Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Trust Computation in the Internet of Things

Internet of Things defines a large number of diverse entities and services which interconnect with each other and individually or cooperatively operate depending on context, conditions and environments, produce a huge personal and sensitive data. In this scenario, the satisfaction of privacy, security and trust objectives plays a critical role in the success of the Internet of Things. Trust here can be considered as a key property to establish trustworthy and seamless connectivity among entities and to guarantee secure services and applications. The aim of this study is to provide a survey on various trust computation strategies and identify future trends in the field. We discuss trust computation methods under several aspects and provide comparison of the approaches based on trust features, performance, advantages, weaknesses and limitations of each strategy. Finally the research discuss on the gap of the trust literature and raise some research directions in trust computation in the Internet of Things.
Antioxidant Activity of Syzygium samarangense L. and Their Endophytic Fungi

Leaves of jambu air (Syzygium samarangense L.) has been used by local residents as medicine for various diseases caused by free radical agents in human’s body. This study aims to find a new source of antioxidants from medicinal plants and their endophytic fungi Syzygium samarangense leaves was fractionated by maceration method using gradient solvent i.e. n-hexane, ethyl acetate, and methanol. The antioxidant activity of the leaf fractions was determined using 1,1diphenyl-2-picryl hydrazyl (DPPH) method. The chemical compound was isolated from active fraction by chromatographic techniques and their chemical structure was identified using spectroscopy techniques. The endophytic fungi was isolated from S. samarangense leaves and continued to cultivation in potato-dextrose broth (PDB) medium for four weeks. The medium was extracted using ethyl acetate and determined its antioxidant activity. Fungi’s isolate with highest activity was analyzed to identify its molecular. Ethyl acetate fraction of S. samarangense leaves showed highest antioxidant activity. Spectroscopy analysis result concluded the isolated compound is 5,7-dihydroxy-6,8-dimethyl flavanone. Four endophytic fungi had been isolated form S. samarangense leaves. Antioxidant activity test showed that ethyl acetate extract of endophytic fungi BJA-1 has the highest value. Molecular identification of BJA-1 shows high homology with Lasiodiplodia venezuelensis strain CBS 129753.
On-screen children's stories : the good, the bad and the ugly

Based on the available empirical evidence, it was investigated in the present dissertation which features of electronic storybooks are beneficial for young children's language and literacy development. In a meta-analysis interactive features like 'hotspots' and games were found to distract children from the story. Switching between listening to a story and playing with interactive elements requires multitasking and might be too demanding for young children's cognitive capacities. In contrast, multimedia elements like animation, background music and sound effects that illustrate the abstract language of narrative stories were found to facilitate children's comprehension of the story and word learning. For instance, motion in animated illustrations were found to attract children's attention to the part of the picture that is relevant to the oral narration and thus facilitate story comprehension. Guidelines for developers, parents and teachers are discussed.
Efficient parallel architecture for implementation of the CMA adaptive antenna

Abstract This paper presents an efficient parallel architecture for implementation of a constant modulus algorithm (CMA) adaptive array antenna. By inserting delay units into the original CMA, a novel delayed CMA (DCMA) that can significantly reduce the associated critical path is derived. Consequently, a pipelining architecture that supports parallel processing is introduced for implementation of the DCMA. In addition to the pipelining technique, a power-of-two multiplier is proposed for the DCMA leading to the efficient FPGA implementation. The effects of delays and finite word-length on the convergence property of CMA are investigated via simulations. Moreover, the synthesized results demonstrate that FPGA implementation of the proposed architecture using power-of-two arithmetic achieves 26.9% resource reduction in comparison with that of fixed-point arithmetic and operating clock frequency higher than 65 MHz. The implemented FPGA was tested to confirm that the designed architecture operates well for CMA.
K-Means Demographic based Crowd Aware Movie Recommendation System

