Reviewerly/exHarmony huggingface dataset:
An Anchor-Free Vehicle Detection Algorithm in Aerial Image Based on Context Information and Transformer

Vehicle detection in the aerial image is an essential and challenging task widely used in industry and agriculture. Deep learning technology has recently achieved rapid development and good object detection results. However, the background of aerial images is complex, targets are densely distributed, and some of them are occluded. For densely distributed targets, we need to predict at each feature point. In the case of complex background and target occlusion, it is often difficult to determine whether a location contains a target if the model only focuses on the local information. Therefore, we need a global perspective and contextual information to help train the model. This paper proposes a new anchor-free small object detection algorithm, which improves feature extraction by fusing contextual semantic information. In addition, a dynamic activation function is also used in our network, which helps us calculate the activation function value for each point from a global perspective. Moreover, we also use the channel attention module and the transformer as the spatial attention module to help the network efficiently obtain global information. We evaluate the effectiveness of our method on the public dataset DLR-3K and VEDAI, and the AP achieves 0.896 and 0.875.
A Novel Low-Light Catenary Image Enhancement Approach for CSCs Detection in High-Speed Railways

The catenary system is essential for ensuring the stable energy transmission of trains in high-speed railways.The non-contact catenary detection is a promising monitoring method, where the catenary image is captured by an industrial camera mounted on the inspection vehicle.The image quality is susceptible to the limitations of the environment and equipment, which adversely affects the catenary location and detection accuracy.In this paper, we propose an unsupervised learning-based catenary image enhancement method for improving localization accuracy.First, the enhancement model is optimized to enhance the catenary image quality, making it sharper and more conducive to detection.Subsequently, an advanced small target location approach, called TPH-YOLOv5, is used to locate the catenary components.Finally, we compared the localization performance of the enhanced image with the low-light image.The experiment results show that the proposed method can effectively enhance the quality of low-light catenary images and improve the positioning accuracy.
PP-DENT: A Privacy-Preserving Framework for Blockchain-Based Mobile/Roaming Transactions

Blockchain technology has attracted many developers in various fields to launch their projects. DENT Wireless, a well-known telecom company, has developed the DENT project to provide blockchain-based eSIM and exchange. This exciting project has attracted many eSIM users. However, DENT cannot support data privacy if requested by the users. This letter offers a privacy-preserving framework based on the DENT project named PP-DENT striking a balance between users’ protection and regulatory policies. The PP-DENT enables privacy for eSIM users and conditional privacy for governments.
A Blockchain Architecture for Trusted Sub-Ledger Operations and Financial Audit Using Decentralized Microservices

Blockchain has become an unavoidable future in enterprise finance, particularly enabling and securing crosscompany transactions.By introducing a comparable notion of smart contract, the trusted sub-ledger operation (TSLO), this article will propose a complete architecture based on the Blockchain to solve the traceability and validity of accounting data by assets groupement.TSLO is a more flexible and adaptable method for asset management in the corporate accounting system and the enterprise resource planner.This method is built on a decentralized microservices tree (DMST) and is an extendable E-Binding form of TEA (Triple Entry Accounting).Instead of using a multi-ledger architecture, the Hyperledger Fabric skeleton, limited to participant channels inside one entity or organization, our approach uses decentralized sub-ledgers with an implementation tree (DMST) for an assets-driven transaction.Furthermore, the government's audit and taxation procedures for financial groups are more accessible by combining Proof of Authority and Proof of Stake to assure the logic of More stake more reputation to preserve.
A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods.
Direct Imaging of Stabilized Optical Flow and Possible Anomalies From Moving Vehicle

Machine perception of dynamic scenes becomes more and more important for autonomous vehicles and vision-based driver-assistance systems. Even with other 3D ranging devices, dense, detailed and instantaneous detection of optical flow is essential for early distinguishing small moving objects in the 3D environment from the moving vehicle. To overcome the limited performance in the immediacy, resolution, accuracy and acuity of existing methods, we provide an optical flow detection scheme based on a three-phase correlation image sensor (3PCIS) that is capable of Fourier-coefficient imaging combined with an exact and direct algorithm derived from the weighted integral method of identifying the differential equation model from a short-duration observation. To utilize inherent performances of the detection scheme by removing the large and rapid disturbances induced by the rotational fluctuations of the platform, we introduce a software operation of gaze in which the image coordinates are fixed on and smoothly pursue a forward stable object so that the optical flow field is relative to the moving coordinate system. In it, the gaze subsystem continuously provides angular velocity and pose between the camera and gaze target, while the imaging subsystem instantaneously obtains two optical flow distributions by cancelling the ego-rotation components and then removing the outwardly diverging components derived mainly from stationary 3D environments. Possible anomalies captured in each frame instantaneously provide candidates of hazardous objects that should be tracked and further investigated. We examine the performance of optical flow stabilization and anomaly detection using image sequences of monocular 3PCIS mounted on a moving vehicle on town roads and a highway.
Exploring Smart Contract Recommendation: Towards Efficient Blockchain Development

