Reviewerly/exHarmony huggingface dataset:
Protection &amp; Privacy Embedding Blockchain Established Fraud Detection

Businesses may use Internet Banking as a means of moving forward with the help of a network of organized bank branches. Clients of financial institutions may access their properties from any of the branch or operational environments over the internet. Authenticity is the key issue with online banking. Internet databases have been hacked to the point that it is impossible to trust on the security of their data. Phishing is a kind of online fact falsification in which the fraudster intends to get sensitive information from the victim, such as their electronic wallet passwords and credit card numbers. It is a criminal hobby to employ social creating planning methods, thus it is important to note this. In order to validate a user before providing them access to protected websites, secret words are frequently utilized. Since it is so simple, there are a lot of people using mystery keys to validate their entries. Passwords from comparable consumers are likely to be the same or almost same, even if they join unique information on a single site or throughout a variety of requests. Employing our framework, various cryptographic natives, such as encryption and pixel distinguishing evidence, could be used to carry out various client verification conspiracies, which needed each character to be informed and skilled in using possible client verification instruments. If the sum and check of money are converted from the supply to the purpose, the unique identification of each coin is also swapped in order to trace its circulation. To put it simply, the suggested framework states that for every dollar our customers give up, an engaging ID may be generated. Individual customers' use of usernames and passwords has skyrocketed as online banking and commercial business models based on the internet have grown exponentially. These are also known as text-based passwords since they combine the terms “username” with “password.” As a result, it is imperative that you keep track of your password, which might be a difficult undertaking. If you are having trouble remembering your login credentials, consider using a photo as a password. Therefore, a great deal of scholars have advocated for the use of a visual password rather than a text password. Computers, ATMs, mobile phones, and other electronic devices may all benefit from the usage of pictorial passwords. Using round tolerance in the graphical password, I created the Cued Click Factor (CCF). As a result, it has been shown that the CCP with circular tolerance is superior than the CCP with square tolerance.
MADDPG: an efficient multi-agent reinforcement learning algorithm

Reinforcement learning has made great progress in solving single agent problems in recent years. However, the development of multi-agent reinforcement learning is much slower. Many existing algorithms perform not very well in the field of multi-agent reinforcement learning. To train a multi-agent reinforcement learning model efficiently, the MADDPG algorithm based on deep neural networks is proposed in this paper. The structure of the neural networks of MADDPG is based on the Actor-Critic framework, which contains centralized critic networks and decentralized actor networks. The result shows that the three agents in the experimental environment learn to cooperate and compete well in just 50 thousand episodes. Although the model of MADDPG algorithm has high computational complexity if the number of agents is too high, it can still perform well in a multi-agent environment.
Graph attention network with edge weights for question answering over knowledge graph

Question Answering over the Knowledge Graphs (KGQA) has attracted extensive attention. The graph neural network can represent the dependent information of the KG, so it is well applied to the KGQA. But most of the KGQA approaches based on graph neural networks model question sentences and candidate answer entities separately. And the influences among questions, relations, and structure are not fully utilized when learning entity representations. To solve these problems, it is proposed that a question answering method based on graph attention network with edge weight to enhance the question relevance of entity representation. For the relationship in the extracted candidate answer subgraph, the Roberta is used to calculate the question’s semantic similarity to be the edge weight. A graph attention network is relied on to fuse the pre-trained entity embeddings and edge weight information for node updates to obtain candidate answer representations. The experimental results show that our proposed model has certain advantages compared with some other benchmark methods.
Study of the Quality of the Conditions for the Implementation of Educational Activities by Educational Institutions of Higher Education in 2020 and 2021

The article presents the results of a study of the quality of the conditions for the implementation of educational activities by educational institutions of higher education in 2020 and 2021. The collection and synthesis of data within the framework of an independent assessment was carried out by order of the Ministry of Science and Higher Education of the Russian Federation in accordance with the "Unified Procedure for Calculating Indicators Characterizing the General Criteria for Assessing the Quality of the Conditions for the Provision of Services by Organizations in the Field of Culture, Health Protection, Education, Social Services and Federal Institutions medical and social expertise”, approved by order of the Ministry of Labor and Social Protection of the Russian Federation of May 31, 2018 No. 344n, taking into account the specifics of the higher education sector. The article compares the integral values of the quality criteria of the conditions for the implementation of educational activities for 2020 and 2021, changes in the values of the criteria for assessing the quality of the conditions for the implementation of educational activities are noted.
Artistic and Journalistic Program As a Means of Civil and Patriotic Education of Students

