Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of IF and Baseband Diversity Combining Receivers

Techniques of diversity reception have advanced rapidly with the advent of over-the-horizon communication. Because of the rapidly fluctuating signals encountered in tropospheric scatter propagation, strong-signal selection (switching diversity) has been largely supplanted by more sophisticated signal-combining techniques. Present tropospheric scatter systems commonly employ either postdetection ratio-squared combining, or more recently, a technique of predetection linear addition. A theoretical and experimental study comparing these two methods is the subject of this paper. Apart from obvious advantages of simplicity, the predetection technique exhibits lower distortion under multipath conditions, due to the linear combining process.
Modeling of Information Asynchronization Caused by Packet Loss and Its Effect on Distributed Optical Networks

In this article, we propose a model to analyze the influence of information asynchronization on networking performance. In the proposed analytical model, we introduce the "distributed network noise" (DNN) parameter to describe information asynchronization of wavelength availability, which is caused by packet loss during the flooding procedure. We also derive the relationship between packet loss rate and DNN, as well as the relationship between DNN and blocking probability. It is shown that the experimental results agree well with the estimate values derived from our proposed analytical model.
A Cost Effective Virtual Cluster with Hadoop Framework for Big Data Analytics

Big data processing is currently becoming increasingly important research field in computer technology professionals due to the continuous growth of the amount of data generated by various fields. However, the processing of large-scale research data requires cluster technology infrastructure which causes huge investments for educational institutions. Hadoop is an open-source framework that allows for distributed storage and processing of very large data sets on computer clusters built from commodity hardware. This technology is being widely used for the analysis of large datasets. This paper focuses on proposes a low cost scalable hadoop virtual cluster platform and the performance of hadoop irtual cluster. We first describe the design and implementation of a virtual datacenter using hadoop framework. Then we perform a set of experiments to investigate the performance of virtual datacenter with standard datasets. For experiment and evaluation, the Cloudera's distribution of Apache Hadoop (CDH) is installed and configured on the DIU Cloud and Big Data Lab as a prototype implementation of a virtual data center and tested with 40 workstations. In this paper, we use TPC BENCHMARK ™ DS” by-Transaction Processing Performance Council (TPC) benchmarks for evaluation virtual data center performance. The contributions of this paper, is to design model and implement a cost effective elastic virtual data center with hadoop framework and resource utilizations for educational institutions to provide high performance for distributed and parallel processing; as well as, Identify the bottlenecks of this systems e.g. bandwidth of network connectivity with all nodes in the lab.
Integrated Optimization Method Using Particle Swarm Optimization and Modeling

In this paper, using Particle Swarm Optimization(PSO) and Radial Basis Function Network(RBFN), a new framework of practical optimization, Integrated Optimization System, is proposed. PSO is used as a globally optimization method, and RBFN is used as a numerical modeling method for approximating a response surface for sample points from a simulator or sensing system in order to reduce the number of accesses to a simulator. On the basis of numerical simulation results using some typical benchmark problems, it is shown that the proposed integrated optimization system enables to obtain a relatively good solution with drastic reduction of the number of function calls for evaluating of performance index values of systems.
Application Domain and Functional Classification of Recommender Systems—A Survey

The amount of scientific and technical information is growing exponentially. As a result, the scientific community has been overwhelmed by the information published in number of new books, journal articles, and conference proceedings. In addition to increasing number of publications, advances in information technology have dramatically reduced the barriers in electronic publishing and distribution of information over networks virtually anywhere in the world. As a result, the scientific community is facing the problem of locating relevant or interesting information. To address the problem of information overload and to sift all available information sources for useful information, recommender systems or filtering systems have emerged. Generally, recommender systems are used online to suggest items that users find interesting, thereby, benefiting both the user and merchant. Recommender systems benefit the user by making him suggestions on items that he is likely to purchase and the business by increase of sales. Filtering information or generation of recommendatio ns by the recommender systems mimic the process of information retrieval systems by incorporating advanced profile building techniques, item/user representation techniques, filtering and recommendation techniques, and profile adaptation techniques. This paper addresses the application domain analysis, functional classification, advantages and disadvantages of various filtering and recommender systems.
The Program for Climate Model Diagnosis and Intercomparison Diagnostic System: Implementing a New Strategy Beyond the Atmospheric Model Intercomparison Project (AMIP)

