Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-efficient video streaming over wireless networks

Recent advances in several key areas such as video coding, wireless radio and silicon technologies have enabled pervasive and ubiquitous access to multimedia-rich mobile applications. As video standards evolve, more bits are being squeezed out while visual quality improves spectacularly. Meanwhile, datarate capacity of wireless links increases exponentially as wireless radio technology evolves from 2G to 4G. On the other hand, silicon technologies are empowering mobile devices with computing capability at exponential rate. However, large scale deployments often pose serious challenges to the resource allocation of the network and mobile devices. Insatiable demands for better visual (from HD to multiview/3D and to ultra HDTV) experience increases the size of video data. User-generated videos and their sharing put significant strains on the wireless networks. Coding and delivery of video often incur significant power cost to mobile devices and wireless networks. Energy-efficient resource allocation and scheduling are thus essential for sustainability of large scale deployment of mobile multimedia applications. This thesis investigates the energy efficiency perspective of resource allocation in video streaming over wireless networks. We consider three scenarios which are typical and common in the applications of wireless video streaming. We first investigate the besteffort video delivery to heterogeneous clients. For this scenario, we consider the wireless multicast of scalable video over wireless mesh networks. The video multicast is a besteffort service which is limited by channel access and energy constraints. We formulate the optimal multicast strategy to maximize the video quality of heterogeneous clients under limited resources constraints. We show that the optimal multicast strategy can be obtained efficiently via a dynamic programming approach. To schedule the scalable video bitstreams for multicast, we propose a quality-impact index to rank the priorities of video frames. Next, we consider the reliable delivery of video frames over a wireless link. For this scenario, we consider energy minimal transmissions of a video frame with reliability and
Text Mining on Big and Complex Biomedical Literature

Driven by the rapidly advancing techniques and increasing interests in biology and medicine, about 2,000 to 4,000 references are added daily to MEDLINE, the US national biomedical bibliographic database. Even for a specific research topic, extracting useful and comprehensive information out of the huge literature data pool is challenging. Text mining techniques become extremely useful when dealing with the abundant biomedical information and they have been applied to various areas in the realm of biomedical research. Instead of providing a brief overview of all text mining techniques and every major biomedical text mining application, this chapter explores in-depth the microRNA profiling area and related text mining tools. As an illustrative example, one rule-based text mining system developed by the authors is discussed in detail. This chapter also includes the discussion of the challenges and potential research areas in biomedical text mining.
Influência do biochar na disponibilidade de água de um Latossolo.

Objetivou-se estudar os efeitos da aplicacao de biochar nas caracteristicas fisico-hidricas de um Latossolo Vermelho-Amarelo Distrofico tipico cultivado com eucalipto. Incorporou-se ao solo a dose de 3,21 Mg ha-1 de biochar de cavacos de eucalipto pirolisados a 450 °C por 8 horas. As amostras de solo foram coletadas tres meses apos a incorporacao nas profundidades de 0-10 e 10-20 cm. Observou-se reducao da densidade e incremento da porosidade total em ambas as profundidades estudadas. Ensaios de extracao de agua demonstraram que, na primeira profundidade, houve incremento na disponibilidade total de agua e na segunda profundidade houve aumento da macroporosidade e reducao da microporosidade. A dose de biochar aplicada aumentou a disponibilidade de agua do solo.
Developing Software Packages for Electromagnetic Simulations

In addition to previous developments on parallel beam dynamics software packages, our efforts have been extended to electromagnetic simulations. These efforts include developing new software packages solving the Maxwell equations in 2D and 3D for time domain and eigenvalue simulations. This paper focuses on software packages for eigenvalue simulations. These new solvers are based on high order numerical methods. Comparative studies of different expansion bases, continuous and discontinuous GALERKIN methods will be discussed. Benchmarks and simulation results will be presented at the end.
Bacterial Fecal Microbiota in Healthy Subjects and Inpatients with Clostridium difficile Infection