Since the development of Blockchain 2.0, the smart contract has become the core of blockchain. However, smart contracts with inaccurate or non-standard codes and settings may cause security vulnerabilities, extra expense cost and wast of computing resource. To avoid these problems and assist users to create new smart contract or apply existing smart contract in a more efficient way, we propose smart contract recommendation by regarding smart contract as a special form of software service in a blockchain system. First, four real-world datasets are obtained from Ethereum and EOSIO for smart contract recommendation. Then, a novel smart contract recommendation framework is proposed and evaluated. In the large-scale experiments, the results validate the feasibility of smart contract recommendation. Additionally, the datasets are publicly released online to other researchers for further studies on smart contract recommendation.
Robust Sparse Weighted Classification For Crowdsourcing

Data collected from nature is usually unlabeled, and it is difficult to be used directly.This issue is well addressed by crowdsourcing, which provides a reasonable way for effectively using these unlabeled data.Generally, workers in crowdsourcing tasks are not professionals, so it is hard to obtain high-quality labels.To address this issue, a robust sparse weighted classification algorithm is proposed, which try to adjust the samples that are not correctly classified in the original lables as much as possible.Specifically, we evalute the ability of different workers(indicator weight matrix) to accurately label different samples by fitting the real data matrix to its weighted reconstruction matrix.And then, l 2,1 -norm and worker labeling ability similarity matrix are added, and negative effects of some bad workers are eliminated through the row sparsity property of l 2,1 -norm.Finally, the optimal indicator weight matrix is obtained by optimizing the two matrices in the objective function simultaneously.Therefore, the obtained optimal indicator weight matrix takes the similarity of worker labeling ability into consideration, and infers all the predicted labels.The results on synthetic and real data sets demonstrate that our algorithm is superior to other state-of-the-art methods.
Development of an electronic system for remote assessment of students' knowledge in cloud-based learning environment

The system for remote assessment of knowledge automates formation of task (tickets) with questions and tasks for conducting intermediate and final monitoring of knowledge of students is offered. In the process of developing an electronic system for knowledge assessment, the basic requirements for a web application and the modules that the system consists of were determined, the main roles of users in the system and their functionality were identified, access rights were established. The technical, functional, and non-functional features of the software product are described, web technologies for creating an application for knowledge assessment are considered. The diagram of system sequences, the use-case diagram, which schematically describing roles and functions of agents in an information system, the diagram of classes of a database structure are presented with UML description. Based on the defined requirements for the resource in cloud-based learning environment, the following technologies for its development were chosen: the server programming language PHP, JavaScript programming language and its libraries were used, as well as the MySQL database with PhpMyAdmin tool for administration of chosen database management system. OpenServer software complex system was used to develop and test the application functionality. The usage of the proposed electronic assessment system of knowledge contributes to the formation of an open information and cloud-based learning environment of a modern educational institution, enhances the efficiency and more rational distribution of teacher time in preparing tests or exams, activates repetition of educational material and knowledge assimilation, indirectly motivates students to more honest learning.
Anti-phishing technique based on dynamic image captcha using multi secret sharing scheme

With the cutting-edge improvement of web, online abuses have been increasing rapidly. Phishing is the most widely recognized abuses performed by digital crooks nowadays. It is an activity to steal private data (for example, client names, passwords and Visa data) in an electronic correspondence. It is a sort of fraud with the end goal of monetary benefit and other fake exercises. It utilizes phony websites that resemble genuine ones. Phishing messages might contain links to sites that are contaminated with malware. In this paper, "an anti-phishing approach using multi secret sharing scheme" is implemented as an answer to this problem. Here, Dynamic Image CAPTCHA based verification using multi secret sharing is performed. Image CAPTCHA is divided into two pieces called shares. Multiple secret pictures are revealed by overlapping the same set of shares at different angles. In the proposed approach, shares are of different modes i.e., user's share is imprinted on a physical transparency while server's share is in digital mode. By using the proposed approach, websites and end clients can cross confirm their identity.
A doubly sparse and low-patch-rank prior model for image restoration

Image restoration is a core problem in computer vision and image processing. In this paper, we introduce a unified low-patch-rank minimization model, which possesses one nuclear norm regularization term promoting the low-patch-rankness, and two sparse regularization terms including the classical total variation (TV) norm and a general sparse term under certain transform such as discrete cosine transform. By setting balancing parameters, our unified model reduces to the classical TV-regularized low-patch-rank minimization model and yields a new non-TV-regularized low-patch-rank prior image restoration model. Due to the multi-block structure of the model, we introduce a three-block alternating minimization algorithm to find approximate solutions of the proposed models. A series of computational results on image inpainting and deblurring further show that our approaches are reliable to recover high-quality images from degraded ones.
Examining fingerprint trace elements in cassiterite: Implications for primary tin deposit exploration

In this study, a machine learning method and a piece of cassiterite trace element composition data were used to find fingerprint trace elements that distinguish different tin (Sn) mineralization types and build tools for exploring primary Sn deposit exploration. The trace element dataset of cassiterite from the granite-related Sn metallogenic system was built using the following two approaches: (1) by analyzing the cassiterite samples from nine Sn deposits in Myanmar using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and (2) collecting published data. The resulting dataset contains 661 records of 12 trace elements in cassiterite from 4 mineralization types: pegmatite, greisen, quartz-vein, and skarn. The spider diagrams of trace elements and the principal component analysis indicate that cassiterite samples from the same one mineralization type are clustered together and have unique geochemical characteristics. Using recursive feature elimination with cross-validation and simulation, tantalum (Ta), niobium (Nb), manganese (Mn), hafnium (Hf), iron (Fe), scandium (Sc) and Sc/Ta, Sc/Hf, and Sc/Mn were selected as the fingerprint elements and ratios, respectively. The cluster distribution in the biplots of the fingerprint elements and ratios indicates that these fingerprints are sensitive to the mineralization type. The distribution in the biplots also reveals that there is an evolutionary sequence of magmatic–hydrothermal fluids from pegmatite to skarn at the cassiterite crystallization environment. This data-driven study improves our understanding of the isomorphism between Sn and ions with similar charge and ionic radii in cassiterite from different hydrothermal environments. The complementary relationships between vanadium (V), Nb, and Ta are also identified. The element V is preferred to form a charge balance pair with Sc in cassiterite from skarn, whereas Ta and Nb are preferred to constitute a charge balance pair in cassiterite from the quartz-vein, pegmatite, and greisen. Our findings demonstrate that trace element compositions of detrital cassiterite grains from stream sediments can be used as an exploration tool to discover concealed primary Sn deposits and to evaluate the economic value based on the grade-tonnage model in the preliminary stage of mineral exploration.
A dimension reduction in neural network using copula matrix

In prediction analysis, there may exist some nonlinear relations between the exploratory variables, which are not captured by traditional correlation-based linear models such as multiple regression, principal component regression, and so on. In this work, we employ a copula matrix to extract principal components of a set of variables which are pair-wisely associated with a copula. By estimating the pairwise copula and its corresponding parameter(s), we suggest an optimization method to extract principal components from a matrix which contains some pairwise measures of association. We use these components as inputs of an artificial neural network to make a more accurate prediction. We test our proposed method using a simulation study and use it to carry out a more accurate prediction in an AIDS as well as a COVID-19 dataset. To increase the reliability of results, we employ a cross-validation technique.
Design of Digital Teaching Platform for Spoken English Based on Virtual Reality

