Reviewerly/exHarmony huggingface dataset:
Viewpoint on ACS PHYS Division Sponsored Virtual Seminars

ADVERTISEMENT RETURN TO ISSUEViewpointNEXTViewpoint on ACS PHYS Division Sponsored Virtual SeminarsHeather Abbott-LyonHeather Abbott-LyonKennesaw State University, Chair-Elect, Astrochemistry subdivisionMore by Heather Abbott-Lyonhttp://orcid.org/0000-0001-6844-2487, Carlos R. BaizCarlos R. BaizUniversity of Texas at Austin, Chair-Elect, Biophysical subdivisionMore by Carlos R. Baizhttp://orcid.org/0000-0003-0699-8468, Partha P. BeraPartha P. BeraNASA-Ames Research Center, Chair, Astrochemistry subdivisionMore by Partha P. Berahttp://orcid.org/0000-0003-0843-3209, Kyle CrabtreeKyle CrabtreeUniversity of California, Davis, Vice-Chair, Astrochemistry subdivisionMore by Kyle Crabtreehttp://orcid.org/0000-0001-5629-5192, Qiang CuiQiang CuiBoston University, Secretary/Treasurer, PHYSMore by Qiang Cuihttp://orcid.org/0000-0001-6214-5211, Ryan C. FortenberryRyan C. FortenberryUniversity of Mississippi, Past-Chair, Astrochemistry subdivisionMore by Ryan C. Fortenberryhttp://orcid.org/0000-0003-4716-8225, Christy F. LandesChristy F. LandesRice University, Vice-Chair, PHYSMore by Christy F. Landeshttp://orcid.org/0000-0003-4163-6497, Anne B. McCoy*Anne B. McCoyUniversity of Washington, Past-Chair, PHYSMore by Anne B. McCoyhttp://orcid.org/0000-0001-6851-6634, Rodrigo NoriegaRodrigo NoriegaUniversity of Utah, PHYSMore by Rodrigo Noriegahttp://orcid.org/0000-0003-1199-0866, and David E. WoonDavid E. WoonUIUC, Secretary, Astrochemistry subdivisionMore by David E. Woonhttp://orcid.org/0000-0003-3831-5078Cite this: J. Phys. Chem. B 2021, 125, 8, 1973Publication Date (Web):January 28, 2021Publication History Published online28 January 2021Published inissue 4 March 2021https://doi.org/10.1021/acs.jpcb.1c00531Copyright © Published 2021 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views466Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (671 KB) Get e-AlertsSUBJECTS:Space chemistry,Luminescence,Physical chemistry Get e-Alerts
Privacy Enhancement on Internet of Things Using Differential Privacy Strategies

Abstract With recent developments in communication and data storage technologies, the Internet is gathering and storing an explosive amount of information. While such a large volume of data provides great opportunities for the exploration of knowledge, organizations do not want to share their information for legal or competitive reasons. This raises the problem of information mining while retaining privacy. Current efficient data mining privacy preservation algorithms are focused on the assumption that all intermediate results during data mining operations are appropriate to be published. It has been shown, however, that such intermediate outcomes can still leak private information. Differential privacy is used in this situation to quantitatively restrict such leakage of information. Differential privacy is a newly emerged concept of privacy that can provide strong observable assurances of privacy. The rapid growth of the Internet of Things has contributed to the pervasive deployment of low-cost smart devices and the widespread use of high-speed wireless networks (IoT). IoT welcomes numerous physical artifacts that have not been active in the conventional Internet and allows a wide variety of IoT applications to be provided through their interaction and cooperation. Many IoT services which require a detailed understanding and analysis of data collected across a large number of physical devices that challenge both the privacy of personal information and IoT creation. IoT information privacy is a broad and complex term since its understanding and interpretation vary between individuals and involves efforts from both legislation and technology to implement it. In this paper we review IoT privacy enhancement using different privacy strategies on various IoT applications and challenges with Security & Privacy. Keywords : Internet of Things, Differential Privacy, Security, Privacy, Data Mining, Data Set, Server.
Corporate Social Responsibility in Nigeria and Rural Youths in Sustainable Traditional Industries Livelihood in Oil Producing Communities

Since the first oil well was drilled in Nigeria, traditional economies have suffered neglect, and rural youths do not see a future for themselves in traditional industries livelihood (TIL). We examine the impact of corporate social responsibility (CSR) of multinational oil companies (MOCs) on youths’ participation in TIL. A total of 1200 youths were sampled across the rural Niger Delta. Results from the use of a logit model indicate a significant relationship between CSR and TIL. The findings suggest increased general memorandum of understanding (GMoU) interventions in canoe-carving, pottery-making, cloth-weaving, mat-making, and basket-weaving to revive the traditional economic activities in Nigeria.
Machine learning methods analysis in the document classification problem

Current situation with official documentary in the world, and especially in Ukraine, requires tools for electronical processing. One of the main tasks at this field is seal (or stamp) detection, which leads to documents classification based on mentioned criterion. Current article analyzes some of existed methods to resolve the problem, describes a new approach to classify documentary and reflects dependence of model accuracy to input data amount. As a result of this work is a convolutional neural network that classify 708 out of 804 images of official documents correctly. A corresponded percentage of model accuracy is 88.03, despite the fact of bias presence in input data.
The Ways of Psychological and Pedagogical Barriers Overcoming between Teachers and Students during COVID-19 Pandemic

The COVID-19 pandemic has affected teaching and learning around the world. As a result, schools and higher learning institutions use the e-learning platform to replace face-to-face teaching and learning. E-learning presents new challenges when compared to a traditional classroom because students are separated from their instructor by a computer screen. It has been established that the psychological and pedagogical barrier is an obstacle arising for a teacher in the process of his professional activity, which reduces its effectiveness. The main psychological and pedagogical barriers are highlighted, which are combined into groups: due to the personal and professional qualities of the teacher; caused by the physical and psychological characteristics of the student himself; determined by the specifics of the subject of interaction; caused by unfavorable factors of the social and material environment in which educational interaction takes place. An in-depth analysis of typical barriers that potentially arise between a teacher and students makes it possible to determine ways to overcome them. A psychological portrait of a personality that potentially creates barriers in interpersonal interaction is outlined. The paper proposes the ways of overcoming psychological and educational barriers between teachers and students during the COVID-19 pandemic.
On Batch-size Selection for Stochastic Training for Graph Neural Networks

Batch size is an important hyper-parameter for training deep learning models with stochastic gradient decent (SGD) method, and it has great influence on the training time and model performance. We study the batch size selection problem for training graph neural network (GNN) with SGD method. To reduce the training time while keeping a decent model performance, we propose a metric that combining both the variance of gradients and compute time for each mini-batch. We theoretically analyze how batch-size influence such a metric and propose the formula to evaluate some rough range of optimal batch size. In GNN, gradients evaluated on samples in a mini-batch are not independent and it is challenging to evaluate the exact variance of gradients. To address the dependency, we analyze an estimator for gradients that considers the randomness arising from two consecutive layers in GNN, and suggest a guideline for picking the appropriate scale of the batch size. We complement our theoretical results with extensive empirical experiments for ClusterGCN, FastGCN and GraphSAINT on 4 datasets: Ogbn-products, Ogbn-arxiv, Reddit and Pubmed. We demonstrate that in contrast to conventional deep learning models, GNNs benefit from large batch sizes.
Verifiable Multi-Party Business Process Automation

Lack of trust is one of the main problems preventing multi-party business process automation. Solutions based on smart contracts and distributed ledgers have been proposed, but suffer from scalability issues. We present a more performant alternative approach to enable trust between business partners, based on authenticated data structures.
Impact of educational project FIEP on development of universities: ten years of history

Presented is experience of Fund for infrastructure and educational programs of Rusnano as to supplying by actual cadres scientific capacity industries. Discussed are effects of participation of universities in educational project of the fund in dynamic for period of 10 years. Shown is, what changes occurred in the structure of educational training, content of educational programs, and infrastructure of the project. Also shown is, how approaches, proposed in the project, influenced on practice of routine activity of the fund, how transformed its image, and was being formed its partnership connections with enterprises of real sector of economics.
Remote teaching dining the COVID-19 pandemic: lessons learned from university extension project

The social distance caused by the COVID-19 pandemic has resulted in the suspension of face-to-face classes around the world Educational methods needed to be quickly adapted to the remote learning This unexpected adaptation impacted students and teachers who, together, committed themselves to experimenting new forms of teaching and learning in order to maintain the continuity of teaching, research and extension activities Therefore, the goal of this paper is to describe the process of adapting a university extension project to the remote learning It is also proposed to share experiences and learnings that can contribute to the design of new pedagogical strategies that provide the student's autonomy in the learning process in this new pandemic reality
e. The use of blockchain technology to improve the food supply chain

The purpose of the article is to show how to use relatively new and very innovative Blockchain technology to improve the food supply chain. In countries such as the United States or Thailand is starting to be an indispensable element in the agri-food sector. At the outset, it should be said what the Blockchain technology is, the use of which is becoming very broad in many sectors of the economy, including in the area of monetary policy of the state. It is used when creating virtual money, i.e. cryptocurrencies, which, despite the controversy they arouse, as well as the world of virtual finance in the COVID-19 era, begin to play a significant role. Blockchain (BCT) in the case of the food supply chain, despite the fact that it is a relatively new digital technology, may revolutionize its functioning. This technology is designed to provide the possibility of storing information in a database of transactions and products, which is decentralized and distributed and not susceptible to changes and manipulations. BCT believes it can play a positive role in ensuring food safety and quality. The main benefit of using this technology is the increased transparency of food supply chains. BCT makes it possible to increase the efficiency of tracking systems and identification of agri-food products in the supply chain. This means that thanks to BTC, it is possible to reduce the number of cases of food adulteration and the unauthorized use of food quality certificates. Nevertheless, BCT, due to the fact that it is a new technology, is not fully developed, i.e. the possibility of scaling BCT may turn out to be ineffective in more extensive and complex supply chains including multi-component products. In addition, it should be remembered that this technology is associated with barriers of a social, economic, legal and financial nature, which may adversely affect the further use of BCT in food supply chains. Despite the growing interest of agri-food sector enterprises in using BCT, its implementation in food supply chains may progress slowly.
FakeSafe: Human Level Data Protection by Disinformation Mapping Using Cycle-Consistent Adversarial Network

The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable private and sensitive data. Huge amount of private data are being generated from personal devices such as smart phone and wearable in recent years. Being able to utilize these personal data will bring big opportunities to design personalized products, conduct precision healthcare and many other tasks that were impossible in the past. However, due to privacy, safety and regulation reasons, it is often difficult to transfer or store data in its original form while keeping them safe. Building a secure data transfer and storage infrastructure to preserving privacy is costly in most cases and there is always a concern of data security due to human errors. In this study, we propose a method, named FakeSafe, to provide human level data protection using generative adversarial network with cycle consistency and conducted experiments using both benchmark and real world data sets to illustrate potential applications of FakeSafe.
The Forecast of the Diamond Content of the Region Based on KMA Materials

Summary In the area of the Kursk magnetic anomaly during geological and geophysical surveys, deep (5–10 km) foci of highly conductive objects were identified. The increased electrical conductivity of terrigenous formations, as well as the detailed high-precision aeromagnetic survey data, made it possible to identify geological and geophysical markers corresponding to the horizons of carbon- and sulfide-containing shales. That is why the forecast of the diamond content of the work area is a very relevant topic. The materials of this work are based on the following aspects: - primary geophysical processing of the magnetic field matrix data according to the M-37 map sheet; - drawing up a forecast map for the most promising areas of work; - consideration of alternative deposits associated with kimberlite pipes to create the most accurate idea of the possibility of detecting the same pipes in the KMA region.
Pengaruh Model Pembelajaran Kooperatif Tipe Student Team Achivement Division (STAD) Terhadap Hasil Belajar Muatan IPS Peserta Didik Kelas V SDN 1 Ampenan Tahun Ajaran 2019/2020

This study aims to determine the effect of the Student Teams Achievement Divisions (STAD) learning model on the learning outcomes of social studies content for class V SDN 1 Ampenan students in the 2019/2020 academic year. The type and research method used is a quantitative approach and is causal comparative (Ex Post Facto). The sampling technique used purposive sampling technique. The total population of this study was 32 students of class V SDN 1 Ampenan. The data collection method used a closed questionnaire with a Likert scale and the social studies learning outcomes document of students. The research instrument test used validity and reliability tests. The prerequisite analysis test used the normality test and linearity test. The data analysis technique used descriptive analysis, simple regression, t test, and the coefficient of determination used to test the hypothesis. The results of hypothesis testing in this study are the p-value of 0.000 at the significance level of 0.05 or 5%, then the p-value is smaller than the significance level (0.000 <0.05), (Ha is accepted and Ho is rejected) and the regression analysis results obtained the value of R Square amounted to 39.6%. Thus it can be concluded that the application of the Student Teams Achievement Divisions learning model has a positive and significant effect on the learning outcomes of social studies content for grade V students at SDN 1 Ampenan for the 2019/2020 academic year.
SECURED WAY TO MANAGE VARIOUS ATTACKS IN CLOUD

Cipher text-Policy Attribute-Based Encryption (CPABE) may be an effective solution to guarantee the confidentiality of data and provide fine-grained access control here. In a CP-ABE based cloud storage system, for example, organizations (e.g., a university such as the University of Texas at San Antonio) and individuals (e.g., students, faculty members and visiting scholars of the university) can first specify access policy over attributes of a potential cloud user. Authorized cloud users then are granted access credentials (i.e., decryption keys) corresponding to their attribute sets (e.g., student role, faculty member role, or visitor role), which can be used to obtain access to the outsourced data. As a robust one-to-many encryption mechanism, CP-ABE offers a reliable method to protect data stored in cloud, but also enables fine-grained access control over the data.
Facial Recognition Processing Using Uniform Pattern Histogram with AI in Multimedia Applications

Nowadays we are using face recognition everywhere. For recognizing the face it has three functions: Detection, Recognition, Checking process. First, it must detect any face. Second, it must recognize the face instantaneously. Finally, it must take some other actions to check whether it allowing the right person to access. Here, Artificial intelligence plays a major role in recognizing the face; the AI-enabled face recognition system captures the person's image from any recorded images, and analyzes the captured image with the image stored in its database. If both the face matches then the person can access any file or they can perform their own actions. Facial recognition can help verify personal identity. It is used in identifying the documents and, most often combined with other biometric technologies such as fingerprint (prevention of ID fraud and identity theft). In this, we are using OpenCV as a tool to recognize the face. By using this developer’s can able to access many advanced computer vision algorithms used for image and video processing in 2D and 3D as part of their program. By using the LBP algorithm we have attained an accuracy rate of face recognition up to 95%, eye detection as 85%, face detection as 80%, face localization as 93%, real time face recognition as 85%.Keywords: face recognition, Uniform pattern histogram Algorithm, artificial intelligence, image comparison.
COVID-19 Emergency Measures and the Impending Authoritarian Pandemic

COVID-19 has brought the world grinding to a halt. As of early August 2020, the greatest public health emergency of the century thus far has registered almost 20 million infected people and claimed over 730,000 lives across all inhabited continents, bringing public health systems to their knees, and causing shutdowns of borders and lockdowns of cities, regions, and even nations unprecedented in the modern era. Yet, as this Article demonstrates—with diverse examples drawn from across the world—there are unmistakable regressions into authoritarianism in governmental efforts to contain the virus. Despite the unprecedented nature of this challenge, there is no sound justification for systemic erosion of rights-protective democratic ideals and institutions beyond that which is strictly demanded by the exigencies of the pandemic. A Wuhan-inspired all-or-nothing approach to viral containment sets a dangerous precedent for future pandemics and disasters, with the global copycat response indicating an impending ‘pandemic’ of a different sort, that of authoritarianization. With a gratuitous toll being inflicted on democracy, civil liberties, fundamental freedoms, healthcare ethics, and human dignity, this has the potential to unleash humanitarian crises no less devastating than COVID-19 in the long run.
VisImages: A Corpus of Images from Visualization Publications

Images in visualization publications contain rich information, e.g., novel visualization designs and common combinations of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Based on a refined taxonomy for visualizations in publications, the dataset includes 35,096 annotated visualizations, as well as their positions. We demonstrate the usefulness of VisImages through three use cases: 1) exploring and analyzing the evolution of visualizations with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing and recognizing visualizations in the images automatically.
Keypoint detection by wave propagation

We propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial–temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to poorly textured objects, which are hardly dealt with by current pipelines based on local invariant features. The impact on the overall algorithm of different numerical wave simulation schemes and their parameters is discussed, and a pyramidal approximation to speed-up the simulation is proposed and validated. Experiments on publicly available datasets show that the proposed algorithm offers state-of-the-art repeatability on a broad set of different images while detecting regions that can be distinctively described and robustly matched.
Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi

The purpose of the study was to classify the factors causing the decline in student achievement during the pandemic using the C4.5 datamining method. Sources of research data were obtained by conducting interviews and distributing questionnaires to 7th semester students of the 2020-2021 school year information system study program. Attributes that used in the classification of the factors causing the decline in student achievement include: Learning Method (C1), Study Time (C2), Material Understanding (C3), Giving Assignments (C4) and Environment (C5). The results of the calculation show that the Material Understanding (C3) attribute is the attribute that most influences the decline in student learning achievement. Testing was also carried out using the help of Rapidminer software and obtained an accuracy of 97.5%. Keywords : Classification, Datamining, C4.5, learning achievement, Pandemic