Reviewerly/exHarmony huggingface dataset:
Comparative Evaluation of Java Virtual Machine-Based Message Queue Services: A Study on Kafka, Artemis, Pulsar, and RocketMQ

Message Queue (MQ) services play a vital role in modern distributed systems as they enable asynchronous communication between services and facilitate the decoupling of various components of the system. Among the many MQ services available, Kafka, Apache Pulsar, Artemis, and RocketMQ are popular choices, each offering unique features and capabilities. As the adoption of MQ services continues to grow, choosing the appropriate service that can meet the requirements of the system has become increasingly challenging. Therefore, a comprehensive comparison of these services is crucial to determine the most suitable one for a specific use-case. This research paper presents a thorough evaluation of these MQ services based on critical metrics such as CPU utilization, memory usage, garbage collection, latency, and throughput. Based on our extensive review, no other research has delved into such a detailed evaluation, thereby establishing our work as a cornerstone in this field. The results of our study offer valuable insights into the strengths and limitations of each service. Our findings indicate that each message queue behaves differently inside the Java Virtual Machine (JVM). This work aims to assist developers and researchers in strategically deploying and optimizing MQ services based on specific system and use-case requirements. In addition to providing machine metrics, our results demonstrate the performance of each message queue under different load scenarios, making it a valuable resource for those seeking to ensure the effective functioning of their MQ services.
Augmenting Vehicular Ad Hoc Network Security and Efficiency with Blockchain: A Probabilistic Identification and Malicious Node Mitigation Strategy

This manuscript delineates the development of an avant garde dual-layer blockchain architecture, which has been meticulously engineered to augment the security and operational efficacy of vehicular ad hoc networks (VANETs). VANETs, which are integral to the infrastructure of intelligent transport systems, facilitate the critical exchange of information between vehicular nodes. Despite their significance, these networks confront an array of formidable security vulnerabilities. Our innovative approach, employing a dual blockchain framework—the event chain and the reputation chain—meticulously tracks network activities, thereby significantly enhancing the trustworthiness and integrity of the system. This research presents a transformative dual-layer blockchain architecture, which was conceived to address the intricate security challenges pervasive in VANETs. The architecture pivots on a sophisticated reputation assessment framework, thus leveraging the principles of Bayesian inference and the analytical rigor of historical data to markedly diminish observational errors, as well as elevate the accuracy of reputation evaluations for vehicular nodes. A salient feature of our methodology is the implementation of an attenuation factor, which has been deftly calibrated to modulate the impact of historical behaviors on current reputation scores, thereby ensuring their relevance and alignment with recent vehicular interactions. Additionally, the numerical threshold serves as an indispensable mechanism, thus establishing a definitive criterion for the early identification of potentially malicious activities and enabling the activation of proactive security measures to safeguard the network’s integrity. Empirical validation of our dual-layer blockchain model has yielded a remarkable 86% efficacy in counteracting malevolent behaviors, thus significantly outperforming extant paradigms. These empirical outcomes underscore the model’s potential as a vanguard in the domain of secure and efficient reputation management within VANETs, thereby heralding a substantial advancement in the sphere of intelligent transportation systems.
An Experimental Evaluation of Path-Based Product Line Integration Testing and Test Coverage Metrics

Product line testing is significant because any faults in a product line platform can lead to widespread impacts on multiple products configured from that platform within a product line. Due to the shared platform, certain testing can be repeatedly performed across different products, leading to unnecessary costs. To enhance quality and reduce costs in product line testing, it is essential to minimize redundant testing of the products in a product line. Because test coverage provides a way to explicitly state the extent to which a software item has been tested, having a clear understanding of test coverage helps avoid unnecessary repetition of tests and ensures that the testing efforts are focused on areas that require attention, ultimately leading to more efficient and effective product line testing. It is necessary to define appropriate test coverage metrics of product line testing that enable testers to identify redundancies in their testing efforts. Path-based integration testing has been proven to be an effective approach to product line integration testing. This paper defines coverage metrics for path-based product line integration testing and demonstrates their effectiveness in preventing redundant testing between platform testing and testing for individual products, while also effectively detecting faults. The experiment results highlight the coverage metrics’ effectiveness in avoiding redundant testing, reducing costs, and covering interfacing across different modules.
Research on Approximate Spatial Keyword Group Queries Based on Differential Privacy and Exclusion Preferences in Road Networks

