Reviewerly/exHarmony huggingface dataset:
Introduction

In the era of Big Data, information explosion is very common in our daily life [1–4]. For instance, Google and Flickr generate more than 20 PB and 3.6 TB data per day, respectively [5]. According to the prediction of the International Data Corporation, the global data sum will go to 175 ZB by 2025 [6]. Therefore, how to effectively and efficiently mine the desired valuable information from large-scale data has become a crucial challenge and has attracted much attention all over the world [7–11].
Improving Robustness of Latent Feature Learning Using L1-Norm

In this era of information explosion, big data are generated from various industrial applications [1–4]. Realizing intelligent recommender systems (RSs) to filter the required information is a very challenging problem [5, 6]. Up to now, various methods have been proposed to implement an RS, among which collaborative filtering (CF) is very popular [7–13].
Domain-specific image classification based on improved residual networks

Aiming at the problems of high target similarity and strong camouflage in the domain-specific, and the traditional image classification technology is difficult to achieve accurate classification, it is proposed to use the deep neural network Resnet50 as the feature extraction network, and combine the attention mechanism and improve it, which can improve the ability of learning effective features; and use depthwise separable convolution to replace standard convolution, which can reduce computational parameters and save computational space. It is verified by experiments that the accuracy of the improved algorithm in this paper is 0.71% higher than that of the Resnet50 prototype, and 0.39 % higher than that of the Resnet50 +SE algorithm model in the image classification of the domain-specific.
Improved sparrow search algorithm based on hybrid strategy

In order to overcome the shortcomings of the sparrow optimization algorithm, which include limited population diversity, low high-latitude solution accuracy, and early convergence, this paper proposes to improve the sparrow search algorithm (FASSA) by using multiple strategies. Firstly, the improved sine chaotic map is used to initialize the population position, which results in a more uniform distribution of the population's initial solution position. Secondly, the position update of the discoverer is improved by introducing the golden sine algorithm with a curve adaptation, which effectively accelerates the speed of convergence and coordinates the capabilities of global and local search. Finally, the firefly algorithm is introduced to combine all sparrows with the optimal sparrows using the firefly perturbation method, so that the quality of the individuals after each iteration is improved. This paper selected eight benchmark functions for testing, and the simulation results of FASSA and the other four meta-heuristic algorithms are compared. According to the experimental findings, the improved algorithm has a great improvement in global search, overcoming local optimization, convergence speed, and convergence accuracy.
Improved adaptive genetic simulated annealing algorithm

This paper aims to use an improved version of the real coding genetic algorithm to achieve a globally optimal solution to complex problems, such as the Traveling Salesman Problem (TSP). The Genetic Algorithm (GA) is prone to fall into local optimal solutions, and the Simulated Annealing algorithm (SA) converges slowly. In this paper, an optimization algorithm based on an improved Adaptive Genetic Simulated Annealing Algorithm (AGSAA) is proposed. Then an adaptive crossover and mutation probability is improved, which can effectively avoid the algorithm from falling into local optimum. Finally, a simulated annealing operator is added according to the evolutionary process of the algorithm, and an adaptive Metropolis criterion and a minimum temperature are improved to make the algorithm more adaptive. The experimental results on the TSP example show that the proposed AGSAA can obtain better optimization results compared with the results of other optimization algorithms.
Reputation evaluation based fault tolerance consensus algorithm for blockchain network

With the development and application of Blockchain technology, some consensus algorithms emerged. Practical Byzantine Fault Tolerance (PBFT), as a type of classic consensus algorithm, has been widely applied in Consortium Blockchain. However, it still has some issues need to be resolved via optimization, such as low node reliability, poor scalability, insufficient dynamics, and frequent view change. To address those problems, this paper proposes a Reputation evaluation-based fault tolerance consensus algorithm, named RPBFT. We introduce a reputation evaluation model based on the service performance and enthusiasm of nodes to participate in consensus. A reward-punishment mechanism is proposed to make nodes with high reliability more likely to participate in block production, which improves the security and reliability of the blockchain. Finally, a two-phase consensus process is presented to achieve a more efficient consensus. The experimental results show that, compared with relevant consensus algorithms, RPBFT is superior in reducing consensus delay and improving communication efficiency.
Multi-intent detection for task-oriented dialogue systems based on joint learning

