Reviewerly/exHarmony huggingface dataset:
SIGNAL TRANSDUCERS OF SENSOR DEVICES ON PHOTOVOLTAIC POWER SUPPLY AND OPTICAL COMMUNICATION

The analysis of the sensor devices operation principles with photovoltaic power and optical communications have been performed. The functioning of such devices is provided by a modulated light flux. An informative signal that forms a sensor device is a QR code. The formation of a QR code is carried out using micropower displays based on e-ink electronic ink technologies, and this QR code is read by a smartphone camera. The main results of photovoltaic sensor devices development based on optical communications are presented, in particular, the analysis and generalization of operation principles, simulations and signal converters prototyping. Examples of a photo-sensor converters studies with transimpedance amplification and gyrator load are analyzed. A layout of signal converters and examples of oscillograms are presented in the course of the considered photosensor devices signal converters experimental studies.
Confine: Fine-grained system call filtering for container attack surface reduction

Reducing the attack surface of the OS kernel is a promising defense-in-depth approach for mitigating the fragile isolation guarantees of container environments. In contrast to hypervisor-based systems, malicious containers can exploit vulnerabilities in the underlying kernel to fully compromise the host and all other containers running on it. Previous container attack surface reduction efforts have relied on dynamic analysis and training using representative workloads to limit the set of system calls exposed to containers. These approaches, however, do not capture exhaustively all the code that can potentially be needed by future workloads or rare runtime conditions, and are thus not appropriate as a generic solution. Aiming to provide a practical solution for the protection of arbitrary containers, in this paper we present a generic approach for the automated generation of restrictive system call policies for Docker containers. Our system, named Confine, uses static code analysis to inspect the containerized application and all its dependencies, identify the superset of system calls required for the correct operation of the container, and generate both a container-wide and application-specific Seccomp system call policy that can be readily enforced while loading the container and launching the main program. We also show that further attack surface reduction is possible by enforcing fine-grained system call policies that do not only consider the system calls used by the target application, but also their argument values. The results of our experimental evaluation with a set of 27 Docker images show that applying container-wide filtering disables more than 145 system calls on average across the entire container, and application-specific filtering increases the number of filtered system calls by 25% on average, as many system calls used exclusively by utilities and scripts during the container’s initialization phase can be safely removed afterwards.
An analysis and review of robot magic shows

Abstract The field of humanoid robotics is constantly evolving, with new advances creating exciting opportunities for research and development. Especially in the entertainment area, robotics applications show significant growth potential. To guide and track the progress of robotics research, good benchmark problems are crucial, but especially investigating human–robot interaction capabilities is difficult. This paper examines robot magic shows as a benchmark and research direction for entertainer robots and discusses flexible and versatile system architectures for robot magicians in the humanoid robot application challenge competition since 2017. The goal is a detailed analysis of magic tricks and magician robots presented in front of audiences. This paper reviews the hardware components and examines the robot platforms of participating teams in the robot magic show, the types of magic performances, the magic tricks and tools, the algorithms for the magic tricks, and the framework for humanoid robots. By providing a comprehensive analysis of these elements, we can gain insight into the capabilities of advanced humanoid robots for high-performance magic tricks. In conclusion, this paper highlights the exciting potential of humanoid robotics and entertainment fusion. In addition, we analyze the use of humanoid robots in magic shows, presenting the industrial potential of entertainer robots.
How Expert Is the Crowd? Insights into Crowd Opinions on the Severity of Earthquake Damage

The evaluation of earthquake damage is central to assessing its severity and damage characteristics. However, the methods of assessment encounter difficulties concerning the subjective judgments and interpretation of the evaluators. Thus, it is mainly geologists, seismologists, and engineers who perform this exhausting task. Here, we explore whether an evaluation made by semiskilled people and by the crowd is equivalent to the experts’ opinions and, thus, can be harnessed as part of the process. Therefore, we conducted surveys in which a cohort of graduate students studying natural hazards (n = 44) and an online crowd (n = 610) were asked to evaluate the level of severity of earthquake damage. The two outcome datasets were then compared with the evaluation made by two of the present authors, who are considered experts in the field. Interestingly, the evaluations of both the semiskilled cohort and the crowd were found to be fairly similar to those of the experts, thus suggesting that they can provide an interpretation close enough to an expert’s opinion on the severity level of earthquake damage. Such an understanding may indicate that although our analysis is preliminary and requires more case studies for this to be verified, there is vast potential encapsulated in crowd-sourced opinion on simple earthquake-related damage, especially if a large amount of data is to be handled.
Emergence of the New Start Up Ecosystem: How Digital Transformation Is Changing Fintech and Payment System in Emerging Markets?

The use of crypto for conducting business presents a host of opportunities and challenges. By and large as with any frontier, there are both unknown dangers and strong incentives. Nowadays, the use of virtual currencies is more frequent in the financial transactions is the main reason for consider to use it. Both the primary and secondary data have been collected by the authors. Surveys and thematic analysis of the information have been done. It gave the clarity of the subject. In the results chapter, survey results have been presented in graphs. Survey has been conducted on the bankers and the responses have been further analyzed with SPSS. The statistical analysis has been performed ensuring the objectives of the research is satisfied. In the discussion chapter, the survey results and the statistical analysis have been further discussed. It has been observed that the digital transformation has helped in making the payments more effective. Correlation has been determined indicating that the digital transformation boost the growth of digital economy. The research helped in understanding the impact of cryptocurrency and the expansion of bitcoin on the digital economy of the emerging market during the pandemic. The research helped in understanding with the help of using secondary qualitative analysis by generating themes that digital economy impacts the emerging markets in a positive way. The research paper also helped in understanding that cryptos help the emerging markets a lot.
Evaluation of Data Visualizations with Bloom’s Six Levels of Understanding

Data visualization is a very relevant topic in today’s world full of data, people see this data in different forms and visualizations. Many studies examine different visualizations and bring new ones to the community. Nowadays it is vital to evaluate the types of visualizations and make decisions about what visualization to use based on the conducted analysis. That is why the evaluation of various types of visualization is an important and interesting topic that has been touched upon in various scientific papers. Since there is no clearly defined list of visualization types in the scientific community, and more importantly – methods for their evaluation, this issue is relevant for further study. This paper describes an experiment conducted amongst 9 respondents to examine the visualization evaluation method using Bloom’s taxonomy (which includes six levels of understanding). The study was inspired by an article written by Burns et al. where the authors apply a framework (based on these six levels of understanding) to evaluate the efficacy of data visualizations. Our experiment consisted of a set of questionnaires about the “classic” data visualization (bar chart) and two more modern and complex ones - infographics and data comics. We wanted to compare them by testing the understanding and perception of presented data by our participants, so all used visualizations have been created on the same data set related to Covid-19 statistics. Even though our research has several limitations we obtained interesting results and insights not only about the types of visualizations but also about the evaluation methodology itself.
Visualization of Business Processes Through Data Comics

Levels of achieving organizational goals of any company reflect the effectiveness of the enterprise management information system, especially with regard to human resources management aspects. Increasing the productivity of each employee reduces the cost of services and improves the quality of their provision. The main purpose of this article is to determine whether data comics is better and easier business process visualization format compared to a modeling language or not. The hypothesis was checked based on a case study about an internal business process of an IT company in Saint Petersburg. The object of study of this work is a company N - an enterprise in the oil and gas industry and its activities in terms of electronic document management. The subject of the work is the electronic document management system used at the enterprise. The sources for searching articles related to the topic of the study were Google.Scholar, ResearchGate, Scopus. The keywords for the search were Visualization of Business Processes, Data Comics, and combinations. Since data comics is quite a new method of visualization and it was necessary to go through practical research, the criteria for filtering were > 2015. However, for theoretical purposes, earlier works were reviewed. The analysis of the survey results is carried out and recommendations for its practical implementation are given. The results of the case study are encouraging for the use of data comics, especially for internal use in the IT company because of many processes that some employees do not correctly understand. Making this study let respondents acquainted with a new and unknown method of data comics (no one of the employees have ever come across it). Creation of data comics for one business process requires not more than a working day and there is no need to hire a new employee (a graphic designer, for example), as data comics modeling is an easy process and there a lot of special tools to draw them (canva.com, makebriefscomics.com, etc.) The further research may be based on a project of implementation data comics in the IT company firstly for internal purposes. After the implementation and getting results data comics may be applied to external business processes and involve customers of the company.
Using the Contrastive Language-Image Pretraining Model for Object Detection on Images Containing Textual Labels

