Reviewerly/exHarmony huggingface dataset:
An Enhanced Tendermint Consensus Protocol Powered by Elliptic Curve VRF for Beacon Chain Model

Blockchain technology has seen rapid adoption of Proof-of-Stake consensus mechanism in lieu of Proof-of-Work due to the former's efficiency and speed. One notable example of Proof-of-Stake is the Tendermint protocol, which has been powering the entirety of Cosmos system - an ecosystem of multiple interlocked chains. However, Tendermint's choice of deterministically deciding the next block proposer presents a huge window for malicious actors to prepare and coordinate attacks on upcoming validator nodes, possibly crippling the attacked chain. Furthermore, randomized number generation in this blockchain ecosystem still proves to be a challenge by reason of blockchain's inherent deterministic nature. Aiming at the above problems, in this paper, we propose an improvement over Tendermint consensus protocol, utilizing Elliptic Curve Verifiable Random Function, a fast and secure pseudorandom generation algorithm suitable for deterministic systems like blockchain. This novel approach will solve the problem of knowing the validator ahead of time, whilst the verifiable random function module will be capable of supplying reliable random numbers to the overlaying beacon chain - a blockchain capable of distributing random numbers to users through smart contracts, and even other blockchains through Cosmos' InterBlockchain Communication Protocol. The performed experiments with a prototype blockchain demonstrated that the new consensus protocol improves Tendermint's resilience against network-layer attack vectors, while maintaining adequate fairness and performance.
Blockchain Oracles: Implications for Smart Contracts in Legal Reasoning and Addressing the Oracle Problem

This paper presents a comprehensive investigation into the role, functionalities, and complexities of blockchain oracles, focusing particularly on the implications for smart contracts in legal reasoning contexts. Oracles serve as a vital bridge to smart contracts' inability to interact with external or "off-chain" data, enabling them to be used in a variety of real-world situations. Oracle's integration, however, introduces a number of complexities, including security vulnerabilities, collectively referred to as the Oracle Problem. In addition to a review of existing literature, we also provide a mathematical analysis quantifying the computational complexity associated with automating legal reasoning and a novel design framework aimed at establishing oracles that are secure, efficient, and legally compliant. The paper aims to serve as a foundational text for researchers, legal practitioners, and blockchain developers, advancing the academic discourse surrounding blockchain oracles and their role in smart contracts.
Cooperation Between The Cimahi Police Criminal Investigation Unit and the Banking Sector in Disclosing Social Engineering Cases

Information technology is increasingly advanced, coupled with the existence of the internet, which is now a global communication system that allows everyone around the world to meet and talk about almost anything. The internet has become a medium of communication, which is done through various media available on it. The internet provides various social media platforms, both for positive and negative reasons, and has become the order of the day because nowadays, the world is so tied to the internet. Unfortunately, not everyone uses the internet for a good cause. There are many people who use social networks to commit crimes, such as cyber-crime in the form of social engineering. Social engineering allows malicious hackers to gain unauthorized access to an organization's networks, user accounts and emails, databases, smart devices, and electronics, such as laptops, personal webcams, and sensors, including network connectivity that allows all of these objects to exchange data. These hackers use a variety of methods to carry out social engineering attacks. The technical complexity of the information systems used in locating, examining, and analyzing relevant transaction data requires sufficient time and technical expertise to gather robust evidence to support the case for social engineering, which involves in-depth analysis of bank transaction data related to the attack. This process can be time-consuming, especially if many transactions need to be traced and analyzed.
¿Qué es un código?

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsEllen BarnettEllen Barnett (ebarnet1@trinity.edu) is an assistant professor at Trinity University in San Antonio, Texas. Karina Méndez Pérez is a doctoral student at the University of Texas at Austin. Rocío Delgado is an associate professor at Trinity University.Karina Méndez PérezEllen Barnett (ebarnet1@trinity.edu) is an assistant professor at Trinity University in San Antonio, Texas. Karina Méndez Pérez is a doctoral student at the University of Texas at Austin. Rocío Delgado is an associate professor at Trinity University.Rocío DelgadoEllen Barnett (ebarnet1@trinity.edu) is an assistant professor at Trinity University in San Antonio, Texas. Karina Méndez Pérez is a doctoral student at the University of Texas at Austin. Rocío Delgado is an associate professor at Trinity University.
From the Editor’s Desk: Moving Toward 3D Instruction

"From the Editor’s Desk: Moving Toward 3D Instruction." Science Scope, 43(1), p. 1 Additional informationNotes on contributorsPatty McginnisPatty McGinnis is a teacher at Arcola Intermediate School in Eagleville, Pennsylvania. You can contact her at PATTYMCGINNIS1@GMAIL.COM or on Twitter: @patty_mcginnis.
Illuminating the Universe: Exploring a Digital Cosmos

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsElizabeth SchibukElizabeth Schibuk (schibuk@gmail.com) is a middle school science teacher at the Conservatory Lab Charter School in Dorchester, Massachusetts. Erika Wright is an education specialist and Mary E. Dussault is a science education program manager, both in the Science Education Department at the Center for Astrophysics | Harvard & Smithsonian in Cambridge, Massachusetts.Erika WrightElizabeth Schibuk (schibuk@gmail.com) is a middle school science teacher at the Conservatory Lab Charter School in Dorchester, Massachusetts. Erika Wright is an education specialist and Mary E. Dussault is a science education program manager, both in the Science Education Department at the Center for Astrophysics | Harvard & Smithsonian in Cambridge, Massachusetts.Mary E. DussaultElizabeth Schibuk (schibuk@gmail.com) is a middle school science teacher at the Conservatory Lab Charter School in Dorchester, Massachusetts. Erika Wright is an education specialist and Mary E. Dussault is a science education program manager, both in the Science Education Department at the Center for Astrophysics | Harvard & Smithsonian in Cambridge, Massachusetts.
From the Editor’s Desk: The Human Side of Our Dynamic Earth

"From the Editor’s Desk: The Human Side of Our Dynamic Earth." Science Scope, 46(1), p. 1 Additional informationNotes on contributorsPatty McginnisPatty McGinnis is an instructional coach and veteran middle school teacher. You can contact her at pattymcginnis1@gmail.com or on Twitter: @patty_mcginnis.
Adaptive Weighted Face Alignment by Multi-Scale Feature and Offset Prediction

Traditional heatmap regression methods have some problems such as the lower limit of theoretical error and the lack of global constraints, which may lead to the collapse of the results in practical application. In this paper, we develop a facial landmark detection model aided by offset prediction to constrain the global shape. First, the hybrid detection model is used to roughly locate the initial coordinates predicted by the backbone network. At the same time, the head rotation attitude prediction module is added to the backbone network, and the Euler angle is used as the adaptive weight to modify the loss function so that the model has better robustness to the large pose image. Then, we introduce an offset prediction network. It uses the heatmap corresponding to the initial coordinates as an attention mask to fuze with the features, so the network can focus on the area around landmarks. This model shares the global features and regresses the offset relative to the real coordinates based on the initial coordinates to further enhance the continuity. In addition, we also add a multi-scale feature pre-extraction module to preprocess features so that we can increase feature scales and receptive fields. Experiments on several challenging public datasets show that our method gets better performance than the existing detection methods, confirming the effectiveness of our method.
Do you see any problem? On the Developers Perceptions in Test Smells Detection

Developers are continuously implementing changes to meet demands coming from users. In the context of test-driven development, before any new code is added, a test case should be written to make sure new changes do not introduce bugs. During this process, developers and testers might adopt bad design choices, which may lead to the introduction of the so-called Test Smells in the code. Test Smells are bad solutions for implementing or designing test code. We perform a broader study to investigate the participants' perceptions about the presence of Test Smells. We analyze whether certain factors related to the participant' profiles concerning background and experience may influence their perception of Test Smells. Also, we analyze if the heuristics adopted by developers influence their perceptions about the existence of Test Smells. We analyze commits of open source projects to identify the introduction of Test Smells. Then, we conduct an empirical study with 25 participants that evaluate instances of 10 different smell types. For each Test Smell type, we analyze the agreement among participants, and we assess the influence of different factors on the participants' evaluations. Altogether, more than 1250 evaluations were made. The results indicate that participants present a low agreement on detecting all 10 Test Smells types analyzed in our study. The results also suggest that factors related to background and experience do not have a consistent effect on the agreement among the participants. On the other hand, the results indicate that the agreement is consistently influenced by specific heuristics employed by participants. Our findings reveal that the participants detect Test Smells in significantly different ways. As a consequence, these findings introduce some questions concerning the results of previous studies that do not consider the different perceptions of participants on detecting Test Smells.
Unveiling Quality in Chatbot Conversations: Quantitative Analysis of Chatbot Requirements