Clostridium difficile is a nosocomial enteric pathogen, considered the main etiological agent of antibiotic-associated diarrhea in hospitals. C. difficile is resistant to several antimicrobial agents used in empiric treatment protocols, which confers selective advantages to this species as compared with other members of the intestinal microbiota. The aim of this study was to evaluate and identify possible alterations of the intestinal microbiota population resulting from the use of antimicrobials associated with infections by C. difficile (CDI). Denaturing Gradient Gel Electrophoresis (DGGE) was used to evaluate fecal samples from two groups: healthy subjects and patients with C. difficile-associated diarrhea (CDAD). A decrease in bacterial diversity was highlighted by the low number of bands in samples from CDAD patients, compared with healthy subjects. This may indicate that antibiotic treatment would affect bacterial diversity, leading to a significant difference between the intestinal microbiota of these two groups, but further studies are still needed. Firmicutes and Verrucomicrobia phyla were detected mainly in healthy individuals, and these could be related to protection factors against the CDI. Klebsiella variicola/K. pneumoniae were found mostly in samples from CDI patients. This study shows the effects of antimicrobials and the CDI itself on human intestinal microbiota.
Distributed Optimization Methods for Large Scale Optimal Control (Gedistribueerde optimalisatie voor grootschalige optimale controle)

This thesis aims to develop and implement both nonlinear and linear distributed optimization methods that are applicable, but not restricted to the optimal control of distributed systems. Such systems are typically large scale, thus the well-established centralized solution strategies may be computationally overly expensive or impossible and the application of alternative control algorithms becomes necessary. Moreover, it is often desired that the information on the coupled subsystems inner dynamics is kept local, making the deployment of a centralized optimal control scheme impossible in practice. In such a case, optimization approaches based on some limited exchange of information remain the only possible option. In the first part of this thesis, we consider nonlinear optimal control problems with distributed structure, for which we design an efficient distributed shooting method resulting in up to 19 times faster integration time when compared to the conventional approaches. The first part reports the testing of the proposed method on two different dynamic optimization problems. The second part of this thesis investigates linear optimal control problems with quadratic cost functions, i.e. quadratic programs (QP). An overview of dual decomposition based approaches is given, followed by the development of a novel dual decomposition scheme. The proposed method employs second-order derivatives. Experimental results suggest that it requires up to 152 times less local optimization steps than classical first-order schemes. Furthermore, as a part of this thesis, an open-source QP solver (PyDQP) with 11 different dual decomposition based methods is implemented. A set of large scale distributed quadratic programs is set up and released for benchmarking purposes.
Flowchart Approach to Scalable Encryption Algorithm Design and Implementation in FPGA

The implementation of encryption/decryption algorithm is the most essential part of the secure communication. In currently existing encryption algorithms there is a tradeoff between implementation cost and resulting performances. Scalable encryption algorithm is targeted for small-embedded application with limited resources (such as memory size, processor capacity). SEA n, b is parametric in the text, key and processor word size and uses a limited instruction set (i.e. NOT, AND, OR, XOR gates, word rotation and modular addition). And it has a provable security against linear and differential cryptanalysis. This paper includes the conversion of loop architecture of SEA into flowchart, in such a way that encryption and decryption process are separated, loop is split into two parts and controlling inputs are removed. By this method it is easy to design in VHDL language, for implementation in FPGA.
Handwritten Digit Recognition by Combining

� Abstract — Recent results in pattern recognition have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classi- fication methods. In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using dif- ferent feature set is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. The ob- tained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets. In our experiments only one of the cooperation schemes exceeds the recognition rate of a single SVM classifier. However, the classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performance.
Parallel K-Medoids algorithm based on MapReduce

In order to solve the bottleneck problems of memory capacity and CPU processing speed when the traditional K-Medoids clustering algorithm is used to deal with massive data,based on the in-depth study of K-Medoids algorithm,a parallel K-Medoids algorithm based on the MapReduce programming model was proposed.The part of Map function is to calculate the distance of each data object to the center point of the cluster and(re)allocation of their respective clusters,and the part of Reduce function is to calculate the new center point of each cluster according to the intermediate results of the Map section.The experimental results show that the parallel K-Medoids algorithm in the Hadoop cluster based on the MapReduce running has good clustering results and scalability,and for large data sets,the algorithm may get close to linear speedup.
File Tracking Method and System Based on Life Cycle Tree

There are many difficult challenges, such as variety, quantity, distribution etc. in the unstructured data tracking management, which lacks an effective file tracking management solution. In this paper, based on the storage mechanism of HBase, we propose a data life cycle tree storage structure, and realize the whole life cycle tracking ȡ of the file. At the same time, the system can provide visual analysis services, such as frequency of operation, distribution of hot spots, data types, the breadth and depth of the life cycle tree, and the data volume trend prediction etc. Compared with common data management systems, this system can be more efficient and fast to query and build the data life cycle tree, and quickly realize the visual analysis of the document. Keywordsdata life cycle; tree storage structure; file tracking management system
Transparence Computing:Concept,Architecture and Example

