Reviewerly/exHarmony huggingface dataset:
Mentoring in Education Using Digital Tools

The article analyzes the phenomenon of mentoring undergraduates in the project educational activities of bachelors using digital tools as the most effective, communicative, practice-oriented experience. The methodology is based on the coaching approach and the principle of mentoring in the process of horizontal peer-to-peer training between students of different levels of training in the same direction and the use of various digital tools (kanban board, Jamboard, chatbots, mind mapping, etc.) for the organization of productive remote interaction
BIT EXCHANGE: A Cryptocurrency Exchange System Based On Blockchain

The paper explains how blockchain functions, addresses these weaknesses, and some outstanding issues. The specifications and assurances of cryptocurrency blockchains don't meet FinTech's demands for security and privacy, from transaction throughput to primitives. It examines how distributed databases are protected and offers a Blockchain- based solution to the problems associated with maintaining information secrecy in them. In this paper, the issue of cryptographic consensus is addressed using blockchain technology. And if there is a way to guarantee that financial activity and transaction activities are saved in a specific database without the participation of the central authority. It shows use scenarios where blockchain may be utilised and examines the key technology aspects and designs of blockchain. API that may be used in the future to advance the development of blockchain-related apps. The coin on this blockchain will be built from the ground up utilising APls. The main goal of this paper is to provide a decentralised blockchain wallet where you can access your money. The paper involves building a generic blockchain.
TripletPower: Deep-Learning Side-Channel Attacks over Few Traces

Deep learning has been utilized as a promising technique in side-channel attacks. However, to recover keys successfully, deep-learning side-channel attacks often require thousands of training traces, which could be challenging for an attacker to obtain in the real world. This paper proposes a new deep-learning side-channel attack which only requires hundreds of training traces. Our proposed method, referred to as TripletPower, trains a triplet network, which learns a robust embedding for side-channel attacks with few traces. We demonstrate the advantage of our method in profiling attacks over power traces collected from AVR XMEGA and ARM STM32 microcontrollers using ChipWhisperer. Specffically, experimental results show that our method only needs as low as 250 training traces to train a classffier successfully recovering keys of unmasked AES on XMEGA (or STM32) while a Convolutional Neural Network needs at least 4,000 training traces in profiling attacks. In addition, we extend our method to non-profiling attacks with on-the-fly labeling. Experimental results suggest that our method can effectively recover keys of unmasked AES on XMEGA with only 525 unlabeled power traces in non-profiling attacks. Our method is also effective over power traces collected from masked AES and traces generated with random delay.
Communication Strategies for Involving the Digital Generation in Scientific Activities

The relevance of the topic is due to the following contradiction. According to comparative international studies there is a decrease in the interest of young people in scientific activity in a number of developed countries. In this regard Russia is an exception since young Russians relate more loyal to science than their peers, for example, in Europe. At the same time, the problem of «depreciation» of scientific personnel in Russia has not been resolved. Efforts are being made to transform high-tech industries, but not all relevant science schools are replenished with promising young people, as representatives of the digital generation prefer to choose more dynamic and open areas of application of their abilities. Against the background of this contradiction, the authors put forward hypothesize about the insufficient involvement of engineering students in scientific research. Basing on the concept of “student involvement”, the consideration of this topic from a sociological point of view allows measuring the level of interest of students in scientific activities and evaluating the effectiveness of measures aimed at increasing the involvement of young people in science. The concept of “student involvement” establishes the relationship between the institutional, including the communication, environment of the university, as well as the behavior of the student and the results of his educational and scientific activities. To develop the research methodology, the authors relied on the concept of the influence of the university on the student (E. Pascarella, P. Terenzini); the theory of the determinacy of student success by its integration into various areas of university life (V. Tinto). To clarify the concept of “scientific communication”, the authors turned to the conceptual model, which differentiating channels of society's access to scientific information (M. Bucchi, B. Trench). The purpose of the study was to determine the degree of student involvement in the scientific activities of the university and to analyze the conditions for the formation of communication strategies that promote involvement. The following results are obtained. First, although students consider science as a significant activity, they do not realize it as a model of their professional identity. Secondly, although science is of interest to many respondents, it remains at the level of ideas, not actions. Desirable career scenarios are not related to scientific activities but to work in business. Students are involved in scientific activities only at the level of the educational process, although they would like to take part in scientific events. Respondents see an obstacle to this in their own lack of confidence in their scientific and technical training. Thirdly, respondents' satisfaction with scientific communications at the university is not uniform. During the research there were identified communication strategies that both arouse interest in science and form in students a sense of progress in mastering research competencies.
Recognition Of Fes Using A Novel Detection Method Based On Convolutional Neural Networks

Currently, the wireless access method utilized by systems is 5G technology. In this study, a lightweight NN for emotion recognition is proposed, where a visual system can be incorporated into devices with a low computational capability to perform face expression classification while simultaneously minimizing the amount of parameters utilized in the classification process. To improve the reliability of the results, it is helpful to package the models as a service that other collaborating systems can use. Face expression resource development frameworks can be tailored to address the requirements of different security models, such as criminal threats, government secrets, etc. This model will have the opposite influence on detection and classification and produces good detection results by identifying photographs inside the dataset, which indicates that the model built into this research is suitable for multi- categorization of facial emotions. In general, comprehended a visual system that can be incorporated on devices with minimal computer capacity to attain result classification and decrease many factors. Following comparison with the algo in recent years, the accuracy of our algo will be higher than theirs, and it has achieved good detection results in photographs inside the dataset after the experimental results.
Detecting Implicit Indeterminates in Symbolic Computation

In the design of symbolic mathematical computation systems, it is a popular choice to use the same syntax for both mathematical indeterminates and programming variables. While mathematical indeterminates are to be used without specific values, programming variables must be initialized before being used in expressions. A problem occurs when mistakenly uninitialized programming variables are silently taken to be mathematical indeterminates. This article explores how this problem can arise and its consequences. An algorithm to analyze programs for this defect is shown along with a Maple implementation.
Hardware Implementation of Two Way Sign Language Conversion System

Hearing disabilities affect people all over the world. Nearly 360 million people have a disabling hearing loss. Due to birth abnormalities, accidents, and oral diseases, the number of people who are deaf and dumb has dramatically increased in recent years. The objective of our project is to form a bridge between the hearing-impaired community and the hearing community; to further commence a two-way communication. A two-way sign language translator that converts Voice to sign language and vice versa in real time is proposed using Convolution Neural Network (CNN) model and raspberry pi module. The model provides 99% accuracy.
Homomorphic Encryption and Prim's Process for Multi-Keyword Exploration in Encrypted Cloud Data for Trades

Due to its low administration costs and convenient accessibility, cloud storage is quickly becoming the norm in today's online infrastructure. By letting cloud users save their massive data in the cloud, the cloud storage system offers crucial advantages. Improving cloud storage's performance at data retrieval requires addressing concerns about data privacy and security. This study presents a new algorithm for bolstering security, called Modified Ring-based Fully Homomorphic Encryption and Prim's (MRFHE-P). The ranking function determines how the index is built for keywords and files. Prim's algorithm generates the dynamic tree based on the ordered keywords. The document is encrypted using a variant of the RSA technique called the Modified Ring-Based Fully Homomorphic Encryption Algorithm (MRFHEP). The encrypted files are indexed in a Two-way Hash Table so that users can quickly get the necessary data. Afterwards, the cloud user uses a shared symmetric key and private key to decrypt the encrypted data. The experimental results show that the suggested technique significantly reduces the time needed to encrypt and decrypt data compared to the current standard. The proposed technique also speeds up indexing and searches for a variety of documents. There was also a decrease in the amount of time spent searching and indexing, and an increase in the efficiency with which keywords could be extracted.
Analyzing Encrypted Cloud Data With Multiple Keywords Productively For Factories

Cloud storage is quickly becoming the norm in today's online infrastructure because of the low administration costs and convenient accessibility. The cloud storage system offers crucial advantages by letting cloud users keep their massive amounts of data in the cloud. Better performance during data retrieval necessitates attending to the privacy and security of data stored in the cloud. The necessary information can be obtained from the cloud using the current improved semantic feature abstraction method E-TFIDF (Enhanced-Term Frequency-Inverse Document Frequency). There was still no solution for the problem of secure cloud-based data access. Current method SEDFS (Searchable Encrypted Data File Sharing) system applied along with limited and precise keyword quest; indexing procedure necessary for improvement. The research focuses on SHE (Secured Hybrid Search), a method for conducting encrypted multi-keyword searches on cloud platforms. Using a category-based method, an index list is built for each document depending on its classification. Clearly, when compared to both the current system and the adaptively secure SSE technique, it has been found that the proposed approach significantly improves encryption and decryption throughput in the shortest possible time. In addition, the suggested algorithm shortens the amount of time required to index and conduct a search on a variety of documents. There was also a decrease in the amount of time spent searching and indexing, and an increase in the efficiency with which keywords could be extracted.
An automatic exposure imaging and enhanced display method of line scan camera for X-ray defect image of solid rocket engine shell

During the long-term operation of solid rockets and equipment welding, some invisible defects will appear inevitably. These defective work pieces will bring major safety hazards during service. Based on the designed imaging device, this paper proposed two new automatic-exposure imaging methods suitable for line scan cameras. An efficient feature statistical welding positioning method was designed to ensure the best imaging quality of the effective welding area. To improve the identification of defect information, a new adaptive high-grayscale pseudo-color enhancement algorithm, dynamically updating the number of quantized colors, was designed. The experimental results show that the automatic exposure algorithm of the line scan camera not only can effectively achieve high-quality imaging of X-ray film, but also can greatly shorten the scanning time and improve the scanning efficiency of the X-ray film. The proposed algorithm has been well applied in the high-definition X-ray film digitization equipment developed.
Content based Image Search Engine with Grey Wolf Optimized Neural Network

The systems with Content-based image retrieval (CBIR) approach enable the search and retrieval of images that are comparable to a particular query image by utilizing attributes that indicate the content visualization of such images. The present study created a mechanism for recovering images indexed in databases based on their visual information without the necessity for linguistic annotations. Feature vectors were obtained from the hybrid of DWT and LBP visual contents. The classification is achieved by using Grey Wolf optimized Neural Network (GWO-NN) technique. The proposed strategy yielded 95.59% accurate findings on publicly available databases.
On the bases of Z<sup>n</sup> lattice

The ${\mathbb{Z}^n}$ lattice is the lattice generated by the set of all orthogonal unit integer vectors. Since it has an orthonormal basis, the shortest vector problem and the closest vector problem are easy to solve in this particular lattice. But, these problems are hard to solve when we consider a rotation of ${\mathbb{Z}^n}$ lattice. In-fact, even though it is known that the ${\mathbb{Z}^n}$-isomorphism problem is in NP ∩ Co-NP, we still don’t have an efficient algorithm to solve it. Motivated by the above, in this paper we investigate the properties of the bases of ${\mathbb{Z}^n}$ lattice which are the sets of column/row vectors of unimodular matrices. We show that an integer primitive vector of norm strictly greater than 1 can be extended to a unimodular matrix U such that the remaining vectors have norm strictly smaller than the initial primitive vector. We also show a reduction from SVP in any lattice isomorphic to ${\mathbb{Z}^n}$ to SVP in n − 1 dimensional sublattice of ${\mathbb{Z}^n}$. We define two new classes of lattice bases and show certain results related to ${\mathbb{Z}^n}$ bases. Finally, we study the relation between any solution to Successive Minima Problem and the set of Voronoi relevant vectors and present some bounds related to the compact bases of ${\mathbb{Z}^n}$.
“JARVIS” - AI Voice Assistant

Developing a personal assistant is the main goal of the research, especially for Windows-based operating systems. JARVIS is an idea inspired by real-life virtual assistants such as her Cortana and Siri for their respective Windows and iOS platforms that perform various tasks from the command line and create a user-friendly interface. This software is a synchronous process that uses and analyses human- dependent artificial language as the basis for speech recognition and processing. It can perform various tasks and serve end-users according to their requirements. The purpose of most of the wizards developed was to customize automation to perform a variety of specific tasks, from sending emails to managing tasks. With the development of technology that has dramatically increased human dependence on computers, people and users want their devices to be simpler and more convenient, and even text-to-speech in providing commands and input. There was a transition. This change marked the beginning of speech recognition. This has created a need to not only get input by voice, but also perform tasks and commands via speech recognition. This was the idea behind the personal voice assistant.
SEF-VSOD: A Static Ensemble Framework For Video Saliency Using Modified Doubly U-Net

In this digital age, video and images are commonly used to process information. Although an image or video contains a lot of information, not all of it is useful. One of the key areas of computer vision is salient object detection, whose major goal is to replicate the human visual system and recognize the scene's most prominent object. The salient object detection techniques have a significant impact on a wide range of applications, including video summarization, automated cropping, etc. Deep learning approaches have recently become more common in the saliency detection problem. However, it is highly challenging for a single model to perform well in all circumstances due to the different constraints in the model creation process (training, parameter adjustment). To address this issue, the author(s) present a static ensemble framework for estimating video saliency. This ensemble approach aided in model performance improvement. The proposed model's performance is evaluated using two well-known publicly available video datasets, namely ViSal and DAVSOD-Easy. Based on the various quantitative evaluation parameters the proposed model performance is compared with the state-of- the-art SOD models.
Fact-checking with explanations