As conversational assistants and natural language interfaces proliferate, the demand for a precise understanding of quality software requirements for chatbots becomes increasingly critical. In this work, we adopted a quantitative methodology, scrutinizing a dataset composed of conversational requirements from a diverse range of agile projects for chatbot development, and identified meaningful patterns in the language and structure utilized. Our investigation led to significant findings, revealing the importance of structured documentation, conversation flow, and user interaction in the development of chatbots, with the most desired quality attributes being capability, naturalness, straightforwardness, and clarity. In addition, a significant emphasis was placed on feature development and meeting acceptance criteria. The research also illuminated the iterative nature of chatbot development, with a recurrent presence of verbs related to improvement or refactoring. While less pronounced, the roles of documentation and testing in ensuring chatbot quality and effectiveness were also noted. This work provides valuable insights into chatbot requirements management and the significance of quality attributes in chatbot development.
Unveiling the Teaching Methods Adopted in DevOps Courses

DevOps practices emerged as an approach focused on coordinating development and operation teams and ensuring rapid and reliable new software releases. Despite the significant demand for preparing new professionals in these practices, there is a reduced number of studies investigating teaching methods in DevOps Education. We investigate the teaching methods reported in the literature. We also performed an analysis based on interviews to identify teaching methods. For both cases, we analyzed their relationship with DevOps educational challenges. Our findings suggest that educators should favor practical methods (e.g., project-based learning) in a collaborative learning environment to teach DevOps.
Enhancing LGPD Compliance: Evaluating a Checklist for LGPD Quality Attributes within a Government Office

The purpose of establishing the Brazilian General Data Protection Law (LGPD) was to introduce regulations for organizations regarding collecting, transmitting, and storing individuals' data. However, understanding the LGPD poses a significant challenge for requirements analysts, particularly in extracting and operationalizing privacy requirements. This experience report proposes to assess and enhance an existing checklist known as LGPD-Check, which serves as a method for evaluating software systems' compliance with the quality attributes specified by the LGPD. The assessment checklist consists of multiple attributes distributed among several evaluation categories, including data transparency, holder consent, holder's rights, data security, and controller's responsibility. The LGPD-Check was applied within a government office to evaluate the checklist's effectiveness, involving eight IT professionals responsible for different web applications, followed by a focus group meeting. Moreover, we evaluate the office's systems regarding compliance with the LGPD. Preliminary findings indicate that the current version of the checklist facilitates the identification of issues related to software systems' compliance with the LGPD and shows that we have a long journey to attend the LGPD in our software systems.
Scrum in Practice: City Reconstruction as a Pedagogical Game Challenge

This paper presents an experience report on the dynamics of the Scrum Game Challenge through the customization of LEGO4Scrum. The game's objective is to teach Scrum concepts through the simulation of the reconstruction of a city of Mariana, Minas Gerais, exploring the use of Scrum and experimenting with tools used by the software industry. As a result, we present the customizations performed, as well as the students' and teachers' perceptions of the advantages and disadvantages of applying the practice of the dynamic in the classroom. Our findings reveal that most students felt that the concepts covered in the dynamics could be applied during their professional practice as software developers in the industry. Moreover, the dynamic allowed them to understand how the application of Scrum works during the software development process in a clear, easy, and fun way. The students and teachers also stated that besides theoretical knowledge, it is essential to consider practical knowledge about the application of agile software development methodologies during Computer Science coursework.
Soft Skills of Software Developers: Exploring the Pandemic's Impact of COVID-19

Context: The software industry has invested efforts to identify and understand the soft skills required for good software developers in recent years. Problem: However, with the face of the COVID-19 pandemic, it was observed that the soft skills of developers suffer impacts, mainly on the mental health and well-being of people and their families. Objective: In this context, this work aims to obtain an overview of the impacts that the soft skills of software developers suffered when facing the COVID-19 pandemic. Method: For this, we conducted a survey with software developers to obtain an overview of this scenario. Results: we received 101 responses from different states in Brazil, in which software developers indicated which soft skills they believe were positively, negatively impacted or those that were not affected. In addition, respondents also indicated which soft skills they believe were acquired after this event. Conclusion: From the results, we observed that most developers believe that their skills were positively impacted after the COVID-19 pandemic. Among them, the following stand out: flexibility, empathy, adaptability, fast learning, and desire to learn.
A Case Study on Data Science Processes in an Academia-Industry Collaboration

Data Science (DS) is emerging in major software development projects and often needs to follow software development practices. Therefore, DS processes will likely continue to attract Software Engineering (SE) practices and vice-versa. This case study aims to map and describe a software development process for Machine Learning(ML)-enabled applications and associated practices used in a real DS project at the Recod.ai laboratory in collaboration with an industrial partner. The focus was to analyze the process and identify the strengths and primary challenges, considering their expertise in robust ML practices and how they can contribute to general software quality. To achieve this, we conducted semi-structured interviews and analyzed them using procedures from the Straussian Grounded Theory. The results showed that the DS development process is iterative, with feedback between activities, which differs from the processes in the literature. Additionally, this process presents a greater involvement of domain experts. Besides, the team prioritizes software quality characteristics (attributes) in these DS projects to ensure some aspects of the final product's quality, i.e., functional correctness and robustness. To achieve those, they use regular accuracy metrics and include explainability and data leakage as quality metrics during training. Finally, the software engineer's role and its responsibilities differ from those of a traditional industry software engineer, as s/he is involved in most of the process steps. These characteristics can contribute to high-quality models achieving the partner needs and, consequently, relevant contributions to the intersection between SE and DS.
DATA-ORIENTED INVERSE KINEMATICS USING THREE CAMERAS’ POINTS OF VIEW

Robots have advantages in operating in hard-to-reach environments for humans, but the modeling of their inverse kinematics is a complex task. Therefore, this article addresses inverse kinematics in soft robots, which present modeling and control challenges due to the non-linear properties of materials. The aim of this work was to present a data-driven inverse kinematics method using three-camera viewpoints to build a robotic skeleton. For the development of the model, three neural network topologies were used: Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Transformer, with the last one presenting a better performance.
AN METHOD TO STOP DIFFERENTIAL MODELS OF FALLING OVER

Robotics is a rapidly growing field with a profound impact on various industries, aiming to design machines that assist humans in practical tasks. Autonomous navigation, a critical aspect of robotics, enables robots to move and operate independently without human intervention. However, navigating different environments presents numerous challenges, including the risk of robots tipping over, leading to damage or potential harm to humans. This article proposes a solution to prevent tipping in differential robots during autonomous navigation. The solution employs behavior trees, a computationally efficient approach, integrated into the robot's navigation system. By continuously monitoring data from an IMU sensor, the plugin effectively detects oscillatory movements and takes corrective actions, ensuring stable and safe robot operation.
Knowledge economy and tools for its implementation in classical banking

The topic of the convergence of the knowledge economy and classical banking is extremely relevant, as it allows to analyze the change in consumer habits and customer expectations, to determine the competitive advantages of technological startups and fintech companies and the challenges of cyber security and the risks of big data analysis, to record the need to create innovative products and services. Implementation of knowledge economy technologies in the financial sector of Ukraine. The rapid development of the financial services market necessitates a deep understanding and implementation of "knowledge economy" methods in the financial sector of the domestic economy. Because the knowledge economy is not an exclusively economic concept. It directly concerns the organization of society's life (people's way of life, worldview and culture, individual and collective life activities). Conceptual definitions of the knowledge economy lead us to generalize that the "knowledge economy" is a convergence of people's aspirations to organize and arrange their lives, based on objective knowledge of reality. Social systems built on the narratives of the "knowledge economy" transform knowledge into more advanced products (goods, services, technologies, etc.) and, which is a mandatory element, increasing the well-being and quality of human life. Knowledge becomes a special and primary factor. At the macro level, the practical implementation of the principles of the "knowledge economy" forms an economy with the predominance of high-tech industries in industry and the development of the knowledge-intensive service sector, which allows us to approach a more perfect social organization. At the micro level, the knowledge economy focuses attention on the justification of the cost of knowledge production. The result of the work was an analysis of the tools of the knowledge economy in the context of the financial market, the competitive advantages of fintech companies were determined, and innovative approaches or tools that could be useful for classical banks and how to implement them were identified.
SMART SENSORS FOR ANOMALY DETECTION DRIVEN BY TINYML IN IOT ARCHITECTURE

In general, the architecture of Internet of Things (IoT) systems is organized in layers, starting with the perception layer, or physical computing, which involves the sensing process, followed by communication, processing, or middleware layer, application, and business layers. In this paper, we propose a new middleware layer positioned just above the perception layer, by integrating more powerful microcontrollers capable of running artificial intelligence algorithms, known as TinyML. We have developed an algorithm based on extreme value theory for real-time anomaly detection. The results indicate the feasibility of implementing the proposed architecture for real-time monitoring and automated decision-making in industrial systems.