Reviewerly/exHarmony huggingface dataset:
Financial Cybercrimes in Poland – In the Search of Victimization Factors

Purpose: Hereby article examines the factors influencing the victimization of adult Poles in the field of financial cybercrimes.Social and demographic factors as well as skills and habits in the field of cybersecurity were taken into account.Approach/Methodology/Design: The method of computer assisted telephone interviews (CATI) was used for this purpose.Descriptive statistics and selected inductive statistics were used in the analyzes.In turn, intra-group differentiation was investigated using a statistical exploratory method called two-step cluster analysis.The analyzes were carried out in the following three groups, those who experienced financial cybercrimes, those who experienced non-financial cybercrimes, and those who did not experience any cybercrime.Findings: The results of foreign studies show contradictions in the answer to the question of what social and behavioral characteristics induce victimization.The use of the cybervictimized persons segmentation procedure allowed to explain this contradiction, which from now on can be treated as apparent.Among the cyber-victimized group there are several groups that differ in terms of sociodemographic (gender, age, education, income, place of residence) and behavioral (IT competences).These are both groups with low IT competences as well as medium or high.They are both young people and middle-aged and elderly people.An important observation is also that the group structures captured in this way are similar among the victimized and non-victimized, and among the victims of financial cybercrimes and non-financial crimes.Practical implications: Identification of significant features (social, demographic, psychographic and the level of competence in the field of avoiding cyber threats) of people particularly vulnerable to victimization, allows to indicate who can be used awarenessraising, educational and remedial measures.The article addresses the urgent and heavily underinvested need to ensure cybersecurity of ordinary Internet users, especially those who are less able to cope with technical issues, for whom age, disability or other reasons prevent such activity.This is of particular importance in the face of the implementation of further digital services as part of e-government activities.Originality/Value: This is the first study on a nationwide sample of adult Poles that reveals the relationship between cybercriminalization in the field of financial crimes, and sociopsycho-demographic features, as well as IT competences and knowledge of the respondents.
Automation And Digitization Of Andhra University, Visakhapatnam College Libraries: A Study

In a digital era there is a tremendous growth of multimedia products and services, so there is need to manage them and disseminate in a wide variety of a formats. The purpose of this paper is to examine the present status of automation and digitization in Andhra University, Visakhapatnam Arts College, Science College, Law College and Engineering College libraries and the challenges they pose to effective information delivery. The library professional plays a vital role in management of digital library. The paper makes a more conscious effort on library automation and implementation of digitization policies.
Exploring Gender-Specific Features from Handwriting for Devanagari Script

Handwriting is a very important mode used for communication. It has been in use for a long time for various purposes. Handwriting is found in many criminal cases and its analysis is quite tedious work to do and even time-consuming. But it would be better if the gender of the writer is known; it would be helpful in narrowing the search of the suspects. In this work, we have tried to explore certain features which could be used for the identification of the gender of the writer. Various researches have been done to determine gender from handwriting in various languages. No such work has been done on any of the Indian languages yet. We have selected the Marathi language, the script used for writing is the Devanagari script, which is the script for some other languages used in India. The database was created and the features were extracted manually including line quality, pen pressure, formation of dots, and many more. However, few features are seen in males while some others are in females. The study is still not conclusive and looking for further work on it.
Improved Chaotic Grey Wolf Optimization for Training Neural Networks

This paper introduces one improved version of the Grey Wolf Optimization algorithm (GWO), one of the newly established nature-inspired metaheuristic algorithms, and the suggested approach is termed Chaotic Grey Wolf Optimization (CGWO). The newly suggested approach CGWO is premeditated by the integration of the chaos technique with the GWO algorithm, aiming to resolve global optimization problems by maintaining a proper balance between exploration and exploitation. In the proposed approach, CGWO is assessed over the classic 23 benchmark functions. The proficiency of the freshly suggested approach, CGWO is verified by comparing it with contemporary methods as well as examined through statistical analysis also. Further, the same CGWO is utilized to train neural networks (MLP) by considering benchmark datasets, for data classification and establishing a better classifier algorithm.
Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing

Abstract Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.
Exploring the Path of Data Intellectual Property Protection in the Context of the Digital Economy

The digital economy's rapid growth has ushered society into a new era, where data holds immense significance. In the field of legal academia, data can be seen as an important carrier of intellectual property rights in a legal sense. Therefore, the research on the path and models of intellectual property protection for data in the context of the digital economy has become a focus of domestic and foreign scholars. This paper aims to address the issues of limited avenues and low effectiveness in the protection of data intellectual property rights in China. Through the definition and comparison of the concepts of data and intellectual property rights in academic terms, this paper analyzes and compares the data protection models in various countries worldwide with the actual data protection situation in China, and summarizes the findings. Furthermore, the paper proposes specific measures for the protection of data intellectual property rights in China, such as establishing clear standards for certifying data compilation works and enacting specialized legislation for data protection. These proposed regulations help to alleviate the current challenges in data intellectual property rights protection to a certain extent.
Research on Price Prediction of Digital Currency Based on Machine Learning

Since the invention of digital currency, the social recognition and demand for special currency and similar cryptocurrencies have increased significantly with the development of digital currency and blockchain technology. The rapid rise in the price of digital currency and its significant volatility attracts a large number of users to invest in it as a digital asset. Before the formation of a regulatory strategy with a standardized system, the development of digital currency will undoubtedly have an increasing impact on society, and its price fluctuation will become an unstable factor in society by increasing the risk to users. Therefore, finding out the factors that affect the price of digital currency and forecasting its price has become the focus of research on Bitcoin in recent years. This will not only help investors and relevant institutions understand Bitcoin and the digital currency market but also help improve the financial market and its policies. In this paper, I use a machine learning model to predict the price of digital currency from both numerical and trend aspects. The main contents include the following elements.First, in terms of data characteristics, I comprehensively summarized and referred to the previous research on the price of special currency at home and abroad. Second, in terms of model prediction, I designed a two-stage feature processing method, used three kinds of recurrent neural network models to compare and predict the price of digital currency, and used recursive feature elimination (RFE) and logical regression (LR), random forest (RF), linear discriminant analysis (LDA) Support Vector Machine (SVM) and Naive Bayes (NB), five commonly used machine learning models, are combined to predict the price trend of Bitcoin.
Extraction of Handwritten Text using Word Beam Search Algorithm and Language Modeling

The challenge of recognizing handwriting in mortgage records is covered in this article. Businesses trying to digitize huge numbers of hand-marked scanned documents or reports have a significant challenge: offline handwritten text recognition from images. In order to translate a picture into a series of characters that match to the text that is contained in the image, this research suggests an innovative language model in combination with a deep convolutional network and a recurrent encoder-decoder network. Using the principles of Deep Learning and Word Beam Search, the complete model is trained as an end-to-end replacement for conventional handwriting recognition techniques. When the Connectionist Temporal Classification (CTC) loss function is trained on the digital form, an RNN is the result. Character probabilities are contained in this matrix for each discrete time step. By translating the character probabilities, a CTC decoding algorithm maps the final text. The token passing mechanism is used to create the recognized text from a list of dictionary words. We offer a novel and highly efficient method for developing restrictive models for classification which might associate entity names in accordance with the data contained in the article on entity types. A benchmark dataset predicated on the Mortgage domain is included. This Mortgage domain is evaluated in the presented model. We tested the model provided below using a set of benchmark mortgage datasets, which are published. The experimental outcomes were compared to the IAM and RIMES datasets, two openly accessible datasets. On the evaluation set of both datasets, word level precision at the cutting edge by 2.5% &amp; 1.3%, respectively.
A Comprehensive Review on Cyber Security and Online Banking Security Frameworks

The rapid growth of online banking and financial transactions has increased the risk of cyber threats and attacks on financial institutions. Cyber security and online banking security frameworks are essential in mitigating these threats and securing sensitive information. This chapter provides a detailed examination of the cyber security and online banking security frameworks utilized by financial institutions to safeguard their assets and clients. The study identifies common threats to online banking systems and discusses the various security measures that can be implemented to prevent these threats. The chapter also evaluates the effectiveness of existing security frameworks and proposes a comprehensive security framework that combines multiple layers of protection to enhance the security of online banking transactions.
Institutions of professional higher education as a condition for successful personality development

The article considers the constituent conditions of self-determination of future social workers in the environment of institutions of professional pre-higher education. The modern level of extra-primary activities in the institution of vocational pre-higher education has been revealed. Special forms and methods of working with students of vocational pre-university education were analyzed and substantiated. The list of necessary conditions for the formation of the system of theoretical knowledge, practical abilities and skills necessary for the effective realization of oneself as an individual in the process of professional training is given in the applicants of vocational education; promoting the development and formation of personal qualities that will contribute to the formation of oneself as a specialist in the social sphere. The purpose of the article was to identify the need to understand the problems of professional training and put forward special requirements for the training of specialists capable of solving social problems. Considerable attention is paid to the problems of extracurricular activities, on the basis of the analysis, the strengths of professional pre-higher education are highlighted, the expediency of using effective methods of extracurricular activities is clarified. She main tasks are defined as follows: to identify and generalize the components of professional self-development as one of the complex forms of transformation of the inner world, which occurs in the process of self-movement of a person along his life path, on which a person knows himself, builds himself, understands the essence of his functioning; to investigate the peculiarities of vocational pre-university education as a focus for personal development on self-building, professional self-determination for the successful growth of professionals in the social sphere. Based on the results of the generalized analysis, it was concluded that the effectiveness and expediency of extracurricular activities is possible under the condition of purposeful activity of the students of professional preliminary higher education, and this, in turn, requires the study and staging of an experiment for self-evaluation and self-affirmation of future social workers as professionals. The conclusions confirmed the substantiation of the necessary pedagogical conditions for improving the quality of extracurricular activities in the environment of institutions of professional pre-higher education regarding future social workers in the conditions of globalization.
Enabling ImageNet-Scale Deep Learning on MCUs for Accurate and Efficient Inference

