Reviewerly/exHarmony huggingface dataset:
A Survey and Comparison of Post-Quantum and Quantum Blockchains

Blockchains have gained substantial attention from academia and industry for their ability to facilitate decentralized trust and communications. However, the rapid progress of quantum computing poses a significant threat to the security of existing blockchain technologies. Notably, the emergence of Shors and Grovers algorithms raises concerns regarding the compromise of the cryptographic systems underlying blockchains. Consequently, it is essential to develop methods that reinforce blockchain technology against quantum attacks. In response to this challenge, two distinct approaches have been proposed. The first approach involves post-quantum blockchains, which aim to utilize classical cryptographic algorithms resilient to quantum attacks. The second approach explores quantum blockchains, which leverage the power of quantum computers and networks to rebuild the foundations of blockchains. This paper aims to provide a comprehensive overview and comparison of post-quantum and quantum blockchains while exploring open questions and remaining challenges in these domains. It offers an in-depth introduction, examines differences in blockchain structure, security, privacy, and other key factors, and concludes by discussing current research trends.
Developing the Concept of Methodological Support for Designing and Assessing the Efficiency of Information Protection Systems of Standard Information Systems Considering Their Vulnerabilities

The Information Protection System (IPS) is an integral part of any Information System (IS). To develop an optimal IPS model at the earliest stages of the IS lifecycle, it is necessary to develop IS resource and threat models. This study is devoted to developing a specific model of IS resources, allowing a detailed description of the relationship between resources and business processes and developing an IS threat model to describe in detail the relationships between threat implementations, various IS vulnerabilities, and the relationships between them. To solve these problems, this study used the methods of set theory, graph theory, probability theory, game theory, random processes theory, mathematical logic, and object-oriented approach. This study simulated different variants of the IPS and found that only a balanced IPS project met the Pareto demands. The projects where the emphasis is on countering only external or internal threats do not meet these demands.
Radar Jamming Recognition via a New Siamese Network

Radar jamming signal identification at low JNR and with limited training data are both open problems. Though multiple studies were performed previously, there still existed two problems needed to be solved. On the one hand, it is difficult to obtain discriminative presentations from the radar jamming signal that are accompanied with the solid Gaussian White Noise (GWN). On the other hand, the available data for training is difficult to obyain from the actual complex noise environment. To solve these problems, we propose a novel siamese network architecture with self attention named SA-Siam for Radar jamming signal classification. Firstly, transforming the jamming signal to the time-frequency (TF) domain where can represent higher dimensional information. Then the intra-class aggregation and inter-class separability of radar jamming signals are enhanced through the siamese network which is beneficial to learn more discriminative features from TF image especially in the case of limited data. In addition, the self attention block (SA) can further capture spatial correlations of the TF image so as to improve the anti-noise performance of the siamese network. We conducted quantitative and qualitative experiments on own dataset with a set of Deep CNNs and classical siamese algorithms. The results verify that our proposed SA-Siam can fully explore the representation of noised jamming data and dramatically improve the radar jamming classification performance under limited training data.
The effectiveness of Jigsaw cooperative learning model in developing students’ oral communication skills and cognitive learning outcomes

This study aims to determine the effectiveness of Jigsaw cooperative learning model in developing communication skills and cognitive learning outcomes of students. This research using the one group pretest-posttest design. The sample is the students of class X MIA 1 who were selected through purposive sampling technique. Data on oral communication skills were carried out by self-assessment, peer assessment and observation which were analyzed using a Likert scale. Meanwhile, data on cognitive learning outcomes were obtained through pretest and posttest scores, then analyzed by performing the n-gain score test. The research data shows that students have oral communication skills in the "high" category, which is as much as 77%. The highest verbal communication skills of students are found in the indicator of delivering detailed information with the "very high" category, while the lowest communication skills are found in the indicator of giving questions in the "enough" category. Then for learning outcomes, statistically student learning outcomes showed a significant difference between initial understanding (pretest) and understanding after learning (posttest) indicated by a significant N-gain value of 0.73 with high criteria. Thus, it can be concluded that the Jigsaw cooperative learning model is effective in developing communication skills and cognitive learning outcomes of students.
Design, Control, and Motion Strategy of TRADY: Tilted‐Rotor‐Equipped Aerial Robot With Autonomous In‐Flight Assembly and Disassembly Ability

