Reviewerly/exHarmony huggingface dataset:
Developing Secure Messaging Software using Post-Quantum Cryptography

In this paper, a technique to develop a secure messaging service utilizing a new post-quantum cryptosystem, termed CryptoMess, is proposed. Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) is utilized to secure key exchange paired with the AES algorithm to protect message content in communication. At the same time, the Rainbow post-quantum digital signature technology is incorporated to assure the integrity and authenticity of communications between the sender and the recipient. As a consequence, the messaging program is able to exchange messages between users, assuring safety, security, integrity, and authenticity. The performance of the program has a transmitting rate of approximately 0.26 s and a receiving rate of approximately 0.22 s. The message signing time is approximately 0.027 s, the message verification speed is approximately 0.22 s, and the key exchange time is approximately 0.0017s.
AI in higher education: Modelling the antecedents of AI usage and effects on 21st century employability skills among postgraduate students in Ghana

The utilisation of Artificial intelligence (AI) in higher education has been a contemporary issue attracting varied perspectives. However, the nuances among the antecedents of AI usage among postgraduate students and the acquisition of 21st-century employability skills are under-researched. This study sought to fill the chasm by adopting a concurrent integrated mixed method to collect data using a questionnaire with closed-ended and open-ended items from 294 postgraduate students in Ghana. Descriptive statistics and partial least squares structural equation modelling (PLS-SEM) techniques were employed for the quantitative data analyses while thematic pattern matching was used to analyse the qualitative data gathered. Results from the descriptive analysis revealed that the most prevalent AI platforms used by postgraduate students were ChartGPT and Quillbot. The two most important uses of such AI tools were just used for learning in general; and searching for literature. The PLS-SEM paths modelling showed that antecedents of postgraduate students' AI usage were benefits, challenges and platform availability. Finally, both AI usage and AI challenges were significant predictors of postgraduate students’ acquisition of 21stcentury employability skills. However, the relationship between AI challenges and the acquisition of 21st-century employability skills was fully mediated by AI usage. Based on the results, recommendations were made for policy and practice of AI usage in higher education.
Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

Abstract The elliptic curve discrete logarithm problem (ECDLP) is a popular choice for cryptosystems due to its high level of security. However, with the advent of the extended Shor’s algorithm, there is concern that ECDLP may soon be vulnerable. While the algorithm does offer hope in solving ECDLP, it is still uncertain whether it can pose a real threat in practice. From the perspective of the quantum circuits of the algorithm, this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s algorithm. We give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime fields, including modular subtraction, three different modular multiplication, and modular inverse. Additionally, we incorporate and improve upon windowed arithmetic in the circuits to reduce the CNOT-counts. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we focus on minimizing the number of CNOT gates in the circuit, which greatly affects the running time of the algorithm on an ion trap quantum computer. Specifically, we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts, along with improved constructions for modular inverse, point addition, and windowed arithmetic. Next, we precisely estimate that, to execute the extended Shor’s algorithm with the improved circuits to factor an n -bit integer, the CNOT-count required is $$1237n^3/\log n+2n^2+n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1237</mml:mn> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>/</mml:mo> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:mn>2</mml:mn> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> . Finally, we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.
Quantum Simulation of Molecular Response Properties in the NISQ Era

Accurate modeling of the response of molecular systems to an external electromagnetic field is challenging on classical computers, especially in the regime of strong electronic correlation. In this article, we develop a quantum linear response (qLR) theory to calculate molecular response properties on near-term quantum computers. Inspired by the recently developed variants of the quantum counterpart of equation of motion (qEOM) theory, the qLR formalism employs "killer condition" satisfying excitation operator manifolds that offer a number of theoretical advantages along with reduced quantum resource requirements. We also used the qEOM framework in this work to calculate the state-specific response properties. Further, through noiseless quantum simulations, we show that response properties calculated using the qLR approach are more accurate than the ones obtained from the classical coupled-cluster-based linear response models due to the improved quality of the ground-state wave function obtained using the ADAPT-VQE algorithm.
Neural networks, AI, and the goals of modeling

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.
Customization of a deep neural network using local data for seismic phase picking

Deep-learning (DL) pickers have demonstrated superior performance in seismic phase picking compared to traditional pickers. DL pickers are extremely effective in processing large amounts of seismic data. Nevertheless, they encounter challenges when handling seismograms from different tectonic environments or source types, and even a slight change in the input waveform can considerably affect their consistency. Here, we fine-tuned a self-trained deep neural network picker using a small amount of local seismic data (26,875 three-component seismograms) recorded by regional seismic networks in South Korea. The self-trained model was developed using publicly available waveform datasets, comprising over two million three-component seismograms. The results revealed that the Korean-fine-tuned phase picker (KFpicker) effectively enhanced picking quality, even when applied to data that were not used during the fine-tuning process. When compared to the performance of the pre-trained model, this improvement was consistently observed regardless of variations in the positions of seismic phases in the input waveform, Furthermore, when the KFpicker predicted the phases for overlapping input windows and used the median value of probabilities as a threshold for phase detection, a considerable decrease was observed in the number of false picks. These findings indicate that fine-tuning a deep neural network using a small amount of local data can improve earthquake detection in the region of interest, while careful data augmentation can enhance the robustness of DL pickers against variations in the input window. The application of KFpicker to the 2016 Gyeongju earthquake sequence yielded approximately twice as many earthquakes compared to previous studies. Consequently, detailed and instantaneous statistical parameters of seismicity can be evaluated, making it possible to assess seismic hazard during an earthquake sequence.
Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images

Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models' robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL's transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.
Cryptoassets: Definitions and accounting treatment under the current International Financial Reporting Standards framework

Abstract This paper provides a first comprehensive definition of cryptoassets for accounting purposes in the types of payment tokens, electronic money (e‐money) tokens, utility tokens and security tokens. The delivery of definitions for accounting purposes addresses some of the concerns raised by the European Financial Reporting Advisory Group (EFRAG) discussion paper and helps accounting regulators adapt current International Financial Reporting Standards (IFRS) standards to blockchain‐based tokens' taxonomy and nature. The paper helps policymakers reconcile Markets in Cryptoassets Regulation Proposal's (MiCA) definitions and classification of cryptoassets with the EFRAG's specific needs for clarification and/or amendment in the IFRS standards and contributes to providing an accounting guide for practitioners in their financial disclosure.
A comparative experiment between textual requirements and model‐based requirements on proxies for contractual safety

Abstract Requirements form the backbone of contracting in acquisition programs. Requirements define the problem boundaries within which contractors try to find acceptable solutions (design systems). At the same time, requirements are the criteria by which a customer measures the extent that their contract has been fulfilled by the supplier. In this context, the quality of a requirement set is determined by the level of contractual safety that it yields. Unfortunately, textual requirements do not provide acceptable levels of contractual safety, as they remain a major source of problems in acquisition programs. Model‐based requirements have been proposed as an alternative to textual requirements, although this promise has not been demonstrated yet. This paper addresses the main question of whether using model‐based requirements improves the contractual safety of acquisition programs compared to using textual requirements. The level of adequate applicability, bounding, necessity, and completeness achieved by model‐based requirements are empirically measured using an experimental study with aerospace engineering and industrial and systems engineering students on a space system application. The results show that model‐based requirements outperform textual requirements in these four variables.
Voice Assistant Using Python

The "Voice Assistant Using Python" project presents the development and implementation of a versatile voice-controlled assistant leveraging Python programming language and associated libraries. The objective of this project was to create an interactive and intuitive system capable of performing various tasks through voice commands, aiming to enhance user convenience and efficiency. The project utilized Python's libraries such as speech recognition, natural language processing, and text-to-speech conversion to enable seamless interaction between users and the voice assistant. The assistant was designed to interpret spoken commands, process natural language, and execute corresponding actions, including retrieving information, managing tasks, controlling applications, and performing basic tasks based on user instructions. The development process involved the integration of speech recognition algorithms to comprehend and interpret user inputs, followed by the implementation of intelligent responses and actions using Python's libraries. Additionally, the project incorporated continuous improvement mechanisms to enhance the assistant's accuracy, responsiveness, and functionality. The report details the methodology, tools, and techniques employed in the development of the voice assistant, along with insights gained during the implementation phase. Results include the successful creation of a functional voice-controlled assistant capable of executing a range of predefined tasks based on voice commands, showcasing the potential for further enhancements and future applications in the domain of voice-based user interfaces. This project not only demonstrates the capabilities of Python for creating interactive voice-controlled systems but also underscores the possibilities of integrating such technology into daily tasks, thereby contributing to the evolution of user-friendly, voice-enabled applications.
Texture Analysis Method – A Survey

Analysing texture plays a pivotal part in image processing, computer vision, and pattern recognition. It plays a vital part in de ciphering complex visual information by characterizing the spatial arrangement of pixels within an image. This check aims to give a comprehensive overview of colorful texture analysis styles, their operations, and recent advancements in the field. From classic statistical approaches to slice- edge deep literacy ways, this check will claw into the rich geography of texture analysis, offering perceptivity into its significance and eventuality for different disciplines similar as medical imaging, remote seeing, and artificial quality control. “The approaches for analysing texture are veritably different, and differ from each other substantially by the system used for rooting textural features. Four orders can be defined 1) Statistical styles. 2) Structural styles. 3) Model grounded styles. 4) Transfigure- grounded styles
Heart Disease Prediction using Machine Learning

This study explores the application of machine learning algorithms for heart disease prediction. Leveraging a dataset comprising various health parameters, we employ classification models to analyze and predict the likelihood of heart disease. Results demonstrate the effectiveness of the proposed approach, showcasing its potential in early detection and preventive healthcare measures
Tool to Summerize Text

Text summarization is the process of making a synopsis from a given text document while keeping the important information and meaning of it. Automatic summarization has become an essential method for accurately locating significant information in vast amounts of text in a short amount of time and with minimal effort. In this project, we propose to implement a web application that can summarize a text or a Wikipedia link. We have additionally been given an opportunity to compare different methods of summarization. Problem Statement - The tremendous abundance of material available on the internet has produced an odd paradox: people are immersed in information, yet they are yearning for wisdom. It is tough to keep up with the internet's daily production of billions of articles. Is there a method to absorb information more effectively in this case without increasing reading time? We are proposing for the above problem a Text Summarizer web app using NLP and NLTK libraries.
The governance technology for blockchain systems: a survey

Abstract After the Ethereum DAO attack in 2016, which resulted in significant economic losses, blockchain governance has become a prominent research area. However, there is a lack of comprehensive and systematic literature review on blockchain governance. To deeply understand the process of blockchain governance and provide guidance for the future design of the blockchain governance model, we provide an in-depth review of blockchain governance. In this paper, first we introduce the consensus algorithms currently used in blockchain and relate them to governance theory. Second, we present the main content of off-chain governance and investigate two well-known off-chain governance projects. Third, we investigate four common on-chain governance voting techniques, then summarize the seven attributes that the on-chain governance voting process should meet, and finally analyze four well-known on-chain governance blockchain projects based on the previous research. We hope this survey will provide an in-depth insight into the potential development direction of blockchain governance and device future research agenda.
Can physics influence world trade?

Abstract The aim of our study is to demonstrate the possibilities of forming students’ interdisciplinary thinking using the analysis of real events. In March 2021, maritime traffic in the Suez Canal was blocked for six days by a container ship. It led to delays in the delivery of goods of approximately 400 million dollars per hour. To analyze this event we used knowledge from seven areas of science: physics, geography, chemistry, engineering, geology, astronomy, economics. The use of an integrated approach made it possible to formulate the consequences of the accident, determine the possible causes of the accident, and substantiate the expediency of the methods of rescue operations. We analyzed only the given facts in news from a scientific point of view. Our analysis does not draw any final conclusions. Project-based learning is proposed as a teaching method in our case. It involves independent work with various sources of information, analysis and systematization of facts from various branches of science, compilation of a list of key words for each branch of science, formulation of conclusions. These skills are useful for the formation of interdisciplinary thinking.
Data science skills for the next generation of statisticians

This paper analyses the future prospects of statistics as a profession and how data science will change it. Indeed, according to Hadley Wickham, Chief Scientist at Rstudio, “a data scientist is a useful statistician”, establishing a strong connection between data science and applied statistics. In this direction, the aim is to look to the future by proposing a structural approach to future scenarios. Some possible definitions of data science are then discussed, considering the relationship with statistics as a scientific discipline. The focus then turns to an assessment of the skills required by the labor market for data scientists and the specific characteristics of this profession. Finally, the phases of a data science project are considered, outlining how these can be exploited by a statistician.
Impacts of using PDCA in the requirements specification process

The requirements specification plays a fundamental role in the software development process, impacting the final product's quality directly. However, many projects have faced challenges related to the requirements specification. In this context, this article presents an experience report of implementing improvements in the requirements specification process of a software project in an academia-industry partnership. This report highlights the importance of enhancing that process to address issues associated with extensive and complex requirements documentation. Thus, we used the PDCA cycle (Plan-Do-Check-Act) and apply the lessons learned from the PDCA's Check stage to improve the requirements specification process, making it clearer, more concise, and comprehensible for the stakeholders in the project. This article provides detailed descriptions of the PDCA cycle stages such as planning improvements, implementing actions and evaluation results and lessons learned. This PDCA-based approach showed to be effective in identifying and resolving problems in requirements specification activities, promoting positive changes in the requirements process.
Vocabulary of Flaky Tests in Javascript

research-article Share on Vocabulary of Flaky Tests in Javascript Authors: Rafael Rampim Soratto Programa de Pós-Graduação em Ciência da Computação, Universidade Tecnológica Federal do Paraná, Brazil Programa de Pós-Graduação em Ciência da Computação, Universidade Tecnológica Federal do Paraná, Brazil 0009-0009-3083-4210Search about this author , Marco Aurélio Graciotto Silva Departamento Acadêmico de Computação, Universidade Tecnológica Federal do Paraná, Brazil Departamento Acadêmico de Computação, Universidade Tecnológica Federal do Paraná, Brazil 0000-0002-1737-8240Search about this author Authors Info & Claims SBQS '23: Proceedings of the XXII Brazilian Symposium on Software QualityNovember 2023Pages 168–177https://doi.org/10.1145/3629479.3629487Published:06 December 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Twenty one years of SBQS (2002 to 2022): A Comprehensive Descriptive and Meta-Scientific Analysis

As the largest scientific conference in Brazil devoted to software quality, the Brazilian Software Quality Symposium (SBQS) groups and pioneers these area in the country, Brazilian institutions and scholars have advanced software quality since 2002. The goal of meta-research is to investigate, promote, and uphold the integrity of rigorous scientific procedures, comprehending a meta-scientific social and cultural complexity through a diverse and prolific community and scientific activity. We present an analytical-descriptive overview of different SBQS aspects covering all editions, 2002 – 2022, based on data from its publications and derivatives, such as more prolific authorship, institutional collaborations, gender distribution, and abstract analysis. Additionally, we calculate and analyze social network metrics, generating collaboration graphs. SBQS features a connected and involved community, female presences in high production and collaboration positions; there was a predominance of Brazilian Portuguese as the main language until 2017, being surpassed by English in the following years. UFRJ stands out in production and authorship, isolated at the top; in terms of collaboration, UFRJ, UFAM, UNIRIO stand out. SBQS authorship are concentrated mainly in the Southeast region of Brazil. The analysis of the abstracts and keywords shows consistency and cohesion between the primary elements of the event and the publications.