Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation classification on the web of data using linked data: a study case: movie popularity classification

Data mining algorithms have been widely used to solve various types of prediction models in movie domain. Classification problems especially to predict the future success of movies have attracted many researchers in order to find efficient ways to address them. However, movie popularity classification has become very complicated as it has too many parameters with different degrees. In this thesis, we review a broad range of literature on (1) movie prediction domain and identify related data, a main data source, and additional data sources to address these problems; (2) on data mining algorithms to build more robust classification models to predict movie popularity. To obtain the robust movie popularity classification model, three experiments were conducted. The first experiment examined five single classifiers (Artificial Neural Network (ANN), Decision Tree (DT), k-NN, Rule Induction (RI) and SVM Polynomial) to develop classification models to predict the future success of movie. The second experiment assessed the use of wrapper-type feature selection algorithms to develop classification models of movie popularity. The last one scrutinized two ensemble methods, bagging and boosting in classifying movie popularity. Based upon the finding and analysis, this thesis contributes in four areas: (1) it demonstrates the capabilities of linked data to get external movie related data sources and shows how additional attributes from external data sources can be used to improve performances of the classification model based on a single data source; (2) it presents the use of Grid Search to get a set of optimal hyper-parameters of Artificial Neural Network (ANN), Decision Tree (DT), Rule Induction (RI) and SVM Polynomial classifiers so as to get more robust classification model; (3) it proves the use of wrapper-type feature selection using Genetic Algorithm suited to those classifiers either using default or optimized parameters in order to get the robust classification model and (4) it establishes the use of ensemble methods (bagging and boosting) to those classifiers either using default or optimized parameters in order to get the model in question.
A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition

The growth of the mobile Internet, smartphones and social networks has brought in huge amounts of picture information, and traditional manual identification is not able to meet the demand well enough. Therefore, the automatical image recognition [1] has been proposed which can help us recognize the image efficiently and get the corresponding information. Although traditional machine learning methods [2] have already been widely used in the field of image recognition, most of these methods are designed to handle one-dimensional vector information. Thus, we should first stretch image matrix to one-dimensional vector or extract features from images to employ traditional image recognition methods, which would lose the adjacent information in images and miss some important features. With the development of computer technology, deep learning [3] is gradually applied to the field of image recognition. It can deal with two-dimensional image data naturally and extract features automatically. Compared with the traditional machine learning methods, deep learning is popular for its good learning ability and low generalization error. In this paper, we compare the differences between SVM [4] and deep learning on image recognition, with an application to handwritten digital images recognition. The results show that the deep learning method is more accurate and more stable in image recognition.
A Journal-Based Replication of “Being Chosen to Lead”

Recent large-scale replications of social science experiments provide important information on the reliability of experimental research. Unfortunately, there exist no mechanisms to ensure replications are done. We propose such a mechanism: journal-based replication, in which the publishing journal contracts for a replication between acceptance and publication. We discuss what we learned from a proof-of-concept journal-based replication at the Journal of Public Economics. Our experience indicates that journal-based replication would be relatively straightforward to implement for laboratory experiments.
Intelligent Traffic Adaptive Resource Allocation for Edge Computing-Based 5G Networks

The popularity of smart mobile devices has led to a tremendous increase in mobile traffic, which has put a considerable strain on the fifth generation of mobile communication networks (5G). Among the three application scenarios covered by 5G, ultra-high reliability and ultra-low latency (uRLLC) communication can best be realized with the assistance of artificial intelligence. For a combined 5G, edge computing and IoT-Cloud (a platform that integrates the Internet of Things and cloud) in particular, there remains many challenges to meet the uRLLC latency and reliability requirements despite a tremendous effort to develop smart data-driven methods. Therefore, this paper mainly focuses on artificial intelligence for controlling mobile-traffic flow. In our approach, we first develop a traffic-flow prediction algorithm that is based on long short-term memory (LSTM) with an attention mechanism to train mobile-traffic data in single-site mode. The algorithm is capable of effectively predicting the peak value of the traffic flow. For a multi-site case, we present an intelligent IoT-based mobile traffic prediction-and-control architecture capable of dynamically dispatching communication and computing resources. In our experiments, we demonstrate the effectiveness of the proposed scheme in reducing communication latency and its impact on lowering packet-loss ratio. Finally, we present future work and discuss some of the open issues.
Research on Vehicle Appearance Component Recognition Based on Mask R-CNN

