Miracle-dz/UniFAR-FTUs huggingface dataset:
Reflective Terahertz Imaging for early diagnosis of skin burn severity

In this thesis we propose that early detection of skin burns is the clinical killer application of Reflective Terahertz imaging. Terahertz imaging has gathered a lot of attention for biomedical applications in the past decade. Properties like sensitivity to water; low energy, non-ionizing nature, transparency to clothing and plastics, and less scattering compared to IR have led to the exploration of THz radiation for imaging of biological tissues. This thesis builds up from the discovery to proof of concept to repeatability of imaging skin burns with Reflective THz imaging system in live rats. Immediate edematous response followed by evolution of burn wound zones, reorganization, redistribution and dissipation of edema was captured in the Terahertz images of burns in rats over a span of three days. Videos made from THz images captured over three days depicting this response have been included as supplemental material to this thesis. Experimental design, modifications and an attempt to quantify the findings are discussed. Statistical tests revealed that distinction among partial and full thickness severities might be possible in early phases of injury. This gives THz imaging an edge over other imaging modalities like Infra red, Laser Doppler imaging and Polarization sensitive coherence tomography etc which are unable to detect the injured area early on.
Sub-Population Genetic Algorithm with Adaptive Strategy in Multi-objective Parallel-Machine Scheduling Problem

This research extends the sub-population genetic algorithm and combines it with a global archive and an adaptive strategy to solve the multiobjective parallel scheduling problems. In this approach, the global archive is applied within each subpopulation and once a better Pareto solution is identified, other subpopulations are able to employ this Pareto solution to further guide the searching direction. In addition, the crossover and mutation rates are continuously adapted according to the performance of the current generation. As a result, the convergence and diversity of the evolutionary processes can be maintained in a very efficient manner. Intensive experimental results indicate that the sub-population genetic algorithm combing the global archive and the adaptive strategy outperforms NSGA II and SPEA II approaches.
On the Metric Regularity of Affine Optimal Control Problems

The paper establishes properties of the type of (strong) metric regularity of the set-valued map associated with the system of necessary optimality conditions for optimal control problems that are affine with respec to the control (shortly, affine problems ). It is shown that for such problems it is reasonable to extend the standard notions of metric regularity by involving two metrics in the image space of the map. This is done b introducing (following an earlier paper by the first and the third named author) the concept of (strong) bi-metric regularity in a general space setting. Lyusternik-Graves-type theorems are proved for (strongly) bi-metrically regular maps, which claim stability of these regularity properties with respect to "appropriately small" perturbations. Based on that, it is proved that in the case of a map associated with affine optimal control problems, the strong bi-metric regularity is invariant with respect to linearization. This result is complemented with a sufficient condition for strong bi-metric regularity for linear-quadratic affine optima control problems, which applies to the "linearization" of a nonlinear affine problem. Thus the same conditions are also sufficient for strong bi-metric regularity in the nonlinear affine problem.
ASPECTS OF STUDYING OF ENGLISH COLOR NAMES : COGNITIVE-DISCURSIVE APPROACH

The article is devoted to the consideration of aspects of studying of English colour names within the cognitive-discursive approach. As one of aspects of study, modeling of knowledge structures defining creation and interpretation of the colour names, which are results of the secondary nomination, is offered. The analysis of simple and parasynthetic words helps to come to a conclusion that their meaning can be defined by the metonymic cognitive model WHOLE – PART (colour) and the propositional cognitive models HAVE QUALITY (COLOUR) LIKE/AS QUALITY (COLOUR), HAVE QUALITY (COLOUR) LIKE OBJECT. Other aspect of studying of colour tone names assumes the analysis of their functioning and the description of cognitive mechanisms of their interpretation.
ANN-ANFIS Based Forecast Model for Predicting PV and Wind Energy Generation