Deep Learning Models or Neural Networks are loosely modeled based on the Human brain. Neural Networks consist of thousands of simple processing units that are densely interconnected, and are a means of performing machine learning. They aim to recognize patterns in data that enable a machine to perform some tasks based on analyzing several examples. One popular field of Deep Learning is Computer Vision that aims to simulate the complex visual system of human beings. An object detection model for instance, might be fed on thousands of images of objects to find visual patterns in the images that consistently correlate with particular labels. However, human visual perception is based on both visual and non-visual modalities of data. This paper aims to propose a Neural Network for Object detection that use various modalities of data, specifically Images and Text, to perform the object detection task
Analysis of ML-Based Classifiers for the Prediction of Breast Cancer

Breast cancer is a significant health issue affecting women globally. Early detection and diagnosis are crucial for effective treatment and improved survival rates. Machine learning (ML)-based classifiers have gained popularity in breast cancer prediction due to their ability to analyze large datasets and identify complex patterns. This study presents an analysis of ML-based classifiers for breast cancer prediction using the UCI Wisconsin breast cancer dataset. Extra Tree Classifier-based feature selection is used to identify the most important features for classification. The classifiers used in this study include Logistics Regression, SVM, kNN, Random Forest, Naive Bayes, and Decision Tree. The performance of each classifier is evaluated based on various metrics, including accuracy, sensitivity, and F1 score. The results show that SVM and Logistics Regression provide the highest performance. This study highlights the importance of feature selection in improving the performance of ML-based classifiers for breast cancer prediction. The findings of this study can contribute to the development of accurate and efficient breast cancer prediction models, which can aid in early detection and timely treatment, ultimately improving patient outcomes.
The Adaptation of the Discrete Rat Swarm Optimization Algorithm to Solve the Quadratic Assignment Problem

The Rat Swarm Optimization (RSO) algorithm is a nature-inspired optimization method that has been shown to be effective in solving continuous and discrete problems. After demonstrating its effectiveness in solving the well-known discrete traveling salesman problem, we aim to apply the RSO algorithm to another complex problem, the Quadratic Assignment Problem (QAP). The QAP is an NP-hard combinatorial problem that seeks to minimize the total cost of constructing and operating facilities, where the benefit of economic activity at any site depends on the presence of other facilities. We evaluated the proposed RSO algorithm on a set of benchmark instances from the QAPLIB library and compared its performance to other algorithms. Our results are encouraging and demonstrate the effectiveness of the proposed approach.
Ponzi Scam Attack on Blockchain

The concept of blockchain has been recently emerged as one of the areas of research. The blockchain enables fully decentralized system which is important feature of it. No one can control the operations of blockchain. It is an open form of distributed ledger which records the transactions carried between the different parties. A Ponzi scheme is a financial-based fraud or scam, in which company promises investors to return high money on the investment made by him/her in terms of money in several schemes by paying new investor’s money to old investor even though there is no circulating money. Recently, blockchain technology is suffering from number of such attacks, one of them is Ponzi scheme or attack. In this paper, architecture for detecting Ponzi scheme on blockchain has been proposed after identifying the research gap through detailed literature survey on Ponzi scheme.
An Approach for Sign Language Recognition with Deep Learning Algorithm

People will communicate with each other to exchange their ideas and thoughts. Among them, a group will suffer to share their feelings in the community. An alternate to such groups is sign language where they need to analyze each sign and convert into meaning information. In order to reduce the complexity of understanding, a model is proposed which converts the sign gestures into the normal people understandable form. In this work, a framework is developed to recognize the sign gestures and face expressions. Signs are recognized by the regions of interest and tracked by utilizing skin division element. Convolution neural networks algorithm helps in preparation and prediction of sign gestures. Such recognitions systems can be applied for various applications such as robotic motions, mechanized house, and game controllers. The proposal can be helpful to group of disabled people during communication with ordinary individuals.
Detection of Sign Language Using TensorFlow and Convolutional Neural Networks

Deaf or hard-of-hearing humans who use sign language can communicate with others by using a variety of text symbols or characters. Since hand signals require recollection of hand body postures, learning them can be difficult. This focuses the requirement for an autonomous gesture recognition system that is widely usable. The classification model image utilized in this experiment was produced using convolutional neural networks (CNNs) and the TensorFlow framework. Sign language detection is used for hearing the speech disable people this can help them in a phenomenal way to understand the communication, this is focused on sign gestures and would be processed using image analysis. After that, a classifier will be used to classify the different signs and forecast the output, and machine learning methods would be used to teach the data sets. In this project, the system is for persons who are unable to communicate verbally. This research aims to develop an intellect that is both sensitive and stable, allowing people with speech and hearing challenges to use it in everyday scenarios.
Hosting an API Documentation Portal Using Swagger and Various AWS

APIs (application programming interfaces) are essential for reusing software. By utilizing an API, software developers can connect features and functions present in some software with their own software. However, understanding how an API works is generally difficult and developers need to spend a significant amount of time getting familiar with the API. To make this task easier, API documentation is essential. In this paper, basic elements of API documentation, different tools available, and related works are discussed. Finally, a method to host an API documentation portal using Swagger, Amazon S3, Amazon Route53, AWS Certificate Manager, and CloudFront is presented. Several criteria are also discussed to evaluate the documentation.
Exploring Spatial Variation of Soils Using Self-organizing Maps (SOMs) and UHPLC Data for Forensic Investigation

Forensic soil analysis is a challenging task attributed to the complexity and diversity of soil composition and properties. Understanding the spatial variability of soils originating from a site can be paramount to forensic soil analysis. This work aims to assess the spatial variation of soils collected from the Nilai, Negeri Sembilan, Malaysia, using self-organizing maps (SOMs) and non-volatile organic profiles. A total of 11 soil samples were sampled and analyzed using ultra-high performance liquid chromatography (UHPLC) coupled with a UV-Visible detector. This resulted in 45 chromatograms, with each described by 2641 variables. The data was carefully preprocessed before studied using principal component analysis (PCA). Then, SOMs were applied to the first five principal components (PC1-PC5). The 45 chromatograms were split into three tight clusters based on the scores plot of PC1 and PC2. Meanwhile, the SOM plot of the 3 × 2 dimension further refined the three clusters into five homogeneous groups and one heterogenous group by simultaneously considering the PC1-PC5. In conclusion, the non-volatile organic profiles of 11 soil samples exhibit relatively low spatial variability.
A New Image Encryption Scheme Using RSA Cryptosystem and Arnold Algorithm

In the present period, it is a pivotal worry that appropriate encryption unscrambling ought to be applied to send the information starting with one spot and then onto the next place across the Web to forestall unapproved access. RSA requires log key sizes and more calculations, huge memory spaces, and furthermore a long execution time; it appears to be important to roll out certain improvements or play it safe during the execution of RSA to give the most extreme security level. Picture Cryptography is a unique sort of encryption method to conceal information in a picture for encryption and unscrambling of unique messages in view of some key worth. A picture document is chosen for encoding and unscrambling utilizing the key age method to move the information starting with one objective and then onto the next. This paper proposes an uneven encryption plot for shading pictures by presenting another effective triple-layered encryption conspire in view of the RSA cryptosystem.
A Novel Integer-Based Framework for Secure Computations over Ciphertext Through Fully Homomorphic Encryption Schemes in Cloud Computing Security

Nowadays many organizations compromise to store the sensitive data to the cloud servers and face challenges while computations are performed on encrypted data. In the cloud computing scenario, to perform any mathematical and analytical functions by the client, it follows the conventional encryption process to store in cloud server and perform any computations, data should be decrypted and resent to the cloud server after computation in encrypted format that results in the exploitation of secure data by learning about the data set by the hacker during the network transmission, and for delegation of computations it requires huge computational cost, time and complexity. To address these problems, Homomorphic Encryption schemes were introduced and it a special kind of cryptographic encryption algorithms that perform analytical calculations over encrypted data without decryption by the server and perform the computations on behalf of the client request. In this paper, we made an attempt to propose a novel secure fully homomorphic encryption scheme using the integers as input and developers allow performing computations on encrypted data, using bootstrapping for refreshing the ciphertext after threshold computations and it is well suited in data processing and data privacy applications including cloud computing. We used a SYTHER tool for automated security protocol verification for analyzing and detecting the falsification of the proposed algorithm and resulting in the unbounded less number of attackers and more secure in terms of integrity, authentication and non-repudiation. Finally, we compared our proposed scheme with an efficient existing state-of-the-art approach and proved our results are work moderates and reduce the time complexity with respect to the time taken for encryption, computation, decryption and ciphertext refresh procedure.