Reviewerly/exHarmony huggingface dataset:
A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network

Deep learning is a branch of machine learning that has grown by leaps and bounds since it was first used in computer vision. The "Olympics" of computer vision, ImageNet Classification, was won by a system that used deep learning and convolutional neural networks in December 2012. Because of how important it is in the field, this competition is sometimes called the "Olympics" of computer vision. (CNN). Since then, people in many different fields, such as medical image analysis, have looked into deep learning. We are going to look into whether or not it would be possible to use deep learning algorithms to analyse medical images. This poll asked people what they thought about the four following topics related to machine learning: 1) How it is now used in computer vision, 2) How machine learning has changed before and after deep learning, 3) What role ML models play in deep learning, and 4) How deep learning can be used to analyse medical photos. Before the invention of deep learning, most machine learning systems relied on inputs called "features." This type of machine learning is called feature-based ML by some (also known as feature-based ML). Studying photographic data can be used to learn through deep learning without the need to separate objects or pull out features. The main difference between the two was this. This was pretty clear when we looked at MLs made before and after deep learning became very popular. This part, along with the model's huge scope, makes deep learning work well. Even though the term "deep learning" is still new, a study on the topic found that photo-input deep-learning algorithms have been available in the field of machine learning for a long time. Even though "deep learning" is a term that has only been around for a short time, this was seen. Even though the idea of "deep learning" is still in its early stages, discoveries like this one have been made. Even before the term "deep learning" was invented, machine learning techniques that used pictures as input were already showing promise for solving a wide range of medical image interpretation problems. Even before the term "deep learning" was made up, this was the case. One of these jobs is to Figure out how lesions are different from other organs and tissues. To solve the problem, an approach to machine learning that is based on images was used. In the next few decades, it is expected that deep learning will completely replace all of the traditional ways that medical images are currently interpreted. This is because applying deep learning and other machine learning techniques to the study of picture data could make medical image analysis much better. "Deep learning," which is the process of teaching computers to "learn" from images, is one of the most promising and quickly growing areas of medical image analysis. Traditional ways of figuring out what a medical image means are likely to be replaced in the next few decades by machine learning that works from pictures.
Using Blockchain and Artificial Intelligence in Energy Management as a Tool to Achieve Energy Efficiency

Improving energy management has received a lot of attention due to environmental issues, energy crises, and growing energy prices in today’s world. Various digital technologies have been developed to enhance energy management to cover these challenges. This article investigates Blockchain and Artificial Intelligence, which have recently attracted increasing attention. The study applies the software of VOSviewer for providing bibliometric analysis. The data (pull of the scientific documents) was generated from the Scopus and Web of Science. There have been done a critical analysis of the literature for evaluating the research in the proposed area. This paper focuses on possible use cases of blockchain and artificial intelligence technologies and considers aspects of technology used to achieve energy efficiency. The findings showed that the technologies are widely applied in energy management. Besides, it is lack of documents that contain the terms ‘energy efficiency and ‘blockchain’. The results of analysis confirm that artificial intelligence has been rapidly integrating with energy management, helping to develop more efficient and secure energy generation techniques.
A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university

Previous studies demonstrate that the use of artificial intelligence (AI) dialogue systems for English as a Foreign Language (EFL) education has effectively improved university students' reading, writing, and listening abilities. However, there are limited systematic reviews focused on the evidence-based interactional competence of EFL university students. This study aims to examine the use of AI dialogue systems to enhance EFL university students' interactional competence. Through the PRISMA process, this study identified 28 articles published between January 2013 and August 2022 in journals and conferences from the most popular databases, including Google Scholar, ProQuest, IEEE, ScienceDirect, and Web of Science. The systematic review identified six dimensions and 25 sub-dimensions that influence the application of AI dialogue systems for EFL learning. The six dimensions include technological integration, task designs, students’ engagement, learning objectives, technological limitations, and the novelty effect. Gaps are identified that (1) components of debate and problem-solving skills in EF acquisition in university education seemed to be overlooked in the AI dialogue system design, and (2) the importance of embedding culture, humor and empathy functions were not taken into consideration in the AI dialogue system. This study finds that the development and implementation of an AI dialogue system in EFL is still in its infancy stage. Future research should emphasize meaning-based communication, intelligibility in language competency, debate, and problem-solving skills in university education.
Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving

This paper utilizes parallel control to investigate the problem of event-triggered deep reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for decision-making of autonomous driving, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without training an explicit triggering condition</i> . Based on the framework of parallel control, the developed ETDQN incorporates information of actions into the feedback and constructs a dynamic control policy. First, in the realization of the dynamic control policy, we integrate the current state and the previous action to construct the augmented state as well as the augmented Markov decision process. Meanwhile, it is shown theoretically that the goal of the developed dynamic control policy is to learn the variation rate of the action. The augmented state contains information of the current state and the previous action, which enables the developed ETDQN to directly design the immediate reward considering communication loss. Then, based on dueling double deep Q-network (dueling DDQN), we establish the augmented action-value, value, and advantage functions to directly learn the optimal event-triggered decision-making policy of autonomous driving without an explicit triggering condition. It is worth noticing that the developed ETDQN applies to various deep Q-networks (DQNs). Empirical results demonstrate that, in event-triggered control, the developed ETDQN outperforms dueling DDQN and reduces communication loss effectively.
Age and gender effects on presence, user experience and usability in virtual environments–first insights

