Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of game theory in wireless communication networks

The ability to model independent decision makers whose actions potentially affect other decision makers makes game theory attractive to analyze the performances of wireless communication systems. Recently, there has been growing interest in adopting game theoretic methods to wireless networks for power control, rate adaptation and channel access schemes. This thesis focuses the application of dynamic game theory and mechanism design in cognitive radio networks, Wireless Local Area Networks (WLAN) and Long Term Evolution (LTE) systems. The first part of the thesis aims to optimize the system performance through the transmission rate adaptation among wireless network users. The optimal transmission policy of each user is analyzed by formulating such a problem under general-sum Markov game framework. Structural results are obtained and provably convergent stochastic approximation algorithm that can estimate the optimal transmission policies are proposed. Especially, in the switching control Markov game theoretic rate adaptation formulation, it is proved that the optimal transmission policies are monotone in channel state and there exists a Nash equilibrium at which every user deploys a monotone transmission policy. This
Testing R Code

The basic premise of this book is that “Testing is the only way to be sure that your code, and your results, are correct”, and many programmers that I know would agree with this1. However, in practice, and for many different reasons, much research software is still developed with only a passing gesture to this principle. The author believes that “Testing code is conceptually easy; the hard part is developing habits to incorporate testing into your workflow”, and this may explain, at least in part, the shortfall of testing. Of course, software testing is a broad field that encompasses many issues from numerical stability and platform dependencies to graphical user interfaces, security and real-time processing. This means it is difficult to provide a small number of good, interoperable generic tools that cover all aspects of software development, whilst facilitating the development of these habits. However, it is possible when a specific programming language or environment is considered in isolation. In my opinion this book, in conjunction with the the tools that it employs, goes a long way towards realising this for the R programming environment. This book is suitable for anyone with a basic knowledge of the R programming language and is relevant for all who program using R, whether it be for creating small scripts, complete packages or anything in between. The book sets out by distinguishing between two types of testing – development time and run time. In the author’s words – “The point of development time testing is to make sure that you [the programmer] have not done something stupid. By contrast, the point of runtime testing is to make sure that the user has not done something stupid. ” These two types are examined separately in Chapters 2 and 3, which detail the two key packages on which the book is based assertive (Cotton 2016) for run time testing (maintained by the author) and testthat (Wickham 2011) for development time testing. They also include numerous examples and exercises, with solutions, which underpin the authors imperative “Attempt all exercises! ”. Having attempted most of them myself it is clear that they are well thought out and instructive. Both chapters are very complete, and at the end of reading them I was pleasantly surprised as to how quickly I could implement the methods they introduced into
Massively multiplayer online games as a sandbox for leadership: the relationship between in and out of game leadership behaviors

xiii Chapter
Visualizing bag-of-words for high-resolution remote sensing image classification

Abstract. Classification of high-resolution remote sensing images is a challenging problem. Bag-of-words (BOW) based classification algorithms have obtained good performances in recent years. However, how the procedures of the BOW framework affect the classification result is still an open question. We present three visualization algorithms to reconstruct images from BOW. After visualization, we can see what the computer actually “sees” in an image feature. We also analyze the procedures of the BOW framework, namely, descriptor extraction and histogram generation, in detail. It is found that the descriptors should not be blamed for wrong classification. The histogram generation strategy should be improved to be robust with image transformation. Then some suggestions are posed for future improvement of BOW-based remote sensing image classification algorithms.
Multichannel implantable stimulation and telemetry system for neuromuscular control

An implantable stimulation and telemetry system is being developed to extend the capabilities of the authors' present implantable neuromuscular stimulators, by incorporating acquisition of command control and feedback signals from implanted transducers and additional channels of stimulation for enhancing motor functions. While the system is implemented for the primary use of establishing hand grasp capabilities, the modular approach of the design will allow reconfiguration of the system for use in other applications. <<ETX>>
A Comprehensive Framework for F0 Estimation and Sampling in Modeling Prosodic Variation in Infant-Directed Speech

