Reviewerly/exHarmony huggingface dataset:
Protection of a Printing Company with Elements of Artificial Intelligence and IIoT from Cyber Threats

The article considers the actual challenges of cyber security that arise at the current stage of information technology development, with the introduction of artificial intelligence and machine learning. On the basis of the received information, it is proposed to carry out the design of the management system of the printing enterprise with the imitation of an expert. When implementing this function, it was decided to use an element of artificial intelligence, namely neural networks. When designing the proposed analytical apparatus, two alternative options for ANN training were developed for comparison. As a result of the simulation of disturbance perception, the ANN trained using TrustScore showed its effectiveness and superiority.
Incidentally Detected Anaemia in Elderly: A Cross-sectional Study from a Tertiary Care Hospital, Pune, Maharashtra, India

Introduction: Iron Deficiency Anaemia (IDA) is most prevalent nutritional deficiency observed worldwide. The elderly population is particularly susceptible. It is a significant risk factor for increased mortality and morbidity, reducing mobility and quality of life. Currently less Indian data is available for incidentally detected anaemia in elderly. The known causes of anaemia in elderly are nutritional, anaemia of chronic disease and malignancy. Aim: To evaluate the haematological profile and to identify potential aetiologies of anaemia in elderly who had an incidental diagnosis of anaemia. Materials and Methods: This was a prospective cross-sectional observational study, which included 90 incidentally diagnosed patients with anaemia above 60 years of age, admitted in Bharati Hospital and Medical College, Pune, Maharashtra, India during March 2021 to September 2022. Complete Blood Counts (CBCs) with peripheral blood smear, Haematocrit (Hct), reticulocyte counts, Red Blood Cells (RBC) indices like Mean Corpuscular Volume (MCV), Mean Corpuscular Haemoglobin (MCH) and cell morphology were all examined for each patient. The statistical programme Statistical Package for the Social Sciences (SPSS) (version 26.0) was used to analyse the data. The Chi-square test was used to evaluate the qualitative parameters. Results: The mean age was 66.44±5.87 years; 52 (57.8%) were men, and 38 (42.2%) were women. The most prevalent co-morbid conditions were diabetes and hypertension. The common clinical signs observed were pallor, oedema, knuckle pigmentation. In this study, IDA was the most frequent aetiology 53 (58.9%), second being megaloblastic anaemia 24 (26.7%), followed by dimorphic anaemia 13 (14.4%). Gastritis was commonest finding on gastroscopy. Conclusion: Anaemia is a common finding amongst elderly population which remains undiagnosed and is ignored. The main cause is nutritional deficiency leading to IDA, megaloblastic anaemia or dimorphic anaemia. Hence, geriatric population should undergo regular screening for early recognition of anaemia. Further large population studies are required for detailed evaluation to determine the causes of anaemia in elderly.
A General Steganalysis Method of QR Codes

With the wide application of quick response (QR) codes, its security has been paid more and more attention. There are many steganography schemes to embed the secret message into QR codes, which can be used in terrorist activities, spread viruses, etc. However, there is currently no effective scheme for detecting stego QR code. This paper divides the spatial QR code-based steganography schemes into three categories and then proposes a steganalysis method for QR codes. The method includes detecting stegao codes and recovering pure QR codes, which is realized by the code regeneration, module comparison, and embedded information filtering operations. Our method can perfectly distinguish the stego code and block the transmission of embedded information for the spatial QR code-based steganography schemes. Theoretical analysis and experiments show that the proposed method is feasible, universal, and robust.
Course Outcomes and Program Outcomes Evaluation with the Recommendation System for the Students

Different kinds of educational systems have been applied in learning and teaching to maximize the slope of the learning curve of the students. One of those educational systems is the OBE system or Outcome-based education. Outcome-based methods have been adopted in education systems around the world, at multiple levels. In this education system, students and teachers have a clear view of what needs to be accomplished by the end of the course. However, in this education system, as many of the students are trying to get the same outcome, weak students are getting behind to achieve their goals. Hence creating inequity, the proposed work uses the prior student data to apply the Course Outcomes and Program Outcomes (CO-PO) attainment model and recommends the students to focus on their individual weaknesses. So as the students are studying through their courses, their results are recorded. Then the data of those students are used to train the application in our proposed work. Consequently, the application can identify pupils who need to learn their needed topics. As a result of this proposed method, students with different abilities can not only rely on the outcome, but also have to develop their required subjects to thrive in their learning process.
Digital Forensics Tool Evaluation on Deleted Files

