Reviewerly/exHarmony huggingface dataset:
Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape

Currently, sustainability is a vital aspect for every nation and organization to accomplish Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG) metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53% of respondents in the BlackRock survey are concerned about the availability of low ESG data, which is critical for determining the organization’s sustainability level. This obstacle can be overcome by implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction, transparency, authentication, and structured data. Based on the review of previous studies, it was determined that only a few studies discussed the implementation of Industry 4.0 technologies for ESG data and evaluation. The objective of the study is to discuss the significance of ESG data and report, which is used for the evaluation of the sustainability of an organization. In this regard, the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG data and suggest that these digital technologies have a crucial role in framing an accurate ESG report. After analysis a few vital conclusions are drawn such as ESG investment has benefited from AI capabilities, which previously relied on self-disclosed, annualized company information that was susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations that can be implemented for future work.
An inception‐ResNet deep learning approach to classify tumours in the ovary as benign and malignant

Abstract The classification of tumours into benign and malignant continues to date to be a very relevant and significant research topic in the cancer research domain. With the advent of Computer Vision and rapid developments in the fields of deep learning, as well as medical devices and instruments, researchers are therefore utilizing the state‐of‐the‐art deep learning architectures to discover patterns in the medical image data and thereby use this information to detect tumours and classify them as benign or malignant. In this paper, we propose a custom state‐of‐the‐art deep learning architecture, the Inception‐ResNet v2 for the classification of ovarian tumours into the two categories of benign and malignant based on a custom dataset with a validation accuracy of 97.5% and a test accuracy of 67%. Furthermore, a quantum convolutional neural network (QCNN) was also implemented with an accuracy of 92% on the validation dataset.
Immersive interactive technologies and virtual shopping experiences: Differences in consumer perceptions between augmented reality (AR) and virtual reality (VR)

Based on the concepts of the Reality-Virtuality (RV) continuum and the stimulus-organism-response (SOR) framework, this study investigated differences between AR and VR in their effects on vividness/interactivity, a sense of presence, users’ sensory brand app experience, attitude, and behavioral intention. For AR, participants downloaded an app to mobile phones. For VR, participants downloaded a VR app to smartphones attached to a Google Cardboard VR headset. Vividness and interactivity directly (or indirectly) impacted a sense of presence, sensory brand app experience, attitude towards technology, and behavioral intention. However, AR and VR differed in how vividness and interactivity influenced sensory brand app experience and attitude towards technology. Overall, a sense of presence was a significant mediator for 1) the relationship between vividness and sensory brand app experience and 2) the relationship between interactivity and attitude towards technology for VR but no such relationship was observed with AR. The relationships among sensory brand app experience, attitude, and behavioral intention were significant in both AR and VR settings. Employing real AR/VR shopping situations, the study provides for practitioners deep understandings of consumers’ actual perceptions of applications of AR/VR and offers practical insights as to the efficiency of AR and VR in improving consumer virtual shopping experiences.
A survey on big data-enabled innovative online education systems during the COVID-19 pandemic

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.
A BERT Framework to Sentiment Analysis of Tweets

Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.
Non-parallel bounded support matrix machine and its application in roller bearing fault diagnosis

At present, the excellent performance of support vector machine (SVM) has made it successfully applied in many fields. However, when SVM is used for two-dimensional matrix data classification, vectorization of these data easily leads to dimension curse and the loss of structural information. Moreover, SVM is sensitive to outliers, which causes the hyperplane to move towards outliers. Therefore, this paper proposes a novel classification method for data in matrix-form, named non-parallel bounded support matrix machine (NPBSMM). In NPBSMM, a constraint norm group (CNG) is constructed and applied to objective function, which can not only suppress the negative impact of outliers on the model, but also make NPBSMM has better sparsity. By constructing CNG, the operation of matrix inversion in dual problem of traditional classification methods is avoided, so NPBSMM is more suitable for solving large-scale data problems. Further, to extract structure information of matrix for modeling, multi-rank left and right projection matrices are employed to establish objective function, which makes NPBSMM has a better ability of data fitting. Experiments performed on three roller bearing fault datasets show that the proposed NPBSMM method has powerful performance and robustness as compared with other typical classification methods.
Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network

Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. With large-scale sensor systems in modern factories, much data will be captured during monitoring and maintenance of complex industrial equipment. Accumulated data facilitates maintenance planning becomes more thorough and timely. Recently, a knowledge graph (KG) was offered to handle large-scale, unorganized maintenance data semantically, resulting in better data usage. Some prior studies have utilized KG for maintenance planning with semantic searching or graph structure-based algorithms, nevertheless neglecting the prediction of potential linkage. To fill this gap, a maintenance-oriented KG is established firstly based on a well-defined domain-specific ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to predict potential solutions and explain fault in maintenance tasks. Lastly, a maintenance case of oil drilling equipment is carried out, where the proposed model is compared with other cutting-edge models to demonstrate its effectiveness in link prediction. This research is anticipated to shed light on future adoption of KG in maintenance planning recommendations.
Convolutional Neural Networks or Vision Transformers: Who Will Win the Race for Action Recognitions in Visual Data?

Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades. Convolutional neural networks (CNN) are a significant component of this topic and play a crucial role in the renown of Deep Learning. Inspired by the human vision system, CNN has been applied to visual data exploitation and has solved various challenges in various computer vision tasks and video/image analysis, including action recognition (AR). However, not long ago, along with the achievement of the transformer in natural language processing (NLP), it began to set new trends in vision tasks, which has created a discussion around whether the Vision Transformer models (ViT) will replace CNN in action recognition in video clips. This paper conducts this trending topic in detail, the study of CNN and Transformer for Action Recognition separately and a comparative study of the accuracy-complexity trade-off. Finally, based on the performance analysis's outcome, the question of whether CNN or Vision Transformers will win the race will be discussed.
Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms

Abstract Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome.
Experimental Mode-Pairing Measurement-Device-Independent Quantum Key Distribution without Global Phase Locking

In the past two decades, quantum key distribution networks based on telecom fibers have been implemented on metropolitan and intercity scales. One of the bottlenecks lies in the exponential decay of the key rate with respect to the transmission distance. Recently proposed schemes mainly focus on achieving longer distances by creating a long-arm single-photon interferometer over two communication parties. Despite their advantageous performance over long communication distances, the requirement of phase locking between two remote lasers is technically challenging. By adopting the recently proposed mode-pairing idea, we realize high-performance quantum key distribution without global phase locking. Using two independent off-the-shelf lasers, we show a quadratic key-rate improvement over the conventional measurement-device-independent schemes in the regime of metropolitan and intercity distances. For longer distances, we also boost the key rate performance by 3 orders of magnitude via 304 km commercial fiber and 407 km ultralow-loss fiber. We expect this ready-to-implement high-performance scheme to be widely used in future intercity quantum communication networks.
Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data

Today's world faces a serious public health problem with cancer. One type of cancer that begins in the breast and spreads to other body areas is breast cancer (BC). Breast cancer is one of the most prevalent cancers that claim the lives of women. It is also becoming clearer that most cases of breast cancer are already advanced when they are brought to the doctor's attention by the patient. The patient may have the evident lesion removed, but the seeds have reached an advanced stage of development or the body's ability to resist them has weakened considerably, rendering them ineffective. Although it is still much more common in more developed nations, it is also quickly spreading to less developed countries. The motivation behind this study is to use an ensemble method for the prediction of BC, as an ensemble model aims to automatically manage the strengths and weaknesses of each of its separate models, resulting in the best decision being made overall. The main objective of this paper is to predict and classify breast cancer using Adaboost ensemble techniques. The weighted entropy is computed for the target column. Taking each attribute's weights results in the weighted entropy. Each class's likelihood is represented by the weights. The amount of information gained increases with a decrease in entropy. Both individual and homogeneous ensemble classifiers, created by mixing Adaboost with different single classifiers, have been used in this work. In order to deal with the class imbalance issue as well as noise, the synthetic minority over-sampling technique (SMOTE) was used as part of the data mining pre-processing. The suggested approach uses a decision tree (DT) and naive Bayes (NB), with Adaboost ensemble techniques. The experimental findings shown 97.95% accuracy for prediction using the Adaboost-random forest classifier.
Emerging information and communication technologies for smart energy systems and renewable transition

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the decarbonization of energy systems is crucial for climate change mitigation. Two major challenges of energy systems decarbonization are renewable transition planning and sustainable systems operations. To address the challenges, incorporating emerging information and communication technologies can facilitate both the design and operations of future smart energy systems with high penetrations of renewable energy and decentralized structures. The present work provides a comprehensive overview of the applicability of emerging information and communication technologies in renewable transition and smart energy systems, including artificial intelligence, quantum computing, blockchain, next-generation communication technologies, and the metaverse. Relevant research directions are introduced through reviewing existing literature. This review concludes with a discussion of the industrial use cases and demonstrations of smart energy technologies.
Medical Image Segmentation via Cascaded Attention Decoding

Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels. Previous works try to overcome this problem by embedding convolutional layers either in the encoder or decoder modules of transformers thus ending up sometimes with inconsistent features. To address this issue, we propose a novel attention-based decoder, namely CASCaded Attention DEcoder (CASCADE), which leverages the multi-scale features of hierarchical vision transformers. CASCADE consists of i) an attention gate which fuses features with skip connections and ii) a convolutional attention module that enhances the long-range and local context by suppressing background information. We use a multi-stage feature and loss aggregation framework due to their faster convergence and better performance. Our experiments demonstrate that transformers with CASCADE significantly outperform state-of-the-art CNN- and transformer-based approaches, obtaining up to 5.07% and 6.16% improvements in DICE and mIoU scores, respectively. CASCADE opens new ways of designing better attention-based decoders.
Autonomous Vehicles Security: Challenges and Solutions Using Blockchain and Artificial Intelligence

The arrival of autonomous vehicles (AVs) promises many great benefits, including increased safety and reduced energy consumption, pollution, and congestion. However, these engines have many security and privacy issues that could undermine the expected benefits if not addressed. AVs will provide new opportunities for hackers to carry out malicious attacks, posing a great threat to the future of mobility and data protection. The research trend in this field indicates that combining Blockchain and AI could bring strong protection for AVs against malicious attacks. Blockchain and AI have different working paradigms, but when merged, they can empower each other, and solve many security and privacy issues of AVs. AI can optimise the construction of the Blockchain to make it more efficient, secure and energy-saving, where Blockchain provides data immutability and trust mechanism for AI-based solutions and makes them more transparent, trustful, and explainable. Although some research is being conducted on this area, the topic of applying Blockchain and AI for securing AVs is not deeply investigated. In this paper, we explore the possible application of an amalgamation of Blockchain and AI solutions for securing AVs. We first introduce a classification of security and privacy threats that may arise from the application of AVs. Then, we provide an overview of recent literature regarding Blockchain and AI usage for securing AVs. Finally, we highlight limitations and challenges that may face the integration of Blockchain and AI with AVs based on our systemic review and suggest potential future directions for research in this field.
Data Augmentation in Classification and Segmentation: A Survey and New Strategies

In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a parameter-free and easy to implement strategy, the random local rotation strategy, which involves randomly selecting the location and size of circular regions in the image and rotating them with random angles. It can be used as an alternative to the traditional rotation strategy, which generally suffers from irregular image boundaries. It can also complement other techniques in data augmentation. Extensive experimental results and comparisons demonstrated that the new strategy consistently outperformed its traditional counterparts in, for example, image classification.
YOLO5Face: Why Reinventing a Face Detector

Tremendous progress has been made on face detection in recent years using convolutional neural networks. While many face detectors use designs designated for the detection of face, we treat face detection as a general object detection task. We implement a face detector based on YOLOv5 object detector and call it YOLO5Face. We add a five-point landmark regression head into it and use the Wing loss function. We design detectors with different model sizes, from a large model to achieve the best performance to a super small model for real-time detection on an embedded or mobile device. Experiment results on the WiderFace dataset show that our face detectors can achieve state-of-the-art performance in almost all the Easy, Medium, and Hard subsets, exceeding the more complex designated face detectors. The code is available at https://github.com/deepcam-cn/yolov5-face .