Accurate estimates of F 0 are essential for modeling how pitch variation is used as an informative cue to linguistic structure. Multiple challenges exist for estimation and valid statistical modeling of F 0 variation. First, certain speech styles, such as infant-directed speech, can involve dramatic pitch variation across utterances. Second, non-modal phonation can cause spurious F 0 values. Third, F 0 samples are not independent of one another, leading to issues with validity in applying generalized linear mixed effect models (GLMMs). To address these problems, we propose a comprehensive framework for accurate F 0 estimation and sampling to model prosodic variation. Our method involves segmentation of speech into utterances, followed by determination of speaker-and utterance-specific pitch range parameters. Regions of non-modal phonation are identified, ensuring that portions of speech leading to spurious F 0 values are rejected early. Next, F 0 stylization at the utterance level ensures robustness to microprosodic variation. Finally, F 0 turning points (e.g., local F 0 minima and maxima) are extracted; these are linguistically significant “control points” in F 0 contours connected by monotonic interpolations. This overall approach not only ensures accurate F 0 estimates, but critically overcomes the problem of non-independence of successive samples for valid statistical treatments within GLMMs.
Understanding natural language about multiple eventualities and continuous eventualities

The main task that this thesis deals with is the provision of a comprehensive analysis covering a meaningful subset of English and developing a computational implementation that is able to show understanding of this language subset, in part via limited visualisation. There is a well accepted analogy that says that eventualities exist in time, in ways that are similar to how objects exist in space. This analogy is used as a framework to investigate in detail those activities that are the eventuality analogue of plural and mass objects—multiple instances of an activity, or continuous occurrence of an activity respectively. These are called extended activities. We examine the ways in which natural language is used to describe these kinds of activities, and discuss ways in which the meanings of such language can be represented. We concentrate on language that is in the form of instructions, and discuss the special relationship between instructions and activities. Using the idea that some of our understanding of language comes from the con¬ text within which the language is being understood, we identify those parts of language about extended activities that are independent of context and indicate the places where context would play a part. Focusing on the context-independent part, the development of a grammar that can be used in an understanding system demonstrates that it is feasible to interpret important aspects of such language computationally. Further, the system includes a semi-graphical visualisation com¬ ponent that depicts in space the internal structure of the extended activity in time. The work in this thesis relies on the notion that language about extended activities is playing a role analogous to that of object quantification. That is, instead of the more common view that such language is playing the role of event modification, we take the view that it plays the role of event quantification. This notion has been introduced by Moltmann, and taking this approach allows the identification and representation of meanings that would otherwise be omitted. Further, incorporat¬ ing this into the computational framework is feasible, and an established approach to object quantification is used to implement event quantification.
Musical Scales in Central Africa and Java: Modeling by Synthesis

The author’s research on the tuning and scale systems of the Central African xylophone and Javanese gamelan has departed from previous Western ethnomusicological studies in these areas. Rather than relying on acoustical measurement as a primary source of data, his team has adopted an interactive approach involving the participation of Central African and Javanese musicians, who work with researchers to demonstrate their tuning processes using synthesizers and MIDI systems. The author presents results of this experimentation and explains the inconsistencies of previous research in terms of differing cultural concepts of scale.
Systematic Assessment of Information Technology Capability Definitions

Examining the relationship between IT capability and firm performance has received ample attention of researchers. However, a sound understanding of how to define IT capability remains underrepresented in existing literature. We extend and modify an existing definition analysis method derived from the Social Sciences and divided each definition in four components: central phenomenon, context, action and consequence. The components serve as basic requirement that help to identify causal explanations from a definition. We analyzed 24 definitions and weighted the relevance of each component using the five-year mean impact factor derived from the Association of Business Schools. To check the robustness of our results, three alternative weights were applied: journal grade, equal weights and citation per article. Our results show that researchers do not define all four components and, contrary to earlier studies, our definition analysis method is able to utilize all words of a definition.
The evaluation of AdBlock technique implementation for enterprise network environment

