Reviewerly/exHarmony huggingface dataset:
Authorship Verification for Hired Plagiarism Detection

Plagiarism detection is an important tool in modern academia. With growing class sizes and the modernization of the internet, there have been more ways that allow plagiarism to excel in modern culture. Methods such as patchwriting – where an individual may copy, paste and possibly modify the content – and commissioned writing – where an individual hires another person to do the work for them – are not considered by modern plagiarism detectors. This work aims to give instructors a way to identify and detect plagiarism in student writing that addresses these difficult-to-detect issues using artificial intelligence. We introduce a tool to aid instructors in detecting plagiarism that adapts to each students’ individual writing style as they submit writing assignments. This work incorporates artificial intelligence and natural language processing that identifies the ways in which a student writes based on a collection of their essays. The proposed Authorship Verification for Hired Plagiarism Detection (AVHPD) tool includes document storage, a clean user interface, and intuitive break-downs of how a given writing sample differs from prior samples.
Penerapan Metode Collaborative Filtering Untuk Personalized Learning Content Pada Learning Management System (LMS)

The delivery of appropriate learning content can be one of the factors that can increase satisfaction, motivation, and interest of learners during learning sessions. But on the other hand, due to a large amount of learning content available on LMS (Learning Management System) and the difficulty of determining learning content that suits the needs and interests of each learner, it often causes some learning content to be overlooked by them. Therefore, this paper aims to design a personalized learning content system in LMS for learners. The main objective of this study is to provide learning content suggestions or recommendations for learners, based on the course module they previously accessed by applying Collaborative Filtering method. This method is used by utilizing dataset in the form of implicit feedback, obtained from the activities of learners when interacting with LMS. The UAT (User Acceptance Test) results show that the personalization system has been well received by as many as 82.67% of learners based on three aspects, those are interface, user, and system interaction aspects. Moreover, the MAE (Mean Absolute Error) calculation shows that this system has the best accuracy rate at a 10% sparsity level with the lowest average value of 0.4514.
An In-depth Security and Performance Investigation in Hyperledger Fabric-configured Distributed Computing Systems

Since its inception as one of the Bitcoin's underpinning technologies, the concept of Blockchain has traversed a long way from being merely a secure distributed ledger meant only for storing cryptocurrencies-based financial transactions to implementing innovative and revolutionary distributed systems for multifarious purposes.One of the contemporary and out-of-the-box Blockchain-based projects, namely Hyperledger, promises to make preeminent use of this technology by promoting cross-industry collaboration in developing Blockchain-based solutions, thus, opening a new chapter in distributed computing.However, attributing to its underlying design, leveraging Hyperledger-Fabric (HF) features still lacks an analysis from the perspective of security risks and efficiency concerns pertaining to real-time distributed computing-based systems and applications.In this regard, we investigate the HF architecture, along with various research endeavours undergone by researchers in recent years to combat its security and performance-related challenges.In light of the identified limitations and bottlenecks, we present our conceptual proposal and feasible insights for improving the efficacy of HF-based systems while not compromising their security.
Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest

The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals ..To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features .In this work, present hybrid intelligent system for the recognition related to the digitally modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM), with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF) classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.
Current Status and Revitalization Plan for Character Education of High Schools under Daejin University Educational Foundation

Current Status and Revitalization Plan for Character Education of High Schools under Daejin University Educational Foundation - Character education;Sangsaeng Daejin character textbook DOUBLE Happiness;Sangsaeng WITH video festival;Experience activities of integrated character program “DOUBLE happiness”;Professional learning community;Online content development
MOBILE ASSISTED LANGUAGE LEARNING IMPLEMENTATION IN SELF-EDITING WRITING PROCESS OF STKIP PGRI JOMBANG STUDENTS

Self-editing writing process has become very important stage in creating good writing. STKIP PGRI students and lecturers pay more attention to writing content then writing mechanics editing. In this pandemic era teaching and learning activities is limited. This results student needs more assistant in editing their writing. MALL (Mobile Assisted Language Learning) deals with installed applications in mobile phone to support editing writing process This study aims at improving self editing in writing process using application in mobile phones. Grammar and spelling checker applications was used for the purpose of the study. The participants of the study were first semester students of English Department, STKIP PGRI Jombang. In order to achieve the objectives of the study, 2 cycle classroom action research was designed. The study succeeds to apply some self editing techniques as follow: (1) MALL Introduction; which contains installing Proofreader application, observing the application features, observing the example work, (2) Self editing awareness; with some activities of inserting writing work into the application, observing the mistakes, choosing suggested correction, peer editing, (3) Self editing final; which activities are comparing manual self editing and MALL editing work and reconstructing the writing.
Achieving multi-dimensional convolutions in photonic frequency synthetic dimensions

We introduce a scheme to achieve arbitrary convolution kernels in multiple dimensions via the frequency synthetic dimension. This scheme incorporates a simple setup consisting of a ring resonator incorporating a phase and an amplitude modulator.
Arbitrary-dimensional reconciliation protocol for continuous-variable quantum key distribution

We report an arbitrary dimensional reconciliation protocol based on elementary reflection transformation, improving the reconciliation efficiency by 3% averagely and obtaining about 50% of improvement of decoding success rate.
Assessing the influences and moderating effects of service quality, security measures, and trust on cloud-computing usage in the healthcare industry

Cloud computing is a technology that is expected to have a positive impact on the healthcare industry. However, this technology should be employed after a thorough investigation of relevant industry-specific requirements. This research aimed to uncover the relative importance and moderating effects of service quality, trust, and security on cloud systems usage in the healthcare industry. A web-based survey provided 291 usable responses from which the researchers examined the survey items' internal consistency, reliability, and discriminant and convergent validity. SmartPLS was utilised to investigate the hypothetical relationships using partial least squares (PLS) structural equation modelling (SEM). The study findings indicated that the research variables security, trust, and service quality (SVQ) were found to be the main predictors of cloud computing usage in healthcare. Cloud providers can use these results to deliver better service to the healthcare industry by focusing on the leading issues perceived by healthcare end-users.
Some Concerns about Automated Teaching in Times of the COVID 19 Pandemic: A Call for Prevention Schemes

The article addresses the question of a possible technological change in teaching, triggered by the COVID 19 pandemic and the development of AI systems in education. On the one side, the pandemic lockdowns and remote education have stimulated reflections on different aspects of educational routine concerning rapid digital expansion into schools. On the other, sophisticated technologies are designated to intelligently support various types of learning. The article presents a brief overview of some ideas from the past, focusing on the automation of teaching. Then it shows how the pandemic situation will help to induce some changes. The socio-cultural perspective presented in the article provides interpretive frameworks for further research.
Evaluation and Redesign of Mixed Teaching Behavior Based on Mosoteach

Mosoteach can well support information-based teaching innovation and promote the teaching reform of universities in China. Based on this, this research constructed a mixed teaching behavior model based on Mosoteach, and then applied this model to the teaching practice of the “Principles of interior Design” course. It focuses on the analysis of the mixed teaching behavior based on Mosoteach by extracting the teaching behavior data collected by Mosoteach. The study found that students performed positively in most teaching activities, while their participation in group tasks and mutual evaluation was not enough. This research proposes an improved design for the evaluation of mixed teaching behaviors, which will improve the validity and reliability of the evaluation reports used by Mosoteach in the Principles of interior Design course. Finally, it is hoped that these mixed-teaching behavior evaluation and redesign based on Mosoteach will provide a reference for the universities in China to promote mixed teaching reform in the era of big data.
The Concept and Phenomenon of Visual Literacy in the Digital Era

Vasyl Kremen
Optimization of Large-Scale Knowledge Forward Reasoning Based on OWL 2 DL Ontology

This paper focuses on the performance of optimized forward reason systems. The main characteristics of forward reasoning are that it is sensitive to the update of data, has a high cost of precomputation closure, and can not be closely related to the characteristics of the specific query. Therefore, it usually makes reason on irrelevant data. Processing this data reduces the performance of the reason system and consumes a lot of memory resources. Backward reasoning can make up for this defect to a certain extent, but its inherent defect of the high cost of online query rewriting cannot make it efficient in reasoning tasks. We design an efficient reason method, which can effectively combine the advantages of forward reason and backward reason to ensure the completeness of reason as much as possible. It can not only reduce the processing cost caused by data updates and desensitize semantic data to a certain extent but also avoid the high cost caused by query rewriting and greatly reduce the cost of precomputation closure. Finally, we implement the proposed method on a prototype of a forward reason system named SUMA-F and compare it with the current forward reason systems with better performance on various datasets of different sizes. Experiments show that the SUMA-F has high reasoning efficiency, is better than other systems, and has high scalability on large-scale datasets.
Multi-objective metaheuristic search algorithms for service composition in IoT

