Reviewerly/exHarmony huggingface dataset:
The Relationship between COVID-19 Blue, Stress, Adaptation to College Life, and Intent to Discontinue Schoolwork among Physical Education University Students

This study was conducted with the aim of providing basic data in seeking educational directions in sports universities after lifting social distancing by investigating the relationship between COVID-19, stress, college life adaptation, and academic interruption intention. The subjects of the study consisted of 400 physical education students from four-year universities in the metropolitan area and Gangwon-do, and a survey was conducted for about two months from March to April 22, and the final 368 copies were used for the final analysis to obtain the following results. First, it was found that COVID-19 of physical education college students had a positive effect on stress. Second, it was found that COVID-19 of physical education college students had a negative effect on college life adaptation. Third, it was found that the stress felt by physical education college students had a negative effect on college life adaptation. Fourth, it was found that the stress felt by sports students had a positive effect on the intention to stop studying. Fifth, it was found that college life adaptation had a negative effect on the intention to stop studying.
A Narrative Study on Adult Learners' Class Experience Applying Blended Learning-Based Innovative Teaching Method

The purpose of this study was to analyze the educational experiences of adult learners who participated in the blended learning-based innovative teaching method application class from a narrative perspective and derive meaningful implications for adult-friendly education. This study conducted in-depth interviews with 5 participants in the blended learning-based innovative teaching class among adult learners at C University, and analyzed the educational experiences of adult learners in blended learning-based innovative class by applying narrative inquiry.&#x0D; As a result of the analysis, adult learners who participated in the blended learning-based innovative class felt fresh about the instructor's new teaching method, and grew through the experience of acknowledging each other's differences and expanding communication and cooperation. Regarding participation in online classes, participants overcame the fear of online learning through the consideration of the instructor and keep up learning. In addition, while experiencing practical-oriented living education both online and offline, meaningful changes and growth were experienced. These results suggest that education for adult learners requires various types of instructional design and application in a direction that considers individual differences but can continue without giving up learning. Based on these results, implications for the direction of adult-friendly education were derived.
Fully (implemented, scalable, and parallel) recurrent photonic neural networks in multimode semiconductor lasers

Photonic neural networks are a highly promising computational system for AI-inspired future information processing. We have recently demonstrated the first fully implemented, photonic neural network realized in multimode semicondcutor lasers. The numerous laser modes acts as the systems neurons, which carrier diffusion and intra-cavity diffraction creating recurrent connections. I will discuss our recent result, where we push the realtime data-rate of the neural network towards GHz levels and use such systems to address highly relevant photonic-technology applications.
Photonic quantum computing

In the race to demonstrate a scalable and fault tolerant quantum computing (QC) platform, quantum photonics stands poised to have a major impact. In particular, by encoding quantum information over fields rather than photons (continuous-variable—CV, rather than discrete-variable—DV, quantum information), record-size quantum processors were demonstrated in spectral and in temporal quantum field combs. These demonstrations used the measurement-based QC paradigm, which is based on cluster entangled states. In this talk, I will outline two of our recent results: proposals for generating hypercubic cluster states by phase modulation of the quantum optical frequency comb and for generating fault-tolerant hybrid CV-DV encodings called Gottesman-Kitaev-Preskill states. The latter leverages CV cluster states along with our experimental ability to perform photon-number-resolving measurements up to 100 photons.
Photonic-chip Fourier convolution processor

Here we introduce a Fourier-Theorem based convolution processors in silicon photonics. The systems leverages an algorithmic homomorphism that utilizes the Fourier transformation provided by a lens along with high-speed optoelectronic signal modulation and read-out. We demonstrate convolution filtering for image processing, convolutional neural network classification tasks. An on-chip lens performs the convolution operation, whereas electro-optic modulators perform the weighting in the Fourier domain at high-speed, followed by detection at a detector array after a 2nd Fourier lens, all on a PIC. Using this accelerator, we demonstrate image filtering and machine learning inference tasks. Given the high SWAP, these accelerators are useful for network-edge AI for the coming Industry-4.0 era.
Pedagogical Conditions for The Implementation of the Use of Digital Tools in Distance Learning

The article deals with the issue of practical application of Internet technologies in the educational process. The main directions of using computer networks in teaching students are indicated. Particular attention is paid to the use of distance learning systems based on the Internet.
A Hybrid Classifier of Cyber Bullying Detection in Social Media Platforms

The increase in online and social media connection has made it simple for hate speech and insulting language to spread. Cyberbullying is the phrase used to describe such online abuse, insults, and assaults. It has become difficult to detect such unauthorized material due to the huge number of user-generated content. Deep neural networks are being used more often by academics to identify cyberbullying than regular machine learning algorithms because to their many benefits over them. Machine learning has several uses in text categorization. Hence, it is fundamental to distinguish and sort CB utilizing profound learning (DL) models in informal organizations to avoid this pattern. FSSDL-CBDC is a fresh out of the plastic new methodology for informal communities that joins profound learning and element subset determination. The SSA-DBN model has demonstrated to be more exact than different calculations with a 99.983% precision rate. Generally speaking, the trials' discoveries showed that the FSSDL-CBDC strategy performs better compared to the contending systems in various ways.
A Trading Model on Block chain Smart Contracts for the Shared Energy in Brazil

The idea of a decentralized peer-to-peer local energy market (LEM) is gaining popularity as local energy output from Renewable Energy Sources (RESs) increases. This study examines a blockchain-based LEM that enables consumers and producers to transact directly in energy within a local community. The Home Energy Management (HEM) system and demurrage mechanism, which are included in the proposed model, enable prosumers and consumers to optimize their energy use and reduce electricity costs. This study reviews the literature on blockchain technology and the energy market, develops a model, puts it into practice, runs experiments, and evaluates network scalability and data production. The approach, which was built using Hyperledger Fabric, allows verified renewable energy trade with anonymous purchasers to shield consumption patterns from disclosure. With 5000 sensors, 5000 customers, and 5000 vendors, the maximum transaction throughput was attained. To assess the network feasibility, the data creation rate by network and the baseline implementation costs were also examined. Additionally, this article offers empirical findings on a subject that are missing from the literature.
Reversible Watermarking Algorithm for Halftone Images Based on Overlapping Blocks Scanning and Central Pixels Flipping

The halftone image is a special kind of image. In order to authenticate halftone images, this research proposes a reversible watermarking algorithm for halftone images by considering both the central pixels’ flippability and the overlapping blocks scanning. The proposed method takes the flippability of the central pixels in 3 × 3 binary blocks as the fundamental method. Then the special pattern pairs are taken as the embedding objects, whose central pixel flippability is nonzero and won’t change after flipping. In this way, the visual quality can remain satisfying after embedding. Moreover, the proposed method uses overlapping blocks scanning to enlarge the embedding capacity. Thus, this research presents a reversible watermarking algorithm for halftone images with high capacity. Test shows that the algorithm can make the watermarked images maintain good visual quality after embedding a large watermark. Compared to original images, the watermarked images are of low visual distortion and human eyes can’t detect the difference.
A review of image authentication techniques based on deep hashing

Image hashing is a key technology for many multimedia security applications. The image content is recognized through a short perceptual digest. In this paper, the latest literatures of image authentication based on hash were researched and analyzed in detail. We introduced a general model for image authentication based on image hashing. In particular, deep hashing algorithms for image authentication based on typical deep neural networks such as convolutional neural networks, convolutional autoencoders, and generative adversarial networks have been discussed. The paper also compares the various existing image hashing algorithms' performances, such as the receiver operating characteristics (ROC) and the area under the ROC curve. Finally, this paper looks forward to the future development trend of image hash authentication technology.
Design of Roadside Vision Perception Algorithm for V2X Technology

With the continuous development of autonomous driving technology, many experts and scholars pay more and more attention to V2X technology. Roadside perception technology is particularly important for autonomous driving perception fusion system based on V2X technology. Roadside vision perception technology is the core of roadside perception technology. Based on the improved YOLOv5 targets detection algorithm and DeepSORT tracking algorithm, accurate and fast perception of roadside targets is realized. It effectively improves the accuracy of targets detection and provides a large number of useful information for autonomous driving perception fusion system. It ensures the safe driving of V2X autonomous vehicles, which is of great significance to the realization of V2X high-level autonomous driving technology.
Analysis of blockchain based smart contract system to understand its performance in different applications

Smart contract technology plays a significant role in various fields for achieving secure and automated transactions without the interference of third parties. There arise certain issues of security and privacy issue during the transaction with the assistance of a smart contract. In this present work, a comparative analysis is carried out between various existing techniques in blockchain-based smart contract techniques. First, analysis is done on the security issue. Second, on the basis of smart contract design. Finally, on privacy issue in smart contract platform. To analyse the performance, metrics such as consumption cost, transaction cost, time overhead and processing time are validated. The cost consumption and time overhead attained for the Auditable Access Control System (AACS) technique is 30,125,894 gas and 3,285 sec. The processing time attained for Online Auto Update Smart Contract (OAUSC) is 2,653 sec. This analysis suggested AACS technique functions better in comparison to other existing techniques.
SEMANTIC RETRIEVAL FOR INDONESIAN QURAN AUTOCOMPLETION