Carbon emissions from economic activity continue to rise and India is the third-largest emitter among individual countries. The Renewable Energy is the way forward and the problems in harvesting it should surmount through policy and technical approaches s. The prime disadvantage with most of the Renewable Energy resources is their susceptibility to the whim and vagaries of nature and becoming a variable random source of power. Predicting the power from these variable power sources define and determine the operation of these system. In this paper ANNANFIS based forecast model for predicting the PV Generation and Wind energy generation is presented. The designed forecast model is trained using historical data. The results of the proposed model are validated and compared by considering data sets from two power generating stations. The proposed model is designed using MATLAB and is presented in the form of a Graphical User Interface (GUI).
Imputation Approaches for Animal Movement Modeling : Supplementary materials A

1.1 Coverage and detection regions To evaluate the performance of the process imputation approach, we compared the posterior distributions of β to the values used to simulate the data through two summary measures. First, we computed the proportion of the time interval during which a pointwise, equal-tailed 95% credible interval contained the true value of the function β, which we term the “coverage region. ” Small coverage regions suggest that the associated method is biased or under-represents uncertainty about model parameters and may be unreliable in practice. In contrast, large coverage regions do not necessarily suggest reliability, because uncertainty about model parameters may be inflated, resulting in high coverage rates without yielding precise inference. A desirable property of posterior credible regions for ∗ Corresponding author: henry.scharf@colostate.edu
Modelo de conectividad de la circunvolución angular en el lenguaje:: metaanálisis de neuroimágenes funcionales

Introduccion. Las tecnicas modernas de neuroimagen funcional permiten analizar la activacion simultanea de diversas areas cerebrales y sugerir modelos de conectividad para funciones cognitivas especificas. Objetivo. Realizar un metaanalisis de las redes funcionales de la region angular (area de Brodmann 39) con relacion al lenguaje. Materiales y metodos. Partiendo de la base de datos BrainMap, se analizaron las coordenadas de activacion en estudios de resonancia magnetica funcional que mostraban una activacion del area de Brodmann 39 durante la realizacion de tareas linguisticas. Se seleccionaron ocho articulos con 13 experimentos, que incluian un total de 155 sujetos, y 265 localizaciones. Resultados. Los resultados mostraron 16 conglomerados de activacion significativos que conformaban una red de coactivacion, la cual incluia las dos regiones angulares, el lobulo parietal superior y la circunvolucion supramarginal derechos, el lobulo temporal izquierdo (cara lateral medial y cara medial inferior) y el lobulo frontal (premotor bilateral y prefrontal izquierdo). Conclusiones. Estos resultados coinciden con los hallazgos obtenidos con tecnicas de conectividad estructural y apoyan el papel integrador de la region angular en funciones linguisticas.
LOVE AND FRIENDSHIP IN CYBERSPACE

Since its birth in the early 1960's the Internet has been growing exponentially in all areas and it is predicted that by the year 2002, 490 million people around the world will have Internet access. Similarly, a rapidly increasing number of people are finding themselves working and playing on the Internet, using computer mediated communication (CMC) to converse, exchange information, debate, court, and show compassion. As a result CMC has become a new way for people to find or meet each other via social Internet tools and form and develop personal relationships. Malcolm R. Parks (1997) compiled a theory of relational development, incorporating seven dimensions along which the nature of interaction changes as relationships develop or deteriorate: 1. Interdependence (influence on each other), 2. Breadth (variety of interaction), 3. Depth (intimacy of interaction), 4. Commitment (expectations that a relationship will continue), 5. Predictability and understanding (familiarity with each other), 6. Code change (creating own linguistic forms and culture) and 7. Network convergence (introducing each other to respective online contacts and social networks). This study investigated the relational development reached in interpersonal relationships initiated and maintained online via social Internet tools. As mainly South Africans responded, results provide first time information about South African Web users' online relationships. Results show that the majority of online relationships reached above average levels of relational development as measured by elevated scores on most of the seven dimensions. The results also show significant differences between the levels of relational development reached in online friendships as opposed to online romantic attachments. The results are consistent with past research and could be used as a point of departure for further investigations into South African's Internet social practices and relational development in online settings. Stellenbosch University http://scholar.sun.ac.za
A Model to Forecast Wind Speed through Singular Spectrum Analysis and Artificial Neural Networks

