Reviewerly/exHarmony huggingface dataset:
Even symmetric chaotic and skewed maps as a technique in video encryption

Abstract The massive growth and use of digital multimedia through computer networks, including video and images, have increased the demand for protecting this digital data. To secure digital video, video encryption is frequently utilized. In this paper, a brand-new video scrambling technique based on two chaotic linearly symmetric maps and one chaotic tent map that has been twisted is suggested. The permutation procedure moves every frame pixel's position using a P-box created by permuting a linearly symmetric chaotic sequence. The diffusion technique employs both linearly symmetric chaos maps and distorted tent maps to create key streams. The keystream closely resembles simple frames because the pixels in the permuted frame indicate which of the two even symmetric chaos maps is replicated each time for the following byte. The information entropy, histogram, neighboring pixel correlation and sensitivity analysis, number of pixels changing regions (NPCR), and unified mean change intensity are used to thoroughly evaluate the recommended method's capacity to improve performance and security (UACI). Comparatively to other methods, the suggested algorithm is resistant to clipping, salt and pepper noise, speckle noise rotation assaults, and clipping. This positive outcome indicates that the plan can be successfully implemented for secure video communication applications.
Scarce data driven deep learning of drones via generalized data distribution space

Abstract Increased drone proliferation in civilian and professional settings has created new threat vectors for airports and national infrastructures. The economic damage for a single major airport from drone incursions is estimated to be millions per day. Due to the lack of balanced representation in drone data, training accurate deep learning drone detection algorithms under scarce data is an open challenge. Existing methods largely rely on collecting diverse and comprehensive experimental drone footage data, artificially induced data augmentation, transfer and meta-learning, as well as physics-informed learning. However, these methods cannot guarantee capturing diverse drone designs and fully understanding the deep feature space of drones. Here, we show how understanding the general distribution of the drone data via a generative adversarial network (GAN), and explaining the under-learned data features using topological data analysis (TDA) can allow us to acquire under-represented data to achieve rapid and more accurate learning. We demonstrate our results on a drone image dataset, which contains both real drone images as well as simulated images from computer-aided design. When compared to random, tag-informed and expert-informed data collections (discriminator accuracy of 94.67%, 94.53% and 91.07%, respectively, after 200 epochs), our proposed GAN-TDA-informed data collection method offers a significant 4% improvement (99.42% after 200 epochs). We believe that this approach of exploiting general data distribution knowledge from neural networks can be applied to a wide range of scarce data open challenges.
FedG2L: a privacy-preserving federated learning scheme base on “G2L” against poisoning attack

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Response Surface Method for Reliability Analysis Based on Iteratively-Reweighted-Least-Square Extreme Learning Machines

A response surface method for reliability analysis based on iteratively-reweighted-least-square extreme learning machines (IRLS-ELM) is explored in this paper, in which, highly nonlinear implicit performance functions of structures are approximated by the IRLS-ELM. Monte Carlo simulation is then carried out on the approximate IRLS-ELM for structural reliability analysis. Some numerical examples are given to illustrate the proposed method. The effects of parameters involved in the IRLS-ELM on accuracy in reliability analysis are respectively discussed. The results exhibit that a proper number of samples and neurons in hidden layer nodes, an appropriate regularization parameter, and the number of iterations for reweighting are of important assurance to obtain reasonable precision in estimating structural failure probability.
RASS: Enabling privacy-preserving and authentication in online AI-driven healthcare applications

Powered by the rapid progress of analytics techniques and the increasing availability of healthcare data, artificial intelligence (AI) is bringing a paradigm shift to healthcare applications. AI techniques offer considerable advantages for the evaluation and assimilation of large amounts of complex healthcare data. However, to effectively use AI tools in healthcare, key issues need to be considered and several limitations must be addressed, such as privacy-preserving and authentication of the healthcare data for analysis in training and inference procedures. Although various techniques ranging from cryptographic tools to obfuscation mechanisms have been proposed to provide privacy guarantees for data in AI-based services, none of them is applicable to online AI-driven healthcare applications. For they require a heavy computational cost on protecting privacy without offering authentication services for third parties. In this paper, we present RASS, an efficient privacy-preserving and authentication scheme for securing analyzed data in an AI-driven healthcare system. The security proofs of our construction indicate that its unforgeability and multi-show unlinkability can defend against the tempering and collusion attacks respectively. Finally, we conduct sufficient efficiency analysis, and the results show that RASS achieves the above security demands without introducing complex computation and communication costs.
The Impact of the COVID-19 Pandemic on Human–Computer Interaction Empirical Research