Recognition of vehicle exterior components is one of the most important core algorithms in the process of intelligent propulsion. This paper focuses on the recognition algorithm of vehicle appearance parts, which can detect the position of vehicle appearance parts and recognize the name of the parts in the image. This paper applies enterprise self-built datasets. Firstly, the vehicle close-range dataset is produced by tailoring in data enhancement. Secondly, the models based on ResNeXt-50+FPN, ResNeXt-101+FPN, ResNeXt-50 backbone network and Mask R-CNN are applied in three stages: panoramic dataset, panoramic dataset and panoramic close-range integrated dataset. Finally, the network model based on ResNeXt-101+FPN and Mask R-CNN is the best on the comprehensive dataset.
A Graph Theoretical Analysis of Functional Brain Networks Related to Memory and Healthy Aging

A Graph Theoretical Analysis of Functional Brain Networks Related to Memory and Healthy Aging Ty Alvin Bodily Neuroscience Center, BYU Master of Science The cognitive decline associated with healthy aging begins in early adulthood and is important to understand as a precursor of and relative to mild cognitive impairment and Alzheimer disease. Anatomical atrophy, functional compensation, and network reorganization have been observed in populations of older adults. In the current study, we examine functional network correlates of memory performance on the Wechsler Memory Scale IV and the Mnemonic Discrimination Task (MST). We report a lack of association between global graph theory metrics and age or memory performance. In addition, we observed a positive association between lure discrimination scores from the MST and right hippocampus centrality. Upon further investigation, we confirmed that old subjects with poor memory performance had lower right hippocampus centrality scores than young subjects with high average memory performance. These novel results connect the role of the hippocampus in global brain network information flow to cognitive function and have implications for better characterizing and predicting memory decline in aging.
A Review of Electrical and Electromagnetic Methods for Coal Mine Exploration in China

During the past 20 years, due to the rapid development of China’s economy, the amount of coal mining activities has rapidly increased, which has led to a large increase in the number of mined-out areas. Therefore, the role of geophysical exploration in coal fields has changed from the detection of coal resources to engineering geological environmental investigation. The safety, efficiency, and green production of coal mines require more advanced geophysical detection technologies. In particular, the accurate detections of the locations of water-rich mined-out areas, as well as concealed water diversion channels, have become the main focuses of coal electrical methods in recent years. Therefore, electromagnetic methods have become the main means of hydrogeological investigations and deep explorations in mine-out coal areas. In this study, a common DC method was briefly introduced, along with an induced polarization method. Then, three representative electromagnetic sounding methods, including a surface frequency-domain electromagnetic method, time-domain electromagnetic method, surface to air electromagnetic method, and mine electromagnetic method, were respectively introduced.
Common Learning and Cooperation in Repeated Games

We study repeated games in which players learn the unknown state of the world by observing a sequence of noisy private signals. We find that for generic signal distributions, the folk theorem obtains using ex post equilibria. In our equilibria, players commonly learn the state, that is, the state becomes asymptotic common knowledge.
Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications

In this paper, we study tail inequalities of the largest eigenvalue of a matrix infinitely divisible (i.d.) series, which is a finite sum of fixed matrices weighted by i.d. random variables. We obtain several types of tail inequalities, including Bennett-type and Bernstein-type inequalities. This allows us to further bound the expectation of the spectral norm of a matrix i.d. series. Moreover, by developing a new lower-bound function for $Q(s)=(s+1)\log (s+1)-s$ that appears in the Bennett-type inequality, we derive a tighter tail inequality of the largest eigenvalue of the matrix i.d. series than the Bernstein-type inequality when the matrix dimension is high. The resulting lower-bound function is of independent interest and can improve any Bennett-type concentration inequality that involves the function $Q(s)$ . The class of i.d. probability distributions is large and includes Gaussian and Poisson distributions, among many others. Therefore, our results encompass the existing work on matrix Gaussian series as a special case. Lastly, we show that the tail inequalities of a matrix i.d. series have applications in several optimization problems including the chance constrained optimization problem and the quadratic optimization problem with orthogonality constraints. In addition, we also use the resulting tail bounds to show that random matrices constructed from i.d. random variables satisfy the restricted isometry property (RIP) when it acts as a measurement matrix in compressed sensing.
Multilevel Neural Network for Reducing Expected Inference Time

