Reviewerly/exHarmony huggingface dataset:
Application of Multiple Intelligence Theory in Junior Middle School English Teaching

This thesis first introduces the Multiple Intelligences Theory and its relevant researches.It then conducts an empirical study to explore the teaching effect of English reading teaching guided by Multiple Intelligences Theory.The new teaching method is carried out in the experimental class, while the traditional one is still used in the control class.At the end of the experiment, a reading test and interview will be held.Finally, the data is analyzed through SPSS.The results show that English reading teaching based on Multiple Intelligences Theory benefits to improve students' reading ability, especially in solving the questions concerning logical-mathematical intelligence and visual-spatial intelligence.It also helps stimulate students' reading interest.A variety of classroom activities greatly enhance their participation and learning passion.This research provides effective references for the practical application of Multiple Intelligences Theory in junior middle school English reading teaching.
Multimodal CT Image Synthesis Using Unsupervised Deep Generative Adversarial Networks for Stroke Lesion Segmentation

Deep learning-based techniques can obtain high precision for multimodal stroke segmentation tasks. However, the performance often requires a large number of training examples. Additionally, existing data extension approaches for the segmentation are less efficient in creating much more realistic images. To overcome these limitations, an unsupervised adversarial data augmentation mechanism (UTC-GAN) is developed to synthesize multimodal computed tomography (CT) brain scans. In our approach, the CT samples generation and cross-modality translation differentiation are accomplished simultaneously by integrating a Siamesed auto-encoder architecture into the generative adversarial network. In addition, a Gaussian mixture translation module is further proposed, which incorporates a translation loss to learn an intrinsic mapping between the latent space and the multimodal translation function. Finally, qualitative and quantitative experiments show that UTC-GAN significantly improves the generation ability. The stroke dataset enriched by the proposed model also provides a superior improvement in segmentation accuracy, compared with the performance of current competing unsupervised models.
A Lithium Niobate on Insulator Based Photonic Neural Network

We propose and simulate a photonic neuromorphic engine that exploits the Lithium-Niobate-on-Insulator (LNOI) integration platform. The resulting reduced-precision vector-matrix multiplier can trade off speed with resolution at a reduced power consumption enabled by LNOI platform.
Privacy-preserved Secure Medical Data Sharing Using Hierarchical Blockchain in Edge Computing

A distributed ledger technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Additionally, edge technology is trending rapidly which brings the computing and data storing facility closer to the user end (device), in order to overcome network bottlenecks. This study, therefore, utilises the transparency, security, efficiency of blockchain technology along with the computing and storing facility at the edge level to establish privacy preserved storing and tracking schemes for electronic health records (EHRs). Since the EHR stored in a block is accessible by the peer-to-peer (P2P) nodes, privacy has always been a matter of great concern for any blockchain-based activities. Therefore, to address this privacy issue, multilevel blockchain, which can enforce and preserve complete privacy and security of any blockchain-based application or environment, has become one of the recent blockchain research trends. In this article, we propose an EHR sharing architecture consisting of three different interrelated multilevel or hierarchical chains confined within three different network layers using edge computing. Furthermore, since EHRs are sensitive, a specific data de-identification or anonymisation strategy is also applied to further strengthen the privacy and security of the data shared.
TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets

Twitter is a social media service that is often used (popular) as a means of communication between users. Twitter's popularity makes spammers spam for personal purposes and gains. Bot spammers are user abuse on Twitter social media. Spammers spread spam repeatedly to other users. This spam is done with the aim of achieving trending topics. Spam activity is carried out by imitating the behavior patterns of real users so that they are not detected as acts of Twitter abuse. in this paper proposed a TF-IDF weighting to detect spammer accounts on Twitter based on tweets and retweet representation of tweets. The purpose of this study is to detect Bot Spammers or Humans using a classification technique using the Naive Bayes algorithm. The best experimental results in the division of 70% training data and 30% test data obtained 92% accuracy with precision and recall of 100% and 87.5%, respectively. This shows that it has successfully detected spammer accounts on Twitter.
More Precise Runtime Analyses of Non-elitist Evolutionary Algorithms in Uncertain Environments

Abstract Real-world applications often involve “uncertain” objectives, i.e., where optimisation algorithms observe objective values as a random variables with positive variance. In the past decade, several rigorous analysis results for evolutionary algorithms (EAs) on discrete problems show that EAs can cope with low-level uncertainties, i.e. when the variance of the uncertain objective value is small, and sometimes even benefit from uncertainty. Previous work showed that a large population combined with a non-elitist selection mechanism is a promising approach to handle high levels of uncertainty. However, the population size and the mutation rate can dramatically impact the performance of non-elitist EAs, and the optimal choices of these parameters depend on the level of uncertainty in the objective function. The performance and the required parameter settings for non-elitist EAs in some common objective-uncertainty scenarios are still unknown. We analyse the runtime of non-elitist EAs on two classical benchmark problems OneMax and LeadingOnes in in the one-bit, the bitwise, the Gaussian, and the symmetric noise models, and the dynamic binary value problem ( DynBV ). Our analyses are more extensive and precise than previous analyses of non-elitist EAs. In several settings, we prove that the non-elitist EAs outperform the current state-of-the-art results. Furthermore, we provide more precise guidance on how to choose the mutation rate, the selective pressure, and the population size as a function of the level of uncertainty.
Evaluation of Usability Quality Between InDriver and Maxim Applications Using Usability Scale (SUS) and Usability Testing Methods

Indriver application and Maxim application are international companies engaged in Information Technology where the company provides a platform that brings drivers and customers together quickly. Both applications come from Russia. These two applications are growing rapidly so they have been downloaded more than 5 million times through the Google Play store. Although it has been downloaded more than 5 million times, some users complain about the two applications. From some of the user feedback, the InDriver application and Maxim application still have problems that occur in their users. This problem is part of the usability problems that exist in both applications. So it is necessary to dig into a more comprehensive problem to explore the problems that occur in both applications. You also need to know the level of usability of these two applications. The level of usability needs to be known to explain the usability of these two applications is already at which level from the user side. This study provides an evaluation to determine the usability problems and the level of usability that exist in both applications using the System Usability Scale (SUS) and Usability Testing methods. The results of this study indicate that the features of the Maxim application are still not accepted by users seen from the sus questionnaire score of 48.3 and on the rating scale including the F rating with a smaller score of 51 so that it can be concluded that the Maxim application is not included in the acceptable category but not acceptable and the InDriver application has a SUS score of 51.25. On the rating scale, the InDriver application is included in the F rating so it can be concluded that the InDriver application is not included in the acceptable category but the low category.
Secure medical image sharing for smart healthcare system based on cellular neural network

Abstract The smart healthcare system (SHCS) facilitates the healthcare process with the widespread use of medical data through the internet of medical things (IoMT). Widespread use of medical data, especially medical images can also lead to privacy issues. Traditional encryption algorithms can address some problems; however, they cannot deter the redistribution of decrypted content. To prevent the decrypted content from being used illegally, the paper proposes a combination scheme of encryption and fingerprinting based on the game of life (GOL) and singular value decomposition (SVD) with the purpose of protecting medical images. First, medical images are performed with discrete wavelet transform (DWT). Second, the highest coefficients bit planes of the approximation component are selected to confuse with GOL. Third, the other bit planes and other detail components are chosen to embed fingerprints. Finally, all subbands are diffused with SVD computing. The proposed privacy protection scheme, as far as we know, is the first privacy protection scheme for the SHCS using GOL and SVD based on a chaotic cellular neural network (CNN). The proposed scheme can provide double-level privacy protection for the SHCS. The experimental results and discussion verify it is effective for the privacy protection of the SHCS.
Algorithms for Calculating the Trajectory of Unmanned Aerial Vehicles for Solving Agricultural Problems

The relevance of using unmanned aerial vehicles (UAV) is substantiated in comparison with other methods of aerospace survey. The paper provides examples of tasks requiring the use of UAVs for aerial photography from different heights. It is shown that the introduction of agricultural robots, including UAVs, increases the speed of fi eld work, allows obtaining unique data necessary for the assessment of agricultural territories, crops processing and plant growth stimulation. It is noted that the problem of constructing the movement trajectories of a multirotor UAV for performing agricultural tasks within a minimum time limit remains unresolved. ( Research purpose ) To reduce the length of the trajectory covering a given area and reduce the flight time of a multirotor UAV, taking into account the analysis of possible obstacles and land plots that are beyond the task scope. ( Materials and methods ) Geometric methods have been used to calculate the UAV flight trajectory covering a given section, the trajectory of movement in an environment with obstacles to the designated point. Photogrammetry methods have been used for processing aerial photography images when forming an orthophotoplane and a terrain map. ( Results and discussion ) The trajectory calculated by the developed algorithm proves to meet all the requirements: it is continuous, has a minimum number of turns, it is smoothed, and feasible for a multirotor UAV. ( Conclusions ) It was determined that according to the proposed algorithm it takes less than 0.05 seconds to calculate the trajectory covering a rectangular section with the sides of 200 by 30 meters. It was found that the trajectory in the fi rst 10,200-square-meter section decreased by 9 percent, and in the second 950,000-square-meter section it reduced by 6 percent, compared with the length of the trajectory built using standard algorithms. The flight time reduced by 32 and 10 percent, respectively. The paper presents the key advantages of using UAV for video shooting such as: guaranteed high resolution of photographic materials and the ability to shoot at a given time, allowing for the crop condition assessment.
Sample Efficiency in Deep Reinforcement Learning based Recommender Systems with Imitation Learning