The basic idea behind the Internet of Things (IoT) is to connect every physical Thing to the Internet. IoT adds ability in Things to sense and communicate with other Things. One of the main concerns in IoT is forming an optimal service composition to fulfil the user requirements while balancing the quality of service (QoS) parameters. So, in this paper, the service composition problem in IoT has been addressed using multi-objective optimisation. An optimal solution to this problem has been provided through proposed novel hybrid and proposed algorithm Multi-Objective Hybrid Hyper-Heuristic Flower Pollination Algorithm (MOHHFPA). The superiority of this algorithm is proved by empirically and statistically comparing it with existing multi-objective algorithms, namely the Non-dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Flower Pollination Algorithm (MOFPA), and the Multi-Objective Hyper-Heuristic Search Algorithm (MOHypEA). The proposed algorithm is empirically evaluated using a real-world case study.
Accelerating artificial bee colony algorithm using elite information

In nature, the foraging behaviour of bee colony is always guided by some elite honeybees with the aim of maximising the overall nectar amount. Being inspired by this phenomenon, we propose an improved artificial bee colony (ABC) variant by using elite information. In our approach, as the main way of generating new offspring, two novel solution search equations are developed based on utilising elite information, which has the advantages of accelerating convergence rate. Moreover, to preserve the search experience of the scout bee phase, a new reinitialisation method is proposed based on using elite information. Extensive experiments are conducted on the CEC 2013 and CEC 2015 test suites, and other four relevant ABC variants are included in the comparison. The results show that our approach has better performance in terms of convergence speed and result accuracy.
FAV-BFT: An Efficient File Authenticity Verification Protocol for Blockchain-Based File-Sharing System

Compared with traditional file-sharing system, the blockchain-based file-sharing system shows its superiority, such as electronic money incentive mechanism, decentralisation, information tamper resistance and so on. Benefiting from those properties, it has attracted tons of users to participate in blockchain-based file-sharing and eventually forms an indestructible electronic library. However, with such a huge amount of files, the problem of file authenticity verification is still not resolved. This paper attempts to address the challenge of file authenticity verification for blockchain-based file-sharing system, specifically, verifying that the file is really stored by the claimer and needed by the file-downloader before the file is downloaded. We propose an efficient file authenticity verification protocol, named File Authenticity Verification Byzantine Fault Tolerant (FAV-BFT). We first apply Verifiable Delay Function to bind the shared file, and then reconstruct it to a challenge-response interactive protocol for file-sharing, and finally embedded with Byzantine Fault Tolerant protocol. Due to the construction, with 2/3 of participants are honest, FAV-BFT can correctly prove how long a file has been stored and whether a file meets the requirement of the file downloader. Moreover, since all the file content is processed by hash function before transformation, FAV-BFT protects the shared-file from content disclosure during the verification process without trusted third parties.
A New Land-Cover Classification Approach in UAV-based Remote Sensing for Solution Ecological Tasks

Summary Nowadays application of UAVs gives a possibility to achieve data with a very high accuracy for solution of different ecological and agricultural problems. The classification of incomplete and high conflicting sources is one of the most important and difficult procedures in Remote Sensing. It has been proposed the new classification approach to applying Dezert-Smarandache Theory in UAV-based remote sensing problems. The classical Dezert-Smarandache fusion rule has been considered in this work. It has been noted, that this theory processes the contradictory information an deals with exhaustive and not mutually exclusive hypotheses. The numerical example has been considered in this work too. The proposed approach to applying the Dezert-Smarandache Theory in land-cover classification can be applied in numerous agricultural tasks, pest control in forestry, prevention of natural disasters, such as forest fires.
Performance evaluation of entropy-based cuboid selection technique for human interaction recognition

Local feature-based approaches are proven to be most successful for the application of human action recognition. This work aims to study the effect of the number of cuboids chosen on the performance of human action recognition. A 3D region, called cuboid, is extracted around every spatio-temporal interest point and an entropy-based cuboid selection method is implemented for choosing the cuboids having maximum information. The proposed method is evaluated using UT interaction and SBU kinect interaction datasets. Results show that maximum classification accuracy and F1 Score are obtained when the top 40 cuboids are selected for the feature extraction. The classification accuracy and F1 Score increase when the number of selected cuboids is increased from 10 to 40 and remain constant when the number is increased beyond 40. The selected 40 cuboids are then used for feature extraction. To carry out this experiment, wavelet coefficients extracted from selected cuboids are used as features.
Fake face detection in video using shallow deep learning architectures