This paper presents the evaluation of AdBlock technique implementation for enterprise network environment. This study has presented the impact of web browsing activities where it is the most active traffic where is consumed the highest inbound bandwidth usage in enterprise network environment. We can conclude that DNS AdBlock is the best solution for enterprise network environment in term of blocking advertisement compare to extension adblock. Adblock technique also reduce network data request by comparing front-end solution (browser extension AdBlock) at client web browser and networks level adblock. The parameters such as HTTP request, TCP connection and network bandwidth are being examined to measure the effectiveness of blocking online advertisement. Both techniques perform the reduction of traffics and bandwidth utilization. The result shows that DNS AdBlock is the most effective in blocking online advertisement using the examined parameters. DNS AdBlock can sustain the usage of web browsing activity for enterprise network and also generate substantial saving across several fonts. This study has identified current web browsing trends traffic in enterprise network where it consumed 50 percent in average. This number increased when industries are moving to cloud web-based consumption. However, industries such as educational sector, web browsing traffic is one of connectivity that enterprises network should be investing to support openness and heavy traffic from educational users.
Word2Vec model for sentiment analysis of product reviews in Indonesian language

Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.
Forecasting methods used for performance's simulation and optimization of photovoltaic grids

The energy-efficiency statistical model presented below is an acceptable and quickly method of photovoltaic grid behavior forecasting, for specific photovoltaic energy producing application. Using the standard method of efficiency evaluation, a statistical model for photovoltaic grids is proposed, transposing collected data in the specified model pattern of the exponential smoothing and autoregressive integrated moving average statistical techniques. These aims were included in a prospective software program (EnergOptimizer), which provide advanced features of designing, forecasting and three-dimensional shading prediction tool. Therefore, a systematic analysis of the photovoltaic grids, in energy-efficiency respect, following this conception will bring benefits in the simulation and optimization background of photovoltaic projects.
A Study of the Structural Similarity Image Quality Measure with Applications to Image Processing

Since its introduction in 2004, the Structural Similarity (SSIM) index has gained widespread popularity as an image quality assessment measure. SSIM is currently recognized to be one of the most powerful methods of assessing the visual closeness of images. That being said, the Mean Squared Error (MSE), which performs very poorly from a perceptual point of view, still remains the most common optimization criterion in image processing applications because of its relative simplicity along with a number of other properties that are deemed important. In this thesis, some necessary tools to assist in the design of SSIM-optimal algorithms are developed. This work combines theoretical developments with experimental research and practical algorithms. The description of the mathematical properties of the SSIM index represents the principal theoretical achievement in this thesis. Indeed, it is demonstrated how the SSIM index can be transformed into a distance metric. Local convexity, quasi-convexity, symmetries and invariance properties are also proved. The study of the SSIM index is also generalized to a family of metrics called normalized (or M-relative) metrics. Various analytical techniques for different kinds of SSIM-based optimization are then devised. For example, the best approximation according to the SSIM is described for orthogonal and redundant basis sets. SSIM-geodesic paths with arclength parameterization are also traced between images. Finally, formulas for SSIM-optimal point estimators are obtained. On the experimental side of the research, the structural self-similarity of images is studied. This leads to the confirmation of the hypothesis that the main source of selfsimilarity of images lies in their regions of low variance. On the practical side, an implementation of local statistical tests on the image residual is proposed for the assessment of denoised images. Also, heuristic estimations of the SSIM index and the MSE are developed. The research performed in this thesis should lead to the development of state-of-the-art image denoising algorithms. A better comprehension of the mathematical properties of the SSIM index represents another step toward the replacement of the MSE with SSIM in image processing applications.
A blind spatial-temporal adaptive equalizer with array antenna using second order statistics

It is known that signal processing with TDL (tapped-delay-line) spatial-temporal array antennas improve communication quality since they can suppress simultaneously undesired signals with space domain processing and ISI with time domain processing. However, conventional array antennas require training sequences to update tap coefficients. Such methods have to waste a fraction of the transmission time to transmit training sequences. In this paper. , we propose a novel spatial-temporal blind array antenna using second order statistics, which does not need training sequences. The proposed algorithm can be applied to the environment in which one transmitter with narrow band signal and white noise or broadband signal exist over the channel. Some computer simulations are shown to verify the effectiveness of the proposed method.
A computerized technique for multi-criteria analysis of seismic disasters - GESKEE database

