ppxscal/embedding_network huggingface dataset:
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Authors: Daniel Pomerantz, Gregory Dudek
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We assume that for each context, a user has a different set of preference weights which are linked by a common, "generic context" set of weights. The approach uses Expectation Maximization (EM) to estimate both the generic context weights and the context specific weights. This improves upon many current recommender systems that do not incorporate context into the recommendations they provide. In this paper, we show that by considering contextual information, we can improve our recommendations, demonstrating that it is useful to consider context in giving ratings. Because the approach does not rely on connecting users via collaborative filtering, users are able to interpret contexts in different ways and invent their own contexts.
Effective multi-label active learning for text classification
Authors: Bishan Yang, Jian-Tao Sun, Tengjiao Wang, Zheng Chen
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. Specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with Support Vector Machines (SVM). An effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. Experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods.
Effective Detection of the Alzheimer Disease by Means of Coronal NMSE SVM Feature Classification
Authors: Javier Ramírez, Rosa Chaves, Juan M. Górriz, Ignacio Álvarez, Diego Salas-Gonzalez, Míriam López, Fermín Segovia
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. Conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. Currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a feature extraction process based on the normalized mean square error (NMSE) features of several coronal slices of interest (SOI) and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach yielding a 97.5% AD diagnosis accuracy.
Crowdsourcing and all-pay auctions
Authors: Dominic DiPalantino, Milan Vojnovic
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

In this paper we present and analyze a model in which users select among, and subsequently compete in, a collection of contests offering various rewards. The objective is to capture the essential features of a crowdsourcing system, an environment in which diverse tasks are presented to a large community. We aim to demonstrate the precise relationship between incentives and participation in such systems. We model contests as all-pay auctions with incomplete information; as a consequence of revenue equivalence, our model may also be interpreted more broadly as one in which users select among auctions of heterogeneous goods. We present two regimes in which we find an explicit correspondence in equilibrium between the offered rewards and the users' participation levels. The regimes respectively model situations in which different contests require similar or unrelated skills. Principally, we find that rewards yield logarithmically diminishing returns with respect to participation levels. We compare these results to empirical data from the crowdsourcing site Taskcn.com; we find that as we condition the data on more experienced users, the model more closely conforms to the empirical data.
Approximation of error concentration parameter for simultaneous circular functional model
Authors: A. G. Hussin, S. F. Hassan, Y. Z. Zubairi, A. Zaharim, K. Sopian
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

In this study, we propose a simple procedure for obtaining estimate of the concentration parameter of the von Mises distribution for the simultaneous circular functional relationship model. Currently, the concentration parameter for this model can only be estimate by assuming the ratio of the error concentration parameter λj is equal to one. This new proposed method can be applied for any value of λ based on the expansion of the asymptotic power series of the modified Bessel function. Finally, the simulation study has been carried out to verify the accuracy of the new proposed procedure.
Models Choice of Grid-Based Decision Support System
Authors: Zhiwu Wang, Yanhui Zhang, Haigang Song, Xueguang Chen
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

Grid-based Decision Support System created a new research area. Decision-making model offers assistant support for decision-makers in model service just like the idea of grid service. How to make a better choice among many model services is the new problem which decision-makers are facing. In this paper, through the definition of model service quality, we transfer the problem of model service choice into multi-attributive decision of model service quality evaluation. TOPSIS method is used to find the solution of this problem, and we also discuss the principle of three kinds of weight mode. At last, we prove the scientific rationality of subjective-objective weight mode by exact samples.
Electroluminescence, charge trapping and quenching in Eu implantes SiO2-Si structures
Authors: S. Tyagulskiy, I. Tyagulskyy, A. Nazarov, V. Lysenko, L. Rebohle, J. Lehmann, W. Skorupa
Venue: Microelectronic Engineering (2009)

This paper reports an analysis of the electroluminescence (EL) spectra changing, charge trapping and EL quenching during operation of the multicolor Eu implanted metal-oxide-silicon light-emitting devices (MOSLEDs). The nature of the changing of the light-emitting color in the MOSLED is discussed. It is shown that the EL life time of the Eu implanted MOSLED is considerably longer than that of high-efficiency MOSLEDs with Tb and Ge implanted oxide. It is demonstrated that a reduced EL intensity, enhanced negative charge trapping and a good EL stability are associated with enhanced clustering in the Eu implanted oxide during high-temperature furnace annealing. The comparison with the operation of the MOSLED fabricated by using the flash lamp annealing technique is performed.
Growth of peas pisum sativum L: in the presence of diesel and bacteria consortia in peat and sandy soil
Authors: Laila Dubova, Olga Muter, Alina Mihailova, Ina Alsina, Vilhelmine Steinberga, Baiba Limane, Dzidra Zarina
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

Remediation of contaminated soils, using plant-based systems, is known as a very perspective and rapidly developing area of biotechnology. In this respect toxicity of diesel for plants is of great importance. In this work, the results of the greenhouse experiments with soils artificially contaminated by diesel are presented. The seeds of peas were sown into peat and sandy soil mended with 1% and 3% of diesel, as well as inoculum of bacteria consortia, which was previously isolated from oil-contaminated sites. After 85 days, when peas were entering the bloom stage in the control samples, plant biomass was harvested. The presence of 1% diesel in sandy soil totally inhibited the growth of peas, although in peat soil an inhibition effect was about 50 %. Harvested biomass was also tested for moist and dry weight, height, content of chlorophyll a and b. Fermentative activity of soils, i.e. FDA (fluoresceine diacetate hydrolysis), dehydrogenase, urease, as well as respiration were measured. The total microbial number in rhizosphere was 10-100 times higher that in average soil sample. The number of colony forming units on the selective medium with diesel was higher in the samples inoculated with bacteria consortia. The results provided additional information on plant response to diesel contamination in the context of toxicity study and perspectives for phytoremediation.
Efficient Provable Secure ID-Based Directed Signature Scheme without Random Oracle
Authors: Jianhong Zhang, Yixian Yang, Xinxin Niu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

As a special signature, a directed signature is a type of signature with verification ability which is restricted. In a directed signature scheme, a designated verifier can exclusively verify the validity of a signature. If necessary, the designated verifier or the signer can prove the correction of a signature to a third party. Directed signature schemes are suitable for applications such as bill of tax and bill of health. In this paper, an ID-based directed signature scheme without random oracle is proposed by combining ID-based cryptology with Waters signature. We also give the syntax and security notion of ID-based directed signature without random oracle: unforgeability and invisiblity. Finally, we show that the proposed scheme is unforgeable under the computational Diffie-Hellman assumption, and invisible under the Decisional Bilinear Diffie-Hellman assumption.
ERS: Evaluating Reputations of Scientific Journals
Authors: Émilie Samuel, Colin de la Higuera
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Current methods for evaluating research are based on counting the number of citations received for publications. Thus, the more an article is cited and the more its impact is considered as important. In this article, we propose a new method for assessing the reputation of scientific journals, based on a Web application in which are gathered the votes of expert researchers. The voting results indicate degrees of preference for one journal over another. Our system uses, in addition, the publications of an expert in order to quantify his expertise in specific fields. These values are coupled with those of votes to determine the relevance, according to the field, of each journal in each topic. An iterative process of transferring values given to journals by experts to values of the experts themselves given their publications has been implemented.
Disambiguating between generic and referential "you" in dialog
Authors: Surabhi Gupta, Matthew Purver, Dan Jurafsky
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

We describe an algorithm for a novel task: disambiguating the pronoun you in conversation. You can be generic or referential; finding referential you is important for tasks such as addressee identification or extracting 'owners' of action items. Our classifier achieves 84% accuracy in two-person conversations; an initial study shows promising performance even on more complex multi-party meetings.
Parallel algorithms development for programmable devices with application from cryptography
Authors: Issam W. Damaj
Venue: International Journal of Parallel Programming (2007)

Reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs), have been witnessing a considerable increase in density. State-of-the-art FPGAs are complex hybrid devices that contain up to several millions of gates. Recently, research effort has been going into higher-level parallelization and hardware synthesis methodologies that can exploit such a programmane technology. In this paper, we explore the effectiveness of one such formal methodology in the design of parallel versions of the Serpent cryptographic algorithm. The suggested methodology adopts a functional programming notation for specifying algorithms and for reasoning about them. The specifications are realized through the use of a combination of function decomposition strategies, data refinement techniques, and off-the-shelf refinements based upon higher-order functions. The refinements are inspired by the operators of Communicating Sequential Processes and map easily to programs in Handel-C (a hardware description language). In the presented research, we obtain several parallel Serpent implementations with different performance characteristics. The developed designs are tested under Celoxica's RC-1000 reconfigurable computer with its two million gates Virtex-E FPGA. Performance analysis and evaluation of these implementations are included.
Adaptronics and Smart Structures: Basics, Materials, Design, and Applications, 2nd edition
Authors: H. Janocha, H. Janocha
Venue:  (2007)

Adaptronic structures and systems can adapt automatically to variable operating and environmental conditions through the use of feedback control. But in contrast to conventional control loops, in which each function is fulfilled by a separate component, adaptronics is characterized by multifunctional components that are preferably integrated into the structure or the system. The goal is a lightweight and simple construction for conserving material and energy resources. The authors of this book have taken on the task of displaying the current state of the art in this highly modern and strongly interdisciplinary field. Selected application examples are also presented and current development trends demonstrated.
Statistical Analysis of Second Order Differential Power Analysis
Authors: Emmanuel Prouff, Matthieu Rivain, Régis Bévan
Venue: IEEE Transactions on Computers (2009)

Second order Differential Power Analysis (2O-DPA) is a powerful side-channel attack that allows an attacker to bypass the widely used masking countermeasure. To thwart 2O-DPA, higher order masking may be employed but it implies a nonnegligible overhead. In this context, there is a need to know how efficient a 2O-DPA can be, in order to evaluate the resistance of an implementation that uses first order masking and, possibly, some hardware countermeasures. Different methods of mounting a practical 2O-DPA attack have been proposed in the literature. However, it is not yet clear which of these methods is the most efficient. In this paper, we give a formal description of the higher order DPA that are mounted against software implementations. We then introduce a framework in which the attack efficiencies may be compared. The attacks we focus on involve the combining of several leakage signals and the computation of correlation coefficients to discriminate the wrong key hypotheses. In the second part of this paper, we pay particular attention to 2O-DPA that involves the product combining or the absolute difference combining. We study them under the assumption that the device leaks the Hamming weight of the processed data together with an independent Gaussian noise. After showing a way to improve the product combining, we argue that in this model, the product combining is more efficient not only than absolute difference combining, but also than all the other combining techniques proposed in the literature.
Predicting tourism loyalty using an integrated Bayesian network mechanism
Authors: Chi-I Hsu, Meng-Long Shih, Biing-Wen Huang, Bing-Yi Lin, Chun-Nan Lin
Venue: Expert Systems with Applications: An International Journal (2009)

For effective Bayesian networks (BN) prediction with prior knowledge, this study proposes an integrated BN mechanism that adopts linear structural relation model (LISREL) to examine the belief or causal relationships which are subsequently used as the BN network structure for predicting tourism loyalty. Four hundred and fifty-two valid samples were collected from tourists with the tour experience of the Toyugi hot spring resort, Taiwan. The proposed mechanism is compared with back-propagation neural networks (BPN) or classification and regression trees (CART) for 10-fold cross-validation. The results indicate that our approach is able to produce effective prediction outcomes.
Principles of E-Learning Systems Engineering
Authors: Lester Gilbert, Veronica Gale
Venue: Chandos Series For Information Professionals (2007)