The Program for Climate Model Diagnosis and Intercomparison (PCMDI) was established in 1989 at the Lawrence Livermore National Laboratory (LLNL) with the principal mission to develop improved methods and tools for the diagnosis, validation and intercomparison of global climate models. The goal of the process is to eventually improve simulation of the regional climate effects of increasing greenhouse gases. In addition to comparing models, PCMDI continues to develop a modeling infrastructure by creating diagnostics that will be shared throughout the research community. PCMDI's early model intercomparison strategy was to solicit a few models that could be run for a specified period with prescribed sea-surface temperatures after being imported and adapted to the LLNL unclassified computer systems. Because of the enormous time required to prepare each model, the experiment was reversed and the modeling groups were asked to perform the controlled simulations themselves. In order to reach out to the entire atmospheric modeling community, the Working Group for Numerical Experimentation (WGNE) became the parent organization and the project was named the Atmospheric Model Intercomparison Project (AMIP). Eventually, more than thirty atmospheric modeling groups joined the effort to compare their model output (Gates et al. 1999). The general results showed that themore » models vary widely for some variables and are tightly clustered for other variables. Fig. 1 characterizes the wide array of results obtained in AMIP and underscores the need to better understand differences among models and between models and observations. As a result of AMIP, model development and improvement strategy has incurred a permanent change. Modeling groups routinely perform AMIP-like simulations as they improve their models and create new versions containing substantial modifications to parameterizations. Other model intercomparison projects (MIPs) have since sprung up, most notably, the Coupled Model Intercomparison Project (CMIP) and the Paleoclimate Model Intercomparison project (PMIP). AMIP itself has undergone substantial changes as we realized that the type and amount of data saved was inadequate. AMIP II was initiated to provide the community with a more comprehensive data set, a longer simulation period, and most important, a chance to evaluate more current models. For the most part the AMIP II simulation have been completed and the data are in the process of being prepared for distribution to research subprojects. « less
Symbolic WCET Analysis of Programs Containing Input-Dependent Branches

Symbolic WCET (worst-case execution time) analysis yields symbolic upper bound expressions for tasks that contain parameters. Quickly evaluated at run-time, such expressions can improve the accuracy of WCET estimate. This paper proposes a symbolic WCET analysis method that is especially for the input-data dependent branches. First, the formulas described by Blieberger is expanded, so that they can express the input-data dependent branches. Simplification of the formulas using control-dependent graph yields conditional symbolic expressions that have different forms corresponding to different input value ranges. Different from the existing methods, the symbolic formulas are directly dependent on input-data, so WCET estimate evaluation at run-time is more simple and straightforward.
[IMPACT OF DIETARY FATTY ACIDS ON LIPID PROFILE, INSULIN SENSITIVITY AND FUNCTIONALITY OF PANCREATIC β CELLS IN TYPE 2 DIABETIC SUBJECTS].

INTRODUCTION the quality of fats could influence the metabolic control of patients with Type 2 Diabetes Mellitus (DM2). OBJECTIVES to determine the relationship between intake and quality of dietary fatty acids to lipid profile, metabolic control, functionality of pancreatic cells and insulin sensivity in subjects with DM2. METHODS we studied 54 subjects with DM2, anthropometric measurements were performed, body composition and dietary lipid intake, saturated fatty acids (SFA), trans, monounsaturated, polyunsaturated, omega 3, omega 6 and dietary cholesterol. Laboratory parameters related to their metabolic control were determined (fasting blood glucose, glycated hemoglobin, and lipid profile). The insulin secretion and insulin sensitivity was determined with the insulin-modified intravenous glucose tolerance test according to the Bergman's minimal model. RESULTS 28 men and 26 women were studied (BMI of 29.5 ± 3.7 kg/m2; age 55.6 ± 6.8 y.), 48% had LDL-C < 100 mg/dL, 12.9% of men c-HDL > 40 mg/dL and 7.4% of women c-HDL > 50 mg/dL. 32% consumed > 10% of AGS and > 300 mg/day of dietary cholesterol. The SFA intake and percentage of calories from fat (G%) were significantly associated with insulin resistance and fasting plasma glucose concentration. The G% predicted 84% variability on c-VLDL. CONCLUSIONS in patients with DM2 a greater intake of fat and saturated fatty acids it associated with greater fasting glycemia and insulin resistance.
Topical Influence Analysis Algorithm based on Information Propagation in Microblogs

With the development and popularization of the Internet, the different microblog topics are disscussed everyday, so the microblog can product a large number of various topics,which can reflect the influence of different users in a given topic. In the microblog topics,the key users of microblog topics are found by discovering the influential sensitive topics and calculating the influence value of the user,which are the focus of attention in the fields of microblog public opinion supervision and safety management. In order to accurately measure the influencers in the given topic and to calculate the user influence value, the thesis proposes the method of constructing the propagation network which is based on attention and forward relationship between users, and then proposes TDN-If algorithm by using PageRank algorithm. When we calculate the transition probability in propagation network by using the TDN-If algorithm, the information propagation is considered to measure influencers. This method can resolve the defects of discovering the user's influence by only using followers of this single indicator in the current microblog topics. The experimental results show that the TDN-If algorithm has important theoretical and practical value, which is better than TwitterRank algorithm and other influential individuals found algorithm. Thus, the method proposed in this paper can not only effectively solve the problem about discovering and persuading the key users in the sensitive topics who have influences and have the unique insights on the significant events, for example, which can provide the strong guarantee for the governments in the fight against terrorism, but also provide the important theory and method for the complex network community discovery, microblog public opinion supervision , microblog safety management and so on.
Comparison of feedback controllers for feedback-error-learning neural network control system with application to a flexible micro-actuator

A neural network approach to on-line learning control and real time implementation for a flexible micro-actuator is presented. The flexible micro-actuator is made of a bimorph piezo-electric high-polymer material (Poly Vinylidene Fluoride). The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. In the process, the neural network learns the inverse dynamics of the system. We make some comparisons between PID and LQG controllers for this neural network controller. Experimental and numerical results for the tracking control of a piezopolymer actuator are presented and they show that the feedback-error-learning neural network is effective in accurately tracking a reference signal.
Corpus-based extension of termino-ontology by linguistic analysis: a use case in biomedical event extraction

The automatic population of a terminoontology is a difficult and challenging task. We propose a text-based ontology extension method that was experimented and evaluated on, for a semantic annotation task in the biomedical domain. It is based on the linguistic analysis of terms and their heads. The head-based method improves both the identification of relevant areas of a termino-ontology and the matching of the corpus terms within these areas.
A VLSI fuzzy logic controller with reconfigurable, cascadable architecture

A general-purpose fuzzy logic inference engine for real-time control applications, designed and fabricated in a 1.1- mu m, 3.3-V, double-level-metal CMOS technology, is discussed. Up to 102 rules are processed in parallel with a single 688 K transistor device. Features include a dynamically reconfigurable and cascadable architecture, TTL-compatible host interface, laser-programmable redundancy, a special mode for testability, RAM rule storage, and on-chip fuzzification and defuzzification. >
Classifying bugs is a tricky business

Abstract : In order to build a computer-based programming tutor for novice programmers, it was necessary to first classify the bugs found in their programs on the basis of type and frequency. However, the enterprise of classification turns out to be a complicated process. While one may want to be able to simply use features in the program itself as the basis for the classification, it turns out that such a scheme will result in classifications that seem to miss the mark, i.e., the classifications will not tell you what misconception the programmer was operating under which caused the bug. To remedy this situation the authors that the programming plans that the programmer intended to use should be the basis for a classification scheme. Thus, a bug classification must take the programmer directly into account. In this paper, they compare several different methods of bug classification currently being used in software engineering projects, and show their weaknesses; while their method of using intended programming plans is not without problems, it is argued that it presents a better alternative than the other methods currently being employed. (Author)
NEW OPTIMIZATION APPROACH FOR SCHEDULING THE BATCH HEAT TREATMENT PROCESS WITH SEQUENCE DEPENDENT SETUP TIMES

The Shaking Optimization Algorithm is among the newly developed optimization algorithms that belong to the Evolutionary Computations Algorithms. It combines heuristics with randomness in the evolution process rather than just apply operators e.g., mutation, recombination, and/or selection rules. It has been used successfully to solve the classic Job Shop Scheduling Problem, the Cutting Stock Problem, and the Unconstrained Engineering Optimization Problems, and the results show that the algorithm is a good competitive to other evolutionary computation algorithms e.g., the Genetic Algorithm and the Particle Swarm Optimization regarding solution time, efficiency, and complicity. This paper uses the SOA algorithm to solve the scheduling problem of the Batch Annealing Process with Sequence-Dependent Setup Times in the heat treatment of steel coils as one of complex scheduling problems. The objective is to determine the movement schedule of shared equipment among parallel bases that gives minimum makespan for processing a given batch size. The bell-type batch annealing process is used here for the comparative study. The results show that the proposed approach is able to solve this class of problems as well in a reasonable time and gives competitive results to other algorithms.
Visual narratives : free-hand sketch for visual search and navigation of video