In a world where data is deleted every millisecond, whether on purpose or unintentionally, the question is whether deleted digital files still exist or if they are simply invisible to us on digital devices. Over the years, researchers have answered the question, but the rapid development of technologies and software makes the topic relevant. The global pandemic (coronavirus disease 2019) affected the physical and cyber worlds. Cyber attacks and data breaches have increased by over 400%. During these attacks, data is frequently deleted, mismanaged, or overwritten, making it difficult for users and digital investigators to recover and trace. Commercial tools that analyze deleted files are often expensive, and the unknown factor of free tools has always been a concern. In this paper, we evaluated two digital forensics tools, Magnet AXIOM, a commercial tool, and Autopsy, a free digital forensics tool, to partially bridge the gap for this era. We also used a differential analysis approach to investigate the persistence of deleted files. Moreover, for the best evaluation of the tools, we created files of various types and activities that mimic the daily usage of an average user on a Windows 11 operating system. The activities are divided into phases based on the processes that will most likely overwrite the deleted files. We also discussed the findings of these phases and presented the recommendations and challenges faced during the research process.
PBPAFL: A Federated Learning Framework with Hybrid Privacy Protection for Sensitive Data

Due to the difficulties of exchanging data securely, data silos have become a critical issue in the era of big data. Federated learning provides an advantageous approach by enabling data holders to train a model collaboratively without sharing local data. However, multiple known inference attacks have made it impossible for a purely federated learning approach to protect privacy well enough. We present a PBPAFL algorithm that combines differential privacy with homomorphic encryption based on federated learning with an assessment module that enables the privacy budget parameters to be flexible in response to varying training requirements. The models trained using our proposed PBPAFL algorithm are capable of preventing inference assaults without a severe loss of precision. To demonstrate the efficacy of our proposed framework, we employ the PBPAFL algorithm to train a collection of face image-sensitive data. The experimental results show that our approach can improve the privacy protection of the model while maintaining precision.
Towards Efficient On-Site CSAM Triage by Clustering Images from a Source Point of View

In digital forensics the Computer Forensics Field Triage Process Model (CFFTPM) addresses use cases, where an immediate on-site processing of digital evidence is necessary to impede ongoing severe criminal offences like child abuse, abduction or extortion. For instance in case of Child Sexual Abuse Material (CSAM) an instant in situ digital forensics investigation of seized devices may reveal digital traces to identify incriminated pictures produced by the suspect himself. In order to protect the victims from further violation the fast and reliable identification of such self produced CSAM files is of utmost importance, however, it is a non-trivial task. In this paper we propose an efficient and effective clustering method as part of the CFFTPM to identify self-produced incriminated images on-site. Our concept extends the classical hash-based identification of chargeable data and makes use of image metadata to cluster pictures according to their source. We successfully evaluate our approach on base of a publicly available image data set and show that our clustering even works in the presence of anti-forensics measures.
Image-to-Image Translation Generative Adversarial Networks for Video Source Camera Falsification

The emerging usage of multimedia devices led to a burst in criminal cases where digital forensics investigations are needed. This necessitate development of accurate digital forensic techniques which require not only the confirmation of the data integrity but also the verification of its origin source. To this end, machine and/or deep learning techniques are widely being employed within forensics tools. Nevertheless, while these techniques became an efficient tool for the forensic investigators, they also provided the attackers with novel methods for the data and source falsification. In this paper, we propose a simple and effective anti-forensics attack that uses generative adversarial networks (GANs) to compromise the video’s camera source traces. In our approach, we adopt the popular image-to-image translation GANs to fool the existing algorithms for video source camera identification. Our experimental results demonstrate that the proposed attack can be implemented to successfully compromise the existing forensic methods with 100% probability for non-flat videos while producing the high quality content. The results indicate the need for attack-prone video source camera identification forensics approaches.
MQTT Traffic Collection and Forensic Analysis Framework

Message Queue Telemetry Transport (MQTT) is a common protocol used for Internet-of-Things (IoT) devices communication. In recent years, IoT devices are deployed in Operational Technology (OT) systems such as building management system (BMS). It enables the capability to control the infrastructure within a building, and can be considered a miniature industrial control system. With the increased use of these devices to further enhance the functionality of such systems, there is also an increased risk of vulnerabilities that come with these devices. Cyber-security must be one of the top priorities to be taken into the consideration at the various stages when designing the BMS to achieve operational reliability. In this paper, we proposed a real-time MQTT logging and abnormal detection framework with push notifications. It can be used to collect digital evidence for forensic investigation and monitor cyber-attacks.
Accurate Registration of 3D Models with Organs in Augmented Reality Through Inertial Sensors

Augmented Reality (AR) can improve the accuracy of surgical procedures by displaying 3D reconstructions of organs directly on the patient’s body. Moreover, it can improve the surgeon’s level of attention by freeing him from the task of mentally associating information from various sources. A crucial aspect for the reliability of an AR system is the registration process, which aims to guarantee the correspondence between the 3D model and the real organ during the whole operation. The research work introduced in this paper aims to improve the accuracy of the registration process by updating the alignment between real and virtual organs even in presence of deformations induced by the insertion of surgical instruments or small displacements that organs might undergo during surgery. By detecting even imperceptible movements, the inertial sensors allow dynamic updating of the alignment between real and virtual organs to ensure an error of a few millimetres. The solution introduced in this study could therefore constitute a first step towards improving precision in AR-guided surgery, although more in-depth studies are needed to assess the optimal positioning of the sensors, which in the future could be done automatically by an application that suggests it in the preparatory phase of surgery.
Assessment of Children Eye Movement Performance: An Eye-Tracker Approach

Eye movement disorders can have various impacts on reading difficulties, such as tracking difficulties, unstable fixation, issues related to visual processing and attention. A comprehensive understanding of patient’s visual functions and reading ability may require a precise evaluation of their eye movements by vision or speech specialists. Eye tracking is a widely applied method for assessing eye movement parameters during reading and other visual tasks. By using eye tracking, it is possible to track eye movements across words and sentences without requiring any overt verbal or motor response from the child. The aim of our study was to develop an objective method for the assessing eye movement performance in children using eye-tracking technology. We tested this method on 53 s-grade school-aged children (7 and 8 years old) using special reading tasks displayed on a computer screen and eye movement recording with a Tobii Pro Fusion eye tracking device (250 Hz). Speech therapists assessed the children's reading skills using the Acadience Reading test. Our results indicated a correlation between the children's reading performance and the number of eye fixations, average fixation duration, and total reading time. Based on our results, we conclude that the developed method based on eye-tracking works well both as a screening method and as a diagnostic method for assessing eye movements during reading. This method will be particularly useful for optometrists, speech therapists, and other specialists involved in children’s vision, health, and academic achievements.
Optimized Machine Learning Input for Evolutionary Source Code to Architecture Mapping

Automatically mapping source code to architectural modules is an interesting and difficult problem. Mapping can be considered a classification problem, and machine learning approaches have been used to automatically generate mappings. Feature engineering is an essential element of machine learning. We study which source code features are important for an algorithm to function effectively. Additionally, we examine stemming and data cleaning. We systematically evaluate various combinations of features on five datasets created from JabRef, TeamMates, ProM, and two Hadoop subsystems. The systems are open-source with well-established mappings. We find that no single set of features consistently provides the highest performance, and even the subsystems of Hadoop have varied optimal feature combinations. Stemming provided minimal benefit, and cleaning the data is not worth the effort, as it also provided minimal benefit.
Predicting on restaurant's hygiene rating: Does customer review emotion and content matter?

Purpose The hygiene factor is always imperative when customers consider a certain restaurant, and the information contained in customer reviews can be an efficient approach to gauge a restaurant's hygiene during gaps in the official inspection. Therefore, the purpose of this study was to investigate whether information obtained from online reviews could predict the upcoming hygiene rating, specifically, evaluating the impact of both qualitative and quantitative content of reviews on the restaurant hygiene rating. Design/methodology/approach The quantitative research method with big data analytic techniques was applied in this study. In total, 127,656 pieces of data collected from 1,710 restaurants in four major cities in the USA were used in the analysis. Both quantitative factors (i.e. reviewer's numerical rating, days to review, readability, useful/funny/cool) and qualitative factors (i.e. eight emotional dimensions of textual reviews) were analyzed from the online customer reviews and considered in predicting the restaurant's hygiene rating. Findings Six out of eight emotional dimensions including anger, disgust, fear, sadness, surprise and trust were identified as having significant influences on the restaurant hygiene ratings. While three quantitative variables including days to review, readability and usefulness were identified with significant impacts on the dependent variable of restaurant hygiene rating. Originality/value This study opens an avenue for innovative research that establishes a connection between customers' reviews and restaurants' inspection systems. The results allow restaurants to predict an impending hygiene inspection rating upon dynamic assessment of review content and aid in adjusting hygiene measures accordingly.
Exploring the Direction of Early Childhood Education in the Age of Artificial Intelligence : Delphi Analysis of a Group of Experts

The purpose of this study is to explore the direction of early childhood education for fostering future human resources in the age of artificial intelligence. For this study, the Delphi survey was conducted on 14 experts engaged in related fields such as artificial intelligence, early childhood education, and convergence education. The study was conducted in a paper-type standard Delphi survey and was conducted in a total of 3 rounds. The degree of agreement was judged based on the analysis of the degree of agreement and the degree of convergence of the Delphi 3rd survey results. As a result of the study, it was agreed that education using storytelling technology (SIRI: Speech Interpretation and Recognition Interface) is possible for young children. In addition, it was agreed that teacher education such as basic education on understanding the concept of artificial intelligence, how and how to use artificial intelligence, ethical aspects of education, and artificial intelligence education suitable for infant development is possible. In addition, a high level of agreement was reached with the opinion that artificial intelligence education through play should be realized and that physical and economic support for artificial intelligence education is required. Through this study, the importance and necessity of artificial intelligence education in early childhood education was confirmed. In addition, It was derived that the development of early childhood education programs and teacher education programs should be prioritized and that both policy and social support were required in order for effective AI education to be implemented. It is expected that the results of this study will be used as basic data to set the right direction for AI education and to develop educational programs related to AI education.
Robots and Artificial Intelligence’s Effects on Employ Prospects in the Future

The prevalence of human-robot connection is expanding as robots have made everyone’s lives more relaxed and pleasant. This study examined the traits and behaviours of numerous robot kinds. They have also looked into how robotics and humans are evolving together. In addition to the many scientists and technicians who work in this field, they have included a few of their contributions in our study. By creating a working system that solves issues and produces good outcomes, they want to understand better how the human brain works. The field of artificial intelligence is enormous and is also making progress in business, healthcare, and quality control. According to several studies, the business sector collaborates with artificial intelligence to evaluate supply and demand. Design and systematize human resource management organizations. The public sector is also developing several intelligent devices for security observation and fault uncovering of nuclear reactors and other crucial systems. Robotics and Artificial Intelligence are also fantastic for safely enforcing law and order. Due to the massive need for intelligent robots across many industries, employment in this field and artificial intelligence are developing. Our main goal is to investigate how people and robots interact.
RESEARCH OF THE OPERATION OF THE SUPPORT WEIGHTENING SYSTEM ON THE EXAMPLE OF MATHEMATICAL AND NATURAL MODELING

Relevance Devices and apparatuses operating in outer space have a high cost due to extreme operating conditions and high requirements for reliability, which, as a result, leads to the need for thorough testing of the performance of all elements, assemblies and subsystems before being sent into space underground conditions. Among the many elements of spacecraft, the process of testing transformable parts that deploy in space from the transport position to the working position is difficult, however, under terrestrial conditions, due to the influence of the weight of the elements on the deployment system and hinges, testing without auxiliary systems does not guarantee reliability of the conducted experiments. Accordingly, one of the solutions is the use of weightless systems that compensate for the weight of the transformable parts of spacecraft. Aim of research Analysis of the operation of a support weightless system based on a mobile robotic platform on holonomic wheels using mathematical and natural modeling. Research methods Mathematical and natural modeling. To compare transient processes, the main quality indicators used in the theory of automatic control are used. Results The transient responses are obtained for the angles of deflection and the angle of deflection of the operation of the model and the full-scale stand of weightless systems. Qualitative and quantitative analysis confirms the efficiency of the method and the closeness of the obtained natural results with the model ones. The weighing accuracy obtained on a full-scale stand is 97.7 %.
Automatic coarsening in Algebraic Multigrid utilizing quality measures for matching-based aggregations

In this paper, we discuss the convergence of an Algebraic MultiGrid (AMG) method for general symmetric positive-definite matrices. The method relies on an aggregation algorithm, named coarsening based on compatible weighted matching, which exploits the interplay between the principle of compatible relaxation and the maximum product matching in undirected weighted graphs. The results are based on a general convergence analysis theory applied to the class of AMG methods employing unsmoothed aggregation and identifying a quality measure for the coarsening; similar quality measures were originally introduced and applied to other methods as tools to obtain good quality aggregates leading to optimal convergence for M-matrices. The analysis, as well as the coarsening procedure, is purely algebraic and, in our case, allows an a posteriori evaluation of the quality of the aggregation procedure which we apply to analyze the impact of approximate algorithms for matching computation and the definition of graph edge weights. We also explore the connection between the choice of the aggregates and the compatible relaxation convergence, confirming the consistency between theories for designing coarsening procedures in purely algebraic multigrid methods and the effectiveness of the coarsening based on compatible weighted matching. We discuss various completely automatic algorithmic approaches to obtain aggregates for which good convergence properties are achieved on various test cases.