Reviewerly/exHarmony huggingface dataset:
Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period

Thai Chana is one of the mobile applications for COVID-19 disease-control tracking, especially among the Thais. The purpose of this study was to determine factors affecting the perceived usability of Thai Chana by integrating protection motivation theory, the extended technology acceptance model, and the system usability scale. In all, 800 Thais participated and filled an online questionnaire with 56 questions during the early COVID-19 omicron period (15 December 2021 to 14 January 2022). Structural equation modeling (SEM) showed that the understanding of COVID-19 has significant effects on perceived severity and perceived vulnerability, which subsequently leads to perceived usefulness. In addition, perceived usefulness and perceived ease of use have significant direct effects on attitude, which subsequently leads to the intention to use, actual use, and perceived usability. This study is one of the first studies that have analyzed the mobile application for COVID-19 disease-control tracking. The significant and substantial findings can be used for a theoretical foundation, particularly in designing a new mobile application for disease-control tracking worldwide. Finally, protection motivation theory, the extended technology acceptance model, and the system usability scale can be used for evaluating other disease-control tracking mobile applications worldwide.
Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.
An image encryption algorithm based on discrete fractional wavelet transform and quantum chaos

Aiming at low security of traditional encryption methods, a new image encryption algorithm based on discrete fractional wavelet transform (DFRWT) and quantum Logistic mapping is proposed in this paper. Firstly, p-order multiscale decomposition is performed for the original image, with DFRWT regarded as decomposition tool. Then the obtained high and low frequency coefficients are shuffled in the time-fractional-frequency domain, followed by an inverse DFRWT to obtain a shuffled image. Finally, XOR operation is carried out between pseudo-random sequence generated by quantum Logistic chaotic and shuffled image, which achieves image diffusion to complete image encryption. Simulation results demonstrate that the algorithm has positive characteristics of large key space, good scrambling performance and quite strong robustness.
A comprehensive and systematic review of the IoT-based medical management systems: Applications, techniques, trends and open issues

The Internet of Things (IoT) is critical in a variety of modern city and society management domains, including intelligent medical management. The intelligent IoT with unlimited networking capacities for medical big data analysis strengthens the interaction across technology and healthcare society in smart cities today. ​IoT enables more convenient ways to remotely and efficiently provide medical services for the patients and monitor the health situation of patients by the doctors, physicians, and nurses over the Internet. Because of the lack of comprehensive and up-to-date review in this domain, the present investigation intends to offer a thorough examination of the role of IoT in medical management systems, discuss the available issues, and answer many empowering technologies and insinuations to numerous usages. Research schemes have been proposed for a 3-cluster categorization (patient data collection, exchange, storage, remote monitoring, and security mechanisms). These terms potentially affect the present and upcoming IoT-based medical system research. The results obtained from the literature review showed that improving security, cost, service time, and efficiency have been the most important goal of researchers in the field of management of medical systems based on IoT. Besides, the findings revealed that IoT is beneficial for governments to boost the condition of society and business relations in terms of well-being; however, IoT needs modern safety infrastructure.
Framework for Product Anti-Counterfeiting using Blockchain Technology

In recent years, blockchain technology has gained more attention, and a plethora of applications have developed. It's a digital money. In addition to solving the problem of double-spending, Bitcoin is a well-known Blockchain application since it does it without relying on a central authority. Blockchain technology provides a tamper-proof basis for any application that relies on it. With this article's help, shoppers will no longer be solely reliant on merchants to verify the legitimacy of things. We present a decentralised Blockchain system for anti-counterfeiting items. This system enables producers to deliver authentic products without managing direct-operated storefronts, considerably lowering the cost of product quality assurance.
Underwater image restoration by red channel compensation and underwater median dark channel prior

