Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Processing and Testing of a MEMS Energy Grooming Structure for Initiator Materials

A common problem experienced by micro-electro-mechanical system (MEMS) initiators is unintentional triggering in a transient high-pulse environment owing to low ignition energy. Herein, therefore, combining the size effect of the Paschen’s law with a modified semi-empirical formula of the secondary electron emission coefficient, a theoretical calculation was carried out to improve the electrostatic grooming structures. It was found that the size effect of the Paschen’s law is owing to the distance-dependent process dominance of the Thomson discharge process and the field-induced electron emission process. A microscale breakdown model was, thus, proposed based on field-induced electron emission and the secondary electron emission coefficient. The structural simulations and the optimization of various materials were carried out using the multiphysics field simulation software (i.e., COMSOL), and an electrostatic grooming device was subsequently fabricated. During experimental testing, the transient voltage of an electrostatic grooming device with a <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{m}$ </tex-math></inline-formula>-wide comb gap could be groomed over 130 V. Furthermore, the model was in good agreement with the experimental results in the range of less than <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula> and more than 9 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>. In the optimum range of 2–<inline-formula> <tex-math notation="LaTeX">$9~\mu \text{m}$ </tex-math></inline-formula>, the result of the test differed from the model by less than 15%. The model, therefore, possesses excellent adaptability in this scale range and can be used to greatly improve the antistatic characteristics of initiators.
Anchoring Events to the Time Axis toward Storyline Construction

To understand the present situations and predict the future, it is essential to look at the past. Since social situations and people’s sense of values are constantly changing, most facts are not true eternally. In order to overview a transition of interpretations about events and things, it is necessary to read texts in a chronological order and organize information along the time axis as a storyline. However, there is a large amount of text on the Web, and since it is no longer impossible to read all the related text manually, a new tool is required. Natural language processing (NLP) is a technology to analyze text using computers and is used in various applications such as information retrieval, opinion analysis, summarization, question answering, and machine translation. A text includes event information in the past, present, and future, so there have been many studies analyzing temporal information in a document. However, they mainly focus on relative local relations between events and between event and time, and there are few studies anchoring events to the time axis with the consideration of global information. In this thesis, we present (i) temporal expressions analysis, (ii) temporal corpus construction, and (iii) temporal information analysis and timeline generation, toward storyline generation. First, we propose a model which recognizes and normalizes temporal expressions in text. Temporal expressions are fundamental for textual temporal analysis and many models have been proposed. However, analyzing loose structures of temporal expressions is a remained problem. To overcome this problem, we propose a neural network model that recognizes and normalizes loose structured temporal expressions via multi-task learning. Then, we propose a new annotation scheme for anchoring expressions in text to the time axis comprehensively. The points of our annotation scheme are two-fold: annotat-
Teaching and learning online – Developing your skills to develop others.

The wide-spread use of Information and Communication Technologies (ICTs) in language learning has not only provided more varied forms of learning for adults but has also posed a number of challenges for language teachers who now need to be multiskilled: They have to be knowledgeable about the ICT tools available for language teaching, able to decide whether a particular tool is consistent with their underlying pedagogic approach, and confident in the use of suitable tools so as to ensure that these tools meet the needs of their learners. It is therefore crucial for language teachers to receive support and encouragement to use ICT tools (more) confidently and to develop their skills further. The DOTS (Developing Online Teaching Skills, http://dots.ecml.at) and MoreDOTS (Using open resources to develop online teaching skills) projects, which have been supported by the European Centre for Modern Languages (ECML, www.ecml.at) since 2008, address teachers’ needs for support by providing a Moodle-based workspace (http://moodle.dots.ecml.at), containing bite-sized training modules and the opportunity to share experience and language teaching materials with other language teachers in Europe (Beaven et al., 2010; Stickler et al., 2010).
Supporting Content Design with an Eye Tracker: The Case of Weather-based Recommendations

Designing content output for weather-aware services based on domain experts can sometimes be arduous due to their limited availability and the amount and complexity of information considered in explaining their recommendations. As an initial step in our work towards generating recommendations that are acceptable and readable, our methodology involving an eye tracker attempts to simplify and capture more valuable data in early design stages. Our pilot study explored which information in weather-based recommendations seemed to be more useful to support users decision making. The results suggest that interactive content could be deployed based on the relevance of informational items and both graphical points of interest and legends could help in delivering content more efﬁciently.
An accelerated algorithm for delayed distributed convex optimization

