Reviewerly/exHarmony huggingface dataset:
Quality Improvement Recommendations of SMAN 5 Bandar Lampung Website using Collaboration of ISO/IEC 25010:2011 and Importance Performance Analysis

For students, schools, and society, a school website is an essential thing. However, it is rare to analyze the quality of school websites, where prior research has focused more on analyzing e-government and university academic websites. In order to remain optimal and maintain its quality, a school website also needs to be evaluated regularly. In general, recommendations for quality improvement of the website will be determined in this report. To understand the existing system as the basis for collecting data and information, UML artifacts are used, i.e., requirement statements, use case diagrams, and class diagrams. The approaches utilized in this study, i.e., ISO/IEC 25010:2011 as a universal quality measurement standard and Importance Performance Analysis (IPA), to determine each item's priority for website improvement to improve quality. According to the analyzed data, the current website's performance score average is 3.29, with a negative gap (&#x2212;0.22). Additionally, the existing website also has issues indicated by items in quadrant I, i.e., US2, US8, FS3, PE2, IQ2, IQ3, and IQ4. These items are the main priority for website improvement. Through these improvements, website performance has increased by 0.41, which means the average improved website performance is 3.70 with a positive gap (0.19). These results indicate that users are satisfied with the improved website performance.
Predictive Control for Waypoint Path Settling Maneuver with Robustness to Initial Vehicle Pose

Recently, interest in autonomous vehicles has increased. Among autonomous driving control technologies, waypoint tracking is a method of driving along a global route created by a global navigation satellite system and is essential from the perspective of lateral motion control. In waypoint tracking control, if an ego vehicle's position and/or direction angle has a sizeable lateral offset with the planned route, the control input may diverge or cannot be computed. There may also be an initial situation when the ego vehicle starts to track the waypoints opposite to the desired direction. In this study, we propose a way for settling to the global route robustly with respect to the initial pose via model predictive control. We derived an integrated motion model of longitudinal and lateral direction. For validation of the proposed method, the experiment was conducted through computational simulation. We confirmed that the vehicle smoothly entered the reference route regardless of the initial position and heading of the ego vehicle.
Improved CyeleGAN for MR to CT synthesis

Radiotherapy treatment planning requires CT images to accurately calculate the dose distribution, but sometimes only MR images can be obtained, therefore it is necessary to generate CT images from its corresponding MR images. In order to synthesize quality pseudo CT images, an improved cycle generative adversarial network (CycleGAN) with residual network (ResNet) and U-Net was proposed. The improved CycleGAN was trained by unpaired data to get better result, and approved by the cycle inconsistency of CycleGAN, the slow-down gradient disappearance of ResNet and the multi-scale fusion of U-Nets. Avoiding the disappearance of input information and the vanishing of gradient information, the improved network synthesized more quality CT images. Compared with the original CycleGAN method, the MAE of the proposed method was reduced by 5.12%, the SSIM increased by 0.7% and the PSNR increased by 1.29%, which was trained and tested on a dataset of 18 patients. Additional, compared with the DCNN method, the atlas-based method, and the pix2pix method, the relative error of MAE was reduced by 1.73%, 2.215% and 0.228% respectively. The proposed method synthesizes more vivid CT images owing to the advantages of deep learning model, which better meets the requirements of clinical analysis.
Stable isotope (δD, δ18O) data on the structural water of two sericite samples from the Cu-Au high-sulphidation deposit Chelopech, Bulgaria – a tentative study

The epithermal high-sulphidation Cu-Au Chelopech deposit is characterized by a well-developed and well-traceable hydrothermal footprint manifested in the volcanic host rocks. The economic ore mineralization is embedded in the strong silicification, included among the advanced argillic zone of alteration, smoothly transitioning to quartz-sericite alteration that evolves into widespread propylitics. The quartz-sericite alteration zone is accessible for exploration only in underground mining galleries and exploration drillings. The main mineral assemblage in this zone is quartz, sericite, pyrite, minor rutile/anatase and relics of apatite and feldspar. According to XRD data from the studied samples, sericite was defined as illite and muscovite/sericite 2M1 polytype. The abundance of heavy stable isotopes (D, 18O) in the structural water of two sericite samples is the object of this study. A special attention was paid to the separation of extraneous waters from the structural one by thermal fractionation. The extracted structural water was converted to hydrogen and carbon dioxide before the isotopic measurements. The obtained results, put into a δD vs. δ18O plot, indicate that sericite structural water is “heavier” than meteoric water, within uncertainty limits.
Classification with Dynamic Data Augmentation

