Reviewerly/exHarmony huggingface dataset:
Reading Habits Among Students in the Digital Era

Reading has a significant number of benefits and the effects of reading practice have been proven in most developed countries. The revolution of technology plays a significant role of unpredictable changes and pervasive effect that has transformed the society nowadays. Indeed, it can fundamentally change the reading habits and indirectly boost efforts in maintaining lifelong reading since Malaysia is now moving towards Vision 2020. Thus, the rapid development and demands of the technology has reinforced the challenges of making reading habits consistent and grows intensively. This study sought to identify and measure the reading habits among students which are their trends and behaviours toward reading that reflect on the impact of digital sources made available through the Internet. This study was conducted in Universiti Teknologi MARA Sarawak, limit to Semester One students, aged between 17-21 years by using a survey method involving 2340 from 10060 diploma students with the questionnaire as the research instrument. The random sampling for the students is involving 14 faculties in UiTM Sarawak branch. Taken together, QR code has been used as an initiative platform in order to conduct and monitor the survey efficiently. Data analyses were collected using Google Sheet and the study found that majority of the students (44%) like reading on websites compared to other choices such as print books, e-books, magazines, comics, novel and newspaper. Fiction is the most preferred type of literature read by 68% the students, followed by non-fiction (32%). The findings also found that majority of the students (49%) preferred reading from their mobile phone, whereas others preferred reading from a print book (44%) and reading from a computer (7%). Findings of the study revealed that the rise of information and technology has extensively changes the trends and behaviors of the student’s reading habits, which slowly moving away from printed books to online source materials.
ProSegNet: A New Network of Prostate Segmentation Based on MR Images

Prostate cancer is the most common cancer in men after lung cancer. Generally, the segmentation of the prostate is the preprocessing work for the diagnosis of prostate cancer. Aiming at the variety of prostate and the similarity of visual characteristics between prostates and their surroundings, this paper proposes a new prostate segmentation network based on MR images, denoted as ProSegNet. ProSegNet consists of two parts: encoder and decoder. To improve the feature extraction capability of the encoder, we use dense blocks as the feature extraction unit, and at the same time introduce a cross-stage partial (CSP) structure to reduce the amount of calculation. In the design of the decoder structure, we integrated the spatial attention mechanism and the channel attention mechanism to enable it to focus on the important features while ignoring the invalid features. In addition, to segment the prostate more accurately, we add a prostate contour segmentation branch to the output of the segmentation network to learn the contour features of the prostate. Finally, to alleviate the problem of small intensity difference between the prostate and surrounding tissues, we designed a truncated intensity stretching image enhancement method. The performance of ProSegNet has been experimentally verified in the Promise12 and ProstateX datasets. On the Promise12 dataset, the dice similarity coefficient (DSC) and hausdorff (Haus) distance are 0.908 and 9.87 respectively. On the ProstateX dataset, the DSC and Haus reach the results of 0.892 and 10.45, respectively. Experimental results show that the ProSegNet can obtain a competitive performance.
Next Generation Cloud Security: State of the Art Machine Learning Model

Recent days have seen an obvious shift in computing platforms and environments with the arrival of cloud computing. Most of the organizations moving towards cloud technology to satisfy varied user demands and requests, and to guard and safeguard the transactions and operations of the organizations. Over the cloud platforms, it is extremely essential to produce a secure and strong environmental support to ensure security and privacy. Machine learning (ML) based models have well-tried their significance to anticipate in finest outcomes to boost the choice of cloud based long-term course of actions. Efficient machine learning models have long been utilized in several application domains that required the identification and prioritization of adverse factors for a threat. The main objective of this paper is to propose a best strategic cloud security enhancement model for next generation computing standards. Efficient machine learning algorithms like convolution neural network gives automatic and responsive approaches to reinforce security in a cloud environment. These models give solutions that incorporate holistic approaches for secure enterprise knowledge throughout all the cloud applications.
Aerobics Action Recognition Algorithm Based on Three-Dimensional Convolutional Neural Network and Multilabel Classification

In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolutional networks and multilabel classification. Since two-dimensional convolutional neural networks (CNNs) lose time information when extracting features, so to overcome this, the proposed research uses three-dimensional convolutional networks to perform video recognition. The feature is taken in time and space, and the extracted features are subjected to multiple binary classifications to achieve the goal of multilabel classification. Various comparison and simulation experiments are conducted for the proposed research, and the experimental results prove the effectiveness and superiority of the approach.
Improving Imbalanced Data Classification in Auto Insurance by the Data Level Approaches