Abstract The purpose of this paper is to assess the effects that the COVID-19 pandemic caused in the human–computer interaction (HCI) research field. Specifically, we aim to investigate how the HCI empirical research methodology changed due to the restrictions caused by COVID-19. For this reason, we analyzed all the papers published in the 2021 edition of The ACM Conference on Human Factors in Computing Systems (CHI 2021), which is generally considered the premier international conference for the field of HCI. Through the analysis of CHI papers, we identified four main effects of the COVID-19 pandemic on HCI research: influence on participants; influence on apparatus; influence on experiment procedure; other influences. These effects are described in detail and broken down into additional subcategories. Moreover, papers on pandemic-related topics were also identified. In addition, we performed some comparisons with the previous and successive edition of the conference, and extended some analysis, e.g categorization, to CHI 2022 papers. The analysis found that 23% of CHI 2021 papers and 36% of CHI 2022 papers reported some influences of the pandemic, the most common being a change in the procedures researchers used to interact with participants in their studies, in most cases based on remote communication technologies.
THE RESULTS OF HISTORICAL SEISMOGRAMS NEW DIGITIZATION TECHNIQUE IMPLEMENTATION AT THE DATA CENTER

For the first time the IGR NNC RK has implemented a new digitization technique allowing digitizing the analogue seismograms with a good quality and saving in advanced seismic data formats. A new technique is based on a Wavetrack software developed by Novosibirsk Regional Center for Information Technologies, and on a range of additional utilities developed by the Michigan State University, USA. The digitization results were compared with NXSCAN results developed by IRIS, USA. The comparison showed some advances of a new technique, and opportunity to use jointly the records digitized by different techniques in a definite frequency range. A database of the digitized nuclear explosions seismograms was created at the moment the digitized seismograms are used for different seismic monitoring tasks.
A dynamic data prepossessing method for parameter identification in power distribution network

Abstract The methods of building a model like Markov chain Monte‐Carlo (MCMC) and sequential model‐based global optimization (SMBO) in power distribution network (PDN) have achieved parameter identification successfully without extra measurement devices. However, the data processing focused on the feeder data is not concerned yet. In this study, the authors present a dynamic data prepossessing method for parameter identification in PDN to successfully obtain a more accurate result. This method considers the similarities of feeder data in both spatial relationship and statistical theory, and then realizes a dynamic aggregation process for new coming data and obtains a set of data with tighter higher dimensional relationship for following identification task. In experiments, the authors applied this data processing method to the actual feeder data with no adjustment of the other condition; identification results with the authors’ processing achieve a 5.3% improvement in accuracy at most.
Automatic Kidney Segmentation Method Based on an Enhanced Generative Adversarial Network

When deciding on a kidney tumor's diagnosis and treatment, it is critical to take its morphometry into account. It is challenging to undertake a quantitative analysis of the association between kidney tumor morphology and clinical outcomes due to a paucity of data and the need for the time-consuming manual measurement of imaging variables. To address this issue, an autonomous kidney segmentation technique, namely SegTGAN, is proposed in this paper, which is based on a conventional generative adversarial network model. Its core framework includes a discriminator network with multi-scale feature extraction and a fully convolutional generator network made up of densely linked blocks. For qualitative and quantitative comparisons with the SegTGAN technique, the widely used and related medical image segmentation networks U-Net, FCN, and SegAN are used. The experimental results show that the Dice similarity coefficient (DSC), volumetric overlap error (VOE), accuracy (ACC), and average surface distance (ASD) of SegTGAN on the Kits19 dataset reach 92.28%, 16.17%, 97.28%, and 0.61 mm, respectively. SegTGAN outscores all the other neural networks, which indicates that our proposed model has the potential to improve the accuracy of CT-based kidney segmentation.
MS_HGNN: a hybrid online fraud detection model to alleviate graph-based data imbalance

