Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multimodal Approach for Monitoring Driving Behavior and Emotions

Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
Do deep neural networks learn shallow learnable examples first

In this paper, we empirically investigate the training journey of deep neural networks relative to fully trained shallow machine learning models. We observe that the deep neural networks (DNNs) train by learning to correctly classify shallow-learnable examples in the early epochs before learning the harder examples. We build on this observation this to suggest a way for partitioning the dataset into hard and easy subsets that can be used for improving the overall training process. Incidentally, we also found evidence of a subset of intriguing examples across all the datasets we considered, that were shallow learnable but not deep-learnable. In order to aid reproducibility, we also duly release our code for this work at https://github.com/karttikeya/Shallow to Deep/
Evolution of auroral asymmetries in the conjugate hemispheres during two substorms

Five hours of simultaneous global imaging data from the conjugate hemispheres are used to examine the dynamics of the auroral substorm. Earlier studies have demonstrated that substorm onset locations in the two hemispheres are systematically displaced due to the orientation of the interplanetary magnetic field. In this paper we present, for the first time, how the asymmetric auroras induced by the IMF orientation at substorm onset disappears during the expansion phase. We suggest that this is the large scale manifestation of auroral arcs as being the sites of magnetic stress release. Magnetic stress on field lines with asymmetric footpoints can lead to a net hemispherical difference in parallel electric field strength which implies that the auroras move with different speeds in the two hemispheres to release the magnetic stress. The relative velocity can be derived from the potential between the hemispheres. During expansion phase the twisted magnetic fields are rectified, bringing the closed magnetic field lines back to the configuration defined by the Earth's interior.
Student Perceptions of Online Homework Tools in Undergraduate Statics Course

Dr. Rossow is a graduate of the University of Michigan with B.S., M.S., and Ph.D. degrees. He is a licensed professional engineer in the state of Illinois. He taught civil engineering for over 35 years, including six years at Washington University in St. Louis and 29 years at Southern Illinois University at Edwardsville, where he was the chair of the Civil Engineering Department for ten years. His areas of expertise are in civil engineering and mechanics. He has consulted for various organizations, including government agencies and an international offshore drilling company. He has published many journal articles and technical reports.
What are the Characteristics of Reopened Pull Requests? A Case Study on Open Source Projects in GitHub

In GitHub, integrators inspect submitted code changes, make evaluation decision, and close pull requests. Some pull requests may be reopened for further modification and code review. It remains unknown why some pull requests are reopened, and how they affect software development. In this paper, we conduct a case study to understand reopened pull requests. We collect 100,622 pull requests from 7 popular projects in GitHub. We study the impacts of reopened pull requests on the code review process. We then qualitatively analyze the root reasons that lead to reopening pull requests. Finally, we examine the characteristics of pull requests caused by various reasons. Our main observations are: 1) Reopened pull requests have lower acceptance rates, more comments, and longer evaluation time than non-reopened pull requests. 2) Several key reasons that lead to reopened pull request are identified such as bugs, change minds, insufficient tests, incompatible version, tests fails, and so on. 3) Reopened pull requests in different categories have various acceptance rates, comment amounts, and evaluation time. Reopened pull requests in the category rebase and change branch has the largest number of comments and the longest evaluation time. These findings provide insights to decrease reopened pull requests, develop a variety of assistant tools, and reduce the maintenance cost of open source projects.
An energy sensitive system framework for cloud robotic network

Cloud robotic network (CRN) is an extension of the traditional robots, and its important significance is to use the Internet and cloud computing to overcome the limitations of single robot and enhance its computing power. As the core technology of CRN, how to achieve the full potential of computation off‐loading is a critical issue. Starting from the basic models of CRN, this paper studies the network architecture and the computation off‐loading method to solve this problem. Firstly, a relatively detailed system model is designed based on the characteristics of CRN to provide theoretical and quantitative basis for subsequent research. Then, based on the theory of connected dominating set, a distributed CDS construction algorithm PCDS is proposed, which provides a virtual backbone with more power for the network. Finally, an energy sensitive computation off‐loading method is proposed to improve the lifetime of the whole network while ensuring the execution time and energy loss of computational tasks. Simulation results show that the proposed framework can effectively improve the performance of CRN and prolong the lifetime to a certain extent.
Engineering Algorithms for Solving Geometric and Graph Problems on Large Data Sets

xi List of Abbreviations and Symbols Used . xii Acknowledgements . xiv Chapter
Integrated Three Theories to Develop a Model of Factors Affecting Students’ Academic Performance in Higher Education

