Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Graphical User Interfaces for Custom-Tailored Applications

ABSTRACT OF THE THESISAdaptive Graphical User Interfaces for Custom-Tailored ApplicationsByAlberto O. Pareja-LecarosMaster of Science in Information and Computer Science University of California, Irvine, 2016Professor Richard N. Taylor, ChairBuilding graphical user interfaces has been a continuing challenge for software developers and designers ever since graphics were introduced in computing. These days, in the world of ubiquitous and mobile computing, building user interfaces to work on every device and platform is tedious at best and requires duplicating work. To tackle this challenge, we introduce an approach which allows clients to modify a software application’s user interface without the application knowing about any of those clients beforehand. Our technique employs the use of mobile code between clients and the application in order for those clients to adapt the application’s user interface to best fit their needs. We use the ComputAtional State Transfer architectural style in order to simplify the design of the communication layer between clients and the application and to ensure secure communication between the entities.We find that building such an application can be practical and that clients are able to introduce code to the application such that the graphical user interface is adapted to those clients. To demonstrate, we created a calculator application whose graphical user interface and related functionality is modified and then consumed by three different clients. The first client uses the application as is, the second client introduces additional functionality to the application, and the third client introduces a change to the presentation of user interface components in the application in order for it to work better on its devices. All three clients are accommodated without the application having any knowledge about what modifications the clients specifically make.
Survey Of Privacy-Preserving Audio Representations With Speaker Diarization

Investigation of the audio features for speaker diarization and verification with privacy in multiple conversations by considering low linguistic information becomes major important issues in Audio Representations. The Major aim of the speaker verification is the process of finding speaker change points in an audio stream. The subsequent aim at group together speech segments on the foundation of speaker characteristics. For this speaker diarization identifying the important features with privacy Linear Prediction (LP) residual is the one of the main model to represent the important audio features with efficient bandwidth calculation and subsignal features also. Previous approaches to privacy sensitive features have paid attention to moreover reinterpreting easy, frame-level heuristics for estimate talking activity in the conversation only with either log files . In this study focus on finding the audio features low linguistic information using deep neural architecture for deriving privacy-sensitive audio features. To admiration this concept of privacy, features might be stored beginning which neither a comprehensible verbal communication nor the lexical contented can be reconstructed. Using this move toward to extract privacy- sensitive features and then to apply diarization. Deep neural network (DNN) with a blockage structural design support audio features with MFCC using the diarization. Finally study the previous methods and deep forward neural network shows better audio feature identification than previous linguistic feature based information.
Skin Condition Estimation Using Mobile Handheld Camera

The fairly recent standard of equipping mobile devices with advanced imaging sensors has opened the possibility of conveniently diagnosing skin conditions, anywhere, anytime. For this application, we attempted to estimate skin conditions from a skin image taken by a mobile handheld camera. To estimate the skin conditions, we specifically identified three skin features (pigmentation, pores, and roughness) that can be measured quantitatively from a skin image. The experimental data indicate that the existing thresholding methods are inappropriate for extracting the pigmentation and pore skin features. Thus, we propose a new line‐fitting based thresholding method for skin feature detection. We thoroughly evaluated our proposed skin condition estimation method using our skin image database. The experimental results show that our proposed thresholding method can better determine the threshold leading to the most visually plausible detection, when compared to existing methods. We also confirmed that skin conditions can be feasibly estimated using a common mobile handheld camera (for example, a smartphone).
Performance Evaluation of Modern Network-on-Chip Router Architectures

On chip interconnection networks simplify the challenges of integrating large number of processing elements. Routers are backbone of networks. Buffers and crossbar in router consumes significant area and power of network. They have huge impact on overall performance and cost of network. Dual Xbar router architecture combines buffered and bufferless feature to reduce buffer read/write energy with dual crossbars. While Switch folding technique introduced to reduce wire density and decrease muxes in crossbar by increasing resource utilization. In this paper, we propose Folded Dual Xbar architecture by combining the Dual Xbar and Folding technique in order to get advantages of both architectures. Performance of architectures is implemented and evaluated using OMNET++ platform by applying multiple traffic patterns under different load conditions. We further calculated buffered and bufferless events to estimate the reduction in buffer read/write energy. Simulation results shows that there is slight increase in throughput and reduction in buffer read/write energy by average 46% at high loads in proposed 2-Folded Dual Xbar as compared to conventional architecture. Proposed 3-Folded Dual Xbar results at least 16.6 % increase in throughput as compared to conventional architecture with 43-45% reduced buffer read/write energy but slight increase in crossbar. Throughput of 3-Folded Dual Xbar decreased only by 5-7% as compared to Dual Xbar with distributed wire density advantage.
A Time Domain Analysis Method for RF Noise