This paper proposes a new pervasive computing paradigm:transparence computing.The hardware environment of this paradigm still consists of clients and servers.However,the clients should be computing devices designed with the transparence computing paradigm.These devices can be desktop or mobile one,such as PDA or mobile phone.The server of this paradigm is a machine with a hard disk,including a general PC.In transparence computing paradigm,no OS,middleware and any application program are installed in the clients,they are loaded from the server through network dynamically when users want to run them in a client.Users can choose any OS and application programs running on it,which has been installed in the server and can be run by the clients.Transparence computing paradigm can improve the security of the information systems,reduce the complexity of the managements,improve the sharing of software and reduce the costs of hardware and software of information systems greatly.This paper introduces the concept and architecture of the transparence computing paradigm.Moreover,we also give an application example of this paradigm.This example is Manageable Multimedia Network Computer (MMNC),which has been developed and commercialized.MMNC system can show the transparence computing is feasible and effective.
The Factors Affecting User Behavior on Mobile Voting in Vietnam

The significant development of Information Technology has delivered to peoples' lives so many conveniences, benefits, and advantages. Convenience is one of the important points considered as the requirement to affect users’ behavior on the adoption of new technology. For enhancing convenience, the mobile voting system was applied as the main point to improve fairness, freedom, and democracy in the Vietnamese voting process. The aim of this study is to clarify the satisfaction, feasibility, and user behavior intention in using the Vietnam Mobile Voting service. This study investigated 3 main group factors as "Social", "Individual" and "Technics" to evaluate the Mobile voting service. The research is assessed based on the data from 228 participants using the questionnaire. The data is used to test the research model and proposed hypothesis. The results of this research can be used to increase the quality of the service and evaluate user intention and satisfaction on Mobile Voting in Vietnam.
Inverse Optimization Model of Linear Programming for Operation Resources Configuration on Logistics Center

This paper builds up an inverse optimal model of linear programming for operation resources configuration on outbound work in the logistics center.The possible difficulty to measure the differential efficiency of shared and alternative resources was taken into account when applied to different tasks.A more practical feasible solution is used to minimize the adjustment of resources efficiency matrix so as to optimize the original model.The result from the actual example has shown that this method can transfer the actual solution into the optimization solution by adjusting resources efficiency matrix and provide a new way to satisfy the actual operation requirement.
Merging photoredox with nickel catalysis: Coupling of α-carboxyl sp3-carbons with aryl halides

A bright outlook for carbon coupling In contemporary organic chemistry, it is straightforward to forge bonds between unsaturated carbons (i.e., carbons already engaged in double bonds) using cross-coupling catalysis. The protocol runs into some trouble, however, if one or both starting carbon centers are saturated (purely single-bonded). Tellis et al. and Zuo et al. independently found that combining a second, light-activated catalyst with a nickel cross-coupling catalyst could achieve selective coupling of saturated and unsaturated reagents (see the Perspective by Lloyd-Jones and Ball). Their methods rely on single-electron transfer from the light-activated catalyst to the saturated carbon, thereby enhancing its reactivity more effectively than the twoelectron mechanisms prevailing in traditional protocols. Science, this issue p. 433, p. 437; see also p. 381 Combining two catalysts, one light-activated, facilitates bond formation between saturated and unsaturated carbons. [Also see Perspective by Lloyd-Jones and Ball] Over the past 40 years, transition metal catalysis has enabled bond formation between aryl and olefinic (sp2) carbons in a selective and predictable manner with high functional group tolerance. Couplings involving alkyl (sp3) carbons have proven more challenging. Here, we demonstrate that the synergistic combination of photoredox catalysis and nickel catalysis provides an alternative cross-coupling paradigm, in which simple and readily available organic molecules can be systematically used as coupling partners. By using this photoredox-metal catalysis approach, we have achieved a direct decarboxylative sp3–sp2 cross-coupling of amino acids, as well as α-O– or phenyl-substituted carboxylic acids, with aryl halides. Moreover, this mode of catalysis can be applied to direct cross-coupling of Csp3–H in dimethylaniline with aryl halides via C–H functionalization.
Optimization methods in machine learning: theory and applications

