Reviewerly/exHarmony huggingface dataset:
COVID-19 pandemic - Social isolation, socio-emotional repercussions and mental health effects

It is known that the COVID-19 pandemic has affected several sectors, several individuals, in a potentiating way, including the elderly, who have been the focus of worrying news, victims of this virus, because they are part of the risk zone, because of their comorbidities involved. In this work, the main object of study is to investigate the impact of social isolation among Brazilians during the COVID-19 pandemic period. To this end, we discuss the repercussions on socio-emotional feelings and the lifting of the effects of the COVID-19 pandemic on mental health. The study was qualitative, based on an exploratory qualitative theoretical perspective. Initially, we had the literature review, bringing authors on the theme presented here. The results showed that social isolation during the COVID-19 pandemic has aggravated problems that affect people's mental health, such as anxiety, depression, insecurity, insomnia. It is concluded, therefore, that the subjects deserve attention, even after the end of isolation, since the damages are real.
Teaching work during the COVID-19 pandemic challenges and learning

During the COVID-19 pandemic outbreak, several changes have become necessary in the world of work, especially regarding the modalities and spaces of its realization. Due to the need for social isolation to minimize the spread of the virus and the consequent contamination of the mass population, the implementation of remote work performed at home, mediated by digital technologies - the so-called home office - has been implemented in several professional areas. In this way, the public and external space, in which productive and salaried work takes place, migrated to the private space, in which unpaid work is performed and dedicated to the reproduction of the conditions of existence, domestic work. Two distinct environments, in which the sexual division of labor and the social relations of sex/gender develop, have been transformed into one. Specifically in the teaching work in Basic Education, despite its already proclaimed precariousness, devaluation, and feminization, the implementation of Emergency Remote Teaching (ERE) added to the overload of the teacher-mother-wife-housewife woman with the realization of a distance educational practice, mediated by unusual digital technologies in public schools, as well as the unforeseen intertwining of their public and domestic work spaces. Unveiling how this moment was experienced by the teachers, its impacts, challenges and difficulties, strategies of resistance and coping, in the face of multiple and simultaneous works, the unusual transformation of their public and private spaces into one, and the compulsory need to use Digital Information and Communication Technologies (DICT) in the educational process, is what this article addresses. It seeks to understand and rethink the training and teaching work in the face of unforeseen situations, the compulsory and unplanned use of digital technologies as tools for teaching-learning, and the sexual division of the domestic work of teachers. The theoretical-empirical research had a qualitative approach, whose subjects are teachers of Basic Education who work in Cycles I and II, of the Municipal Public Education Network of Belo Horizonte, who were interviewed in person using a semi-structured script. Subsequently, excerpts from the interviewees' speeches, which help to answer the research questions, were selected and analyzed in the light of the theories on Teacher Education and Work and Sexual Division of Labor.
Analytical Study on Punctuation Errors in the Persuasive Writing of English Language Learners

The study was conducted to determine the types of punctuation errors and the most frequently occurring punctuation errors in persuasive writing committed by English language learners in higher education. The descriptive-qualitative design was employed to obtain the purpose of the study. The study participants were eighteen (18) third-year students of Bachelor of Arts in the English Language in one of the state universities in Negros Occidental during the academic year 2022–2023, chosen through purposeful sampling. The data source is the participants' independently composed persuasive essays with a provided topic. The errors were identified according to the rules of punctuation by Straus et al. and classified according to the surface strategy taxonomy by Dulay et al.: omission, addition, misformation, and misordering. The results revealed that omission obtained the highest frequency among the four (4) types of error, and comma is the most frequent punctuation error they committed, obtaining 67.7% and 77.4%, respectively. The researchers recommend that English language learners engage in self-correction and peer-correction activities to build competence and gain command and intelligibility in English language writing.
PESAN PROFETIK CERPEN BERTEMA ‘AMPLOP KIAI’, SEBUAH KAJIAN SOSIOLOGIS DAN IMPLEMENTASINYA DALAM PEMBELAJARAN SASTRA DI SEKOLAH

AbstractThis study took four short stories as material objects, namely "Amplop susulan" by Teguh Effendi, (Effendi, 2017) "Kiai Amplop" written by Edy Yuswanto, (Yuswantoro, 2011) "Amplop ustadz manfaat" is a short story by Effendi Thahar, (Thahar, 2016) "Amplop-Amplop Abu-Abu" written by a kiai named "A. Mustofa Bisri" (Bisri, 2017). The research method used in this research is qualitative method. The stages carried out are research and take noted and then describing. The purpose of the research is to find the perception or response of the kiai or ustadz to the envelope he received, to find prophetic messages in the short stories studied, and their implementation in learning literature at school.
Rancang Bangun Mesin Penetas Telur Burung Love Bird Otomatis Berbasis Arduino Uno R3

Caring for birds is in great demand among bird lovers both in terms of maintenance and maintenance which is quite easy, just pay attention to diet and cleanliness of the place. Birds that enter the adult mass will experience overheat, which is a sign that they are ready to be bred and ready to breed. However, problems arise when the bird does not want to incubate its eggs due to certain factors. A fully automatic bird egg incubator using Arduino UNO is needed, which is a tool that functions to incubate bird eggs automatically and perfectly without having to be incubated by the parent, with the aim of reduce the failure rate in raising birds and increase the process of hatching the eggs. This machine regulates the temperature and humidity levels as well as egg rotation so that the resulting temperature can be evenly distributed automatically without having to touch the eggs during the hatching process as a substitute for the natural incubation process by the mother. The temperature needed to incubate the eggs ranges from 37.5°C to 40°C and humidity ranges from 35% to 60%.&#x0D;
APPLICATION OF INTUITIONISTIC FUZZY SETS IN DETERMINING RESEARCH TOPICS FOR MATHEMATICS EDUCATION STUDENTS THROUGH THE NORMALIZED EUCLIDEAN DISTANCE METHOD

An intuitionistic fuzzy set (IFS) can be helpful in decision-making as a concept to describe uncertainty. This study proposes the application of IFS in determining research topics for students of the mathematics education study program using the normalized Euclidean distance method. This study also shows the differences in the analysis results using the max-min composition method revised by De et al. (2001) with the normalized Hamming distance method and the normalized Euclidean distance method. The results show that the normalized Euclidean distance method can determine student research topics more accurately than other methods because they are careful in looking at distance differences. The normalized Euclidean distance method provides the best distance measure with a high confidence level in terms of accuracy.
Single image dehazing using frequency-guided filtering and progressive physics learning

Abstract Current learning-based dehazing ways simply rely on the paired synthetic datasets and physical models, which can hardly describe the complicated degradation for practical applications. These methods still struggle to achieve the haze removal, color distortion and detail restoration instantly, by ignoring the frequency characteristic differences and prior knowledge importance. To address these problems, we propose an unpaired stage-wise framework integrating Frequency-guided filtering and Progressive physics learning in an adversarial Dehazing network, called as FPD-Net. To be specific, a guided filter based on frequency information is employed to decompose the high and low frequency components for better feature extraction. We further merge the prior and physical knowledge to form progressive physics learning, which can produce pleasing haze-free outputs with visibility and reality. For better atmospheric light estimation, the variational auto-encoder and KL loss are included to represent the illumination message. Extensive experiments on both synthetic and real datasets prove that our designed FPD-Net achieves better performance visually and quantitatively than the comparing dehazing models.
Design of n to 2n quantum reversible decoder using Modified Toffoli gate

Abstract Decoder is a combinational logic circuit; it changes a code into a collection of signals. Decoder is incredibly important circuit employed in combinational logic. Reversible logic may be a new research area due to its reduction in power dissipation and lots of researchers proposed differing types of approaches to construct a quantum reversible decoder. In this paper we tried to design n to 2 n reversible decoder using modified TOFFOLI reversible logic gates. We proposed 1 to 2 1 , 2 to 2 2 and 3 to 2 3 , 4 to 2 4 and 5 to 2 5 reversible decoder and analyzed its lead in respect of quantum cost and garbage output. We tried to propose an algorithm that helps to design n to 2 n reversible decoder. All circuit of the decoder are tested by using IBM Quantum Experience circuit composer.
Comparative analysis of enhanced hybrid encryption algorithm with single encryption algorithm

To accomplish end users data security, it appears that the methods currently used to ensure data security, privacy, integrity, and availability to cloud users are insufficient. An encryption method will be required to guarantee the private preservation of users data while it is in transit or persistent state. The security of cloud data is ensured by a hybrid encryption algorithm that includes the use of the symmetric Advanced Encryption Standard (AES) and an asymmetric RSA algorithm ,combining an asymmetric algorithm (public-key cryptography, or PKC) with a symmetric algorithm (also called private key cryptography), which employs a single key for encryption and decryption, to increase the security of cloud data. Hybird encryption has significant benefits over single encryption in terms of security, speed and computation time. By first encrypting data with AES and then encrypting the AES key with RSA while in the cloud, the suggested method offers a hybrid encryption scheme that improves the security of cloud data. This paper examines various forms of cryptographic encryption and also offers a enchanced hybrid encryption technique for online applications.
Privacy Preservation Techniques for Secure Data Broadcasting using in Distributed Environments