Attending lectures is a common way to learn Islamic knowledge. The speaker talks in front of the forum, and participants take notes on the lecture material. Many participants listen to the lecture while taking notes either in books or on other digital devices to avoid forgetting the discussed topics. However, note-taking during the lecture can be challenging, with no complementing module from the speaker. Lecturers have different paces and varying ways of delivering. In addition, sometimes, participants cannot always focus during the lecture. Those factors can cause problems in the note-taking process: some details can be lost or even shift the meaning. For note-taking on sensitive topics, such as verses from the Quran, the note-taking process must be done carefully and avoid mistakes. In this study, we proposed an autocomplete system for the Indonesian translation of the Quran that will help the user in note-taking Islamic lectures. The user writes down words, the parts of the Quran verse that he hears, and the system will retrieve the most similar verse. With semantic retrieval, the user does not need to write down the exact words of the verses he heard. The system can also handle typographical-error that usually occur in note-taking. We use Fasttext and calculate the cosine distance between the query and verses for the retrieval process. We also performed several optimization steps to create a robust system for the production stage. The system is evaluated by comparing how close the returned verse is with the ground truth. The proposed method's result accuracy reached 79.41% for the top 5 retrieved verse and 85.29% for the top 10 retrieved verse.
Long Text Semantic Span Extraction Algorithm for Medical Electronic Medical Record

Named entity recognition (NER) is one of the hotspots in the field of Natural Language Processing (NLP). NER, as one of the most important sub tasks in the field of information extraction, aims to find some key phrases in massive texts, such as names of people, places, diseases and drugs. At present, researchers have proposed some models that are easy to implement and have good effects. However, because entity span usually contains little semantics and is not easy to understand, and in the medical field, there are high requirements for the semantics of the extracted span, so the semantic span extraction task appears. Semantic span may be a word or phrase, or it may be composed of one or more sentences. Due to the limitation of coding length, the extraction of semantic span from long text is subject to many restrictions. Because of the particularity of this task, so far, there is little research on the direction of semantic span extraction. We tried a variety of models on our medical electronic medical record dataset. The experimental results show that the proposed model is simple and effective for extracting semantic span from long text.
Research on Image Enhancement of Rural Orchard Security Monitoring under Low Illumination

It is great significance to improve the quality of the monitoring image for the low illumination situation and to improve the security of the rural orchard. In this paper, an improved block homomorphic filtering algorithm is proposed to improve the quality of video surveillance image due to low illuminance in real environment. On the basis of image block adaptation and according to different illumination conditions, the filtering effect is improved by adding and adjusting the weight factor of the transfer function, and the blocking edge effect is alleviated by smoothing filtering. In order to solve the problem of low saturation of low illumination image, the image saturation is adjusted hierarchically and the saturation lower than the threshold value is nonlinear pulled up, so as to restore the color saturation of the image as much as possible. The experimental results show that the PSNR, SSIM and IE are improved by 6%, 21% and 4% respectively. Ultimately, it can improve effectively the image quality of orchard video monitoring under low illumination.
Single Image Defogging Method Based on optimized Double Dark Channel with Gaussian Weighting

In this paper, we propose a single image defogging methods based on an optimized double dark channel with Gaussian weighting to address the problems of artefacts and residual fog at depth-of-field abrupt changes in the defogged images obtained by the traditional dark channel a priori algorithm. The two dark channels are first obtained using super pixel block filtering and median square filtering for each layer, the two filtered dark channels are then combined at the pixel level and enhanced by constructing a Gaussian weighting function, and then the transmittance is optimized using a guided filtering method. The fog map is then converted to HSV color space and the white areas containing the sky etc. are extracted, the probability function was introduced to take the average of the brightness components of the first 10% of the pixel points in the white area as the atmospheric light estimate. Finally, the contrast stretching method was used to improve the image brightness. The experimental results show that the proposed algorithm can better preserve the details of the image and remove the residual fog at the depth-of-field abrupt changes, and improve the artifacts with good visual effects.
Tight Bounds for Repeated Balls-into-Bins

We study the repeated balls-into-bins process introduced by Becchetti, Clementi, Natale, Pasquale and Posta (2019). This process starts with $m$ balls arbitrarily distributed across $n$ bins. At each round $t=1,2,\ldots$, one ball is selected from each non-empty bin, and then placed it into a bin chosen independently and uniformly at random. We prove the following results: $\quad \bullet$ For any $n \leq m \leq \mathrm{poly}(n)$, we prove a lower bound of $\Omega(m/n \cdot \log n)$ on the maximum load. For the special case $m=n$, this matches the upper bound of $O(\log n)$, as shown in [BCNPP19]. It also provides a positive answer to the conjecture in [BCNPP19] that for $m=n$ the maximum load is $\omega(\log n/ \log \log n)$ at least once in a polynomially large time interval. For $m\in [\omega(n),n\log n]$, our new lower bound disproves the conjecture in [BCNPP19] that the maximum load remains $O(\log n)$. $\quad \bullet$ For any $n\leq m\leq\mathrm{poly}(n)$, we prove an upper bound of $O(m/n\cdot\log n)$ on the maximum load for all steps of a polynomially large time interval. This matches our lower bound up to multiplicative constants. $\quad \bullet$ For any $m\geq n$, our analysis also implies an $O(m^2/n)$ waiting time to reach a configuration with a $O(m/n\cdot\log m)$ maximum load, even for worst-case initial distributions. $\quad \bullet$ For any $m \geq n$, we show that every ball visits every bin in $O(m\log m)$ rounds. For $m = n$, this improves the previous upper bound of $O(n \log^2 n)$ in [BCNPP19]. We also prove that the upper bound is tight up to multiplicative constants for any $n \leq m \leq \mathrm{poly}(n)$.
Data center power consumption prediction based on principal component analysis and DeepAR

The era of big data and cloud computing has driven the rapid expansion of the number and scale of data centers worldwide, and the ensuing huge power consumption has put pressure on resources and the environment. Accurate prediction of data center power consumption can provide an important basis for current power management techniques, while effectively improving the efficiency of intelligent operation and maintenance of modern data centers. To address this problem, a server power consumption prediction model based on a combination of principal component analysis (PCA) and DeepAR is proposed in the paper. The model uses the time series of server power consumption and performance index data from the Zhengzhou Inspur data center to predict future moment power consumption, performs principal component analysis on the performance index, and inputs the effective principal components and historical power consumption data into the DeepAR network for prediction. The model is experimentally validated on all three server datasets, and the results show that the model outperform the DeepAR network model as well as other comparison models in terms of prediction. When compared with the DeepAR network, the MAPE of this model is reduced by 0.23%, 0.12%, and 0.05% on the data1, data2, and data3 datasets, respectively.
Unsupervised Abnormal Transaction Order Detection Method Based on Deep Learning Time Factor

Abstract Abnormal transaction order detection plays an important role in identifying transaction data changes and abnormal transaction behaviors of users. The existing abnormal order detection methods have low generalization ability in different transaction time series, and the exception itself has a variety of patterns. In addition, the positive sample points with a large proportion carry much more information than the abnormal sample points, which will cause the classifier to be disturbed in the learning process and inaccurate classification. The research proposes an unsupervised abnormal transaction order detection method based on the depth learning time factor. By processing the original data column, the first feature sequence is obtained, and then the later feature sequence is trained through the depth learning model. The abnormal order is detected by comparing the differences between the two feature sequences. The results show that the research effectively solves the practical problem of too few abnormal transaction samples, can be applied to the transaction process of different modes, reduces the noise in the original transaction data, and has obvious improvement in each evaluation index compared with the traditional method.
Performance Analysis of You Only Look Once Object Detection Models on Custom Datasets

Object detection, localization, and classification are now thought to be common study areas. The advancement of Deep Learning has aided researchers working in the field of object detection. By decreasing the model’s loss functi on, the accuracy of object detection may be boosted. Training and executing this model on a suitable GPUenabled machine can also cut down on total time. In this article, we employed the YOLO family models, namely YOLO v3, YOLO v4, and YOLO v5, and trained them using the transfer learning approach on custom datasets. These cutting- edge performers outperformed the competition on the Custom Dataset of Open Image Dataset V6 for various classes, achieving mean average precision mAP @0.5 97 %, 89 %, and 88 %, respectively. This is better than the initial trained models, which had mAP @0.5 values of 57.9 %, 43.5 %, and 33 % for YOLO v3, YOLO v4, and YOLO v5, respectively