Reviewerly/exHarmony huggingface dataset:
Reactive Software Architectures in IoT

Reactive architecture is a rapidly growing new trend in the context of the Internet of Things. While design principles around reactive architecture have been identified, i.e., through the use of the reactive manifesto, it is difficult to understand existing research solutions for the reactive system clearly. In this article, we apply the systematic mapping study methodology to identify, classify and evaluate the current state of the art in reactive architecture from the following four perspectives: publishing trends, challenges, architectural diagrams/views, and used quality attributes. Furthermore, we systematically defined a classification framework to categorize research on reactive architecture and applied it rigorously to the 17 selected studies. We synthesized the data obtained and produced a clear view of state of the art. This provides a solid foundation for planning future research and reactive architecture applications.
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks

Decentralized federated learning (DFL) is an ef-fective approach to train a deep learning model at multiple nodes over a multi-hop network, without the need of a server having direct connections to all nodes. In general, as long as nodes are connected via multiple hops, the DFL process will eventually allow each node to experience the effects of models from all other nodes via either direct connections or multi-hop paths, and thus is able to train a high-fidelity model at each node. We consider an effective attack that uses jammers to prevent the model exchanges between nodes. There are two attack scenarios. First, the adversary can attack any link under a certain budget. Once attacked, two end nodes of a link cannot exchange their models. Secondly, some jammers with limited jamming ranges are deployed in the network and a jammer can only jam nodes within its jamming range. Once a directional link is attacked, the receiver node cannot receive the model from the transmitter node. We design algorithms to select links to be attacked for both scenarios. For the second scenario, we also design algorithms to deploy jammers at optimal locations so that they can attack critical nodes and achieve the highest impact on the DFL process. We evaluate these algorithms by using wireless signal classification over a large network area as the use case and identify how these attack mechanisms exploits various learning, connectivity, and sensing aspects. We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.
E-Business as Catalyst for Global Transformation of SMEs: A Middle East Perspective

Small and medium enterprises (SMEs) are carriers of the economic development on local, regional, and national level. With high levels of flexibility, agility and adaptability, small and medium enterprises have the ability to transform and embrace new emerging technologies and opportunities. In the Industry 4.0 world, characterized by the intensive development and integration of digital solutions into all levels of operations, SMEs are among early adopters of such solutions. The purpose of the paper is to examine how small and medium enterprises in the Middle East are increasing their competitiveness, using innovative digital and e-business solutions as key transformative tools. Within the research, a systematic review of support ecosystem for development of the sector in the Middle East has been performed. Based on the systematic review and literature findings, the 5I framework, consisting of internationalization, innovation, integration, implementation, and intensifying, has been suggested as the model for transformative development of small and medium enterprises. In the future research, it is possible to examine how fast-growing economies that create supportive environment for the development of small and medium enterprises, simultaneously recognizing digital and e-business solutions as important pillar of increasing competitiveness and innovativeness, might benefit from successful implementation of 5I framework for successful global transformation.
Detection of offensive terms in resource-poor language using machine learning algorithms

The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.
Attention feature fusion awareness network for vehicle target detection in SAR images

ABSTRACTSynthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36% and 97.26% on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods.KEYWORDS: Synthetic aperture radar (SAR)deep learningvehicle target detectionfeature awarenessfeature fusion AcknowledgementsAll authors would sincerely thank the reviewers and editors for their beneficial, careful, and detailed comments and suggestions for improving the paper.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the National Natural Science Foundation of China [42201077,61671465,62172338].
Harnessing CRNN Technology for Image-Based Sequence Recognition

We are studying the challenge of finding text in everyday images using advanced computer techniques, specifically deep learning. Our system can accurately spot and read text in images. It uses Convolutional Recurrent Neural Networks (CRNNs), a type of deep learning model, to locate and understand the text. Instead of relying on manual rules, our model learns from the data to identify text patterns. Our projects unique feature is that our model is trained on single- word images, making it versatile in recognizing different text styles and backgrounds. We trained our model using a large dataset, including the “MJ Synthetic Word Dataset”, which consists of 50,000 out of which we have taken 35000 astraining images, 15000 as validation images. The results have been promising, indicating potential real-world applications such as searching for words in large sets of images. Key Words: Advanced computer techniques, Deep learning, System, Spot and read text, Convolutional Neural Networks (CRNNs), Deep learning model
An approach to cybercrime issues in Dandume Local Government Area of Katsina State, Nigeria