To forecast wind speed is a crucial process to the design, the development and the operation of a windelectric plants. The speed and the direction of the wind are generally intermittent and it is necessary a robust model that be able to predict correctly their variations. This paper proposes a new wind speed forecasting model based on artificial neural networks and singular spectrum analysis. This model is able to provide forecasts from 1 to 24 hours ahead, and has been experimented using a Brazilian wind-power generation database. The obtained results have demonstrated that the model proposed in this paper is robust and reliable for real applications. Keywords—Wind speed forecasting, singular spectrum analysis, windelectric plant, artificial neural network
Auditor Experience, Work Load, Personality Type, And Professional Auditor Skeptisism Against Auditors 'Ability In Detecting Fraud

This study aims to examine the effect of auditor experience, workload, personality type professional skepticism, on the auditor's ability to detect fraud. Respondents in this study were KAP auditors in the Jakarta, Surabaya and Makassar regions with purposive sampling techniques. The data collection method used in this study was by distributing questionnaires to the respondents. The data analysis method used is multiple linear regression analysis. This research is very useful for auditors to detect fraud. The results showed that audit experience variables, workload, and professional skepticism had a positive effect on the auditor's ability to detect fraud, while personality type variables did not influence the auditor's ability to detect fraud
Fully Integrated Memristor and Its Application on the Scroll-Controllable Hyperchaotic System

In this paper, a fully integrated memristor emulator using operational amplifiers (OAs) and analog multipliers is simulated. Based on the fully integrated memristor, a scroll-controllable hyperchaotic system is presented. By controlling the nonlinear function with programmable switches, the memristor-based hyperchaotic system achieves controllable scroll numbers. Moreover, the memristorbased hyperchaotic system is fully integrated in one single chip, and it achieves lower supply voltage, lower power dissipation, and smaller chip area. The fully integrated memristor and memristor-based hyperchaotic system are verified with the GlobalFoundries’ 0.18 μm CMOS process using Cadence IC Design Tools. The postlayout simulation results demonstrate that the memristor-based fully integrated hyperchaotic system consumes 90.5mW from ±2.5V supply voltage and it takes a compact chip area of 1.8mm.
Deep Curiosity Networks

We introduce Deep Curiosity Networks (DCN), a technique for guiding exploration of reinforcement learning agents towards states that both novel and useful for predicting future observations. In DCN, an agent receives an exploration bonus that is proportional to the product of a scalar curiosity score and the error of a neural network trained to predict features of the next observation. The curiosity score approximates how useful an observation will be for training the predictor network: i
Title ML symbol synchronization for OFDM-based WLANs in unknownfrequency-selective fading channels

Based on the maximum-likelihood principle and the preamble structure of IEEE 802.11a WLAN standard, this paper proposes a new symbol synchronization algorithm for IEEE 802.11a WLANs over frequency-selective fading channels. In addition to the physical channel, the effects of filtering and unknown sampling phase offset are also considered. Loss in system performance due to synchronization error is used as a performance criterion. Computer simulations show that the proposed algorithm has comparable performances to the algorithm based on the generalized Akaike information criterion (GAIC), but the proposed algorithm exhibits reduced complexity.
Entomopathogenic Nematodes: Potential For Exploration and Use in South America