It is widely known that deep neural networks (DNNs) can perform well in many applications, and can sometimes exceed human ability. However, their cost limits their impact in a variety of real-world applications, such as IoT and mobile computing. Recently, many DNN compression and acceleration methods have been employed to overcome this problem. Most methods succeed in reducing the number of parameters and FLOPs, but only a few can speed up expected inference times because of either the overhead generated from using such methods or DNN framework deficiencies. Edge–cloud computing has recently emerged and presents an opportunity for new model acceleration and compression techniques. To address the aforementioned problem, we propose a novel technique to speed up expected inference times by using several networks that perform the exact same task with different strengths. Although our method is based on edge–cloud computing, it is suitable for any other hierarchical computing paradigm. Using a simple yet strong enough estimator, the system predicts whether the data should be passed to a larger network or not. Extensive experimental results demonstrate that the proposed technique can speed up expected inference times and beat almost all state-of-the-art compression techniques, including pruning, low-rank approximation, knowledge distillation, and branchy-type networks, on both CPUs and GPUs.
The Implementation of Station Rotation and Flipped Classroom Models of Blended Learning in EFL Learning

This research was conducted to analyze the implementation of blended learning. This study focused on the implementation Station Rotation and Flipped Classroom models of blended learning in academic writing course in English Education Study Program of UNISNU Jepara. Observation and interview were done to collect the data of the implementation of Station Rotation and Flipped Classroom model. The study found that station rotation was implemented in 3 cycles/rotations; 1) teacher led instruction, 2) independent work or ollaborative activities , and 3) online learning. Whereas flipped classroom was implemented in 2 steps; 1) lecturer share online assignment about the topic through SIAkad Unisnu Jepara, and 2) face-to-face meeting in the classroom. It also found that blended learning can facilitate the flexibiliy and make students learn more active and urge their curiousity.
Model-Based Systems Thinking: Assessing Engineering Student Teams

Contribution: A rubric for assessing the systems thinking expressed in conceptual models of technological systems has been constructed and assessed using a formal methodology. The rubric, a synthesis of prior findings in science and engineering education, forms a framework for improving communication between science and engineering educators. Background: Systems thinking is an important skill in engineering, but to date no rubric for assessing this skill based on a formal methodology has been published with reliability and validity measures. Research Questions: (a) What attributes should a rubric for assessing the systems thinking of engineering students comprise? (b) To what extent can such a rubric serve in assessing the systems thinking level of engineering students, as expressed in their conceptual system models? Methodology: Based on a literature review of systems thinking assessment in science and engineering education, the authors classified the systems thinking attributes they had identified into system function, structure, and behavior. Scoring instructions were developed for conceptual system models based on Object-Process Methodology ISO 19450, a formal methodology and language for model-based systems engineering. A total of 142 undergraduate engineering students in 32 teams modeled Web-based systems using the methodology. Each team submitted two models of the same system, at the middle and end of semester. Models were scored using the rubric, and its reliability and validity were evaluated. Findings: Indications of interrater reliability, internal consistency, and construct validity were acceptable, excluding the system function aspect, implying the rubric may be used reliably for its intended purpose.
Training and evaluation in a large-scale virtual environment for a location-based mobile application

Virtual reality (VR) training has been applied to multiple different tasks and compared to conventional training methods. The aim of this research is to evaluate training in a large-scale CAVE-based virtual environment (VE) for a location-based mobile application designed to support an address verification task via walking. This training required a simulated neighborhood environment, a user interface for VE navigation, and communication between the VE and the mobile application. In this research, four main elements interacted together to provide the user experience: the VE, the mobile application, the task, and the user’s spatial ability. The first part of the research examined the impact of training in VR. The training was applied in two different environments with two different groups (in the field and VR). The effectiveness of training was measured using a field test for both training groups. There were statistically significant improvements in both training groups after training. There were no significant differences between the two training groups in performance (time, distance, task errors) in the testing session. The second part evaluated the interaction with the mobile application by assessing the impact of training on the use of the mobile application and the usability issues experienced with the app. Usability was measured using quantitative (taps in the application) and qualitative (usability questionnaire) methods. Results showed statistically significant improvements in the app interaction in both real-world and virtual training groups where both groups reduced taps significantly in the testing session compared to the training session. There were no significant differences between the two training groups in taps in the testing session. The usability questionnaire documented issues related to feedback and map design. However, the
Optimizing Shipping Container Damage Prediction and Maritime Vessel Service Time in Commercial Maritime Ports Through High Level Information Fusion

