Reviewerly/exHarmony huggingface dataset:
Psychological features of value orientations of young people in conditions of uncertainty

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
An energy and area efficient, all digital entropy source compatible with modern standards based on jitter pipelining

This paper proposes an energy and area efficient entropy source, suitable for true random number generation, accompanied with a stochastic model in a 28nm CMOS technology. The design uses a jitter pipelining architecture together with an increased timing resolution to achieve a maximal throughput of 298 Mbit/s and a best energy efficiency of 1.46 pJ/bit at a supply of 0.8V. The generated random bits pass the NIST SP 800-90B IID tests with a min entropy rate of 0.933 bit/bit, which is more than required by the AIS-31 standard. The all digital design allows for effortless transfer to other technology nodes, taking advantage of all benefits related to further technology scaling.
Strategy to Improve Employee Security Awareness at Information Technology Directorate Bank XYZ

Bank handles private information like customer financial transactions and personal data. There was a 63% increase in cyberattacks attempted against Bank XYZ in 2021, and 1,323 attempted attacks on corporate email Bank XYZ. Therefore, implementing security awareness training for all employees is crucial for Bank XYZ. The information security awareness program must be assessed to determine the program's efficiency and the level of information security awareness among employees. Therefore, this study assesses the information security awareness at Bank XYZ, especially the Information Technology (IT) Directorate using the Human Aspect of Information Security Questionnaire (HAIS-Q) method. The findings of this study revealed that employees at Bank XYZ in the information security work unit had a "Good" level of awareness. In contrast, the results from other IT work units were “Medium”. Based on the assessment results, Bank XYZ's security awareness strategy recommendation is to align awareness content with information security policies and procedures, use a variety of media awareness, and focus on the "Internet Use" and "Information Handling" awareness areas. As a way of determining the achievement of information security Key Performance Indicators (KPI), security awareness measurement must be done regularly, for example, once a year.&#x0D;
Fitting second-order cone constraints to microbial growth data

Second-order cone programming is a highly tractable convex optimization class. In this paper, we fit general second-order cone constraints to data. This is of use when one must solve large-scale, nonlinear optimization problems, but modeling is either impractical or does not lead to second-order cone or otherwise tractable constraints. Our motivating application is biochemical process optimization, in which we seek to fit second-order cone constraints to microbial growth data. The fitting problem is nonconvex. We solve it using the concave–convex procedure, which takes the form of a sequence of second-order cone programs. We validate our approach on simulated and experimental microbial growth data, and compare its performance with conventional nonlinear least-squares fitting.
Performance of End-to-end Model Based on Convolutional LSTM for Human Activity Recognition

Human activity recognition (HAR) is a key technology in many applications, such as smart signage, smart healthcare, smart home, etc. In HAR, deep learning-based methods have been proposed to recognize activity data effectively from video streams. In this paper, the end-to-end model based on convolutional long short-term memory (LSTM) is proposed to recognize human activities. Convolutional LSTM can learn features of spatial and temporal simultaneously from video stream data. Also, the number of learning weights can be diminished by employing convolutional LSTM with an end-to-end model. The proposed HAR model was optimized with various simulation environments using activities data from the AI hub. From simulation results, it can be confirmed that the proposed model can be outperformed compared with the conventional model.
Machine Learning in OCR Technology: Performance Analysis of Different OCR Methods for Slide-to-Text Conversion in Lecture Videos

A significant percentage of a lecture video's content shown is text.Video text can therefore be a crucial source for automated video indexing.Researchers have recognised printed and handwritten text extracted from pictures using a variety of machine learning techniques and tools before digitising it.A machine learning technology called optical character recognition (OCR) enables us to recognise and retrieve text information from documents, converting it into searchable and editable data.This study primarily focuses on text extraction from lecture slides using Google Cloud Vision (GCV), Tesseract, Abbyy Finereader, and Transym OCR and compares the results to develop a lecture video indexing scheme for the non-linear steering in lecture videos to watch only the interesting points of topics.We have taken a total of 438 key-frames in 10 categories from seven different lecture videos that range in length.First, binary and greyscale versions of the input colour images are created.Before using the OCR APIs, the frames are additionally preprocessed to improve the image quality.The recognition accuracy demonstrated that the GCV OCR performs effectively, saving computing time by collecting image text with the highest accuracy of other tools, 96.7 percent.
Effectiveness of the Inquiry-Based Method in English Language Teaching of Ukrainian University Students Through Technology-Enabled Learning

