Reviewerly/exHarmony huggingface dataset:
A Robot Foreign Object Inspection Algorithm for Transmission Line Based on Improved YOLOv5

Aiming at the problems of slow detection rate and low accuracy of traditional transmission line inspection methods, a transmission line target detection model based on improved YOLOv5 is proposed in this paper. Firstly, the Bottleneck module in the Backbone network is replaced to improve the lightweight of the model; then the coordinate attention (CA) module is introduced to design the Backbone network to improve the performance of model detection; finally, the frame regression loss function is changed to improve the accuracy of detection. After the transmission line images are further expanded, the foreign object data sets of transmission line are constructed. Experiments on the above data sets show that: Compared with YOLOv5, the detection accuracy of the optimized model is improved by 6.7%, the mean average precision (mAP) reaches 87.0%, and the detection speed is improved by 16.0%. The YOLOv5 lightweight model proposed in this paper reduces the power consumption of the platform and improves the model detection speed and accuracy. It is more conducive to the deployment of the target detection model in the mobile terminal.
FedTD: Efficiently Share Telemedicine Data with Federated Distillation Learning

With the Internet of Things and medical technology development, patients use wearable telemedicine devices to transmit health data to hospitals. The need for data sharing for public health has become more urgent under the COVID-19 pandemic. Previously, security protection technology was difficult to solve the increasing security risks and challenges of telemedicine. To address the above hindrances, Federated learning (FL) solves the difficulty for companies and institutions to share user data securely. The global server iterative aggregates the model parameters from the local server instead of uploading the user's data directly to the cloud server. We propose a new model of federated distillation learning called FedTD, which allows the different models between local hospital servers and global servers. Unlike traditional federated learning, we combine the knowledge distillation method to solve the non-Independent Identically Distribution (non-IID) problem of patient medical data. It provides a security solution for sharing patients’ medical information among hospitals. We tested our approach on the COVID-19 Radiography and COVID-Chestxray datasets to improve the model performance and reduce communication costs. Extensive experiments show that our FedTD significantly outperforms the state-of-the-art.
Data Security Risk Prediction of Labor Relationship Rights Protection Network Platform Based on Machine Learning

In order to improve the effect of data security risk prediction of labor relations rights protection network platform, this paper introduces machine learning algorithm into this field, and designs a new network platform data security risk prediction method. Determine the data risk index of the network platform of labor relationship rights protection, calculate the weight of the risk data in the website data, and find the risk data characteristics in the website. Build the decision tree, calculate the data entropy involved in the decision tree, summarize the characteristics of the risk data, create the nodes of the decision tree, and get the status of the risk data of the labor relationship rights protection network platform. The obtained risk data status is brought into the Bayesian network probability definition to analyze the risk degree of the risk data. The experimental results show that the design method can effectively shorten the evaluation time and improve the risk prediction accuracy.
A Lattice-Based Aggregate Signature Based on Revocable Identity

In the practical application of identity-based aggregated signature schemes, the problems of singer’s key loss and signer’s identity invalidation often occur. This makes the identity-based aggregate signature scheme need to realize the revocation of the signer, and the existing revocation of identities method needs to rebuild the system master key. Combined with the revocation method proposed by Boldyreva [1], this paper improves the aggregated signature scheme to realize the dynamic revocation of singer’s identities. At the same time, in order to deal with the threat brought by the development of quantum computer to the traditional cryptosystem, a lattice-based aggregate signature scheme based on revocable identity is proposed in this paper.
Explanation-Guided Minimum Adversarial Attack

Machine learning has been tremendously successful in various fields, rang-ing from image classification to natural language processing. Despite it has been gained ubiquitous, its application in high-risk domains has been hindered by the opacity of its decision-making, i.e., users do not understand the reason for the given prediction result. To circumvent this limitation, explainable artificial intelligence (XAI) is being developed from multiple perspectives and at multiple levels. However, the auxiliary information provided by XAI helps to build a trust bridge between users and models, while inevitably increasing the risk of the model being attacked. In this paper, we prove that explanation information has a certain risk of attack on the model, and to explore how the adversary can use explanation information to reduce the attack dimension. Our proposed attack method can reduce the perturbation range to a certain extent, i.e., the adversary can add perturbation in a very small range. It can ensure the distortion and success rate at the same time, reduce the perturbation amplitude, and obtain the adversary samples that can not be discernible by human eyes. Extensive evaluations results show that the explanation information provided by XAI provides a set of sensitive features for the adversary. On the CIFAR-10 dataset, the scope of our attack is 90% smaller than the C &W attack, while maintaining a similar success rate and distortion. At the same time, we verify that our method can still achieve good attack effect even in black box.
Differentially Private Generative Model with Ratio-Based Gradient Clipping