A new spatial keyword group query method is proposed in this paper to address the existing issue of user privacy leakage and exclusion of preferences in road networks. The proposed query method is based on the IGgram-tree index and minimum hash set. To deal with this problem effectively, this paper proposes a query method based on the IGgram-tree index and minimum hash set. The IGgram-tree index is proposed for the first time to deal with the approximate keyword query problem in the road network. This index significantly improves the efficiency of calculating the road network distance and querying approximate keywords. Considering that spatial keyword group queries are caused by NP-hard problems with high time complexity, this paper proposes a data structure that uses the minimum hash set, which can efficiently search for the result set. To address the problem that the traditional spatial keyword group query does not consider user privacy leakage and the limitations of existing privacy protection techniques, this method proposes a differential privacy-based allocation method to better protect the privacy of data. The theoretical study and experimental analysis show that the proposed method can better handle the approximate spatial keyword group query problem based on its use of differential privacy and exclusion preferences in road networks.
An Analysis of Air Flow in the Baking Chamber of a Tunnel-Type Electric Oven

The baking process in tunnel ovens can be influenced by many parameters. Among these, the most important can be considered as: the baking time, the volume of dough pieces, the texture and humidity of the dough, the distribution of temperature inside the oven, as well as the flow of air currents applied in the baking chamber. In order to obtain a constant quality of bakery or pastry products, and for the efficient operation of the oven, it is necessary that the solution made by the designers be subjected to modelling, simulation and analysis processes, before their manufacture, and in this sense it can be applied to the Computational Fluid Dynamics (CFD) numerical simulation tool. In this study, we made an analysis of the air flow inside the baking chamber of an oven. The analyzed oven was used very frequently on the pastry lines. After performing the modelling and simulation, the temperature distribution inside the oven was obtained in the longitudinal and transverse planes. For the experimental validation of the temperatures obtained in the computer-assisted simulation, the temperatures inside the analyzed electric oven were measured. The measured temperatures validated the simulation results with a maximum error of 7.6%.
Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges.
Real-Time Recognition of Handwritten Characters Using CNN

The objective of our paper is to create a system that can recognize and classify characters written in three dimensions in the air based on a variety of parameters. Air-writing is a revolutionary approach for producing linguistic signals or words in free space through the use of hand or finger movements. In our paper, we are using the object of a pen’s tip to write in the air. This approach allows for greater precision and control in the writing process. This paper could combine computer vision and machine learning for handwriting recognition. The air-writing recognition system uses the computer’s digital camera to track the movement of the pen’s tip as it writes characters and numbers in the air. Using a convolutional neural network, the letters, numerals, and other symbols are then classified into one of the available classes. While many previous systems rely on complex and costly pursuit configurations, we created a system that can recognize gestures using a significantly simpler and less costly pursue configuration. The proposed model achieved an overall accuracy of 89.62%.
Assessing the Efficacy of Different BERT Variants for Distinguishing Types of Cyberbullying on Twitter

BERT is a highly developed language model that has excelled in a wide range of tasks, including question-answering and language comprehension. It now outperforms both the performance of older state-of-the-art computer models and human intelligence. Due to its exceptional capabilities, a large number of businesses, academic institutions, and divisions of Google are actively developing the BERT model architecture through supervised training, with the goal of either enhancing its effectiveness or customizing it for particular tasks by pre-training it with specific contextual representations. In this paper, we classify the category of cyberbullying in each tweet posted on Twitter taken into our dataset. Our research aims to test out different variations of BERT-ALBERT, RoBERTa, and DistilBERT and extrapolate the efficiency of each model concerning the multi-label classification of tweets.
Analysis and Study of Bug Classification Quintessence and Techniques for Forecasting Software Faults

Developers frequently save past faults in storage to better understand issues affecting software systems and these flaws can be divided into families. A flaw may occur at any step of the software coding cycle. The most popular names include bug, defect, flaw, fault, or error. In this paper, we examined various defect classification schemes and techniques for forecasting software faults. We also reviewed the severity of software bug, as well as the fault-type classification schema along with software system bugs. The IBM Orthogonal Defect Classification is a popular form of categorization (ODC). Based on a lot of knowledge about the faults, including their symptoms and semantics, their root causes, and a host of other factors, ODC proposes multiple orthogonal classifications of defects. Another method for categorizing defects is the IEEE 1044-2009 Standard. It has six families with severe defects. The research's objective is to learn about and investigate the major types of defects. A thorough grasp of the traits of software defects is necessary to create efficient models for the detection of software errors and their recovery. In this paper, we have also focused on various fault forecasting techniques, used datasets, fault percentage, performance measures, study objectives, and outcomes along with future scope.
A Blockchain-Based Custom Clearance Solution for International Trade Using IPFS and Non-fungible Tokens

Imports and exports of a nation heavily impact its GDP, interest rates, exchange rate, and inflation rate. With the ongoing transition from traditional to digital methods, the procedure of importing-exporting goods also witnessed a trend in the paper-based traditional approach where the custom department's officers verify all of the paperwork in accordance with national customs law before approving imports and exports. However, this approach is extremely time-consuming and has a higher risk of manipulation/tampering with the documents to make illegal imports and exports happen. To digitalize the previous approach, the development of systems with centralized servers came into existence which could connect the exports and imports and thus, reduce the time factor in communication but increase the risk of data(documents) getting mutated or lost (in case of server crash). With the evolution of blockchain technology, an efficient alternative to providing solutions for the above-mentioned drawbacks in import–export exists. Security, encryption, and transactions amongst individuals (such as exporters, importers, banks, customs offices) that cannot be changed are all provided by blockchain technology. This paper presents a blockchain-based solution for automating Customs Clearance between trading countries by employing non-fungible tokens (NFTs) for Certificate of Origin (COO) and Letter of Credit (LCs) and communicating agreement files on the blockchain network using Inter Planetary File System (IPFS).
Building Resilience Against Ongoing and Future Pandemics: Blockchain Technology to the Rescue

While the world grappled with the COVID-19 pandemic and its externalities, scientists have recommended that the global community brace against potential future pandemics. The need to build resilient systems has never been this urgent. The world, especially emerging economies, faces acute food insecurity, high food prices, failing health infrastructure and rampant misinformation spread, among others. Since blockchain technology (BCT) has been discussed in the supply chain resilience context, and it offers the potential to develop resilient systems, we aim to outline the potential of BCT to help build resilience against ongoing and future pandemics. Mainly, we focus on BCT for healthcare management, disruption management of food supply chains, human resource management, modern education and certification and governance and administration.
On the Semiotic Embodiment of British Satirical Drama Films Take Chaplins Monsieur Verdoux and The Gold Rush as an Example

Film semiotics is a theory within the professional discipline of film and television that is rich in meaning. The works of many playwrights can be referred to as textbooks for the application of film semiotics analysis. This paper focuses on two works of drama legend Chaplins The Gold Rush and Monsieur Verdoux to compare and analyze the meaning of film semiotics. The Gold Rush, with The Gold Rush as the background and money as the symbol, satirizes human greed and unlimited pursuit of money and power, describes the weakness of human nature and the flaws of society, and reminds people to cherish the present moment. Monsieur Verdoux, with love as the clue and emotion as the symbol, embodies modern peoples pursuit of love and marriage, reflecting the changes and diversification of the concepts of marriage and love in contemporary society, and the pursuit of freedom and independence, reminding people to cherish freedom and independence, and the pursuit of true happiness. According to the research findings, the future research on movie semiotics still needs to be studied in depth.
Avatar Design in Education: Realistic vs. Cartoonish Representation and Acceptability

