Reviewerly/exHarmony huggingface dataset:
The System of Organization of Volunteer Activity in a Pedagogical University

The article is devoted to the study of the organization of volunteer activities in a pedagogical university as a socially significant phenomenon. The authors considered the content of the concepts “volunteer activity”, “educational space”. The features of the organization of volunteer activities in a pedagogical university are highlighted. It is determined that volunteering is one of the important factors in the formation of the civil and personal position of students in today’s complex world. The development of the volunteer movement, the process of its institutionalization, shows its scale, focus on various spheres of human life, and is of great interest for research. The system of organizing volunteer activities in a pedagogical university has a number of features related to interpersonal communication of student youth, professional orientation and preparation for independent living. Volunteer activity carried out at the Pedagogical University acts as a kind of connecting link between theory and practice, since the inclusion of future teachers in it contributes to the development of an active professional position and the development of professional values in practice. The authors conducted a survey of 3-4 year students in the amount of 120 people from February 5, 2022 to March 10, 2022 in order to study the motivation and involvement of young people in voluntary work in the name of the common good. The accumulation and analysis of the data obtained led to the conclusion that volunteering is attractive to students, as it allows solving certain social issues.
Blockchain for IoT and Big Data Applications: A Comprehensive Survey on Security Issues

Today’s world is full of new technologies and advancements in every field. With more unique, advanced technologies coming up daily in the modern world, along with the ease of living comes narrowing of the privacy of an individual. With more technologies coming up, taking more information about an individual, the privacy of the individual is get violated and, thus, brings a threat to the security of the individual too. In this chapter, two such modern technologies that have now become an inevitable part of the human life are discussed along with what security measures they lack, why or how they lack these security measures, and what can be done to secure these. But before going to their security aspects, let’s first know something about the technologies like what role do these play in society and what is their importance in daily human lives.
Image Forgery Detection Techniques: A Brief Review

As the digital camera came into the picture, it is very common practice to capture the image and share it with the help of the internet. Even nowadays documents are also shared with the same methods, and there comes the need for security. As the research says that most people are not able to discriminate the real and fake images or edited documents, therefore the chances of forgery improved. In this digital era, the system will arrange tools or weapon in the form of software and make this forgery easy and cheap. However, there are also various techniques and software developed by researchers from all over the world to counter this forgery. In this present paper, the process of image forgery will be highlighted and their various types are discussed. Numerous detection techniques are also conferred in this presented paper.
Improving the performance of deep learning-based classification when a sample has various appearances

In the general case, the performance of deep learning-based classification models depends on the ability of capturing features. When a sample has various appearances, the increased features may lower the performance of these models. In this case, training more models on different appearances can be a choice to improve the accuracy. In this paper, we built a new framework that generates a network of models to improve the accuracy. First, our framework built a strategy to increase the number of models to well capture the increased features. We then utilise our recursive Bayesian methods on the selected outputs of trained models, which is to reduce the similarity among these outputs for higher accuracy. The experimental results show that our framework can be a good choice to improve the performance of deep learning applications.
Internet Banking Safety Framework

The internet is part and parcel of our everyday lives. Internet banking is playing a major role. But the risk is always there: malpractitioners always ready with their unethical and illegal ways of making crimes. Internet banking is the updated medium of communication to serve customers. Recently, the use of the internet in the banking sector of Bangladesh has increased rapidly. Through the internet banking system, banks have made a consequential growth and the number of frauds also has increased. Like other countries, Bangladesh is facing security problems. It is not totally free form risk and fraudulent activities. This chapter unveils internet banking crimes methodology and the limitations of existing security systems. To mitigate the internet security problems, manifold-layered security systems should be ensured among the clients and the banks. The internet banking security structure and its manifold-layered security systems have been developed and evaluated through an expert evaluation method.
Multi-size drone detection using YOLOv5 network

With increasingly modern technology, advanced and flexible functions, compact design, and low cost, drones are recently used in many fields with different effective purposes. Unlike beneficial applications, hostile forces leverage drones to explore the terrain, carry illegal explosives, and so on. Those applications can seriously threaten national security and defense. To prevent illegal drones effectively, we apply deep neural networks to detect the illegal drones in a variety of conditions and different sizes of drones. Accordingly, a computer-based system using modern cameras combined with an algorithmic model can solve the complex drone detection problem. Therefore, an emerging complex neural network approach based on YOLOv5 is proposed in this paper. With the method, we achieve a very expected result (confidence of 0.993 @0.5IOU), which meets the requirements of the drone detection problem.
Blockchain Application in Digital Platform FieldWork 4 RES used for Planning and Realization of Renewable Energy Sources Projects

Considering the global warming and negative effects of greenhouse gasses on the Earth’s atmosphere, renewable energy sources (RES) are of key importance for the planet’s sustainability. In a view of the EU’s 2050 long-term strategy, it is expected that the RES capacities will continue to rise significantly, as one of the main goals of the strategy is to fully decarbonize Europe’s energy supply. This paper describes a FieldWork 4 RES system intended for providing the application support for planning and realization of RES projects, from their infrastructure construction until asset management and maintenance. The main objective of the FieldWork 4 RES platform is to tackle the problems of legal inconsistencies and uncertainties, administrative complexities, and a long journey from idea to execution when dealing with RES projects. FieldWork 4 RES system uses Common Data Environment (CDE) concept and is based upon a clearly defined catalog for construction and maintenance of RES power plants. The system is designed as a support application for monitoring and managing existing business processes which consist of multiple modules. That ensures adaptability to each business scenario and different types of users from RES developers to contractors and project offices.
Decision Support System based on Application of the Second Generation Neural Network

The issue of the feasibility of using existing statistical and hydrological methods for short-term and early forecasting in the framework of forecasting the levels of water rise in water bodies is considered: a comparative review is given, which describes their advantages and disadvantages. In the course of analyzing the shortcomings of these methods, the problem of operational and early (advance) forecasting of water rise levels was identified. To solve this problem, a decision support system is proposed for predicting the water rise levels in advance, based on a neural network (intelligent) analysis of retrospective data (date, water level, air temperature, atmospheric pressure and wind speed) to calculate the water level values for 5 days in advance. The artificial neural network itself is based on the freely distributed library of machine learning programs "TensorFlow", and a modified backpropagation method is used as training, the main difference of which is an increase in the learning rate of an artificial neural network. The results of the analysis of the effectiveness showed that the proposed decision support system is more accurate (the error between the real and calculated values does not exceed 2.10 %), compared to existing common methods/systems (8.36 %). This will allow to give the necessary time to special services for the implementation of flood control measures to prepare for the protection of technical facilities of enterprises.
Exploring electronic lab notebooks (ELNs) at a R1 institution in the Southeast USA

Purpose This study aims to build a better understanding of researcher needs regarding support for data that you create, store, and/or manage using an electronic lab notebook (ELN), also referred to as electronic research notebook (ERN). The study also articulates the need for risk assessment for ELN products used by researchers for both open data and sensitive data that require standards. Design/methodology/approach The author used a participatory action research mixed-methods approach. A working group was formed from an ELN initial meeting. The working group team investigated several institutional ERN solutions by setting up trials, speaking with representatives from other research universities with ERN solutions and conducting internal and external research. This culminated in a broader-scale survey exploration. Findings Findings reveal there is no single institutional ELN license solution to satisfy all scientific disciplines. There is a need to develop foundational tools needed by all, provide additional tools and uses cases with best practices that can be tailored to various labs and research processes and develop a how-to guide on how to assemble the parts to create a useful ELN solution. Research limitations/implications The research implications include providing support for researchers selecting an ERN solution through a combination of online guides, short tutorials and training. There is a need to develop foundational tools, uses cases with best practices that can be tailored to various labs and research processes and how-to guide on how to assemble the parts to create a useful hybrid-ELN solution. Practical implications Practical implications include aligning available ERN solutions with other institution provided technologies across the research life cycle to provide researchers a suite of tools to conduct and manage their research. Further investigating educational license discounts for courses using eLabJournal, RSpace, Protocols.io, Open Science Framework, LabArchives or other ERNs currently funded by student course fees via grant funded projects are key implications. Social implications Social implications include the research computing environments of researchers that use ELN solutions approved through institutional risk assessment for open data are in compliance with university regulatory frameworks for use of the software in research. Originality/value The originality of this study includes risk assessments of ELNs solutions to better guide researchers in the selection process. To the best of the author’s knowledge, this survey was the first exploration of ELN on campus resulting in a final report to senior stakeholders. This study also highlights a developing grant proposal to further develop support across labs and campus.
Formation of Innovative Potential of youth by Means Design Laboratory