In this paper we put forward a novel technique of K-Means based crowd-aware recommender system in which we select the closest crowd to the particular user and locate the preference of those to suggest that user a set of movies at that time in the same province. K-Means is the clustering algorithm that is used to cluster the specifiedspot among the set of spots in the dataset. Here we make groups of the crowd and then locate in which cluster a particular user belongs. Following classify the user in particular category of the crowd, system will find the set of movies among the crowd, which is most preferred by that set of crowd in which the user is classified. The proposed approach is likely to attain the high precision, effectiveness and will take less time as judge against other techniques for movie recommendation.
Performance improvement of automatic pathological voice quality assessment based on higher-order statistics = 고차 통계량에 기반한 자동 장애 음성 평가알고리즘의 성능 개선

This thesis presents new parameters based on the HOS (Higher-Order Statistics) analysis to improve the classification performance of a multi-stage pathological voice assessment system. Automatic pathological diagnosis is a field which still demands further investigation mainly due to the difficulty in quantifying or standardizing the speech pathologists’ diagnoses. In recent years, various speech signal processing techniques have been proposed and applied for the voice disorder diagnosis. The objective is to quantitatively measure the degree of deviation of the pathological from the normal voice patterns with some acoustic analyses. And, objective supports of the diagnostics have some advantages to be adopted directly into the everyday life rather easily with less cost. Although most of the previous researches made novel contributions to the automatic detection of voice disorders and to voice quality assessment, their achievements are not easy to be compared with each other due to the lack of
Disambiguating evolutionary algorithms: composition and communication with ESDL

Evolutionary Computation (EC) has been developing as a field for many years. Encompassing a range of intelligent and adaptive search, optimisation and decision-making algorithms, there is a wealth of potential for EC to be applied to problems in many domains. People who are new to the field may want to learn, understand or apply EC, while those who are more experienced are looking to extend, develop and teach. Unfortunately, it is not clear how best to approach these tasks. Ideally, students should have guidance through the field, including how EC works and approaches to algorithm design; researchers should have canonical structures, implementations, presentation formats and comparison frameworks; developers should have easy access to interested users, as well as the potential to differentiate their work in terms of performance, flexibility and aesthetics. This thesis provides a model of the structure of Evolutionary Algorithms (EAs) based on operator composition. A small number of discrete element types and their interactions are defined, forming an algorithm architecture that supports existing concepts and provides direction for those looking to understand, use and improve EAs. A simple description language based on these elements is created to support communication between authors, readers, designers and software. Implementation concerns, ideas and potential are discussed to assist those with an interest in developing the simulation tools and frameworks used within the field. The model and description language are shown to concisely and unambiguously describe EAs in a directly publishable form.
Neural sequential associator and its application to stock price prediction

A neural sequential associator using feedback multilayer neural networks is proposed to predict long-term time series data. The neural network analyzes the inherent structure in the sequence and predicts the future sequence based on these structures. Feedback multilayer neural networks are used in duplicate and the inputs to such models are functions of time to represent time correlations of temporal data in the synaptic weights during learning. It is shown that the method gives better performance than neural networks without feedback when applied to the prediction of long-term stock prices. <<ETX>>
MODELLING NATURAL LANGUAGE WITH FINITE AUTOMATA

Finite-state devices are used as a core technology in many fields of natural language processing. The applications include speech recognition and generation, spelling correction, fact extraction, information retrieval and approaches to translation. It has been shown that substantial aspects of natural languages can be processed with finite-state methods. Finitestate devices are very attractive for natural language processing, because they provide several advantages for language modelling as well as for mass data processing. The advantage for language modelling is that the framework allows for a uniform modelling of information which can be formulated with rules and of information which due to the social and historical development of natural languages is more word specific and must be lexicalized. In addition, weighted automata allow for accounting for the variability of data. The advantages for mass data processing are manifold: finite-state automata are remarkably fast and they provide compressed representations of the data, which at the same time serve as search structures. Finite-state automata are mathematically well understood, and their algebraic foundation allows for a modular design and automatic compilation of system components. Due to the availability of high-level specifications they are easily maintainable and well-scalable. Modelling natural language with finite state automata has also provided much insight into structures and principles of natural languages and their complexity.
Conversação homem-máquina. Caracterização e avaliação do estado actual das soluções de speech recognition, speech synthesis e sistemas de conversação homem-máquina

VII ÍNDICE DE FIGURAS . IX ÍNDICE DE QUADROS . XI LISTA DE SIGLAS . XIII ÍNDICE. XVII INTRODUÇÃO . 1 1.1 ENQUADRAMENTO DO TEMA . 1 1.2 INTRODUÇÃO AO TEMA. 3 1.3 OBJETIVOS . 5 1.4 ORGANIZAÇÃO DO DOCUMENTO . 7 ESTADO DA ARTE . 9 2.1 COMPONENTES BÁSICOS DE UM SISTEMA DE CONVERSAÇÃO POR VOZ ENTRE HOMEM-MÁQUINA 9 2.1.1. UTILIZADOR . 10 2.1.2. SPEECH RECOGNITION . 10 2.1.3. LANGUAGE UNDERSTANDING . 14 2.1.4. DIALOGUE MANAGEMENT . 15 2.1. 5. LANGUAGE GENERATION . 19 2.1. 6. SPEECH SYNTHESIS . 20 2.1. 7. EXTERNAL SYSTEM . 21 2.2 ESTRATÉGIAS DE DIÁLOGO . 23 2.2.1. CONTROLO DO DIÁLOGO . 23 2.2.2. MÉTODOS DE APRENDIZAGEM . 26 2.2.2.1. Supervised Learning . 27 2.2.2.2. Unsupervised Learning . 27 2.2.2.3. Reinforcement Learning . 28 2.3 DESAFIOS DE UM SISTEMA DE CONVERSAÇÃO POR VOZ . 31 2.3. 1. OUTPUT INADEQUADO AO TIPO DE INFORMAÇÃO . 31 2.3. 2. TRABALHAR EM AMBIENTES REAIS . 32 2.3. 3. OBTER DADOS . 32 2.3. 4. AVALIAÇÃO E TREINO . 32 2.3. 5. INCAPACIDADE HUMANA DE COMUNICAR . 34 2.3. 6. COMPREENSÃO DA LINGUAGEM FALADA . 34
Texting and language learning

The use of text-messaging has increased dramatically across the world in recent years, and with it has come an abbreviated, casual writing style, often called textese, characterized by orthographic innovations called textisms. The research summarized in this chapter confirms that the use of textese is not associated with poorer literacy skills in general. In children, it is even linked with better reading and spelling ability, although the picture is less clear in adults. The way that textism use is studied is important, as different methodologies can result in different estimates. As the written language of text messaging continues to evolve, investigators should continue to develop ways of collecting and studying the message data in children and adults. Rather than being a distraction in the cL1ssrnom, mobile technology has the potential to act as a versatile tool for learning, and die research reviewed here could help to inform future studies in this area.
A Hybrid Algorithm Combining Weighted and Hash T Apriori Algorithms in Map Reduce Model Using Data Analysis Cloud Platform

Data mining performs a primary role in knowledge analysis that blessings improvement of enterprise. In modern generation, distributed data mining turns into a first-rate studies space owing to distributed computing. digital distribution and distributed records mining receives its most vital impact on massive facts analysis with the assistance of cloud and grid computing. This studies paintings plays statistics mining in an exceedingly cloud surroundings and Hadoop platform. affiliation rule set of rules, Apriori, to guide Map Reduce parallel computing platform. we tend to enforce and assess the planned set of rules on Amazon EC2 Map Reduce platform. This work mines distributed statistics in an exceedingly cloud surroundings the utilization of hybrid apriori association rule algorithmic rule which mixes the benefits of each hash-t and weighted apriori algorithms. to explore "huge information" on clouds, it's miles terribly essential to analysis data processing methods based mostly completely on cloud computing paradigm from each theoretical and sensible views finally the work compares the general performance with the present implementation of weighted and hash-t algorithms. The performance of our approach is manifested with the help of the initial experimental results documented during this paper.
The role of working memory in comprehension of doubly embedded relative clauses: a self-paced reading and eye tracking study