Virtual Reality (VR) is applied in various areas were a high User Experience is essential. The sense of Presence while being in VR and its relation to User Experience therefore form crucial aspects, which are yet to be understood. This study aims at quantifying age and gender effects on this connection, involving 57 participants in VR, and performing a geocaching game using a mobile phone as experimental task to answer questionnaires measuring Presence (ITC-SOPI), User Experience (UEQ) and Usability (SUS). A higher Presence was found for the older participants, but there was no gender difference nor any interaction effects of age and gender. These findings are contractionary to preexisting limited work which has shown higher Presence for males and decreases of Presence with age. Four aspects discriminating this study from literature are discussed as explanations and as a starting point for future investigations into the topic. The results further showed higher ratings in favor of User Experience and lower ratings towards Usability for the older participants.
Predicting photovoltaic power production using high-uncertainty weather forecasts

A growing interest in renewable power increases its impact on the energy grid, posing significant challenges to reliability, stability, and planning. Weather-based prediction methods help relieve these issues. However, their real-world accuracy is limited by weather forecast errors. To help resolve this limitation, we introduce the SolarPredictor model. Publicly available weather forecasts are used to predict solar power production by a target photovoltaic power plant. To achieve high prediction accuracy, the model is trained on genuine weather forecasts, including errors and mispredictions. Further, we introduce the SolarDB dataset, comprising one year of power production data for 16 power plants. The dataset includes hourly weather forecasts with seven days of history, allowing our model to anticipate errors in the meteorological features. The prediction accuracy is evaluated on a wide range of weather forecast ages, accurately reflecting real-world performance. The SolarPredictor model is compared against 17 techniques, reaching an average RRMSE of 6.15 for 1-day, 8.54 for 3-day, and 8.89 for 7-day predictions on the SolarDB dataset. Finally, we analyze the effects of weather forecast uncertainty on prediction accuracy, showing there is at least a 23% performance gap compared to using zero-error weather. Data and additional resources are available at cphoto.fit.vutbr.cz/solar.
A Brief Analysis of “ChatGPT” – A Revolutionary Tool Designed by OpenAI

The ChatGPT, a powerful conversational tool trained by OpenAI is considered to be a revolutionary model in the field of artificial intelligence and natural language processing. It has received a lot of attention because of its potential to automate a variety of tasks and possibly have an impact on sectors like translation, customer service, and content creation. It uses GPT-3 (Generative Pre-training Transformer 3) language model to process user queries. GPT-3 has been trained on a very large dataset, which includes a wide range of texts from the internet and other sources. This has given it a broad knowledge base and has allowed it to generate responses to a wide range of prompts that are coherent and human-like. GPT-3 is one of the largest and most powerful language models to date, and it has the ability to perform a wide range of natural language processing tasks. After its release, ChatGPT has become a trending tool for the public to experiment and explore what it is capable of. In this article, we want to clarify what ChatGPT is. How does it work? What makes it different from other chatbots or search engines like Google? What are the major challenges and future prospects for it?
Blocking blockchain: Examining the social, cultural, and institutional factors causing innovation resistance to digital technology in seafood supply chains

Digital technologies such as blockchain, smart contracts, QR codes, and fintech are forecast to improve the transparency, sustainability, and efficiency of supply-chains in numerous sectors. However, such innovations may face greater resistance and implementation challenges in some sectors more than others. Here, we conduct 24 interviews with fishers, aquaculturists, restaurants, and industry experts around existing blockchain applications seeking to enhance seafood traceability and trackability in Australia. We combine innovation resistance theory, principal-agent theory, and the theory of planned behaviour to reveal how institutional norms and socio-cultural dynamics are deterring blockchain adoption. For example, the seafood industry exhibits limited digitization and automation, and an oligopoly of wholesalers with the power and networks to control the supply chain – against a backdrop of food fraud claims. Results suggest blockchain could threaten the competitive advantage of wholesalers, since it would reverse existing asymmetries around trade, price, and provenance information. Furthermore, despite favouring the decentralisation and information sharing that blockchain provides, other supply chain actors were hesitant to adopt it through fear of jeopardising relationships with these wholesalers upon which their business models rely. We assert that blockchain will only drive transformative change if the most influential supply chain actors see value in using it.
Quantum Embedding Method for the Simulation of Strongly Correlated Systems on Quantum Computers

Quantum computing has emerged as a promising platform for simulating strongly correlated systems in chemistry, for which the standard quantum chemistry methods are either qualitatively inaccurate or too expensive. However, due to the hardware limitations of the available noisy near-term quantum devices, their application is currently limited only to small chemical systems. One way for extending the range of applicability can be achieved within the quantum embedding approach. Herein, we employ the projection-based embedding method for combining the variational quantum eigensolver (VQE) algorithm, although not limited to, with density functional theory (DFT). The developed VQE-in-DFT method is then implemented efficiently on a real quantum device and employed for simulating the triple bond breaking process in butyronitrile. The results presented herein show that the developed method is a promising approach for simulating systems with a strongly correlated fragment on a quantum computer.
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original data. However, the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying. Thus, it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy. Moreover, selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training, which further affects the model accuracy. In this paper, we propose a vehicle selection scheme, which maximizes the learning accuracy while ensuring the stability of the cache queue, where the statuses of all the vehicles in the coverage of edge server are taken into account. The performance of this scheme is evaluated through simulation experiments, which indicates that our proposed scheme can perform better than the known benchmark scheme.
Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems

The cross-domain fault diagnosis (CDFD) method can provide accurate fault diagnosis models for HVAC systems in the case of incomplete labeled data. However, the relationship between the three key factors (i.e., the similarity between the source domain and the target domain, the availability of target domain labeled data, and the type of classifier) and the diagnosis accuracy of CDFD methods are still unclear. Therefore, this study sets 728 diagnostic scenarios to evaluate the influence of the above three key factors on accuracy. The CDFD methods involved include the direct prediction-based CDFD (DPFD) method, the feature transformation transfer learning-based CDFD (FTFD) method, and the pre-training and fine-tuning transfer learning-based CDFD (PFFD) method. The evaluation is based on an experimental HVAC system and its simulation model. There are three main findings. First, the weaker the similarity, the worse the accuracy, yet the sensitivity between similarity and accuracy is gradually weakened for DPFD, FTFD and PFFD methods. Secondly, expanding target domain data availability can improve the performance of FTFD and PFFD methods, the performance improvement is small in the strong similarity scenarios but significant in the weak similarity scenarios. Thirdly, for the DPFD and FTFD methods, the models obtained by different classifiers have significant performance differences. Specifically, shallow machine learning classifier has performance advantages over deep learning classifier, and the support vector machine is the most prominent. The insights obtained can provide practical guidance and ensure that the obtained models provide satisfactory accuracy.
Head-mounted display augmented reality in manufacturing: A systematic review

Head-mounted display (HMD) augmented reality (AR) has attracted more and more attention in manufacturing activities, as it enables operators to access visual guidance in front of their view directly while freeing human's two hands. Nevertheless, HMD AR has not been widely adopted in manufacturing fields as humans expected since the release of Google Glass in 2012, and thus it is important to understand the related issues arising from the actual deployments of HMD AR on the shop floor. To the best of the authors’ knowledge, there have not been comprehensive discussions on HMD AR in manufacturing from a holistic perspective. This article aims to provide an extensive map for the distribution of HMD AR in various manufacturing activities and a systematic overview of underlying technical perspectives associated with their actual industrial applications between 2010 and 2022, involving AR visualization, tracking and registration, context awareness, human-machine interaction, as well as ergonomics and usability, which are significant for the actual AR deployments for human-centric manufacturing in Industry 5.0. It is also worth mentioning that this work presents a historical overview of the current research on the development of HMD AR, as well as a summary of the existing methods and open problems for HMD AR in manufacturing. It is helpful to understand the current technical situations of HMD AR while providing insights to deploy industrial AR applications and perform academic research in the future.
Explainability and causability in digital pathology

The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
A comprehensive study on cybersecurity challenges and opportunities in the <scp>IoT</scp> world

Abstract It has become possible to link anything and everything to the Internet in recent decades due to the expanding Internet of Things (IoT). As a result, our usage of technology has changed a lot, causing digital disruption in the real world. IoT allows drones, sensors, digital set‐top boxes, surveillance cameras, wearable technology, and medical equipment to be connected to the internet. Healthcare, manufacturing, utilities, transportation, and housing are among the various sectors that has become intelligent. Recently, we have seen a surge in cybersecurity challenges and opportunities for the improvement of various IoT applications. Although cybersecurity and the IoT are extensively researched, there is a dearth of studies that exclusively focus on the evolution of cybersecurity challenges in the area of AI and machine learning, blockchain and zero trust, lightweight security, integration of IoT with 5G networks, and many more in the IoT world. The availability of environment‐capturing sensors and internet‐connected tracking devices allows for private life surveillance and cloud data transmission. Therefore, a significant problem for researchers and developers is to ensure the CIA (Confidentiality, Integrity, and Availability) security triangle for people. This paper presents a comprehensive study of cybersecurity applications, challenges, and opportunities in the IoT world. The IoT architectural layer, attacks against the IoT layer, and related issues are highlighted. Furthermore, cybersecurity issues and challenges in IoT along with the strength and weaknesses of existing techniques are discussed in detail. Our study will provide insight into various current cybersecurity research trends in the IoT world.