The article deals with the modern socio-cultural situation associated with the educational process in higher educational institutions. The most important goal is the formation of a citizen's personality, focused on Russian traditional spiritual and moral values.&#x0D; The concept of the development of forms and methods of extracurricular and educational work involves the creation of a projective model of the personality of a graduate with a higher education, the implementation of which, as the ultimate goal, should be guided by the entire educational process of the institute.&#x0D; Patriotism as a value largely determines the ideas contained in the President's annual messages to the Federal Assembly of the Russian Federation. The programs proposed by the President are aimed at harmonizing modern Russian society, focusing primarily on young people.&#x0D; The main task of the educational process is to carry out systematic and purposeful work on the formation of a Russian civic identity in student youth, a feeling of love for the Fatherland, responsibility for its condition and development, an active civic position, and readiness to fulfill civic duty.&#x0D; As priority values in the educational activities of the institute, the student acts as a person in his originality and uniqueness, his world in the unity of external manifestations and internal experiences, his rights and vital interests.&#x0D; Technological methods of organizing art and journalistic programs are proposed for consideration and practical implementation.&#x0D; The paper presents the specifics of the organization of an artistic and publicistic program in the context of modern innovative creative activity. The problem of civil-patriotic education of young people is highlighted as one of the most important problems of state youth policy. The art and journalistic program is presented as a bright spectacular form of cultural and leisure activities.
An Efficient Cryptography VLSI Design for Data Security

An electronic data encryption standard is known as Advanced Encryption Standard (AES). This standard is used in both software and hardware, and today it is one of the most widely used encryption techniques. Implementation of asymmetric cryptography algorithm with good security on a field-programmable gate array (FPGA). An 8-bit data channel as well as five primary blocks are included in the proposed architecture. In our project, to keep plaintext, keys, and intermediate data create two separate register banks Key Register and State Register. Shift Rows are integrated into the State Register to save space. This research work proposes an efficient block of 8-bit with four internal registers to take and send an 8-bit data to fit the Mix Column to an 8-bit data path. Advanced shared Sub-Bytes are also used for the key expansion and encryption process.
Special Session: Effective In-field Testing of Deep Neural Network Hardware Accelerators

Ongoing research to obtain high performance Deep Neural Network (DNN) executions have led to the development of customized purpose-built deep learning inference accelerators. DNN accelerators are susceptible to faults, due to high-energy particles, process variations, temperature and structural deformities manifesting as latent defects. These faults can introduce misclassification, thereby jeopardizing the Functional Safety (FuSa) of the accelerator in mission mode, which can eventuate to disastrous consequences, including loss of human lives. In this paper, we explore the impact of such faults on the FuSa of a DNN accelerator by varying the network parameters, position and characteristics of the injected fault across multiple exhaustive datasets. Furthermore, we analyze the efficiency of a software-based self test scheme to detect FuSa violations in the accelerator in mission mode, that employs functional test patterns, akin to instances in the application dataset. The test patterns, selected from the dataset of the DNN, furnish up to 100% coverage with cardinality as low as 0.1% of the entire test dataset.
Multi - Authority Based Approach for Manipulation of Encrypted Data in Cloud

In this paper, we address the protection concerns of cloud storage under the scenario where users encrypt-then-outsource data, share their outsourced data with other users, and so the service provider may be queried for searching and retrieval of encrypted data. Because the main distinctive, we propose a security approach for storage, sharing, and retrieval of encrypted data within the fully as constructed supported attribute-based encryption (ABE) thus enabling access control mechanisms over both the encrypted data and also for the data retrieval task through search access control. Efforts have studied problems around this application scenario on different fronts: efficiency, flexibility, reliability, and security. Our suggested secure Multi-authority CP-ABKS (MABKS) system addresses such limitations and minimizes the computation and storage burden on resource-limited devices in cloud systems. Additionally, the MABKS system is extended to support malicious attribute authority tracing and attribute update. proposed a practical CP-ABE scheme, which offers user revocation and attributes updates. We proposed an efficient and feasible MABKS system to support multiple authorities, to avoid having performance bottlenecks at one point in cloud systems. Furthermore the presented MABKS system allows us to trace malicious.
Vavilov’s Life Activity Analysis As an Element of Patriotic Education Among Students of Saratov State Agrarian University