Humans have an innate ability to communicate visually; the earliest forms of communication were cave drawings, and children can communicate visual descriptions of scenes through drawings well before they can write. Drawings and sketches offer an intuitive and efficient means for communicating visual concepts. Today, society faces a deluge of digital visual content driven by a surge in the generation of video on social media and the online availability of video archives. Mobile devices are emerging as the dominant platform for consuming this content, with Cisco predicting that by 2018 over 80% of mobile traffic will be video. Sketch offers a familiar and expressive modality for interacting with video on the touch-screens commonly present on such devices. This thesis contributes several new algorithms for searching and manipulating video using free-hand sketches. We propose the Visual Narrative (VN); a storyboarded sequence of one or more actions in the form of sketch that collectively describe an event. We show that VNs can be used to both efficiently search video repositories, and to synthesise video clips. First, we describe a sketch based video retrieval (SBVR) system that fuses multiple modalities (shape, colour, semantics, and motion) in order to find relevant video clips. An efficient multi-modal video descriptor is proposed enabling the search of hundreds of videos in milliseconds. This contrasts with prior SBVR that lacks an efficient index representation, and take minutes or hours to search similar datasets. This contribution not only makes SBVR practical at interactive speeds, but also enables user-refinement of results through relevance feedback to resolve sketch ambiguity, including the relative priority of the different VN modalities. Second, we present the first algorithm for sketch based pose retrieval. A pictographic representation (stick-men) is used to specify a desired human pose within the VN, and similar poses found within a video dataset. We use archival dance performance footage from the UK National Resource Centre for Dance (UK-NRCD), containing diverse examples of human pose. We investigate appropriate descriptors for sketch and video, and propose a novel manifold learning technique for mapping between the two descriptor spaces and so performing sketched pose retrieval. We show that domain adaptation can be applied to boost the performance of this system through a novel piece-wise feature-space warping technique. Third, we present a graph representation for VNs comprising multiple actions. We focus on the extension of our pose retrieval system to a sequence of poses interspersed with actions (e.g. jump, twirl). We show that our graph representation can be used for multiple applications: 1) to retrieve sequences of video comprising multiple actions; 2) to navigate in pictorial form, the retrieved video sequences; 3) to synthesise new video sequences by retrieving and concatenating video fragments from archival footage.
Optimal Temporal Decoupling in Task Scheduling with Preferences

Multi-agent planning and scheduling concerns finding a joint plan to achieve some set of common goals with several independent agents each aiming to find a plan or schedule for their part of the goals. To avoid conflicts in these individual plans or schedules decoupling is used. Such a decoupling entails adding local constraints for the agents such that they can schedule autonomously within those constraints, while they guarantee that a conflict-free global solution can be constructed from the individual agents’ schedules. In this paper we investigate finding an ‘optimal’ decoupling, that maximizes the sum of the agents’ preferences about their scheduling of tasks. We show using a Linear Programming (LP) approach that optimal decouplings can be found efficiently by exploiting the properties of a task scheduling instance.
Doğum Sonrası Ruhsal Durumun Emzirme ile İlişkisi

Gunumuzde anne sutu ve emzirmenin onemi kadar onemli olan bir baska konu da dogum sonrasi annenin ruh sagligidir. Annenin bebegine saglikli bir sekilde bakabilmesi ve uzun donem emzirebilmesi icin oncelikle kendi beden ve ruh sagliginin yerinde olmasi gerekir. Anne ruh sagliginin kotu olmasi, bebegin iyi beslenememesi ve fiziksel sagliginda bozulmalarla sonuclanmaktadir. Depresif annelerin emzirme konusunda daha fazla kaygilandiklari, sutlerinin besleyiciligi ve yeterliligi konusunda olumsuz dusuncelere sahip olduklari soylenebilir. Emzirme ile ilgili yeterli egitim ve destegin yani sira bebek besleme konusundaki olumsuz sonuclari azaltmak icin depresyondaki anneyi tanimak ve tedavi etmek oldukca onemlidir. Emzirme egitimi verilirken gercekci hedeflerin saptanmasi, olgu bazinda yaklasim ve olabilecek komplikasyonlari tanimaya yonelik egitimlerin anneye ve cevresindekilere verilmesi gerekir.
Finding Information on the Web: Does the Amount of Whitespace Really Matter?

It has been a long-held notion that the use of open space or "whitespace" adds not only to the attractiveness of the design of a written publication, but adds to the functionality as well. For example,it has been stated that whitespace plays the crucial role of "directing the viewer's attention to the regions where important information is provided and allowing the global structure of the composition to assume a meaningful configuration" (Mullet & Sano, 1995, p. 126). It is contended that Whitespace"gives the eye a place to rest.It can help to organize the material on the page. It can tie successivepages together by repetition of identifiable areas" (White, 1974, p. 48). However, it has been asserted by Web usability researcher Jared Spool that these assumptions should not apply to Web design. That is, in his book,