Reviewerly/exHarmony huggingface dataset:
Boosting Adversarial Training with Hypersphere Embedding

Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedding (HE) mechanism into the AT procedure by regularizing the features onto compact manifolds, which constitutes a lightweight yet effective module to blend in the strength of representation learning. Our extensive analyses reveal that AT and HE are well coupled to benefit the robustness of the adversarially trained models from several aspects. We validate the effectiveness and adaptability of HE by embedding it into the popular AT frameworks including PGD-AT, ALP, and TRADES, as well as the FreeAT and FastAT strategies. In the experiments, we evaluate our methods under a wide range of adversarial attacks on the CIFAR-10 and ImageNet datasets, which verifies that integrating HE can consistently enhance the model robustness for each AT framework with little extra computation.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising

Text summarization aims to extract essential information from a piece of text and transform the text into a concise version.Existing unsupervised abstractive summarization models leverage recurrent neural networks framework while the recently proposed transformer exhibits much more capability.Moreover, most of previous summarization models ignore abundant unlabeled corpora resources available for pretraining.In order to address these issues, we propose TED, a transformerbased unsupervised abstractive summarization system with pretraining on large-scale data.We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora.Next, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of generated summaries.Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles.Further analysis shows that the summaries generated by TED are highly abstractive, and each component in the objective function of TED is highly effective.Pretrained Transformer Encoders/Decoders theme loss article generated summary denoised sent.noisy sent.cross-entropy loss sent.
Measured and satellite-derived albedo data for estimating bifacial photovoltaic system performance

The albedo of the ground surface is an important factor in the cost-effectiveness of a bifacial photovoltaic (PV) system. To improve the availability of reliable albedo data, datasets of ground albedo and associated meteorological data were developed by using existing measurement network data and data measured by the PV industry. The measured datasets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Satellite-derived values of albedo are available from the National Solar Radiation Data Base (NSRDB). The NSRDB albedos were compared to the measured albedos for Surface Radiation budget (SURFRAD) network locations for the period 2001–2017, and the mean bias difference results were from −0.044 to +0.056. These differences are greater than the albedo measurement uncertainty of ±0.02; consequently, the NSRDB albedos should be used with caution for estimating the performance of bifacial PV systems. Differences between SURFRAD and NSRDB albedos are attributed to the NSRDB method for determining albedo and to the ground surfaces within the NSRDB 4 km spatial resolution pixel consisting of a mixture of surface types rather than just the single surface types viewed by the albedometers at the SURFRAD stations.
Yoga Asana Identification: A Deep Learning Approach

Abstract Yoga is a healthy practice that originated from India, to rejuvenate a man in his physical, mental, and spiritual wellness. Moving with the brisk technology advancements, there is a vast opportunity for computational probing in all social domains. But still, the utilization of artificial intelligence and machine learning techniques for applying to an interdisciplinary domain like yoga is quite challenging. In this work, a system that recognizes a yoga posture from an image or a frame of a video has been developed with the help of deep learning techniques like convolutional neural networks (CNN) and transfer learning. We have considered images of 10 different asanas for training the model as well as evaluating the prediction accuracy. The prediction model backed with transfer learning shows promising results with 85% prediction accuracy and this system can be considered as an initial step to build an automated yoga image and video analysis tool.
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification

Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy.Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information.To this end, we formulate the text-label semantics relationship as a semantic matching problem and thus propose a hierarchy-aware label semantics matching network (HiMatch).First, we project text semantics and label semantics into a joint embedding space.We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.Our model captures the text-label semantics matching relationship among coarse-grained labels and fine-grained labels in a hierarchy-aware manner.The experimental results on various benchmark datasets verify that our model achieves state-of-the-art results.
Amassing the Security: An Enhanced Authentication Protocol for Drone Communications over 5G Networks

At present, the great progress made by the Internet of Things (IoT) has led to the emergence of the Internet of Drones (IoD). IoD is an extension of the IoT, which is used to control and manipulate drones entering the flight area. Now, the fifth-generation mobile communication technology (5G) has been introduced into the IoD; it can transmit ultra-high-definition data, make the drones respond to ground commands faster and provide more secure data transmission in the IoD. However, because the drones communicate on the public channel, they are vulnerable to security attacks; furthermore, drones can be easily captured by attackers. Therefore, to solve the security problem of the IoD, Hussain et al. recently proposed a three-party authentication protocol in an IoD environment. The protocol is applied to the supervision of smart cities and collects real-time data about the smart city through drones. However, we find that the protocol is vulnerable to drone capture attacks, privileged insider attacks and session key disclosure attacks. Based on the security of the above protocol, we designed an improved protocol. Through informal analysis, we proved that the protocol could resist known security attacks. In addition, we used the real-oracle random model and ProVerif tool to prove the security and effectiveness of the protocol. Finally, through comparison, we conclude that the protocol is secure compared with recent protocols.
Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering

Reading customer reviews before purchasing items online has become a common practice; however, some companies use machine learning (ML) algorithms to generate false reviews in order to create positive brand images of their own products and negative images of competitors' offerings. Existing techniques use review content to identify fraudulent reviewers; however, spammers become more intelligent, started to learn from their mistakes, and changed their tactics in order to avoid detection techniques. Thus, investigating fraudulent accounts' behaviour of generating fake negative or positive reviews for competitors or themselves and the necessity of ML classifiers to identify fraudulent reviews, is more important than ever. In this research, we present a novel feature engineering approach in which we (1) extract several “review-centric” and “reviewer-centric” features from a dataset; (2) combine the cumulative effects of features distributions into a unified model that represents overall behavior of the fraudulent reviewers; (3) investigate the role of effective data pre-processing to improve detection accuracy; and (4) develop a probabilistic approach to detect fraudulent reviewers by learning a novel M-SMOTE model over a derived balanced dataset and feature distributions, which outperforms other ML models. Our study contributes to the literature on digital platforms and fraudulent review detection with significant managerial and theoretical implications through these novel findings.
Early diagnosis of Alzheimer's disease based on deep learning: A systematic review

The improvement of health indicators and life expectancy, especially in developed countries, has led to population growth and increased age-related diseases, including Alzheimer's disease (AD). Thus, the early detection of AD is valuable to stop its progress at an early stage. This study systematically reviewed the current state of using deep learning methods on neuroimaging data for timely diagnose of AD. We reviewed different deep models, modalities, feature extraction strategies, and parameter initialization methods to find out which model or strategy could offer better performance. Our search in eight different databases resulted in 736 studies, from which 74 studies were included to be reviewed for data analysis. Most studies have reported the normal control (NC)/AD classification and have shown desirable results. Although recent studies showed promising results of utilizing deep models on the NC/mild cognitive impairment (MCI) and NC/early MCI (eMCI), other classification groups should be taken into consideration and improved. The results of our review indicate that the comparative analysis is challenging in this area due to the lack of a benchmark platform; however, convolutional neural network (CNN)-based models, especially in an ensemble way, seem to perform better than other deep models. The transfer learning approach also could efficiently improve the performance and time complexity. Further research on designing a benchmark platform to facilitate the comparative analysis is recommended.
Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can only load the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement; the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently-computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market dataset to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time-series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.
Effectiveness of Virtual Reference Services in Academic Libraries: A Qualitative Study Based on the 5E Learning Model

This study explores library users' perceptions and preferences of virtual reference services (VRS) in an academic library in Hong Kong and examines the impact of VRS on students' learning for the future improvement of the library's services. A qualitative semi-structured interview was conducted with ten students from different faculties of a comprehensive university in Hong Kong. Proceedings of the interviews were recorded using Zoom, transcribed, and analyzed into theme tables according to the 5E learning model. Results indicate that WhatsApp is the most preferred VRS due to its convenience, response immediacy, textual record, and minimal learning efforts. Phone, email, and Zoom are less preferred due to users' perceived embarrassment, formality, and time constraints. The findings demonstrate that VRS is vital in students' different learning stages. It arouses students' interest in learning and information search, allows the exploration of new learning resources, provides a good platform to solve learning problems, and offers efficient and instant help for learning. Unlike previous studies, this study identifies users' perceived impact of VRS on their learning process based on the 5E learning model. It provides important implications and suggestions to improve VRS and staff training to enhance the library's overall reference services.
A systematic review of social media-based sentiment analysis: Emerging trends and challenges

In the present information age, a wide and significant variety of social media platforms have been developed and become an important part of modern life. Massive amounts of user-generated data sourced from various social networking platforms also provide new insights for businesses and governments. However, it has become difficult to extract useful information from the vast amount of information effectively. Sentiment analysis provides an automated method of analyzing sentiment, emotion and opinion in written language to address this issue. In the existing literature, a large number of scholars have worked on improving the performance of various sentiment classifiers or applying them to various domains using data from social networking platforms. This paper explores the challenges that scholars have encountered and other potential problems in studying sentiment analysis in social media. It gives insights into the goals of the sentiment analysis task, the implementation process, and the ways in which it is utilized in various application domains. It also provides a comparison of different studies and highlights several challenges related to the datasets, text languages, analysis methods and evaluation metrics. The paper contributes to the research on sentiment analysis and can help practitioners select a suitable methodology for their applications.
Blockchain for sustainable supply chain management: trends and ways forward

