Reviewerly/exHarmony huggingface dataset:
A Recommender System Based On Collaborative Filtering Using Polarity Improvement in Sentiment Analysis

Sentiment Analysis, which is a new subfield of the processing of natural language and text mining, categorizes the texts based on the sentiment expressed in them. Sentiment plays a significant role in decision-making. So sentiment analysis technology has a broad scope for scientific applications. On the other hand, a huge amount of information in the world today is in the form of text. Therefore, text mining techniques are important. Exploring comments or analyzing sentiment as a branch of text mining, means finding the author's perspective on a specific subject. The Internet allows users to easily express their opinions and get informed about the opinions of others. The high volume and the lack of proper structure for the text of the comments provided on the web, make it difficult to use hidden knowledge within them. Therefore, it is important to provide methods that can prepare and provide this knowledge in a summarized and structured way. In this research, it has been tried to provide a fuzzy method for analyzing the following comments on news sites according to the text of the report. In this regard, it has been tried to investigate the relationship with the author's commentary and opinion in light of the subject of the text using the grammatical features of texts such as noun and verb, as well as sentimental load analysis of sentences. Subsequently, the method is evaluated by implementing it on the dataset collected from news and comments. The proposed method has 87% diagnosis accuracy.
Uso de dispositivos móviles para la enseñanza y aprendizaje en estudiantes de ingeniería: Hacia el uso de tecnología 5G

Education is inextricably linked to the different stages of the history of mankind. In this sense, today's education is strongly influenced, by the digital age, by the great advance of information and communication technologies, by the global village and the neoliberal economic model. This forces us to assume the challenge that the incorporation of technology imposes on the formative processes in the classroom. The present work has as objective, to determine the use of mobile devices of students of the engineering careers in the Technological of Superior Studies of Jilotepec in the processes of learning and to idendificar those processes that foment their use. The study is based on the application of a survey in which the different engineering careers were considered, number of participating students by gender, age, use of equipment, use of mobile devices to read or study, time of use of mobile to study or consult subjects related to the different subjects as well as the type of networks consulted on the Internet. Finally, it shows the technological trend with the generation 5G and its application in the teaching and learning process in the very near future.
The Use of Mobile Technology in Enhancing the Critical Reading Skills of Pre-University ESL Students

Critical thinking skills are one of the most soughed after criteria for studies and work in the 21 st century, impacted by the industrial revolution that revamped the nature of the job, lifestyle and education.Critical thinking skills develop through the process of critical reading which involves the high order thinking, are lacking among the graduates, contributing to the statistic of unemployment in the nation.The past studies revealed the tertiary students' critical reading skills do not surpass the comprehension level and need for critical reading in English text training.Critical reading skills can be instilled in the tertiary students through continuous practice.In addressing to this problem, a mobile phone application is developed as practising tool, with practice module divided into themes with different levels of difficulties.The purpose of this paper is to meet the need to enhance the critical reading skills of the pre university program students in Samarahan District, Sarawak, Malaysia via purposing the use of ReadMe mobile application as effective language practising tool.The research methodology employed the quantitative approach and data was collected via survey questionnaire with quasi experimental approach involved the administration of pretest, post test and document analysis.It was found that half of the respondents showed improvement in the critical reading competency thus enhanced their interest in reading.
Service Action Recognition in Power Supply Business Hall with 3D-Fused ConvNet

For the purpose of improving the service quality, video surveillance systems are widely used to standardize the service process in power supply business halls. If the employers check surveillance video to ensure predefined process of staff behaviours, it will be characterized as time-consuming. In recent years, great progress has been made in intelligent action recognition using Convolution Neural Networks (CNNs). However, due to the small range of staffs' motion and similar scene information of power supply business halls, the performance of using traditional CNNs to recognize service actions, e.g. bowing, standing and sitting, is general. For improving the recognition rate, this paper proposes a 3D-fused Convolutional Network (ConvNet) for service actions recognition, which focuses on detecting the actions in the typical scene of one staff person and one customer with a well-segmented video clip. The well-segmented video clips are sent as input to the 3D-fused ConvNet for action recognition. The 3D-fused ConvNet consists of two base learners, optical flow base learner and RGB base learner. Both learners use the Convolutional 3D (C3D) architecture. Specifically, the RGB learner can be used to capture the features of small staffs' motion while the optical flow base learner can be viewed as the key part to eliminate the influence of the background, especially in a similar scene. Furthermore, prediction scores of two base learners can be weighted by the softmax function according to the performance of each base learner. Finally, the prediction scores of the two base learners are fused to obtain the prediction result, namely the specific actions of the staffs in the videos. The experiment result shows that the proposed method achieves 92.41% accuracy on the service action dataset of the power supply business hall.
Development of Android-based mobile learning modules on the immune system for Madrasah Aliyah Negeri (MAN) 9 Jakarta

