Reviewerly/exHarmony huggingface dataset:
Reference Solar Spectra and Their Generation Models

Reference solar spectra are used in many solar applications. Utilizing a reference solar spectrum in a unified manner is necessary for reliable evaluation for each application. Since reference solar spectra have been updated only recently, the most recent spectra have not yet been uniformly adopted for some applications. To improve this situation, understanding the evolution and details of reference solar spectra is important. However, there have been few reports on reference solar spectra and their generation models. This paper reviews the evolution of reference solar spectra and their generation models, particularly ASTM G173-03 and CIE 241:2020 reference solar spectra and their Simple Model for the Atmospheric Radiative Transfer of Sunshine (SMARTS) generation model. In addition, the key points in the SMARTS model that affect the calculation accuracy of the spectral irradiance are explained. Moreover, the issue of further comparing the SMARTS model with a rigorous model is revealed. The information presented will help users to understand the newly updated reference solar spectra and correctly use the SMARTS model in practice.
Research on Reinforcement Learning Explainable Strategies Based on Advantage Saliency

Deep reinforcement learning is increasingly being used in difficult environments with sparse rewards and high-dimensional inputs, and it performs well, but its decision-making processes are largely unclear and difficult to explain to end users. Saliency map methods explain an agent's behavior by highlighting state features relevant for the agent to take an action. In this paper, we use the perturbation-based saliency map method, propose the use of advantage function to replace the existing method of calculating state saliency, realize the combination of advantage function and perturbation-based saliency map. A saliency map is generated by noting the saliency of the dependent elements of the agent's chosen action in the Atari game environment. Experimental comparisons show that our method generates more accurate explanatory saliency maps.
Flipped Classroom of Electrical and Electronic Technology based on Multisim Simulation

In view of the phenomenon that traditional teaching focuses on Teachers' narration and theoretical derivation, and students passively accept knowledge points. This topic introduces the flipped classroom. The results show that under the flipped classroom teaching mode, students can internalize the knowledge learned before class in limited classroom time and output a visual Multisim Simulation circuit. The results show that the students' creativity, knowledge transfer ability and practical ability are stimulated when designing the circuit. At the same time, in order to solve the phenomenon that higher vocational students generally have poor learning initiative and do not carefully watch pre class teaching videos, this paper puts forward the calculation formula of mutual learning. Practice shows that under the supervision of the formula, students basically form the habit of mutual aid learning.
Homomorphic Encryption and its Application to Blockchain

The concept, method, algorithm and application of the advanced field of cryptography, homomorphic encryption, as well as its application to the field of blockchain are discussed in this paper. There are various forms of homomorphic encryption and they can be classified into three categories: partially homomorphic encryption, hierarchical homomorphic encryption and fully homomorphic encryption. We explain the concept, method and algorithm for each category with down to earth examples. The milestone example of RSA algorithm is explained which is still widely used in the internet and in blockchain as well. Applications of homomorphic encryption depends on efficiency of implementation which has been in good progress in recent years.
Cross-age face synthesis based on conditional adversarial autoencoder

Face aging aims to render face images with desired age attribute. It has tremendous impact to a wide-range of applications, e.g., criminal investigation, entertainment. The rapid development of generative adversarial networks (GANs) has shown impressive results in face aging. Among them, the Conditional Adversarial Autoencoder (CAAE) proposed in 2017 has achieved good results in face aging. However, the generated faces still have the problems that the aging features are not obvious and the identity information are not well maintained. In addition, research have shown that the human aging process is affected by genes. Different races have different external characteristics of aging. However, the current research does not take the racial factor into account, ignores the racial differences in the aging process. It affects the accuracy of transformation. To solve the above problems, this paper proposes a cross-age face synthesis based on conditional adversarial autoencoder: First, a conditional adversarial autoencoder is used as the infrastructure to build a cross-age face synthesis model based on race constraints. Secondly, the discriminator is composed of a discriminant network and a classification network, and a category loss function is designed to generate a real face that matches the target age; Finally, the model uses a identity feature extractor and a discriminator of the multi-scale architecture. Through multi-level discrimination from pixel values to high-level semantic information, the loss of personal identity features is minimized. UTKFace and MegaAge-Asian datasets are used in the experiment. Three comparative experiments are designed for the above improvements. The results show that the racial constraints make the generated images effectively maintain the racial characteristics, such as skin color and texture; The classification function of the discriminator improves the aging effect; The design of the multi-scale discriminator enables the generated face to have more stable local structure and identity information. Through qualitative and quantitative analysis, it is shown that this method has higher aging accuracy and identity retention than the CAAE method.
Application of Virtual Reality in Different Fields