Summary: The book integrates the principles of software engineering with the principles of educational theory, and applies them to the problems of e-learning development, thus establishing the discipline of E-learning systems engineering. For the first time, these principles are collected and organised into the coherent framework that this book provides. Both newcomers to and established practitioners in the field are provided with integrated and grounded advice on theory and practice. The book presents strong practical and theoretical frameworks for the design and development of technology-based materials and environments which have teaching, training or educational value. It brings together a complete range of the specific theories and detailed techniques involved in the design, development and delivery of materials such as business presentations, web-based presentations, training courses and academic lessons. Although the methods and theories discussed are generally appropriate to all forms and levels of learning and teaching, the book illustrates their use in and focuses its approach upon e-learning with adults. Key Features: 1.Integrates the principles of software engineering with the principles of educational theory 2. Provides a coherent process for developing e-learning activities 3.Provides a coherent framework for the content and structure of e-learning activities 4.Overviews current research in e-learning technologies The Authors: Lester has developed and applied teaching and learning materials and environments in the higher education sector, having taught courses in cognitive psychology, research methods and computer systems analysis and design. As one of the senior members of the Learning Technologies Research Group at the University of Southampton, he is currently working on a number of funded research projects in the area of e-learning. Veronica works as a freelance training consultant, developing and applying instructional systems design, mainly in the financial services sector, where she has been engaged by many banks and insurers. Readership: Developers of e-learning who are new to or inexperienced in the use of learning technologies. Students enrolled on any college or university course concerned with the use of learning technologies.Experienced instructional developers seeking a reference source for the major techniques of e-learning systems development. Contents: Introduction General systems theory Project development framework E-learning systems structure and content Feasibility Front-end analysis Project plan Initial design Detailed design: e-learning transactions Detailed design: lesson plans Detailed design: remaining steps Production Pilot, distribution, use and evaluation Quality management Project management References Index
Evaluation of Flow-Aware Networking (FAN) architectures under GridFTP traffic
Authors: César Cárdenas, Maurice Gagnaire
Venue: Future Generation Computer Systems (2009)

QoS in grid networks is a very important issue. In this paper, we compare two scheduling algorithms of the Flow Aware Networking (FAN) architecture under Grid traffic. The metrics used to compare both serve to allocate the bandwidth. Additionally, we provide the comparison of one of the FAN architectures against DiffServ and under Grid traffic. Metrics studied here are average goodput and average delay. Finally, we provide a survey of the most important IP-based QoS architectures for Grid networks. We assume that Grid traffic is only composed by GridFTP sessions that arrive following a stationary Poisson process. We conclude that FAN based on the Priority Fair Queuing (PFQ) scheduling algorithm gets the better performance and that FAN architectures perform better than Diffserv.
Mutual information based non-rigid image registration using adaptive grid generation: gpu implementation and application to breast mri
Authors: Hua-Mei Chen
Venue:  (2008)

In this dissertation a new approach for non-rigid image registration using mutual information is introduced. A fast method for non-rigid registration is developed by adjusting divergence and curl of an intermediate vector field from which the deformation field is computed using finite difference method. The similarity measure mutual information is employed in the gradient-based cost minimization (or mutual information maximization) of the registration. The huge amount of data associated with MRI is handled by fully automated algorithm optimized with a multi-resolution topology preserving regridding scheme. The adaptive grid system naturally distributes more grids to deprived areas. The positive monitor function disallows grid folding and provides a mean to control the ratio of the areas between the original and transformed domain. The flexibility of the adaptive grid allocation could dramatically reduce processing time with quality preserved. Mutual information facilitates robust registration between different image modalities. Different types of joint histogram estimation are compared and integrated with the system. The whole system is also implemented on GPU which allows efficient parallel computation of vast amount of 3D data in SIMT manner during different procedures. The GPU implementation offers up to 221 times speed up in the gradient normalization routine and around 40 times speed up in the overall calculation. This scheme is applied on 3D dynamic contrast-enhanced breast MRI, which requires the registration algorithm to be non-rigid, contrast-enhanced features preserving and within clinical visit time limit. Experiments show promising results and great potential for future extension.