In many large-scale optimization problems arising in the context of machine learning the decision variable is of high-dimension and the objective function decomposes into a sum over a large number of terms (one for each instance in the training data set). In this setting, second-order optimization methods such as Newton or quasi-Newton methods, are not tractable due to the complexity of evaluating and inverting the Hessian, or an approximation thereof. Also, the vast amount of data available is spread around multiple servers making a centralized optimization solution sub optimal or impossible. Therefore we concentrate on first order methods that are scalable in a decentralized setting.In this thesis we provide a framework for distributed delayed convex optimization methods for networks in a master-server setting. Our goal is to optimize a global objective function which is the sum of the local objective functions of the agents in the network.We review Nesterov's accelerated algorithm for centralized optimization since it is the optimal algorithm for the class of convex, and strongly convex functions and to modify it accordingly for decentralized optimization in the master-server setting.It is natural that in an asynchronous setting the current value of the server node is a past value of the master node communicated some time steps ago, and thus gives rise to delays in the analysis. We have proven that a delayed accelerated method maintains the optimality of the algorithm with a convergence rate of O(1/t²). We have also performed simulations and we have verified that the accelerated algorithm performs better that the alternative algorithms for decentralized optimization in a master server setting. %%%%Dans de nombreux problemes d'optimisation a grande echelle qui se posent dans le cadre de la machine l'apprentissage , la variable de decision est de haute dimension et la fonction objectif se decompose en une somme sur un grand nombre de termes (une pour chaque instance dans l'ensemble de donnees d'apprentissage). Dans ce cadre , les methodes d'optimisation de second ordre tels en tant que methode de Newton ou quasi- Newton , ne sont pas traitables par la complexite de et l'evaluation de l'inversion de la Hessian ou une approximation de celui-ci. Par consequent , nous nous concentrons sur les methodes du premier ordre qui sont evolutives dans un cadre decentralise.Dans cette these, nous fournissons un systeme cadre pour des methodes d'optimisation distribues retardes convexes pour les reseaux multi-agents dans un cadre serveur-maitre. Notre objectif est d' optimiser une fonction objectif globale qui est la somme des fonctions objectifs locales des agents dans le reseau. Nous introduisons l'idee d' accelerer l'algorithme de Nesterov pour l'optimisation centralisee dans le cadre serveur maitre decentralisee puisqu'il est l'algorithme optimal pour le classe de fonctions qui nous interessent. Il est naturel que dans un cadre asynchrone, la valeur actuelle du noeud de serveur est une valeur passee du noeud maitre communiquee…
Network-based Protocol Implementation Engineering and A Study on MachineLearning based Binary Code Analysis

Network-based Protocol Implementation Engineering and A Study on Machine Learning based Binary Code Analysis Network-based protocol implementations play a significant role in the communication area, but the feature creep and increasing vulnerabilities have become serious problems. To deal with these problems, we proposed TOSS, an online server systems tailoring tool through binary rewriting. TOSS minify binary programs with just-enough features to reduce the potential attack surface and deal with the debloating problem. Besides, this paper also gives a comprehensive study of machine learning based binary code analysis to find the relationship between binary code and machine learning algorithm. For networkbased protocol implementations whose source code is available, we proposed a packet generation and analysis functions identification tool through slicing, and test it with several network-based protocol applications.
Functional Properties of Triticale Bran Bioactives Using In Vitro and In Vivo Model Systems

Part of the solution to reducing the prevalence of chronic diseases is the employment of food as medicine. Whole grain consumption has been associated with a variety of health benefits owing to their bioactive constituents, namely fibre and phytochemicals. The overall aim of this study was to discover the underlying mechanisms behind the positive physiological effects of whole grains and further elucidate their nutritional value. In particular, the bioactive properties of alkylresorcinols (ARs), phenolic lipids present almost exclusively in cereal grains, and soluble dietary fibre, potential prebiotics, were investigated using triticale bran (TB), an underestimated cereal crop. Specific objectives were met through the following projects. Conditions for large-scale extraction of ARs from cereal grains were optimized using response surface methodology (RSM). Isolation of ARs for 16-24 h at a solid-to-solvent ratio of 1:40 (weight per volume) and temperature of 24 °C produced the highest yield with the least amount of co-extractives/unknowns. The antioxidant and anti-inflammatory activities of ARs were evidenced by in vitro model systems, ORAC, DPPH, and COX assay whereby results were in agreement with ex vivo model systems, RAW 264.7 macrophage cell cultures induced by AAPH or LPS. Subsequently, the in vivo effects of ARs were evaluated and oxidative stress markers in liver and heart tissues of mice supplemented 0.5% ARs and mice fed 10% TB demonstrated antioxidant protection. Additionally, water extractable polysaccharides (WEP) from cereal samples were isolated by varying treatments; the highest yield with the least amount of impurities was achieved by boiling water extraction followed by successive enzyme treatments, dialysis and successive ethanol fractionations. Using yogurt as fermentation model, probiotic yogurt in the presence of TB produced significantly higher TTA values, lower pH levels, and improved microbial viability compared to controls. In addition to the prebiotic potential of TB, likely owing to its high fibre content, isolates of WEP also demonstrated antioxidant activity as measured by ORAC assay. Findings of this study will contribute to a better understanding of the underlying mechanisms behind the health benefitting properties of whole grains.
Development of an approach to language identification based on language-dependent phone recognition

The goal of Language Identification (LID) is to quickly and accurately identify the language being spoken. Although the differences among different (spoken) languages are generally large by any sensible measure, automatic language identification remains a major challenge (perhaps indicating the immaturity of the field of speech processing). Current language identification systems vary greatly in terms of information utilization and system complexity. Understanding all of these approaches in a unified framework is one of the major challenges in automatic language identification. In this dissertation we provide a partial unification by studying the roles of acoustic, phonotactic and prosodic information in a particular system for language identification. A comparative study was first conducted on a common two-language task (English and Japanese) to get a grasp of these issues. The results from the comparative experiments were used as basis for the development of a general purpose language-identification base-line system. Within this framework, two novel LID information sources (backward language model and a context-dependent duration model) were introduced. These two models increased language modeling accuracy at a moderate cost in terms of training data. Also, a novel optimization method was introduced to enhance the discrimination between different languages. These methods led to substantial improvements in system performance. Preliminary studies into channel normalization, conversational speech and system adaptation to new languages were also pursued. A general purpose LID software tool kit was developed based on the algorithm developed in this thesis work. The final LID system developed attained correct rates of 91% (45-second segments) and 77% (ten-second segments) on a commonly used nine-language task. This is one of the best results reported to date on these tasks.
Handbook of RF and Microwave Power Amplifiers: Frontmatter

1. Silicon LDMOS and VDMOS transistors: physics, design and technology Wayne Burger and Chris Dragon 2. GaAs FETs: physics, design and models Rob Davis 3. Wide band gap transistors - SiC and GaN: physics, design and models Bob Trew 4. Amplifier classes, A to S Steve Cripps 5. Computer-aided design of power amplifiers Steve Maas 6. Practical HF/VHF/UHF RF power amplifier realization Dan Myer 7. Microwave hybrid amplifier realization Dominic Fitzpatrick 8. Monolithic power amplifiers Inder Bahl 9. RF power amplifier thermal design Mali Mahalingam 10. Reliability Bill Roesch 11. Power amplifier applications Mustafa Akkul and Wolfgang Bosch 12. Amplifier measurements Michael Hiebel.
The study of similarity score calculation methods for minutia-based fingerprint matching algorithms

The study of similarity score calculation methods for minutia-based ngerprint matching algorithms A.J. de Kock Department of Mathematical Sciences, Division of Applied Mathematics, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa. Thesis: MSc December 2016 This study aims to establish guidelines for calculating the similarity score between two minutia point representations of ngerprints for minutia-based ngerprint matching. Existing research does not provide clear guidelines on how to calculate the similarity score between two minutia point representations and the reported performance of most existing algorithms include those comparisons for which the point matching algorithm failed. This study therefore compares the performance of existing similarity score calculation methods after the erroneous comparisons from the point matching algorithm have been removed. It furthermore investigates in which way and to what extent these methods are a ected by intra-class variations and inter-class similarities. The results indicate that none of the existing similarity score calculation methods is superior to all the others when implemented on the FVC2002 and FVC2004 ngerprint databases. This study also proposes an improved local descriptor for local similarity score calculation and investigates whether the combination of di erent types of similarity score calculation methods better addresses intraclass variations and inter-class similarities and therefore improves pro ciency. The results indicate that similarity score calculation methods that address both global and local inter-class similarities, and are robust to intra-class variations, perform better across multiple databases. Even though this study concludes that the combination of di erent types of similarity score calculation methods generally improves pro ciency, high levels of noise and nonlinear distortion still adversely a ect performance. Future work should therefore focus on improving the stages preceding the similarity score calculation stage, i.e. minutia extraction and point matching. ii Stellenbosch University https://scholar.sun.ac.za
Evolving programs to build artificial neural networks

The biological brain has inspired many artificial intelligence algorithms, especially artificial neural networks (ANNs). Originally designed to replicate neural functionality [10], these models are now used in a wide variety of applications and have shown impressive ability in computer vision, natural language processing, and control. Despite these significant advances, contemporary ANNs are far removed from their biological sources and do not demonstrate the same ability to generalize to new tasks and in new environments as biological organisms [4, 7]. We suggest that the focus on synaptic weight change as the learning mechanisms in ANNs is a contributing factor to their limitations in generalization. As neural development, which changes network structure, is an integral part of biological learning, we propose that structural development should be included in learning in ANNs.
A Comparison of Sentiment Analysis Methods on Amazon Reviews of Mobile Phones

The consumer reviews serve as feedback for busi-nesses in terms of performance, product quality, and consumer service. In this research, we predict consumer opinion based on mobile phone reviews, in addition to providing an analysis of the most important factors behind reviews being classified as either positive, negative, or neutral. This insight could help companies improve their products as well as helping potential buyers to make the right decision. The research presented in this paper was carried out as follows: the data was pre-processed, before being converted from text to vector representation using a range of feature extraction techniques such as bag-of-words, TF-IDF, Glove, and word2vec. We study the performance of different machine learning algorithms, such as logistic regression, stochastic gradient descent, naive Bayes and convolutional neural networks. In addition, we evaluate our models using accuracy, F1-score, precision, recall and log loss function. Moreover, we apply Lime technique to provide analytical reasons for the reviews being classified as either positive, negative or neutral. Our experiments revealed that convolutional neural network with word2vec as a feature extraction technique provides the best results for both the unbalanced and balanced versions of the dataset.
Comparison of Ten Agent-Oriented Methodologies

This chapter provides a comparison of the 10 agent-oriented software engineering methodologies presented in the preceding chapters. An evaluation framework comprising process-related, technique-related, model-related and supportive-feature criteria is used in this comparison. As each application entails a different set of requirements that indicate which evaluation criteria are the most important and should be supported by the chosen methodology, the “best” methodology is dependent on the target application. The results provide a useful framework to assist the developer in selecting the most appropriate methodology for any target application.
The Design of Novel Kni-10729 Based Compound for Inhibition of HTLV-1 Protease as Novel Insight for Development of Anti-HTLV Drugs

The Design of Novel Kni-10729 Based Compound for Inhibition of HTLV-1 Protease as Novel Insight for Development of Anti-HTLV Drugs Masoud Keikha 1, 2 and Mohsen Karbalaei 3, * Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Department of Microbiology and Virology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Department of Microbiology and Virology, School of Medicine, Jiroft University of Medical Sciences, Jiroft, Iran Corresponding author: Department of Microbiology and Virology, School of Medicine, Jiroft University of Medical Sciences, Jiroft, Iran. Email: karbalaeim931@mums.ac. ir
ANALYSIS AND COMPARISON OF ROUTING PROTOCOL OF WIRELESS SENSOR NETWORK

There are many differences between the routing protocol design of a wireless sensor network and wireless ad-hoc network. Highly restricted resource and frequent node failure are two major challenges. Relevant technical research into wireless sensor network has become an issue of intense interest. In recent years, achievements in research on routing protocol of wireless sensor network have been summarized, analyzed and compared. Features of wireless sensor networks have been introduced and key factors of its routing protocol design have been introduced. According to the realization features of protocol, routing protocol of wireless sensor network has been divided into five catego-ries, and each category of important protocols has been elaborated and analyzed. Finally, the features of these pro-tocols are summarized and compared and the development trend of such research has been forecasted.