Reviewerly/exHarmony huggingface dataset:
SYSTEMATIC SELECTION OF BLOCKCHAIN PLATFORMS USING FUZZY AHP-TOPSIS

Various businesses and industries such as financial, medical care management, supply chain management, data management, Internet of Things (IoT) and government supremacy, have been using blockchain technology to develop systems. During the selection of blockchain platforms, many criteria need to be taken into account depending on the organization, project and use case requirements. This study proposes a systematic selection method based on the Fuzzy AHP-TOPSIS approach which compares and selects alternative blockchain platforms against a set of selection criteria that cover both features and non-functional properties. A case study was conducted to evaluate the applicability of the proposed selection method. The proposed selection method which consists of three main stages was applied for the comparison and selection of the most appropriate blockchain platform for two projects. In the case study, three blockchain platforms were selected and ranked for each project based on selection criteria derived from the project requirements. Both project representatives showed strong agreement with the applicability aspects of the proposed selection method. It is concluded that the proposed selection criteria and selection method can be applied practically to support the decision-makers in blockchain platform selection for real-world projects.
RECOMMENDING JAVA API METHODS BASED ON PROGRAMMING TASK DESCRIPTIONS BY NOVICE PROGRAMMERS

The overwhelming number of Application Programming Interfaces (APIs) and the lexical gap between novices’ programming task descriptions in their search queries and API documentations deter novice programmers from finding suitable API methods to be used in their code. To address the lexical gap, this study investigated novice programmers’ descriptions of their programming tasks and used the found insights in a novel approach (APIFind) for recommending relevant API methods for the programming tasks. Queries written by novice programmers were collected and analysed using term frequency and constituency parsing. Four common patterns related to the return type of an API method and/or API class that provides an implementation for the API method were found and captured in the Novice Programming Task Description Model (NPTDM). APIFind uses NPTDM that was operationalised in a rule-based module, a WordNet map of API word-synonyms, a programming task dataset comprising the collected queries, a Java API class and method repository, a Stack Overflow Q&amp;A thread repository, and the BM25 model in Apache Lucene, to produce the top-5 API methods relevant to a search query. Benchmarking results using mean average precision @ 5 and mean reciprocal rank @ 5 as the evaluation metrics show that APIFind outperformed BIKER and CROKAGE when the novice queries test dataset was used. It performed slightly better than BIKER but slightly worse than CROKAGE when the reduced BIKER test dataset was used. In conclusion, common patterns exist in novice programmers’ search queries and can be used in API recommendations for novice programmers.
Cloud-based Disaster Warning System

A disaster is often defined as a 'sudden accident or a natural catastrophe that causes great damage or loss of life.Disasters aren't always predictable and not all kinds of disasters occur everywhere.Disaster management refers to the conservation of lives and property during a disaster.In most cases, the most significant thing for effective disaster management is to get the information at the right time.There have been several instances in the past where disasters such as landslides, forest fires, etc. were reported too late leaving the authorities with no options to mitigate its effects.Though the disasters are being monitored at various levels by the authorities, due to the large geographic area and diverse conditions there have been delays in being informed about the occurrence of a disaster.There are several disaster monitoring centers and also methods including satellite-based/drone-based disaster monitoring systems.But these have many limitations.There is a need for a much more cost-effective system that can do the same in a more efficient way.Here we propose a system that is cost-effective and can be deployed on a large scale.The use of this system that we propose will enable 24x7 monitoring of a large area from a single location.
PR-DESIGN TRAINING IN THE SPHERE OF SOCIAL AND CULTURAL ACTIVITIES

The paper deals with the issue of teaching the theoretical and practical aspects of PR-design for bachelor degree students in the field of training 51.03.03 Socio-Cultural Activities, “Management of State Institutions and non-State Organizations of the Socio-Cultural Sphere”. The use of educational PR projects in the educational process contributes to the formation of professional competencies that are in demand by modern employers.
IMPLEMENTATION OF THE RIJNDAEL ALGORITHM ON WEB-BASED WHISTLEBLOWING SYSTEM