Online transaction fraud has become increasingly rampant due to the convenience of mobile payment. Fraud detection is critical to ensure the security of online transactions. With the development of graph neural network, researchers have applied it to the field of fraud detection. The existing fraud detection methods will solve the class imbalance by sampling, but they do not fully consider the various imbalances in the heterogeneous graph, and the data imbalance will directly affect the performance of the model. This work proposes a hybrid graph neural network model for online fraud detection to address this issue. The three types of imbalance in online transactions are feature imbalance, category imbalance, and relation imbalance, and they are all addressed in the proposed model. The entities with the feature most closely related to the fraudsters will be determined for the feature imbalance, and samples will be taken for further identification in the subsequent training phase. The hybrid model then uses under-sampling in combination with the long-distance sampling to find nodes with high similarity of features for the category imbalance. Finally, we propose a reward/punishment mechanism based on reinforcement learning for relation imbalance, which uses the threshold created by training as the sampling weight between relations. This paper conducts experiments on the public datasets Amazon and Yelp. The experimental results show that the model proposed is 5.61% higher than the best model in the comparison model on Amazon dataset, and 1.58% higher on Yelp dataset.
On good encodings for quantum annealer and digital optimization solvers

Several optimization solvers inspired by quantum annealing have been recently developed, either running on actual quantum hardware or simulating it on traditional digital computers. Industry and academics look at their potential in solving hard combinatorial optimization problems. Formally, they provide heuristic solutions for Ising models, which are equivalent to quadratic unconstrained binary optimization (QUBO). Constraints on solutions feasibility need to be properly encoded. We experiment on different ways of performing such an encoding. As benchmark we consider the cardinality constrained quadratic knapsack problem (CQKP), a minimal extension of QUBO with one inequality and one equality constraint. We consider different strategies of constraints penalization and variables encoding. We compare three QUBO solvers: quantum annealing on quantum hardware (D-Wave Advantage), probabilistic algorithms on digital hardware and mathematical programming solvers. We analyze their QUBO resolution quality and time, and the persistence values extracted in the quantum annealing sampling process. Our results show that a linear penalization of CQKP inequality improves current best practice. Furthermore, using such a linear penalization, persistence values produced by quantum hardware in a generic way allow to match a specific CQKP metric from literature. They are therefore suitable for general purpose variable fixing in core algorithms for combinatorial optimization.
The Analysis of the Risks and Improvements of ERC20 Tokens

Blockchain is a decentralized, shared ledger that aggregates blocks of data in a factual data structure in chronological order using a chain structure. Two hundred sixty thousand tokens, or 98% of the around 2.5 million smart contracts in Ethereum network, are ERC-20 tokens. The ERC20 standard specifies constructors for token contracts that establish and initialize the contract state. The Ethereum wallet and Ethereum decentralized, centralized applications can access tokens through these standard interfaces. Security vulnerabilities in ERC-20 have drawn a lot of attention lately in recent years. This paper summarizes some basic security vulnerabilities and suggests avoiding multiple withdrawal attacks. Tokens can be used to support dApps, access blockchain services, trade, and obtain voting rights. Various tokens exist depending on their properties and use cases, including governance, utility, security, transaction, and platform tokens. Tokens can be sorted as fungible tokens and non-fungible tokens. Tokens that can be fungible are identical, divisible, and can be instead of money. Non-fungible tokens have a single owner and are unique.
Study for Performance of MobileNetV1 and MobileNetV2 based on Breast Cancer

Artificial intelligence is constantly evolving and can provide effective help in all aspects of people's lives. The experiment is mainly to study the use of artificial intelligence in the field of medicine. The purpose of this experiment was to compare which of MobileNetV1 and MobileNetV2 models was better at detecting histopathological images of the breast downloaded at Kaggle. When the doctor looks at the pathological image, there may be errors that lead to errors in judgment, and the observation speed is slow. Rational use of artificial intelligence can effectively reduce the error of doctor diagnosis in breast cancer judgment and speed up doctor diagnosis. The dataset was downloaded from Kaggle and then normalized. The basic principle of the experiment is to let the neural network model learn the downloaded data set. Then find the pattern and be able to judge on your own whether breast tissue is cancer. In the dataset, benign tumor pictures and malignant tumor pictures have been classified, of which 198738 are benign tumor pictures and 78, 786 are malignant tumor pictures. After calling MobileNetV1 and MobileNetV2, the dataset is trained separately, the training accuracy and validation accuracy rate are obtained, and the image is drawn. It can be observed that MobileNetV1 has better validation accuracy and overfit during MobileNetV2 training. From the experimental results, it can be seen that in the case of processing this dataset, MobileNetV1 is much better than MobileNetV2.
Facial Expression Recognition based on Mini_Xception