Predicting the frequency of insurance claims has become a significant challenge due to the imbalanced datasets since the number of occurring claims is usually significantly lower than the number of non-occurring claims.As a result, classification models tend to have a limited ability to predict the occurrence of claims.So, in this paper, we'll use various data level approaches to try to solve the imbalanced data problem in the insurance industry.We developed 32 machine learning models for predicting insurance claims occurrence {(undersampling, over-sampling, the combination of over-and undersampling (hybrid), and SMOTE)× (three Decision tree models, three boosting models, and two bagging models) = 32}, and we compared the models' accuracies, sensitivities, and specificities to comprehend the prediction performance of the built models.The dataset contains 81628 claims, each of which is a car insurance claim.There were 5714 claims that occurred and 75914 claims that didn't occur.According to the findings, the AdaBoost classifier with oversampling and the hybrid method had the most accurate predictions, with a sensitivity of 92.94%, a specificity of 99.82%, and an accuracy of 99.4%.And with a sensitivity of 92.48%, a specificity of 99.63%, and an accuracy of 99.1%, respectively.This paper confirmed that when analyzing imbalanced data, the AdaBoost classifier, whether using oversampling or the hybrid process, could generate more accurate models than other boosting models, Decision tree models, and bagging models.
Accelerating Neural Architecture Search for Natural Language Processing with Knowledge Distillation and Earth Mover's Distance

Recent AI research has witnessed increasing interests in automatically designing the architecture of deep neural networks, which is coined as neural architecture search (NAS). The automatically searched network architectures via NAS methods have outperformed manually designed architectures on some NLP tasks. However, training a large number of model configurations for efficient NAS is computationally expensive, creating a substantial barrier for applying NAS methods in real-life applications. In this paper, we propose to accelerate neural architecture search for natural language processing based on knowledge distillation (called KD-NAS). Specifically, instead of searching the optimal network architecture on the validation set conditioned on the optimal network weights on the training set, we learn the optimal network by minimizing the knowledge loss transferred from a pre-trained teacher network to the searching network based on Earth Mover's Distance (EMD). Experiments on five datasets show that our method achieves promising performance compared to strong competitors on both accuracy and searching speed. For reproducibility, we submit the code at: https://github.com/lxk00/KD-NAS-EMD.
Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.
Implementation of Mobile Learning in Mathematics Instruction for Elementary Second Graders

In this study, a mobile learning system (MLS) was developed and adopted to facilitate elementary second-grade students to learn mathematics. A quasi-experimental design was adopted. There were two learning models, including the typical instruction group (TI group) and MLS group. The learning content is the topic of multiplication. A total of 93 s-grade students from four classes in a public elementary school in Northern Taiwan participated in this research. Participants were randomly divided into the MLS group (47 participants: 22 boys and 25 girls) and the TI group (46 participants: 26 boys and 20 girls). Participants in the MLS group received mathematics instruction in the MLS, whereas those in the TI group received direct instruction in typical classrooms. All students took the pretest and posttest of mathematics learning achievement test and mathematics learning interest scale assess their improvement of learning achievement and learning interest after the learning activities. The findings revealed that students in the MLS group had significantly better improvement in their mathematics learning interest and mathematics learning achievement than those in the TI group. In addition, students in the MLS group had significantly better performance in answering items of comprehension and application levels in the mathematics learning achievement test.
A New Framework for Matching Forensic Composite Sketches With Digital Images

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.
The Impact of Knowledge Management (KM) Strategies on Employee Job Satisfaction: A Study of RMG in Bangladesh

Knowledge management (KM) strategy is a crucial part of personnel management and retention of talents. This paper aims at identifying the impact and relation of knowledge management on employee satisfaction. The study is based on hypothesis and the data was collected by a questionnaire survey from 35 employees of reputed garments organization of Bangladesh. Satisfaction rely on factors like compensation structure, user friendly, relation with co-worker, autonomy, workload etc. The core findings of the study is codified strategy is more user friendly, but the practice of tacit strategy with proper incentives increase the overall satisfaction of employee though there are more workload. The employees are more comfortable with tacit strategy than codified strategy. The study is only focused on garments employee where KM strategy is widely practice all spheres of Human Resource Management (HRM). However, The paper reveal how KM strategy can increase employee satisfaction. The HR people and decision maker can understand and design appropriate KM strategy from this study. Therefore, organization also can manage and retain the talents by designing and applying findings of the study.
Digital twin development of a solar power plant

The article is devoted to the development of a digital twin of a solar power plant. An overview of existing solutions in this area is given. An approach to building a digital twin based on ontological engineering is discussed in more detail. The main steps of ontological engineering are described. A fragment of the system of ontologies of photovoltaic systems is presented. A mathematical model designed to simulate a solar power plant taking into account changes in environmental parameters is considered. The article also focuses on the design of the architecture of the digital twin, consisting of: Digital Shadow, Digital Model and control system. The article also describes the stage of implementation of a prototype of a digital twin of a solar power plant, which currently includes a database and a component for calculating the output characteristics of solar panels and inverters.
Evaluating Text Generation from Discourse Representation Structures