Language processing has been a focus of working memory research since Baddeley introduced his Model of Working Memory in the 1970’s. There has been continued discussion over whether the same working memory (WM) system that underlies verbally-mediated tasks relying on conscious, controlled, processing also provides the resources used in language processing. Recently, Caplan, DeDe, Waters, & Michaud (2011) found that increased reading times at only the most difficult point of the most difficult sentences presented in their study (sentences with doubly embedded relative clauses) correlated with improved comprehension. They hypothesized that this correlation occurs because at these points where normal parsing fails, individuals with high working memory capacities use ancillary comprehension mechanisms that rely on verbal working memory. Caplan and Waters (2013) proposed that use of verbal working memory for ancillary comprehension in sentence processing may appear behaviorally as improved comprehension with longer reading times in self-paced reading tasks and as regressive eye movements out of these points where parsing is thought to fail. This thesis attempted to replicate the above mentioned finding of Caplan et al. (2011). This study also added an eyetracking task to enable measurement of regressive eye movements and a measure of working memory to permit analysis of individual
Current Steganography Approaches: A survey

Steganography is the art of hiding the fact that communication is taking place. Steganographic systems can hide messages inside images or other digital objects. To a casual observer inspecting these images, the messages are invisible. In this paper we have first explained concept, and also the history of steganography. After that we have mentioned its basis; and as the main part of the paper, we have cited and explained different methods of steganography. We have also tried to mention advantages and disadvantages of them, and also the way each algorithm works.
A Survey on Applications of Voronoi Diagrams

The Voronoi diagram is a fundamental data structure about space partitioning. It has been employed in a variety of geometry-related fields since 1900s. With the popularity and development of computing technology, the area of its applications is still being widened each year. Based on the references obtained from Internet, this paper try to give a broad and deep survey on the applications, especially those since 1990s. Emphasis is put on w hat problems it solves and why it is employed.
Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps

Considers two well-known pattern recognition techniques using texture analysis. The first is the co-occurrence matrix method which relies on statistics and the second is the Kohonen map which comes from the artificial neural networks domain. Both methods are used as feature extraction methods. The extracted feature vectors are fed to a second Kohonen map used as classifier. The authors report briefly some results of their experimental assessment of the merit of each technique when applied to the problem of classifying the seabed from sequences of real images.
Study of Web Information Extraction Technology Based on Automatically Generated Template

In online public opinion analysis,the people need for automatic tools to find the exact information among the magnanimous information sources for further analysis.This paper presented an approach based on automatically generated template to eliminate noise content and extract information from web pages efficiently.The web page was translated into Document Object Model,and then the extraction rules according to the user demand were established.Based on the above process,the templates of web pages were automatically generated.Finally,these templates extraction rules were employed to directly extract information.Experimental result shows this extraction method which has a high recall and precision is reasonable and efficient.
High Speed Digital Sample Rate Conversion for a Real-Time Ground Station Receiver