Entomopathogenic nematodes have potential for biological control of insect pests. They are currently used for the control of soil and cryptic pests in North America, Europe, Asia and Australia. Entomopathogenic nematodes can be easily mass-produced and applied using conventional spray equipment. They have a broad host range and are safe to the environment. Due to the history of success with implementation of biological pest control programs, South American countries have a great opportunity to develop and implement the use of entomopathogenic nematodes. Recent emphasis on fruit and vegetable production in Brazil also stresses a need to implement safer and effective pest control methods. This paper provides an overview of recent developments in entomopathogenic nematode research and commercialization, evaluates their potential for use and exploration in Brazil and other South American countries, and makes recommendations for establishing entomopathogenic nematology.
Utilization of Unlicensed Spectrum in Effective Coexistence of eMBB and uRLLC

In the emerging 5G mobile network, ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two inﬂuential service classes. The coexistence of uRLLC and eMBB services on the same radio resource leads to a challenging scheduling problem because of the trade-off among latency, reliability and spectral efﬁciency. In this paper, a puncturing scheme based coexistence approach between uRLLC and eMBB trafﬁc is proposed for the upcoming 5G mobile networks, while eMBB users are supported by additional unlicensed spectrum resources. We present two heuristic algorithms to resolve the resoruce allocation problems of eMBB and uRLLC users. Simulation results show the beneﬁts of the proposed approach over other baseline methods in terms of the minimum achieved rate and fairness among eMBB users.
Distributionof Nodal Vacancies in Random Graphs : Connectivity Management for Sensor-Fusion and Mobile Networks

Distributed sensor networks are systems of sensor nodes interconnected by communication networks. The networks support advanced detection algorithms that use distributed computing to detect events as emergent phenomena of the system of sensors. These systems depend on connected and reliable communication networks. The systems are characterized typically by random nodal distribution and are directly related to mobile peer-to-peer networks having similar characteristics and problems. This paper explores models of these networks based on techniques of analyses of random graphs. Through evaluation of areas within the communication network uncovered by nodes, a model for connectivity of networks is developed.
ELF Optimizing Backend Compile Install Input Output dex code IR native code Transform Code Generation Optimization

With the introduction of Android 5 Lollipop, the Android Runtime (ART) superseded the Dalvik Virtual Machine (DVM) by introducing ahead-of-time compilation and native execution of applications, effectively deprecating seminal works such as TaintDroid that hitherto depend on the DVM. In this paper, we discuss alternatives to overcome those restrictions and highlight advantages for the security community that can be derived from ART’s novel on-device compiler dex2oat and its accompanying runtime components. To this end, we introduce ARTist, a compiler-based application instrumentation solution for Android that does not depend on operating system modifications and solely operates on the application layer. Since dex2oat is yet uncharted, our approach required first and foremost a thorough study of the compiler suite’s internals and in particular of the new default compiler backend called Optimizing. We document the results of this study in this paper to facilitate independent research on this topic and exemplify the viability of ARTist by realizing two use cases. In particular, we conduct a case study on whether taint tracking can be re-instantiated using a compiler-based app instrumentation framework. Overall, our results provide compelling arguments for the community to choose compilerbased approaches over alternative bytecode or binary rewriting approaches for security solutions on Android.
Scalable Multi-Class Gaussian Process Classification via Data Augmentation

This paper proposes a new scalable multi–class Gaussian process classification approach building on a novel modified softmax likelihood function. This form of likelihood allows for a latent variable augmentation that leads to a conditionally conjugate model and enables efficient variational inference via block coordinate ascent updates. Our experiments show that our method outperforms state-of-the-art Gaussian process based methods in terms of speed while achieving competitive prediction performance.
Named Entity Recognition in Tatar : Corpus-Based Algorithm

Named entities recognition is one of the urgent tasks in the researches of language using electronic language corpuses. This article discusses the main methods for solving this problem, including algorithms based on various machine learning models, regular expressions and dictionaries. Also in the article, the authors proposed their own algorithm, which allows named entities recognition on the basis of search queries using direct and reverse search. The results of the algorithm, presented in the article, suggest what additional functions are necessary to achieve the best results. The proposed algorithm is used in the “Tugan Tel” corpus management system and can be used both with the electronic corpus of the Tatar language and with corpuses of other languages.
Adoption of Technology and Performance of Kenya Revenue Authority

: Kenya Revenue Authority has been missing tax targets set by the national treasury despite introducing and adoption of technology to help in tax administration. The degree of tax avoidance and evasion has also increased in tax administration. The adoption of technology will assists tax payers and the Kenya Revenue authority staff to file and administer taxes and offer e-filling and e-payments instead of using paper based system. The general objective of this study was to establish the effect of adopting technology and performance of Kenya Revenue Authority while the specific objectives was to find out the effect of perceived usefulness of adoption of technology on performance of Kenya Revenue Authority, to determine the perceived ease of use of technology on performance of Kenya Revenue Authority, to explore the effects of perceived behavioral control of technology on performance of Kenya Revenue Authority and lastly to determine the effects of perceived security of technology on performance of Kenya Revenue Authority. Technology Adoption Model and the Theory of Reasoned Action/Theory of Planned Behavior was used to guide the study. The study adopted a survey design in order to obtain the necessary data.T-test was used to determine the degree and significance of the relationship between variables. All the questionnaires were adequately checked for reliability and verification using tables, charts and graphs. The study was carried in the Nairobi’s head office withtarget population of 220 respondents comprising of 20 Senior Assistant Commissioners, 30 Assistant Commissioners, 50 Senior Revenue Officers and 120 Revenue Officers of head office who add up to a total 220 respondents. Israel (2013) sample determination formula was utilized to determine the sample size and stratified random sampling used to identify the particular respondents to participate in the study. The required primary data was collected using structured questionnaire. Both quantitative and qualitative analysis was done using statistical package of social sciences (SPSS) (version 20). The analyzed data was then used to make conclusions, recommendations and suggestions for further research.
EFFECTS OF HYDROPRIMING ON METABOLIC ACTIVITY , SEED GERMINATION AND SEEDLING EMERGENCE OF CARROT Summary

The aim of the experiments has been to investigate the effect of four methods of hydroconditioning, in cluding single and two-step soaking, on the carrot 'Amsterdamska' seed s quality. For this purpose, the commercial seeds o f this variety have been hydrated with four methods at 20°C, using a si ngle or two-step soaking for 2, 4, 6, 12 and 24 hou rs or in doses of the aerated water to the different moisture content (10 to52%). Next, the moistened seeds with the mention ed methods have been incubated at 18-20 C in airtight containers for 1, 2, 4 or 6 days, wit h daily aeration and assessing their moisture content. Then, the hydroprimed seeds have been examine d by the evaluation of the dynamics and number of g erminated seeds, mean time of seed germination at 20°C, permeability of cytoplasmic membranes, total activity of dehydr ogenases, dynamics of emergence, number of emerged seedlings and their height after 18 days after sowing. The results ind icate that the carrot 'Amsterdamska' seeds after hydration up to 40% of w ater content and incubation at 20°C for 2-4 days a nd drying down to an initial moisture content, affects in the higher vigor of seeds, accelerate their germination, often in greater percentages and accelerate emergence and growth of seedlings. R apid penetration of water into the embryos does not damage the cytoplasmic membranes.
A Forensics Approach for Hypervisor Lokendra

Cloud Forensics defines as a post investigation and discussion of the survey results generated by the cyber attacks over Cloud. The exponential growth of the Cloud in private and public Sectors has also increased the Cyber Crimes in the Cloud. Virtualization is the Techniques running at the back of Cloud computing in which virtual machines simultaneously operates and application that controls and managed them is hypervisor. Many models for security of virtualization have been proposed for the protection of resources but still virtualization is being vulnerable to many attacks. Hypervisor forensics is a post approach to investigate and analyze security threats at hypervisor level. In this paper we have proposed an algorithm and implement this framework which will work for maintaining the data log file in terms of attacks graphs.