In order to improve the effect of digital teaching of spoken English, this paper combines virtual reality technology to design a digital teaching platform for spoken English and combines virtual reality technology to digitally process spoken English. The signal processed by the front-end amplifier circuit is converted into a digital signal through analog-to-digital conversion, and the digital receiving system is realized by means of software radio, which reduces the dependence on the hardware circuit and enhances the portability of the system. In addition, this paper selects filters according to the needs of digital teaching of spoken English and constructs a digital teaching platform for spoken English based on virtual reality. The experiment verifies that the digital teaching platform for spoken English based on virtual reality has a good effect of digital spoken English teaching.
BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Comprehensive performance evaluation is an important basis for improving the training effect of enterprise employees and the effective allocation of enterprise resources. Based on AHP and BP neural network theory, this paper constructs a comprehensive performance evaluation method for enterprises, AHP is used to calculate the weight of the index, and then the importance index is screened. The model proposes a conceptual model of comprehensive performance of manufacturing enterprises from the support layer, core layer, and promotion layer and constructs a manufacturing system from horizontal and vertical. The influencing factors of comprehensive performance solve the quantification problem of enterprise comprehensive performance evaluation and have obvious guiding value for the research on the integration mode and path of industrialization and industrialization of regional manufacturing enterprises. In the simulation process, the weight of each index in the evaluation system is first determined by the analytic hierarchy process; then the evaluation index membership score table is established, and fuzzy mathematics is used to calculate the expert’s score, so as to solve the problem caused by the intermediate value. The uncertainty caused by the jump is finally established by the analytic hierarchy process, and the neural network is used to simulate the sample. The experimental results show that by using AHP to collect training samples for neural network evaluation, the comprehensive performance evaluation system has good fitness and achieves the best comprehensive consideration of accuracy and training time when there are 17 hidden layer neurons. The maximum relative error is 1.64%, which is much lower than the general accuracy requirement of 5%, which effectively improves the performance and calculation accuracy of the network.
Immersive Therapy for Improving Anxiety in Health Professionals of a Regional Hospital during the COVID-19 Pandemic: A Quasi-Experimental Pilot Study

Background: Immersive therapy through virtual reality represents a novel strategy used in psychological interventions, but there is still a need to strengthen the evidence on its effects on health professionals’ mental health. Objective: To analyze the results of immersive therapy through virtual reality on the levels of anxiety and well-being of the health professionals working in a regional hospital in Olot (Spain). Methods: Pilot quasi-experimental study including a group of 35 female (mean age = 45.7, SD = 8.43) health professionals who undertook immersive therapy for 8 weeks. The intervention was implemented through virtual reality, and its effect on anxiety levels and well-being was evaluated through the Hamilton and Eudemon scales, respectively. Data on age, gender, active pharmacological or psychological treatment, mental health disorders and number of sessions were also collected. Results: A statistically significant (p < 0.001) improvement in anxiety and well-being was found, with large and moderate effect sizes (0.90 and 0.63 respectively). In addition, these changes were clinically significant. No significant associations were found between the improvements and the different variables, but a greater trend was identified among the group of professionals with untreated or unidentified levels of anxiety. Conclusion: This group of health professionals showed a statistically and clinically significant improvement in anxiety and well-being after the application of immersive therapy using virtual reality. Further studies with a control group are necessary to further analyze this novel intervention.
Transformer-Based Deep Reinforcement Learning in VizDoom

Transformers is a novel neural network architecture that is successfully used in natural language processing tasks and is starting to be used in other areas such as video processing and image processing. However, transformers are yet to be studied in different aspects of reinforcement learning scenarios. In this work we combine transformer architectures with reinforcement learning and train them in the VizDoom game environment, producing agents that play better in comparison to traditional neural network architectures.
A Classification-Based Blockchain Architecture for Smart Home with Hierarchical PoW Mechanism

Smart home, as a typical Internet of Things (IoT) application, provides people with a variety of conveniences. Unfortunately, it may suffer from security and privacy issues. Currently, blockchain theory is considered as one of the potential solutions to the IoT security problem. However, according to the rules of blockchain, it requires large storage to store distributed ledgers and undertakes long latency caused by proof of work (PoW), which cannot be performed by resource-constrained IoT devices. To address the issue, we propose a classification-based blockchain architecture with a hierarchical PoW mechanism, which can reduce the storage consumption and decrease the latency. In our architecture, we divide IoT devices into several child nodes by data classification and convert the data storage into partial network storage. Furthermore, we try to set the moderate-cost security grades (SG) to adjust the difficulty of PoW for reduction of latency. Finally, comparing the performance of our scheme with the traditional method and current technology, the proposed architecture not only takes up less storage (i.e., almost 90% reduction) but also increases efficiency (i.e., almost 50% running time saving) while ensuring safety.