Data augmentation has improved the accuracy and robustness of deep neural networks. Research has focused on finding an optimal augmentation policy that generates good quality training images to improve classification accuracy. However, searching for this optimal augmentation policy is computationally expensive and is dependent on the neural architecture. In this work, we design a dynamic augmentation approach that automatically adjusts the number of transformation operations and their magnitudes during the training of deep neural networks. We also address the shift in the test data distribution by proposing to perform augmentation on the test data. We validate the effectiveness of our solution on CIFAR-10, CIFAR-100, ImageNet, and the perturbed datasets including CIFAR-10-C, CIFAR-100-C, ImageNet-A, ImageNet-C and ImageNet-P. Experiment results show that our proposed dynamic augmentation approach is scalable and gives good performances on clean, adversarial and corrupt datasets, reducing the best published results by a significant margin.
An Electric Equipment Analysis Method based on Knowledge Reasoning

Abstract Facing the increasingly complex power grid architecture and high equipment failure risk, a comprehensive equipment condition analysis method based on knowledge reasoning is proposed in this paper, mainly using the large amount of characteristic information to realize the condition evaluation, fault detection and early warning. Firstly, it statistically analyzes the historical data, extracts the characteristic information of equipment health status and builds a knowledge mapping library of key factors for equipment-centered status analysis; secondly, it establishes an intelligent early warning library of equipment index data, and gets the probability of equipment defects and fault risks through induction-based knowledge reasoning method; finally, it gets the equipment status rating through logic and rule-based knowledge reasoning method and the closed-loop system of equipment status evaluation is established. The reasonableness of the evaluation method is verified by the examples, which realizes supervision equipment operation status mining early warning, sensing equipment operation status in advance and reducing potential operation risk of power grid.
Space/Spatial-Frequency Filtering Based on the Local Frequency Estimation: From the Noisy Signal, through the Space/Spatial-Frequency Domain to the Estimated one

Space/spatial frequency filtering, as a possibility for estimation of two-dimensional highly nonstationary noisy signals, is considered in this paper. Principles of transferring from two-dimensional (2D) to 4D space/spatial-frequency domain, of the local frequency (LF) estimation, and the 2D signal estimation based on the estimated LFs are considered in detail. The filter, based on the considered principles, is tested and verified on a real-life nonstationary noisy 2D signal.
What Can Attention Module Do in Knowledge Distillation?

Knowledge distillation is an effective method to transfer knowledge from teacher model or ensemble models to student. KD based on the intermediate layers makes the feature representations of student imitate the teacher. However, the feature maps of teacher contain less obvious information levels, which limits student’s efficient learning. In this paper, we propose a knowledge distillation method based on attention module representation (AM-KD). We believe that the feature representation of attention network with richer information levels can help student to learn the knowledge of teacher preferably. By multiplying the attention weight and the original network feature representation, we obtain knowledge with both hierarchical and global information. Experiments are carried out on CIFAR and Tiny-ImageNet datasets to verify the effectiveness of our method. When there is a large gap between the student and the teacher, the improvement of AM-KD is higher than that of the distillation method using attention weight alone.
Application of People Flow and Face Mask Detection for Smart Anti-Epidemic

During the COVID-19 pandemic, World Health Organization (WHO) published two protection methods. One is maintaining social distance; the other is wearing the mask. Many reports indicate that keeping a safe distance and wearing face masks can reduce the risk of transmission during this pandemic. In this paper, we present a deep learning pipeline that can achieve pedestrian flow statistics, identify correct and incorrect mask-wearing simultaneously from real-time video streams. In our proposed system, the live video stream is taken as input. The alert will be broadcasted when people crowed and someone do not wear the mask. Experimental results show that the proposed system can achieve an effective detection solution and reduce the risk of infection.
Computer intelligent assessment model and potential impacts of blockchain technology on grocery store by mathematical statistics

This paper investigates the impact of blockchain technology on the supermarket (retal industry) in NZ from a technological and functional perspective. This research employs secondary research methods including desktop research, literature review and case studies. Blockchain technology affects the business process of supermarkets mainly by securing payments and provides an end-to-end supply chain that improves food security. However, this process is hampered bu a number of challenges including difficulty in adapting sound logistics, lack of adequate skills and resistance to change by employees. This can be addressed by imparting education/training and raising the awareness about the benefits of blockchain.
BERT Based Cross-Task Sentiment Analysis with Adversarial Learning

Sentiment Analysis (SA) is an essential task in natural language processing. Generally, previous sentiment analysis models focus on a single subtask. However, a generalized SA agent is expected with the ability to learn knowledge from one task and use it in other relevant tasks. Consequently, we formulate this challenge as an unsupervised task adaption problem and propose TAL-IS, a simple and efficient approach to finetune cross-task SA model. In this approach, we use Task Adversarial Learning (TAL) with a BERT-specific Input Standardization (IS) scheme to obtain both emotion-discriminative and task-invariant contextual features. To the best of our knowledge, our work is the first attempt to propose a cross-task model for SA subtasks with unsupervised task adaption. Experiments show that our proposed model outperforms the general finetuning method and can learn knowledge effectively cross SA subtasks.
Introduction Conceptualization of Security, Forensics, and Privacy of Internet of Things: An Artificial Intelligence Perspective

The Internet of Things (IoT) is a rapidly evolving technology that empowers billions of globally distributed physical things to be interconnected over the internet to capture, collect, exchange, and share a wide variety of vast amounts of data. These physical things incorporate all of the connectable devices ranging from conventional household devices to complex industrial devices (Javed et al. in IEEE Commun. Surv. Tutorials, 2018). The emergence of INDUSTRY 4.0 has revolutionized the industrial domain globally with a large and ever-increasing number of embedded devices, and computerized chips because of the expanding availability, affordability, microprocessors, sensors’ capacity, and omnipresent communication technologies. Connecting such a very large number of heterogeneous objects and implanting sensors to them leads to some degree of digital intelligence at devices that, empowering them to communicate instantaneous data with no human intervention.
Abstract Syntax Tree (AST) and Control Flow Graph (CFG) Construction of Notasi Algoritmik

Abstract Syntax Tree (AST) and Control Flow Graph (CFG) are program code representations widely used for static analysis. One of the uses of static analysis is for automated grading programming exercises. Notasi Algoritmik is a notation used in our institution for learning programming, including those related to the evaluation of programming exercises. However, the current condition in our institution is that the grading process of programming exercises using Notasi Algoritmik is still done manually, which has an error-prone risk. Before our work, there was no research about how AST and CFG are constructed from Notasi Algoritmik, even though it is possible to develop an automated grader to solve manual assessment in the evaluation of programming learning. Therefore, in this paper, research was conducted to construct the AST and CFG from Notasi Algoritmik, to make developing an automated grader for programming assignments that uses Notasi Algoritmik possible in the future. The first thing that we need to do is to define the grammar of Notasi Algoritmik. After the grammar is defined, we need to do lexical and syntax analysis to construct the AST. AST itself will be used to represent the Notasi Algoritmik in abstract form to construct the CFG. An algorithm created by Tim Teitelbaum (2008) alongside some modifications can be used as a reference to construct the CFG. The research shows that AST is well constructed after two-phase lexical analysis and syntax analysis. Meanwhile, CFG can be constructed using the recursive technique that traverses nodes in the AST. With the result of this research, the development of an automated grader for Notasi Algoritmik can be tried in the future.
Research on Classifiers Used to Identify Dangerous Goods Transportation Vehicles

With the continuous development of the national economy, the domestic demand for dangerous goods has also increased year by year. Once a traffic accident occurs, it will have a huge impact on the natural environment, road safety, and the safety of people’s lives and property. In addition, Advanced Driver Assistance Systems (ADAS) based on sensor technology and advanced control technology provide a good solution for car driving safety. Sensors play a very important role in advanced driver assistance systems. Commonly used sensors mainly include cameras, millimeter wave radars, lidars, etc., which can be used to obtain vehicle internal and external information. This information can help the driver complete the driving task more safely. Therefore, this paper summarizes the current research status of relevant aspects at home and abroad, and compares various vehicle identification and detection algorithms, and uses Haar-features and AdaBoost cascade classifier algorithm to identify dangerous goods transportation vehicles. A total of four classifiers are trained, and the number of positive samples of each classifier is 800, 1200, 1600 and 2000 respectively. Through comparative analysis, it is found that the classifier trained from 1600 positive samples has the best effect.
Kinematics Analysis of Aerobics Movement Decomposition Based on Multi-target Video Tracking Algorithm

Computer vision is currently an important research field. In computer vision, target tracking is one of the entry points for research, it is widely used in motion analysis, video surveillance, robot navigation, human–computer interaction and many other fields. In so many application scenarios, target tracking algorithms are required, it has high durability and efficiency. However, in real complex scenes, there are often different situations such as light changes, mutual occlusion between targets, interaction between targets, and blurred target motion, making the recorded video scene complex and changeable. Therefore, it is still a very difficult task to effectively monitor multiple moving targets at the same time. This article explores and analyzes the aerobics movement decomposition kinematics based on the video tracking algorithm of multiple sports targets. After consulting related materials, summarizes the feasibility of the aerobics movement decomposition kinematics based on the multiple sports target video tracking algorithm. The experiment of aerobics action decomposition kinematic analysis of the motion target video tracking algorithm, through the experiment, it is concluded that the angle of the right hip joint is 167.45°, when the right hip is fully extended at the beginning of the set of competitive aerobics action. It can be seen from the angle of the two knee joints that the legs are relatively straight and maintain a good posture. When the jumping foot hits the ground, the difference between the right hip angle between the two moments is 29.8°, that is, the right hip angle decreases and the right hip angle decreases, indicating that the upper body is facing the right side, the upper body begins to bend downward, and the left hip continue to abduct, its value increases, and at the same time the left lateral muscle stretches, so that the hip and shoulder joints form a larger rotation angle.
Applicability Testing Technique of Intelligent Processor for Embedded Computing System