A time domain analysis method is developed for the calculation of the longitudinal oscillations caused by the RF noise in the storage ring. This method is based on the impulse response model, and it could calculates the change of transient field caused by beam oscillation and RF noise turn by turn. By means of discrete spectrum analysis, the spectrum of the beam is obtained. The synchronous oscillation of the excited by high RF source with a phase modulation is predicted in simulation program, and the corresponding experimental measurements are carried out on HLS II. The fitting results are in agreed with the experimental measurements. INTRODUCTION Phase and amplitude noises in the RF system excite the beam longitudinal oscillations, it produce the longitudinal emittance growth and debase the quality of the beam [1, 2]. The effects of RF noise has always been concerned by accelerator laboratories all over the world. Hitherto, many scholars have put forward a few calculation methods [3, 4], some of these method is quite effective, such as Pedersen models and iterative equations and so on. The field caused by the beam is changed due to the longitudinal oscillations of beam. Likewise, the change of field affects the beam movement turn by turn, so the relationship between them is really complicated. Most of the conventional RF noise analyse method are based on the longitudinal motion equation
Hierarchical Structure Based Hybrid Approach to Sentiment Analysis of Chinese Micro Blog and Its Feature Extraction

With the development of Web 2.0,micro blog has drawn substantial attention from both academia and industry communities.This paper utilizes micro blog API from Sina and carries out sentiment analysis on Chinese micro blog.We compare performances of three method,based on the emoticon,the sentiment lexicon and the hybrid approach over hierarchical structure using SVM,respectively.Through the experiments,we find that SVM based hybrid approach achieves the best performance.Furthermore,we analyze the contribution of various features in this model,including target-independent features and target-dependent features.Experimental results show that SVM based method can gain an accuracy of 66.467% with target-independent features,and an improved accuracy of 67.283% with the addition of target-dependent features.
A connectionist account of the object-substance distinction in early noun learning

A Connectionist Account of the Object-Substance distinction in Early Noun Learning Young children learning vastly different languages generalize nouns in the same systematic way. One task that has demonstrated this is the Novel Noun Generalization task. In this task, children are shown an exemplar, it is named, and then the children are asked what other things are called by the same name. Young children extend the name of a solid object to other objects that match the exemplar in shape irrespective of their color, size or material. However, when generalizing the name of a non-solid substance, children consider material to be more important. In brief, young children seem to know that objects and substances are fundamentally different and classified in different ways. Where does this knowledge come from? One proposal is that it comes from innate ideas reflecting an ontological difference between objects of substances. In this research I present evidence for an opposite idea: that it is learned. I propose that the concepts of object and substance emerge as the natural product of associative learning and generalization by similarity. More specifically, I propose that the formative associative mix consists of correlated perceptual properties, syntactic cues, and most notably, the lexical category structure of early learned nouns. I use connectionist networks to simulate both children's performance in the Novel Noun Extension task and the knowledge they bring to the task. In nine experiments with children, neural networks and adults, I support the claim that abstract ideas of object and substance are created by the statistical regularities inherent in the early noun lexicon by: (1) demonstrating that a connectionist network trained on specific instances does act as if it has general knowledge of abstract kinds, (2) demonstrating that these associative processes can explain previous findings in the literature, and (3) generating, testing and verifying new predictions.
Wavelet based Blood Vessel Segmentation in Retinal Image Analysis