The relevance of this article is due to the consideration of the problem of the formation patriotic education of students, especially in those universities where the export of educational services is actively developing. The results of the study value orientations of student youth allow us to conclude that it seems justified and significant to include in the formation of patriotic education of students the Vavilov’s life activity study, whose name is the Saratov State Agrarian University. Active forms of education, joint extracurricular activities of the university and targeted guidance from teachers contribute to solving this problem. The dynamics of the development patriotic education of students is presented on the example of the Vavilov’s life activity analysis.
Computer Vision based Automated Spare Part Finder App

The “Automated car spare part finder app” aims at building a spare part detection mobile application using image recognition technique that will assist users in finding desired spare parts and provide a list of websites where those spare parts can be found. The developed system accomplishes the goal of integrating a deep learning model with a mobile application. A YOLOv5s model was trained on a custom dataset (spanning over 8 classes), which was built from scratch due to the unavailability of a spare part dataset sample. Following the training recommendations of YOLOv5, two models were generated by varying the run parameters and the data augmentation techniques, whilst maintaining the default model architecture. A lite version of the trained model was deployed on a mobile application, implemented using Flutter. The application allows users to select an image and run predictions on the selected image to identify and classify the part if present. Following detection, the application prompted users to input the make and model of the vehicle which would then be coupled with the predicted part name to search the web and generate a list of spare part suppliers for the specified part. This was achieved by using a web scrapper also implemented in Flutter. The resulting system, has an accuracy percentage of around 80% and has accomplished all requirements set.
Cutting tool wear detection method based on fusion of ELM and wavelet packet decomposition

Aimed at the puzzle that is difficult to detect the cutting tool wear during machining process, the paper discussed a method of tool wear detection based on fusion between Wavelet packet decomposition and extreme learning machine (ELM). In this paper, it firstly analysed the time-frequency correlated characteristics in sound signal of cutting tool, an then based on wavelet packet decomposition it explored the extraction method of statistical feature for cutting tool status-sensitive spectrum energy, and finally based on sound feature recognition, it constructed a sort of fast ELM detection model. Taken the sound signal identification of cutting wear as example in some operation site, the data of actual measuring in work site verified that the explored method above could get faster response speed and higher detection accuracy than other traditionally used methods. The experimental simulation results show that the discussed method is effective and reasonable.
Exploiting spatiotemporal inconsistencies to detect deepfake videos in the wild

Cyberspace is an emerging battlefield and deepfakes are being constantly weaponized by malicious actors. With rapid advancements in media synthesis technologies, detecting deepfakes is becoming increasingly difficult. The following paper presents a unified approach focusing on the fusion of Convolutional Neural Networks and Long Short Term Memory Networks for spatial and temporal analysis of deepfake videos. This study compares the performance of the most prevalent and frequently used deepfake detection methods- convolutional neural networks (CNN) and convolutional neural networks combined with long-short term memory networks (CNN-LSTM) with our architecture on a combined dataset consisting of videos from Face Forensics++ and Deepfake Detection Challenge Dataset, which consists of multiple types of manipulated media- Deepfakes, Faceswaps, Neural Textures, Face Shifter and Face2Face. We find that our architecture provides a 2.5% increase in detection accuracy over the most frequently used current deepfake detection method (CNN-LSTM).
Impacts of the Covid-19 Pandemic on the Educational Process: Use of Innovative Educational Technologies

The Covid-19 pandemic affected all aspects of life. Individual countries are struggling with the effects of the pandemic. The pandemic has resulted in significant global social and economic disruption , including the largest global recession since the Great Depression . Many countries are introducing lockdowns; that is, individual institutions, restaurants, factories, and schools are closed; people’s stays are restricted (thus eliminating physical contact); and many preventive measures are introduced. Due to the Covid-19 pandemic, individual educational institutions were forced to switch from full-time to distance learning. As part of distance education, individual institutions had to react promptly and adapt their educational process. This chapter compares students’ approach to study before and during a pandemic and analyzes in more detail the method of distance learning during a pandemic. The research analyzes the forms and content of teaching, especially the use of innovative educational technologies and methods and ways of communication between teachers and students, and compares teaching at the beginning of the Covid-19 pandemic and during its second wave.
English Translation Recognition Model Based on Optimized GLR Algorithm

In the current era of information technology, people’s requirements for English translation are gradually increasing, and the need for a computer to understand and translate English language is becoming more urgent. In order to accurately identify phrases, this paper proposes an English translation recognition model based on optimized GLR algorithm, which can improve the accuracy of recognition by locating phrases in the text.
Classification of Desk Workers’ Behaviors for a Service Robot

