Reviewerly/exHarmony huggingface dataset:
Indonesian Textbook Based on Character Education Through Active Learning for the Elementary School Students

The lack of success in learning Indonesian in fifth-grade elementary schools is dominant in the low productive language skills. The limitations of learning resources such as textbooks are one of the causes of the emergence of these problems. This study aims to develop Indonesian language textbooks containing character education through the Active Learning model by utilizing literature as a learning resource. The type of research used is development research with the following stages: exploration, model development, and model testing. The subjects of this research were the fifth-grade elementary school students at the exploration stage, as many as 30 students. At the development stage, as many as 56 students and as many as 165 students at the testing stage. Data was collected through documentation studies, observations, interviews, and questionnaires. The data analysis technique was conducted qualitatively using an interactive model at the exploration and product development stages. The statistical analysis used an independent sample t-test at the model test stage. The test begins with prerequisite analysis and hypothesis testing. The research results at the exploration stage showed that the textbooks used were not by the needs of elementary school students and teachers, then a needs analysis and development of a textbook prototype were carried out. At the stage of testing the effectiveness with the main field trial, it showed a difference between the experimental and control groups. The study concludes that Indonesian language textbooks are stated to improve student's language skills in elementary school. The use of textbooks is recommended to support effective language learning.
A Deep Learning-Based Approach to Solve the Height-Slope Ambiguity in Phase Measuring Deflectometry

Phase Measuring Deflectometry (PMD) shows excellent performance in measuring shape of specular surfaces. However, the existing height-slope ambiguity affects the measurement of PMD. In this paper, we proposed a deep learning-based method to solve the ambiguity and reconstruct the specular surface. A neural network is built to reconstruct the surface. A set of simulation surfaces are generated using two-dimensional Legendre polynomials to train and test the network. A spherical-shaped surface was reconstructed in a simulation to evaluate the validity of the proposed method, and compare our method to the state-of-the-art method. Experimental results demonstrate a 35.88% improvement in this method's accuracy, it has great improvement in measurement accuracy and cost efficiency.
Accelerating GNN Training on CPU$$+$$Multi-FPGA Heterogeneous Platform

Training Graph Neural Networks (GNNs) has become time consuming as the graphs grow larger. Thus, many works have been proposed to accelerate GNN training on multi-GPU platforms. Though GPUs feature high computation power, training GNNs on GPU suffers from low resource utilization. We propose to accelerate GNN training on a CPU+Multi-FPGA heterogeneous platform. By utilizing the customizable hardware resources on the FPGAs, we instantiate multiple hardware kernels with optimized data access pattern and memory organization. The optimized hardware kernels can efficiently access graph-structured data and thus achieve high training performance. However, training GNN with multiple FPGAs also leads to high FPGA-to-FPGA communication overhead and workload imbalance. We develop optimized graph partitioning techniques to minimize FPGA-to-FPGA data communication, and develop a task scheduler to balance the workload among the FPGAs. Compared with the state-of-the-art GNN training implementation on a multi-GPU platform, our work achieves up to 24.7 $$\times $$ bandwidth efficiency; this superior efficiency enables our work to achieve up to 3.88 $$\times $$ speedup and 7.18 $$\times $$ energy efficiency using much less compute power and memory bandwidth than GPUs.
Blockchain Multi-signature Wallet System Based on QR Code Communication

In order to avoid the risk of theft and loss of the private key of the blockchain wallet, a novel secure and stable blockchain multi-party signature system combining software and hardware is proposed. First, at the software level, multi-party key management method is adopted to divide the blockchain wallet key into multiple fragments for multi-point storage. Threshold signature technology is adopted to provide key escrow and retrieval services. Then a cold wallet for storing keys is designed at the hardware level. In order to avoid the risk of being attacked by hackers through network contact, a visible light communication method based on Quick Response Code is proposed. The Base45 coding scheme is adopted to improve the coding efficiency of QR Code, and the GG18 multi-party signature process is optimized. Finally, this paper analyzes the security of visible light communication, and verifies the efficiency of this method by experiments.
A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

One of today’s inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent decision making capacity has been widely used for threshold selection in the last decade. Furthermore, a 2D histogram was constructed using local average intensity values resulting in loss of some edge information. To address these problems, this study proposes a new methodology using a novel practical decisive row-class entropy (PDRCE) based fitness function for multilevel thresholding. The PDRCE values are computed using the newly constructed 2D histogram-based on normal local variance. Further, an opposition flow directional algorithm (OFDA) is proposed to maximize the fitness function. The performance of the proposed technique is compared with five state-of-the-art 2D histogram-based entropic fitness functions. Moreover, the performance of OFDA is investigated through comparison with other global optimizers namely the genetic algorithm, particle swarm optimization and artificial bee colony. An image segmentation evaluation dataset (BSDS500) is used in this experiment. It is witnessed that the proposal is more efficient than state-of-the-art methods. Our fitness function would be useful for registration, segmentation, fusion, etc.
Enterprise Cartography

This chapter describes Enterprise Cartography (EC) primitives. Thus, after an introduction to EC concepts, EC definitions are proposed (including enterprise model, enterprise meta-model, architectural sentence, alive artifact, transformation initiative, architectural maps, enterprise observation, knowledge base, as-was state, as-is state, to-be state, enterprise architect, and enterprise cartographer). Next, five EC primitives are described. The system representation and the EC formalization are presented next.
Development of mathematics mobile learning application: Examining learning outcomes and cognitive skills through math questions

Mobile technologies have started to be preferred as a new learning technology in teaching and learning.Considering the need for adequate and rapid feedback in solving mathematical problems that students have difficulty with reflects the need for a different educational practice and technology.In this study, based on design-based research (DBR) model, a mobile application was developed, which enables the extension of the traditional classroom to a virtual space, where students can practice mathematical problem solving and easily access solutions.The purpose of this study was to determine which mathematics questions that students have difficulty in solving are mainly distributed over which subjects and learning outcomes, and to reveal the cognitive levels of the questions sent.The findings revealed that 'numbers and algebra' was the learning domain, and 'derivate' was the subject, which students had the most difficulty with.Moreover, the questions were found to be mostly at the cognitive levels of understanding and applying in Bloom's Taxonomy.Based on the results, it can be concluded that the mathematics mobile application can be used as an alternative tool examining the learning outcomes and cognitive levels of students.
Autonomous Evaluation of Unmanned Vehicles: A Survey

With the continuous improvement of the technical intelligence level of unmanned vehicle, the autonomy of unmanned vehicle has become one of the important performance indicators. However, the autonomy evaluation of unmanned vehicles is still in the early stage of development, and a complete and scalable metric system is required to promote the further development of the technology. Firstly, we investigates the autonomy framework of the current unmanned system, arranges the autonomy evaluation framework of unmanned vehicle, and constructs a more comprehensive autonomy evaluation framework through the external and internal evaluation of autonomy. Then the evaluation systems of environment complexity, task complexity, manual intervention, perception and cognition, decision-making and planning, control and execution are summarized and reviewed. Finally, we count the distribution of the reviewed literature in our proposed framework, which proves the rationality, comprehensiveness and compatibility of our proposed framework for autonomous vehicle autonomy.
Certificateless authentication application of the Modbus protocol in industrial control systems

in recent years, with the development of modern industrial network, the safety of industrial control system has received wide attention. Modbus protocol is widely used in industrial control systems, but it lacks a secure authentication scheme and is vulnerable to malicious attacks. We studied and analyzed the identity authentication problem of Modbus. By using the certificateless public key cryptography, a certificateless signature authentication scheme is designed, which solves the certificates management problem in traditional public key cryptography and the key escrow problem in identity-based cryptography. Finally, the proposed certificateless signature scheme is applied to the authentication process of Modbus protocol in industrial control system. It not only implements key negotiation, but also proves that the scheme can resist replay attack, camouflage attack, man-in-the-middle attack and denial of service attack using security analysis.
Background Suppressed and Motion Enhanced Network for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (VAD) is a significant and challenging task in the surveillance video analysis field, locating anomalous motion frames using only video-level label information. In general, weakly supervised VAD mainly faces two obstacles. Firstly, the greatly changed background always causes false detection. Secondly, the motion changes are implicit in real-world surveillance videos caused by unsuitable focal distance, illumination, etc. This paper proposes a background suppressed and motion enhanced network (BSMEN) for Weakly Supervised VAD to solve the problems. The BSMEN utilizes the multi-head self-attention mechanism to construct a triple branch framework to suppress the influence from the background and enhance the motion significance in the VAD process. Moreover, a motion discrimination sequence extraction (MDSE) module is devised to locate the anomaly motion frames more accurately in the BSMEN. Extensive experiments on two mainstream VAD evaluation datasets validate that BSMEN outperforms state-of-the-art weakly supervised VAD methods.
Deepfake Creation and Detection using Ensemble Deep Learning Models