In carrying out its responsibilities, an employee works for an agency or company and also works with his colleagues, whether they are co-workers or their own superiors. So it is very important for an employee to gain trust in his work environment. If there is a violation or behavior that deviates from an employee in the work environment, then there must be someone who reports it but of course by protecting the identity of the reporter. Based on these problems, the authors make and design a web-based whistle blowing application to protect the identity of people who report violations that occur in their work environment. This whistle blowing web is created using cryptographic algorithm methods. Cryptographic algorithms work by disguising data or information into a form of password that has no meaning. The author uses the Rijndael algorithm to encrypt the complainant's data. So that by using the Rijndael algorithm on this web-based Whistleblowing system, the data or reporting information will be safe in the database and it is hoped that an optimal system will be created for data and information security
Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and specific handwritten images, while the vector of locally aggregated descriptors with double power normalization is employed in aggregating local and global features. Moreover, handwritten image segmentation, preprocessing, enhancement, optimization of neural network architecture, and normalization for local and global features are exploited, significantly improving system performance. The proposed system is evaluated on Computer Vision Lab (CVL) datasets and the International Conference on Document Analysis and Recognition (ICDAR) 2013 datasets. The results show that it represents good generalizability and achieves state-of-the-art performance. Furthermore, the system performs better when training complete handwriting patches with the normalization method. The experimental result indicates that it’s significant to segment handwriting reasonably while dealing with handwriting overlap, which reduces visual burstiness.
BELIEFS AND ATTITUDES ABOUT ARTIFICIAL INTELLIGENCE (AI) AMONG COLONOSCOPIST PARTICIPANTS IN A PRAGMATIC IMPLEMENTATION TRIAL OF COMPUTER-AIDED DETECTION (CADE) OF POLYPS THAT DID NOT REPLICATE THE POSITIVE RESULTS OF RANDOMIZED TRIALS

Stanford University, USA; University of California San Diego, USA.
Adaptive Gaussian Search : A Novel Non-linear Minimization Technique [Presidential Award Proceedings]

A novel minimization technique was developed. The proposed method is based on a Gaussian random search in the parameter space and it can handle a wide range of problems, including bi-exponential curve fitting, in a reasonable time. It uses only function values, and does not require gradients.
Research on Machine Translation (MT) System Based on Deep Reinforcement Learning

With the deepening of globalization and the rapid development of human society and economic society in the world, the demand for machine translation in human society is also increasing rapidly, and the progress of artificial intelligence technology has put forward new requirements for the quality of machine translation. At the same time, the development of machine translation research has set a benchmark for other fields of natural language processing. Therefore, the research on machine translation not only has high practical value, but also can promote the progress of the theoretical research on natural language processing. Based on the idea of deep learning, this paper selects English language features as feature parameters, uses fine-tuning Python model training technology and feature-based deep learning method, and conducts deep reinforcement learning on Anglo-German MACHINE translation model. The comparative experiment of Indian English-German neural machine translation model shows that, The machine translation system based on deep learning is obviously better than traditional machine translation, and the translation effect is obviously improved.
Robust Zero-Watermarking for Medical Images Based on Deep Learning Feature Extraction

Internet data transfer has increased in recent years, as well as the need to generate protection and authentication of multimedia data. The use of watermarks on digital images serves for copyright protection and ownership authentication. Zero-watermarking does not embed the watermark into the host image. The imperceptibility of the watermark is required for some tasks without modifying pixels on the image, such as medical images, because the medical image has a low distortion and may generate a wrong diagnosis. In this paper, we propose a zero-watermarking algorithm for medical images with patient authentication purposes and avoid image tampering based on a deep learning neural network model as a feature extractor using the Context Encoder. Thus, is extracted a feature map with the most representative image features. Therefore, an or-exclusive is applied to merge the watermark sequence and the extracted feature map. The watermark signal consists of a pseudorandom sequence and contains the patient’s information. The bit error rate and the normalized cross-correlation demonstrate the robustness of the zero-watermark technique against geometric and image processing attacks.
PENGGUNAAN MEDIA CROSSWORD PUZZLE UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN IPAS KELAS IV SDN BADER 03

Learning media is one of the methods used in teaching and learning activities. We as teachers must utilize Learning Media to support the learning process of students in order to get maximum results. The purpose of this study is to use crossword puzzle media, researchers want to increase teacher activity, student activity &amp; student learning outcomes. This study used the Classroom Action Research method using qualitative and quantitative data analysis techniques. This study consisted of 3 cycles with data collection techniques using the Observation &amp; Test method. There was an increase in teacher activity during the period of 3 cycles, the first cycle was 65%, the second cycle was 75% and in the third cycle it increased to 87%. The increase also occurred in student activity from cycle I, namely 62%, cycle II 74% and cycle III increased to 85%. Student learning outcomes also increased from cycle I 50%, cycle II 72% and cycle III 87%. Based on these results, it can be concluded that the use of Crossword Puzzle media can improve student learning outcomes in the Science Class IV SDN Bader 03 subject.
OffSig-SinGAN: A Deep Learning-Based Image Augmentation Model for Offline Signature Verification

Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This study proposed OffSig-SinGAN: a deep learning-based image augmentation model to address the limited number of signatures problem on offline signature verification. The proposed model is capable of augmenting better quality signatures with diversity from a single signature image only. It is empirically evaluated on widely used public datasets; GPDSsyntheticSignature. The quality of augmented signature images is assessed using four metrics like pixel-by-pixel difference, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and frechet inception distance (FID). Furthermore, various experiments were organised to evaluate the proposed image augmentation model’s performance on selected DL-based OfSV systems and to prove whether it helped to improve the verification accuracy rate. Experiment results showed that the proposed augmentation model performed better on the GPDSsyntheticSignature dataset than other augmentation methods. The improved verification accuracy rate of the selected DL-based OfSV system proved the effectiveness of the proposed augmentation model.
Secured Framework for Banking Chatbots using AI, ML and NLP

Artificial intelligence (AI) Machine Learning (ML), and Natural Language Processing (NLP)are being employed in a growing number of sectors and is evolving as the technology of the future. It has been widely used to improve the quality of decision-making and problem-solving. This research study proposes a secured framework for banking chatbots using AI-ML and NLP. The framework based on qualitative in-depth experts interviews can be used for developing efficient chartbots for managing customer interactions of various data forms. AI enabled chatbots involves use of machine learning algorithms that can communicate with consumers in a conversational manner. AI enabled chatbots can speak common man’s language language and utilize voice as a user interface. Chatbots are used in the public and private sectors to provide virtual help in areas such as telecommunications, media, tourism, retail, stock markets, and banking. In the banking business, chatbot can act as a communication tool between customers and banks. It has a special advantage in financial industry as it can be automated and does not necessitate human interaction; and can enhance accessibility and provide a lot of features to customers.
HISTORICAL AND PEDAGOGICAL PREREQUISITES FOR THE DEVELOPMENT OF RESEARCH ACTIVITIES OF RUSSIAN UNIVERSITY STUDENTS

The article is devoted to the analysis of historical and pedagogical prerequisites for the de-velopment of research activities of students of higher educational institutions of the Russian Federation. It was noted that the research work of students has a long history of development — the first student societies began to be created in the Russian Empire at the end of the 18th century, the research activities of students actively developed during the USSR. After its collapse, stagnation occurred, and a new impetus for the development of research work of university students was given in the late 90s thanks primarily to the implementation of the Federal Target Program "State Support for the Integration of Higher Education and Fundamental Science for 1997-2000". The structure and content of higher education has been modernized, which has also led to a change in the organization of research activities of students — a research approach in teaching and education has become widespread, and one of the priorities of higher education has become the formation and development of research thinking in the learning process.
EXPERIENCE OF USING DIGITAL DIDACTICS IN THE EDUCATIONAL SPACE OF A MEDICAL UNIVERSITY

The article describes the experience of using digital didactics in the educational process of Samara State Medical University. The authors reveal the concept of «learning tools», considering them as means of managing the educational process. The degree of study of the issue of digitalization and learning tools in the digital educational space is revealed. At the same time, the authors note that the identified issues are sufficiently developed, but there are gaps in the projection of the allocated training funds on the educational processes of universities, taking into account their specifics. Based on the principles of learning revealed within the framework of personality-oriented, personality-developing, personalized learning technologies, as well as focusing on the conclusions drawn within the framework of the anthropological approach, the authors prove the feasibility of using simulation technologies and educational tracks in the educational process of a medical university as digital learning tools. A brief, but rather deep retrospective analysis of the work on the topic of simulation technologies is given by the authors in the section «background». As a result, concluding that there are a number of unresolved problems related to the integrity and consistency of the acquired knowledge, skills and abilities, as well as the costs in the formation of competencies. The main part of the article describes the experience of using simulation technologies in the educational process of the university, as well as to assess the level of conflict between future doctors and current nurses. The reasonableness of the conclusions is given by the positive results of the study conducted by the authors on the diagnosis of anticipatory abilities, empathic skills, and the motivational sphere. The authors also note the versatility of the VR simulator and describe the practice of its use at the Samara Medical University to determine the personality characteristics of future managers (personnel reserve) in order to assist them in the future in correcting existing behaviors, not corresponding to the role of the head. The authors also note the expediency of using educational tracks in teaching as a means to develop systemic thinking and ensure the integrity of knowledge in future doctors.
Acute Leukaemia Diagnosis Using Transfer Learning on Resnet-50