Abstract Blockchain operates on a highly secured framework, and its decentralized consensus has benefits for supply chain sustainability. Scholars have recognized the growing importance of sustainability in supply chains and studied the potential of blockchain for sustainable supply chain management. However, no study has taken stock of high-quality research in this area. To address this gap, this paper aims to provide a state-of-the-art overview of high-quality research on blockchain for sustainable supply chain management. To do so, this paper conducts a systematic literature review using a bibliometric analysis of 146 high-quality articles on blockchain for sustainable supply chain management that have been published in journals ranked “A*”, “A”, and “B” by the Australian Business Deans Council and retrieved from the Scopus database. In doing so, this paper unpacks the most prominent journals, authors, institutions, and countries that have contributed to three major themes in the field, namely blockchain for sustainable business activities, decision support systems using blockchain, and blockchain for intelligent transportation system. This paper also reveals the use of blockchain for sustainable supply chain management across four major sectors, namely food, healthcare, manufacturing, and infrastructure, and concludes with suggestions for future research in each sector.
A dynamically hybrid path planning for unmanned surface vehicles based on non-uniform Theta* and improved dynamic windows approach

This paper presents a dynamically hybrid path planning scheme incorporating the global guidance, real-time motion planning and collision-free maneuvering for Unmanned Surface Vehicles (USVs). To ensure safe navigation in narrow waters, the non-uniform costmaps with risk function is designed for global planning. Non-uniform Theta* (NT*) performs a reverse search from the goal node to establish the mapping relation containing the parent node and minimum path-cost of each cell. To prevent USV trapping into local minimum after avoiding the dynamic obstacles, the parent node is dynamically selected as the local target of improved dynamic window approach (IDWA), rather than tracing the sub-target fixed in global path as DWA does. In order to intensify navigation efficiency of the USV, the minimum path-cost from current cell to the goal is also implemented to the objective function of IDWA for evaluating the predicted trajectories. The proposed algorithms are validated by comparative numerical analysis and proven to work effectively.
A visually secure image encryption scheme using adaptive-thresholding sparsification compression sensing model and newly-designed memristive chaotic map

Aiming at the deficiencies of existing image compression-encryption algorithms, such as unstable reconstruction performance and weak crypticity, a novel visually secure image encryption scheme based on the adaptive-thresholding sparsification compression sensing model and the newly designed memristive chaotic map is proposed in this paper. First, the plain image is sparsely represented in the wavelet packet domain. Then, the coefficient matrix is scrambled and compressed using parallel compressive sensing with assistance from the newly developed adaptive-thresholding sparsification model. Next, the compressed image is re-encrypted by the FAN transform and diffusion to generate a noise-like secret image. Finally, the reversible matrix encoding-based embedding method is employed to create a meaningful cipher image. To improve the execution efficiency of the encryption algorithm without affecting the security performance, a low-dimensional discrete memristive chaotic map is designed and applied in the compression, encryption and embedding stages. Additionally, the Lp matrix norm is utilized to establish an adaptive threshold model to effectively enhance the compression performance. Simulation results and analyses are presented to demonstrate the effectiveness, the compressibility, and the secrecy of the proposed scheme.
Random neighbor elite guided differential evolution for global numerical optimization

Optimization problems not only become more and more ubiquitous in various fields, but also become more and more difficult to optimize nowadays, which seriously challenge the effectiveness of existing optimizers like different evolution (DE). To effectively solve this kind of problems, this paper proposes a random neighbor elite guided differential evolution (RNEGDE) algorithm. Specifically, to let individuals explore and exploit the solution space properly, a novel random neighbor elite guided mutation strategy named “DE/current-to-rnbest/1” is first proposed to mutate individuals. In this mutation strategy, several individuals randomly selected from the population for each individual to be updated along with the individual itself form a neighbor region, and then the best one in such a region is adopted as the guiding exemplar to mutate the individual. Due to the random selection of neighbors and the directional guidance of elites, this strategy is expected to direct individuals to promising areas fast without serious loss of diversity. Notably, it is found that two popular mutation strategies, namely “DE/current-to-best/1” and “DE/current-to-pbest/1”, are two special cases of the proposed “DE/current-to-rnbest/1”. Further, to alleviate the sensitivity of the proposed algorithm to the involved parameters, this paper utilizes the Gaussian distribution and the Cauchy distribution to adaptively generate parameter values for each individual with the mean value of the Gaussian distribution and the position value of the Cauchy distribution adaptively adjusted based on the evolutionary information of the population. With the above two techniques, the proposed algorithm is expected to effectively search the solution space. At last, extensive experiments conducted on one widely used benchmark function set with three different dimension sizes demonstrate that the proposed algorithm achieves highly competitive or even much better performance than several compared state-of-the-art peer methods.
Review on Interpretable Machine Learning in Smart Grid

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.
An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation.