Reviewerly/exHarmony huggingface dataset:
Evaluating the Usability and Functionality of Intelligent Source Code Completion Assistants: A Comprehensive Review

As artificial intelligence advances, source code completion assistants are becoming more advanced and powerful. Existing traditional assistants are no longer up to all the developers’ challenges. Traditional assistants usually present proposals in alphabetically sorted lists, which does not make a developer’s tasks any easier (i.e., they still have to search and filter an appropriate proposal manually). As a possible solution to the presented issue, intelligent assistants that can classify suggestions according to relevance in particular contexts have emerged. Artificial intelligence methods have proven to be successful in solving such problems. Advanced intelligent assistants not only take into account the context of a particular source code but also, more importantly, examine other available projects in detail to extract possible patterns related to particular source code intentions. This is how intelligent assistants try to provide developers with relevant suggestions. By conducting a systematic literature review, we examined the current intelligent assistant landscape. Based on our review, we tested four intelligent assistants and compared them according to their functionality. GitHub Copilot, which stood out, allows suggestions in the form of complete source code sections. One would expect that intelligent assistants, with their outstanding functionalities, would be one of the most popular helpers in a developer’s toolbox. However, through a survey we conducted among practitioners, the results, surprisingly, contradicted this idea. Although intelligent assistants promise high usability, our questionnaires indicate that usability improvements are still needed. However, our research data show that experienced developers value intelligent assistants highly, highlighting their significant utility for the experienced developers group when compared to less experienced individuals. The unexpectedly low net promoter score (NPS) for intelligent code assistants in our study was quite surprising, highlighting a stark contrast between the anticipated impact of these advanced tools and their actual reception among developers.
Attendance Management System using Face Recognition

The main purpose of this project is to build a face recognition-based attendance monitoring system for educational institution to enhance and upgrade the current attendance system into more efficient and effective as compared to before. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Many problems arise when the authority is unable to enforce the regulation that exist in the old system. The technology working behind will be the face recognition system. The human face is one of the natural traits that can uniquely identify an individual. Therefore, it is used to trace identity as the possibilities for a face to deviate or being duplicated is low. In this project, face databases will be created to pump data into the recognizer algorithm. Then, during the attendance taking session, faces will be compared against the database to seek for identity. When an individual is identified, its attendance will be taken down automatically saving necessary information into a excel sheet. At the end of the day, the excel sheet containing attendance information regarding all individuals are mailed to the respective faculty.
Using Level-Based Multiple Reasoning in a Web-Based Intelligent System for the Diagnosis of Farmed Fish Diseases

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.
Civil Liability of Stem Cell Banks

Objective: This research examined the civil liability of stem cell banks. It studied the phenomenon with reference to the Jordanian Civil Code, particularly in the instructions issued under the stem cell system, within the instructions for insurance of stored stem cells and its amendments of 2014.&#x0D; &#x0D; Method: The descriptive analytical approach was used through extrapolating and analyzing the legal texts regulating the obligations of stem cell banks and the terms of the agreement related to contracts for storing and preserving stem cells, and deriving civil liability provisions accordingly.&#x0D; &#x0D; Result: The Jordanian legislator did not define the contract for preserving and storing stem cells. It recognized the necessity of financial compensation for damage to stem cells. However, it did not address the obligation to compensate for material or moral damage if the bank breached its obligation to deliver stem cells for the purposes of treatment.&#x0D; &#x0D; Conclusion: Stem cells are at the forefront of research in the medical fields, as stem cells have been proven to provide solutions to incurable diseases, such as cancer, Alzheimer’s, diabetes, and other diseases.
Online Language Learning in Participatory Culture: Digital Pedagogy Practices in the Post-Pandemic Era

