comparative study on diabetes disease diagnosis using neural networks
Authors: Hasan Temurtas, Nejat Yumusak, Feyzullah Temurtas
Venue: Expert Systems with Applications: An International Journal (2009)

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. In this study, a comparative pima-diabetes disease diagnosis was realized. For this purpose, a multilayer neural network structure which was trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neural network structure were used. The results of the study were compared with the results of the pervious studies reported focusing on diabetes disease diagnosis and using the same UCI machine learning database.
Constant-partially accelerated life tests for Burr type-XII distribution with progressive type-II censoring
Authors: Alaa H. Abdel-Hamid
Venue: Computational Statistics Data Analysis (2009)

Based on progressively type-II censored samples, constant-partially accelerated life tests (PALTs) when the lifetime of items under use condition follow the two-parameter Burr type-XII (Burr(c,k)) distribution are considered. The likelihood equations of the involved parameters are derived and then reduced to a single nonlinear equation to be solved numerically to obtain the maximum likelihood estimates (MLEs) of the parameters. The observed Fisher information matrix, as well as the asymptotic variance-covariance matrix of the MLEs are derived. Approximate confidence intervals (CIs) for the parameters, based on normal approximation to the asymptotic distribution of MLEs, studentized-t and percentile bootstrap CIs are derived. A Monte Carlo simulation study is carried out to investigate the precision of the MLEs and to compare the performance of the CIs considered. Finally, two examples presented to illustrate our results are followed by conclusions.
Conoscopic holograms analysis using variations of the Hough transform
Authors: Julius Butime, Luis Galo Corzo, Luis Fontan, Íñigo Gutiérrez García, Ainhoa Galarza, Iñaki Sancho
Venue: International Journal of Imaging Systems and Technology (2009)

The analysis of holograms obtained using a Conoscopic setup is a very complex subject, given their nature. Holograms contain the 3D depth information about an object whose surface is to be measured and later reconstructed. The recovery of depth information has so far been carried out using mathematical transforms in combination with linear regression techniques. Here the Hough transform, a useful Computer Vision technique for detecting features in images is adapted to the analysis of holograms in order to establish distance relationships for a given object. The captured images of holograms are pre-processed and subsequently analyzed for characteristic patterns that are later used in finding the distance to an object. &copy; 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 10&ndash;13, 2009