Retinal image analysis is currently a very vivid field in biomedical image analysis. Retinal image can be analysed to detect a number of cardiovascular diseases as well as diseases of the eye proper.One important component of the retinal image analysis is blood vessel segmentation. Blood vessel carry a lot of information regarding various disease conditions. Blood vessels segmenatation can be carried out by a number of methods. Of this methods the wavelet based methods are found to be most efficient as it provides both time and frequency components. Here we propose a wavelet based technique for blood vessel segmentation. We take manually generated blood vessel segmented images as the ground truth and compare our results with it to attain various parameters such as sensitivity, specificity and accuracy.The images generated by our method can be utilised for taking decisions regarding disease symptoms of which are known using using retina images.
A Dynamic Interactive Decision Analysis and Support System (DIDASS). Users Guide (May 1983)

The Interactive Decision Analysis group at IIASA has recently developed an interactive decision support system called DIDASS (dynamic interactive decision analysis and support system). The major advantage of this system over most other computerized approaches to decision problems is that it is interactive, that is, it involves the decision maker in the decision process. It is an attempt to combine the analytical power of the "hard" computer model with the qualitative assessments of the decision maker. DIDASS is an interactive multicriteria programming package based on the reference (aspiration) approach to multicriteria analysis, and is capable of dealing with both linear and nonlinear problems. It has been written in FORTRAN 77, avoiding the use of any operating-system-dependent statements or commands, which means that it can be transferred to almost any computer without difficulty. This guide has been prepared for users both at IIASA and at the many collaborating institutions where DIDASS is now running, and is based on the version of DIDASS available on tape from IIASA.
Information Modeling Methods and Methodologies

This book is disseminates the research results and best practices from researchers and practitioners interested in and working on modelling methods and methodologies. Though the need for such studies is well-recognized, there is a paucity of such research in the literature. What specifically distinguishes this book is that it looks at various research domains and areas such as enterprise, process, goal, object-orientation, data, requirements, ontology, and component modelling, to provide an overview of existing approaches and best practices in these conceptually closely-related fields.
XML Data Integration: Merging, Query Processing and Conflict Resolution

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, we discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). We also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.
A Simplified FDDL Algorithm for Face Recognition

In this paper, a modified Fisher Discrimination Dictionary Learning method is introduced, which is suitable for face recognition without and with disguise. In the proposed method the new optimization model is establish, so as to preserve intrinsic complementary penalty as well as able to simplify calculation of original algorithm. The effective and efficient of the proposed method is demonstrated experimentally by considering the YALE, ORL, and AR face database.
Verbmobil A Translation System For Face To Face Dialog

Thank you for downloading verbmobil a translation system for face to face dialog. As you may know, people have look numerous times for their chosen books like this verbmobil a translation system for face to face dialog, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.
Retrieval using document structure and annotations

Successful retrieval of information from text collections requires effective use of the information present in a collection. The structure of documents in the collection and the relationships between elements within a document and other documents contain important information about the meaning of these elements. For example, the words present in the title of a web page may contain important clues about that page's content. The text of a link to the web page may also be an important indicator of the page's content. Researchers have long recognized that structure can be an important indicator of relevance. Yet the majority of prior work is limited to experiments on small test collections and evaluated on a single retrieval task. These limitations hamper the generality of the conclusions. The recent construction of large and diverse test collections provides us the opportunity to reconsider the general task of retrieval in collections with structure. This dissertation draws on three retrieval tasks to identify important properties of retrieval systems supporting the use of structure and annotations. We investigate known-item finding of web pages, retrieving elements from XML articles, and the retrieval of answer-bearing sentences as a component of a question-answering system. The retrieval model, an adaptation of the Inference Network model, clarifies the query language and simplifies the process of smoothing using multiple representations. The experiments in this dissertation show state-of-the-art results for these tasks and also provide novel insights to the shape of the parameter space when using mixtures of language models. Our experiments with question-answering further show how semantic predicates automatically annotated on a collection can be used to improve a system's ability to retrieve answer-bearing sentences.
Model Predictive Path Integral Control: From Theory to Parallel Computation

In this paper, a model predictive path integral control algorithm based on a generalized importance sampling scheme is developed and parallel optimization via sampling is performed using a graphics processing unit. The proposed generalized importance sampling scheme allows for changes in the drift and diffusion terms of stochastic diffusion processes and plays a significant role in the performance of the model predictive control algorithm. The proposed algorithm is compared in simulation with a model predictive control version of differential dynamic programming on nonlinear systems. Finally, the proposed algorithm is applied on multiple vehicles for the task of navigating through a cluttered environment. The current simulations illustrate the efficiency and robustness of the proposed approach and demonstrate the advantages of computational frameworks that incorporate concepts from statistical physics, control theory, and parallelization against more traditional approaches of optimal control theory.
A caller-side inline reference monitor for object-oriented intermediate language: extended version

Runtime security policy enforcement systems are crucial to limit the risks associated with running untrustworthy (malicious or buggy) code. The inlined reference monitor approach to policy enforcement, pioneered by Erlingsson and Schneider, implements runtime enforcement through program rewriting: security checks are inserted inside untrusted programs. Ensuring complete mediation – the guarantee that every security-relevant event is actually intercepted by the monitor – is non-trivial when the program rewriter operates on an object-oriented intermediate language with state-of-the-art features such as virtual methods and delegates. This paper proposes a caller-side rewriting algorithm for MSIL – the bytecode of the . NET virtual machine – where security checks are inserted around calls to security-relevant methods. We prove that this algorithm achieves sound and complete mediation and transparency for a simplified model of MSIL, and we report on our experiences with the implementation of the algorithm for full MSIL.
Online Classification Algorithm Based on Confidence-weighted Learning

Gaussian process has the ability to deal with complicated problems such as high dimension,small samples,and nonlinearity.A new algorithm called confidence-weighted learning is proposed by introducing Gaussian process into the online classification.It maintains a Gaussian distribution over weight vectors and makes adjustment over the weight vector with every sample instance.By this way,it correctly classifies examples with a specific probability.Experimental results show that the confidence-weighted classifiers are always more accurate than the perceptron classifier in the simulated datasets,as well as in the real datasets.
Preferences for activities in French textbooks used in Slovak secondary schools analysed with Birova´s Communication Curve Tool

Abstract Which textbooks are used in French classes? Can the struggle to choose a good textbook affect the communication procedure in French? What is the proportion of language, intercultural and communicative activities in selected textbooks? What are the reasons for purchasing language-based and communication-based textbooks? The present study explores these questions and presents results with broad implications for teachers of French at secondary schools. It points out some significant markers such as textbook type activities preferences and arguments for choosing textbooks for French classes.
Research Gaps and Trends in Cloud Computing: A Systematic Mapping Study

Context: Since 2009 a large number of papers have been presented at IEEE International Conference on Cloud Computing (CLOUD). Studying these papers and identifying relevant research areas from them are both difficult and time consuming. Objective: To address this problem, we aim to provide a structure to the entire collection of fulllength papers published in the proceedings of the past CLOUD conferences (from CLOUD 2009 to CLOUD 2014). Method: We undertake a Systematic Mapping study of these papers by categorizing them. Results: 523 relevant papers are selected and assessed in terms of their research topic, contribution and research type. Thematic and frequency analysis is then conducted to identify research gaps and trends. Conclusions: By research topic, technology oriented papers extensively outnumbered business oriented papers. By contribution, methods significantly outstripped other types of contribution whereas there is a desperate shortage of contribution from practical experience. By research type, empirical research papers have dominated the past six CLOUD conferences whereas theoretical research is close to none.
WASAR imaging with backprojection based group complex approximate message passing

Group sparsity based wide angle synthetic aperture radar imaging model is developed and propose a novel algorithm called backprojection based group complex approximate message passing to recover the anisotropic scene. Compared with conventional backprojection based complex approximate message passing algorithm for the recovery of isotropic scene, the proposed method accommodates aspect dependent scattering behaviour better and can produce better imagery. Simulated and experimental results are presented to demonstrate the validity of the proposed algorithm.
Privacy preserving distributed mining of association rules

It is crucial to preserve user's privacy without disclosing individual transactions in distributed mining.This paper addresses a secure distributed mining algorithm PPDMA(Privacy Preserving Distributed Mining Algorithms) for association rules.The method incorporates cryptographic techniques to minimize the information shared.By encrypting the constrained sub-tree and other information being transmitted,PPDMA can secure user's privacy.Theory analysis shows the feasibility and effectiveness of the algorithm.