Aerial Robot with the Ability to Assemble in Mid-Air In article 2300191, Junichiro Sugihara, Moju Zhao, and co-workers introduce TRADY, an innovative aerial robot. TRADY can autonomously assemble/disassemble mid-air, enhancing control and torque. The prototype boasts a 90% success rate in the assembly motion, delivering nine times the torque of a single unit when assembled.
Navigating Financial Evolution: Business Process Optimization and Digital Transformation in the Finance Sector

Purpose: This study presents a thorough analysis of the financial industry's digital transformation environment with a particular emphasis on business process improvement. Financial institutions are navigating a changing environment where cutting-edge technology is being employed widely. Financial operations are changing as a result of advances in cloud computing, blockchain, as well as artificial intelligence (AI), which enable scalability, and cost-efficiency, along with improved consumer experiences. However, this change is not without its difficulties, such as the complexity of regulatory compliance, worries about data security, including the necessity of talent acquisition. The purpose of the study is to develop an in-depth assessment of the alternations in numerous financial aspects, where there has been a rise in process optimization and digital transformation. With the help of the study, the examination of the various measures included with the aid of digital technology for the intensification of company processes optimization has been achieved.&#x0D; Methodology: The methodology implemented in the development of the study is that of secondary analysis. Through the application of interpretivism, addressing the issues have been provided, where deductive reasoning has been inculcated for reaching the expected outcome. The collection of information had been enabled from academic journals and other data sources from PubMed, IEEE Xplore, and Google Scholar. The application of literature analysis has been enabled for assessing the various concepts.&#x0D; Findings: As per the findings of the study, it has been noted that digital revolution has been extremely fruitful for the growth of financial services. A harmonic balance between technological innovations including compliance needs to be attained via cooperative efforts between financial institutions and regulatory organizations. Efficiency gains, particularly in risk assessment procedures, are being driven by robotic process automation (RPA) and AI-based algorithms. This study highlights the necessity of constant flexibility inside financial institutions to be robust as well as competitive in the rapidly changing digital ecosystem. AI has helped in increasing data security within the financial firms and increased the rates of customer satisfaction.&#x0D; Unique Contribution to Theory, Policy and Practice: The unique contribution of the study towards the theory, policy and practices relates to the betterment of theory of digital disruption for increasing the overall potential of the digital services.
Unsupervised Domain Adaptation for Mitigating Sensor Variability and Interspecies Heterogeneity in Animal Activity Recognition

Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability and the difficulty of obtaining labeled datasets. To address this issue, this study intensively investigates the impact of unsupervised domain adaptation (UDA) for AAR. We compared three distinct types of UDA techniques: minimizing divergence-based, adversarial-based, and reconstruction-based approaches. By leveraging UDA, AAR classifiers enable the model to learn domain-invariant features, allowing classifiers trained on the source domain to perform well on the target domain without labels. We evaluated the effectiveness of UDA techniques using dog movement sensor data and additional data from horses. The application of UDA across sensor positions (neck and back), sizes (middle-sized and large-sized), and gender (female and male) within the dog data, as well as across species (dog and horses), exhibits significant improvements in the classification performance and reduced the domain discrepancy. The results highlight the potential of UDA to mitigate the domain shift and enhance AAR in various settings and for different animal species, providing valuable insights for practical applications in real-world scenarios where labeled data is scarce.
Improving Sentiment Classification for Hotel Recommender System through Deep Learning and Data Balancing

A recommender system is a type of information filtering system that predicts and recommends items or products to users based on their preferences and past behaviors. It is commonly used in e-commerce and social media to suggest items that a user may be interested in purchasing, reading, watching, or listening to. Sentiment analysis is an area of natural language processing that has emerged as a popular way for organizations to detect and categorize opinions about a product, idea or service. In recent years, many attempts have been made to apply sentiment analysis in designing recommender systems, in order to recommend various items, such as hotels. It is thought that providing a quality hotel suggestion based on the requirements and preferences of users is a challenge and, naturally, alluring effort for tourism applications. In this paper, the quality of decision making for hotel recommender system based on sentiment analysis, deep learning and data balancing techniques has been improve. Multiple approaches are used with our proposed system to provide high-quality hotel recommendations. To achieve this goal, first, the existing dataset is balanced, using the translating and text paraphrasing policy by a transformer-based model called T5. Afterwards, an integrated method, including the transformer-based XLM-RoBERTa model is used along with the attention mechanism for sentiment analysis. The result of the comparison of our proposed model with the four best non-transformer-based models; RNN, GRU, LSTM, Bi-LST, and the most recent transformer-based model, En-RFBERT, on the TripAdvisor dataset showed the superiority of our proposed method. Our propose system beats En-RFBERT by 3%, 7%, and 5% in Macro Precision, Recall, and F1-score, respectively and performs better than En-RFBERT when it comes to responsiveness time.
USING CHATGPT IN THE PROCESS OF LEARNING ENGLISH: ADVANTAGES AND OPPORTUNITIES

This research paper examines the use of ChatGPT, a statistical language model, developed by OpenAI, as an effective tool for learning English. It examines the benefits of this technology for English language learners and describes its potential for improving oral and written communication skills, vocabulary and grammar. Possible challenges and limitations of using ChatGPT are also discussed and some recommendations are given for the optimal use of this technology in the educational process.&#x0D; The article offers a detailed overview of the possibilities, advantages and limitations of using ChatGPT in the English language learning, and also experimentally proves the increase in learning productivity indicators. The study highlights the importance of this technology as an innovative tool for the English language learners and promotes further research and development of this area in the academic environment.
Challenges to Use Role Playing in Software Engineering Education: A Rapid Review

Role playing is a teaching method widely used to enhance students learning and engagement, by allowing them to adopt specific roles and interact with others in simulating real-world scenarios, thus applying their theoretical knowledge in a practical context. In Software Engineering Education (SEE), role playing may help students to develop key skills (like teamwork, problem-solving, and critical thinking), to understand the complexities and challenges of software development, and to appreciate the importance of collaboration and effective communication. To use role playing effectively, SEE teachers need to understand the challenges that arising from using it. This paper presents the design, execution and results of a rapid literature review to identify these challenges. Several well known digital libraries (Web of Science, Scopus, and IEEE Xplore) yield 44 articles, which after inclusion/exclusion filters left a total of 23 articles. Key findings are that: (1) most role playing is used to teach skills linked to software development and teamwork/"soft skills", and secondarily to software design, quality assurance, and process management; rather than project management or quality assurance; (2) challenges and generic considerations for implementing role playing were identified; and (3) challenges for applying role playing in SEE were identified by virtue of the SE specialty classification. In summary, role playing is a mature teaching technique used to in other fields, and has made limited inroads in SEE, mainly in disciplines dear to agile development (like development and teamwork).
Generalized Program Sketching by Abstract Interpretation and Logical Abduction

This paper presents a new approach for synthesizing missing parts from imperative programs by using abstract interpretation and logical abduction. Given a partial program with missing arbitrary expressions, our approach synthesizes concrete expressions that are strong enough to prove the assertions in the given program. Furthermore, the synthesized elements by our approach are the simplest and the weakest among all possible that guarantee the validity of assertions. In particular, we use a combination of forward and backward numerical analyses based on abstract interpretation to generate constraints that are solved by using the logical abduction technique. We have implemented our approach in a prototype synthesis tool for C programs, and we show that the proposed approach is able to successfully synthesize arithmetic and boolean expressions for various C programs.
RANCANGAN WEBSITE LAYANAN PENGADUAN PADA DINAS PERUMAHAN DAN KAWASAN PERMUKIMAN KOTA

Improving public services is a form of government participation in serving the community. Public complaints are a form of participation to play a role in building the environment as well as a form of monitoring the performance of government agencies. Reports and community aspirations can be used as input for evaluating policies and regulations that have been made or regulations that have been implemented and to find out problems that exist in the community effectively, quickly and up to date and can immediately provide solutions and solutions. The Department of Housing and Settlement Areas of Binjai City has the main task of assisting the Governor in carrying out decentralization and deconcentration authority in the Housing, Settlement and Land areas in accordance with the policies established by laws and regulations. In this case, the community still finds it difficult to report complaints about problems, one of which is regarding illegal settlement development, because the community must come to the Binjai City Housing and Settlement Area Office to report the problem. This becomes ineffective and inefficient because many sub-districts are far from the city center, making it difficult for remote communities to complain to the Binjai City Housing and Settlement Area Office. For this reason, a web-based public complaint service information system is needed to make it easier for the public to report all existing problems. This facility is expected to help connect the community with the Binjai City Housing and Settlement Area Office as effectively and efficiently as possible.
Depth estimation from a single-shot fringe pattern based on DD-Inceptionv2-UNet

The quick and accurate retrieval of an object’s depth from a single-shot fringe pattern in fringe projection profilometry has been a topic of ongoing research. In recent years, with the development of deep learning, a deep learning technique to FPP for single-shot 3D measurement is being used. To improve the accuracy of depth estimation from a single-shot fringe pattern, we propose the depthwise separable Dilation Inceptionv2-UNet (DD-Inceptionv2-UNet) by adjusting the depth and width of the network model simultaneously. And we evaluate the model on both simulated and experimental datasets. The experimental results show that the error between the depth map predicted by the proposed method and the label is smaller, and the depth curve map is closer to the ground truth. And on the simulated dataset, the MAE of the proposed method decreased by 35.22%, compared to UNet. On the experimental dataset, the MAE of the proposed method decreased by 34.62%, compared to UNet. The proposed method is relatively outstanding in both quantitative and qualitative evaluations, effectively improving the accuracy of 3D measurement results from a single-shot fringe pattern.
Meta-Networking: Beyond the Shannon Limit with Multi-Faceted Information

The conventional network infrastructure is struggling to keep up with the rapidly growing demands of modern society. The explosion of data, the increasing number of connected devices, and the growing reliance on real-time applications are all putting pressure on the current network, which almost reaches Shannon's limit. In this article, we propose Meta-Networking, an advanced networking architecture that can provide beyond Shannon communications by utilizing multi-faceted information from different domains, based on an intelligent collaboration among distributed network entities. An overview of Meta-Networking is provided and the key principles and components of Meta-Networking, including the quality-of-experience characterization, AI-empowered semantic encoding, and information density improvement, are analyzed. It enables a groundbreaking communication system where a much larger amount of information is transmitted without increasing the size of binary digits. Furthermore, an application scenario for image transmission in the Internet of Vehicles (loV) is discussed, which shows a significant performance improvement as compared with conventional communications. It is believed that Meta-Networking has the potential for revolutionizing communication systems with higher efficiency, stronger reliability, and intelligence awareness.
A New Lightweight and Secure Certificateless Aggregate Signcryption Scheme for Industrial Internet of Things

The Industrial Internet of Things (IIoT) combines several smart devices which communicate continuously with each other via the Internet, to reshape the traditional industrial process and to enable faster production and optimize processes, by monitoring machine status, environment, and data management from the crowdsourced industrial devices. Industry 4.0 is accelerated with modern computing platforms and communication technologies. The storage and data movement over the network is often exposed to many security and efficiency challenges due to the interconnections of millions of smart devices across the globe and the fact that these smart/constrained devices have limited power. The cryptographic algorithms provide a solution for the privacy concerns in the IIoT network in terms of authentication and data integrity to data transmission across the network. However, the traditional cryptographic approaches are complex in nature require high computational costs and are time-consuming. Hence, dedicated cost-effective lightweight and cryptographic algorithms are in high demand and are essential. This paper intends to develop a new lightweight and secure aggregate signcryption scheme for crowdsourced industrial data using certificateless cryptography. This scheme effectively reduces computational cost and time consumption. In addition, this scheme avoids the key-escrow problem and achieves stronger security properties. Finally, we evaluate our PF-CLASC scheme’s performance and will compare results with recent state-of-the-art schemes.
InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks

Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consuming and tedious nature. While existing automatic methods alleviate some of the burden on users, they often fail to cater to users' specific interests. In response to these limitations, we present InkSight, a mixed-initiative computational notebook plugin that generates finding documentation based on the user's intent. InkSight allows users to express their intent in specific data subsets through sketching atop visualizations intuitively. To facilitate this, we designed two types of sketches, i.e., open-path and closed-path sketch. Upon receiving a user's sketch, InkSight identifies the sketch type and corresponding selected data items. Subsequently, it filters data fact types based on the sketch and selected data items before employing existing automatic data fact recommendation algorithms to infer data facts. Using large language models (GPT-3.5), InkSight converts data facts into effective natural language documentation. Users can conveniently fine-tune the generated documentation within InkSight. A user study with 12 participants demonstrated the usability and effectiveness of InkSight in expressing user intent and facilitating chart finding documentation.