This paper examines the variations in attitudes towards avatars across different age groups. The study reveals that middle-aged individuals prioritize the economic advantages of avatars but express dissatisfaction with their lack of authenticity and emotional communication capabilities. In contrast, the younger generation gravitates towards interacting with real people as they offer a more genuine and personalized communication experience. Real individuals can convey information through facial expressions, voice, and body language, meeting the youth's desire for effective and sensory communication. Furthermore, real people can adapt and respond flexibly to user feedback and contextual cues, aligning with the youth's expectations for customization and individuality. Opinions within the teenage group differ, with some acknowledging the potential of avatars to provide a wealth of knowledge and customization. They appreciate the ability to tailor the avatar's characteristics and services to their specific needs. Conversely, another portion of the teenage group perceives avatars as lacking authenticity and the ability to share personal experiences. They value the human element in communication and find avatars unable to replicate the depth of emotional connection that real individuals offer. Overall, the attitudes towards avatars among different age groups are influenced by various factors such as economic considerations, human nature, sensory experiences, and effectiveness. These differences reflect the diverse needs and values of each age group. Understanding these nuanced perspectives can inform the design and implementation of avatar technologies to better cater to the expectations and preferences of different age demographics.
Ubiquitous multi-occupant detection in smart environments

Abstract Recent advancements in ubiquitous computing have emphasized the need for privacy-preserving occupancy detection in smart environments to enhance security. This work presents a novel occupancy detection solution utilizing privacy-aware sensing technologies. The solution analyzes time-series data to detect not only occupancy as a binary problem, but also determines whether one or multiple individuals are present in an indoor environment. On three real-world datasets, our models outperformed various state-of-the-art algorithms, achieving F1-scores up to 94.91% in single-occupancy detection and a macro F1-score of 91.55% in multi-occupancy detection. This makes our approach a promising solution for improving security in smart environments.
Blockchain and Artificial Intelligence Technology in Professional Services

In recent years, scholarly focus has shifted towards exploring the applications of disruptive technologies in professional services. These studies emphasise the need for further research in this domain. This research aims to comprehensively review the existing literature on the uses of blockchain, artificial intelligence (AI) and machine learning (ML) algorithms in professional services such as higher education, healthcare, financial securities firms and smart energy consulting. The rapid innovation and advancement in technology have led to substantial improvements in work efficiency and productivity. As industries transition towards sustainability and digitalisation, the role of energy-efficient systems becomes important in shaping smart factory designs and in further implementations. The uses of AI and other disruptive technologies for business operations not only boost production efficiency but also enhance customer satisfaction. Central to this transformation are strategies like deep learning and data/text mining, which facilitate the shift from conventional manufacturing practices to smart manufacturing. Apart from smart energy systems, the fields of higher education, healthcare and financial securities are witnessing a surge in the applications of AI, ML algorithms and blockchain technology and their contributions in emerging service economy. This study undertakes a comprehensive investigation into various factors associated with the application of disruptive technologies, evaluating their impact on the operational efficiency of professional service firms (PSFs). Through this research, we aim to identify gaps in the current literature and will suggest the directions for future research.
Some notes on the algebraic structure of linear recurrent sequences

Abstract Several operations can be defined on the set of all linear recurrent sequences, such as the binomial convolution (Hurwitz product) or the multinomial convolution (Newton product). Using elementary techniques, we prove that this set equipped with the termwise sum and the aforementioned products is an R -algebra, given any commutative ring R with identity. Moreover, we provide explicitly a characteristic polynomial of the Hurwitz product and Newton product of any two linear recurrent sequences. Finally, we also investigate whether these R -algebras are isomorphic, considering also the R -algebras obtained using the Hadamard product and the convolution product.
Dynamics of a Quantum Common-Pool Resource Game with Homogeneous Players’ Expectations

In this work, we analyse a common-pool resource game with homogeneous players (both have boundedly rational expectations) and entanglement between players' strategies. The quantum model with homogeneous expectations is a differential approach to the game since, to the best of our knowledge, it has hardly been considered in previous works. The game is represented using a Cournot type payoff functions, limited to the maximum capacity of the resource. The behaviour of the dynamics is studied considering how the fixed points (particularly the Nash equilibrium) and the stability of the system vary depending on the different values of the parameters involved in the model. In the analysis of this game, it is especially relevant to consider the extent to which the resource is exploited, since the output of the players is highly affected by this issue. It is studied in which cases the resource can be overexploited, adjusting the parameters of the model to avoid this scenario when it is possible. The results are obtained from an analytical point of view and also graphically using bifurcation diagrams to show the behaviour of the dynamics.