This study investigated cybercrime issues in Dandume Local Government Area of Katsina State. The study aimed to understand the various types of cybercrimes that the community faces and the resulting impacts, particularly educational and psychological effects on the victims. Additionally, the study discussed the main causes of these crimes, particularly among young people. To achieve these aims, the study collected data from 115 residents of Dandume using a structured questionnaire. The collected data was analyzed using statistical tables. The results showed that cybercrime is prevalent in the Dandume community, with many of the participants reporting that they had experienced one form of cybercrime or the other. The most common types of cybercrime reported by the participants were yahoo attack, social media hijacking, credit card threat, government offer scam, and airtime scam. Furthermore, the study revealed that many of the participants were not aware of the various ways they could protect themselves from cybercrime. Furthermore, the study highlighted the effects of these crimes on the victims and discussed the main causes of cybercrimes among users of internet in the area. The paper made some recommendations for minimizing the challenges of cybercrime in Nigeria. The study underscored the importance of community education and awareness-raising on the dangers of cybercrime and suggests the need for increased efforts to implement security measures to protect residents from cybercriminals.
Digital Certificate Validation Using Blockchain: A Survey

The advantages of blockchain technology include decentralization, distribution, transparency, and immutability. These attributes make blockchain (BC) a good solution to prevent and reduce the counterfeiting in many domain of our life. Personal official digital certificates such as university certificate, passport, birth certificates, identification cards, etc. are the most significant documents that the government offer to citizens. However, fake personal certificates can be easily issued by certain people or entities to obtain illegal services, because the procedures for issuing such certificates contain security or operational vulnerabilities, such as the possibility of a third party to modify the certificate information, as well as weaknesses in the certification verification procedures. Recently, blockchain technology has become a crucial technology in the fight against fraud and abuse of documents, as well as an applicable method for verifying the validity of documents. This study attempts to explore and discuss the up-to-date research studies of using Blockchain technology in preventing or reducing counterfeit of digital certificates.
How vlogging can build seafarer candidate self-confidence in speaking English

In 1973 the IMO Sea Security Committee concurred at its twenty-seventh session that English has been used as a common language for navigational purposes. Mix nation languages and lack of proper English communication skill onboard become barriers which had contributed to marine safety and even loss of property onboard. In consequence, seafarer candidates must be able to speak English well so they can avoid misunderstanding on shore-to-ship (and vice versa), ship-to-ship and on-board communications. Nevertheless, this skill becomes a crucial problem for some seafarer candidates who have lack confidence in speaking English. This study was conducted to investigate how vlogging could build the seafarer candidates’ English speaking skills whilst they had a lack of confidence. This research used descriptive qualitative research. It was conducted at Politeknik Maritim Negeri Indonesia. The samples of this study were 28 Marine Engineering students in the third semester. They were from A and B classes. This research used three techniques in collecting data namely questionnaire, observation, and interview. The questionnaire was given in an online form which contained 10 questions. To analyze the data, the writer used Likert scale. The writer also managed observation and an interview by giving them 10 open-ended questions. To support the data, the writer also analyzed the video result. The result of the study showed that Vlogs can be used to improve the seafarer candidate's confidence in speaking English.
Understanding the Impact of CBDCs on Financial Stability

The development of digital banking and the introduction of cryptocurrencies have drawn considerable attention to central bank digital currencies (CBDCs) in the global financial system. Policymakers, economists, and business experts are debating the deployment of CBDCs and their effects on financial stability as central banks examine their potential. In order to investigate the possible risks and advantages of CBDCs for financial stability, this study presents a thorough narrative literature review that critically evaluates a wide range of academic publications, reports, and research studies. This study also highlights the necessity of a user-centric financial ecosystem that incorporates Web 3 concepts. It also explores the significance of carefully constructed CBDC frameworks by looking at the potential impacts of CBDC adoption on monetary policy, systemic risks, and banking intermediation.
From Tweets to the Streets: Twitter and Extremist Protests in the United States

How does social media affect the composition of political protests in the United States? Using early adoption of Twitter at the 2007 South by Southwest (SXSW) festival as a plausibly exogenous source of variation in county-level Twitter penetration (Müller & Schwarz, 2023), and comprehensive data on protest events, this paper finds that Twitter penetration increases the frequency of protests overall, but also radicalizes them. Twitter disproportionately fuels protests with participation of “extreme" groups—groups that are particularly militant, radical, or hateful. These effects do not depend on the topic of the protest nor political leaning. I also present survey evidence suggesting that coordination is not the only mechanism driving these results: An increase in county-level Twitter penetration implies an increase in respondent's willingness to justify violence against other people, normalizing the participation in extreme groups and extreme protests.
Thermal Sensation Analysis in the Building of Universitas Ichsan Gorontalo

Gorontalo City is characterized by a tropical climate, where thermal comfort is a crucial component to achieve, particularly for educational activities aimed at students. The thermal comfort is affected by various factors, namely building design, internal, and external factors. The university building represents a facility where face-to-face learning and teaching activities occur. The purpose of the current study is to analyze the perceived thermal comfort of students during classroom activities with three different air conditioning (AC) settings (16, 22, and 27 oC) in theUniversitas Ichsan Gorontalo campus building. This research employed a quantitative descriptive method, comprising thermal measurements inside the classroom and questionnaire distribution to students to collect thermal sensation data. The research object was conducted in three separate classrooms during the time range of 08.00-15.00 Central Indonesia time. The results revealed that most students felt neutral and comfortable in the three different AC settings inside the classroom. Nevertheless, it should be noted that thermal comfort could be influenced by other factors, such as physical activity and clothing. Consequently, further research could be conducted by taking into consideration these factors and engaging the participation of teachers and staff to acquire a more comprehensive understanding of thermal comfort in the campus building.
Clustering Mining Equipment Productivity Data Using K-Means Algorihtm

There are many factors that affect the productivity of mining equipment, one of which is the condition of the mining equipment when it is used. Knowing the condition of the machines on mining equipment is mandatory for supervisors in mining activities. In analyzing data on the condition of mining equipment in large quantities requires a lot of energy and time, so a classification model is needed that can categorize the mining equipment based on its performance. Data Mining is a process that uses statistical, mathematical, artificial intelligence and machine learning techniques to extract information. The results of data mining for mining productivity data are obtained duration of mining equipment work has a impact on productivity based on correlation value between work duration and maintenance duration of a tool. Data mining analysis is also carried out by clustering mining equipment using the K-Means model. The results obtained a conclusion that supports mining equipment with less working duration affects to productivity. The result shows variables that affect the productivity of mining equipment and divide mining equipment categories based on tool performance with data mining clustering techniques.
Customer Needs Classification from Online Social Media Using Bag-of-Concepts Representation

Social media platforms are now a very powerful tool for digital marketing strategy because it helps companies to be in direct contact with their customers. A communication problem in digital social media is several customer needs phrases posted on social media, making it difficult for businesses to find relevant posts and respond to customers immediately. Therefore, knowing and understanding the customer requirements for a product can help the product owner to propose the right product to the right customer. This study focuses on understanding customer needs in Thai and classifying them into certain concepts. This study aims to classify customer needs for products in online social media community groups. The model focuses on understanding Thai customer need phrases. We then use a bag of concepts representation, including pattern analysis that applies n-grams together with POS and synonym replacement, conceptual analysis, pattern matching, and class labeling that applies concept sets obtained from the FP-Growth algorithm and represents TD-IDF value in a bag of concepts. The effectiveness of the proposed model is evaluated on five classification algorithms, including Decision Tree, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbor, and RBF Neural Network. The results show that Decision Tree can yield higher accuracy and F-measure values than the others. As this study is an initial step of a personalized product recommendation system in the future, this study will apply the model to the remaining domains for future work.
An improved technique for software cost estimations in agile software development using soft computing

Several software businesses are being encouraged to work because of their high failure rates to manage and estimate agile projects and create successful software projects. The process of accurately estimating a project&apos;s overall effort and expense is crucial. Throughout the past few decades, software initiatives have helped to increase the variety of development methodologies. Therefore, it is a big problem to determine the exact amount of effort and cost incurred for various program undertakings that are based on unique improvement models and have fresh and innovative software development processes. Software businesses have used several distinct development approaches, including 1The COCOMO (Constructive Cost Model), which enables more precise cost estimation of software projects, was introduced in this study. It is feasible to forecast the financial success or failure of a new endeavor using historical company data. The Nave Bayes method is crucial and offers good accuracy in machine learning that uses historical data to predict the future. To test the suggested system&apos;s behavior in this case, we used the SEERA dataset. The results show that the accuracy of our proposed method in predicting profit and loss is 89.59% and 26.8 percent, respectively. The accuracy of the overall effort computation is also higher, at 97.06 percent, when compared to the SVM&apos;s accuracy of 94.45 percent.
Multi-Language Program Understanding Tool

Open-source programs have gained popularity due to their decentralized, quick development cycles and accessibility to everyone. Program understanding is vital for open-source software developers to modify or improve the code. However, one problem open-source developers face is the difficulty in understanding the programs as the program grows large and becomes complex. The current program understanding tool is inefficient because it only supports one programming language, while open-source programs are written in various languages. This paper discusses a new program understanding technique that facilitates multi-language program understanding. The proposed technique helps developers to understand open-source programs by supporting two unique features: multimedia and additional comments. We carried out this study in four stages. First, we examined available tools and techniques in software understanding to identify their strengths and weaknesses. Second, we proposed a new technique. Third, we designed a new tool to implement the new technique. Lastly, we evaluated the tool using a survey. We invited twenty users, including students and programmers, to use the system and ask for their feedback. The evaluation of the proposed techniques shows that the respondents have a positive perception as they agree that the technique helped them better understand the program. The multimedia support and an additional comment provided by the tool significantly improve user understanding of the program. For future work, we would like to explore the possibility of utilizing some machine-learning techniques to enhance the process of program understanding.