We present an approach for automated fact-checking, given a trusted knowledge base and a natural language text. The FACE (FAct Checker with Explanations) system is capable of extracting the knowledge behind the sentences, and decide what is entailed in the trusted sources and what is in conflict with them, providing also explanations and counter speeches in English. The system also specifies the provenance of each of its argument, thus it can be traced back to the source of the information.Description logic representation of the input is obtained using the FRED machine reader, which is further improved by detecting and handling translation patterns. The obtained ontology is aligned to the knowledge base using the WordNet database in a custom algorithm, then entailment and conflict detection is performed with the Hermit reasoner, through which we obtain the explanations and counter speeches which are verbalized to Attempto Controlled English.The fact checker is demonstrated on Covid-19 related sentences, however it is domain independent, and can be used with other knowledge bases as well.
W-Net Plus: Dnn For Spatial Saliency Prediction In Videos

Deep neural networks (DNNs) have been employed recently to identify saliency in images and videos. Different models have been suggested to predict spatiotemporal as well as temporal and spatial saliency. In this study, a model called W-net Plus is proposed for predicting spatial saliency in videos. The newly proposed model not only translates information from the encoder to the decoder but also from the decoder to the encoder, and it is inspired by U -net architecture. The proposed model has been evaluated by the author(s) on various data sets, and they have obtained noteworthy findings for the F - Max, structural similarity (S-measure), and Mean Absolute Error (MAE) measurement parameters, demonstrating its effectiveness and survivability. No stage of the training involves the utilization of transfer learning in the work.
Prediction of Breast Cancer Risk Using Microarrays and Deep Learning

More than 1.15 million new instances of breast cancer are identified each year. In the clinic, only a few reliable prognostic and predictive indicators are utilized to make decisions about the treatment of breast cancer patients. The mortality rate of breast cancer patients may be lowered and their survival time extended by early identification. Analysis and processing of Microarray images, the principal test used for screening and early diagnosis, are the keys to improving breast cancer prognosis and are at the heart of this study. The Fuzzy C-means (FCM) approach is used for image segmentation in microarray for the detection of breast cancer. After features are retrieved from the segmented areas and the system is fully trained, the efficient classifier is used to assign microarrays to their respective classes. Techniques such as Multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), and Gray-level Co-occurrence Matrix (GLCM) are used to extract texture information. Differentiating masses and microarray image calcifications from the surrounding tissue is achieved with the aid of morphological operators and the classification of these features is handled by the Deep Convolution Neural Network (DCNN) algorithm. In a microarray, the tumor’s borders are highlighted and exhibited to the doctor, who may then assess the extent of the growth.
Product Authentication System using Blockchain<sup>*</sup>

Every well-known business has scammers who sell counterfeit goods at reduced prices. Due to a lack of transparency, supply chain management has frequently encountered issues such as service redundancy, poor departmental collaboration, and a compromise of standards. Counterfeiters in the market generate major challenges for legitimate businesses. Still, a significant number of individuals are unaware of the greatest extent of the harm that these products have on brands. As a result, it is essential to have a system that allows the end user to verify all details about the products purchased for the customer to determine the product’s authenticity. Combining these features with blockchain-based technology can create a coherent, effective counterfeit-reduction strategy.
Growing Threats Of Cyber Security: Protecting Yourself In A Digital World

Cybersecurity has become a key concern for organizations and individuals worldwide. The interconnected nature of the digital world, combined with the continued development of new technologies, has created a landscape rife with potential threats. This paper examines the nature of cyber security threats in various fields, namely the Internet of Things (IoT), blockchain, healthcare, government, and finance. It discusses the various security vulnerabilities related to these technologies and industries and provides an overview of the measures that can be taken to mitigate the associated risks. The paper looks at the role and importance of cyber security in the current digital age and the need for increased vigilance to protect against malicious actors. Along with an overview, this paper aims to focus on cyber security threats in the field of blockchain technology, more significantly on crypto transactions. The proposed ETVS is an effectual transaction verification structure that acts as an additional protection to the transactions.