The COVID-19 pandemic dramatically accelerated the digitalisation of education around the world in a short period of time, which presented a unique opportunity for language teachers and policy makers to reconsider assumptions about language learning in higher education. Against this background, this study examines how digital pedagogy can enhance the experience of language learning in online settings in the post-pandemic era by drawing on a case study of educational activities developed during the pandemic for foreign language modules at a UK university. In particular, this study delves into the different dimensions of participatory culture in relation to digital pedagogy practices for language teaching and learning by adopting an interdisciplinary approach. Ultimately, we argue that online language education should aid students, i.e., the Gen Z cohort, in acquiring and developing digital literacy, or the capacity to communicate effectively by creating a variety of online texts and interreacting and collaborating with other people by means of various digital technologies. Therefore, it has also been argued that language teachers need to play a role as facilitators who can foster interactive, participatory environments to help students to develop student-centred, sustaining learning communities.
Measurement of the Cybersecurity Strategy Effectiveness with a Scorecard Based On Risk Analysis

Although the rapid acceleration of technology offers solutions that will make life easier, it also brings technological threats that will negatively affect human life and cause serious problems. Attacks, thefts and espionage using technology increase exponentially every year compared to the previous. In order to eliminate this problem that affects the whole world, many countries give priority to creating cybersecurity strategies in order to protect their information and re-sources, and develop effective implementation methods. Despite the abundant literature, there is a large gap in the effective implementation of strategies. While evaluating the strategy, measurement is made regardless of the risks that will arise if the action plan is not fulfilled. For this reason, it is recommended to evaluate the risk that will arise if the action titles are not implemented in order to eliminate this shortcoming. The aim is to emphasize that sustainability and continuous improvement will be increased by using the scorecard based on risk analysis by implementing the proposed action plan through the strategy model. The use of scorecards to over-come the increasing challenges arising from digital transformation today will contribute to the evaluation of the strategy and eliminate its shortcomings, as well as provide self-assessment. This study suggests that measuring as a method of increasing efficiency, which has not been discussed much until now, may be the solution. In order to ensure the security of the smart world, there is a need for a sustainable and effective strategy that can keep up with digital realities, renewing itself.
Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

&lt;abstract&gt; &lt;p&gt;A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m&lt;sup&gt;2&lt;/sup&gt;, 300 W/m&lt;sup&gt;2&lt;/sup&gt;, 700 W/m&lt;sup&gt;2&lt;/sup&gt;, 1000 W/m&lt;sup&gt;2&lt;/sup&gt;, 800 W/m&lt;sup&gt;2&lt;/sup&gt; and 400 W/m&lt;sup&gt;2&lt;/sup&gt;) and by varying the temperature values (15℃, 35℃, 45℃ and 5℃). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%; the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings.&lt;/p&gt; &lt;/abstract&gt;
Enhancing computational scalability in Blockchain by leveraging improvement in consensus algorithm

Accommodating an increasing number of users in the Blockchain network has moved to the forefront of discussion. It is also evident that without jeopardizing the data security in Blockchain, it is of indispensable need to devise an appropriate method for improving the scalability trait of Blockchain. In this article, we have proposed a consensus method that is having the potential to improve the scalability of the Private Blockchain. The system, at first, mitigates latency arising from kernel schedulers, ensuring that the application consistently has access to an available core for transaction processing. Secondly, the committee system alleviates the network's workload, preventing spurious transactions from monopolizing network resources and impeding its efficiency. Extensive experimentation is made by considering various scenarios of transaction with CPU isolation and application sticking to core 2 with varied priority. Based on the number of transactions performed per second, the proposed system is compared with different existing consensus mechanisms working in various types of Blockchains. Also, a detailed discussion is presented on the critical analysis of the adopted research mechanism. Overall, the proposed systems outperforms to other systems in various parameters of blockchain network scalability.
Artificial Intelligence and Machine Learning with IoT

For decades, humans have been intrigued by the concept of an intelligent and independent self-learning machine. The idea behind Machine Learning (ML) is to simplify the development of analytical models such that, with the help of available data, algorithms can learn continuously. Internet of Things (IoT)-enabled devices are the major sources of data generation for creating all the data in a variety of ways. Making smart IoT applications involves intelligent processing and analysis of this generated data (Big Data). ML may be used in cases where the desired effect is defined (supervised learning) or where data itself is not defined beforehand (unsupervised learning) or where learning is the outcome of the interaction among the learning model and the environment (reinforcing learning). In this chapter, we present and discuss a classification of machine learning algorithms, which can be used in conjunction with IoT. Furthermore, how different machine learning techniques are applied to derive higher-level information from the data is illustrated. Lastly, we investigate the real-world IoT data characteristics that involve an interpretation of the data.
Human Activity Recognition: Approaches, Datasets, Applications, and Challenges

Human Activity Recognition (HAR) has been such a demanding problem that needs to be solved. The main focus area of HAR is in healthcare, providing assisted living, especially to elderly people and physically disabled people. This field is getting so much importance as this technique can avoid long hospitalization by monitoring the patients at their residents only. This is done with the help of some other technologies like the Internet of Things (IoT). HAR is made possible by the use of sensors (wearable and non-wearable), smartphones, and images. The data collected by these sensors are stored in the form of a dataset. Then data pre-processing, feature extraction, and classification algorithms are applied to these datasets to get the desired outcome. In this chapter, different publicly available datasets are discussed along with the approaches that can be used to deal with HAR, then its different applications and the challenges that are being faced in the proper execution of HAR systems have been discussed.
IoT and Blockchain-Enabled Charging Station for Electric Vehicles

Transportation and pollution together are the major issues in the present-day scenario. The burning of fossil fuels constitutes a major part of global warming, which in turn influences climatic change. A suitable replacement for fossil fuels could be from renewable sources. But the introduction of electric vehicles is the best-suited alternate in the field of transportation. Switching to an electric model of transportation has a few problems. One of them is the availability of charging stations to charge the vehicle’s battery. This is mainly because there is a lot of background work that needs to be done by a company to set up a charging station and the cost that is involved. Setting up of Blockchain-Based Electric Vehicle Charging Station (EVCS) is the proposed idea. This proposed integrated system connects all the charging stations within a particular cell. The electric vehicles are routed to the allotted charging station inside that cell using a blockchain-based algorithm. This blockchain-based algorithm allocates a charging station to a particular vehicle that has the charging requirements depending on certain factors. Since blockchain is a distributed ledger, it also helps in the futuristic analysis and development of charging stations over a particular area considering factors like peak demand, profit, etc. The proposed system is not entirely based on blockchain but is an integration of blockchain with IoT.
Learning Approaches for Security and Privacy in Internet of Things

The Internet of Things (IoT) is an evolving pattern that concentrates on the links between machine, “things,” the internet, and users. One of the core technologies to achieve Internet of things is Cyber-Physical System (CPS). A new paradigm that pursues the convergence of the physical and cyber spaces in which people live is the cyber-physical system. It is tightly integrated with different cyber and physical systems in terms of scale and level. Consequently, the CPS suffers from some CPS problems that could threaten the lives directly, while the CPS environment, including its various layers, is related to threats on the spot that require studying the safety of CPS. Hence, this survey is based on cybersecurity-based machine learning on Internet of things. This addresses the survey based on various attacks, security, and privacy authentication in cybersecurity, and finally, analyzes the privacy based on future research in cybersecurity.
Blockchain Security for the Internet of Things

Blockchain is immutable and shared distributed ledgers for storing the transaction in distributed ledgers. Blockchain technology provides impossibility to change or modify the information that is recorded in distributed ledgers. Blockchain technology can be applied in the real-time IoT operating system, voting model, real estate management platform, and supply chain management. The Internet of Things (IoT) also has similar topologies that we have used in Blockchain (peer to peer network). However, it still suffered from privacy and security vulnerabilities. The combination of Blockchain and IoT is more powerful, and we have to use different applications. This book chapter discusses the blockchain application in different areas. The areas include advertising, insurance and social applications, blockchain music, energy financing, automotive, and agriculture.
Semantic-Layout-Guided Image Synthesis for High-Quality Synthetic-Aperature Radar Detection Sample Generation

