Reviewerly/exHarmony huggingface dataset:
Sustainable Analysis of Insulator Fault Detection Based on Fine-Grained Visual Optimization

Insulators of the kind used for overhead transmission lines institute important kinds of insulation control, namely, electrical insulation and mechanical fixing. Because of their large exposure to the environment, they are affected by factors such as climate, temperature, durability, the easy occurrence of explosions, damage, the threat of going missing, and other faults. These seriously influence the safety of the power transmission, so insulation monitoring must be conducted. With the development of unmanned technology, the staff used unmanned aircraft to take aerial photos of the detected insulators, and the insulator images were obtained by naked eye observation. Although this method looks very reliable, in practice, due to the large quantity of insulator-collected seismic data, and the complex background, workers are usually relying on their experience to make judgements, so it is easy for mistakes to appear. In recent years, with the rapid development of computer technology, more and more attention has been paid to fault detection and identification in insulators by computer-aided workers. In order to improve the detection accuracy of self-exploding insulators, especially in bad weather environments, and to overcome the influence of fog on target detection, a regression attention convolutional neural network is used for optimization. Through the recursive operation of multi-scale attention, multi-scale feature information is connected in series, the regional focus is recursively generated from coarse to fine, and the target region is detected to achieve optimal results. The experimental results show that the proposed method can effectively improve the fault diagnosis ability of insulators. Compared with the accuracy of other basic models, such as FCAN and MG-CNN, the accuracy of RA-CNN in multi-layer cascade optimization is higher than that in the previous two models, which is 74.9% and 75.6%, respectively. In addition, the results of the ablation experiments at different scales showed that the identification results of different two-level combinations were 78.2%, 81.4%, and 83.6%, and the accuracy of selecting three-level combinations was up to 85.3%, which was significantly higher than the other models.
Automatic Bounding Box Annotation with Small Training Datasets for Industrial Manufacturing

In the past few years, object detection has attracted a lot of attention in the context of human-robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object's label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph

The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation methods depend on the interaction result (e.g., completion rate, grades, etc.). However, the long period required to complete a course, especially large-scale online courses in higher education, can lead to serious sparsity of interaction results. In view of this, we propose a novel course recommendation method named HGE-CRec, which utilizes context formation for heterogeneous graphs to model students and courses. HGE-CRec develops meta-path embedding simulation and meta-path weight fusion to enhance the meta-path embedding set, which can expand the learning space of the prediction model and improve the representation ability of meta-path embedding, thereby avoiding tedious manual setting of the meta-path and improving the effectiveness of the resulting recommendations. Extensive experiments show that the proposed approach has advantages over a number of existing baseline methods.
Identification of Chaotic Dynamics in Jerky-Based Systems by Recurrent Wavelet First-Order Neural Networks with a Morlet Wavelet Activation Function

Considering that chaotic systems are immersed in multiple areas of science and nature and that their dynamics are governed by a great sensitivity to the initial conditions and variations in their parameters, it is of great interest for the scientific community to have tools to characterize and reproduce these trajectories. Two dynamic chaotic systems whose equations are based on the jerky system are used as benchmarks, i.e., the Memristive Shaking Chaotic System (MSCS) and the Unstable Dissipative System of type I (UDSI). One characteristic common to them is their simple mathematical structure and the complexity of their solutions. Therefore, this paper presents a strategy for identifying chaotic trajectories using a recurrent wavelet first-order neural network (RWFONN) that is trained online with an error filtering algorithm and considering the Morlet-wavelet as an activation function. The parameters of the network are adjusted considering the Euclidean distance between the solutions. Finally, the results depict proper identification of the chaotic systems studied through analysis and numerical simulation to validate the behavior and functionality of the proposed network.
Towards personalized privacy preference aware data trading: A contract theory based approach

A large number of IoT devices geographically distributed in urban areas makes it attractive to collect massive data in a crowdsourced manner. To fully exploit the value of crowdsourced data, there has been a significant growth in demand for data trading recently. However, trading data exposes data owners to privacy violations. Existing studies based on centralized differential privacy are impractical due to the assumption of a trustworthy data collector, the high risk of privacy leakage, and extensive communication costs, let alone the data owners' personalized requirements on privacy protection. To this end, we propose a contract theory-based personalized privacy-aware data trading approach that provides a set of optimal contracts specifying different privacy-preserving levels and data trading prices to selfish data owners who upload perturbed data according to the negotiated privacy-preserving level, and finally aggregates the data using a group-weighted maximum likelihood estimation method. The proposed private data trading approach not only achieves desirable data utility in terms of accuracy but also satisfies budget feasibility, individual rationality, and incentive compatibility through theoretical analysis and extensive experiments.
ThumbDet: One thumbnail image is enough for object detection