Econometric scaling approach of vulnerability and risk on building stock to major seismic events is essential for understanding the impact and the consequences of these earthquakes. It also has a predictive value for loss estimation of next events. Due to use of a large amount of information in such multicriteria approaches, it is a high necessity to develop computerized techniques to automate the calculation process, to manage the data easier and also to represent the results in a manner as suggestive as possible. Therefore, the paper presents the GESKEE (Global Econometric Scaling using Knowledge on Earthquake Effects) Database developed by authors which is an indispensable tool for econometric scaling approach for seismic risk assessment. For this aim, the database was structured starting from special requirements imposed by GESKEE Disaster Scale (2010 version). As a result, according to necessity of econometric calculation and graphical representation of GESKEE Disaster Scale (2010 version), a flexible and helpful database (GESKEE Database) was developed. Actually, building up this Database was the main key to improving, developing, updating and to a better interpretation of the GESKEE Disaster Scale (from 1998 version to 2010 version). The results are promising and appear to provide an increased predictive value for the GESKEE Disaster Scale. We consider that this application will be greatly useful and convenient for a transparent and a more efficient seismic risk assessment and loss scaling, providing a better understanding of past seismic events impact and so, a real help in calibration of earthquake disaster prevention policies. In order to point out the innovative edge of GESKEE Database, which is an important tool in social and economic quantification of seismic vulnerability of building stocks and earthquake loss, we present some examples of graphic representations and also some interpretations of the results.
EFFICIENT BIG DATA ANALYSIS USING HADOOP FRAMEWORK FOR SENSOR NETWORK DATA

In the era of rapid development of the technology, Wireless Sensor Networks is also a field that is been developing and already reached a stage where thousands of nodes in a network will be present and they store and also deliver a vast amount of data. We are living in the age where an explosive amount of data is being generated every day. Majorly the data from sensors, mobile devices, social networking websites, scientific data & enterprises are contributing to a maximum extent for this huge explosion of data. The major problem will be the amount of data collected, its storage and processing. So in an order to store, load and process the large scale sensory data we propose a paper that enable the efficient and effective analysis of the Big Data and also handling the large volumes carried out on the Hadoop platform.
Plasma Treatment of Random and Aligned Electrospun PCL Nanofibers

Plasma treatment of electrospun poly(e-caprolactone) (PCL) nanofiber random mats and aligned meshes is studied. The changes in the surface chemistry, and mechanical and biological properties of the PCL nanofibers induced by NH3 + O2 plasma treatment are evaluated using surface contact angle measurements, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), tensile tests, and cell culture. It was found that plasma treatment resulted in a significant increase in surface hydrophilicity of the PCL nanofibers, with the water contact angle reduced from ~135° to 0°. XPS surface characterization indicates that the plasma treatment introduced new functional and polar groups on the fiber surface. Tensile test results show that, after the plasma treatment, the ultimate tensile strength and the ultimate strain of both the PCL nanofiber random mats and aligned meshes were reduced. The phenomenon indicates that the plasma etching effect occurred on the PCL nanofiber surfaces. When cultured with mouse osteoblast cells (MC3T3-E1), the plasma-treated PCL nanofiber random mats and aligned meshes yielded much higher cell proliferation rates compared to those obtained for the untreated controls. Environmental SEM examination shows that the plasma treatment significantly enhanced cell growth along the aligned PCL nanofibers. These results indicate that plasma surface modification of electrospun nanofibers has great potential in the development of novel polymeric scaffolds for tissue engineering applications, such as bone healing and cartilage repair.
Use of integral experiments in support to the validation of JEFF-3.2 nuclear data evaluation

For many years now, IRSN has developed its own Monte Carlo continuous energy capability, which allows testing various nuclear data libraries. In that prospect, a validation database of 1136 experiments was built from cases used for the validation of the APOLLO2-MORET 5 multigroup route of the CRISTAL V2.0 package. In this paper, the k eff obtained for more than 200 benchmarks using the JEFF-3.1. 1 and JEFF-3.2 libraries are compared to benchmark k eff values and main discrepancies are analyzed regarding the neutron spectrum. Special attention is paid on benchmarks for which the results have been highly modified between both JEFF-3 versions.