Abstract The 21st century is marked by the use of technology in learning. One example is the use of smartphones as learning media in the form of Mobile Learning Modules, which are expected to make it easier for students to learn biology material. This study aims to develop biology learning media in the form of Android-based Mobile Learning Modules on the Immune System material. The research method used is Research and Development (R&amp;D). Research procedures include concepts, design, material collection, product manufacture, product testing, and distribution. The study was conducted at MAN 9 Jakarta. The research involved media experts, material experts, students, and biology teachers. Data analysis using Kolmogorov Smirnov, statistical tests using Paired Sample t-Test. The results showed that the Android-based Mobile Learning Module for the Immune System material was rated by an average score of 91.4%. The average pretest and post-test scores were 57 and 78.8. The conclusion of this study shows that this Android-based Mobile Learning Module has fulfilled the requirements as a learning medium and can improve student learning outcomes on the Immune System material.
Framework and Application of Live Video Streaming as a Virtual Reality Gaming Technology: A Study of Function and Performance

Live video streaming of outdoor and built environment could be a source of scenarios generations for training games, especially when scenarios are on real-time dynamics of the environment. Such so-called live virtual reality (LVR) is, among other things, one of the most recent gaming technologies. There is an increasing need for effective solutions that can be utilized to process videos in attempts to gain desirable result, as the requirement of video quality is becoming higher and higher. The issue at the core of real-time transcoding is to control the right combination of transmission bitrate and resolution. This work aims to develop a prototypical workflow for dewarping videos from fisheye camera to the cloud and re-stream it to a client. This prototype is used for testing combinations of bitrate and resolution in various living scenarios of indoor, outdoor and street traffic environments. The study of performance is enabled by KTH students who supply feedbacks about quality of experience. Comments of observers prove the promising use of LVR in training and education games. The results reveal that the combination of bitrate 3.5–4.5 Mbps and resolution 720p is best suited for transmission in order to avoid noticeable lagging.
Feature Construction Through Inductive Transfer Learning in Computer Vision

This paper will look into the application of transfer learning in computer vision, where once the training is completed or learned, we can use that learning to be applied for other new domain with respect to attributes if we only do the system or code transfer or does we need a degree to training which will sufficiently reduce the training time thus making the computer vision more robust and reliable. The paper will look and compare the details and will publish the experiment results for Inception V3, VGG16, and Resnet50 models on the ImageNet dataset by varying the image sizes for source and target datasets, for Inductive transfer Learning and various techniques or methods specially by doing the model transfer.
Expert system for diagnosing narcissistic personality disorders using certainty factor and forward chaining methods

Abstract Narcissistic disorder originates from the Greek legend Narcissus, a young boy who falls in love with his reflection in a pond. Personal with this disorder has a purpose with interests that are not reality, excessive, the nature is known as grandiosity. People with narcissistic disorders expect others to praise and follow their desires and requests and a narcissist is less sensitive to the needs of those around him. Research associations of the All India Institute of Medical Science which results in a period of six years, there were 259 people worldwide died with a percentage of men 72.5 percent and women 27.5 percent due to selfies due to excessive narcissism. The method used is Certainty Factor and Forward Chaining as an inference engine to determine the diagnosis results based on the prescribed symptoms. Calculations in this method combine the measure of increased belief and measure of increased disbelief values obtained from the expert and also classify the narcissistic personality disorder so that this expert system helps the user to find the level of narcissism that matches the user’s experience and obtain the value of trust and recommendations for handling narcissistic personality disorders.
Analysis of Hybrid Technique for Motion Planning of Humanoid NAO

The navigation of a humanoid robot is essential because it is the basic requirement of any assigned task. Singly used motion planning techniques may take a long path to reach the target and increase the computational cost. Therefore, in this article, a hybrid controller is employed in the humanoid NAO for motion planning assignment. The Eagle strategy (ES) with Ant colony optimization (ACO) is introduced in this article for evaluating precise steering angles for humanoid robots as they traverse a route from a reference point to a target point. This enables the robot to achieve its specific target more quickly by avoiding barriers and obtaining the minimal global direction. The hybridized ES-ACO approach is critical in determining precise steering angles to escape obstacles. The details of terrain are obtained using vision and ultrasonic sensors, which also include the barriers ranges to the ES as an input variable. The ES's input parameters are the barrier ranges from the NAO in front, left, and right directions, and the technique's output variable is the precise steering angle. The designed controller is tested in both a simulation and an experimental setting with a variety of obstacles. The outcomes of both simulation and experimental conditions are compared, and a strong correlation is identified in those with the fewest deviations.
Software Engineering in Australasia

Six months ago an important call was made for researchers globally to provide insights into the way Software Engineering is done in their region. Heeding this call, we hereby outline the position Software Engineering in Australasia (New Zealand and Australia). This article first considers the software development methods, practices and tools that are popular in the Australasian software engineering community. We then briefly review the particular strengths of software engineering researchers in Australasia. Finally, we make an open call for collaborators by reflecting on our current position and identifying future opportunities.
Dual Gradient Feature Pair Based Face Recognition for Aging and Pose Changes

This paper proposes a novel dual gradient feature pair (DGFP) based face recognition system that is robust against pose and age variation. This DGFP method, initially segments the rectangular face region into 6 regions which forms 3 pairs which is almost symmetrical to each other. From each pair highly matched HOG features are extracted and the dual gradient features like the positive gradients and negative gradients are extracted from the symmetrical regions of the face gets matched. DGFP is extracted in both the training and testing images. The matching is obtained using the minimum Euclidean distance with a threshold value. Experimental results are evaluated using the datasets such as MORPH, FGNET and Yale dataset and the results reveals that the proposed DGFP face recognition system has achieved an average recognition rate of 92.07%, average time of feature extraction is 253s and the average time of face recognition is 0.1533s
Application of adaptive square root cubature Kalman filter in turbofan engine gas path performance monitoring

Abstract Kalman filters are very popular in turbofan engine community for health monitoring purposes. In this study, in order to get better performance in terms of filtering accuracy and noise adaptability, an Adaptive Square Root Cubature Kalman Filter (ASRCKF) was proposed to estimate health parameters of the turbofan engine gas path components. In the ASRCKF algorithm, the mean value and covariance of the engine nonlinear function were calculated by cubature rule-based numerical integration method and used as a substitute for nonlinear model in nonlinear Kalman filter. The latest information of measurement parameters in the recursion and filtering process was used to estimate and self-adjust the noises cross-covariance by removable window method to get higher filtering accuracy. Compared with the Extended Kalman Filter (EKF) and Square Root Cubature Kalman Filter (SRCKF), the simulation results in the gradual and rapid deterioration process of turbofan engine gas path components indicate that the higher accuracy and faster convergence can be obtained through ASRUKF, which can be used in health parameters estimation and condition monitoring of turbofan engine gas path. 1
Construction Crane Detection under Transmission Line Based on Improved Vibe Algorithm

Abstract In this paper, the moving object detection part of crane’s illegal construction detection under transmission line is studied, and two scenes are simulated. The Visual background extraction (Vibe) algorithm is selected as the moving object detection algorithm used in this paper. For the problems of the algorithm, including ghost phenomenon in detection results and the the presence of falsely detected foreground points under dynamic background, the Vibe algorithm is improved by the reconstructed background model, dynamic radius threshold and connected domain analysis. Results of the experiment show that ghost area in the detection results of the improved Vibe algorithm can quickly disappear, and the number of falsely detected foreground points is significantly reduced. Compared with the original Vibe algorithm, the average value of the comprehensive evaluation index F1 is increased by 5.8%, and the detection effect of moving object is significantly improved.
고령화 시대의 노년기 문법교육의 설계와 전망

The study is a basic discussion of grammar education in the age of aging, with the aim of setting the direction of the theoretical approach to this topic. There are the following three points in the study.First, the meaning of grammar education for older learners is divided into two. This is a kind of education for older adults (aged adults) who live in an aging age and at the same time it is a kind of education for all people living in an aging age.Second, there are two design directions for grammar education in old age. Grammar education for the continuation of productive activities (especially career life) and for the prevention of social aging, can be set up to do well in cultural adaptation and social integration.Third, the main task of research and implementation of grammar education in the future is to prevent the rapidly spreading problem of hate expression for aged adults in society. the second task is to incubate empowerment centered on older learners in the Grammar education. Finally, Language Awareness program for all age learners should be attempted.
SComm: A Real-Time Mutually Authenticated Secure Communication Framework for Smart Grids

Motivated by recent Denial of Service (DoS) attacks at the control center and multiple remote power generation sites of a registered entity in the U.S. power grid, we seek to address the lack of sufficient real-time mutual authentication and secure communication between smart grid components. We introduce SComm, a real-time mutually authenticated secure communication framework that consists of a commitment-based enrolment protocol and a mutually authenticated key establishment protocol by which components can authenticate each other and carry out secure communication to prevent DoS attacks. Our framework applies a Zero-Knowledge Elliptic Curve Diffie-Hellman (ZK-ECDH) to establish a unique cryptographic session key for secure communication. We analysed our framework with respect to its security and performance, and the results show that our frame-work enhances the security of components and communication in real-time to efficiently deal with unauthentication and DoS attack. As proof of concept, we apply our framework to mitigate the DoS attacks at the registered entity.
Video Quality Assessment by Sparse Representation and Dynamic Atom Classification

Finding that not all dictionary atoms are closely related to degradation in visual signal, we innovatively design a distortion sensitivity guided Dynamic Atom Classification (DAC) strategy to separate distorted signal. Then, we propose a novel DAC-based full-reference video quality assessment (VQA) method. The method includes two parts: spatial quality evaluation and temporal quality evaluation. Spatially, we train a distortion-aware dictionary, get sparse representation of video patches, and dynamically classify activated dictionary atoms. Every frame is separated into difference and basic components, and spatial similarity is aggregated by component similarities. Temporally, we calculate gradient similarity of frame difference to capture motion information. The experimental results indicate the effectiveness of the proposed algorithm compared with state-of-art VQA methods.
Phase-sensitive nonclassical properties in quantum metrology with a displaced squeezed vacuum state

We predict that the phase-dependent error distribution of locally unentangled quantum states directly affects quantum parameter estimation accuracy. Therefore, we employ the displaced squeezed vacuum (DSV) state as a probe state and investigate an interesting question of the phase-sensitive nonclassical properties in DSV's metrology. We found that the accuracy limit of parameter estimation is a function of the phase-sensitive parameter $\phi -\theta /2$ with a period $\pi $. We show that when $\phi -\theta /2$ $\in \left[ k\pi/2,3k\pi /4\right) \left( k\in \mathbb{Z}\right)$, we can obtain the accuracy of parameter estimation approaching the ultimate quantum limit through using the DSV state with the larger displacement and squeezing strength, whereas $\phi -\theta /2$ $\in \left(3k\pi /4,k\pi \right] \left( k\in \mathbb{Z}\right) $, the optimal estimation accuracy can be acquired only when the DSV state degenerates to a squeezed-vacuum state.
Effect of sliding pendulum isolator bearing to the structural performance of Becakayu Ahmad-Yani continuous span bridge

&lt;p&gt;Structural assessment is an important procedure to be done for checking and reviewing the realization of the expected structural performance level. In a normal circumstance, it is common that the bridge is designed with life-safety performance that implies undamaged superstructures and foundations, and damaged substructures. This design philosophy is normally known as “ductile substructure and elastic superstructure” concept. The location of the damages shall be predefined to allow easy access for future repairs after earthquake events. Nowadays, a different design approach is introduced through the use of passive control devices. One of the most common technology for bridges is the seismic isolation system using sliding pendulum isolator bearing as the seismic controlling device. Ahmad Yani Continuous Span Bridge in Becakayu Toll Road Project is designed with pendulum isolator bearings. Through non-linear time history analysis, the structural performance of Ahmad Yani Bridge will be evaluated. It is expected that all the dissipation mechanism occurs in the pendulum bearings, with both superstructure and substructures are still in elastic and essentially elastic conditions.&lt;/p&gt;
Research on MOOC Course Evaluation Method Based on Cloud Model and Fuzzy Comprehensive Evaluation

Aiming at the problems in the quality evaluation of MOOC courses, the cloud model and fuzzy comprehensive evaluation are used to evaluate the quality of MOOC courses. By analyzing the main factors that affect MOOC courses, a MOOC course evaluation index system is constructed, and then the quality of MOOC courses is evaluated based on the fuzzy randomness of the cloud model. Finally, an example is used for analysis, and the result shows that the evaluation method in this paper has high applicability.
Personal Software Process: A Study from an Academic Perspective

The study presented in this article is the outcome of an investigation that collects and analyses the results obtained from the students’ responses after finishing a course on Personal Software Process (PSP). The objective of the course is for future Computer Engineers to learn and improve the software construction process. When they finished the course, a short questionnaire was conducted to find out their perception of how it was carried out and the answers were analysed from two different points of views. From a quantitative point of view, the answers were analysed to find out if the course is truly interesting for the students; from a qualitative point of view, the responses were analysed to extract the positive and negative aspects and the improvements that the students perceive about the course. As conclusions, it must be indicated that the results obtained have allowed us to contrast our results with other studies previously carried out by other authors, and it has also allowed us to make proposals for improvements for new courses to be carried out in the future.