Dialogue system uses natural language as a medium to achieve friendly communication between humans and machines. The performance of intent detection is crucial to the effectiveness of a task-oriented dialogue system. When developing a task-oriented dialogue system in a specific domain, users typically express multiple intents in the same sentence. In this study, we compare two approaches for multi-intent detection at the sentence level, which aims to investigate multi-intent detection for task-oriented dialogue systems based on joint learning. Experiment results on the ATIS and MixATIS datasets show that the multi-classification approach improves slot prediction by combining relevant intent information, whereas the multi-label approach based on joint learning improves intent detection by making predictions at each possible intent.
A method for improving the robustness of federal learning systems based on input transformation

Federated learning is an effective method to solve the problem of data silos, but adversarial attacks launched based on adversarial samples pose a great threat to the security of federated learning models. This makes the application and promotion of federated learning somewhat affected. Therefore, this paper verifies the performance of a defense method for adversarial attacks in federated learning scenario, which is proposed in the traditional machine learning. The method defends adversarial attacks mainly by performing an input transformation before feeding images to the model. We conducted experiments on the EMnist dataset, and the experimental results show that this defense strategy can also improve the robustness of federation learning under different adversarial attacks.
Research on the design of coal mine safety monitoring substation based on edge computing

This paper designs a safety monitoring substation based on edge computing, which can realize the self-diagnosis and self-recognition of the faults of each component module of the coal mine safety monitoring substation and give a voice alarm through the voice alarm module to prompt the operator to repair in time. Through the self-diagnosis of the fault, it facilitates the operation of the maintenance personnel, shortens the time of fault repair, and ensures the timeliness of the operation of the management personnel through the voice prompt as well as avoids the further development and expansion of potential safety hazards. In addition, the substation can also give prompts related to the system operation through the voice module, which greatly facilitates the use and maintenance personnel, avoids the occurrence of mis-operation or missing operation, and ensures the stability and safety of the system operation.
Electricity carbon quota trading scheme based on blockchain and proxy re-signature

To solve the security and privacy issues in power carbon quota trading, a secure power carbon quota trading scheme is proposed by utilizing proxy re-signature and blockchain. The proxy re-signature technology is used to convert the signature of the buyer's enterprise into the signature of the environmental administrative institution, which protects the identity and privacy of the buyer's enterprise. The seller enterprise uses the one-time public key as the payment address of the transaction, which ensures the privacy of the seller enterprise in the carbon quota transaction. In addition, the signatures of the buyer and seller and the tamper-proof nature of the blockchain ensure the integrity and unforgeability of electricity carbon quota transactions. The analysis results show that the proposed scheme provides the anonymity and fairness of electricity carbon quota trading.
Harris hawks algorithm based on teaching-learning and adaptive t-distribution disturbance

The TLTHHO algorithm is proposed to address the shortcomings of Harris Hawks optimization in dealing with certain complex problems that are prone to local optimality, slow convergence, and inability to find the global optimal solution. Firstly, a teaching-learning-based optimization algorithm is used to improve the population's low-quality solutions, enhance communication between populations, and improve solution quality. Secondly, to perturb the global optimal solution and improve the algorithm's ability to jump out of the local optimal solution, a dynamic adaptive t-distribution variation strategy is introduced. Finally, a greedy strategy is used to select between the perturbed individuals and the hawk individuals, thus improving the optimization ability and enhancing the optimization accuracy of the hawk population. The performance of TLTHHO is validated by 10 test functions with unimodal and multimodal characteristics, and the simulation experiments show that the proposed algorithm's performance is effectively improved, and the convergence speed and accuracy are faster and more accurate than the comparison algorithms.
Design of STM32-based RGB lighting control system

The wave of the "internet celebrity" era has promoted the development of fill lights, photography lights, etc. To achieve better visual effects and precise lighting adjustments, a PWM-based RGB photography lighting control system with STM32 as the core is designed. The overall hardware platform architecture with the STM32G070 series chip as the core processor is described, and the hardware principle is introduced in detail from a modular perspective, followed by the PWM dimming principle and the software algorithm process. This system is portable across different operating systems, having strong timeliness and low power consumption. It can also communicate with a mobile phone APP through the Bluetooth protocol to achieve remote and accurate digital control of photographic illumination. It can run stably and be successfully applied to the product.
Design and implementation of enterprise web application common framework based on model-view-viewmodel architecture

Based on the shortcomings of MVVM framework in enterprise level applications, this paper proposes a flexible common framework for enterprise level Web applications based on MVVM framework, designs core functions such as custom tag components and dynamic page component assembly mechanism and implements them based on the lightweight front-end framework knockout. The public framework designed and implemented in this paper introduces MVVM excellent architecture mode, flexible customization mode and high browser compatibility for traditional enterprises.
Automatic identification method of micro-expressions based on three types of algorithms

The recognition and analysis of micro-expressions based on algorithmic model is the common research content of computer and psychology, it is of theoretical and practical significance to accurately identify and analyze micro expressions. This paper introduces the definition, characteristics and recognition of micro-expression, and describes several algorithms commonly used for micro-expression recognition, to help people understand the status of automatic recognition of micro-expression. In future research, more algorithms or feature analysis methods can be used to further improve the accuracy of automatic micro-expression recognition and promote the further development of this field.
Domain adaptive foggy object detection method based on YOLOv5

In order to improve the performance of automatic driving target detection in foggy scenarios, this paper proposes an end-to-end domain adaptive target detection method based on YOLOV5 algorithm. The domain adaptive network is added to the last layer of the feature extractor of YOLOV5 algorithm, and the gradient inversion layer (GRL) is added between the label classifier and the domain classifier by adversarial training strategy, which confuses the ability of the network to distinguish the source domain from the target domain and maximizes the feature extractor to generate domain-invariant features. According to the training results on the dataset Cityscapes, compared with directly using the original YOLOV5 model, the average accuracy of the proposed algorithm is improved by 10.3%, and the FPS of the model reaches 42.8. Compared with the two-stage detection model Faster-RCNN, it is more suitable for the real-time scene of automatic driving.
Human face image colorization with dual-scale attention U-Net

In order to colorize grayscale human face image and solve the problems of boundary leakage and detail loss in current colorization algorithms, we propose the dual-scale attention U-Net. First, on the basis of U-Net, we improve the original convolution block and select two convolution kernels of different sizes to extract features, which improves the ability of deep feature extraction for different scales. Second, integrating CBAM into skip connections helps network to focus on salient regions and suppress unnecessary regions while sharing features and reducing information loss. In addition, inspired by the field of image super resolution, MS-SSIM-L1 is adopted as the loss function, and the edge and texture information of the image can be taken into account when coloring. Experiments show that compared with other colorization algorithms, the proposed method not only reduces boundary leakage and detail loss, but also has outstanding performance in colorizing old photos of historical figures.
Blockchain-based data sharing scheme for Internet of Vehicles

Internet of Vehicles (IoV) can effectively utilize vehicle dynamic information in the network system, provide vehicles services, and make traffic smoother. For the data sharing problem between different cryptosystems in IoV, an efficient and secure blockchain-based data sharing scheme for the IoV is proposed. Heterogeneous aggregated signcryption technology is used to realize secure communication between certificateless cryptography (CLC) and public key infrastructure (PKI). The outsourced computing operation of smart contracts based on blockchain greatly reduces the computing overhead of roadside units and on-board units, while improving the security of data sharing in the IoV. Furthermore, the analysis results show that the proposed scheme can guarantee the confidentiality and integrity of IoV data sharing and has high computational efficiency.
Design and implementation of commercial SMS supervision model based on blockchain

With the rapid development of the telecommunications industry, commercial short message service (SMS) provides enterprises with low-cost marketing channels. The increasing illegal marketing behavior not only causes troubles for mobile users, but also has a negative impact on the normal supervision of the industry. Under the traditional operation mode, it is challenging to know whether enterprises or service providers (SP) provide commercial SMS according to the wishes of mobile users. Another reason that makes the regulation of commercial SMS more challenging is the lack of transparency in the identification of business entities in the existing commercial SMS service model. Usually, the business SMS service process will generate corresponding communication records, called “Call Details Record” (CDR). When dealing with user complaints or violations, industry regulators spend a long time verifying the effectiveness of CDR, which is also one of the reasons for the decline in the supervision of commercial SMS. In view of the above problems, this paper proposes a commercial SMS supervision model based on blockchain that uses blockchain technology to optimize the traditional SMS supervision methods and combines smart contracts and asymmetric encryption algorithms to achieve the security and trust of CDR management. The model aims to solve the problems of fuzzy identity of business subjects in commercial SMS, difficulty in CDR traceability of commercial SMS, and long time-consuming CDR verification in the process of SMS supervision. At the same time, the model is conducive to reducing the obstacles of data sharing among multiple parties. Finally, this research designs and implements a business SMS supervision prototype system based on blockchain under Hyperledger Fabric. The performance test results demonstrate the feasibility of the model proposed in this research.
3D object detection based on local feature fusion

The existing 3D object detection networks based on the cross-attention mechanism from the bird's-eye view perspective mostly use the parameters calibrated by the camera to take the features corresponding to project each 3D feature point from the front view feature by projecting the 3D point onto the two-dimensional image. The features obtained by this method are affected by the calibration parameters and the projection position. To reduce the influence of the camera calibration parameters and the accuracy of the projection position on the obtained features, a local feature fusion network is proposed, which fuses the features within a certain range near the projection point to represent the corresponding features of the 3D point, thus improving the feature representation capability. The experimental results on the nuScenes val set show that the NDS value is increased by 1.4% compared with the benchmark. In addition, the network has robustness. Under the same noise, its accuracy reduction rate is reduced by more than 56% compared with the benchmark and has a practical value in automatic driving.
Learning for Data Synthesis: Joint Local Salient Projection and Adversarial Network Optimization for Vehicle Re-Identification

The problem of vehicle re-identification in surveillance scenarios has grown in popularity as a research topic. Deep learning has been successfully applied in re-identification tasks in the last few years due to its superior performance. However, deep learning approaches require a large volume of training data, and it is particularly crucial in vehicle re-identification tasks to have a sufficient amount of varying image samples for each vehicle. To collect and construct such a large and diverse dataset from natural environments is labor intensive. We offer a novel image sample synthesis framework to automatically generate new variants of training data by augmentation. First, we use an attention module to locate a local salient projection region in an image sample. Then, a lightweight convolutional neural network, the parameter agent network, is responsible for generating further image transformation states. Finally, an adversarial module is employed to ensure that the images in the dataset are distorted, while retaining their structural identities. This adversarial module helps to generate more appropriate and difficult training samples for vehicle re-identification. Moreover, we select the most difficult sample and update the parameter agent network accordingly to improve the performance. Our method draws on the adversarial networks strategy and the self-attention mechanism, which can dynamically decide the region selection and transformation degree of the synthesis images. Extensive experiments on the VeRi-776, VehicleID, and VERI-Wild datasets achieve good performance. Specifically, our method outperforms the state-of-the-art in MAP accuracy on VeRi-776 by 2.15%. Moreover, on VERI-Wil, a significant improvement of 7.15% is achieved.
Analysis and novel methods for capture of normative eye-tracking data in 2.5-month old infants

Development of attention systems is essential for both cognitive and social behavior maturation. Visual behavior has been used to assess development of these attention systems. Yet, given its importance, there is a notable lack of literature detailing successful methods and procedures for using eye-tracking in early infancy to assess oculomotor and attention dynamics. Here we show that eye-tracking technology can be used to automatically record and assess visual behavior in infants as young as 2.5 months, and present normative data describing fixation and saccade behavior at this age. Features of oculomotor dynamics were analyzed from 2.5-month old infants who viewed videos depicting live action, cartoons, geometric shapes, social and non-social scenes. Of the 54 infants enrolled, 50 infants successfully completed the eye-tracking task and high-quality data was collected for 32 of those infants. We demonstrate that modifications specifically tailored for the infant population allowed for consistent tracking of pupil and corneal reflection and minimal data loss. Additionally, we found consistent fixation and saccade behaviors across the entire six-minute duration of the videos, indicating that this is a feasible task for 2.5-month old infants. Moreover, normative oculomotor metrics for a free-viewing task in 2.5-month old infants are documented for the first time as a result of this high-quality data collection.
It takes a pirate to know one: ethical hackers for healthcare cybersecurity

Healthcare cybersecurity is increasingly targeted by malicious hackers. This sector has many vulnerabilities and health data is very sensitive and valuable. Consequently, any damage caused by malicious intrusions is particularly alarming. The consequences of these attacks can be enormous and endanger patient care. Amongst the already-implemented cybersecurity measures and the ones that need to be further improved, this paper aims to demonstrate how penetration tests can greatly benefit healthcare cybersecurity. It is already proven that this approach has enforced cybersecurity in other sectors. However, it is not popular in healthcare since many prejudices still surround the hacking practice and there is a lack of education on hackers' categories and their ethics. The present analysis aims to comprehend what hacker ethics is and who ethical hackers are. Currently, hacker ethics has the status of personal ethics; however, to employ penetration testers in healthcare, it is recommended to draft an official code of ethics, comprising principles, standards, expectations, and best practices. Additionally, it is important to distinguish between malicious hackers and ethical hackers. Amongst the latter, penetration testers are only a sub-category. Acknowledging the subtle differences between ethical hackers and penetration testers allows to better understand why and how the latter can offer their services to healthcare facilities.