Reviewerly/exHarmony huggingface dataset:
Conceptualization of blockchain enabled interconnected smart microgrids

Power systems are undergoing rapid transitions to incorporate renewable sources of generation and to combat climate change. The next stage of transitions will lead to a shift from large-scale, centralized systems to networks of small-sized, distributed electricity systems, which require distributed or decentralized ledgers for database management for efficient transactions. Distributed Ledger Technology (DLT) are a form of decentralized ledgers where the transactions (energy, information, and money) among various entities are maintained. One such DLT is blockchain technology which offers several advantages. Data recorded in blockchains are difficult to tamper with; have privacy protection; facilitate fast, accurate, and real-time settlement of financial transactions. Contemporary research has started focusing on their possible applications in energy systems. State-of-the-art suggests that while business and market aspects have been extensively discussed, the electrical constraints and implementation methodologies have not been adequately addressed. Furthermore, all the reviewed projects have implemented only peer-to-peer transactions that are not scalable. To incorporate the new entities like prosumers, inter-microgrid transactions, and interactions with the legacy power grid, new structural and operational frameworks are necessary. The proposed research explores the possibility of developing blockchain enabled smart microgrids (BSMG) with the above frameworks. It aims to build a conceptual framework of BSMG, including the transaction protocols and process flows. It proposes the inclusion of network constraints in a three-level transaction setup executed over a four-layered architecture. Another practical challenge is that BSMGs may be set up on different blockchain platforms. Hence, this paper also proposes implementing Inter-Blockchain Protocol for the first time to include interoperability and communication between different platforms. Finally, the performance metrics that will be used to validate the BSMGs are outlined. • Blockchain Enabled Smart Microgrids will play a pivotal role in Energy industry. • Architecture is simplified to four distinct layers based on their functionality. • Process flow modified to take electrical constraints into account. • InterBlockchain Communication Protocol between microgrids proposed for first time. • Qualitative and quantitative benchmarking measures to validate developed models.
Detecting Spam Email with Machine Learning Optimized with Harris Hawks optimizer (HHO) Algorithm

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris’ Hawks’ cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model’s spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3.
Signal Modulation Classification Based on the Transformer Network

In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared with four other deep models and two traditional methods. Simulation results show that the proposed one has a higher classification accuracy especially at low signal to noise ratios (SNRs), and the number of training parameters of the proposed model is less than those of the other deep models, which makes it more suitable for practical applications.
DeblurGAN-CNN: Effective Image Denoising and Recognition for Noisy Handwritten Characters

Many problems can reduce handwritten character recognition performance, such as image degradation, light conditions, low-resolution images, and even the quality of the capture devices. However, in this research, we have focused on the noise in the character images that could decrease the accuracy of handwritten character recognition. Many types of noise penalties influence the recognition performance, for example, low resolution, Gaussian noise, low contrast, and blur. First, this research proposes a method that learns from the noisy handwritten character images and synthesizes clean character images using the robust deblur generative adversarial network (DeblurGAN). Second, we combine the DeblurGAN architecture with a convolutional neural network (CNN), called DeblurGAN-CNN. Subsequently, two state-of-the-art CNN architectures are combined with DeblurGAN, namely DeblurGAN-DenseNet121 and DeblurGAN-MobileNetV2, to address many noise problems and enhance the recognition performance of the handwritten character images. Finally, the DeblurGAN-CNN could transform the noisy characters to the new clean characters and recognize clean characters simultaneously. We have evaluated and compared the experimental results of the proposed DeblurGAN-CNN architectures with the existing methods on four handwritten character datasets: n-THI-C68, n-MNIST, THI-C68, and THCC-67. For the n-THI-C68 dataset, the DeblurGAN-CNN achieved above 98% and outperformed the other existing methods. For the n-MNIST, the proposed DeblurGAN-CNN achieved an accuracy of 97.59% when the AWGN+Contrast noise method was applied to the handwritten digits. We have evaluated the DeblurGAN-CNN on the THCC-67 dataset. The result showed that the proposed DeblurGAN-CNN achieved an accuracy of 80.68%, which is significantly higher than the existing method, approximately 10%.
Text-Based Emotion Recognition Using Deep Learning Approach

Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.
Novel logic mining incorporating log linear approach

Mining the best logical rule from the data is a challenging task because not all attribute of the dataset will contribute towards the optimal logical representation. Even if the correct attributes were selected, wrong logical connection in the logical formula will lead to suboptimal logical representation of the datasets. These two factors must be carefully considered in creating more robust logic mining method. In this paper, we proposed a novel logic mining by introducing log-linear analysis to select the best attributes which formulate the logical rule that will be embedded into the energy-based ANN named Discrete Hopfield Neural Network (DHNN). In log-linear phase, the test of the association for each attributes will be carried out where the attributes that have a significant level less than α will be selected before proceeding to the logic mining phase. By using DHNN, the selected attributes via log-linear will be learned and retrieved the optimal induced logic with classification ability. The proposed hybrid model has been tested using various real-life datasets and was compared with several established logic mining methods. Based on the findings, several winning points for the proposed model where the proposed model dominates 3 metrics out of 5 in the average rank. The metrics that achieve the highest average rank are Accuracy (1.800), Precision (3.500), and Mathews Correlation Coefficient (2.700). In accordance with the experimental result obtained, the proposed model has achieved optimal performance with a statistically significant p-value. Hence, these findings lead to an advancement of the existing logic mining via the statistical method.
Predictors of contact tracing app adoption: Integrating the UTAUT, HBM and contextual factors

Contact tracing apps (CTAs) have been introduced as a means to identify and isolate possible cases infected with COVID-19. Since the adoption rate determines the effectiveness of CTAs, it is important to examine what factors contribute to a higher CTA uptake. This study aimed to use an integrative approach to explain early CTA adoption, whereby three perspectives are distinguished: technology-related (derived from the Unified Theory of Acceptance and Use of Technology [UTAUT]), health-related (derived from the Health Belief Model [HBM]), and context-related. A survey was administered among a representative sample of the Dutch population (N = 1865). A hierarchical logistic regression analysis was performed in which the models were compared. Results showed that an integrative model including all three perspectives (i.e., UTAUT, HBM, and context-related variables) resulted in better model fit than any of the other models. All UTAUT variables were associated with CTA adoption in the expected directions. Regarding the HBM, self-efficacy, perceived barriers and perceived benefits were associated with CTA adoption in the expected directions. Several context-related variables, such as fear, were associated with CTA adoption. Our findings demonstrate that extending the UTAUT with preventive health-behavioral factors and contextual factors contribute to better understanding of CTA adoption.
Improved Algorithm for Insulator and Its Defect Detection Based on YOLOX

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.
A real time crime scene intelligent video surveillance systems in violence detection framework using deep learning techniques

• Currently tremendous growth is observed in research of surveillance system. • The surveillance cameras installed at various public places like offices, hospitals, schools, highways, etc. • This research proposed novel technique in detecting crime scene video surveillance system in real time violence detection using deep learning architectures. • Its purpose is to detect signals of hostility and violence in real time, allowing abnormalities to be distinguished from typical patterns. Surveillance system research is now experiencing great expansion. Surveillance cameras put in public locations such as offices, hospitals, schools, roads, and other locations can be utilised to capture important activities and movements for event prediction, online monitoring, goal-driven analysis, and intrusion detection . This research proposed novel technique in detecting crime scene video surveillance system in real time violence detection using deep learning architectures. Here the aim is to collect the real time crime scene video of surveillance system and extract the features using spatio temporal (ST) technique with Deep Reinforcement neural network (DRNN) based classification technique . The input video has been processed and converted as video frames and from the video frames the features has been extracted and classified. Its purpose is to detect signals of hostility and violence in real time, allowing abnormalities to be distinguished from typical patterns. To validate our system's performance, it is trained as well as tested in large-scale UCF Crime anomaly dataset. The experimental results reveal that the suggested technique performs well in real-time datasets, with accuracy of 98%, precision of 96%, recall of 80%t, and F-1 score of 78%.
Beyond the hype: how blockchain affects supply chain performance

Purpose This paper aims to contribute to the debate about the value of blockchain for supply chain management by assessing empirical evidence on the relationship between blockchain and supply chain performance. Design/methodology/approach The authors conducted a structured review of the academic literature to identify and assess papers providing empirical insight on operational blockchain applications. The authors complement the findings from this review with primary empirical data from 11 interviews with blockchain providers, users and experts involved in four recent projects. Findings The paper presents an integrated research framework that illustrates the impact of blockchain on supply chain performance. The findings highlight that blockchain can affect supply chain performance directly – via one of its core technological features – and indirectly via the broader business project through which blockchain technology is implemented. Practical implications Insights from this paper should provide managers with a more nuanced understanding of how blockchain technology can be leveraged to address important supply chain management challenges. Originality/value Prior research addressing the relationship between blockchain and supply chain performance mostly discusses potential performance effects of blockchain, presents individual blockchain applications and/or provides little explanation for how the core technological features of blockchain affect supply chain performance. This paper systematically assesses the ways in which blockchain can affect supply chain performance. In doing so, it goes beyond the initial hype around blockchain technology while countering some of the more recent critiques.
A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey different meanings and are "weighted" differently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efficiency in the definition of regulations for ethical and reliable AI development. We show how our taxonomy and definition of interpretable AI differ from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a-highly needed-standard for the communication among interdisciplinary areas of AI.
Secure DNA-Coding Image Optical Communication Using Non-Degenerate Hyperchaos and Dynamic Secret-Key

With the aim of tackling insufficient security in the chaotic encryption algorithm for digital images in the Optical Access Network, a color image encryption scheme combining non-degenerate discrete hyperchaotic system and deoxyribonucleic acid (DNA) dynamic encoding is proposed. First, a new non-degenerate hyperchaotic system is constructed with all positive Lyapunov and more complex dynamic characteristics. Furthermore, the key sequence based on non-degenerate hyperchaotic system is generated using plaintext correlation to achieve the effect of a dynamic secret key. Next, a binary bit-planes permutation is performed on the image using one of the key sequences. Then, the chaotic key sequence is used to sequentially perform DNA encoding, obfuscation, and decoding. Finally, a binary bit-planes obfuscation is performed to obtain the final ciphertext. The research results show that the non-degenerate chaotic sequence can pass the NIST 800-22 test, and the corresponding encryption algorithm can resist various common attacks and has a strong anti-interference ability. In addition, the algorithm is verified on ARM-Embedded, which proves that the encryption system proposed in this paper is a feasible secure communication technology scheme. Therefore, the scheme proposed in this paper is helpful to provide new ideas for the design and application of high-security cryptosystem in optical access network.
Buffer bots: The role of virtual service agents in mitigating negative effects when service fails

Abstract In recent years, marketers have placed increased reliance upon artificial intelligence (AI) and, subsequently, the use of virtual agents in customer service contexts is on the rise. Despite such service digitalization, service can still fail. While there is an increasing literature on the effect of virtual agents in service settings, questions remain as to how customers react to service failure that results from interactions with virtual service agents. To this end, we deconstruct the effect of virtual agent service failure across two studies: one involving a process service failure and another involving an outcome service failure. We specifically manipulate the type of service agent that causes the service failure (human vs. virtual agent) and the magnitude of the failure (small vs. large). Results show that firms can leverage virtual service agents to mitigate or buffer the negative effects of service failure. From a managerial perspective, our findings suggest that firms could engage virtual service agents in situations where there may be a risk of outcome service failure—particularly in settings where relatively large magnitude failures may be experienced. In such a setting, we find that virtual service agents can mitigate the negative effects of service failure, more so than when the failure results from an interaction with a human service agent.
Low-rank 2D local discriminant graph embedding for robust image feature extraction

As a popular feature extraction algorithm, the 2D local preserving projections (2DLPP) algorithm has been successfully applied in many fields. Using 2D image representation, the 2DLPP algorithm preserves the manifold attributes and retains the local information of high-dimensional space data. However, the 2DLPP algorithm may encounter some problems in real-world applications, such as a lack of discriminatory ability, singularity problems, and sensitivity to occlusion and noise in data. Therefore, this paper introduces low-rank into the 2DLPP algorithm and proposes a new feature extraction algorithm, which is the low-rank two-dimensional local discriminant graph embedding (LR-2DLDGE), to solve these problems. To improve the LR-2DLDGE algorithm robustness, we fuse the discriminant information in graph embedding and the low-rank properties of the data. The algorithm has three advantages: First, the algorithm uses a graph embedding (GE) framework to maintain the local neighbourhood discrimination information between data. Second, the LR-2DLDGE algorithm ensures that the data points are as independent as possible from different classes in the feature space. Finally, the algorithm uses the L1-norm as a constraint and reduces the influence of noise and corruption through low-rank learning. The theoretical computational complexity and convergence of the algorithm are explicated and proved. Extensive experimental results on three occluded and noisy image datasets confirm the effectively and robustness of LR-2DLDGE, respectively.
Web-based online course training higher school mathematics teachers

The article looks into the problem of theoretical aspects of using Web 2.0 technology in higher education. This paper describes answers of 87 respondents who have helped to identify the most required types of educational content for the integration to pages of the online course training higher school mathematics teachers. The authors carry out a theoretical analysis of researches and resources that consider the development of theoretical aspects of using web tools in higher education. The research presents the characteristics common to online courses, principles of providing a functioning and physical placement of online systems in webspace. The paper discusses the approaches of creating and using animated content in online systems. The authors describe the methods of publishing video content in web systems, in particular, the creation and use of video lectures, animation, presentations. This paper also discusses several of the existing options of integrating presentations on web pages and methods of integrating mathematical expressions in web content. It is reasonable to make a conclusion about the expediency of promoting online courses, the purpose of which is to get mathematics teachers acquainted with the technical capabilities of creating educational content developed on Web 2.0 technology.