Deep learning techniques have been used in various disciplines, ranging from simple data processing to complicated image classification tasks. Deepfakes is a deep learning approach with benefits and drawbacks impacting the world. However, deepfakes are now cutting-edge technology being exploited for nefarious purposes such as the breach of human privacy and identity. Because deep learning is advancing rapidly daily, people use AI to produce deepfakes videos and images. Hence newer AI technology to detect deepfakes is critical. Therefore, the study has proposed detecting video and image deepfakes based on convolutional neural network (CNN) combined with the long short-term memory (LSTM) model, which constructs a deep learning model classifying images and video deepfakes. The proposed model investigated a novel approach to research more powerful models which can be applied to any large dataset. The experimental results demonstrated that the proposed method had achieved promising performance on modified datasets from Celeb-DF with high AUC performance up to 0.7584 and MCC reaching 0.558. Besides, this paper presents brief research on creating and detecting the image and video deepfakes technologies and points out the challenges of using deepfakes in many different contexts.
Quantum Random Number Generation Based on Spatial Modal Superposition over Few-Mode-Fibers

A quantum random number generator based on few-mode fiber technology is presented. The randomness originates from measurements of spatial modal quantum superpositions of the LP 11 a and LP 11 b modes. The generated sequences have passed NIST tests.
Theoretical and methodological foundations of a systems approach in pedagogical science

The concept of “system” is used in various fields of knowledge, since each science deals with the study and practical application of complex system objects and must find ways to accurately describe, study and build them. Not a single science can do without the use of systems research methods. The purpose of the systems approach is to single out integral systemic phenomena as objects of study, find integration mechanisms, identify necessary and sufficient conditions for the existence of a certain systemic phenomenon, study integrative systemic qualities, design an object that realizes a certain goal. The aim of the study is to determine the theoretical and methodological foundations of a systems approach in pedagogical science. To reach this aim, scientific and methodological literature were analyzed in the work to define the concepts of system, systems approach, and pedagogical system; to identify the main components of the systems approach, its structure, functioning and significance in practical application; and to identify differences between the systems approach and holistic and integrated approaches. At present, in educational activity there is a need for integration and differentiation of its components, aimed at the processes of intensification. In this regard, the question arises about the mechanisms for minimizing the constituent elements of the system without losing its quality and functioning. Consistency in pedagogical activity is often considered from a theoretical and managerial point of view. The theoretical meaning of this concept includes the creation of a pedagogical system based on methodological principles and conceptual approaches to building educational activities. The managerial meaning of the system lies in the control and regulation of educational activities aimed at their improvement. The systems approach is considered more broadly than the integrated and holistic approaches because it includes connections that are both at the same and different ordinal levels. In turn, holistic and integrated approaches can be considered as identical concepts that have mutually conditioning relationships. The systems approach in pedagogical activity plays a role not only in systematizing the elements of the system (determining their place and location in it), but also in their hierarchy, revealing interdependent relationships that imply the creation of more effective educational activities.
Knowledge Graph Based Chicken Disease Diagnosis Question Answering System

With the rapid development of natural language processing technology and knowledge graph technology, knowledge graph based intelligent question answering systems are being increasingly applied in various fields and industries. In the poultry industry, it is of great importance for farmers to promptly obtain scientific information of poultry disease diagnosis and curing measurements, where knowledge graph based question answering systems can contribute richly. In this paper, we design and implement Knowledge Graph based Chicken Disease Diagnosis Question Answering System (CDD-QAS) via deep learning models. We build a knowledge graph for chicken disease diagnosis, which contains 28 common chicken diseases and their corresponding symptoms, prevention and curing measures. In the construction of intelligent question answering system, we use BERT-TextCNN to realize the task of intention recognition and use BiLSTM-CRF to realize the task of entity recognition. Experimental results show that our proposed system can achieve better performance than other models, and possess great interactivity and accuracy. The proposed system can make great contribution to poultry industry and sets a good example of applying knowledge graph and deep learning methods in building question answering system.