This paper aimed to alleviate the disparity in the literature regarding social media use for collaboration and communication and its influence on the performance of students at higher education. A questionnaire survey on constructivism theory, technology acceptance model, and communication theory were utilized as the key method for collecting data and was circulated among a total of 863 university students. The obtained outcomes of students’ behavioral intention to utilize social media to collaborate learning and online communication indicates a positive effect on their academic works in higher education institutes, while male students were not completely satisfied with interaction with peers for collaboration learning. The study indicates that collaboration learning, as well as online communication over social media enhances, the students learning activities and enable to sharing knowledge, information, and discussions, and hence, we recommend students to utilize social media for education purpose and should have encouraged them through lecturers at higher level education institutions.
ICGICT Allocation of DG for IEEE 33 BusSystems

Recently, integration of Distributed generation (DG) in distribution system has increased to high penetration levels. The impact of DG on various aspects of distribution system operation, such as reliability and energy loss depend highly on DG location in distribution feeder . Optimal DG placement plays an important role . This paper proposes the application of Particle Swarm Optimization for the placement of DG in the radial distribution systems to reduce the real power losses and to improve the voltage profile . The proposed technique is tested on standard IEEE-33 bus test system.
Online Reconstruction of Indoor Scenes With Local Manhattan Frame Growing

We propose an efficient approach for robust reconstruction of indoor scenes by taking advantage of the geometric relation between consecutive Manhattan keyframes and local pose refinement to improve the accuracy and fidelity of the reconstructed models. At the core of our framework, we have a Local Manhattan Frame Growing system, which finds the principal directions of the scene and aligns point clouds with the dominant plane, and a Local Pose Optimization, which refines the pose estimation for a specific range of frames. During the reconstruction process, we use Manhattan keyframes for a planar pre-alignment to provide a robust initialization for the final surface registration. All Manhattan keyframes are integrated using a frame-to-model scheme to create local models based on the refined camera poses. The final dense model is reconstructed by adopting a geometric registration between local segments and integrating them into a global frame. The experimental results show the effectiveness of our approach to reduce the cumulative registration error and overall geometric drift.
Identifying and Mitigating Bias in Machine Learning Applications

This study addresses the existence of bias in machine learning applications and examines techniques for identifying and mitigating bias using scholarly literature published between 2012 and 2019. The intended audience is machine learning engineers, system analysts, and data analysts of any industry. This study is significant because there may be considerable ethical implications caused by machine learning bias; identifying and mitigating these biases is key to the development and deployment of effective machine learning algorithms.
Frequent itemset mining: A 25 years review

Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data. Insights from such pattern analysis offer important benefits in decision‐making processes. However, algorithmic solutions for mining such kind of patterns are not straightforward since the computational complexity exponentially increases with the number of items in data. This issue, together with the significant memory consumption that is present in the mining process, makes it necessary to propose extremely efficient solutions. Since the FIM problem was first described in the early 1990s, multiple solutions have been proposed by considering centralized systems as well as parallel (shared or nonshared memory) architectures. Solutions can also be divided into exhaustive search and nonexhaustive search models. Many of such approaches are extensions of other solutions and it is therefore necessary to analyze how this task has been considered during the last decades.
Statistical Identification in Svars - Monte Carlo Experiments and a Comparative Assessment of the Role of Economic Uncertainties for the US Business Cycle

Structural vector autoregressive analysis aims to trace the contemporaneous linkages among (macroeconomic) variables back to underlying orthogonal structural shocks. In homoskedastic Gaussian models the identification of these linkages deserves external and typically notdata-based information. Statistical data characteristics (e.g, heteroskedasticity or non-Gaussian independent components) allow for unique identification. Studying distinct covariance changes and distributional frameworks, we compare alternative data-driven identification procedures and identification by means of sign restrictions. The application of sign restrictions results in estimation biases as a reflection of censored sampling from a space of covariance decompositions. Statistical identification schemes are robust under distinct data structures to some extent. The detection of independent components appears most flexible unless the underlying shocks are (close to) Gaussianity. For analyzing linkages among the US business cycle and distinct sources of uncertainty we benefit from simulation-based evidence to point at two most suitable identification schemes. We detect a unidirectional effect of financial uncertainty on real economic activity and mutual causality between macroeconomic uncertainty and business cycles.
Racing Bib Number Recognition Using Deep Learning

This research investigates the use of deep convolutional neural networks for racing bib number recognition in sport images. As labelled images of racing bib numbers are scarce, this paper investigates the potential benefits of transfer learning, by training models partly and fully on the Street View House Numbers (SVHN) dataset. Several deep neural network architectures are studied. The best recognition results were obtained by a model that had been trained on a hybrid dataset of the SVHN images plus an additional 262,131 images of racing bib numbers, with a recall of 0.93, precision of 0.95, and F-measure of 0.94 on the RBNR Dataset and a recall of 0.98, precision of 0.98, and F-measure of 0.98 on the private dataset. Our results are much higher than those reported in related work and show that deep learning can effectively be used for racing bib number recognition. Also, the effectiveness of transfer learning for this problem is demonstrated.
Batch Mode Reinforcement Learning for Controlling Gene Regulatory Networks and Multi-model Gene Expression Data Enrichment Framework

BATCH MODE REINFORCEMENT LEARNING FOR CONTROLLING GENE REGULATORY NETWORKS AND MULTI-MODEL GENE EXPRESSION DATA ENRICHMENT FRAMEWORK RN, UTKU M.S., Department of Computer Engineering Supervisor : Prof. Dr. Faruk Polat July 2013, 132 pages Over the last decade, modeling and controlling gene regulation has received much attention. In this thesis, we have attempted to solve (i) controlling gene regulation systems and (ii) generating high quality arti cial gene expression data problems. For controlling gene regulation systems, we have proposed three control solutions based on Batch Mode Reinforcement Learning (Batch RL) techniques. We have proposed one control solution for fully, and two control solutions for partially observable gene regulation systems. For controlling fully observable gene regulation systems, we have proposed a method producing approximate control policies directly from gene expression data without making use of any computational model. Results show that our proposed method is able to produce approximate control policies for gene regulation systems of several thousands of genes just in seconds without loosing signi cant performance; whereas existing studies get stuck even for several tens of genes. For controlling partially observable gene regulation systems, rstly, we have proposed a novel Batch RL framework for partially observable environments, Batch Mode TD(λ). Its idea is to produce approximate stochastic control policies mapping observations directly to actions probabilistically without estimating actual internal states of the regulation system. Results show that Batch Mode TD(λ) is able to produce successful stochastic policies for regulation systems of several thousands of genes in seconds; whereas existing studies cannot produce control solution for regulation systems of several tens of genes. To our best knowledge, Batch Mode TD(λ) is the rst framework for solving non-Markovian decision tasks with limited number of experience tuples. For controlling partially observable gene regulation systems, secondly, we have proposed a method
Medical and health sciences academics’ behaviours and attitudes towards open access publishing in scholarly journals: a perspective from South Korea

This article seeks to extend the knowledge of the behaviour and attitudes towards open access publishing through a survey that focusses on the attitudes and behaviours of academic researchers in Korea working in medicine and healthcare. Issues covered include: use of and intentions regarding OAP, and perceptions regarding advantages and disadvantages of OAP, journal article publication services, peer review, and re-use. A significant proportion of the articles (mean 58%) published by this group are published gold open access, consistent with the push in Korea towards international impact for their research. Researchers were more positive about the benefits of OAP than they were negative about its disadvantages. Analysis of responses on the basis of gender, and experience in publishing, showed some significant differences in attitudes to some statements.
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-Modality Cardiac Segmentation

Deep convolutional networks have demonstrated state-of-the-art performance on various challenging medical image processing tasks. Leveraging images from different modalities for the same analysis task holds large clinical benefits. However, the generalization capability of deep networks on test data sampled from different distribution remains as a major challenge. In this paper, we propose a plug-and-play adversarial domain adaptation network (PnP-AdaNet) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We tackle the significant domain shift by aligning the feature spaces of source and target domains at multiple scales in an unsupervised manner. With the adversarial loss, we learn a domain adaptation module which flexibly replaces the early encoder layers of the source network, and the higher layers are shared between two domains. We validate our domain adaptation method on cardiac segmentation in unpaired MRI and CT, with four different anatomical structures. The average Dice achieved 63.9%, which is a significant recover from the complete failure (Dice score of 13.2%) if we directly test an MRI segmentation network on CT data. In addition, our proposed PnP-AdaNet outperforms many state-of-the-art unsupervised domain adaptation approaches on the same dataset. The experimental results with comprehensive ablation studies have demonstrated the excellent efficacy of our proposed method for unsupervised cross-modality domain adaptation. Our code is publically available at https://github.com/carrenD/Medical-Cross-Modality-Domain-Adaptation
Earning Movement Prediction Using Machine Learning-support Vector Machines (SVM)