The overwhelming majority of global trade is executed over maritime infrastructure, and port-side optimization problems are significant given that commercial maritime ports are hubs at which sea trade routes and land/rail trade routes converge. Therefore, optimizing maritime operations brings the promise of improvements with global impact. Major performance bottlenecks in maritime trade process include the handling of insurance claims on shipping containers and vessel service time at port. The former has high input dimensionality and includes data pertaining to environmental and human attributes, as well as operational attributes such as the weight balance of a shipping container; and therefore lends itself to multiple classification methodologies, many of which are explored in this work. In order to compare their performance, a first-of-its-kind dataset was developed with carefully curated attributes. The performance of these methodologies was improved by exploring metalearning techniques to improve the collective performance of a subset of these classifiers. The latter problem formulated as a schedule optimization, solved with a fuzzy system to control port-side resource deployment; whose parameters are optimized by a multi-objective evolutionary algorithm which outperforms current industry practice (as mined from real-world data). This methodology has been applied to multiple ports across the globe to demonstrate its generalizability, and improves upon current industry practice even with synthetically increased vessel traffic.
Microfinance in evolution An industry between crisis and advancement

Excessive market growth combined with insufficient institutional capabilities has caused problems in many countries. While initially it was assumed that the industry’s problems were triggered by the global crisis and the following recession, a consensus is now emerging that problems are rooted much deeper, i.e. within the characteristics of the microfinance industry as they have developed over time.
A quantitative investigation of students’ attitudes towards electronic book technology

The purpose of this study is to analyse the factors that have an impact on technology adoption for e-books utilizing the Analytic Hierarchy Process and Multiple Regression Analysis methods. Findings indicate that perceived usefulness and ease of use are the most significant determinants in using e-books. Of key significance is that Analytic Hierarchy Process results show that consumers make pairwise comparisons, adding environmental concerns to the selection process. Recognizing the importance of all these factors is valuable to e-book developers and marketers in presenting products that meet all consumer choice criteria. The Analytic Hierarchy Process provides researchers with a more thorough decision-making analysis.
Mathematical and probabilistic modeling approach for estimation of surface settlements due to TBM tunneling

Tunnel Boring Machines (TBMs) for soils have become the preferred option for construction of tunnels, especially in urban areas. Rapid excavation process, safe working environment and relatively low impact on inducing ground movements at the surface have been the main factors for its employment. Even though, during tunneling, few tools exist that may provide an estimation of surface settlements due to the TBM performance. Another aspect to consider should be the variability of geotechnical parameters and ground profile stratigraphy because constitute a significant source of uncertainty for the assessment of ground movements. These uncertainties may be considered at the design during modeling by using probabilistic approaches that often require significant computation effort. The present research aims to propose methodological approach, where first a rational and efficient probabilistic framework to deal with the uncertainties of geotechnical properties and stratigraphic profile on tunneling-induced ground movement is presented to finally describe a mathematical procedure to propose a model, that considers variability of geotechnical parameters, for estimation of surface settlements during TBM tunneling. The proposed methodological approach was applied to the recently constructed extension of Line 5 of São Paulo Metro. The probabilistic approach based on the application of a hybrid point estimation method employs a reduced number of computations without losing accuracy. The framework also relies on sensitivity analyses. Two-dimensional (2D) and three-dimensional (3D) numerical analyses are carried out for Line 5 using a commercial finite element code. Three probabilistic scenarios based on the lower, mean, and upper bound values of the coefficient of variation of input variables are considered in the analysis of ground response for Line 5. The results obtained using the proposed framework allowed to estimate the probabilistic scenario that better provides an ideal coefficient of variation to adopt for input variables. The ground elastic properties and topsoil layer thickness had the most significant influence on ground movements. The mathematical approach is based on the behavior of ground movement due to applied TBM support pressure. Maximum surface settlement and Volume loss were considered as the variables to describe ground movement, and TBM face support pressure are considered as the variable for describing the tunnel internal support pressure. Set of candidate models are considered from the literature review, and a selection model criterion on these models are used in order to choose the model that better described the system response. It was found that the nonlinear stress-strain curve proposed by Duncan & Chang (1970) provide an excellent fitting to the data taken from the case study. Lower and upper bounds are proposed to this model for consideration of variability of geotechnical parameters and, finally, the physical meaning of the model parameters are proposed.