Conventional approaches to TinyML achieve high accuracy by deploying the largest deep learning model with highest input resolutions that fit within the size constraints imposed by the microcontroller’s (MCUs) fast internal storage and memory. In this paper, we perform an in-depth analysis of prior works to show that models derived within these constraints suffer from low accuracy and, surprisingly, high latency. We propose an alternative approach that enables the deployment of efficient models with low inference latency, but free from the constraints of internal memory. We take a holistic view of typical MCU architectures, and utilise plentiful but slower external memories to relax internal storage and memory constraints. To avoid the lower speed of external memory impacting inference latency, we build on the TinyOps inference framework, which performs operation partitioning and uses overlays via DMA, to accelerate the latency. Using insights from our study, we deploy efficient models from the TinyOps design space onto a range of embedded MCUs achieving record performance on TinyML ImageNet classification with up to 6.7% higher accuracy and 1.4x faster latency compared to state-of-the-art internal memory approaches.
QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing

Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there are few attempts to explicitly address the dependencies between current question and question context. This paper presents QURG, a novel QUestion Rewriting Guided approach to help the models achieve adequate contextual understanding. Specifically, we first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model the rewriting relations between question and context, and the schema linking relations between natural language and structured schema. Experimental results show that QURG significantly improves the performances on two large-scale context-dependent datasets SParC and CoSQL, especially for hard and long-turn questions.
Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing. A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature from the directional sub-aperture LF data cube. For the enhancement of the depth map, dual iteration-based weighted median filtering (WMF) is used to reduce surface noise and enhance the accuracy of the reconstruction. Generating a 3D point cloud involves combining two key elements: the enhanced depth map and the central view of the light field image. The proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing (HCI) and real-world LFM datasets. The results are compared with different state-of-the-art methods. The structural similarity index (SSIM) gain for boxes, cotton, pillow, and pens are 0.9760, 0.9806, 0.9940, and 0.9907, respectively. Moreover, the discrete entropy (DE) value for LFM depth maps exhibited better performance than other existing methods.
A Study on Design Method of Sample Transfer Control Software by Integrating Petri Net Model and Object Oriented Design

In recent years, the semiconductor manufacturing industry has seen a diversification of demand and the importance of high mix low volume production has increased. In the manufacture of semiconductors using process etching equipment, a device called a linear cluster tool is used, in which process modules that process one by one wafer are linearly connected by multiple links. In a production system such as a linear cluster tool, it is required to avoid phenomena such as deadlocks and collisions caused by resource constraints such as machines, parallel operations, and so on. However, it is difficult for control methods that assume mass production of a small variety of products to cope with high mix low volume production in which the process is frequently changed. In particular, the software that controls the equipment has become increasingly complex and large in scale to address this challenge, and this has also caused a problem of reduced maintainability. The purpose of this study is to propose an efficient control method design by integrating Petri net model and object oriented design method to solve these problems and to propose a method to develop highly reliable equipment control software.
GENERAL THEORETICAL ISSUES OF FORMATION OF PROFESSIONAL COMPETENCE OF FUTURE SPECIALISTS IN THE NATURAL SCIENCES

The article is devoted to the problem of forming the professional competence of future specialists in the natural sciences. The purpose of the article is to determine the directions of successful formation of professional competence of future specialists in the natural sciences. The research methods are theoretical (analysis of scientific sources, educational and professional programs in the natural sciences, websites and platforms for organizing distance learning, synthesis and generalization of results); empirical (generalization of pedagogical experience). The article analyzes the approaches of domestic scientists to defining the essence of the concept of «professional competence» and presents the author's position of the essence of the concept of «professional competence of future specialists in the natural sciences». It has been found that the professional training of future specialists in the natural sciences at universities is carried out in accordance with the Standards of Higher Education of Ukraine (field of knowledge 10 Natural Sciences). Based on the theoretical analysis of the Standards of Higher Education of Ukraine (field of knowledge 10 Natural Sciences), educational and professional training programs for specialists in the natural sciences, experience in organizing the educational process and training of specialists in this field at V.N.Karazin Kharkiv National University and H.S.Skovoroda Kharkiv National Pedagogical University (for the period of 2021-2023 academic years), the priority areas for the successful formation of professional competence in the languages of transformation are identified.