The use of Artificial Intelligence to create falsified videos using Deep Neural Networks is posing a serious problem in distinguishing the real from the counterfeit. These counterfeit videos are known as “Deepfakes”. Due to their realistic appearance and their subsequent ability to influence perceptions and mass sentiment, deepfakes must be monitored. Malicious deepfakes must be detected, and their circulation is immediately controlled. Many deepfake detection technologies have been developed that use particular features to classify fabricated media. This paper proposes the framework of deepfake detection using deep neural network models. The hybrid combination of deep learning models predicts deepfakes with better accuracy. The proposed model is tested and evaluated on the DFDC and CelebDF dataset that classifies more deepfake videos.
The Impact of Mobile Educational Games on Contemporary Users' Learning Behavior

The purpose is to cope with mobile educational game users' low continuous utilization rate. This thesis innovatively introduces the deep learning technology to study and the relationship between the mobile educational game and learners' learning behavior. It is found that learners' game experience is significantly correlated with game satisfaction. The estimation algorithm is designed based on the convolutional neural network (CNN), and the questionnaire method is adopted. The design tool of the estimation algorithm is a computer system, and the social software is used for the questionnaire. The dataset source used in the estimation algorithm design is “World Sudoku Championship.” The game satisfaction questionnaire and learning behavior questionnaire subjects are 60 primary school students and 300 college students, respectively.
Geoinformation support of the school course of geography

The current level of development of information systems, the introduction of technology in all aspects of life and the ever-accelerating process of digitalization also affect school education. Students increasingly notice the gap between the classical form of presenting material and the capabilities of modern information systems. Especially acute, in our opinion, this problem concerns the study of geography. The study of geography by traditional methods is not only ineffective and discourages students from learning but does not meet the requirements of the federal state educational standard. The teaching of geography should be conducted with the introduction of geoinformation technologies. Based on this thesis, to solve the problem we have voiced, the creation of a textbook is proposed. In our demonstration manual, based on the requirements of the federal state educational standard, 4 blocks of tasks were identified. The solution of these problems is aimed at developing the ability of students to form relationships and see dependencies using a layered organization of data. A set of layers for solving problems consists of the actual data needed to solve a specific task. All tasks are printed on translucent paper so that students have the opportunity to see the previous layers and analyze their relationship, formulate conclusions about the connection of phenomena. Also the manual has a section with questions aimed at helping students to formulate their thoughts when writing an answer, and ready-made templates with sample answers for self-testing and understanding the general principle of an answer generation. Due to epidemiological restrictions, the testing of the textbook took place remotely in one of the schools in Perm.
A Hybrid Learning Particle Swarm Optimization With Fuzzy Logic for Sentiment Classification Problems

Methods based on deep learning have great utility in the current field of sentiment classification. To better optimize the setting of hyper-parameters in deep learning, a hybrid learning particle swarm optimization with fuzzy logic (HLPSO-FL) is proposed in this paper. Hybrid learning strategies are divided into mainstream learning strategies and random learning strategies. The mainstream learning strategy is to define the mainstream particles in the cluster and build a scale-free network through the mainstream particles. The random learning strategy makes full use of historical information and speeds up the convergence of the algorithm. Furthermore, fuzzy logic is used to control algorithm parameters to balance algorithm exploration and exploration performance. HLPSO-FL has completed comparison experiments on benchmark functions and real sentiment classification problems respectively. The experimental results show that HLPSO-FL can effectively complete the hyperparameter optimization of sentiment classification problem in deep learning and has strong convergence.
Scaling Adversarial Training to Large Perturbation Bounds