The requirements of the time and significant changes in the educational system with emphasis on distant learning due to the Covid-19 pandemic force teachers to reconsider methods, approaches, techniques, and tools in foreign language teaching to ensure the effectiveness of the learning process. This study describes the implementation of the inquiry-based method in the process of English language acquisition by Ukrainian university students specializing in cybernetics and information technology. It aims to determine the effectiveness of inquiry-based learning in developing Ukrainian university students’ comprehension and communication skills in professional English through technology-enabled learning. The main research question is to investigate the benefits and challenges of inquiry-based learning for Ukrainian university students to ensure impactful results. The methodology presupposes activities that encourage the independent and collaborative acquisition of the material, increase learners’ intrinsic motivation, enhance communicative and digital skills, and provoke critical thinking and meaningful learning. The teacher becomes a facilitator and organizer directing students’ learning process, fostering their active thinking and interest. It contributes to their better comprehension and meaningful study. The research findings proved the undeniable benefit of the inquiry-based method for university students as it develops their curiosity, inspires deeper understanding and learning, and motivates them to make discoveries and achievements
On the Implications of Human-Paper Interaction for Software Requirements Engineering Education

It is broadly accepted that requirements engineering is one of the most important phases of a software project, and requires tools to be effective.For a variety of reasons, paper as a tool has lasted for millennia and remains ubiquitous.This paper makes a case for a contextual, conscientious, and evidence-based use of paper in a competency-oriented approach to software requirements engineering education (REE).It argues that the prophecies for the obsolescence of paper are premature, there are unique benefits in the use of paper, and the decision to use paper should be based on [0, 1] rather than {0, 1}.In this regard, a need-centered conceptual model for human-paper interaction is proposed.The characteristics of paper that make it historically unique are reported and the affordances of paper relevant to REE are discussed.The REE-related activities that benefit from viewing paper as a boundary object and using different types of paper are highlighted and illustrated by means of examples.In advocating polyliteracy, the potential for a convergence of paper and digital media towards a harmonic coexistence is underscored.
Analysis of the Features and Structure Behind Availability in Blockchain Using Altcoin

Data availability is a security factor in a blockchain that is challenging to understand and explain because it cannot be described cryptographically or mathematically because of its behavior-dependent properties on the volunteers who maintain the distributed ledger in the network. Most previous approaches have frequently used successful projects with empirically effective availability, such as Bitcoin and Ethereum, to analyze more active blockchain networks. Therefore, even if much of the Altcoin forked from Bitcoin had nodes in the network, contrary to intuition because they were unpopular and failed projects, they were not the focus of much attention in the analysis. However, it is interesting to note that both can maintain availability. Considering the network structure that supports availability, it is possible to investigate Altcoin’s features as a minimal component because it is a failed project, whereas it is challenging to do so with Bitcoin and Ethereum because of their complex components. Thus, we specifically selected Altcoin in decline based on our user role model. We investigated the features of the network that maintain availability and the user structure underlying Altcoin, focusing on factors associated with continuity, such as initial node survivability, interest in software updates, and nodes with security risks. We discovered that compared with a stand-alone organization, the actions of early users and few community-friendly users provide greater availability. We also discuss the motivations of these users based on the cost of installing the nodes.
Convergence and optimality of policy gradient primal-dual method for constrained Markov decision processes

We study constrained Markov decision processes with finite state and action spaces. The optimal solution of a discounted infinite-horizon optimal control problem is obtained using a Policy Gradient Primal-Dual (PG-PD) method without any policy parametrization. This method updates the primal variable via projected policy gradient ascent and the dual variable via projected sub-gradient descent. Despite the lack of concavity of the constrained maximization problem in policy space, we exploit the underlying structure to provide non-asymptotic global convergence guarantees with sublinear rates in terms of both the optimality gap and the constraint violation. Furthermore, for a sample-based PG-PD algorithm, we quantify sample complexity and offer computational experiments to demonstrate the effectiveness of our results.
Current Status and Future Development of Quantum Computation

Quantum computation, as part of the broader field of quantum information, represents an assembly of concepts and techniques that concern the nature and processing of information based on quantum mechanics. Quantum computation utilizes unique resources such as quantum superposition and quantum entanglement to encode and process information and has been proved to be dominantly advantageous over classical computation on certain important scientific and engineering problems. Potential applications of quantum computation are expected to influence future information technology and many other related fields deeply and significantly. In this article, we briefly review the history of quantum computation, including how its fundamental ideas and concepts came into being and the development of its significant theories and algorithms. We also discuss the status and outlook of several representative technical routes in this field, including superconducting quantum computation, distributed superconducting quantum computation, photonic quantum computation, trapped-ion quantum computation, silicon-based quantum computation, as well as other systems. Furthermore, by analyzing certain common issues faced by all routes, we propose some thoughts and suggestions for future development of quantum computation in China. We particularly emphasize the following: reinforcement of strategic planning at a national level, establishment of a research team of high caliber, and boost of relevant fundamental research and development of core techniques and critical instruments.
PENGARUH TINGKAT AKURASI DALAM IDENTIFIKASI GEJALA DAN TANDA PENYAKIT PADA TANAMAN

The development of technology in the industrial era 4.0 is now very advanced from year to year. Initially, only a small set of tools and simple machines in industries, then the development has become a modern orbit so that it can help to range from solving problems with human control to machines that can make their own decisions from the analysis they do. This called artificial intelligence. Specifically in the field of agriculture, artificial intelligence technology can be implemented to facilitate and even provide knowledge about technology for farmers and those working in agriculture. In this case, to see the effect of the level of accuracy in identifying symptoms and signs of disease in plants using the ResNet model architecture.
Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer

Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients.A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study.The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis.In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.
A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks

With the development of deep learning theory and the decrease of the cost of acquiring massive data, the image semantic segmentation algorithm based on Convolutional Neural Networks (CNNs) is gradually replacing the conventional segmentation algorithm by its high accuracy segmentation performance. By increasing the amount of training data and stacking more convolutional layers to form Deep Convolutional Neural Networks (DCNNs), a neural network model with higher segmentation accuracy can be obtained, but it faces the problems of serious memory consumption and long latency. For some special application scenarios, such as augmented reality and mobile interaction, real-time processing cannot be performed. To improve the speed of semantic segmentation while obtaining the most accurate segmentation results as possible, this paper proposes a semantic segmentation algorithm based on lightweight convolutional neural networks. Taking the computational complexity and segmentation accuracy into account, the algorithm starts from the perspective of extracting high-level semantic features and introduces a position-attention mechanism with richer contextual information to model the relationship between different pixels, avoiding the convolutional local perceptual field to be too small. To recover clearer target boundaries, a channel attention mechanism is introduced in the decoding part of the model to mine more useful feature channel information and effectively improve the fusion of low-level features with high-level features. By verifying the effectiveness of the above model on a publicly available dataset and comparing it with the more popular semantic segmentation methods, the model proposed in this paper has higher semantic segmentation accuracy and reflects certain advantages in objective evaluation.
A Generative Hyper-Heuristic based on Multi-Objective Reinforcement Learning: the UAV Swarm Use Case

The interest in Unmanned Aerial Vehicles (UAVs) for civilian applications has seen a drastic increase in the past few years. Indeed, UAVs feature unique properties such as three-dimensional mobility and payload flexibility which provide unprecedented advantages when conducting missions like infrastructure inspection or search and rescue. However their current usage is mainly limited to a single operated or autonomous device which brings several limitations like its range of action and resilience. Using several UAVs as a swarm is one promising approach to address those limitations. However, manually designing globally efficient swarming approaches that solely rely on distributed behaviours is a complex task. The goal of this work is thus to automate the design of UAV swarming behaviours to tackle an area coverage problem. The first contribution of this work consists in modelling this problem as a multi-objective optimisation problem. The second contribution is a hyper-heuristic based on multi-objective reinforcement learning for generating distributed heuristics for that problem. Experimental results demonstrate the good stability of the generated heuristic on instances with different sizes and its capacity to well balance the multiple objectives of the optimisation problem.
From Banal Surveillance to Function Creep: Automated License Plate Recognition (ALPR) in Denmark