Currently, many people are working their works at a desk at an office or their homes, and work is the one problem that makes humans stressed. A service robot is the one type of robot created to interact with a customer, and the service robot can be a friend while we are working. It can know a customer’s behavior and make an action intimately, and it will help us reduce the stress. Therefore, this research aims at customer behavior classification while working at a desk for a service robot. This research method consists of 3 steps that are 1) skeleton feature extraction, 2) data processing, and 3) behavior classification. We have first gotten the human skeleton information on an input RGB image using the OpenPose framework. Then, we have calculated the vectors and angles of the skeleton information to be the input features of a model. Finally, we used the input features from the previous step to classify human behavior using the Deep Learning algorithm. This research method can predict customer behavior with the F1 score of each behavior is above 0.95.
Uniform Distribution of Multiple Features for Remote Sensing Optical Image Matching

Uniform distribution of selected features is one of the important factors in optical image matching. In this paper, an effective scheme is proposed to obtain a uniform distribution of the extracted features. At first, two different types of operators namely scale invariant feature transform (SIFT) and multi-scale Harris detector are employed to extract initial features from the optical images. Then, the well distributed and robust features are selected from these initial features and these are considered as final features. After that, the input optical images are matched through the feature matching process by using the selected features. The proposed scheme can obtain better distribution of matching pairs than the state-of-the-art methods. Experiments on three sets of remote sensing optical image pairs verify the performance of the developed algorithm.
Modeling Guided Inquiry and School Librarian Instructional Partnerships to Pre-Service Teachers Through Digital Video Production

The last ten years in school library research reflect an expanded definition of information literacy along with a stronger emphasis on in-depth information literacy development, concluding that a fundamental shift in instruction provided by school librarians is needed; one that not only helps students find information, but develops students’ abilities to interact with, and learn from information, engaging with it in critical ways. Collaboratively designed and implemented through an instructional partnership between the school librarian and a classroom teacher, Guided Inquiry instruction helps students gain meaningful understanding and develop a personal perspective by exploring, comparing, and contrasting multiple information sources. Despite the frequently touted benefits of instructional partnerships between school librarians and classroom teachers, these structures are rarely, if ever, modeled by school library and pre-service teacher educators. This study examined the process and challenges inherit in designing and modeling Guided Inquiry units of instruction, through a school librarian instructional partnership model, in pre-service teacher education, exploring its impact on teacher candidate willingness to identify school librarians as co-teachers. Findings from the present study indicate Guided Inquiry units co-taught by school library educators and teacher educators help teacher candidates both successfully navigate the research process and develop a mental model of the school librarian as a co-teacher.
Wind Load Calculation Acting on PV Plant with Solar Tracking System

The article presents an overview of photovoltaic plant design software. The considered programs do not allow to analyze the value of the wind load on a photovoltaic plant. This aspect must be taken into account when designing photovoltaic plants with solar tracking systems. A computational 3D model of the photovoltaic plant with a dual-axis solar tracking system of the Orenburg State University has been developed in the SolidWorks. An analysis of the wind loads on a plant has been carried out at wind speeds of 32 m/s, 7.9 m/s and at various inclination angles of photovoltaic modules. Revealed that the photovoltaic plant will be under significant wind load at a wind speed of 32 m/s and inclination angle <tex>$\beta=45^{\circ}$</tex> and <tex>$\beta=79^{\circ}$</tex>. Long-term operation of the photovoltaic plant under these conditions will lead to a significant reduction in service life. In this regard, the control system must be equipped with protection of the photovoltaic plant from wind loads.
Emotion Recognition in Tweets Using Multi-Modal Data

People on an average tend to express their emotions and opinions farmore freely in social media community than anywhere else. Understanding human emotional states is necessary to improve Human computer interaction (HCI). Emotion classification systems use unsupervised, supervised or hybrid learning techniques to classify emotions expressed in text, images etc. In this paper, we propose a fuzzy-rule based unsupervised learning technique for textual data and a late fusion model formed by fusion of objects and places based Convolutional Neural Network (CNN) classifiers for images to identify the emotions associated with social media posts. The proposed fuzzy system integrates Word Sense Disambiguation and Natural language processing using a novel rule base to identify emotions with their intensity. The images are annotated and its relevance with respect to the related tweet is checked by calculating the cosine similarity between them.