This paper conducts research on the applicability technology of intelligent processors in embedded devices. From the aspects of the complexity of intelligent tasks, the real-time performance and accuracy requirements of intelligent tasks, the high-performance density requirements of the embedded system and the working environment requirements of embedded devices, the relevant characteristics of intelligent applications in the embedded environment are analyzed. Based on the above analysis, a series of testing indexes for the applicability of intelligent processors for embedded environments are proposed, including support for different types of intelligent algorithms, processing performance, processing accuracy, power consumption, and working environment. Using typical deep neural network models, the applicability of a certain type of domestic intelligent processor is tested and analyzed to verify the validity of the proposed indexes.
Evaluation Model of Regional Sports Development Level Based on Fuzzy Neural Network

Fuzzy neural network fully combines the advantages of the fuzzy system and neural network and has the abilities of Learning Association, recognition, adaptation, and fuzzy information processing. In the process of using a fuzzy neural network to model practical application cases, the key is a learning algorithm. In order to establish a fuzzy neural network model which is suitable for the evaluation of regional sports development levels, we used a BP algorithm, an RBF algorithm, a momentum gradient descent BP algorithm a and a PSO algorithm to train the fuzzy neural network model. The results show that the fuzzy neural network model of a PSO algorithm can better evaluate the regional sports development level.
Vision: Computing and Authentication Practicesin Global Oil and Gas Fields

Oil and gas fields are a critical part of our infrastructure, and vulnerable to attack by powerful adversaries. In addition, these are often difficult work environments, with constraints on space, clothing, and more. Yet there is little research on the technology practices and constraints of workers in these environments. We present what we believe is the first survey of oil- and gas-field workers located around the world. We establish the presence and status of a variety of computing devices and of the security practices that govern their use. We also determine the working conditions (such as personal protective equipment) under which these devices are used, which impacts usable security aspects like feasible forms of authentication. Our preliminary work suggests many directions for improving security in this critical sector.
Formation of secondary school students’ information culture in the context of digital transformation of biological education

This paper examines the possibilities within the digital transformation of biology education, especially for the formation of senior students information culture. Genesis disclosure of the information culture concept within the framework of bibliographic, philosophical and pedagogical approaches allowed us to reveal the following tendency: the dynamically changing information world determines the evolution of such a multicomponent phenomenon as information culture. The paper presents the methodology implementation for the formation of secondary school students information culture by means of mobile technologies in the context of blended learning in the system of modern biological education. Criteria and indicators have been determined that make it possible to judge the level of information culture formation within the framework of cognitive, axiological and technological components. The paper analyzes the results of a pedagogical experiment, which finally allowed to state that the developed model of the formation of modern secondary school students information culture by using mobile learning technologies and the organizational and pedagogical conditions for its implementation contribute to the achievement of positive dynamics of indicators and improve the quality of biological education in a secondary school.
Event Extraction from News Media Based on Knowledge and Data-Driven Methods

The paper analyzes up-to-date methods for Event Extraction from unstructured data. The proposed approach is based on both knowledge and data-driven methods for Event Extraction, in particular, from news data streams displaying the agenda. Firstly, syntactic dependency relations between extracted components of events are identified, representing coherence of the text fragments (collocation candidates). T-score association measure is applied at the next stage of collocation extraction. The advantage of the developed technology is that both the grammatical structure of the language and the statistical association between event components are analyzed. The effectiveness of the developed technology is assessed and exceeds the precision results of the other approaches. The benefits of the proposed technology are to improve the quality of news stream processing.
Text Image Super Resolution Using Deep Attention Neural Network

In this paper, we propose a super-resolution method for text images to improve the accuracy of optical character recognition (OCR). The accuracy of OCR is closely related to the resolution of the image, and when OCR is applied to low resolution text images, satisfactory results are often not obtained. In the proposed method, we extract more representative feature information from text images by combining channel and spatial attention. Furthermore, we propose a new loss function called “edge loss”. Experimental results show that the recognition accuracy of text images by our SR method is 5.87% higher than that of the original low-resolution images, and also higher than the results of BICUBIC and the baseline model.