The prediction of earnings movement is used to evaluate corporate performance and make investment decisions. This study presents a detailed model for predicting the movement of company future earnings using Support Vector Machines (SVM) technique and comprehensive financial data extracted from the Securities Exchange Commission (SEC) mandated eXtensible Business Reporting Language (XBRL). XBRL does not change the disclosure itself; still it can facilitate information gathering and processing, since it is easily downloaded from the internet and translated into EXCEL format, which should be beneficial to users of the financial reporting information. The model, using XBRL data, was able to classify the companies correctly on average, about 63.4% of the time, less than the traditional method of Stepwise Multivariate Logistic Regression which had an average prediction rate of 68.1%. However, when disseminating the data, based on industry, the accuracy level reaches 84.2% using SVM, while with Stepwise Multivariate Logistic Regression model accuracy only reaches 71.6%. These results suggest that the model presented has merit and can be used as a financial analysis tool.
A Hybrid Passivity-Based Control Strategy for Three-Level T-Type Inverter in LVRT Operation

The fast dynamic performance of a photovoltaic (PV) inverter is one of the most important issues in low-voltage ride-through (LVRT) operation, as the voltage dip period is short. Nevertheless, it is difficult to control the output currents with conventional methods, especially during transients or under low-power factor operation. In order to satisfy the LVRT requirements and improve the performances of a three-level T-type inverter (3LT<inline-formula> <tex-math notation="LaTeX">$^{2}\text{I}$ </tex-math></inline-formula>) in a PV system, the hybrid passivity-based control (HPBC) strategy with the fast transient response and strong robustness to system parameter changes and external disturbances is proposed in this paper. First, the Euler–Lagrange model of the 3LT<inline-formula> <tex-math notation="LaTeX">$^{2}\text{I}$ </tex-math></inline-formula> under the LVRT condition is derived and analyzed. Then, the 3LT<inline-formula> <tex-math notation="LaTeX">$^{2}\text{I}$ </tex-math></inline-formula> system is theoretically proved to be strictly passive by using some ingenious arithmetic and differential calculation techniques. Furthermore, based on the error energy function and the Lyapunov stability criterion, a passivity-based controller including a negative sequence of grid voltage feedforward control terms is designed to suppress the negative-sequence currents effectively. The balanced grid currents are obtained, which improve the stability of the system in LVRT operation. Compared with the conventional methods, the proposed HPBC scheme has the superior characteristics of fast and non-overshoot response in a transient state and accurate current tracking performance in a steady state. The theoretical analysis and effectiveness of the proposed strategy have been validated by means of both simulations and experiments.
STUDENT TEACHERS’ EXPERIENCES OF TEACHING PRACTICE AT OPEN AND DISTANCE LEARNING INSTITUTION IN SOUTH AFRICA

This small-scale study focused on the experiences of student teachers towards teaching practice in an open and distance learning (ODL) institution in South Africa. The sample consisted of 65 fourth year students enrolled for Bachelor of Education, specializing in secondary school teaching. The mixed-method research design consisting of quantitative and qualitative approaches was used. Questionnaire and individual interview discussions were used as instruments for data collection. Descriptive statistics was used to analyze quantitative data. Content analysis was used to analyze qualitative data. The study revealed that student teachers experienced challenges with regard to on-time placement in schools, supervision and mentoring. Based on the findings, recommendations were made. Among others was that mentors and university contracted supervisors should be constantly empowered through workshops to work effectively in leading and guiding student teachers. On the issues of placement of students in approved schools, the university should consider implementing a system which will enable students to place themselves online. Placing students in approved schools is a major challenge for the ODL institution in South Africa given the great number of students that have to be placed in schools every year and the slow pace at which the institution is moving to integrate technology in addressing the problem.