High speed digital sample rate conversion for a real-time ground station receiver by Brian J. Carrick, Masters of Science Utah State University, 2016 Major Professor: Dr. Jacob Gunther Department: Electrical and Computer Engineering Small satellites are becoming a popular spacecraft option because of their low cost, and complexity. Past small satellites had simple communication systems with low data rates that rarely exceeded 38.4 kbps. Engineers, researchers, and students, from Space Dynamics Laboratory and Utah State University, have recently created and launched a pair of small satellites called the Dynamic Ionosphere CubeSat Experiment (DICE). This mission was revolutionary because of its high-speed data link, with data rates up to 1.5 Mbps. Due to the high-speed data link, it was necessary to create a ground station capable of receiving and processing the received data. For the DICE mission the ground station developed was not capable of receiving and processing the data in real-time. Because the current ground station was not capable of running in real-time it became necessary to develop a real-time solution. The real-time ground station receiver is a hardware solution, that implements signal processing algorithms in an FPGA in real-time. This report describes the theory, design, simulation, implementation, and testing of the sampling rate conversion portion of the system, as well as, other work that can be done to complete the real-time ground station receiver. (109 pages)
Anion Distributions in Sodium Silicate Solutions. Characterization by 29SI NMR and Infrared Spectroscopies, and Vapor Phase Osmometry

Soluble silicates are complex mixtures of silicate anions. The larger anions are two- or three-dimensional condensation products of silicate monomer, SiO4-4. In more siliceous silicate solutions, i.e., SiO2:Na2O ratio >2.0, some of the silicate condenses to polymeric (colloidal) silica. Negative charges on the anions are balanced by protons or by cations, typically alkali metal or quaternary alkylammonium cations. The distribution of these anions varies with the concentration of dissolved silicate solids, the relative molar concentrations of cations and silica, and to a lesser extent, trace impurities. 29Silicon NMR spectroscopy of silicate solutions has been a powerful method for studying the connectivity of silicon and oxygen atoms in silicate solutions. In this paper we will show how variations in silicate band shape and peak location in the mid-infrared spectrum from 700 to 1300 cm-1 depend on concentration and silica:alkali ratio. We will interpret the infrared data in consonance with NMR results. We.
Use of Bit 0x20 in DNS Labels to Improve Transaction Identity

The small (16-bit) size of the DNS transaction ID has made it a frequent target for forgery, with the unhappy result of many cache pollution vulnerabilities demonstrated throughout Internet history. Even with perfectly and unpredictably random transaction ID's, random and birthday attacks are still theoretically feasible. This document describes a method by which an initiator can improve transaction identity using the 0x20 bit in DNS labels.
Solving NTRU Challenges Using the New Progressive BKZ Library

NTRU is a public-key cryptosystem, where the underlying mathematical problem is currently safe against large-scale quantum computer attacks. The system is not as well investigated, as for example RSA, and the company behind NTRU has created the NTRU Challenges, to remedy this. These challenges consist of 27 different public keys of increasing size, where the task in each challenge is to calculate (something similar to) the private key. The goal of this thesis was to examine different attacks against the NTRU Challenges and solve as many challenges as possible. By lattice reduction attacks, using a recently published new progressive BKZ algorithm, the first five challenges were solved, while the current biggest solved challenge by any researcher is challenge number seven. (Less)
Development of a Continuous‐Flow System for Catalysis with Palladium(0) Particles.

Heterogeneous catalysis for organic synthesis under continuous-flow conditions becomes possible by a new reactor-based approach. Continuous-flow reactors with a monolithic glass/polymer composite interior are loaded with palladium particles by ion exchange followed by reduction. When incorporated into a continuous-flow setup (PASSflow) this reactor allows the transfer-hydrogenation of alkenes, alkynes, nitro-substituted aromatic compounds and benzyl ethers in the flow-through mode. In addition, the activity of the catalysts is well suited to achieve Suzuki, Sonogashira and Heck cross-coupling reactions in the absence of phosphanes or any other ligands, resulting in a greatly simplified purification. As an extension to this concept a bifunctional support was prepared inside the reactor consisting of Pd particles and an ion-exchange group (hydroxide form). In the Suzuki− Miyaura reaction the reactor serves as a base for immobilisation and activation of the boronic acid as boronate and as a catalyst for promoting the C−C coupling reaction under continuous-flow conditions. (© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2004)