Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Collision Detection for Rigid Object in Deformable Environment

Collision Detection for rigid object in deformable environment is very popular in the field of virtual reality such as surgery simulation and so on, but few research has been made. This paper proposes a collision detection method based on fixed direction hull bounding volume tree which provides an effective way for such problem. Fixed direction hulls is a special convex hull whose outward normal of facets comes from a fixed direction set. It overcomes limitations of other bounding volumes and makes a promise between tightness and simpleness. It can be determined that two fixed direction hull bounding volumes do not overlap by checking if one of their bound intervals on direction axis defined by the fixed direction set does not overlap. Thus the intersection test between two bounding volumes need only at most n comparisons ( n is the size of fixed direction set). Updating bounding volumes after rotation is an important problem of hierarchical bounding volume approach. An updating algorithm based on linear programming is proposed in this paper based on the definition and property of fixed direction hulls. This method need not add any additional computation and memory during building bounding volume hierarchies. It can update a bounding volume through 3 n multiplications. Deformation of object is a difficulty in collision detection. This paper analyses two kinds of deformations and proposes resolving method respectively. A bottom up updating method is proposed especially which can compute the bounding volume of parent node through bounding volumes of the two children only by n comparisons.It is proved through experiment that our method can not only solve collision detection between rigid objects efficiently but also solve collision detection between rigid objects and deformable objects.
Deep learning with application to hashing

Deep Learning and Learning to Hash are two important research areas in machine learning, which have rapid improvements in recent years. What I mainly researched on is an inter-discipline field: deep learning for cross view hashing. Multiple layers of representation in deep learning has the property of abstracting representation from input data, while, in the cross view similarity search, the biggest difficulty is to represent items from one domain to another. Here, I want to take advantage of the latest deep learning technology to solve the cross view similarity search problem. Hashing is used to accelerate this process. This thesis mainly contains three parts. Chapter 2 is a literature survey. It contains a deep learning survey and a learning to hash survey. The deep learning survey briefly introduces fundamental technology of deep learning and its recent development including the latest technology. The Learning to Hash survey brief introduces some widely used learning to hash algorithms. Chapter 3 is an experiment about comparison of some state of the arts learning to hash algorithms. Chapter 4 is cross view hashing based on deep learning. I present a cross view feature hashing technique using deep learning and show some results. These three chapters are main chapters. Chapter 1 and Chapter 5 are introduction and conclusion.
Implementing commitment HR practices: line manager attributions and employee reactions

Abstract Findings of positive relationships between a set of well-configured commitment human resource practices and organizational performance outcomes are well documented in the strategic human resource management (HRM) literature. However, several recent empirical studies have found significant gaps between the HR practices that organizations report using and employees’ experiences of those HR practices, raising a question about the extent to which organizationally intended HR practices are effectively implemented within an organization. Drawing on attribution theory, we address this problem by focusing on the role of frontline managers (FLMs) in implementing commitment HR practices. Data collected from 195 employees in 61 work groups in two time periods showed that FLMs’ commitment HR attributions affected their implementation of commitment HR practices, which in turn affected employees’ perceptions of HR practice implementation and the average level of organizational citizenship behavior in their work group. The findings contribute to the strategic HRM literature by demonstrating the significant role of FLMs’ attributions in attaining the unrealized potential benefits of commitment HR practices through the effective implementation of commitment HR practices.
Advancing Towards a Cashless Society

This exploratory research study looked into the younger population of Brunei Darussalam in terms of the feasibility of eWallets. By utilizing the unified theory of acceptance and use of technology (UTAUT) model established by Venkatesh et al., this research hoped to assess the country’s technological readiness and the level of acceptance of eWallet adoption in a future of an e-Payment economy for the purpose of to improve the efficiency of financial institutions as well as for the provision of new services for the convenience of the customers. The study found that none of the main four constructs of the UTAUT model to be predictors of behavioral intention but rather, attitude towards using technology and anxiety. This may be as a result of the younger populace being in constant interaction with various types of technology, paired with the rising internet connectivity which led to the minimal impact of a new technology, in this case, eWallets. Advancing Towards a Cashless Society: The Acceptance of eWallet in Brunei Darussalam
Automatic Context-Relevant Off-Activity Talk Suggestion for Dialogue Contribution