With the widespread application and functional complexity of deep neural networks (DNNs), the demand for training samples is increasing. This elevated requirement also extends to DNN-based SAR object detection. Most public SAR object detection datasets are oriented to marine targets such as ships, while data sets oriented to land targets are relatively rare, though they are an effective way to improve the land object detection capability of deep models through SAR sample generation. In this paper, a synthesis generation collaborative SAR sample augmentation framework is proposed to achieve flexible and diverse high-quality sample augmentation. First, a semantic-layout-guided image synthesis strategy is proposed to generate diverse detection samples. The issues of object location rationality and object layout diversity are also addressed. Meanwhile, a pix2pixGAN network guided by layout maps is utilized to achieve diverse background augmentation. Second, a progressive training strategy of diffusion models is proposed to achieve semantically controllable SAR sample generation to further improve the diversity of scene clutter. Finally, a sample cleaning method considering distribution migration and network filtering is employed to further improve the quality of detection samples. The experimental results show that this semantic synthesis generation method can outperform existing sample augmentation methods, leading to a comprehensive improvement in the accuracy metrics of classical detection networks.
Four attributes of intelligence, a thousand questions

Jeff Hawkins is one of those rare individuals who speaks the languages of both AI and neuroscience. In his recent book, "A Thousand Brains: A New Theory of Intelligence", Hawkins proposes that current learning algorithms lack four attributes which will be necessary for true machine intelligence. Here we demonstrate that a minimal learning system which satisfies all four points can be constructed using only simple, classical machine learning techniques. We illustrate that such a system falls short of biological intelligence in some important ways. We suggest that Hawkins’ list is a useful model, but the “recipe” for true intelligence—if there is one—may not be so easily defined.
Applied Solutions for ESG Management of Entrepreneurship in Digital Economy Markets with the Help of Blockchain

Purpose: This chapter is aimed at the generalisation of the specifics of using blockchain to solve the tasks of ESG management and at the study of applied solutions connected with the management of entrepreneurship in the environmental, social and governance spheres in digital markets with the help of blockchain technologies. Design/methodology/approach: The interdisciplinary approach is used, which combines the managerial ESG concept and digital technology solutions based on blockchain, as well as the systems approach, which allows for the comprehensive interpretation of the results obtained. The methods used include observation and generalisation, as well as the graphical method. Findings: We elaborate on the relevance and specifics of the ESG concept in the management of entrepreneurship in digital markets, consider the advantages of blockchain and its potential to solve the tasks of the environmental, social and governance character in digital markets and discover which directions and goals of sustainable development are implemented and achieved within ESG management. Based on this, we provide applied solutions that are based on blockchain; they are used to solve the problems of ESG management. Originality/value: The results presented have scientific and practical value, because they characterise the value of blockchain technologies for solving the tasks of ESG management, define the main directions for their use from the position of achievement of the Sustainable Development Goals and coordinate them. The results obtained help understand due to what and which exactly environmental, social and governance tasks can be solved during the management of entrepreneurship in digital economy markets.
Underwater image contrast enhancement through an intensity-randomised approach incorporating a swarm intelligence technique with unsupervised dual-step fusion

Analysing objects in an underwater medium is difficult due to light problems in such an environment. Low contrast and visibility in water medium result in restricted information extraction. Some previous methods inadequately reduce underwater colour cast and improve the image contrast minimally. This work proposes intensity-randomised underwater image contrast enhancement (IRUCE), an improved enhancement method that integrates an unsupervised dual-step fusion technique to reduce blue-green colour cast, improve contrast, and enhance image brightness, especially in turbid and deep underwater media. IRUCE enhances the inferior colour channels and moderates the dominant colour channel to reduce the colour cast before the intensity-randomised approach is implemented. The intensity-randomised approach is designed to effectively distribute image intensity across all intensity levels. Next, a swarm intelligence algorithm is fused to perform median equalisation on all colour channels. The median intensity values of inferior colour channels are shifted towards the dominant colour channel. The unsharp masking technique is employed in the last step to increase image sharpness. The effectiveness of this approach is validated through quantitative and qualitative evaluations, and the results are compared with those of other state-of-the-art methods. Outcomes indicate that the proposed method improves underwater image quality significantly and outperforms other contemporary techniques.