We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input representation, and evaluate character-level and word-level decoders.We obtain very encouraging results in terms of reference-based automatic metrics such as BLEU.But because such metrics only evaluate the surface level of generated output, we develop a new metric, ROSE, that targets specific semantic phenomena.We do this with five DRS generation challenge sets focusing on tense, grammatical number, polarity, named entities and quantities.The aim of these challenge sets is to assess the neural generator's systematicity and generalization to unseen inputs.
Rain Classification for Autonomous Vehicle Navigation Using Machine Learning

Autonomous vehicles (AV) has gained popularity in research and development in many countries due to the advancement of sensor technology that is used in the AV system. Despite that, sensing and perceiving in harsh weather conditions has been an issue in this modern sensor technology as it needs the ability to adapt to human behaviour in various situations. This paper aims to classify clear and rainy weather using a physical-based simulator to imitate the real-world environment which consists of roads, vehicles, and buildings. The real-world environment was constructed in a physical-based simulator to publish the data logging and testing using the ROS network. Point cloud data generated from LiDAR with a different frame of different weather are to be coupled with three machine learning models namely Naïve Bayes (NB), Random Forest (RF), and k-Nearest Neighbour (kNN) as classifiers. The preliminary analysis demonstrated that with the proposed methodology, the RF machine learning model attained a test classification accuracy (CA) of 99.9% on the test dataset, followed by kNN with a test CA of 99.4% and NB at 92.4%. Therefore, the proposed strategy has the potential to classify clear and rainy weather that provides objective-based judgement.
Heart Disease Prediction Using Logistic Regression

Abstract Heart is the main organ of the human body. Heart disease is one of the disease that contribute most death in the world. It occurs when the heart is not functioning with full potential to pump the blood to all parts of the body. In this study, the researcher attempt to determine the significant variables between absence or presence of heart disease with others variables such as age, resting blood pressure, serum cholesterol, maximum heart rate achieved and others variables such as gender, chest pain type, resting electrocardiographic result, fasting blood sugar, oldpeak, slope, number of major vessels and thalassemia. The provided data covers 270 patients information. The three methods were applied to heart disease data which are were binary logistic regression (BLR) models, BLR models with least quartile difference (LQD) method and BLR models with median absolute deviation (MAD) method. After comparing among three methods, it is was found that the binary logistic with applied MAD model tend to be the best model with the highest percentage of accuracy. According to the final model, it is were shown that. Only chest pain type, number of major vessels and thalassemia is significant and positively associated to heart diseases. This kind of study is to gain public awareness about the most important factor that can lead to heart disease so that they can take prompt action or make a prevention to avoid this disease from occurring. This study also gather new information to the public to learn about logistic regression and robust method since it demonstrated step-by-step in this research. It will make easier for them to understand how this statistical method operates and gives benefit to all
Parallel best-first search algorithms for planning problems on multi-core processors

The multiplication of computing cores in modern processor units permits revisiting the design of classical algorithms to improve computational performance in complex application domains. Artificial Intelligence planning is one of those applications where large search spaces require intelligent and more exhaustive search control. In this paper, parallel planning algorithms, derived from best-first search, are proposed for shared memory architectures. The parallel algorithms, based on the asynchronous work pool paradigm, maintain good thread occupancy in multi-core CPUs. All algorithms use one ordered global list of states stored in shared memory from where they select nodes for expansion. A parallel best-first search algorithm that develops new states with depth equal to one is proposed first. Then, we propose an extension of this parallel algorithm that features a diversification strategy in order to escape local minima. We study and analyse a set of computational experiments for problems that come from the International Planning Competition and real-world industry applications. The empirical evaluation shows that the parallel algorithms solve most of the domains efficiently without incurring higher solutions costs. In those problems with partial results, we highlight the potential shortcomings of the proposed approaches for promising future directions.
Overhead effects of data encryption on TCP throughput across IPSEC secured network

The transmission control protocol (TCP) is an internet protocol used to send data across networks. When encrypted data are sent across networks, additional overhead is incurred. The data to be secured through encryption must be transformed from plain text to Ciphertext, resulting in data size increase. In this paper, the overhead effect of data encryption on TCP throughput across an internet protocol security (IPSec) network was investigated. In particular, we compared the additional data sizes of encrypted data to that of non-encrypted data, and their speed during transmission, respectively, using the iPerf3 simulator. Specifically, a comparative analysis between the actual transfer time of encrypted and non-encrypted data was investigated. The result showed that transmission times vary for different data sizes that were secured through encryption. Encrypted data had higher transfer time across the network. Additionally, the transfer time across the network was found to be consistent as data size increased with non-encrypted data. Encrypted data, on the other hand, were found to have inconsistent increase in data size with the same set of data sizes due to padding resulting in higher transfer time across thenetwork.