Underwater images often show low contrast, blurring, and color distortion due to the absorption and scattering of light. In contrast to existing underwater image restoration methods, we propose an underwater image restoration method with red channel compensation and blue-green channel restoration. First, a proposed approach relies on the hue and attenuation differences between different color channels of the underwater image to estimate the background light. Then, the red channel is enhanced according to a perfect reflection assumption algorithm. Finally, a new median underwater dark channel prior (MUDCP) is proposed to precisely estimate the blue-green channel transmission map. Experimental results show that our method significantly improves contrast, removes color bias, and preserves more detail than other underwater restoration techniques.
News-based sentiment and bitcoin volatility

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.
Refined One-Stage Oriented Object Detection Method for Remote Sensing Images

Multi-class object detection in remote sensing images plays an important role in many applications but remains a challenging task because of scale imbalance and arbitrary orientations of the objects with extreme aspect ratios. In this paper, the Asymmetric Feature Pyramid Network (AFPN), Dynamic Feature Alignment (DFA) module, and Area-IoU regression loss are proposed on the basis of a one-stage cascaded detection method for the detection of multi-class objects with arbitrary orientations in remote sensing images. The designed asymmetric convolutional block is embedded into the AFPN for handling objects with extreme aspect ratios and improving the space representation with ignorable increases in calculation. The DFA module is proposed to dynamically align mismatched features, which are caused by the deviation between predefined anchors and arbitrarily oriented predicted boxes. The refined Area-IoU regression loss, which reconciles two new regression loss functions, the area-guided regression loss and IoU-guided regression loss, is proposed to simultaneously solve the scale imbalance problem and angle sensitivity problem. Experiments on three publicly available datasets, DOTA, HRSC2016, and ICDAR2015, show the effectiveness of the proposed method.
HashFormer: Vision Transformer Based Deep Hashing for Image Retrieval

Deep image hashing aims to map an input image to compact binary codes by deep neural network, to enable efficient image retrieval across large-scale dataset. Due to the explosive growth of modern data, deep hashing has gained growing attention from research community. Recently, convolutional neural networks like ResNet have dominated in deep hashing. Nevertheless, motivated by the recent advancements of vision transformers, we propose a pure transformer-based framework, called as HashFormer, to tackle the deep hashing task. Specifically, we utilize vision transformer (ViT) as our backbone, and treat binary codes as the intermediate representations for our surrogate task, <i>i.e.</i>, image classification. In addition, we observe that the binary codes suitable for classification are sub-optimal for retrieval. To mitigate this problem, we present a novel average precision loss, which enables us to directly optimize the retrieval accuracy. To the best of our knowledge, our work is one of the pioneer works to address deep hashing learning problems without convolutional neural networks (CNNs). We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUSWIDE and ImageNet. The proposed method demonstrates promising results against existing state-of-the-art works, validating the advantages and merits of our HashFormer.
Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of our knowledge, however, there is not a comprehensive survey of MDE. This paper aims to bridge this gap by reviewing 197 relevant articles published between 1970 and 2021. In particular, we provide a comprehensive survey of MDE covering various methods, introduce the popular performance evaluation metrics and summarize publically available datasets. We also summarize available open-source implementations of some representative methods and compare their performances. Furthermore, we review the application of MDE in some important robotic tasks. Finally, we conclude this paper by presenting some promising directions for future research. This survey is expected to assist readers to navigate this research field.
Automatic Modulation Classification Based on Decentralized Learning and Ensemble Learning

To deal with the deep learning-based automatic modulation classification (AMC) in the scenario that the training dataset are distributed over a network without gathering the data at a centralized location, the decentralized learning-based AMC (DecentAMC) had been presented. However, there exists frequent model parameter uploading and downloading in DecentAMC method, which cause high communication overhead. In this paper, an innovative learning framework are proposed for AMC (named DeEnAMC), in which the framework is realized by utilizing the combination of decentralized learning and ensemble learning. Our results show that the proposed DeEnAMC reduces communication overhead while keeping a similar classification performance to DecentAMC.
Detecting deepfake videos based on spatiotemporal attention and convolutional LSTM

Fake face detection is in dilemma with the rapid development of face manipulation technology. One way to improve the effectiveness of detector is to make full use of intra and inter frame information. In this paper, a novel Xception-LSTM algorithm is proposed by using our new spatiotemporal attention mechanism and convolutional long short-term memory (ConvLSTM). In the algorithm, the spatiotemporal attention mechanism, including spatial and temporal attention mechanism, is proposed to capture and enhance spatiotemporal correlations before dimension reduction of Xception. Thereafter, the ConvLSTM is introduced to consider frame structure information while modeling temporal information. The experimental results on three widely used datasets demonstrate that the proposed algorithms perform better than the state-of-the-art algorithms. In addition, the effectiveness of the spatiotemporal attention mechanism and ConvLSTM are illustrated in ablation experiments.
Maximum-Entropy-Based Decision-Making Trial and Evaluation Laboratory and Its Application in Emergency Management

Decision-making trial and evaluation laboratory (DEMATEL) can visualize the structure of complex causal relationships, so it is widely used in decision making. One of the important steps in DEMATEL is normalization, and it has received much attention in recent years. Maximum entropy (MaxEnt) is a universal principle, and it is an effective tool for determining the amount of information existed in evidence. In this paper, the authors propose MaxEnt-based DEMATEL. The greatest contribution in this paper is the use of MaxEnt principle to determine the normalized direct influence matrix, which allows to obtain the normalized matrix with minimal information loss. The authors illustrate emergency management to show the superiority of the proposed method.
A secure kNN query processing algorithm using homomorphic encryption on outsourced database

With the adoption of cloud computing, database outsourcing has emerged as a new platform. Due to the serious privacy concerns associated with cloud computing, databases must be encrypted before being outsourced to the cloud. Therefore, various k-nearest neighbor (kNN) query processing techniques have been proposed for encrypted databases. However, existing schemes are either insecure or inefficient. In this paper, we propose a new secure kNN query processing algorithm. Our algorithm guarantees the confidentiality of both encrypted data and users’ query records. To achieve a high level of query processing efficiency, we also devise an encrypted index search scheme that performs data filtering without revealing data access patterns. A performance analysis shows that the proposed scheme outperforms the existing scheme in terms of query processing costs while preserving data privacy.
Modeling Arabic subjectivity and sentiment in lexical space

Abstract In spite of the vast amount of work on subjectivity and sentiment analysis (SSA), it is not yet particularly clear how lexical information can best be modeled in a morphologically-richness language. To bridge this gap, we report successful models targeting lexical input in Arabic, a language of very complex morphology. Namely, we measure the impact of both gold and automatic segmentation on the task and build effective models achieving significantly higher than our baselines. Our models exploiting predicted segments improve subjectivity classification by 6.02% F1-measure and sentiment classification by 4.50% F1-measure against the majority class baseline surface word forms. We also perform in-depth (error) analyses of the behavior of the models and provide detailed explanations of subjectivity and sentiment expression in Arabic against the morphological richness background in which the work is situated.
Shaving Data Center Power Demand Peaks Through Energy Storage and Workload Shifting Control

This paper proposes efficient strategies that shave Data Centers (DCs)' monthly peak power demand with the aim of reducing the DCs' monthly expenses. Specifically, the proposed strategies allow to decide: i) when and how much of the DC's workload should be delayed given that the workload is made up of multiple classes where each class has a certain delay tolerance and delay cost, and ii) when and how much energy should be charged/discharged into DCs' batteries. We first consider the case where the DC's power demands throughout the whole billing cycle are known and present an optimal peak shaving control strategy for it. We then relax this assumption and propose an efficient control strategy for the case when (accurate/noisy) predictions of the DC's power demands are only known for short durations in the future. Several comparative studies based on real traces from a Google DC are conducted in order to validate the proposed techniques.
A Scalable Attribute-Based Access Control Scheme with Flexible Delegation cum Sharing of Access Privileges for Cloud Storage

Nowadays cloud servers have become the primary choice to store and share data with multiple users across the globe. The major challenge in sharing data using cloud servers is to protect data against untrusted cloud service provider and illegitimate users. Attribute-Based Encryption (ABE) has emerged as a useful cryptographic technique to securely share data with legitimate recipients in fine-grained manner. Several solutions employing ABE have been proposed to securely share data using cloud servers. However, most of the solutions are data owner-centric and focus on providing data owner complete control on his outsourced data. The existing solutions in cloud computing fail to provide shared access privileges among users and to enable cloud users to delegate their access privileges in a flexible manner. In order to simultaneously achieve the notion of fine-grained access control, scalability and to provide cloud users shared access privileges and flexibility on delegation of their access privileges, we propose a scalable attribute-based access control scheme for cloud storage. The scheme extends the ciphertext policy attribute-based encryption to achieve flexible delegation of access privileges and shared access privileges along with scalability and fine-grained access control. The scheme achieves scalability by employing hierarchical structure of users. Furthermore, we formally prove the security of our proposed scheme based on security of the ciphertext-policy attribute-based encryption. We also implement the algorithm to show its scalability and efficiency.
Dust removal from high turbid underwater images using convolutional neural networks

In recent years, underwater image processing has been a focus of many studies. Most underwater image processing technologies are focused on descattering, absorption correction and reflection correction. For a deep sea mining machine, dust seriously affects visual acuity. To correct for this, a two-part dust removal approach is proposed. Underwater red-green minimum channel prior descattering is used to remove fine dust in the first stage. However, the impact of dust streaks on images is always undesirable. Consequently, we propose a further deep convolutional neural-network-based dust removal method. The experimental results show that the proposed method performs better in removing haze-like scatters and dust-like scatters.
Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis

High-dimensional non-Gaussian data are ubiquitous in many real applications. Face recognition is a typical example of such scenarios. The sampled face images of each person in the original data space are more closely located to each other than to those of the same individuals due to the changes of various conditions like illumination, pose variation, and facial expression. They are often non-Gaussian and differentiating the importance of each data point has been recognized as an effective approach to process the high-dimensional non-Gaussian data. In this paper, to embed non-Gaussian data well, we propose a novel unified framework named adaptive discriminative analysis (ADA), which combines the sample's importance measurement and subspace learning in a unified framework. Therefore, our ADA can preserve the within-class local structure and learn the discriminative transformation functions simultaneously by minimizing the distances of the projected samples within the same classes while maximizing the between-class separability. Meanwhile, an efficient method is developed to solve our formulated problem. Comprehensive analyses, including convergence behavior and parameter determination, together with the relationship to other related approaches, are as well presented. Systematical experiments are conducted to understand the work of our proposed ADA. Promising experimental results on various types of real-world benchmark data sets are provided to examine the effectiveness of our algorithm. Furthermore, we have also evaluated our method in face recognition. They all validate the effectiveness of our method on processing the high-dimensional non-Gaussian data.
A Privacy-Preserving Framework for Outsourcing Location-Based Services to the Cloud

Thanks to the popularity of mobile devices numerous location-based services (LBS) have emerged. While several privacy-preserving solutions for LBS have been proposed, most of these solutions do not consider the fact that LBS are typically cloud-based nowadays. Outsourcing data and computation to the cloud raises a number of significant challenges related to data confidentiality, user identity and query privacy, fine-grained access control, and query expressiveness. In this work, we propose a privacy-preserving framework for outsourcing LBS to the cloud. The framework supports multi-location queries with fine-grained access control, and search by location attributes, while providing semantic security. In particular, the framework implements a new model that allows the user to govern the trade-off between precision and privacy on a dynamic per-query basis. We also provide a security analysis to show that the proposed scheme preserves privacy in the presence of different threats. We also show the viability of our proposed solution and scalability with the number of locations through an experimental evaluation, using a real-life OpenStreetMap dataset.