Virtual reality technology is a rapid development of technology in recent years, has a vital role in the development of different fields. Using image display, human-computer interaction and other technologies to generate three-dimensional environment. Enable participants to interact and manipulate virtual environments and objects in real time. This paper analyses and summarizes the development of VR from three areas. Firstly, it analyses the immersive experience brought by VR in games and discusses the influence of VR on games. Then it summarizes the application of VR in medical care and other fields, bringing more effective means to the treatment of diseases. Doctors can use VR headsets to practice more surgeries and train the younger generation. Finally, the differences between VR films and traditional films are analysed from the perspective of films. At the end of this paper, the development of VR in the above three fields are summarized and prospected.
Monocular Occlusion-Robust Tracking Novel for Campus Intelligent Monitor System

In practical applications, the need for moving target recognition is growing, necessitating the development of an intelligent monitoring system that can achieve the same level of precision while assuring real-time performance. We present a campus intelligent video surveillance system that is created utilizing a target detecting approach that combines frame differential method with background removal method, as well as a dynamic target tracking method that combines particle filter and color feature matching. Experiments proved that the system works well and can solve the problems of background interference in target detection and occlusion-robust effectively.
A Multi-target Tracking Method Against Intelligent RGPO Jamming

In this paper, considering the serious defects of intelligent Range Gate Pull-off (iRGPO) jamming to the performance of the radar multi-target tracking, a novel multi-target method against iRGPO jamming is investigated. The problem of long-time stable target tracking against the iRGPO jamming is solved by proposing a multi-target data association algorithm based on backtracking. Firstly, the association probability between measurements and target is selected as the optimization function to construct the model. Next, a target tracking method based on backtracking is proposed to obtain the measurements of false targets, and then a filtering process is used to correct the trajectory further. Finally, simulation results are provided to verify the validity of the proposed algorithm.
SECURE AND EFFICIENT MANAGEMENT OF DEVICE MEMORY USING “DSRC_PTR” POINTER FOR INCREASE OF THE COMPETITIVENESS OF IT PRODUCTS IN THE MARKET

A smart pointer for partially automated control of dynamic memory called dsrc_ptr has been developed. It is designed to detect memory errors, as a result of which IT products will become more competitive in the market. One of the clearest indicators of the relevance and importance of the problem is the report released by the US National Security Agency in the fall of 2022, which warned against developing software that may have similar problems. The presented pointer took into account the mentioned problems. It can be implemented both at the level of the programming language and provided by the library. The pointer provides a toolkit for detecting memory errors in software during debugging. No third-party tools will be needed to locate and fix memory errors. In the release version of the software implementation, the pointer gets a similar pointer behavior and has no speed loss beyond the programmer's settings. The advantage of the proposed changes over the existing alternatives is that the pointer independently detects errors during the debugging process, and does not incur additional costs in the release version. A software implementation of the proposed concept is given in C++ programming language.
Information Hiding Method for Multi-channel Flexible Sensors

Information hiding is one of the most critical techniques in information security. The goal of this task is to encrypt the maximum number of digits while introducing the minimum modification to the original medium. Soft sensors have been applied to the domain of human motion tracking and shown their advantages over existing optics and inertial sensors. However, soft sensors still suffer from low signal-to-noise ratios. This flaw provides inherent robustness to information decryption. Our work introduces the use of soft sensor signals as the medium for information hiding and develops a novel method to select the optimal channel given a multi-sensor scenario. We use a Sudoku matrix to encrypt the information in the signal flow of soft sensors. And an encryption channel selection algorithm based on tracking error is proposed to reduce the impact on information loss. The experimental results show that the soft sensors can effectively serve as the medium of information hiding with our proposed method.
Assessing the Predictability of Bitcoin Using AI and Statistical Models