This article discusses how Automated License Plate Recognition (ALPR) has been implemented in Denmark across three different sectors: parking, environmental zoning, and policing. ALPR systems are deployed as a configuration of cameras, servers, and algorithms of computer vision that automatically reads and records license plates of passing cars. Through digital ethnography and interviews with key stakeholders in Denmark, we contribute to the fields of critical algorithm and surveillance studies with a concrete empirical study on how ALPR systems are configured according to user-specific demands. Each case gives nuance to how ALPR systems are implemented: (1) how the seamless charging for a “barrier-free” parking experience poses particular challenges for customers and companies; (2) how environmental zoning enforcement through automated fines avoids dragnet data collection through customized design and regulation; and (3) how the Danish Police has widened its dragnet data collection with little public oversight and questionable efficacy. We argue that ALPR enacts a form of “banal surveillance” because such systems operate inconspicuously under the radar of public attention. As the central analytic perspective, banality highlights how the demand for increasing efficiency in different domains makes surveillance socially and politically acceptable in the long run. Although we find that legal and civic modes of regulation are important for shaping the deployment of ALPR, the potential for function creep is embedded into the very process of infrastructuring due to a lack of public understanding of these technologies. We discuss wider consequences of ALPR as a specific and overlooked instance of algorithmic surveillance, contributing to academic and public debates around the embedding of algorithmic governance and computer vision into everyday life.
FATORES DE RISCO E SOROPREVALÊNCIA DE ASSINTOMÁTICOS DA LEISHMANIOSE VISCERAL HUMANA EM ÁREA DE TRANSMISSÃO INTENSA EM PETROLINA/PE, BRASIL

OBJECTIVES: To identify the seroprevalence of asymptomatic individuals in areas of disease transmission and analyze the environmental, socioeconomic and demographic risk factors in the municipality of Petrolina/PE, Brazil. METHODS: We approached 92 individuals in 19 localities with VL cases recorded in the Information System for Notifiable Diseases (SINAN) between 2016 and 2017. In each visit, two questionnaires were applied seeking to obtain clinical, social, economic and environmental variables. Viable blood samples collected during the visits (n=90) were submitted to the Indirect Immunofluorescence Reaction to identify anti-Leishmania antibodies. For data analysis, descriptive statistical tests were performed using Stata® 14.0 software. RESULTS: The presence of antibodies was detected in 2.2% (2/90) of the samples, in which one individual presented symptoms and the other was classified as asymptomatic. No risk factors related to the presence of anti-Leishmania antibodies were identified. CONCLUSION: The identification of one asymptomatic individual reinforces the importance of expanding human prevalence studies in endemic areas. Despite the low prevalence, it was possible to identify a possible case with manifestation of the clinical form of chronic VL that needs proper care.
Design of human-computer interaction gesture operation based on STM32 and MPU6050

In order to improve the convenience of computer use, a human-computer interaction mode of gesture operation is proposed. With the main functions of collecting and processing user environment information and obtaining their own state, and with the characteristics of fast, convenient, attached and anytime, anywhere, a wearable gesture operated human-computer interaction device is designed. The system is composed of STM32F103 single chip microcomputer, six axis sensor mpu6050, Bluetooth hid module XM-04-HID-K, etc. the six axis sensor fixed on the back of the hand obtains the hand inclination angle in real time. The CPU determines the instructions to be executed according to the received data and converts the operation into instructions. All instructions are integrated in a specific packet format and sent to the upper computer through the Bluetooth hid module, The test shows that the mouse pointer can be controlled by the movement of the hand.
An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern

Portable digital devices such as PDAs and camera phones are the easiest and most widely used methods to preserve and collect information. Capturing a document image using this method always has warping issues, especially when capturing pages from a book and rolled-up documents. In this article, we propose an effective method to correct the warping of the captured document image. The proposed method uses a checkerboard calibration pattern to calculate the world and image points. A radial distortion algorithm is used to handle the warping problem based on the computed image and world points. The proposed method obtained an error rate of 3% using a document de-warping dataset (CBDAR 2007). The proposed method achieved a high level of quality compared with other previous methods. Our method fixes the problem of warping in document images acquired with different levels of complexity, such as poor lighting, low quality, and different layouts.