Computer vision fields have witnessed great success thanks to deep convolutional neural networks (CNNs). However, state-of-the-art methods often benefit from large models and datasets, which introduce heavy parameters and computational requirements. Deploying such large models in real-world applications is very difficult because of the limited computing resources. Although many researchers focus on designing efficient block structures to compress model parameters, they ignore that the role of large-scale input images is also an important factor for algorithm efficiency. Reducing input resolution is a useful method to boost runtime efficiency, however, traditional interpolation methods assume a fixed degradation criterion that greatly hurts performance. To solve the above problems, in this paper, we propose a novel framework named ThumbDet for reducing model computation while maintaining detection accuracy. In our framework, we first design an image down-sampling module to learn a small-scale image that looks realistic and contains discriminative properties. Furthermore, we propose a distillation-boost supervision strategy to maintain the detection performance of small-scaled images as the original-size inputs. Extensive experiments conducted on a standard object detection dataset MS COCO demonstrate the effectiveness of the proposed method when using very low-resolution images (i.e. 4× down-sampling) as inputs. In particular, ThumbDet achieves satisfactory detection performance (i.e. 32.3% in mAP) while drastically reducing computation and memory requirements (i.e. speed up of 1.26×), outperforming the traditional interpolation methods (e.g. bicubic) by +3.2% absolutely in terms of mAP.
Noise-related face image recognition based on double dictionary transform learning

The existing single dictionary learning algorithms are applied to face recognition and achieve satisfactory results. However, their performance is poor when dealing with noisy images and images involving complex variations such as large pose variations and occlusions. In this paper, a novel noise-related face image recognition method based on double dictionary transform learning (DDTL) is proposed. On the one hand, DDTL introduces the L2,p-norm to remove the redundant information in the dictionary and the noise involved in the training images, which makes the learned dictionary more discriminative. On the other hand, DDTL introduces the analysis dictionary and performs double dictionary transform learning with the synthetic dictionary. This can better reveal the relationship between the samples and the representation coefficients, and improve the accuracy of the learned dictionary and representation coefficients. Besides, DDTL introduces a linear regression term in the model learning process, which can distinguish and expand the differences between classes. Experimental results on six databases show that DDTL is superior to existing methods.
The construction of a theory‐based augmented reality‐featured context‐aware ubiquitous learning facilitation framework for oral communication development

Abstract Background Given the advancement of mobile and sensing technology, the incorporation of augmented reality (AR) in context‐aware ubiquitous learning (CAUL) has offered significant benefits to oral communication development in foreign language learning. Although a great number of studies have been dedicated in this field, only a little research was conducted to formulate a facilitation framework aligning with established learning theories or pedagogies. Objectives This review thereby examined the facilitation tenets conducive to the best practice of AR‐based CAUL projects for oral communication enhancement and further established a AR‐guided CAUL framework. Methods The researchers systematically reviewed 17 related empirical AR studies from Web of Science from 2000 to 2021 and synthesized the results with focused second language acquisition theories. Results and Conclusions Three practical tenets were identified, including facilitating learner‐centred pedagogies with personal and contextual learning supports, incorporating constructivist learning design, and encouraging collaborative learning and higher‐order cognitive skills. Furthermore, in accordance with the synthesized results, a facilitation framework combining situated learning, learning‐by‐doing, and social constructivist design was developed to engage students in a learning, applying, and reflecting process in formal and informal learning contexts. Major Takeaways For the best practice of oral communication through AR, this review study offers practical facilitation tenets and theoretical framework which are in alignment with research‐proven theories. It is expected that the framework could provide frontline educational practitioners with guidance to foster students' learning outcomes and interactions.
Implementing Partnerships in Energy Supply Chain Cybersecurity Resilience

This study describes the implementation of an energy sector community to examine the practice of cybersecurity for operational technology environments and their supply chains. Evaluating cybersecurity from the perspectives of different actors participating in the energy sector, the progress and challenges of operators and suppliers in delivering cybersecurity for the sector are explored. While regulatory frameworks incentivize individual organizations to improve their cybersecurity, operational services contain contributions from many organizations, and this supply chain of activity needs to be influenced and managed to achieve desired security and resilience outcomes. Through collaborations and systems engineering approaches, a reference model is created to facilitate improvements in managing the cybersecurity of supply chains for different actors, including service operators, maintainers, manufacturers, and systems integrators. This study provides an illustration of implementing a common vision of cybersecurity improvement across a community of actors. It utilizes a collaborative framework that has facilitated the co-production of cybersecurity guidance for energy sector participants.
Privacy‐preserving remote sensing images recognition based on limited visual cryptography

Abstract With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19

In 2019, the Covid-19 pandemic led to emergency changes in the educational sector as a precautionary measure to limit the spread of the Covid-19 virus and protect the health and safety of students. Educational institutes couldn't escape this havoc; by April 2020, 189 countries had suspended school, affecting 89 percent of the world's students. Since the epidemic began, online learning has completely taken over the educational industry, leaving students with no choice but to adapt to the brand-new virtual learning environment. Consequently, people turned to social media, such as Twitter, to express their feelings, opinions, and concerns about online learning as an alternative to traditional physical classes. The new online learning platforms, associated technologies, and procedures have been widely discussed on Twitter. In the proposed study, we have presented a systematic approach to analyze the public opinions and perceptions about online learning using Twitter sentiment analysis (TSA) through Twitter's API and term frequency-inverse document frequency (TF-IDF) technique. Further, we classified the sentiments into certain clusters, such as positive, negative, and neutral, using a text mining approach (i.e., lexicons). Moreover, we have uncovered these sentiments and visualized the clusters using visualization techniques such as word clouds and bar charts. Additionally, by using TF-IDF, we measured the strength of words that people use to express their opinions about online schooling and explored to what extent it affects the overall results of our analysis.
Computationally efficient image encryption technique based on selective pixel diffusion