As a branch of deep learning, conditional Generative Adversarial Net (cGAN) has achieved remarkable results in target-oriented image generation, but the process of training the model lacks privacy guarantee. Differential Privacy (DP) is a privacy-preserving mechanism that could be utilized in the Stochastic Gradient Descent (SGD) of deep learning to maintain the confidentiality of training data. However, the quality and diversity of differentially private conditional image synthesis remain large room for improvement because traditional mechanisms with thick granularities and rigid clipping bounds in Differentially Private SGD (DPSGD) could lead to huge performance loss. To tackle this, we propose three algorithms with different clipping granularities and clipping bound distributions, and investigate the effects on enhancing conditional image generation under Differentially Private Projection cGAN (DP-PcGAN). We compare and analyze the performance of our strategies with methods concerned in terms of seven metrics, finding ratio-based gradient clipping highly effective and robust in training DP-PcGAN for generating visually promising images. Specifically, the Per-layer RGC surpasses previous methods under all evaluation metrics utilizing the second least privacy expense for MNIST dataset, while Per-unit RGC attains encouraging outcomes with a moderate converging speed under FashionMNIST dataset.
Bayesian Based Security Detection Method for Vehicle CAN Bus Network

The existing on-board can bus network security detection methods have low accuracy in detecting the number of abnormal messages and long detection time. Therefore, combined with Bayesian algorithm, this paper designs a on-board can bus network security detection method. For data preprocessing, establish a classification model, classify network attacks, calculate the information entropy of CAN bus, establish a Bayesian model, learn Bayesian network, continue to reason, and realize the security detection of vehicle can bus network based on Bayesian. The experimental results show that the proposed security detection method has high detection accuracy and short detection time, and can find internal security vulnerabilities in time, providing technical support for the safe operation of vehicles.
A Method for Information Security Analysis Using Information Graphs

The paper defines the concept of an information graph and proposes its use to describe information links and analyze the information flow in any organization regarding information security. Research on information graphs in this aspect should be developed, especially in identifying “dangerous graph structures” such as the Hamiltonian graphs or cliques. Information graphs containing such a structure as a Hamilton cycle can be analyzed using results from the theory of finding and existence of Hamilton cycles, an essential branch of graph theory. The paper also describes a graph of potential security conflicts, a thematic graph, i.e. a specific information graph. Two theorems concerning the properties of information graphs are also proved.
Using Group Theory to Generate Initial Population for a Genetic Algorithm for Solving Traveling Salesman

In this chapter, we propose a novel algorithm that uses Genetic algorithm with group theory for initial population generation and also propose a novel crossover for solving Traveling Salesman Problem. In the group tour construction method, each individual/initial tour has distinct start city provided that population size is equal to total number of cities. In the initial population, each individual/tour has a distinct starting city. The distinct starting cites of each tour provide genetic material for exploration for the whole search space. Therefore, a heterogeneous starting city of a tour in initial population is generated to have rich diversity. Proposed crossover based on greedy method of sub-tour connection drives the efficient local search, followed by 2-opt mutation for improvement of tour for enhanced/optimal solution. The result of the proposed algorithm is compared with other standard algorithms followed by conclusion.
Psychology and Solutions of Perpetrators in Cyber Violence

As of December 2021, the number of Chinese Internet users reached 1.032 billion, an increase of 42.96 million from December 2020, and the Internet penetration rate reached 73.0%. In the Internet era, people’s freedom of speech has been greatly released, and many insulting and inflammatory remarks and pictures posted on the Internet have brought great harm to people’s personal health, especially teenagers [1]. Therefore, this paper studies the psychology of online abusers and tries to provide corresponding solutions. It can be concluded that the psychology of abusers is divided into subjective and objective parts, and the corresponding solutions are to improve the relevant legal construction, strengthen the platform management, and deepen legal education.
Pathway for Elaborating Model of Culture of Innovation and Creativity at Universities as Prerequisite for Their Performance

