Reviewerly/exHarmony huggingface dataset:
Development of a Novel Hyperchaos-Based Image Encryption Algorithm Consisting of Two Scrambling-Diffusion Operations

This paper proposes a hyperchaos-based image encryption algorithm, which consists of two scrambling-diffusion operations and one scrambling operation. In the proposed algorithm, the initial conditions of a hyperchaotic Chen system are first generated using the Message Digest 5 (MD5) value of digital images and given initial values, and then the images will be encrypted using the keystream created by the system. Since the initial values of hyperchaotic Chen systems are related to plaintext and the encryption process is related to the images, this algorithm is able to effectively protect images against selective plaintext attacks. Simulation results demonstrate that the present algorithm offers enhanced encryption performance, high security, and strong resistance to known attacks. Therefore, it may find wide application in image encryption transmission. Compared to other image encryption algorithms, the proposed algorithm uses different keystreams when encrypting different images and is capable of effectively resisting various plain image and differential attacks faster.
Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

Translation is an important index to assess students’ English learning ability. In the current English translation teaching, there are still dilemmas such as poor professionalism of curriculum, insufficient knowledge reserve of students, and single teaching methods and approaches. Therefore, this study carries out research on English translation teaching based on the deep learning model to improve the EM algorithm and the guidance of Production-Oriented Approach (POA). The experiment proves that POA can effectively mobilize students’ enthusiasm in translation learning, improve the efficiency of internalizing human knowledge into output ability, optimize teachers’ teaching effect, and significantly improve students' English translation ability and output application ability by more than 30%.
Analysis on the Effect of Wushu Project Propagation in Nonmaterial Cultural Field of Environmental Protection Based on Artificial Intelligence Analysis Technology

Since the beginning of the new century, the party and the government have always attached great importance to youth sports work and cared about the healthy growth of youth. The CPC Central Committee and the State Council have issued the opinions on strengthening youth sports and strengthening youth physique, which has initially formed a social consensus on promoting the healthy growth of young students. With the process of school education reform and the promulgation and implementation of the Ministry of Education's opinions on implementing and ensuring the time of daily sports activities for primary and middle school students, especially the official launch of the "national sunshine sports for hundreds of millions of students" in 2021, the school sports work in China has entered a new historical stage. Under this premise, the status and importance of Wushu in the intangible cultural field of schools have been gradually improved. As an important part of school sports, Wushu in the field of intangible culture plays an important role in strengthening students' physique, improving students' health, and promoting students' intellectual development. It is also one of the basic ways to realize school sports. How to better play its role has attracted the attention of many experts and scholars. This study focuses on the current situation of Wushu projects in the field of intangible culture in Weixian Middle School of Xingtai City, trying to understand its advantages and existing problems through the current situation, and put forward targeted solutions, so as to enable Wushu projects in the field of intangible culture to play their educational and fitness functions in school sports and provide help for the comprehensive development of middle school students.
Survey Paper: Study of Sentiment Analysis and Machine Translation using Natural Language Processing and its Applications

Natural Language Processing is a technique where machine can become more human and NLP makes human to communicate with the machine easily. Trained machine can think like as human and predict continuous, discrete value. NLP as a tool improves decision making for target customers. NLP seems to be the natural next step in integrating technology with daily lives of people. In this paper the basics of sentiment analysis, use of machine learning in natural language processing, Challenges in machine translation, common problems and applications are discussed in brief.
Research through and through design

An intriguing aspect of the notion of research through design is how much we seem to gain from its proposition and how informative it appears to be, even as we do not state precisely what is meant by the terms research or design. But, what is it that this notion of through calls to the foreground? In what follows, I discuss three interpretations of what through might refer to by looking at it from different perspectives: through practice , through making and through judgement . Different perspectives bring different possibilities to the foreground, and therefore they also suggest slightly different future trajectories. But this also means that some issues fade into the background, something new perspectives can help us get a glimpse of. This is perhaps especially evident in the case of judgement, the least familiar of the three. Looking across the range of research trajectories that these three perspectives open up, there are reasons to think that we can make research through design become a more extensive, inclusive and a far more radical research proposition than its name reveals.
Performance Analysis of E-Certificate Generation and Verification using Blockchain and IPFS

From many years to the present, people have used strong copies of degree certificates as proof of graduation. But in recent days, due to advanced technology, these certificates have been forged. As a result, undeserving people get job opportunities, which tarnish the image of these paper certificates. Therefore, overcoming such certification problems using blockchain technology is encouraged. Blockchain is a system of storing information that makes it difficult or impossible to repair, hack a system, ad save paper costs. With the help of the blockchain, an anti-forge, fixed, electronic-certified electronic certificate is produced. Students will not be able to manipulate the content of e certificates. The function of this system is that: a computer file (e-file) of the certificate i.e. The institution produces the electronic certificate At the same time, that student's record is kept in blockchain blocks using hash values. The hash value is generated from the interplanetary file system (IPFS) network. Accompanying electronic certificate, associated unique serial number (unique id) were also given to the applicant. The searching system (e.g. The Company the applicant applied for) can verify the authenticity of the electronic file using a unique hash value based on the data stored in the blockchain. The proposed method improves the performance concerning storage and speed.
Perceptual Quality Aware Adaptive 360-Degree Video Streaming with Deep Reinforcement Learning

As 360-degree videos are with high data volume, it is a great challenge to deliver the content and provide a high quality of experience (QoE) for users. In this paper, we investigate the tile-level rate allocation problem with the purpose of optimizing users’ QoE. Specifically, we find the nonlinearity between video quality and bitrate through extensive experiments. Thus, a QoE metric is defined to better measure the perceptual quality. Considering the sequential decision nature of video streaming, we formulate the rate decision problem as an Markov Decision Process. Then we propose a deep reinforcement learning based rate adaptive streaming approach to solve this problem. However, the solution space is large as a 360-degree video is spatially partitioned into multiple tiles. In order to address the problem of combinatorial explosion, we propose a tile classification method based on the predicted viewpoint. Experimental results based on real-world traces show that our algorithm can improve the overall QoE by 16% − 22% compared to existing algorithms.
A Comparative Study of Pre-trained Word Embeddings for Arabic Sentiment Analysis

In this paper, we conduct a series of experiments to systematically study both context-independent and context-dependent word embeddings for the purpose of Arabic sentiment analysis. We use pretrained word embeddings as fixed features extractors to provide input features for a CNN model. Experimental results with two different Arabic sentiment analysis datasets indicate that the pre-trained contextualized AraBERT model is the most suitable for such tasks. AraBERT reaches an accuracy score of 91.4% and 95.49% on the large Arabic book reviews dataset (LABR) and the hotel Arabic-reviews dataset (HARD), respectively.
Research and implementation of resource recovery supply chain financial platform based on blockchain technology

Faced with the heavy utilization of the earth's limited resources and the limitation of waste disposal capacity, implementing recycling is an effective method to improve resource utilization, low carbon and sustainable economic development. In view of the problems of long capital recovery period and lack of trust in industrial chain data for material recycling enterprises, this paper proposes to build a supply chain supply chain financial information platform based on blockchain technology, using the technical characteristics of blockchain technology such as secure encryption, non-tampering, and traceability., which can increase the end-to-end data transparency of the supply chain, reduce transaction costs, and endorse the trust between the upstream and downstream of the supply chain. Provide an effective financial model for companies with limited funds, and then help small and medium-sized enterprises in resource recovery to solve their disadvantaged position in the financing process. The resource recovery supply chain financial platform has built a cross-regional, cross-industry, cross-organization, and industry association, Industry chain alliances such as supervision and capital have formed a material recycling supply chain financial platform. In addition to providing convenience for tax supervision and financial services, it also helps reduce the burden on staff in the resource recycling industry and facilitates online business transactions. Finally, the deployment of smart contracts on the platform is shown, and the advantages of the platform in the application of resource recovery supply chain financial systems are discussed and analyzed.
Cycle-Consistent Learning for Weakly Supervised Semantic Segmentation

We investigate a principle way to accomplish the weakly supervised semantic segmentation, only using scribbles as supervision. The key challenge of this task lies in how to accurately propagate the semantic labels from the annotated scribbles to those unlabeled regions so that accurate pseudo masks can be harvested to learn better segmentation models. To tackle this issue, we propose a simple, strong, and unified framework named Cycle-Consistent Learning (CCL) in this work. To be specific, our CCL first utilizes the given scribbles for training and makes a prediction for those unlabeled regions. Then, the predicted regions, in turn, serve as supervision for learning to predict the labeled scribbles. With such a cycle-consistent constraint, the accurate scribbles can reversely help ease those potential noises existing in the unlabeled regions, resulting in better pseudo masks. The training process of our CCL is looped until the network converges in an end-to-end way. We conduct extensive experiments on the popular PASCAL VOC benchmark and achieve a comparable result with the state-of-the-art method. The training mechanism of the CCL is straightforward and can be easily embedded into any future weakly supervised semantic segmentation approach.
Two-branch Objectness-centric Open World Detection

