Reviewerly/exHarmony huggingface dataset:
A semantic representation model incorporating knowledge point information for test question duplication detection

The repetition of test questions raises a significant challenge to test fairness. When processing the test texts, we found that the repetition of test questions is not only the similarity of context but also the context difference with the similar semantics, which is more challenging to identify. In addition, the existence of formulas in the test questions brings a greater challenge to detect the repetition of test questions. To address the above problems, we propose a semantic embedding representation model for test question duplication detection. The model incorporates knowledge point information and extracts the semantic information of test questions through a dual encoder structure and set a threshold to obtain top-n test questions with high similarity by calculating the cosine similarity between test questions. The experimental results show that this paper's proposed model greatly improved over the traditional repetitive detection model.
Research on automatic maintenance algorithm of bridge axis based on Beidou RTK

In this paper, the precise orientation data provided by Beidou RTK equipment for the gate bridge is used as data input. Through application analysis, model construction and algorithm research, the precision power output is provided for the gate bridge, and a feasible technical path is provided for the automatic maintenance of the axis of the floating bridge. In this paper, aiming at many environmental factors of gate bridge and dynamic characteristics of power boat engine, a stable power output solution is put forward, which is an innovation of traditional floating bridge axis maintenance logic, aiming at promoting the unmanned and intelligent development of floating bridge erection.
Research on evaluation of equipment support capability of land aviation brigade based on FAHP

To test to evaluate the aviation brigade combat equipment support ability, based on the combat equipment safeguard drill results in recent years, with the relevant regulations standard, through consulting literature, investigation and other methods, constructed the aviation brigade combat equipment support ability evaluation index system, and through the AHP to determine evaluation index weight of every layer. The fuzzy comprehensive evaluation method is used to calculate the evaluation results, and finally a more scientific and comprehensive evaluation is made for the actual combat equipment support capability of the army aviation brigade.
Ship detection in SAR image based on improved YOLOv5 network

In order to improve the accuracy of YOLO series algorithm in detecting small ship targets in SAR images, a target detection algorithm based on improved yolov5 is proposed in this paper. In this paper, The Multi-Scale Channel Attention Module (MS_CAM) is added to the network structure to aggregate local and global feature information in the way of channel attention, which can alleviate the problem of large semantic gap between different scales to a certain extent. In addition, the PANet fusion structure in YOLOv5 was replaced by BiFPN structure to make the network better weight of learning features. The experiment on the open RSDD-SAR dataset shows that compared with the traditional method, the AP value and recall rate of the whole dataset are improved.
Vehicle target detection from remote sensing images based on deep learning method

Aiming at the problem of small scale and little characteristic information of vehicles in remote sensing images, this paper proposes a multi-scale attention detection network. Firstly, VGG16 backbone network is used to obtain multi-level image features, and the cascade feature pyramid module and the dilated attention module are added to enhance the multiscale features of the target. Secondly, the focal loss function is added to the classification task to improve the fitting ability of the network to samples. Finally, the comparative experiments are conducted on the dataset. The results show that the multi-scale attention detection network has a high detection accuracy and its mAP reaches 94.56%.
Development of Large-Scale Precision Farming Monitoring System based on REST API and SignalR

Farming is one of the primary resources to provide human food therefore, it takes a large amount of cultivated land to meet food needs. Currently, technologies such as IoT have significant role in advancing agriculture in this digitalize era by improving conventional agriculture methods by not only making it optimal but also making it cost efficient for farmer to produce quality of crops. The aims of this paper to develop system in the form of REST API service by using .net 6 as framework to efficiently manage the growth of IoT devices from the ground up in medium scale to large scale cultivated land with easy to scale up based on the SignalR library and crops health monitoring. The system will assist institute farmer and technician by visualizing data from each deployed IoT device (Temperature, humidity, soil moisture, Ph) from each section of cultivated land and management of each new IoT device such as sensor, actuator and microcontroller that will be deployed and by using SignalR library as an interconnection between device to API. To ensure the quality of the system author measured using the JMeter application by conducting simulation with high peak of user access and transmitted data of IoT device to give a validation of load testing that the system will run smoothly without any problem. The research concludes based on the testing that been done has successfully delivered and shown no latency or error with performance result on each test 21ms functional and 0.50ms on SignalR testing.
Technology Acceptance in Learning History Subject Using Augmented Reality Towards Smart Mobile Learning Environment: Case in Malaysia

In alignment with smart city initiatives, Malaysia is shifting its educational landscape to a smart learning environment. The Ministry of Education (MoE) has made History a mandatory subject for passing the Malaysian Certificate of Education to grow awareness and instil patriotism among Malaysian students. However, History has been known as one of the difficult subjects to study for many students. On the other hand, the Malaysian Government Education Blueprint 2013-2025 seeks to “leverage ICT scale up quality learning” across the country. Therefore, this study aims to identify the factors that influence the intention to use Augmented Reality (AR) for mobile learning in learning History subject among secondary school students in Malaysia. Quantitative approach has been chosen as the research method for this study. A direct survey was conducted on 400 secondary school students in one of the smart cities in Malaysia as the target respondents. The collected data are analysed through descriptive statistics and Multiple Linear Regression analysis by using Statistical Package for the Social Sciences. Based on the results, the identified factors that influence the intention to use AR for mobile learning in learning History subject are Gender, Perceived Usefulness, Perceived Enjoyment, and Attitude Towards Use. The identified factors can be a good reference for schools and teachers to strategize their teaching and learning methods in pertaining to History subject among secondary school students in Malaysia. Future studies may include the study of various types of schools in Malaysia and explore more moderating effects of demographic factors.
An Automatic Student Attendance Monitoring System Using an Integrated HAAR Cascade with CNN for Face Recognition with Mask

In the olden day's many organizations including private and government finds it difficult to mark the attendance manually.A few decades back with the research on biometrics and image processing many smart applications like face recognizers and scanners came into existence but all these apps suffer from single face scanning problem but from the past 5 years many object detection algorithms help us to classify many objects or faces at a time based on multi facial points using boundary boxes to segment the regions.Many research works are carried out for the recognition of faces without masks.With the help of detection algorithms, the proposed algorithm tries to recognize the face of the students with or without masks to mark the attendance in this pandemic situation by designing HAAR integrated with LBP and CNN to find the multiple persons based on the facial points associated with the upper nose, eyes and other regions to extract the features.
Maintenance and Support

As you know, this book aims to help you build applications that do not require maintenance and that always work—without production support teams. You can achieve this objective by following the various recommendations that I presented in the previous chapters. So, what is left to discuss in the current chapter?
PEDAGOGICAL CONDITIONS THE FORMATION OF SPECIAL COMPETENCIES OF THE FUTURE PRIMARY SCHOOL TEACHERS ON THE BASIS OF THE PROJECT METHOD

The article deals with the pedagogical conditions of forming special competences of future primary school teachers on the basis of the project method.In this context, the authors of the article conclude that the formation of special competencies of future primary school teachers on the basis of the project method forms professional training during the implementation of the proposed pedagogical conditions.Today, the system of higher education in our country is undergoing significant changes.In accordance with the existing regulatory documents related to the field of education, higher education provides for the training of highly qualified personnel in all major areas of socially useful activities in accordance with the needs of society and the state.The modern dynamically developing world imposes qualitatively new requirementsforpedagogical staff training.High professionalism and competence, mobility, ability for professional adaptation, constant self-improvement are required from the specialist.In the authors' opinion, to solve these problems it is necessary to widely use innovative teaching technologies, to develop the creative abilities of future teachers, the desire for independent learning, strengthening the connection between theory and practice.
Classifying Sequences by Combining Context-Free Grammars and OWL Ontologies

This paper describes a pattern to formalise context-free grammars in OWL and its use for sequence classification. The proposed approach is compared to existing methods in terms of computational complexity as well as pragmatic applicability, with examples in the music domain.
Towards the Development of a Performance Evaluation of a Hyperledger Fabric Blockchain-based Application for Traceability of Distribution and Recall Transactions in Pharmaceutical Supply Chain

Blockchain technology is a disruptive technology that is currently being adapted for many enterprise use cases and as the applications of blockchain technology continues to widen, the pharmaceutical industry remains an optimistic industry with various challenges to provide solutions for.In this literature review, the researchers gathered and examined relevant studies to the opportunities of blockchain in the pharmaceutical industry and challenges of blockchain performance.The study identified that one of the largest opportunity areas for blockchain in the pharmaceutical industry is within its supply chain.Issues within the pharmaceutical supply chain involve illegal distribution practices, counterfeit drugs and inefficient drug recall management.With specific challenges and issues identified in blockchain performance, this study proposed the development of a specific blockchain implementation focused on Distribution and Recall transactions within the pharmaceutical supply chain.The proposed system is eyed for a performance evaluation as evaluating the performance of a specific blockchain implementation still yields importance.
Unlocking the Full Potential of Augmented Reality for Decision Making Under Battlefield Stress

Augmented reality on the battlefield has the potential to revolutionize tactical decision making, especially in support of a multi-domain operation (MDO).Previous work comparing more traditional tools such as sand tables and tablets to the newer mixed-reality options do not show clear superiority of the newer technology.However, consideration of the surrounding literature suggests that there may be inherent advantages, and design interventions, that ultimately improve the utility of augmented reality tools such that they are preferable to previous technologies.Specifically, in high-risk activities, such as those encountered in an active battlefield, the augmented reality interface, and the information available through that technology, may be designed so as to convey communication advantages.The proposed study compares design interventions for augmented reality using a HoloLens 2 to get information quickly for tactical decision-making across any MDO with similar representation on a tablet.Using the situation awareness framework and cognitive workload measures (based on NASA-TLX), we look at training on augmented reality technology to determine the best options.Furthermore, by studying the presentation of augmented reality images on the HoloLens 2, we would provide recommendations for how to quickly integrate the technology as a training tool and, eventually, a tool in the field.By determining the best ways to provide training on the new technology, the use of augmented reality can prevent costly and/or dangerous mistakes that would jeopardize the safety of the warfighter and increase the utility of the augmented system in scenarios where maintaining situation awareness is vital.
Predicting Bank Loan Eligibility Using Machine Learning Models and Comparison Analysis

As people's demands grow, so does the need for bank loans.Every day, banks get many loan applications from customers and other individuals but not every applicant is accepted.Typically, banks execute a loan application after verifying and evaluating the applicant's eligibility, which is a time-consuming and challenging process.When examining loan applications and making credit approval decisions, most banks use their credit score and risk assessment systems.Despite this, some applicants fail to pay their bills each year, causing financial institutions to lose a substantial amount of money.In this study, Machine Learning (ML) algorithms are employed to extract patterns from a common loan-approved dataset and predict deserving loan applicants.Customers' previous data will be used to undertake the study, including their age, income type, loan annuity, last credit bureau report, Type of organization they work for, and length of employment.ML methods such as Random Forest, XGBoost, Adaboost, Lightgbm, Decision tree, and K-Nearest Neighbor were used to discover the maximum relevant features, i.e., the elements that have the most impact on the prediction output.These mentioned algorithms are compared and assessed against one another using standard metrics.Among these, Logistic Regression achieved the highest accuracy of 92%.It was also determined as the best model and performed significantly well better than other machine learning methods in terms of F1-Score, which is 96%.
Designing a Business Process Based on Knowledge Management and Product Development Process in IT Industry

Recent studies have shown that knowledge management holds a very important role in determining a company innovation, especially when it comes to their product development process.This study aims to design a business process that will combine both product development process and knowledge management for IT Industry as it is one of the industries with the highest innovation growth in Indonesia.This study will use risk management framework to identify, eliminate and minimize risk in designing the product development process.Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) will be used in to assess which knowledge management method is the suitable activity that can support knowledge conversion process for each step of product development process.At last, both product development process and knowledge management activities will be combined and mapped as one whole business process as an innovation process guideline for IT Industry in Indonesia.
Development of Radiation Protective Mobile Case

The Radiation protective mobile cover is a product that blocks or reflects radiation emitting from a communication device.These gadgets are continually processing data, thus they are always active.Different components tend to generate and emit microwave radiation while processing this data and most of the radiation is emitted through the display unit.In this study, the flip cover has been designed in a systematic approach of concurrent engineering to obtain a higher overall shielding from radiation generated by the display unit as well as the other components.The cover is made of low-cost materials (aluminum foil, rubber) in order to be both economical and healthy for all clients.The developed protective cover can protect the customer effectively by filtering 96.07% of the generated radiation.This study designed a cover that protects against dangerous energy released by mobile phones.Other sorts of dangers, such as fire, chemical, biological, and projectile hazards, can be protected using the principles of the present invention.
Twisted quantum walks, generalised Dirac equation and Fermion doubling

Quantum discrete-time walkers have since their introduction demonstrated applications in algorithmics and to model and simulate a wide range of transport phenomena. They have long been considered the discrete-time and discrete space analogue of the Dirac equation and have been used as a primitive to simulate quantum field theories precisely because of some of their internal symmetries. In this paper we introduce a new family of quantum walks, said twisted, which admits, as continuous limit, a generalised Dirac operator equipped with a dispersion term. Moreover, this quadratic term in the energy spectrum acts as an effective mass, leading to a regularization of the well-known Fermion doubling problem.
Foreignization and Domestication on Translating Life-nurturing Terms in Three Versions of Huangdi Neijing - Su Wen

Huangdi Neijing - Su Wen (Su Wen) is the first recommended book for foreign readers who are interested in preservation of health and ancient Chinese culture. It is a pity that many life-nurturing terms in the classic were translated differently in different English versions, which may confuse and even misguide the readers. This study aimed at investigating foreignization and domestication on rendering life-nurturing terms in three representative versions of Su Wen. A corpus of 101 life-nurturing Chinese terms and their corresponding English versions was established. The translation methods of the terms without and with specific TCM concepts were identified and the translation strategies related to foreignization and domestication were analyzed to reveal the linguistic features of the translated life-nurturing terms in three versions. Our study found that translation methods such as literal translation, transliteration with additional explanation, and transliteration-literal translation could achieve the effect of foreignization while such translation methods as free translation, interpretive translation and literal translation with annotation could produce the effect of domestication. The results of the study suggest that foreignization rather than domestication is mainly applied in rendering the life-nurturing terms with specific TCM concepts to retain their unique linguistic and cultural features accurately, concisely and elegantly. The study of foreignization and domestication in the translation of life-nurturing terms is of great help to the translation of traditional Chinese medical classics with accuracy in meaning, smoothness in expression and faithfulness in style.
Using COBIT-19 to Create Information Assurance of COVID-19 Application

In the new era of covid-19 Pandemic, there is some COVID-19 Alert system which record patients exposed to covid-19 in around the world and also each provinces of Indonesia. The research goal is to make model to audit the COVID-19 Alert System with the issue of information assurance. The proposed model offers the audit assessment model to calculate the capability system in the perspective of Information Assurance. The method of assessment model is based on COBIT 2019 and also ISO / IEC 15504-2:2003 about requirement for performing Software Process Assessment. The result is the audit model equipped by the design factor for assessment model of COVID-19 Alert System, some process activities of audit and chosen mapping of control objectives to alignment goals for enterprise architect and auditor
Comparison and analysis of the accuracy of multiple machine learning algorithms in the field of spam classification

With the continuous advancement of science and technology, network data dissemination technology has been rapidly developed, but at the same time, the problem of spam is becoming more and more serious. This paper aims to analyse the classification principle of each machine learning classifier and compare the differences in the classification effect of each classifier. For the classification of spam, this paper mainly does the following aspects of the work: first, the selection of mail datasets and the pre-processing of email text information, the second is the extraction of the text characteristics of the email, The data is then divided into two parts, one for training and one for testing, using each machine to train the model, using the test set to test the model. Finally, parameters such as accuracy, precision, recall, and F1 score are calculated according to the mail classification results, comparing each model. The results show that the best classification effect is the neural network model and the naïve Bayes model, which can obtain better generalization ability in the test data and should be applied more in practice.
Workshop safety helmet detection algorithm based on improved YOLOv5

For workshop workers, helmets are one of the most important protective tools. Wearing helmets during operation can further protect the lives of workers. Aiming at the situation that the current safety helmet detection model has missed detection and false detection of small targets and dense targets in complex environments such as workshops, this paper proposes a workshop operation safety helmet detection algorithm based on improved YOLOv5 in combination with practical application scenarios. The CBAM attention mechanism is added to the backbone network, so that the network pays more attention to the safety helmet target to be detected, which can improve the detection effect in complex backgrounds. The EIoU _ Loss function is used to replace the GIoU _ Loss function to improve the convergence effect of the module. By comparing the accuracy and detection speed of the improved YOLOv5 target detection algorithm with the original YOLOv5 target detection algorithm on the self-made safety helmet detection data set, the results show that the average accuracy of the improved YOLOv5 model is 1.4 % higher than that of the original model. Achieved in a complex environment for small targets and dense target detection requirements.