As the premise of emotion recognition, facial expression recognition has received extensive attention and discussion, which aims to capture face information by computer, understand and classify it according to the way of thinking of people to improve the effect of human interaction. Through previous efforts have significantly improved the accuracy, few of them consider the application effect in specific scenarios. In this paper, we make a summarize of the existing research methods, and explore the generalization ability of the network model in different scenarios based on the MiniXception network. We also explore the scope with smaller or bigger impact on network performance, and make assumptions and prospects for the optimization method and development direction in this scenario.
Chatbot Improves the Customer Service in Four Important Industries

Chatbot was first introduced in 1966. With the development of Artificial Intelligence for decades, the function and usage of Chatbot have been enhanced to a very high level. In this paper, we are going to talk about how the use of Chatbot improves the customer service in four industries: healthcare, advisory, commercial, and education. They are the four important social fields, and they all have very large demands and uncountable customers every day. The human agents are facing tons of customers from various backgrounds. They need the help from artificial intelligence. Apart from that, we will also discuss some special advantages of AI-based chatbots which them work so effectively in customer service. These three advantages are automation, trust, and productivity. With the combination of these advantages, chatbot can be highly supportive in the field of customer service and improve the quality of customer service. Eventually, we will talk about some challenges the chatbots are facing now, and how to make it function better in customer service to meet more users’ expectations.
Studies Advanced in Image Super-resolution Reconstruction based on Deep Learning

Image super-resolution reconstruction (SR) has always been a research hotspot in the computer vision community, whose purpose is to convert low-resolution (LR) images into high-resolution (HR) images by algorithms without changing the hardware. Early SR works were mainly based on manual features and mathematical statistical models, relying on interpolation to achieve high-resolution image reconstruction. Thanks to the rapid development of convolutional neural networks, SR based on deep learning has achieved breakthroughs in both accuracy and speed. This paper first introduces the basic principles of image super-resolution reconstruction, including the basic framework and loss function of SR. Then, we introduce the representative super-resolution reconstruction methods such as SRCNN, SRGAN, and TTSR. We also introduce the common public datasets and evaluation indexes, and compare the performance of several SR methods to analyze their advantages and disadvantages. Finally, we summarize the existing challenges in the image super-resolution reconstruction, and give a look out for the future development direction of SR.
Robust design and task‐priority control for rescue robot HURCULES

Abstract This paper proposes a novel design strategy and task‐priority‐based control methodology for a robot to successfully complete a rescue operation in an extremely unstructured environment. The mechanical structure is designed to obtain both versatile manipulability and all‐terrain mobility. The regularized hierarchical quadratic program is used for whole‐body motion and force control. The optimization strategy is reasoning about regularization and thus it ensures convergence of the solution in the face of singularities while taking into account equality and inequality constraints. We demonstrate the effectiveness of the online optimization‐based control algorithms through extensive real‐world numerical and experimental results. Finally, we highlight that the rescue robot can successfully execute missions to extract a casualty and dispose of a dangerous object both indoor and outdoor environments.
Template-guided frequency attention and adaptive cross-entropy loss for UAV visual tracking

This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles (UAVs). Most Siamese trackers are used to regard object tracking as classification and regression problems. However, it is difficult for these trackers to accurately classify in the face of similar objects, background clutters and other common challenges in UAV scenes. So, a reliable classifier is the key to improving UAV tracking performance. In this paper, a simple yet efficient tracker following the basic architecture of the Siamese neural network is proposed, which improves the classification ability from three stages. First, the frequency channel attention module is introduced to enhance the target features via frequency domain learning. Second, a template-guided attention module is designed to promote information exchange between the template branch and the search branch, which can get reliable classification response maps. Third, adaptive cross-entropy loss is proposed to make the tracker focus on hard samples that contribute more to the training process, solving the data imbalance between positive and negative samples. To evaluate the performance of the proposed tracker, comprehensive experiments are conducted on two challenging aerial datasets, including UAV123 and UAVDT. Experimental results demonstrate that the proposed tracker achieves favorable tracking performances in aerial benchmarks beyond 41 frames/s. We conducted experiments in real UAV scenes to further verify the efficiency of our tracker in the real world.
Impact learning: A learning method from feature’s impact and competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm.