Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proposed Two Phase Hybrid Feature Selection Method using Backward Elimination and PSO

Feature selection is an exigent task in the field of data mining, which deals with a wide range applications of more number of features. The most demanding task in machine learning and pattern recognition domains is to eliminate the redundant and irrelevant features. The strategies of feature selection include filter and wrapper methods. Filter method weights the relevance of features based on the attribute weighting criterion. The wrapper approach evaluates the efficiency of the feature selection algorithms by extracting feature subsets by refining the feature space in its search space. Both methods of feature selection involve merits and demerits. A hybrid feature selection method (HFSM) based on backward elimination and particle swarm optimization is proposed to overcome the drawbacks of both filter and wrapper methods and thereby including the strengths of both filter and wrapper methods. Experiments are carried out with UCI (University of California, Irvine) data sets. The capability of the proposed method is compared with the existing methods which outperforms the existing methods in terms of performance measures like accuracy of the classifiers, number of relevant features and calculation efficiency.
Application of Three Mathematical Models for Marine Economy Forecasting in China

On the basis of the marine economy statistic data available,the paper adopts three mathematic models to carry out the forecast on main marine industries output value in China from 2007 to 2010.Comparing history data fitting of three predicting models,the paper presents that the fitting errors of grey system model is the smallest and the fitting precision of the ARIMA model is unsatisfactory.
Eye-hand Coordination based Human-Computer Interaction

Human-computer interaction (HCI) has great interactive applications in many fields. A novel eye-hand coordination based HCI approach has been proposed. According to the fact that the eye gazing starts prior to the hand movement and reaches the target in advance, an eye-hand coordination model in a non-contact way is constructed by facial orientation and skeletal joints of hand. Both temporal median filtering and moving average filter strategies are developed to overcome some fluctuation influences during HCI. The diversity of interactive habits among multiple users is considered in an ordinary hardware from a crowded scene without any hypothesis for the scenario contents in advance. Comparative comparisons with state-of-the-arts have highlighted the superior performance of the proposed approach.
Professors and instructors' experiences with transitions and policies for the online environment

5 Acknowledgement.6 Chapter One: Introduction.7 Chapter Two: Literature Review.8 Online learning defined.8 Online learning.9 Learning (e-learning) in higher education.11 Define policies (elaboration on policies) . 14 Transitions to online teaching.15 Transition research: professors making the transition and implementing policies.19 Chapter Three: Research Study.22 Research Questions.22 Methods.22 Data collection.24 Background of the participants.25 Chapter Four: Data Findings.26 Transitions.26 Transitions from traditional to online.27 Participants’ enjoyment of the transition to online teaching.32 Distance Learning vs. Online Learning as perceived in the experience.32 Course outlines.33 Course outlines and their origin.34 POLICIES AND TRANSITIONS TO ONLINE 3 Course outline policies challenged by students.37 Course outline policy revisions.39 Other course challenges and course outline policy implementation challenges.43 Availability of course outline at registration.46 Course outline covering all policy scenarios.49 Assessment.51 Technological backdrop.53 Technology requirements.53 Fairness and effectiveness with technology.57 Having a back-up plan for when the technology fails.63 Synchronous and a-synchronous settings.64 Policy categories.65 Mandatory university policies.65 Challenging the institutional policies required by the university.66 Guidance for best practices within online learning settings.67 Managing student expectations.68 Policies and age demographics.69 Netiquette policy.70 Summary of findings.70 Chapter Five: Document Analysis.73 Chapter Six: Discussion.75 Chapter Seven: Findings.82 Chapter Eight: Recommendations.84 POLICIES AND TRANSITIONS TO ONLINE 4 Chapter Nine: Conclusions.86 Appendix A: Online Course Policy Writing Tips.88 Appendix B: Research & Survey Questions.90 References.92 POLICIES AND TRANSITIONS TO ONLINE
The early detection of motion boundaries

This thesis shows how to detect boundaries on the basis of motion information alone. The detection is performed in two stages: (i) the local estimation of motion discontinuities and of the visual flow field; (ii) the extraction of complete boundaries belonging to differently moving objects. For the first stage, three new methods are presented: the ``Bimodality Tests,'''' the ``Bi-distribution Test,'''' and the ``Dynamic Occlusion Method. '''' The second stage consists of applying the ``Structural Saliency Method,'''' by Sha''ashua and Ullman to extract complete and unique boundaries from the output of the first stage. The developed methods can successfully segment complex motion sequences.
Improving Compiler Optimizations using Program Annotations

In this work we describe a framework that combines the results of from program testing, formal verification and compiler optimization. We focus on how the informations manually provided by the developer, by crowd source formal verification or by static formal verification tools can be used to help the compilation process. This framework is built on top of the LLVM architecture and can be useful to software developers as it will improve performance while significantly enhancing security. By using the results coming from program analyzer’s analysis we show how our framework can reduce the overhead incurred to ensure array bounds checking and how it can help compiler analysis and improve compiler optimizations.
Explore and Reform of Linux Operating System

The content of the current operating system mainly includes basic concepts and basic theo-ry, most students have to interest to learn. Based on Linux, discuses the teaching attitudes, contents and methods of operating system, and develops the basic idea of the curriculum re-form, the contents and methods of the experimental teaching are reformed, it emphasis on enhancing the quality of student training, the technology and the innovation capability.
Analysis and design of proportionate-type normalized least mean square algorithms

In this thesis a thorough examination of proportionate-type normalized least square (PtNLMS) algorithm is presented. The main application of these algorithms is network echo cancellation. The examination presented includes the analysis of both steady-state and transient regimes of the PtNLMS algorithm. Additionally, the applicability and limitations of common assumptions, such as Gaussianity, used for the joint probability density function of the weight deviation are studied. In fact, a closed form solution for the conditional joint probability density function of the current weight deviation given the preceding weight deviation is presented and shown to differ from Gaussianity assumption in many situations. The examination of PtNLMS algorithms performed in this work also includes the development of new PtNLMS algorithms by choosing gains which optimize user-defined criteria, such as mean square error, at all times. While most of the PtNLMS algorithms examined are designed to operate under the assumption that the impulse response is sparse, a set of algorithms is developed which do not rely on this assumption. The PtNLMS algorithm is then extended from real-valued signals to complex-valued signals. This thesis then closes with an examination of the computational complexity of the algorithms discussed.
Torque control of a cable-driven series elastic actuator using the 2-DOF method

Series elastic actuators (SEA) are playing an increasingly important role in the fields of rehabilitation robotics and physical human-robot interaction for their inherent compliance and safety. Cable-driven series elastic actuation has further advantages of low inertia, flexible installation, etc. In this paper, a cable-driven SEA platform has been built up, and the 2 degrees of freedom (2-DOF) approach has been taken to achieve efficient torque control performance. The model of the cable-driven SEA system was built up by taking the motor as a PI-controlled velocity source. Then, the stabilizing 2-DOF controllers are parameterized. This control strategy provides a solution for separately pursuing the goals of reference tracking and robustness. Based on this, the torque controller can be designed to achieve the optimal performance tuned by three parameters. The simulations have validated the efficiency of the 2-DOF torque control method for the cable-driven SEA.
A 32nm EGPU Parallel Multiprocessor Based on Co-issue and Multi-Dimensional Parallelism Architecture

In this paper, a Parallel Multiprocessor (PM) based on SIMT (Single Instruction and Multiple Threads) architecture is proposed. With co-issue architecture and multidimensional parallelism implemented in high-effective PM, Embedded Graphics Processing Unit (EGPU) provides great performance for various situations, such as general purpose computing, 3D scene rendering, and graphics processing. Application programs are departed into separated threads. Allocated by Thread Processing Unit (TPU), separated threads can be executed in parallel. Parallelism in different hierarchy and dimension are implemented by Multi-Dimensional Parallelism Processor (MD-PP), which has made a proper trade-off between performance and cost. Additionally, PM improves the hardware occupancy with its co-issue architecture and internal bus accessing mechanism to meet the demand of processing capability. Its unified shading architecture also helps to hide processing latency. PM can execute 4 basic operations in the best case and 2 in the worst case within each clock cycle. With 32nm process technology and 200MHz clock frequency, PM’s area is about 5104494um 2 , power consumption is about 101.838mW, and it can process nearly 28M vertices or fragments in average. Experimental results show that the MD-PP based PM can process data with high performance and get a balance between efficiency and hardware consumption simultaneously.
Reaching the Unreached through Blended Learning: A Case Study from the Maldives National University

With the advancement of interactive technology, many educational institutions have opted for blended learning as a modern higher education model to reach disadvantaged groups in societies. This paper explores how the Maldives National University approached the dispersed community of the Indian Ocean archipelago through adaptation and application of blended learning. It also focuses on how the University introduced and developed flexible learning, the delivery model being used, challenges faced and the strategies being used to overcome them. The barriers include perceptions of general public about flexible learning, learning habits and styles of the learners, the internet speed and bandwidth and limited staff capacity. However, statistics of the recent years shows that this learning model is appropriate in Maldives as flexible learning courses are quite popular among the dispersed island community. In the last six years the student enrollment has stayed steady and thousands of working adults are studying at various levels of blended learning programs up to postgraduate level. Our success, despite the challenges indicates that blended learning is an effective mode of delivery for the people who live in this small island nation.
Delving Deep into Extractive Question Answering Data

The impact of large-scale pre-trained language 001 models on Question Answering in recent times 002 is undeniably positive. Few prior works have 003 attempted however to provide detailed insight 004 into how such models learn from QA dataset 005 component parts. For example, what specific 006 kinds of examples are most important for mod- 007 els to learn from? In this paper, we examine 008 two English QA datasets, namely SQuAD1.1 009 and NewsQA, and report findings on the inter- 010 nal characteristics of these widely employed 011 extractive QA datasets. Experiment results re- 012 veal: (i) Models learn relatively independently 013 of examples from outside a given question type 014 (the performance on each question type mainly 015 comes from that data belonging to that same 016 question type); (ii) Increased difficulty in the 017 training data results in better performance; (iii) 018 Learning from QA data approximates to the 019 process of learning question-answer matches. 020
Similarity Search Using Metric Trees

The traditional method of comparing values against database might work well for small database but when the database size increases invariably then the efficiency takes a hit. For instance,image plagiarism detection software has large amount of images stored in the database, when we compare the hash values of input query image against that of database then the comparison may consume a good amount of time. For this reason we introduce the concept of using metric trees which makes use metric space properties for indexing in databases. In this paper, we have provided the working of various nearest neighbor search trees for speeding up this task. Various spatial partition and indexing techniques are explained in detail and their performance in real-time world is provided.
Total Synthesis of (−)-Macrocarpal C. Stereoselective Coupling Reaction with a Novel Hexasubstituted Benzene Cr(CO)3 Complex as a Biomimetic Chiral Benzyl Cation Equivalent

The first total synthesis of (−)-macrocarpal C (3) is described. The synthesis features a highly stereoselective coupling reaction of silyldienol ether 6 with biomimetic benzyl cation species (R)- and (S)-B, which were generated from novel hexasubstituted benzene chromium tricarbonyl complexes (R)- and (S)-17. At the final step of the total synthesis, we developed the tris-O-demethylation of macrocarpal C trimethyl ether 34 under basic conditions using lithium p-thiocresolate. Moreover, spectroscopic evidence shows that the synthetic (−)-3 is identical to natural macrocarpal G (4).
Scalable and efficient outlier detection in large distributed data sets with mixed-type attributes

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates.
A Human Affect Recognition System for Socially Interactive Robots

This chapter presents a real-time robust affect classification methodology for socially interactive robots engaging in one-on-one human-robot-interactions (HRI). The methodology is based on identifying a person’s body language in order to determine how accessible he/she is to a robot during the interactions. Static human body poses are determined by first identifying individual body parts and then utilizing an indirect 3D human body model that is invariant to different body shapes and sizes. The authors implemented and tested their technique using two different sensory systems in social HRI scenarios to motivate its robustness for the proposed application. In particular, the experiments consisted of integrating the proposed body language recognition and affect classification methodology with imaging-based sensory systems onto the human-like socially interactive robot Brian 2.0 in order for the robot to recognize affective body language during one-on-one interactions. DOI: 10.4018/978-1-4666-2211-1.ch029
Selection intrusion detection parameters using entropy

Feature selection methods for intrusion detection now available are influenced by the statistical characteristics of evaluation data and the detection methods.A feature selection method independent of detection method is presented.The method is based on the intrusion detection model and the entropy is used as the criterion.The simulation results show that it is effective to increase the detection speed and reduce of the memory,without sacrificing the detection correctness by using the selected features based on entropy.
A Test Suite Reduction Technique for Partial Coverage of Test Requirements

During software system development,one has to face with frequent local updates and partial fault corrections,where selective-form regression testing is required.To save its test effort,one needs to solve the partial coverage test suite reduction problem.Under selective-form regression testing,this paper formally defines the multi-objective test suite reduction problem,and develops the HATS algorithm to solve the problem.The HATS conforms to the heuristic greedy search framework,and defines the weighting factor α to balance the two objectives: one is to reduce the test suite size;and the other is to avoid coverage of irrelevant test requirements.The experiment results show that,compared with current test suite reduction techniques and algorithms,with proper setting of the factor α,the HATS can reduce the test suite size,cut down the coverage of irrelevant test requirements,while less compromise the fault detection ability.Original contributions of this paper include two aspects: First defined the multi-objective test suite reduction problem under selective-form regression testing;Second designed the HATS algorithm which used fewer test cases to maintain the fault detection effectiveness.
Bayesian Artificial Intelligence, Second Edition

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web ResourceThe books website at www.csse. monash.edu. au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.
Developing Writing Skills in English Using Content-Specific Computer-Generated Feedback with EssayCritic

This paper presents a study of Norwegian Upper Secondary School students’ writing process in English with: 1) feedback from an essay critiquing system (EssayCritic) (target class) and 2) feedback from collaborating peers (comparison class). The students in both classes significantly improved their grades. In the target class, the feedback from EssayCritic gave content-specific cues and the students included more ideas in their essays than the students in the comparison class who struggled when giving feedback to each other.
The Mechanism and Applications of Berkley DB

As the development of embedded-computing technology, many embedded systems deploy embedded database. The basic characteristic of the embedded databse is firstly presented. An excellent embedded database-Berkeley DB is secondly talked. Including the basic concept and the architecture of it,how to use the Berkeley DB is thirdly described.
The Inventions Of Leonardo Da Vinci

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