This Data security and privacy are essential things nowadays due to a large amount of sensitive data broadcasting on social media websites. Most of Internet of Things (IoT) and health care applications having quite challenges to achieve privacy preservation when number of distributed resources has involved during the data broadcasting. In this paper, we proposed a distributed data analysis and privacy preservation framework. In this paper, we introduced numerous privacy preservation techniques during data distribution to achieve high privacy. Some traditional methods, data anonymization, generalization, random permutation, specialization, top-down and bottom-up data generalization, fingerprint insertion etc., are also evaluated on extensive data when distributing with multi parties: the proposed one-way hashing privacy, XOR operation for generating multiple secure copies. First, we develop a few dynamic policies for each copy using XOR operation and insert fingerprints in individual documents. The collaboration of XOR operation and custom policies archives higher security from internal as well as external attacks. On the other hand, the data recovery approach has been designed to extract a fingerprint from secure copies. In the extensive experimental analysis, we evaluate proposed results with numerous existing systems and show the effectiveness of proposed modules in a distributed environment.
Histopathologic brain age estimation via context‐aware attention‐based deep multiple instance learning

Abstract Background While a large list of age‐related changes have been described in the human brain, the extent to which they represent the result of common pathological reactions versus signatures of underlying brain aging remains unclear. One approach to sorting out the relative importance of the myriad of changes is deep learning, which has emerged as a powerful tool for image analysis and computer vision. However, large digitized whole slide image datasets from human aging brains have not been previously available. Method In this study, we leveraged a large novel collection of uniformly processed digitized human post‐mortem brain tissue sections to create a histological brain age estimation model. We further investigated the effect of cognitive impairment and exogenous stress on the model. This was accomplished by developing a context‐aware attention‐based deep multiple instance learning model on 702 human brain tissues sections (age range 50‐110 yr) from the hippocampus stained with Luxol Fast Blue counterstained with hematoxylin and eosin (LH&amp;E) on a brain age estimation task. Result Our model estimated brain age within a mean absolute error of 6.2 years. Learned attention weights corresponded to neuroanatomical regions known to be vulnerable to age‐related change. We found that deviations from our estimated histopathologic brain age significantly correlated with the clinical marker of cognitive status (p = 0.042). In addition, we found evidence of significantly accelerated age (p = 1.12×10‐23) in a cohort of subjects with a neuropathological diagnosis of chronic traumatic encephalopathy (CTE), a neurodegenerative disease caused by mild yet repetitive traumatic brain injury, that displays features that overlap with aging. Conclusion These data indicate that estimated histopathologic age can be used as a reliable pathologic correlate to identify factors that contribute to accelerated or decelerated brain aging.
The effect of virtual reality on executive function in older adults with mild cognitive impairment: A systematic review and meta‐analysis

Abstract Background Virtual reality (VR) ‐ a computer simulation of a real or imagined three‐dimensional (3‐D) environment, which allows users to have the same experiences they would get in a similar real situation, has stimulated the interest of researchers and clinicians since its first use in 1994. Compared to traditional pen‐and‐paper training, it is symbolized as a systematic and controllable intervention that makes use of data visualization and provides immediate feedback based on the participants’ performance. VR is usually categorized into three types according to the form of connection with the physical world, and includes non‐immersive, semi‐immersive, and full‐immersive VR. Method This review aimed to summarize the currently available premium evidence to determine the effect of virtual reality (VR) on executive function (EF) in older adults with mild cognitive impairment (MCI), and to detect what level of immersive VR would be the most beneficial. Five electronic databases, namely, PubMed, Embase, PsycINFO, CINAHL and Cochrane Library were searched. Our research team screened the studies and extracted data according to our inclusion criteria. The methodological quality of each study was rated using the PEDro scale. When three or more studies reported the same outcome, a meta‐analysis was conducted using Review manager 5.4.1 Result Finally, 14 randomized controlled trials with a total of 518 participants were included. VR training had an overall positive effect on cognitive flexibility, global cognitive function, attention, and short‐term memory compared to the control groups. Additionally, semi‐immersive VR was more effective in improving cognitive flexibility compared to the other two types of VR. The application of non‐immersive level of VR had a significant effect on global cognitive function, attention, short‐term memory, and cognitive flexibility. Conclusion VR may be effective in improving EF in older adults with MCI. However, the level of immersive VR that would be the most beneficial on EF still needs to be investigated with a greater number of well‐designed studies.
Development of Virtual Assistance Strategy for Students Using Blockchain and Artificial Intelligence Framework

The education sector is important and continuously upgrades in all aspects. Some essential needs are learning materials, teacher involvement, and student potential to use the resources for better knowledge gain. In a post-pandemic scenario, looking back at pandemic situations, educational reformation took place quickly. Virtual training, virtual laboratories, and online educational facilities must provide flexible training to students, working professionals, and corporate certifications to stay current in this technological era. Hence, this paper presents the virtual assistance strategy using artificial intelligence and blockchain to schedule various tasks at the convenience of the learner and instructor. The paper also uses quantitative analysis to present the post-research feasibility analysis for the proposed system.
Co‐designing a virtual reality social connecting space for older adults living with dementia

Abstract Background Older adults living with dementia often experience reduced social connectedness and must be supported to enhance or maintain their social connections. Digital technology is increasingly being used to address this area. In particular, virtual reality (VR) has shown promising benefits for sociability despite not being designed for this purpose. Research has highlighted the need to design social VR spaces which can enable multi‐user experiences to foster social connectedness. Such a design process needs to meaningfully involve older adults living with dementia and their support persons from the outset. Methods Action research was used to co‐design a VR social connecting space for older adults living with dementia. The research aimed to explore one’s experience and perception of social connectedness and how VR may be used to help older adults living with dementia stay socially connected. Using participatory methods of data collection, older adults living with dementia and their support person had the opportunity to use a VR technology probe and participate in a series of semi‐structured interviews, group meetings and design workshops. Reflexive thematic analysis was used to analyze transcripts alongside observational field notes taken by the researcher. The findings from each phase of data collection were used to inform subsequent research activities. Results The meaning and importance of social connectedness is dynamic and transient. Older adults living with dementia experience several barriers and facilitators to social connectedness and their support person often plays an important role in maintaining existing connections. VR was considered a novel and enticing technology to enhance and maintain one’s connections. Older adults living with dementia and their support person expressed preferences for shared experiences and these were translated into the virtual environment (VE). Design preferences to facilitate co‐location and co‐presence in the VE were also integrated into the social connecting space. Conclusion VR was considered an enticing technology to enhance or maintain the social connectedness of older adults living with dementia. Older adults living with dementia and their support person contributed to the design decisions which shaped the development of the social connecting space.
Cloud based AI-driven Video Super-Resolution Solution

High production costs are delaying a widespread deployment of UHD broadcast offerings. Only a few special events tend to be produced and broadcast in UHD, with most 4K content coming from streaming providers such as Netflix, Amazon Video or Disney+, and even on those cases, availability is still significantly more limited than for their non-4K libraries. As a result, the potential of UHD displays is not fully exploited as the picture representation relies on the viewing device upscale capabilities, usually highly constrained by computing and power consumption limitations. High-quality up-conversion can be a viable solution to accelerate 4K availability, since costs can be reduced not only by complementing UHD offerings with high-quality content upscaled from existing HD libraries, but also by leveraging on existing production pipelines all the way up to a final up-conversion stage, while retaining control over how content will be rendered on 4K screens. We propose a cloud-based AI driven Super-Resolution upscaling solution that vastly outperforms traditional methods while retaining low and scalable operational costs.
Improving Netflix video quality with neural networks

Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input video is downscaled into multiple lower-resolution videos. This is typically done by a conventional resampling filter like Lanczos. In this work, we describe how we improved Netflix video quality by developing neural networks for video downscaling and deploying them at scale.
A real-time semantic segmentation method for end-to-end autonomous driving in low-light environments

To solve the problem that the existing semantic segmentation method is challenging to balance the two indicators of reasoning speed and accuracy under low-light conditions, a lightweight real-time semantic segmentation method that can run on embedded vehicle terminals under low-light conditions, namely, LCG-BiSeNetV2 is proposed. Firstly, the Zero-DCE++ method is introduced to preprocess the low-light video frames; Secondly, by improving the module of the BiSeNetV2 method and optimizing the downsampling convolution in the encoder structure, the semantic feature extraction ability of the BiSeNetV2 method has been significantly improved; Finally, the self-made road scene semantic segmentation dataset and the CAMVID dataset were used for training and testing, the mIoU coefficient reaches 72.37% and 76.73%, and the reasoning speed reaches 148.91 Frames Per Second (FPS) and 136.75 FPS, which shows that the LCG-BiSeNetV2 method has achieved fast reasoning and precise semantic segmentation.