Recently, there has been an unprecedented interest in using reinforcement learning (RL) for recommender systems (RSs), due to its unique ability in taking into account the dynamic and long-term user engagement.However, sample ineciency is a major challenge in applying RL to problems with very dynamic environments and huge actions spaces.In this paper, we present Imitation, Reinforcement learning based Recommender System (IR2S) to combine RL with imitation learning to alleviate this problem.More specically, by utilizing demonstrations (available user ratings), we show that IR2S can optimize its behavior faster and more eciently.The proposed IR2S, built on top of Deep Q Network (DQN), shows superior performance compared to baselines in experiments.
On Mixup Regularization

Mixup is a data augmentation technique that creates new examples as convex combinations of training points and labels. This simple technique has empirically shown to improve the accuracy of many state-of-the-art models in different settings and applications, but the reasons behind this empirical success remain poorly understood. In this paper we take a substantial step in explaining the theoretical foundations of Mixup, by clarifying its regularization effects. We show that Mixup can be interpreted as standard empirical risk minimization estimator subject to a combination of data transformation and random perturbation of the transformed data. We gain two core insights from this new interpretation. First, the data transformation suggests that, at test time, a model trained with Mixup should also be applied to transformed data, a one-line change in code that we show empirically to improve both accuracy and calibration of the prediction. Second, we show how the random perturbation of the new interpretation of Mixup induces multiple known regularization schemes, including label smoothing and reduction of the Lipschitz constant of the estimator. These schemes interact synergistically with each other, resulting in a self calibrated and effective regularization effect that prevents overfitting and overconfident predictions. We corroborate our theoretical analysis with experiments that support our conclusions.
Analisis Peran Orang Tua Terhadap Motivasi Belajar Siswa Kelas V SD Negeri Kalicari 01 Semarang Di Masa Pandemi Covid-19

This study aims to describe the role of parents in the learning motivation of V SD Negeri Kalicari 01 Semarang during the Covid-19 pandemic. This type of research uses a qualitative approach. Sources of data in this study were classroom teachers, parents, and students. The data analysis technique used is the Miles and Huberman Analysis Interactive model. The results showed that the role of parents in the learning motivation of fifth graders at SD Negeri Kalicari 01 Semarang during the Covid-19 pandemic had been going well, it was known from observations made by parents in distributing questionnaires. The role of parents on student learning motivation is to educate, guide, accompany, motivate children to learn well. Based on the results of the research and discussion, it was concluded that the role of parents on the learning motivation of fifth graders at SD Negeri Kalicari 01 Semarang has been carried out well by parents and students.
Blind Image Inpainting Using Low-Dimensional Manifold Regularization

In this paper, we present a novel method for blind image inpainting, which can restore images with missing or corrupted pixels, or images where the location of the damaged pixels is unknown. The method applies weighted nonlocal Laplacian to address the problem of blind image inpainting using low-dimensional manifold model (LDMM) regularization, and uses semi-local blocks instead of point integrals to implement constraints in LDMM. This solves the problem of low solution efficiency caused by the asymmetry of the linear equations solved by point integration, and the problem of the high iteration count to get good restoration effect. Experiments show that our method is competitive with latest methods in terms of both repairing images with large missing pixels rate and inpainting speed.
Investigating the Perception of Success in Software Projects Among Developers from a Brazilian Software Company

Understanding and properly applying agile practices in software development projects is a common challenge for developers. Consequently, the expected benefits of these practices may not be perceived, hampering developers from reaching critical success factors of software projects. This work aims to investigate the perception of success in software projects in the context of a Brazilian software company whose development process is based on Scrum. Besides, the development teams from this company follow several technical and non-technical agile practices. For this purpose, we conducted an opinion survey with 17 developers distributed among four development teams of this company. The survey findings revealed prevailing gaps in the teams' perception of critical success factors in their projects despite following a common subset of agile practices addressing them. Among others, the gaps include a lack of perceiving realistic schedules, clear requirements, and good quality management. Aiming at improving this scenario in the company, we plan to conduct a collaborative action research with one of the development teams investigated.
PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive Learning

Encouraged by the success of contrastive learning on image classification tasks, we propose a new self-supervised method for the structured regression task of 3D hand pose estimation. Contrastive learning makes use of unlabeled data for the purpose of representation learning via a loss formulation that encourages the learned feature representations to be invariant under any image transformation. For 3D hand pose estimation, it too is desirable to have invariance to appearance transformation such as color jitter. However, the task requires equivariance under affine transformations, such as rotation and translation. To address this issue, we propose an equivariant contrastive objective and demonstrate its effectiveness in the context of 3D hand pose estimation. We experimentally investigate the impact of invariant and equivariant contrastive objectives and show that learning equivariant features leads to better representations for the task of 3D hand pose estimation. Furthermore, we show that standard ResNets with sufficient depth, trained on additional unlabeled data, attain improvements of up to 14.5% in PA-EPE on FreiHAND and thus achieves state-of-the-art performance without any task specific, specialized architectures. Code and models are available at https://ait.ethz.ch/projects/2021/PeCLR/
The Discrete Gaussian for Differential Privacy

A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples from continuous distributions, and previous work has demonstrated that seemingly innocuous numerical errors can entirely destroy privacy. Moreover, when the underlying data is itself discrete (e.g., population counts), adding continuous noise makes the result less interpretable. With these shortcomings in mind, we introduce and analyze the discrete Gaussian in the context of differential privacy. Specifically, we theoretically and experimentally show that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuous Gaussian noise. We also present an simple and efficient algorithm for exact sampling from this distribution. This demonstrates its applicability for privately answering counting queries, or more generally, low-sensitivity integer-valued queries.
Literacy Education

Background: The development of technology has led to an increase in the interest of teenagers and students towards modern literary works so that literary works based on local wisdom are starting to be neglected. Purpose: This is the basis of this study developed to improve student literacy competence through the making of It-Based Bilingual Book of Folklore Drama Script which also aims to foster students’ interest in local culture. Design and methods: Classroom Action Research Method is used in this study. Results: The improving literacy competence experienced by students can be seen from the evaluation of the performance of the assignments they do. In Routine Task, students have an average score of 86.54. In Critical Journal Review task, the students' average has a literacy competence score of 87.90. In the Critical Book Review task, students have an average score of 89.33. Through the Mini Research task, the average score was 91.90. In the Idea Engineering tasks, the average score of students reached 90.27. In project assignments, it can be seen that the students' literacy competence increases in the aspects of reading, understanding and speaking with an average score of 94.09.
Efficient and Secure ECDSA Algorithm and its Applications: A Survey

Public-key cryptography algorithms, especially elliptic curve cryptography (ECC) and elliptic curve digital signature algorithm (ECDSA) have been attracting attention from many researchers in different institutions because these algorithms provide security and high performance when being used in many areas such as electronic-healthcare, electronic-banking, electronic-commerce, electronic-vehicular, and electronic-governance. These algorithms heighten security against various attacks and at the same time improve performance to obtain efficiencies (time, memory, reduced computation complexity, and energy saving) in an environment of the constrained source and large systems. This paper presents detailed and a comprehensive survey of an update of the ECDSA algorithm in terms of performance, security, and applications.
An Analysis of Metaverse Studies in English Education in Korea

This study analyzed metaverse studies in English education to provide directions for the research on and educational applications of metaverse in language teaching and learning. Through key words search and review of abstracts, 34 research articles, which were published in peer-reviewed journals in Korea, were selected for analysis. To sort and analyze the collected studies, classification criteria were developed: publication years, types of metaverse, target population, research methods, and metaverse programs. The analysis of the studies showed the abrupt increase of metaverse-related research since 2021 across all types of metaverse: virtual world, augmented reality, and mirror world, In terms of research methods, four types of research were defined: experimental, survey, developmental, and review studies. In general, collected studies reported positive effects of metaverse education on affective aspects of learners. Implications of the findings and suggestions for future research and practices were presented.