The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending attacks constrained within low magnitude Lp norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to achieve adversarial robustness within larger bounds, against perturbations that may be perceptible, but do not change human (or Oracle) prediction. The presence of images that flip Oracle predictions and those that do not makes this a challenging setting for adversarial robustness. We discuss the ideal goals of an adversarial defense algorithm beyond perceptual limits, and further highlight the shortcomings of naively extending existing training algorithms to higher perturbation bounds. In order to overcome these shortcomings, we propose a novel defense, Oracle-Aligned Adversarial Training (OA-AT), to align the predictions of the network with that of an Oracle during adversarial training. The proposed approach achieves state-of-the-art performance at large epsilon bounds (such as an L-inf bound of 16/255 on CIFAR-10) while outperforming existing defenses (AWP, TRADES, PGD-AT) at standard bounds (8/255) as well.
Towards Synthetic Social Media Data

There is an increasing need for training and testing data that can be used for the development of technologies and research.Due to data protection regulations, a lot of the realworld data -especially the data from social media -becomes unavailable to use.The problem can be solved by generating synthetic data that imitates the properties of real-world data.In this paper, we present Fabulator -a social media generator that combines text and graph structure to be used for the generation of synthetic data and, in the future, for the simulation of various events on social media.
Active Data Science for Improving Clinical Risk Prediction

Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.
A Robust Image Watermarking Algorithm Based on Content Authentication and Intelligent Optimization

Aiming at some shortcomings of current digital watermarking algorithms, a color image double watermarking algorithm based on content authentication and machine learning is proposed in this paper. Firstly, the algorithm combines discrete wavelet transform (DWT) and discrete cosine transform (DCT), and embeds the watermark through singular value decomposition (SVD) modulation. Secondly, using the learning and classification characteristics of Support Vector Machine (SVM), the watermark detection model is obtained through training a large number of data, and finally the robust watermark is extracted automatically. In order to realize content authentication and tamper location, the algorithm embeds a fragile watermark at the same time, and uses the relationship between the high-frequency coefficients of wavelet transform to embed and extract the watermark. Through the simulation results and data analysis, the proposed double watermarking algorithm not only realizes the intelligent watermark extraction process, but also can accurately locate malicious tampering while maintaining strong robustness.
Attitude of University Students to the Information Content of Electronic Educational Platforms

Due to the widespread transition of universities to a remote work format, a survey of students was conducted to identify information-content characteristics and the attitudes of university students to electronic educational platforms. The study took place in two stages from September 2020 to November 2021. Surveys were conducted using Google forms and the MS Teams platform, as well as observations and interviews. The respondents were students at several Russian universities. The legal framework for educational online platforms is presented; modern approaches and classification of educational platforms are given.
Low Light Image Enhancement Based on Multi-Scale Network Fusion

At present, researchers have made great progress in the research of object detection, however, these studies mainly focus on the object detection of images under normal lighting, ignoring the target detection under low light.And images in the fields of automatic driving at night and surveillance are usually obtained in low-light environments.These images have problems such as poor brightness, low contrast, and obvious noise, which lead to a large amount of information loss in the image.And the performance of object detection in low light is reduced.In this paper, we propose a low-light image enhancement method based on multi-scale network fusion to solve the problems of images in low-light environments.Aiming at the problem that the effective information of low-light images is relatively small, we propose a preprocessing method for image nonlinear transformation and fusion, which improves the amount of available information in the light image.Then, in order to obtain a better enhancement effect, a multi-scale feature fusion method is proposed, which fuses features from different resolution levels in the network.The details of low-light areas in the image are improved, and the problem of feature loss caused by too deep network layers is solved.The experimental results show that our proposed method can achieve better enhancement effects on different datasets compared with the current mainstream methods.The average recall value of the object detection with our method is improved by 38.25%, which shows that our proposed method is effective and can promote the development of autonomous driving, monitoring, and other fields.