The formation of innovative human potential is the key to the development of a competitive and sustainable economy. The problem of the formation of the innovative potential of young people, in particular, is solved by higher educational institutions that train highly qualified specialists with specified competencies. The experience of the Penza State Technological University (hereinafter – PenzGTU) shows the effectiveness of the development of an innovator student through design laboratories as a well-founded, purposeful and practice-oriented innovation of the educational environment. Within the framework of the project laboratory, various forms can be used: conducting an intensive training course, interacting with mentors and consultants directly or indirectly, using electronic assistants, holding plenary sessions, creative workshops and project competitions. The purpose of the article is to present the experience of organizing and conducting a project laboratory in Penza State Technical University as a condition for increasing the social activity of young people through active involvement in innovative project activities. Methodology and methods of research. The study was conducted on the basis of an environmental approach. The environmental approach is understood as a set of modern opportunities that allow to form the creative and scientific-innovative potential of young people, to produce a socially significant product and, as a result, to cause a high degree of social activity. The analyzed sources were the works of Yu. S. Manuylov, E. S. Polat, V. V. Pasechnik, V. D. Simonenko, V. A. Yasvin and others. Research methods: analysis and synthesis of pedagogical and psychological literature, practical experience. Conclusion. In conclusion, quantitative and qualitative indicators of the project laboratory activity and its impact on the formation of the innovative potential of young people are described.
PENDAMPINGAN PELAKSANAAN PEMBELAJARAN BERBASIS BAHASA IBU DI TKK OLAEWA KECAMATAN BOAWAE KABUPATEN NAGEKEO

It has been identified that children use their mother tongue in their daily lives. This is not surprising because mother tongue is the first language that is known and used by children to communicate. This intense communication using the mother tongue causes children to have difficulty understanding the material when participating in the learning process at school. Because the language of instruction used in schools is Indonesian. This problem is a serious concern for schools so that schools can use their mother tongue as the language of instruction in order to help children more easily understand the material presented by the teacher. The purpose of this service activity is to assist teachers in the implementation of mother tongue-based learning. The method of implementing the service is through monitoring and evaluation method, observation, and face-to-face discussions. This service activity was carried out at Olaewa Kindergarten as one of the target schools for using mother tongue in learning activities. The results of the mentoring activities are that children are more actively involved in playing and learning activities and create learning situations that are interactive, fun and stimulate learning motivation for children.
Fine-grained and fair identity authentication scheme for mobile networks based on blockchain

With the popularity of the internet, users hope to better protect their privacy while obtaining network services. However, in the traditional centralized authentication scheme, identity information such as the user's private key is generated, stored, and managed by the network operator. Users can't control their identity information, which will lead to a great threat to the privacy of users. Based on redactable blockchain, we propose a fine-grained and fair identity authentication scheme for mobile networks. In our proposed scheme, the user's identity information is generated and controlled by the users. We first propose a notion of score chameleon hash (SCH), which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services. We propose another notion of self-updating secret sharing (SUSS), which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor. Experimental results show that, compared with the immutable blockchain Bit-coin, the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions, and can be adopted with a small additional overhead.
How to buy/own/mint one milliliter of the ocean from the South China sea

opinion Share on How to buy/own/mint one milliliter of the ocean from the South China sea Authors: Winnie Soon Aarhus University Aarhus UniversitySearch about this author , Tzu Tung Lee Massachusetts Institute of Technology Massachusetts Institute of TechnologySearch about this author Authors Info & Claims InteractionsVolume 29Issue 4July - August 2022 pp 10–13https://doi.org/10.1145/3543624Online:29 June 2022Publication History 0citation213DownloadsMetricsTotal Citations0Total Downloads213Last 12 Months213Last 6 weeks30 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Short Answer Detection for Open Questions: A Sequence Labeling Approach with Deep Learning Models

Evaluating the response to open questions is a complex process since it requires prior knowledge of a specific topic and language. The computational challenge is to analyze the text by learning from a set of correct examples to train a model and then predict unseen cases. Thus, we will be able to capture patterns that characterize answers to open questions. In this work, we used a sequence labeling and deep learning approach to detect if a text segment corresponds to the answer to an open question. We focused our efforts on analyzing the general objective of a thesis according to three methodological questions: Q1: What will be done? Q2: Why is it going to be done? Q3: How is it going to be done? First, we use the Beginning-Inside-Outside (BIO) format to label a corpus of targets with the help of two annotators. Subsequently, we adapted four state-of-the-art architectures to analyze the objective: Bidirectional Encoder Representations from Transformers (BERT-BETO) for Spanish, Code Switching Embeddings from Language Model (CS-ELMo), Multitask Neural Network (MTNN), and Bidirectional Long Short-Term Memory (Bi-LSTM). The results of the F-measure for detection of the answers to the three questions indicate that the BERT-BETO and CS-ELMo architecture obtained the best effectivity. The architecture that obtained the best results was BERT-BETO. BERT was the architecture that obtained more accurate results. The result of a detection analysis for Q1, Q2 and Q3 on a non-annotated corpus at the graduate and undergraduate levels is also reported. We found that for detecting the three questions, only the doctoral academic level reached 100%; that is, the doctoral objectives did contain the answer to the three questions.
Impact of the COVID-19 Pandemic on Cyber Security Issues in the Healthcare Domain

The world is facing the biggest pandemic in 100 years: COVID-19. This has led us to live in new ways like social distancing, which caused India to endorse the use of the internet and technology. This shift to greater online use has created a digital dependency that favors attackers in terms of exploiting a vulnerability in every sector. Cyber-attacks are one of the global risks in today’s digital era. Sectors like banking and retail have become victims of and been badly affected by different types of attacks. With the recent trends, healthcare has become a popular new target during the pandemic. Due to the complex IT infrastructure of healthcare organizations, the threat of cyber-attack and the resulting disruption to medical services could impact a patient’s care or may even be a threat to the patient’s life. During COVID-19, when people around the world were working from home using video conferencing software such as Zoom, Skype, and Google Meet, many accounts were compromised and sold on the dark web. While medical professionals were busy developing a vaccine for COVID-19, attackers targeted the research institutes and many healthcare organizations and demanded ransom. Due to digital innovations in the healthcare industry, it has become a new target for the theft of clinical information. The cyber-attacks have targeted healthcare organizations, particularly healthcare bodies, pharmaceutical companies, and research organizations. The healthcare sector is behind other sectors in securing medical data; hence, it is incumbent on us to focus on ensuring confidentiality and protecting patient information from unauthorized access. In the past few years, healthcare information security has gained attention from all sectors of research and business. The primary reason is the innovations in information management, big data, information security, and cyber security in the area of healthcare. This has driven healthcare organizations to face new challenges. The insights in this chapter can be helpful for managers and decision-makers to manage the organizational information security practices that enterprises faced during COVID-19.
Analyzing and de-anonymizing Bitcoin networks: An IP matching method with clustering and heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research. Since Bitcoin was created by Nakamoto in 2009, it has, to some extent, deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations. In this paper, the power-law distribution that the Bitcoin network obeys is given with mathematical proof, while traditional de-anonymous methods such as clustering fail to satisfy it. Therefore, considering the profit-oriented characteristics of Bitcoin traders in such occasion, we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT), with realtime exchange rate of Bitcoin involved. With heuristic approach used for de-anonymity, algorithm that deals with the adjacency matrix and transition probability matrix are also put forward, which then makes it possible to apply clustering to the IP matching method. Basing on the heuristic approach and additional algorithm for clustering, finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain. Experiments on IP matching method are applied to the actual data. It turns out that similar behavioral pattern between IP and transaction records are shown, which indicates the superiority of IP matching method.