This chapter analyses Bitcoin’s predictability using AI and statistical models, and then identifies the model that produces the most accurate results. A multivariate time series dataset was created to train an AI (LSTM) and statistical model (ARIMA) to predict the price of Bitcoin over time. The LSTM model achieved the highest accuracy of 94% and the lowest MAPE of 5%. ARIMA had the best overall metrics, but when it came to forecasting the future, it performed poorly. The results show that it is possible to predict the BTC with a reasonable error rate; however, Bitcoin is extremely volatile, making it difficult to obtain results that can be confidently used to assert its value ahead of time. The result of the study suggested that it is possible to forecast the Bitcoin price with minimal error rates, refuting the null hypothesis. However, because the bitcoin price index is affected by various external sources, forecasting time series problems is intrinsically challenging. When attempting to predict stated sort of data, the following constraints must be considered: the models (1) do not account for exogenous variable uncertainty, and (2) do not account for the fact that forecast-error variances vary with time (Fair, Chapter 33 Evaluating the predictive accuracy of models. In: Handbook of econometrics, vol 3, pp 1979–1995. Elsevier [Online]. Available at: https://doi.org/10.1016/S1573-4412(86)03013-1 . Accessed 19 Jan 2022, 1986).
Filter Bank Networks for Few-Shot Class-Incremental Learning

Deep Convolution Neural Networks (DCNNs) can capture discriminative features from large datasets. However, how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world, e.g., classifying newly discovered fish species, remains an open problem. We address an even more challenging and realistic setting of this problem where new class samples are insufficient, i.e., Few-Shot Class-Incremental Learning (FSCIL). Current FSCIL methods augment the training data to alleviate the overfitting of novel classes. By contrast, we propose Filter Bank Networks (FBNs) that augment the learnable filters to capture fine-detailed features for adapting to future new classes. In the forward pass, FBNs augment each convolutional filter to a virtual filter bank containing the canonical one, i.e., itself, and multiple transformed versions. During back-propagation, FBNs explicitly stimulate fine-detailed features to emerge and collectively align all gradients of each filter bank to learn the canonical one. FBNs capture pattern variants that do not yet exist in the pretraining session, thus making it easy to incorporate new classes in the incremental learning phase. Moreover, FBNs introduce model-level prior knowledge to efficiently utilize the limited few-shot data. Extensive experiments on MNIST, CIFAR100, CUB200, and Mini-ImageNet datasets show that FBNs consistently outperform the baseline by a significant margin, reporting new state-of-the-art FSCIL results. In addition, we contribute a challenging FSCIL benchmark, Fishshot1K, which contains 8261 underwater images covering 1000 ocean fish species. The code is included in the supplementary materials.
A Color Image Encryption Scheme Based on Singular Values and Chaos

The security of digital images transmitted via the Internet or other public media is of the utmost importance. Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities. This study provides an approach to color image encryption that could find practical use in various contexts. The proposed method, which combines four chaotic systems, employs singular value decomposition and a chaotic sequence, making it both secure and compression-friendly. The unified average change intensity, the number of pixels’ change rate, information entropy analysis, correlation coefficient analysis, compression friendliness, and security against brute force, statistical analysis and differential attacks are all used to evaluate the algorithm’s performance. Following a thorough investigation of the experimental data, it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.
Dynamic Resource Allocation for Containerized Applications in Edge Computing

As a portable and extensible open-source platform for managing containerized workloads and services, Kubernetes has been widely deployed. Its native Horizontal Pod Autoscaler (HPA) mechanism is based on static configuration templates for expansion and contraction (Wu et al. in Dynamically adjusting scale of a Kubernetes cluster under qos guarantee. IEEE, 2019 [1], Zhong and Rajkumar in ACM Trans Internet Technol (TOIT) 20(2):1–24, 2020 [2], Zhang et al. in IEEE Trans Parallel Distrib Syst 32(8):2086–2100, 2021 [3], Zhou et al. in Proc IEEE 107(8):1738–1762, 2019 [4], Zhou et al. in Adaptive parallel execution of deep neural networks on heterogeneous edge devices, 2019 [5], Qiniu Container Cloud Team in Kubernetes in action (Chinese Version). Publishing House of Electronics Industry, Wanshou Road, Haidian District, Beijing, 2019 [6]). The amount of resources applied by each replica is the same and fixed. There is excess resource allocation in the process of cluster scaling based on multi-dimensional resource constraints. Therefore, we propose a dynamic template (DT)-based cluster expansion mechanism, which reduces the number of resources applied for a single request and is more friendly to edge scenarios with limited resources.
Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may have an adverse effect on te model. Therefore, we propose the Federated Reinforcement Learning (FedRL) model, which consists of multiple users collaboratively training the model. Each household trains a local model on local data. These local data never leave the local area, and only the encrypted parameters are uploaded to the central server to participate in the secure aggregation of the global model. We improve FedAvg by incorporating a Q-learning algorithm to assign weights to each locally uploaded local model. And the model has improved predictive performance. We validate the performance of the FedRL model by testing it on a real-world dataset and compare the experimental results with other models. The performance of our proposed method in most of the evaluation metrics is improved compared to both the centralized and distributed models.
Blockchain-Based Novel Solution for Online Fraud Prevention and Detection

Online fraud and identity theft have become common terms associated with users working over the internet across the globe. It often leads to several problems like damaging users’ credit status and costing time and money, affecting users’ trust during online transactions. Therefore, the objective of this paper is to build a system that includes a blockchain-based non-fungible token that will have the user’s personal data on a decentralized blockchain system as this system uses features of blockchain, so it will allow everyone to know who has access to their data which will help them to prevent the identity theft. This system also uses AI/ML algorithms using users’ past behavior analytics to detect fraud during interactions with the website. An identity verification system entirely owned by the individual will increase trust that the data is genuine and reliable.
Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle (UAV) self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale, which can quickly and accurately complete complex tasks such as path planning, situational awareness, and information transmission. Due to the openness of the network, the UAV cluster is more vulnerable to passive eavesdropping, active interference, and other attacks, which makes the system face serious security threats. This paper proposes a Blockchain-Based Data Acquisition (BDA) scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario. Each UAV cluster has an aggregate unmanned aerial vehicle (AGV) that can batch-verify the acquisition reports within its administrative domain. After successful verification, AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage. There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports, respectively. The security analysis shows that the BDA construction can protect the privacy and authenticity of acquisition data, and effectively resist a malicious key generation center and the public-key substitution attack. It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem (ECDLP) assumption. The performance analysis demonstrates that compared with other schemes, the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.
A Truthful Revenue-Maximizing Resource Allocation Auction Mechanism in the Internet of Vehicles Cloud-Edge Environment

The Internet of Vehicles has an important role in environmental perception and data processing for autonomous driving. A system framework of cloud-edge collaboration is proposed to make full use of cloud computing and edge computing resources in view of the mixed scenarios of autonomous driving and assisted driving on the Internet of Vehicles, in this paper. An online auction mechanism is designed to solve the problem of resource competition among Internet of Vehicles users to maximize resource provider revenue and social welfare. First, we propose a priority based on the degree of resource requirement as an important factor in resource allocation to improve social welfare. In addition, we propose a neural network-based payment pricing algorithm to maximize resource provider revenue, where the degree of resource requirement calculated by the resource allocation algorithm is used as the input data of the neural network. Finally, the experimental results show that our method yields high revenue, and high social welfare.
Blockchain-Based Authentication in IoT Environments: A Survey

The rapid growth in IoT adoption in different areas of our lives has made these devices a significant target for malicious actors. Attacks varied from full-device control, to botnet infections in IoT environments making them difficult to defend. One of the areas that attackers commonly target is authentication. As a response to these increasing attacks on authentication mechanisms, many state-of-the-art research was conducted to investigate employing blockchain in IoT authentication. In this chapter, we survey this research and provide insightful conclusions on the current state, and the future of blockchain research in IoT authentication.