Reviewerly/exHarmony huggingface dataset:
Aeronautic maturity cybersecurity: a framework

This article presents the results of a systematic review of the methods to implement cybersecurity maturity. Based on them, it proposes a framework for excellence in aeronautical cybersecurity that integrates the objectives of aeronautical cybersecurity with capabilities and requirements, which contributes to increasing the maturity of aeronautical cybersecurity. It exposes 13 objectives with 120 capabilities and 5 functional maturity levels to gradually meet up to 600 aeronautical cybersecurity requirements. Which were taken from articles with good practices associated with publications of the last 5 years and the criteria resulting from the collaboration of managers and the validation of experts in the industry, which allowed it to be enriched with good practices associated with the management of cybersecurity and the resilience of these infrastructures.
Discernment and Perusal of Software Vulnerability

The examination of vulnerabilities is vital to network security, and finding the route to the flaw's source is essential for the analysis and mitigation of software vulnerabilities that attackers can exploit. The automation of secure software development can be easily achieved by using vulnerability ID. Prior to this, manually tagged communications with vulnerability ID was a laborious process that had scalability problems and room for human mistakes. To facilitate code examination, several vulnerability detection techniques have been developed and to support code inspection, several vulnerability detection techniques have been developed. A series of research that uses machine learning approaches and provide encouraging outcomes are among these strategies. This chapter discusses recent research trend that uses deep learning to identify vulnerabilities and demonstrates how cutting-edge neural approaches are used to identify potentially problematic code patterns. It also highlights a few publications from research that have analyzed vulnerability identification using deep learning.
Blockchain-Enabled Secured Smart City Services

Smart city incorporates multiple living methods for the sustainability of resources. As the number of interconnected devices is expanding day by day, we need to secure the transmission of data. Blockchain is seen as a way to eliminate tampering and reduce the risks of malware threats with IoT devices and data. In smart cities, blockchain-based IoT applications can be used for a wide variety of services in a secure and decentralized way. This chapter introduces blockchain technology and how it secures the data and then explores the integration of blockchain technology in smart city services like healthcare, transportation, supply chain management, smart grid, and banking.
Knowledge Repository on Cyber Security

Cyber-attacks are serious problems for the IT sector. According to a survey, 66 percent of businesses have experienced these attacks. It's difficult and tiresome to fend against these attacks. Cyber analysts are now studying hacker intentions and purposes for incidents in addition to analysing cyber occurrences as a result of the rise in cyberattacks. DBMS may also establish itself as a key technology in the field of computer security and addressing cyber threats among the several technologies being designed and developed to avoid cybercrime. Research on cybercrime and hacker's interpretation can be honed with the use of DBMS. Through its logical and physical models and query processing, it aids in supplying sufficient knowledge to understand upcoming cyber-attacks.
Design and Development of the e-Review

Requirements review is a formal review process in which several reviewers read all or part of software requirement specification (SRS) and search for defects. In order to ensure that the system requirements have been completely and clearly identified, reviewers use many different review techniques. Despite of the availability of many reading techniques such as perspective-based reading techniques, checklist-based reading techniques and a few more, we could not find any existing tool to support two or more reading techniques in order to guide reviewer while reviewing a document. Besides, currently, in order for the reviewers to review an SRS document, they need to gather physically, make a session and give their review feedback during the session. In such cases, the requirement engineers need to schedule the review session based on the availability of the reviewers. The review leader needs to manually organize the session and review outcomes also need to be manually consolidated. Thus, the objectives of this project are to develop a web-based tool in order to; (1) enable reviewers to review SRS document using two different reviewing techniques such as checklist-based reading (CBR) and perspective-based reading (PBR); (2) enable review leader to assign reviewer for requirement review; and (3) allow compilation of the review feedback to be generated. The project applied Iterative process model to manage the software development process. The implementation of the project is using PHP JavaScript, HTML, CSS, Bootstrap, JQuery, and Ajax
Cues to Deception in Islamic Websites Text and Design

Deception is not always a lie, but sometimes a factual truth deliberately misleads. The deception continues to grow with the proliferation of the growth of the internet and reliance on internet information to make decisions. Thus, the absence of a deception judgment for websites will make users believe false and deceiving information which poses a serious problem, and there is still little scholarly work on this issue. Hence, the purpose is to investigate the distribution of cues to deception in real websites. A case study approach was carried out using content analysis. The cues were tested against some websites, and the context for the case study is the Prophet Muhammad’s biography. As a result, a list of cues was generated and grouped into nine heuristics; five heuristics related to text, and four heuristics related to design. Under each of these developed heuristics are lists of cues. The analysis from the case study confirmed the existence of the cues and enriched and improved the heuristics with more new cues
Case: No podemos abandonar las ciencias: Supporting Science Home Learning with Multilingual Families

In this speculative case, we imagine the life of an elementary scienceElementary science teacher, Sonia, several years after the COVID-19 pandemicCOVID-19 has receded, and schooling has returned to “normal.” However, the new normal for students and teachers is likely to require a greater reliance on technologyTechnology-supported home learning, as well as a renewed attention to “basic skillsBasic skills” for students who are perceived as “at riskAt risk” for falling further behind grade level standards. That is, the learning expectations and assumptions developed during the pandemic, with students and families taking on more of the content learning at home through a range of virtual learning platforms, is likely to continue as part of normal learning moving forward, with teachers needing to adapt accordingly. These new expectations have created both opportunities and challenges for teachers. This case study presents the complex intersection of several of these opportunities and challenges, including technologyTechnology access, language support, and a greater reliance on basic skills curricula that leave elementary teachers with little time for supporting robust scienceScience learning experiences.
Numerical Comparisons of Different Imaging Algorithms

Image processing is the set of operations performed to extract “information” from the image. An interesting problem in digital image processing is the restoration of degraded images. It often happens that the resulting image is different from the expected image. Our problem will therefore be to recover an image close to the original image from a poor quality image (that has been skewed by Gaussian and additive noise). There are several algorithms on how we can improve the broken image in better quality. We present in this paper our numerical results obtained with the models of Tikhonov regularization, ROF, Vese Osher, anisotropic and isotropic TV denoising algorithms.
Conducting Qualitative Interviews with AI

Qualitative interviews are one of the fundamental tools of empirical social science research and give individuals the opportunity to explain how they understand and interpret the world, allowing researchers to capture detailed and nuanced insights into complex phenomena. However, qualitative interviews are seldom used in economics and other disciplines inclined toward quantitative data analysis, likely due to concerns about limited scalability, high costs, and low generalizability. In this paper, we introduce an AI-assisted method to conduct semi-structured interviews. This approach retains the depth of traditional qualitative research while enabling large-scale, cost-effective data collection suitable for quantitative analysis. We demonstrate the feasibility of this approach through a large-scale data collection to understand the stock market participation puzzle. Our 395 interviews allow for quantitative analysis that we demonstrate yields richer and more robust conclusions compared to qualitative interviews with traditional sample sizes as well as to survey responses to a single open-ended question. We also demonstrate high interviewee satisfaction with the AI-assisted interviews. In fact, a majority of respondents indicate a strict preference for AI-assisted interviews over human-led interviews. Our novel AI-assisted approach bridges the divide between qualitative and quantitative data analysis and substantially lowers the barriers and costs of conducting qualitative interviews at scale.
A Study in Federated Learning Analytics for UAV

The world of AI and Data Science is changing fast, and so are its successor technologies. One of the latest additions to this stack is Federated analytics. Federated analytics is a systematic method used for data analytics that does not provide input data from individual devices. Where conventional data science brings much of the information into a typical central data lake, federated analytics collects information from far-distributed datasets without collecting it in one central server. Collaborative data science without data collection, as Google terms it. Federated Learning is a way to train centralized machine learning models on decentralized data. A dominant application of Federated Learning has been In the medical sector, where data privacy and accuracy are of prime importance. Another application where Federated Learning can make a mark is federated analytics for drone technology. An application that connects multiple drone data for varied applications like agriculture, animal disease deduction, defence system, etc. Drone technology is booming for its easy-to-use architecture and its ability to collect data where human reach ability is restricted. Drones combined with IOT devices and connected to cloud services can generate massive data. To train these algorithms better, you still need to access more data. A single decentralized system that can execute a machine learning algorithm on a single node And then pass the trained model to another node for further training. A final generated model can then be downloaded at both nodes without leaking data. Hence, the algorithm is trained better, and data is protected since only the model travelled from one node to another. In our study, we will be viewing both methods and see how the shortcoming of conventional Federated Learning schemes has led to the introduction of Decentralized Federated Learning for UAV Networks (DFL-UN). To overcome the shortfall of central server schemes in Federated Learning, DFL-UN was developed with the basic idea that UAVs train a local model based on local data and do not require a central node for global model aggregation.
PREVENTING POTENTIAL ROBBERY CRIMES USING DEEP LEARNING ALGORITHM OF DATA PROCESSING

Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics. With the increasing number of robbed apartments and houses every year, addressing this issue has become one of the highest priorities in today's society. By leveraging deep learning techniques, such as Neural Networks, in mechatronics and robotics, innovative solutions can be developed to enhance security systems, enabling more effective detection and prevention of apartment crimes. To evaluate the performance of our trained network, we conducted extensive experiments on a separate test dataset that was distinct from the training data. We meticulously labeled this dataset to obtain accurate ground truth annotations for comparison. By measuring precision scores, we determined the effectiveness of our model in detecting potential crimes. Our experiments yielded an accuracy rate of 97% in the detection of potential crimes. This achievement demonstrates the capability of YOLO and the effectiveness of our trained network in accurately identifying criminal activities. The high accuracy rate indicates that our system can effectively assist in property protection efforts, providing a valuable tool for security personnel and law enforcement agencies.
TiGen – Title Generator Based on Deep NLP Transformer Model for Scholarly Literature

Finding an appropriate title that comprehends readers clearly about the research is a challenging task. In this paper, we present a tool TiGen to generate suitable titles for their scholarly literature after parsing its abstract as input. We used GPT-2, a deep natural language processing transformer model to generate title suggestions. It is an auto-regressive pre-trained model with masked self-attention. The model has been trained with arXiv dataset which is scraped off selectively to keep the fields of the obtained dataset limited only to “Natural Language Processing”. TiGen is fed with a research abstract as input via a transformer architecture of neural networks corresponding to the decoder segment, for generating a list of titles. The results are evaluated using ROUGE-1 and ROUGE-L scores and validated against the state-of-the-art research work in this domain. To prove the usability of our work, the title of this paper is also auto-generated by TiGen.
Earthquakes and Other Seismic Processes as Part of Impact Theory

When considering seismic phenomena, geophysicists proceed from the fact that the mechanism of earthquakes is explained by the process of a sharp shift of blocks of the earth's crust or mantle relative to each other when they are loaded by external forces. This process is justified in the theory of Elastic recoil by Mr. Reid [1], which is a postulate of modern geophysics. The basic idea of Elastic recoil is that at the moment of the shift of the blocks of the Earth's crust and mantle, a dynamic impact occurs, and the main source of earthquake energy according to the theory of Elastic recoil is the elastic energy stored by the blocks of the rocks of the Earth's crust, which at the moment of collision of the blocks of rocks of the massif passes into a force pulse, which leads to a significant change in the velocities of the points of impact blocks with the formation of shock waves, which produce destruction on the surface of the Earth. In the opinion of many geophysicists, the Elastic recoil postulate is false and has been repeatedly criticized by scientists for contradicting the fundamental laws of sciences [2, 3]. In this article, in contrast to the Elastic recoil, the mechanism of an earthquake is described, in which the energy of seismic processes is not the elastic energy of deformations of rocks, but the energy of a shock wave, the front of which propagating in the mountain massif in the form of a monotonously increasing sphere produces destruction in the form of crushing of the mountain massif and buildings on its surface. Naturally, the more monolithic the rock mass is, the more intensively it will absorb seismic energy. But, if a mountain massif has a geological disturbance in its volume, then the mechanical impulse will “feel” almost instantly (within < 10-5 seconds) this violation will determine the distance and direction of movement to it and, like lightning in the atmosphere, in accordance with the Second Law of thermodynamics, it will necessarily rush to this weakened place, because there are energetically favorable conditions for the passage of a mechanical pulse. Consequently, it can be concluded that nature uses blocks of rocks with geological defects as energy conductors to dump the “extra” energy of an explosion suddenly formed in a mountain massif and the mechanism of earthquakes develops in accordance with a well–known physical postulate - any movement of any matter occurs along the path of least resistance with maximum work and minimum costs, and the process of earthquakes are mechanical, chemical and thermonuclear explosions in a mountain massif that turn into seismic energy radiation. In this article, we will present the theoretical foundations of seismic processes from alpha to omega.
New Ant Colony Optimization Including Weights

NP-hard problems are difficult to be solved. They need huge amount of computational resources like time and memory. The computational time grows exponentially, when the size of the problem grows. Combinatorial Optimization Problems (COP) belong to the class of NP-hard problems. The traditional numerical methods can not solve this type of problems using reasonable computational resources. Therefore special methods are developed to solve COPs in a resonable time. These are so called metaheuristic methods, most of which mimic some nature process. One of the most successful metaheuristic method is Ant Colony Optimization (ACO). ACO algorithm was inspired by real ants behavior. An important thing is how the ants can find the shortest path between food sources and their nest. Like in a nature, the “artificial” ants communicate depositing information, called pheromone, on their path. We improve the ACO algorithm adding weights. These weights are taken in to account, when an ant decide to move. It is a kind of diversification of the search in a search space. As a test problem is used Multiple Knapsack Problem (MKP).
THE RELATIONSHIP BETWEEN WORK CHARACTERISTICS AND SUBJECTIVE WELL-BEING OF EDUCATIONAL ORGANIZATION MANAGERS AND EMPLOYEES

Aim: To empirically investigate the relationship between work characteristics and subjective well-being of managers and employees in educational organizations.Materials and Methods: Analysis of work characteristics was done using The Work Design Questionnaire (WDQ). The modified BBC Subjective Well-being scale (BBC-SWB) was used to analyze educational staff's well-being. The research data were processed using mathematical statistics: descriptive statistics (mean, std. deviation), Pearson correlation, and independent samples t-test. 345 respondents from the Central region of Ukraine participated in the survey.Results: Although educational staff had a generally positive assessment of work characteristics, there were certain disproportions in their assessments of individual work characteristics: work autonomy characteristics were rated lower than task characteristics, while social characteristics and work context characteristics were rated lower than knowledge characteristics. In addition, educational organization managers rated the vast majority of work characteristics higher than employees, which suggests a more favorable organizational environment for managers than employees. There were three work characteristics groups in the context of their associations with staff's subjective well-being: 1) work characteristics that had a positive statically significant relationship with both managers and employees' subjective well-being indicators; 2) work characteristics that had positive and negative statistically significant relationships with employees' subjective well-being indicators; 3) work characteristics that had positive and negative statistically significant relationships with managers' subjective well-being indicators.Conclusions: The obtained findings may be helpful for managers and employees of educational organizations in promoting staff's subjective well-being and increasing educational organizations' effectiveness.
Solar Radiation Modelling for Bulgaria Based on Assimilated Surface Data

The solar radiation heats the surface of the land and water bodies, warms the atmosphere, and powers the ocean currents. The sun provides the energy that drives the Earth’s climate system. The precise knowledge of solar radiation at certain place of interest is essential also for the precise quantitative description and construction of photovoltaic facilities. The three primary sun radiation components—global horizontal irradiance (GHI), direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are most frequently not all present as observational readings at the same time. The numerical modelling approach is a pragmatic workaround for the lacking data. This study describes the simulation skills of the solar radiation model DISC utilizing assimilated observational GHI-data as the primary input parameter and satellite records as reference. The numerical experiment is conducted over the region, which frames completely Bulgaria with a grid with fine resolution. It is performed for the year 2015 and on an hourly basis. The main outcome is that the assimilated observational GHI-data are systematically smaller than the satellite records. In contrast, the modelled with DISC DNI, as well as the derived from the DNI parameter sunshine duration, are positively biased from the reference.
QoE-Driven Data Communication Framework for Consumer Electronics in Tele-Healthcare System

The COVID-19 pandemic has elicited interest in the development of novel, low-cost solutions for tele-healthcare that patients can use to consult with doctors using consumer electronics (CEs). Android CEs typically have multiple network interfaces. Simultaneous access to multiple interfaces can greatly enhance users’ quality of experience (QoE). To enhance data transmission throughput and robustness in tele-healthcare systems, we propose a QoE-driven data communication framework which can cater to all types of healthcare apps. The framework includes a QoE-driven congestion controller (QCC) and a QoE-driven MPTCP (multipath transmission control protocol) scheduler (QMS). Different app types correspond to specific QoE parameters that influence the QCC and QMS for meeting diverse healthcare app requirements. No server-side modifications are needed for this framework; only changes on the Android client side are necessary. We implement and experiment with the framework on an Android 13 smartphone in a real environment. Experimental results confirm the framework’s effectiveness and availability, and show that the QCC and QMS reduce completion time for transferring small files by at least 4.71% and for transferring big file by at least 13.94% compared with existing solutions.