This thesis investigates the possibility of utilizing online resources for off-activity dialogue contribution. It is a first attempt to propose a tool which automatically suggests off-activity talks in form of some sentences relevant to the dialogue context. We propose four approaches and comparatively evaluate them over two test-sets of open domain and health-related queries in a conversational quiz-like setting. The evaluation results show satisfying performance for some of the proposed approaches. The results suggest that the modular architecture implemented throughout this work provides an applicable and effective system to process dialogue context and suggest relevant off-activity talks.
Improving the Consistency of Injection Molding Products by Intelligent Temperature Compensation Control

Temperature stability is critical to the consistency of product quality in the injection molding process, and it is very necessary to improve the temperature control accuracy under dynamic conditions. However, due to the large time delay, strong coupling, and the dynamic characteristics existing in the system, it is not an easy task to achieve precise temperature control in the injection molding process. In this paper, a new intelligent temperature compensation control strategy for the injection molding process under dynamic conditions is proposed in order to solve two key problems in the compensation control strategy: the compensation time and compensation quantity. A data-based feedforward iterative learning control (ILC) algorithm is designed to learn the optimal compensation time. Once the optimal compensation time is learned, a deep Q-learning algorithm which combined Q-learning with an artificial neural network (ANN) is proposed to learn the optimal compensation quantity. An experimental platform is designed to validate the superiority of the proposed method. Experimental results show that the proposed method can effectively improve temperature control accuracy under dynamic conditions. Meanwhile, the product consistency has also been improved.
Automatic and Invariant Recognition of Traffic Sign with Color-HMAX and Deep ELM Based Classifier

One of the several challenges in the development of an automatic traffic sign recognition system is the invariant recognition of various traffic signs irrespective of the occlusion and changes in position, size, orientation, illumination and viewpoint. An automatic traffic sign recognition system usually consists of two constituent parts, namely features extractor and features classifier. Most popular Traffic Sign Recognition (TSR) systems usually comprises of a CNN (Convolutional Neural Network) based features extractor and classifier, which achieve significant results but at the cost of a very complex and huge network. Furthermore, since classical NN (Neural Network) based classifiers are trained by Gradient Descent for weights tuning, therefore their generalization capability is limited. To improve the performance further, in this paper we make use of the hierarchical feed-forward model HMAX for the feature extraction and deep extreme learning machine based classifier for the classification of the traffic signs. HMAX model generalizes well and facilitates the features extraction invariant to position, size, orientation, viewpoint and luminance. This robust feature representation is then fed to the deep ELM based feature classifier for the classification of the traffic signs to the respective class category. The recognition and classification accuracy of the developed algorithm was experimentally tested on German Traffic Sign Recognition Benchmark (GTSRB). Achievement of excellent recognition performance is demonstrated with improved generalized, invariant and robust feature representation.
PECULIARITIES OF PERSONAL SELF-IMPROVEMENT IN EARLY AND LATE ADOLESCENCE

The article analyzes the peculiarities of personal self-improvement in early and late adolescence. The age, individual psychological and gender characteristics of adolescents influencing their real and ideal self-images, the directions of desired changes and self-improvement motivation are revealed. The peculiarities of self-improvement of girls and boys are characterized; self-improvement tendencies during the specified age periods are analyzed. The process of personal self-improvement has distinct individual-psychological colors, which is determined primarily by the peculiarities of personal characteristics, self-awareness and motivations.
AN EVALUATION OF THE EFFECTIVENESS OF THE CYPRUS AND CYPRUS TURKISH HISTORY COURSE CURRICULUM OBJECTIVES IN SCHOOL PRACTICES

The aim of this study is to determine the effectiveness of the Cyprus and Cyprus Turkish History course curriculum’s objectiveness and the teachers’ implementation of the curriculum in schools. The research was conducted using a qualitative research approach and a descriptive research model. The researcher prepared semi-structured interview form to reveal the teachers’ views. Content analysis method was applied through using Nvivo 10 software in the analysis of qualitative data. The analysis of the survey data was conducted by encoding the results, as well as dividing and categorizing the teacher’s feedback and opinions; furthermore, direct opinions have also been noted. According to the findings, not only is there a disparity between the application of the program and its original objectives, but the effectiveness of the curriculum is also questioned. Keywords: Education, Curriculum, Cyprus and Cyprus Turkish History, Qualitative
An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates

This chapter applies the Backpropagation Neural Network (BPNN) trained with different numerical algorithms and technical analysis indicators as inputs to forecast daily US/Canada, US/Euro, US/Japan, US/Korea, US/Swiss, and US/UK exchange rate future price. The training algorithms are the FletcherReeves, Polak-Ribiére, Powell-Beale, quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), and the Levenberg-Marquardt (LM). The standard Auto Regressive Moving Average (ARMA) process is adopted as a reference model for comparison. The performance of each BPNN and ARMA process is measured by computing the Mean Absolute Error (MAE), Mean Absolute Deviation (MAD), and Mean of Squared Errors (MSE). The simulation results reveal that the LM algorithm is the best performer and show strong evidence of the superiority of the BPNN over ARMA process. In sum, because of the simplicity and effectiveness of the approach, it could be implemented for real business application problems to predict US currency exchange rate future price.
Performance-Oriented Contracting—A Review

This paper provides a systematic review of multiple writings addressing quality performance-oriented contracting and individual driven performance. The study systematically reviewed 155 refereed papers across various fields published between from 2000 to 2018. A categorization framework of performance-oriented contracting is proposed addressing key dimensions of its plan and management. The relative analysis of fundamental articles advancing interdisciplinary perspective encourages learning, supports strategic decision making, and provides knowledge about quality management in firms especially at individual and organizational stages. The paper reviews performance contracting articles in public and private organizations, which are a milestone toward quality performance. These outcomes demonstrate that performance contracting is useful in performance improvement if job design and knowledge sharing exist across the organization. The results indicate that much of the contemporary wide-ranging performance contracting study do not have robust theoretical grounds. The review submits managerial implications which potentially assist practitioners, consultants, and academicians understand the concept of performance contracting, quality design, and management. This paper proposes a categorization structure of performance contracting study and proposes directions for future study on performance contracting, which should explicitly connect to contemporary study themes. This paper informs further systematic endeavours to conduct more beneficial literature reviews about performance contracting in diverse organizational setups.
Crosstalk minimization in two-layer channel routing

As fabrication technology advances, devices and interconnection wires are placed in closer proximity and circuits operate at higher frequencies. This results in crosstalk between wire segments. Work on routing channels with reduced crosstalk is a very important area of current research. In this paper we show that the crosstalk minimization problem in the reserved two-layer Manhattan routing model is NP-complete, even for the channels without any vertical constraints. Since minimizing crosstalk is NP-complete, we propose simple and efficient polynomial time heuristic algorithms that reduce crosstalk more than 35% on average on a given routing solution of minimum area.
A Discourse Analysis Study of Comic Words in the American and British Sitcoms

This paper investigates discourse analysis and its role in studying comic words and their meaning in different contexts. This study aims at showing the relation between discourse analysis and comedy or comic words, presenting the types of comedy and comic words and how they give meaning of mocking, teasing and sarcasm, presenting the difference between mocking, teasing and sarcasm, displaying interjections and their relation with comic words, in addition to analyzing the data in terms of tables. It is hypothesized that many people do not know the meaning of discourse analysis and comedy. Second, they do not have the capacity to differentiate between comedy, mocking, sarcasm and teasing. They also do not know how to use comic words in expressions. Above all, many of them cannot analyze a particular episode properly and people cannot know the intentions of the speaker concerning comic words. The value of this study is for people who are interested in linguistics. The data used in this study are the American sitcom ‘Friends’ and the British one titled ‘Bottom’. The data is analyzed on the basis of the theories of the Cooperative Principle and Taflinger model. The results of “Friends”, the American sitcom and “Bottom”, the British one, show that there are many comic words. The characters use interjections or onomatopoeia to communicate laughter and excitement. The use of teasing is also emphasized by the use of other comic words like: a*s, breasts, and idiot for presenting jokes and laughter sense.
An exploration on blood donation intention among population in Hawassa city, Southern Ethiopia; theory of planned behaviour approach: A qualitative study

Millions of lives are saved each year through blood transfusions. Blood donation is therefore considered as a humanitarian act and a positive behavioural phenomenon. To explore intention to donate blood among Hawassa city population, southern Ethiopia, 2017, a qualitative study using the theory of planned behaviour was employed. Data collection was done using a semi-structured interview guide with open-ended questions. Focus group discussion (FGD) and in-depth interviews were employed for data collection. Data analysis begun with translation as it was the first step in data analysis for the qualitative study. Transcripts were coded using the Atlas.Ti. 7 software. For coding the data, the basic constructs of the theory of planned behavior was used. Almost all participants do not know the minimum and maximum age to donate blood. Television and friends are mentioned as the main source of information about blood donation. Majority of the participants had mentioned that it is very difficult for them to know for whom the donated blood is given. Almost all participants in this study see blood donation as a humanitarian act. For married participants, wives and husbands were stated as subjective norms. The influence of subjective norm is not so much considered. Majority of the participants stated that they had fear that they will suffer from some health problem if they donate blood. Being anemic and weak is reported by the majority. Key words: Blood donation, voluntary donation, qualitative research, Hawassa, theory of planned behaviour.
A Generic Performance Framework for Ambulance Services: An Australian Health Services Perspective

This review explores a selection of existing methods that measure ambulance service performance and identifies a performance framework that could be useful in a variety of contextual settings. It drew on a selection of well-known performance frameworks currently in use within emergency, health and ambulance systems. It is recommended that ambulance authorities adopt a health services performance framework in preference to the widely used emergency services framework with its heavy emphasis on time intervals such as response times. This suggested framework should be able to accommodate a wide range of validated clinical and non-clinical performance indicators that better reflect the position of ambulance services as part of the health system.
Hardware/software co-design for multimedia

Multimedia computing systems are typically heterogeneous multiprocessors which are highly optimized for both performance and implementation cost. This makes them well-suited to hardware/software co-design techniques, which simultaneously optimize the hardware and software architectures of a system to meet system requirements. This paper surveys important results in co-design with emphasis on techniques necessary for the design of multimedia computing systems, particularly the analysis and synthesis of memory systems.
Biomarqueurs dans deux troubles des conduites bien caractérisés : trouble des conduites alimentaires et trouble des conduites suicidaires

Les troubles des conduites alimentaires (TCA) et les troubles des conduites suicidaires (TCS) sont des pathologies mentales graves. Ces deux troubles representent un probleme de sante publique majeur. En effet, il s’agit de pathologies pour lesquelles les pronostics sont mediocres, ayant un impact lourd sur la vie des patients et de leur entourage et representant un cout eleve pour la societe. Il n’existe actuellement aucun biomarqueur valide dans l’une ou l’autre de ces pathologies. L’objectif de ce travail est de reussir a identifier de potentiels biomarqueurs dans ces deux troubles. L’identification de biomarqueurs potentiels pourrait etre utile dans divers domaines : faciliter le diagnostic des patients, ameliorer la prise en charge des patients et enfin servir de cibles therapeutiques dans le but d’elaborer de nouveaux traitements plus efficaces. Nous presenterons dans ce travail l’interet et l’utilisation des biomarqueurs en psychiatrie puis deux axes d’exploration des TCA ainsi qu’un axe d’exploration des TCS: l’approche neuropsychologique ainsi que l’approche biologique pour les TCA et l’approche genetique pour les TCS. Concernant les TCA, nos resultats suggerent un interet potentiel des oestro-progestatifs dans l’amelioration des fonctions cognitives des patientes. Au sujet des TCS, il semblerait que deux des polymorphismes genetiques que nous avons etudies (l’un du gene MOR et l’autre du gene SKA2) soient associes avec le risque d’emergence d’idees suicidaires au cours de l’initiation d’un traitement antidepresseur. Si ces etudes etaient repliquees, ces divers elements pourraient s’averer etre des outils diagnostics et therapeutiques.
Knowledge Driven Search Intent Mining

Jadhav, Ashutosh. Ph.D. Department of Computer Science and Engineering, Wright State University, 2016. Knowledge Driven Search Intent Mining. Rich background knowledge from biomedical knowledge bases and Wikipedia enables development of e↵ective methods for: I) Intent mining from health-related search queries in disease agnostic manner II)E cient browsing of informative health information shared on social media. Understanding users' latent intents behind search queries is essential for satisfying a user's search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Hence, there has been increasing attention on studying how to e↵ectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domainspecific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and forms one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven,