Reviewerly/exHarmony huggingface dataset:
COVID-19 Contact Tracing Apps

As a response to the COVID-19 pandemic, many countries, including Switzerland, have put enormous efforts to break chains of infections using automated contact tracing apps for smartphones. As a result, different approaches such as centralized and decentralized apps with a wide range of frameworks have been deployed, including DP-3T and PEPP-PT. It immediately raised privacy concerns and intensified the importance of ensuring interoperability between the various apps, especially in border regions. Those frameworks were examined regarding its privacy architecture and interoperability, including usage of the tracing apps of Switzerland, France, and Germany. Applicable regulations such as the GDPR must be complied with, and user concerns and the general effectiveness of automated tracing applications respected. Finally, the conclusion answers the main objectives and provides an outlook with potential solutions.
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces.

Today, state-of-the-art Neural Architecture Search (NAS) methods cannot scale to many hardware platforms or scenarios at a low training costs and/or can only handle non-diverse, heavily constrained architectural search-spaces. To solve these issues, we present DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid and diverse NAS, that scales to many user scenarios. In DONNA, a search consists of three phases. First, an accuracy predictor is built using blockwise knowledge distillation. This predictor enables searching across diverse networks with varying macro-architectural parameters such as layer types and attention mechanisms as well as across micro-architectural parameters such as block repeats and expansion rates. Second, a rapid evolutionary search phase finds a set of Pareto-optimal architectures for any scenario using the accuracy predictor and on-device measurements. Third, optimal models are quickly finetuned to training-from-scratch accuracy. With this approach, DONNA is up to 100x faster than MNasNet in finding state-of-the-art architectures on-device. Classifying ImageNet, DONNA architectures are 20% faster than EfficientNet-B0 and MobileNetV2 on a Nvidia V100 GPU and 10% faster with 0.5% higher accuracy than MobileNetV2-1.4x on a Samsung S20 smartphone. In addition to NAS, DONNA is used for search-space extension and exploration, as well as hardware-aware model compression.
신규 간호사의 학부 감염 관리 교육 경험과 교육 만족도에 따른 손위생 의도 및 손위생 수행도의 차이

Purpose: This study aims to identify the degree of intention to hand hygiene and hand hygiene behavior in new nurses based on education experience on infection control. Method: The participants comprised 108 new nurses working in a general hospital in S city, Korea. Data were collected from September to December, 2020. Data were analyzed using descriptive statistics, independent t-test, analysis of variance (ANOVA), and Pearson correlation with SPSS 26.0 program. Results: There were statistically significant differences in intention to hand hygiene (F=3.39, p=.021) and hand hygiene behavior (F=3.33, p=.022) depending on education experience related to infection control during undergraduate courses. Hand hygiene intention (F=4.41, p=.008) and hand hygiene behavior (F=4.13, p=.008) showed statistically significant differences depending on educational satisfaction with infection control during undergraduate courses. There were significant positive correlations between intention to hand hygiene and hand hygiene behavior (r=.21, p=.026). Conclusion: This suggests that education in infection control can reinforce intention to hand hygiene and hand hygiene behavior in new nurses.
Analisis Jurnal Elektronik Bidang Lingkungan Terakreditasi Tahun 2020 pada SINTA

Environmental problems are seen in the news. Scientific studies related to environmental problems are important in providing recommendations for solutions to environmental problems. The analysis of accredited electronic journals in the environmental field is carried out by descriptive methods, using data sources downloaded from http://issn.lipi.go.id/ and from http://sinta.ristekbrin.go.id/ with the aim of knowing: 1) Journal title and journal accreditation status; 2) Issuer; 3) Frequency of issue; 4) City of rise; 5) Journal citations. Furthermore the data is presented in tabular form for further analysis and conclusion drawing. The results of the study are known as many as 40 journal titles have been accredited with the level of SINTA 2 (22.50%), SINTA 3 (30%), SINTA 4 (27.50%), SINTA 5 (8%). Journal publishers are 27 state universities (67.50%), private universities 4 titles, (10%), state Islamic universities 1 (2.50%), public polytechnics 5 (12.50%), ministries 1 (2, 50%) and research institute 1 (2.50%) there is one professional association.( Asosiasi Pembina Hukum Lingkungan Indonesia). It is known that the frequency of publication of the journal is twice a year as many as 27 titles (67.50%), three times a year amounting to 9 (22, 50%), and four times a year 4 (10.00%). Bandung, Semarang, Jakarta, and Yogyakarta are the most published cities, each with 4 titles. There are three major journals which are widely cited, namely Enviroscienteae: Jurnal Ilmiah Bidang Pengelolaan Sumberdaya Alam dan Lingkungan (1,233 times), Jurnal Wilayah dan Lingkungan (1,294 times), and Jurnal Sumberdaya Alam dan Lingkungan (1,196 times). Research conclusions show that in Indonesia there are no accredited electronic journals at the SINTA level 1. Most publishers are state universities, and the Enviroscienteae : Jurnal Ilmiah Bidang Pengelolaan Sumberdaya Alam dan Lingkungan , published by the Bogor Institute of Agriculture, is  the most cited journal (1,233 times).
City Cleanliness Drive Web Portal Using Region Based Convolutional Neural Networks

Identification or detection of object played an important role in Computer Vision, in implementations like city construction process Managers had often wasted lot of their energy, time and resources in cleaning up the garbage, which was unexpectedly showed up. When deep network systems increased its complexity, the systems are constrained by the training data availability. Due to this, Open CV, Google AI released the Open images dataset publicly, so that the research and development would happen in study and analysis of images. As a result, virtual street cleanliness been at most important in this project, however the existing system has disadvantages like collection of garbage is not automated. It doesn’t use the best real time algorithm for identifying the objects. This project will embed the above said things in the system, making the work of managers to keep the city/construction site clean very simple and effective.
Copyright Protection of E-books by Data Hiding Based on Integer Factorization

A data hiding method based on integer factorization via e-books in the EPUB format with XHTML and CSS files for copyright protection is proposed.Firstly, a fixed number m of leading bits in a message are transformed into an integer which is then factorized to yield k results.One of the k factorizations is chosen according to the decimal value of a number n of the subsequent message bits with n being decided as the binary logarithm of k.Next, the chosen factorization, denoted as a × b, is utilized to create a combined use of the and <span> elements in the XHTML files to embed the m + n message bits by including into the two elements a class selector named according to the value of a as well as a text segment with b characters.The class selector is created by the use of a CSS pseudo-element.The resulting web pages are of no visual difference from the original, achieving a steganographic effect.The security of the embedded message is also considered by randomizing the message bits before they are embedded.Good experimental results and comparisons with exiting methods show the feasibility of the proposed method for copyright protection of e-books.
An End-to-End Transformer Model for 3D Object Detection

We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block. Specifically, we find that a standard Transformer with non-parametric queries and Fourier positional embeddings is competitive with specialized architectures that employ libraries of 3D-specific operators with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge. Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research.
A new vision for infratech: governance and value network integration through federated data spaces and advanced infrastructure services for a resilient and sustainable future : Policy brief

Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalised, plagued by delays, cost overruns and benefit shortfalls (Cantarelli et al. 2008; Flyvbjerg, 2007; Flyvbjerg et al., 2003; Flyvbjerg et al., 2004). The root cause is the prevailing fragmentation of the infrastructure sector (Fellows and Liu, 2012). To help overcome these challenges, integration of the value chain is needed. This could be achieved through a use-case-based creation of federated ecosystems connecting open and trusted data spaces and advanced services applied to infrastructure projects. Such digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. Digital federation enables secure and sovereign data exchange and thus collaboration across the silos within the infrastructure sector and between industries as well as within and between countries. Such an approach to infrastructure technology policy would not rely on technological solutionism but proposes the development of open and trusted data alliances. Federated data spaces provide access to the emerging data economy, especially for SMEs, and can foster the innovation of new digital services. Such responsible digital governance can help make the infrastructure sector more resilient, efficient and aligned with the realisation of ambitious decarbonisation and environmental protection targets. The European Union and the United States have already developed architectures for sovereign and secure data exchange.
TEKNOLOGI CLOUD COMPUTING MENGGUNAKAN METODE RST BERBASIS ANDROID UNTUK PENGADUAN KERUSAKAN JALAN : (STUDI KASUS: KECAMATAN MEDAN TEMBUNG)

Road damage can interfere with the activities of a vehicle. If the road damage is slow, of course it will be very disturbing for the people who use the road, this is based on accidents, congestion and various incidents that cause the community to suffer losses. So that from these problems, an application that can be used as a report on road damage by the relevant agencies is needed. Many applications can be developed or created by developers, but there are also many paid applications. Therefore, to maintain, complaints about road damage can take advantage of cloud computing technology that provides free access with a certain capacity. One of them is owncloud cloud computing application. Owncloud is a cloud computing application that can be used as a free local client server application. Owncloud application also applies the REST method to make the request request process faster. In this application owncloud application is used as a road damage complaint application with a client application system that functions as a reporter and a server application that functions to manage data and manage report data with owncloud technology.
Application of Computer Artificial Intelligence and Information Technology In Industry Automation

Artificial intelligence (AI) has greatly changed people's lifestyles and habits, and has been widely used in life. AI is widely used in computer network technology, which accelerates the development of computer network technology. In order to make quick and scientific decisions on enterprise problems according to information, optimize enterprise resource allocation and improve enterprise core competitiveness, this paper puts forward the types and general situation of real-time software platform for enterprise automation and informatization and the core technologies of real-time software platform for enterprise automation and informatization according to the problems existing in the development of computer AI. So as to further research in related fields, make its role play effectively, and promote the stable development of modern society. This scheme makes effective use of all kinds of information in the office of enterprises, and arranges, processes, processes and feeds back all kinds of information in time, so as to facilitate the sharing and exchange of business information of enterprises. Systematic research on the theory and technology of real-time database under the network environment requires a lot of work, not only in theory, but more importantly, the theoretical results can stand the test of practice.
Educational Practice as a Tool in the Formation of Professional Competence of Future Primary Education Teachers

This article is aimed at the essence, content, methods and forms of organization of training and internships in higher education institutions, as well as the formation of professional competence
Research on the innovation of senior high school English classroom teaching model under the new college entrance examination system

In today's high school English classrooms, the traditional teaching model has been unable to meet the demand for the cultivation of innovative talents under the background of the new college entrance examination system.For this reason, innovative research on teaching models is proposed, and conventional teaching concepts are changed to enable students to understand the concepts of listening and cognition.Popularize emotional teaching strategies for faculty and staff, and apply the hierarchical teaching model.And use the results of field investigation to verify the effectiveness of the new teaching model.
STML Students Perception on the Implementation of Online Learning in Education

The expansive nature of the Internet and the accessibility of technology have generated a surge in the demand for web based teaching and learning. The purpose of this study was to describe STML students’ perception on the implementation of online learning in education using a quantitative content analysis. Besides that, the purpose of this study to identify the effectiveness, and impact of online learning. By using quantitative research method, the data collected through questionnaire. The respondents involving 252 STML students. This questionnaire’s online learning dependency was analyzed using 6-point Likert- scale. Data was analyzed using SPSS Statistics version 25. The implementation of online learning in education can be used by students in their learning activities in line with current time.
Research guides: GEOS: Geoscience: Need more help?

This guide includes resources to support students studying Earth Science (Geoscience) at Camosun College.
Kebiasaan Menyontek Pekerjaan Mahasiswa Selama Pandemi Covid 19 Pendidikan Akuntansi FKIP Universitas Muhammadiyah Surakarta Angkatan 2016

This study aims to describe the habit of cheating on the work of students during the COVID-19 pandemic, accounting education at the Faculty of Teacher Training and Education, Muhammadiyah University, Surakarta, class of 2016. This research is a qualitative research with ethnographic research design. Collecting data through interviews and documentation. The results showed that there were 2 factors in the habit of cheating by students, namely internal factors including student orientation towards grades not science, laziness to try, getting grades easily, lack of self-confidence. External factors include demands for high scores, low sanctions. The method and form of cheating is done by exchanging answers via WhatsApp and searching for answers on the internet site. The conclusion of this study is that internal and external factors cause the habit of cheating by students during the COVID-19 pandemic.
Analytical study of financing policies for the agricultural sector in Sudan (1998-2018)

The objective of the study is to shed light on the sources and policies of financing the agricultural sector in Sudan, addition to checkup the impact of both internal and external financing on theagricultural production in the short and long run during the period(1998-2018), using the Autoregressive Distributed Lag (ARDL) bounds testing approach. The results arrived that there is a positive impact for each of the government developmental expenditures in the agricultural sector and foreign direct investment in the agricultural sector on the agricultural production in the short and long run, Whereas the effect of bank financing was in the long run only. The study recommended that priority should be given to the development of the agricultural sector through allocating an estimated part of development financing to this sector, in addition to the good management of the monetary financing policy and directing it towards the agricultural sectors.
Fire Mirrorcle Extinguisher / Shazwani Shamsir

Fire Armour Sdn Bhd is a partnership company that manufactures fire protection equipment. This company is located in Petaling Jaya, Selangor. This company produces various kinds of fire extinguisher such as ABC Dry Powder Fire Extinguisher, Carbon Dioxide Fire Extinguisher, Foam Fire Extinguisher, and a few equipment such as fire hose, fire blanket, home kit and smoke alarm. This company comes up with a new product which is fire extinguisher with new technology. The product is called the Fire Mirrorcle Extinguisher. The word “Mirrorcle” represents the viewing window that is attached at the top of the fire extinguisher tank. Besides that, this product comes with a glow-in-the-dark and reflective tape that will produce light in the dark to ease users to find the fire extinguisher when they are trapped in a dark situation. The target market for this company is those people who are in the M40 and T20 income level as they can afford to buy the fire extinguisher. Besides that, the target market for this company is those who aged between 22 years old to 55 years old since the people in this age range are the ones who have high concern about safety. In the first few years of launching, this company targets to sell this product throughout the Peninsular Malaysia through wholesalers such as Aeon, Giant and Tesco.
Reconstruct the Curriculum to Cultivate New Talents Who Know and Love Agriculture in the New Era

The new agricultural science is not a new starting point, and the construction of new agricultural science should seize the key point of multidisciplinary cross-fertilization. Building smart agriculture, realizing the deep integration of agriculture and information technologies such as Internet of Things, and cultivating new talents who know and love agriculture in the new era have become the development trend of new agricultural and forestry talents training in China. In the face of the development needs of agricultural industrialization, large-scale and intelligence, we must change the current phenomenon of overly fine and narrow professional division and the disconnection between talent training and social needs, in order to cultivate compound agricultural talents that match the current agricultural development needs. Smart agriculture is a typical interdisciplinary field. Based on the specialty of agricultural electrification, the curriculum system is reconstructed, and the two curriculum groups of smart agriculture and smart electricity are constructed, gradually changing from strong electricity to weak electricity, and adding agricultural related courses to form the talent training program of smart agriculture. With the mission of strengthening agriculture and loving agriculture, and taking social demand as the target, we reverse the teaching system and build a professional training roadmap containing basic competencies, professional core competencies and expanded competencies, so as to truly solve the problem of disconnection between talent training and market, and provide valuable reference for the talent training of smart agriculture in China.
ZFlow: Gated Appearance Flow-Based Virtual Try-On With 3D Priors

Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has garnered significant research interest due to its immense practical applicability. Recent methods involve a two stage process: i) warping of the garment to align with the model ii) texture fusion of the warped garment and target model to generate the try-on output. Issues arise due to the non-rigid nature of garments and the lack of geometric information about the model or the garment. It often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns regarding geometric and textural integrity (such as pose, depth-ordering, skin and neckline reproduction) through a combination of gated aggregation of hierarchical flow estimates termed Gated Appearance Flow, and dense structural priors at various stage of the network. ZFlow achieves state-of-the-art results as observed qualitatively, and on quantitative benchmarks of image quality (PSNR, SSIM, and FID). The paper presents extensive comparisons with other existing solutions including a detailed user study and ablation studies to gauge the effect of each of our contributions on multiple datasets.
Learning Action Completeness From Points for Weakly-Supervised Temporal Action Localization

We tackle the problem of localizing temporal intervals of actions with only a single frame label for each action instance for training. Owing to label sparsity, existing work fails to learn action completeness, resulting in fragmentary action predictions. In this paper, we propose a novel framework, where dense pseudo-labels are generated to provide completeness guidance for the model. Concretely, we first select pseudo background points to supplement point-level action labels. Then, by taking the points as seeds, we search for the optimal sequence that is likely to contain complete action instances while agreeing with the seeds. To learn completeness from the obtained sequence, we introduce two novel losses that contrast action instances with background ones in terms of action score and feature similarity, respectively. Experimental results demonstrate that our completeness guidance indeed helps the model to locate complete action instances, leading to large performance gains especially under high IoU thresholds. Moreover, we demonstrate the superiority of our method over existing state-of-the-art methods on four benchmarks: THUMOS'14, GTEA, BEOID, and ActivityNet. Notably, our method even performs comparably to recent fully-supervised methods, at the 6 times cheaper annotation cost. Our code is available at this https URL.