This paper presents an automated model for leukaemia detection that is based on the computational power of a deep pre-trained model Resnet-50. The conventional manual method to detect the disease from microscopic blood cell images is time driven and the diagnosis is subjective due to the variation of technical expertise of the hematologists and may vary from one pathologist to other. Hence a model is proposed that exploits the transfer learning technique on Resnet-50 to learn the features of microscopic blood cell images from the Acute Lymphoblastic Leukemia Image Database for Image Processing (ALL-IDB1) to classify them into diseased and healthy. As the number of images in the dataset is very less for training on deep-network, the model may overfit. As a precautionary measure, augmentation of images is performed during the training. Apart from image augmentation, L2 regularization is also used to reduce overfitting. The proposed model demonstrates 100% accuracy on unseen test images with Resnet50. The comparison of the obtained results is done with state-of-the-art work performed by contemporary researchers.
Deep learning can predict presence of TP53 aberrations and IGHV mutational status from peripheral blood smears of chronic lymphocytic leukemia

Introduction: Patient stratification based on genomic biomarkers is critical to optimize treatment of chronic lymphocytic leukemia (CLL). Using digitized, genomically annotated peripheral blood smears (PBS) of patients with CLL, we evaluated whether deep learning can predict presence of TP53 aberrations (del[17p] and/or TP53 mutation) and unmutated IGHV status solely based on cytomorphology. Methods: The workflow consisted of sample collection and digitization, manual annotation, segmentation, morphological feature extraction, and aberration classification. Whole slide images were generated from PBS of 414 patients with previously untreated CLL (CLL14, GIVe, BAAG). Manual annotation was conducted using Qupath software (v0.3.2) with ImageJ. A tailored Stardist model was used for semantic segmentation. Morphological features were collected from each segmented white blood cell (WBC) and fed into the classification model. Two neural network (NN) models were generated for each aberration (TP53 and IGHV) based on a cohort split into 70/30% training and testing datasets. The models were evaluated using AUC (Area Under the Curve of ROC [Receiver Operating Curve]). Results: In preparation of model training, 4,962 manual cells from 18 randomly selected patients were manually annotated. For the TP53 classifier, 141,873 WBC from 52 patients were segmented using the Stardist model. For the IGHV classifier, 131,410 WBC from 57 patients were segmented. The model was trained on 256 × 256 pixels patches obtained from original images with 200 epochs and 100 batch_size whilst making use of data augmentation. An intersection over union (IoU) value of 89.6% confirmed good model performance, i.e. accurate WBC segmentation. Lymphocyte cells were then segmented on each frame image. In total, 28 single-cell morphological features were captured and 8 best representatives fed into the NN model. The collected metadata vector features were randomly assigned to non-overlapping training and testing sets. Separate datasets were used for the TP53 and IGHV classifier. On a single-cell level, the models achieved an AUC of 86% for TP53 aberrations and IGHV status, respectively. For independent validation, the TP53 and IGHV classifiers were tested on a set of unseen images of 33 and 22 patients, respectively. AUC values of 78% and 72%, respectively, were achieved, confirming good model performance. To classify individual patients, a threshold of ≥50% positive cells/image was used to either classify a patient as TP53 aberrated or IGHV unmutated. Using this approach, 70% of patients with TP53 aberration and 68% with IGHV unmutated status were accurately classified. Keywords: Bioinformatics, Chronic Lymphocytic Leukemia (CLL), Computational and Systems Biology, Diagnostic and Prognostic Biomarkers No conflicts of interests pertinent to the abstract.
THE PSYCHOLOGICAL EFFECTS OF VIDEO GAME ADDICTION

Many teenagers' routines now include extensive time spent playing videogames and computer games.Teens and young adults spend countless hours each week playing violent video games.Media that show someone trying to hurt others on purpose is considered violent.Most of these studies' research locations were wealthy, industrialized nations.The nation's future lies in its children.The results showed that the connection between fundamental psychological requirements and compulsive internet gaming was mediated in a serial fashion by a sense of duty and purpose in life.The results also suggested that having purpose in life and taking on responsibilities might help explain the inverse association between compulsive internet gaming and meeting fundamental psychological requirements.The current study's findings are significant because they imply that satisfying adolescents' fundamental psychological needs may aid in preventing their addiction to online gaming.