Reviewerly/exHarmony huggingface dataset:
A Rendering Method of Product Feature Tree Structure Based on Recursive Algorithm

Due to the rapid advance of informationization process, in the process of test data accumulated a large number of product features, product features data exponentially, traditional product characteristics data rendering is often divided into the hierarchical data list metadata form rendering techniques such as rendering, properties, it is difficult to intuitively show features hierarchy, feature relation, It is impossible to display the product characteristic data of various types and complex forms. In this paper, we design a product characteristics based on recursive algorithm tree structure graphics rendering method, according to the user from the database query below the currently selected target category all feature level data, the characteristics of metadata, product features are formed by recursive algorithm, data structure and loads it into the plug-in, use plug-in rendering the page, formed as a plug-in tags of target feature tree structure, Users more intuitive preview, reading more strong, more easily identify the structure of various product features data relationship.
Spatial Color Mixture Model of Digital Camouflage Based on Visual Transmission Characteristics

Digital camouflage has been widely used because of its good camouflage effect. However, the pre-evaluation of digital camouflage design is still lags behind its widespread application. So a new model based on visual transmission characteristics is proposed in this thesis, in order to simulate the spatial color mixture effect of digital camouflage pattern. According to experimental results, the new model is proved to have a good performance. The new model can provide guidance for later modification, reduce the risk of construction, and improve the effect of digital camouflage.
Privacy protection scheme for edge computing based on function encryption

In the response to COVID-19, the big data can achieve a new data application mode with reduced manpower and material costs, which will help the government understand the supply and demand status of medical supplies and make precise decisions to ensure the supply of materials and prevent the spread of the epidemic. However, in the medical big data solution based on cloud computing, the user data storage and computing process are all carried out in the cloud, the risk of medical data is increasingly exposed, and the privacy protection of medical data is worrying. Edge computing makes data privacy protection more operational. Because the data collection and calculation are based on the local, and no longer need to be transmitted to the cloud, some important information, especially sensitive information, cannot be transmitted through the network, effectively solving the problem of user privacy leakage and data security. Function encryption provides data with a very flexible method that meets data confidentiality and effective access control. So based on function encryption, this paper proposes a privacy protection scheme under the edge computing paradigm, introduce the data interaction process for the scheme and demonstrates the privacy protection ability and computing efficiency of the scheme in the main scenario of smart medical care.
Dynamic Overhead Queue-based Efficient Service Delivery at Edge for Adaptive Live Streaming

The live broadcast business has developed rapidly in recent years, but low latency, smoothness, and high user quality of experience (QoE) are still the problems that we need to solve. At the same time, the arise of edge computing has made up for the lack of traditional cloud computing in live broadcast services and brought low-latency effects, but also brought a heavy burden to the edge network, making edge load balancing an important issue to be solved urgently. Meanwhile, the real-time and low-latency characteristics of live broadcast put forward higher requirements on the network, which increases the difficulty of load balancing optimization. Therefore, to cope with the development trend of adaptive live streaming and improve the QoE, this article first proposes an adaptive live streaming transmission framework under the edge network, which integrates edge service clusters, multicast, and client adaptive bitrate(ABR) algorithms. Then, we proposed a load optimization scheme for edge service clusters, focusing on the dynamics of load and transmission costs. According to the two factors directly affected by the ABR algorithm, i.e. edge server CPU load and bandwidth overhead, The dynamic overhead queue load-balancing algorithm is proposed. Compared with the other three load optimization algorithms, the simulation results show the superiority of our scheme in term of load balancing and overhead.
Authentication and Key Agreement Protocol for Edge Computing

The rise of edge computing has promoted the large-scale deployment of terminal embedded devices. Although the convenience of data collection is increased, the security of private data is facing serious threats. This paper designs an identity authentication and key agreement based on elliptic curve public key cryptography Protocol. It consists of four phases: system setup phase, embedded registration phase, edge registration phase, and identity authentication phase. This article uses BAN-logic to give a formal security analysis of the protocol. Security analysis and performance evaluation show that in the terminal embedded system, the security of the key is significantly enhanced, and a variety of attacks against the embedded system can be effectively resisted by the key. The additional overhead added by this protocol will not have a significant impact on system performance.
A study on content-based instruction activities in middle school English textbooks based on the 2015 Revised National Curriculum

This study investigated the content-based instruction (CBI) activities in middle school English textbooks based on the 2015 Revised National Curriculum. It investigated whether the CBI activities were designed to provide language learning opportunities and integrate various knowledge and skills. To this end, the analysis focused on the questions regarding the content subjects and topics provided in the activities of the textbooks, learning objectives of these activities and a detailed analysis of the activities. The results indicated that the CBI activities were designed to meet the goal of ‘creative convergence education’ by appropriately connecting various knowledge and skills with the topic of the unit. Yet, the activities were largely limited to a small number of subjects such as social studies, ethics, and technology and family. Also, neither clear language objectives nor content vocabulary were provided. Finally, the activities were mostly intended for group works, indicating that English proficiency levels will likely affect learning outcome. This study suggests that middle school English convergence education would benefit from including clear language objectives for the integrated activities as well as content vocabulary, and a variety of topics beyond the scope of daily life should be covered in the textbooks.
Peer Review Declaration Form Guidance

On the following page you will find the declaration form. • Please answer each question. • You should submit the form along with the rest of your submission files. • The deadline is the submission date written in your publishing agreement. All conference organisers/editors are required to declare details about their peer review. We will published the information you provide as part of your proceedings. All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: blind (authors cannot see reviewer names, but reviewers can see author names.) • Conference submission management system: EDAS • Number of submissions received: 83 • Number of submissions sent for review: 80 • Number of submissions accepted: 76 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 91.57% • Average number of reviews per paper: 2.3 • Total number of reviewers involved: 60 • Any additional info on review process : • Contact person for queries : Name : Eng Swee Kheng Affiliation: Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis Pauh Putra Campus, 02600 Arau, Perlis, Malaysia Email : kheng@unimap.edu.my
Super Exponentials in Linear Logic

Following the idea of Subexponential Linear Logic and Stratified Bounded Linear Logic, we propose a new parameterized version of Linear Logic which subsumes other systems like ELL, LLL or SLL, by including variants of the exponential rules. We call this system Superexponential Linear Logic (superLL). Assuming some appropriate constraints on the parameters of superLL, we give a generic proof of cut elimination. This implies that each variant of Linear Logic which appears as a valid instance of superLL also satisfies cut elimination.
Comparative study of CNN, VGG16 with LSTM and VGG16 with Bidirectional LSTM using kitchen activity dataset

Computer-based human activity detection of everyday life has lately attracted a lot of attention due to its relevance to contextual assisted living. Such applications necessitate the automatic recognition of high-level events involving many human actions in a specific context. Kitchen activity recognition aims to monitor the activities of the inhabitant and identify deviations from the normal in kitchen. This paper presents a deep neural architecture for recognizing kitchen activities that employs a combination of machine learning models. In this work, comparative study of kitchen activity detection is performed using kitchen video dataset with three models, namely CNN, VGG16 with LSTM and VGG16 with Bidirectional LSTM. The results of the experiments reveal that an accuracy of 31% for training and 28% for testing is achieved with CNN model, whereas VGG16 with LSTM model achieves 92% for training and 96% for testing, VGG16 with Bidirectional LSTM model achieves 95% for training and 94% for testing.
Register cartography and Giovanni Parodi&amp;#8217;s research: Registerial profiles of school subjects and university disciplines

In this article, I explore the functional varieties of language, i.e. registers (or genres), used in different school subjects and university disciplines, examining the ranges of registers used in a selection of subjects and disciplines - their registerial profiles. I draw on Giovanni Parodiâs research into registerial profiles of university disciplines and on systemic functional work on registerial profiles of school subjects, showing how they complement one another. I sketch an outline of a comprehensive view informed by these pioneering contributions so that future research can fill in gaps and further illuminate registerial profiles of different fields of study, curricular composition and sequence of registers and learner paths through institutions of education seen in terms of expanding personal registerial repertoires. For the sake of conceptual clarification, I also touch on different approaches to the phenomenon of functional variation within language according contexts of use under the headings of âregisterâ and âgenreâ.
Text Analytics in Big Data Environments

Text analytics is used to analyze the large volume of natural text, and it discovers lexical or linguistic patterns from the unstructured documents. Its main effort is to elucidate the information overload problem by using the natural language processing and machine learning techniques. Preprocess the documents, storage of intermediate representations and analysis of represented documents and visualization of results are the significant steps in text analytics. It is the confluence of different areas such as statistics, computational linguistics, and Artificial Intelligence. Big Data is a concept designating the tremendous amount of information within the form of structured, unstructured, and semi-structured. Big Data text analytics is a method of extracting and producing useful data and information from structured and unstructured data, which can make knowledge management more efficient via its categorization, visualization, and analysis. Hence, text analytics is necessary to analyze the huge volume of unstructured data. Text analytics becomes essential for the different domains such as healthcare, social media, education, telecommunications, industrial automation, aerospace, defense, weather forecasting, and so many. In future industry revolution 4.0, the text data analytics would be an essential task and it will produce optimal results.
Peningkatan Akses Penyimpanan Data Client Menggunakan Website Pada Inside Lombok

The service on improving access to client data storage using the website at Inside Lombok is carried out so that data storage can be carried out effectively and efficiently. With this web, it is hoped that it will make it easier for Lombok insiders to enter and access client data wherever they are. The purpose of this activity is the development of a client database information system in Inside Lombok so that it can be accessed easily and can be done anywhere. The types of this activity are manufacture and training. This activity was carried out for 20 days with Inside Lombok employee participants. The evaluation carried out is divided into two. The first evaluation of the website creation that has been developed consists of a functional test and a satisfaction test, the second is an evaluation of satisfaction regarding the use of the web that has been used. The result of this activity is that the team has succeeded in creating a website to store client data so that Inside Lombok employees do not need to use excel anymore, which is where this website can save time for employees who want to enter client data because the format for entering data already exists and can be accessed anywhere.
Copyright Policies and Guidelines for Thesis in Universities

Join us for ETD2021 (Electronic Theses and Dissertations) Conference in November 2021 in the United Arab Emirates. Our theme, ‘Open scholarship in a post-pandemic world’ addresses the enhanced need for discoverability of research findings in a virtual world. Since the global impact of the COVID-19 pandemic has constrained international travel and mobility, we have all come to rely on working in the virtual world. These developments have merely accelerated the existing trends towards more electronic and digital repositories. Since 1991, the Networked Digital Libraries of Theses and Dissertations (NDLTD) have contributed to research collaboration by identifying and aggregating digital repositories of theses and dissertations as a previously fugitive class of research resources.
Design and Implementation of Distributed Fault-Tolerant Computer Middleware of Deterministic Communication

In this paper, the distributed fault-tolerant computer middleware of deterministic communication is designed, under the distributed system architecture; key technologies such as distributed node synchronization/communication technology, distributed system membership management, and membership consistency guarantee are mainly discussed. First, the distributed fault-tolerant computer architecture and related technologies of deterministic communication are introduced, then the software design is explained, and the design and implementation of middleware software are mainly discussed.
Reducing false-positive recalls by adding temporal changes information to an AI system for breast cancer detection

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
Object detection process of domestic high-resolution building remote sensing image based on weak light enhancement

Remote sensing images play an important role in acquiring geographic data, acquiring earth resources and emergency disasters. However, the obtained remote sensing images often have some problems, such as low contrast, poor visibility, blur and so on. This paper presents a building object detection process based on weak light enhancement of domestic high-resolution remote sensing images. The whole process mainly includes image enhancement and object detection. Using unsupervised Generative Adversarial Net, image enhancement training can be carried out without low / normal light image pairs, and the enhanced image effect can be detected through object detection algorithm. The experimental results show that the detection process is very helpful to the accuracy and efficiency of building object detection.
An Intelligent Approach for Protecting Privacy in Distributed Information Mining Using Secured Computation of Multiple Participating Sites

Information mining is a very well-known task using which managers can take a better decision regarding data operations.For that purpose, they need to get useful information from a large amount of raw data.This kind of big data mining is usually carried out on unstructured data that is huge in context of its size.Due to the vast size, the data mining process faces confidentiality and security breaches issues.There are many technologies through which we can search for useful information for getting fruitful results towards the fulfilment of organizational goals.However, it's a big challenge for researchers to get knowledge from the large amount of data that is owned by multiple parties which are located at different sites.For that, they need to perform Distributed Data Mining task with the concern of privacy leakage.While secure multiparty computation presents the solution to this problem but there are some issues which are untouched yet.In this paper, we presented two data privacy issues not even solved by multiparty computation.The presented algorithms are designed using the process of matrix computation with the help of encoding and decomposition methods.The implementation of the work is based on a secure Multi-Party Computation (MPC) protocol named SPDZ so that we can perform the sharing of data values.We analyzed the results produced by single machine and proposed design and implementation to show the similarity between both.Experimental results show the effectiveness of implemented algorithms and their implementation to preserve the privacy of distributed data in the process of distributed data mining.
Assistência em Saúde ao Idoso com quadro Drepressivo / Health Care for the Elderly with Drepressive Signs

Resumo: Com o aumento da população idosa, e por ser uma fase da vida frágil e conforme os aparecimentos de doenças, com o encerramento trajetória profissional, perda familiar, mudança de hábitos, tudo isso vem o sentimento de frustações, é comum que nessa fase os idosos fiquem deprimidos. Tornando assim bastante importante o acompanhamento qualificado e humanizado por parte dos profissionais de saúde. Por tanto cabe a estes profissionais proceder em buas de uma assistência adequada. Objetivo desse estudo é analisar e descrever a importância da assistência em saúde ao paciente com depressão na terceira idade. Metodologia: trata-se de uma revisão integrativa da literatura, que foi realizada através da pesquisa bibliográfica com os seguintes descritores: Terceira idade, depressão, assistência da enfermagem e saúde do idoso.com os estudos dos artigos selecionados os resultados foram distribuídos em três categorias temáticas: Fatores contribuintes para depressão na terceira idade; Ações da enfermagem para prevenir e amenizar a depressão na terceira idade; A importância da assistência qualificada do profissional da enfermagem ao idoso com depressão. Notou-se o quanto é importante uma assistência adequada e humanizada da enfermagem ao idoso com depressão, elaborando assim ações que vá ajudar o idoso na prevenção à amenização dos sintomas da depressão. Espero que esse estudo ajude com futuras pesquisa relacionado a esse tema, fortalecendo o interesse dos enfermeiros a saúde do idoso. Palavras-chaves: Depressão. Terceira idade. Assistência. Saúde do idoso.Abstract: With the increase of the elderly population, and because it is a fragile stage of life and according to the onset of diseases, with the closure of the professional trajectory, family loss, change of habits, all this comes with the feeling of frustration, it is common for the elderly at this stage get depressed. Thus, qualified and humanized monitoring by health professionals is very important. Therefore, it is up to these professionals to provide adequate assistance. The aim of this study is to analyze and describe the importance of health care for patients with depression in the elderly. Methodology: this is an integrative literature review, which was carried out through bibliographical research with the following descriptors: Third age, depression, nursing care and elderly health. With the studies of the selected articles, the results were divided into three categories thematic: Contributing factors for depression in old age; Nursing actions to prevent and alleviate depression in old age; The importance of qualified nursing professional assistance for the elderly with depression. It was noted how important an adequate and humanized nursing care for the elderly with depression is, thus developing actions that will help the elderly in preventing the alleviation of symptoms of depression. I hope this study will help with future research related to this topic, strengthening nurses' interest in the health of the elderly. Keywords: Depression. Third Age. Assistance. Elderly health.
Research on real-time video key frame extraction technology based on clustering

A keyframe is a crucial image frame used to describe a shot, and the use of keyframe technology can significantly reduce the amount of data for video retrieval. For example,video-on-demand, face recognition under the camera, key lens retrieval of medical images, etc. Aiming at the problems in the current video keyframe extraction process that the extraction accuracy is low and cannot meet the real-time performance, this paper proposes a real-time video keyframe extraction algorithm CTM-NN based on the inter-frame difference method combined with clustering and neural network. The algorithm uses the inter-frame difference method based on the set threshold, HOG plus HSV first-order moment feature extraction algorithm, and uses the K-means++ clustering algorithm to finally train its own ResNet-50 model, aiming to accurately and efficiently extract real-time video Keyframes. In order to verify the algorithm proposed in this paper, experiments were carried out in the finished news video, landscape video, and real-time concrete mixing video. The experimental results show that the method proposed in this paper can meet the extraction accuracy and meet the keyframe extraction speed of the real-time video so that it can save the keyframes, automatically label while maintaining the time sequence. All in all, the CTM-NN algorithm proposed in this paper has achieved good results in the extraction and storage of real-time video keyframes