Medical images play a vital role in disease diagnosis. When the medical images are communicated through an insecure transmission channel, their chances of being accessed by an unauthorized user increase resulting in the loss of patients' sensitive personal data. Thus, providing security to such image data while transmitting it over an insecure communication network becomes crucial. This work presents a computationally efficient cryptosystem for encrypting medical images. The encryption process consists of multiple phases. In the first phase the control parameters and initial values for the various chaotic maps used, are evaluated. This phase is followed by encryption in which these evaluated values are used to obtain the chaotic sequences for encryption. In the subsequent phases, we make use of a new approach of selective, pixel-dependent diffusion to obtain the cipher image. The effectiveness of our cryptosystem is evaluated using security analysis and execution time analysis. The obtained outcome shows a high-security level compared to already existing state-of-the-art techniques. In addition, the computational complexity of our scheme is very small (0.1sec for encrypting a 256×256 image) making it suitable for real-time smart health applications.
Support Vector Machine and Convolutional Neural Network Approach to Customer Review Sentiment Analysis

Sentiment Analysis is a branch of Natural Language Processing which intends to identify the sentiment in the data being analyzed through polarity analysis and emotion analysis. It can be performed with approaches which are based on Machine Learning as well as the approaches which are based on Lexicons which in turn rely on corpus and dictionaries. Thus, the unprecedented growth of E-Commerce Platforms resulting in an exponential increase in the amount of customer reviews prompts us to choose the most appropriate and optimized models of Sentiment Analysis to produce high accuracy. Since the customer reviews are one of the most important factors which influence the brand value, advertising and customer services of a company, harnessing Sentiment Analysis to get more insight into the reviews is the need of the hour. In this paper, the Customer Review Sentiment Analysis for polarity classification has been performed using Support Vector Machine Model and Convolutional Neural Network Model on a real world dataset of web scraped customer reviews, following which the Support Vector Machine Model was deployed to a web application. By choosing the most appropriate methods of dataset cleaning, text preprocessing and hyper parameter tuning, the Support Vector Machine and the Convolutional Neural Network Model achieved high accuracies of 96% and 94% respectively. Thus, both the Sentiment Analysis models have achieved much higher accuracy and minimal error rate in contrast to the existing models.
Design and Analysis of Automated Machine Learning (AutoML) in PowerBI Application Using PyCaret

Massively using conventional machine learning requires many people to understand the process. However, the design of an automatic machine learning system using the Pycaret Library can make it easier for users by reducing some of the operations of machine learning in general. This automated machine learning design is managed in the Power BI application so that the display form can be better understood and easily visualized. By carrying out several method stages in the form of literature studies, data collection, designing classification methods using conventional machine learning and AutoML in Power BI, analysis of results, and reporting/publication. The result of comparison of machine learning and Auto ML: In machine learning, 98 lines of line code are used, while in AutoML, eight lines of code are used. Another advantage derived from using AutoML in PowerBI is the feature to be able to visualize processed datasets so that they can obtain information in the form of diagrams, charts, and others. In testing using the breast cancer dataset, the use of Machine Learning is considered better based on the score obtained. Machine Learning received the highest Accuracy using GaussianNB is 0.94. In testing using the Diabetes dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using CatBoost Model is 0.777. In testing using the heart failure dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using Random Forest Model with a score of 0.85.
Counterfeit Product Detection In Supply Chain Management With Blockchain

Lately, fake items have taken on an undeniably significant role in the businesses that make different items. This affects the standing of the organization, as well as their deals and benefits. The execution of blockchain innovation takes into consideration the recognizable proof of certified items as well as the recognition of fake products. The blockchain is a sort of dispersed, decentralized, and computerized record that keeps value-based data as blocks in various data sets that are associated with the chains. Blockchain innovation is otherwise called Distributed Ledger Technology (DLT). Because of the way that blockchain innovation is a protected innovation, any block can neither be modified nor be hacked. Clients never again need to depend on the affirmation of item security given by outsider because of the use of Blockchain innovation. Quick Response (QR) codes are an incredible asset that can be used in this undertaking to battle the broad issue of item fraud. These codes exploit ongoing advancements in portable and remote advancements. The utilization of a Quick Response code scanner, in which the Quick Response code of the product is connected to a Blockchain, is the technique by which counterfeit merchandise are recognized and dispensed with. Therefore, this method has the potential to be utilised in order to keep product particulars and the created unique code of that product in the database in the form of blocks. It does a comparison of the code with the entries in the Blockchain database after it has obtained the user's unique code and stored it. The consumer will receive a notification indicating the product is authentic if the code is a match; otherwise, the customer will receive a notification that the product is counterfeit