In recent years, with the development of deep learning, object detection has made great progress and has been widely used in many tasks. However, the previous models are all performed on closed sets, while there are many unknown categories in the real open world. Directly applying a model trained on known categories to the unknown classes will lead to misclassification. In this paper, we propose a two-branch objectness-centric open world object detection framework consisting of the bias-guided detector and the objectness-centric calibrator to effectively capture the objectness of both known and unknown instances and make the accurate prediction for known classes. The bias-guided detector trained with the known labels can predict the classes and boxes for known classes accurately. While the objectness-centric calibrator can localize the instances of any class, and does not affect the classification and regression of known classes. In the inference stage, we use the objectness-centric affirmation to confirm the results for known classes and predict the unknown instances. Comprehensive experiments conducted on the open world object detection benchmark validate the effectiveness of our method compared to state-of-the-art open world object detection approaches.
Semi-Supervised Learning with Limited Data for Automatic Speech Recognition

In this paper, we analyze the performance of semi-supervised learning (SSL) methods for the automatic speech recognition (ASR) task. We focus on the case of model adaptation using small unlabeled datasets. The basic SSL method that we apply uses pseudo-labels generated by the adapted model itself, however, we also propose and analyze a number of improvements to SSL. Furthermore, we investigate the possibility of using these methods on the datasets with the token distributions significantly different from the one represented by the training data. We show that in certain conditions, even very small amounts of data can improve the ASR model performance. Using the proposed SSL variant, we were able to reduce WER by 12-22%, depending on the dataset.
Comparison of Crowdsourced and Remote Subjective User Studies: A Case Study of Investigative Child Interviews

Crowdsourced and remote user studies have recently gained popularity as alternatives to traditional laboratory studies. However, they are subject to unreliability, and it is challenging to ensure that valid results are collected, especially when conducting user studies with experts. Experts are a sparse resource, usually having busy schedules and heavy workloads, and are not necessarily geographically close. They are therefore often unwilling to participate in studies which require physical attendance. In this paper, we compare three alternative methods: crowd sourced user study with non-experts, remote user study with non-experts, and remote user study with domain experts, for a use case involving investigative child interview training. We present the results from three subjective studies about the perception of AI-generated child avatars, which is developed using various technologies such as dialogue models, game engine, text-to-speech and speech-to-text components. The study was conducted with three different user groups, and our results indicate the importance of using best practice measures for ensuring the collection of reliable results in crowdsourced settings as compared to remote studies, and highlight the difference between the perspectives of domain experts and non-experts.
A Novel Cosine Transformed Chebyshev Chaotic Map based Image Encryption

Digital chaos has become a popular approach for developing secure and efficient image encryption techniques. On the other hand, image encryption techniques are vulnerable to the differential, statistical, and known/chosen plaintext attacks. This paper creates a novel approach for encrypting medical images using the novel cosine transformed Chebyshev chaotic map. The proposed novel chaotic map is analyzed using parameters like bifurcation diagram and Lyapunov Exponent. In the proposed approach, first, both seed and control parameter are initialized using the secret key and initial vector. After that, the intermediate key for each color image component is generated using the propoed chaotic map and the shared key. Finally, the cipher image is created using the combination of the intermediate key and the chaotic map. This scheme has been analyzed using various security parameters like brute force attack, known/chosen plaintext attack, Number of Pixel Change Rate (NPCR), Unified Averaged Changed Intensity (UACI), Corrleation Coefficient (CC), and Entropy.
A-pillar Blind Spot Display Algorithm Based on Line of Sight

The closed load-bearing structure of a car is designed to decrease the injuries to drivers in traffic accidents, however, such a design also results in many blind spots in driving which are the main cause of a number of car crashes. A-pillar blind spots elimination can make for safer driving. Therefore, this paper proposes an algorithm that utilizes a screen mounted on the A-pillar to display its blind spots in real time and uses a wide-angle camera to capture a panoramic view outside the car. We use a binocular vision system detecting facial features to calculate the driver’s head pose and the 3D coordinates of the pupil center. After obtaining the pupil centers and coordinates of the four corners of the A-pillar display screen, the blind spot can be calculated through the angular relationship between the driver’s line of sight and the outside wide-angle camera’s angle of view. Considering the possibility that the pupil center detection may fail when the driver is not looking at the A-pillar, we propose a compensation method to indirectly calculate the pupil center by using the camera above the driver’s head. We carried out a simulation experiment using a display screen replacing the A-pillar, which showed that our algorithm could display the blind spots occluded by the display screen well.
Using Digital Tools for Contact Tracing to Improve COVID-19 Safety in Schools: Qualitative Study Exploring Views and Experiences Among School Staff

Throughout the pandemic, governments worldwide have issued guidelines to manage the spread and impact of COVID-19 in schools, including measures around social distancing and contact tracing. Whether schools required support to implement these guidelines has not yet been explored in depth. Despite the development of a range of technologies to tackle COVID-19, such as contact-tracing apps and electronic vaccine certificates, research on their usefulness in school settings has been limited.The aim of the study was to explore the needs of school staff in managing COVID-19 and their experiences and perspectives on technological support in relation to contact tracing. School staff are the ones likely to make key implementation decisions regarding new technologies, and they are also the ones responsible for using the new tools daily. Including both management staff and class teachers in the development of school-based technologies can lead to their successful adoption by schools.Semistructured interviews were conducted with UK school staff, including primary and secondary school teachers and school managers. Thematic analysis, facilitated by NVivo, was used to analyze the data. Two of the authors independently coded 5 (28%) of the interviews and reached a consensus on a coding framework.Via purposive sampling, we recruited 18 participants from 5 schools. Findings showed that primary schools did not perform contact tracing, while in secondary schools, digital seating plans were used to identify close contacts in the classroom and manual investigations were also conducted identify social contacts. Participants reported that despite their efforts, high-risk interactions between students were not adequately monitored. There was a need to improve accuracy when identifying close contacts in common areas where students congregate. Proximity tracking, use of access cards, and closed-circuit television (CCTV) emerged as potential solutions, but there were concerns surrounding false alerts, burden, and security.School staff have found it difficult to monitor and implement social distancing and contact-tracing provisions. There are opportunities for mobile digital technologies and CCTV to support school staff in keeping their students and colleagues safe; however, these must place minimal demands on staff and prioritize security measures. Study findings can help researchers and practitioners who work in different contexts and settings understand what particular challenges are faced by school staff, and inform further research on the design and application of digital solutions for contact tracing.
Documentosophy as the philosophical conceptualization of document entity

The author continues to substantiate the goal set by Arkady Sokolov to conceptualize the document entity. As the initial point, he took Sokolov’s definition of documentosophy as the segment of sociocultural space to build, transfer, preserve and user meaningful communication messages referred to as documents. Sokolov also refers to the sphere of sociocultural production where the documents emerge, circulate and are preserved, as the documentosphere. The author argues that the adequate definition of the document is possible in its dialectic object-subject perception according to the international definition: the document is an object or recorded information provided that in the given documentation process (rather than generally!) it can be considered (by a subject) as a discrete whole. This results in the need to specify restrictions and to formulate narrower definition of the document specifically for each individual documentation process. The fundamental provision of relativity, conformity and conventionality of the concept “document” is explained; the author criticizes the mediaology claiming to replace documentive concepts with medialogical structures. In fact, the subject of documentology coincides with that of mediaology. However, firstly, each science has its own term, and, secondly, these are just their approaches that differ, i. e. the ontological approach is applied in documentology, and the communicative one in mediaology. The mediaology has been diligently though unreasonably avoiding the concepts of “document” and “information”. Thirdly, as opposed to documentology, the mediaology has been still unable to propose any brand-new and valuable insight into the document phenomenon.
‘Your Strength Is Inspirational’: How Naomi Osaka’s Twitter Announcement Destigmatizes Mental Health Disclosures

On May 31, 2021, Naomi Osaka, one of the top-ranked female tennis players, and one of the highest-paid female athletes in the world, announced her withdrawal from the French Open on her social media (Twitter) account, citing mental health issues. There exists a stigma around mental health; and people suffering from mental health conditions often experience “discrimination and stigma” (World Health Organization, 2019). Such disclosures by a noted sportsperson provide an opportunity to help combat the stigma. The present study uses unsupervised machine learning and qualitative thematic analysis to analyze 11,800 English language responses to her tweet. Results indicate that Osaka’s tweet mostly garnered a lot of support and encouragement. However, there also existed some negative comments. Additionally, 40% of the negative comments were disseminated by bot-like automated accounts. Practical implications for sports communication are also discussed.