We look at the integral role played by convex optimization in various machine learning problems. Over the last few years there has been a lot of machine learning problems which have a (non)smooth convex optimization at its core. These problems generally call for fast first order iterative methods as obtaining the exact minimum is often impossible and second order methods or higher become prohibitively expensive even on moderately sized datasets. We look at a few such optimization problems that arise in different contexts and show that a class of smoothing strategies due to Nesterov can be applied to these seemingly very different problems to obtain theoretically faster rates of convergence than existing methods. Our experimental results validate the speed and efficacy of our methods and scale significantly well over a broad range of datasets. This thesis also explores an often used but understudied optimization algorithm, namely the cyclic coordinate descent method, and provides a novel theoretical analysis of the first non-asymptotic convergence rates of cyclic coordinate descent under certain assumptions. This work also sheds light on some of the recent advances in online convex optimization to minimize regret in the presence of smooth unknown functions. We also look at online learning from the point of view of stability and provide a new integral framework which encompasses the regret analysis of all existing algorithms as specific cases of this framework. We investigate related methods of analysis and the central role played by optimization in all these seemingly different but connected domains of machine learning research.
Advanced compact model for the charges and capacitances of short-channel MOS transistors

This paper presents a new compact model for the intrinsic charges and (trans) capacitances of the MOSFET including short-channel effects such as drain induced barrier lowering (DIBL), channel length modulation (CLM) and carrier velocity saturation. Explicit and compact expressions for charges and (trans) capacitances valid in all regimes of operation are presented. Simulations examples that illustrate short-channel effects in charges and (trans) capacitances are shown.
Cluster Head Energy Optimization in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of many sensor nodes which are vital to various applications in our daily lives. Optimizing energy usage is a key challenge for WSNs. Improving energy utilization can help with controlling traffic, saving energy and eventually, resulting in a better lifetime. In this paper, a modification of the Energy Efficient Credit-Based routing algorithm is proposed, which selects the most optimal Cluster Head based on the priority of relay nodes. Simulation results demonstrate that the proposed algorithm achieves a more efficient load-balancing, a better lifetime, and lower energy consumption, at the expense of slightly higher packet loss and lower data delivery rate. The results are compared with the commonly used Low-Energy Adaptive Clustering Hierarchy algorithm.
Semantic Monitoring of Service-Oriented Business Processes

Monitoring of business processes and service-oriented systems is a critical enabling technology for improving visibility into business operations, allowing their optimization, adjustments and restructuring. One of the major problems of current monitoring methods is the prevalent heterogeneity of various types existing among the applications and services used in complex distributed business process. In this chapter we propose semantic monitoring as a possible solution addressing some of the heterogeneity problems. The idea of semantic monitoring is to apply semantic annotations, using ontologies, to descriptions of event types and event instances emitted during interactions with integrated applications. We introduce a generic semantic monitoring framework consisting of a modular monitoring ontology and appropriate event detection mechanisms. The monitoring ontology defines generic, language independent monitoring concepts, and the language specific modules defining taxonomy of event types specific to a particular process modeling language/methodology. We present two such modules, one we have developed for the OWL-S Semantic Web services process models, and the other that we have developed for a business artifact-centric approach to business process specification. Next, we describe mechanisms for specification and detection of semantic composite events. We present a language based on an event algebra combined with semantic event-filtering expressions using description logics atoms enriched with OWL datatypes and SWRL builtins. Semantic filtering allows detection of such events that would otherwise be impossible without the use of semantic descriptions. We also discuss detection mechanisms suitable for runtime execution and after-execution
Enhancement in Security Implementation using Chaotic Map in Wireless Sensor Network

— Wireless Sensor Network (WSN) is a group of large number of sensor nodes. Security techniques for wireless sensor networks (WSN) face a great appeal due to their limited size and energy . To overcome these limitations chaotic maps is used. Chaotic map and genetic operations is a security mechanism used in sensor networks to provide data confidentiality. This algorithm is used to encode the data, in addition SET IBS and SET IBOOS are also used to increase the security by verifying whether the data is sent by an authorized user.
The Improvement of Test Case Selection for the Process Software Maintenance

Software maintenance is one of the critical parts in the processes of Software-Development Life Cycle. The body knowledge of maintaining the new software versions base on the abilities of fixing bugs, the revision of programs, the plan of preventing future constraints, and the software-development in piece and performance. While the modification has been retaining, the structure of code has to be retested to gain a level of assurance that it will be conducted relying on the requirement functions and faults in the program. According to this situation, the test cases of each program must be generated in minimum numbers in order to improve the revised modules and reduce execution times through the entire software system, including saving cost of maintaining codes. Concepts of resolving this problem is ongoing by the techniques of regression test such as random and safe selection. The traditional techniques focus building methods for reduce the numbers of selected test cases, including the percent errors. Therefore, this article proposes the new technique that gives the lower numbers of test cases, while the faultless rate is higher than the traditional techniques.