In the twenty-first century universities, as intellectual potential formative institutions and tool for creating effective national system are needed to be much more focused on innovation and creativity, rather than on stability and standardization. An innovative and creative universities boost to the development of globally competitive economics, through forming productive and flexible workforce, idea generation, creating technologies and their dissemination. Hence, those who want to create a world class university will need to develop an organizational environment which boosts to innovative and creative behavior of its members leading to university performance and the economic development of the country. In terms of innovation capability and Skills, Georgia takes quite law positions that is proved by “The Global Competitiveness Report, 2019” according to which it takes 91st and 46th places out of 141 countries. Thereof it’s vitally important to investigate the leading countries’ (USA, Switzerland, Germany, Finland, Denmark, Singapore, Italy, Israel, Japan, Luxemburg and South Korea) world-class universities’ (THE World University Rankings 2021) key determinants of innovative and creative organizational environment as well as to outline and analyze possible strategies and pathways for elaborating model of innovative and creative environment that will fit to Georgian reality.
CSESnet: A deep learning P-wave detection model based on UNet++ designed for China Seismic Experimental Site

Accurate detection of P-wave arrivals has important applications in real-time seismic data processing, such as earthquake monitoring and earthquake early warning. The Sichuan and Yunnan regions, where the China Seismic Experimental Site (CSES) is located, has frequent strong earthquakes and large amount small earthquakes, resulting in serious earthquake disasters. In this paper, we modify the UNet++ network structure and use 490,000 event waveform data and 78,000 noisy data from the CSES as the data set, and analyze the effects of the training set quality, labeled data and loss function on the model performance to obtain a new P-wave detection model-CSESnet. The recall, precision and F1 score of this model are 94.6%, 85.4% and 89.7%, respectively. The tests in Beijing Capital Circle (BCC) indicates the performance of the CSESnet decrease little and has good generalization. The test in Luxian M6.0 earthquake shows that CSESnet can also predict the P-wave arrival times of large earthquakes and process strong motion data very well. CSESnet provides a new detection model to improve the earthquake detection capability in CSES.
From savages to snowflakes: Race and the enemies of free speech

Abstract Right-wing free speech advocacy is increasingly shaping global politics. In IR, free speech has generally been viewed within human rights and international legal frameworks. However, this article shows that contemporary free speech advocates often ignore or oppose human rights and international law, focusing instead on (what they describe as) a defence of the nation state against the enemies of free speech. This article examines this articulation of free speech's enemies: first historically as the ‘savage’ in John Stuart Mill's influential formulation of free speech; and then contemporarily as the ‘snowflake’, ‘mob’, and ‘cultural Marxist’ by elected officials and lobbyists in the UK and US. The article argues that John Stuart Mill's savage is figured within a racialised civilisational hierarchy of degrees of humanity. Today, right-wing free speech advocates extend and reconfigure this hierarchy, imagining the ‘snowflake’, ‘mob’, and ‘cultural Marxist’ as lesser human, subhuman, and extra-human, respectively. Thus, in contrast to rights-based analyses of free speech advocacy – which assume or assess the promotion of rights as a ‘public good’ – the article argues that narratives of free speech's enemies are deployed by right-wing free speech advocates to underwrite racialised policy responses and global hierarchies.
Analysis of adversarial attack based on FGSM

With more and more applications of deep learning, including neural networks, the lack of explanatory make it easy to under external attacks. This paper mainly focuses on adversarial attack, that is, by adding slight perturbation to the input data, which cannot be detected by human being, can lead to a wrong output of the model and maximize the model’s prediction error, resulting in a distrastic decline in the performance of the model, including prediction accuracy, etc. But so far, there is still no sufficient theoretical support for why adversarial attack that can not be detected can lead to serious performance degradation of neural network models, some attempts at explaining this phenomenon focused on the reason of over-fitteing or the linear or unlinear nature of the neural network. In this paper, several experiments based on Fast Gradient Sign Attack Algorithm to resist adversarial attack are designed and implemented towards the model of neural network on the tasks of the image recognition and classification, and some regular experimental results are obtained. The experimental results provide some evidence for the argument that the reason why adversarial examples and adversarial attack can work is over-fitting, but it is possible that the evaluation methods can not measure such special kind of overfitting.
The applications of Generative Adversarial Network in surgical videos

Unstructured data e.g. images and videos are widely used in the medical field. Because the generative adversarial network (GAN) has the ability to process images with fewer labels and better feature extraction, the application of GAN in surgical video can promote the development of medical fields such as surgeon training and telemedicine. From three aspects, surgical procedure, video enhancement and imitation learning, the article summarizes the current applications of generative adversarial network (GAN) in surgical video processing. The first is two specific applications in terms of surgical procedure, step prediction (i.e. Supr-GAN) and surgical image generation. Second is about video enhancement. Based on the real-time performance and video processing effect, the paper introduces three types of applications in real-time video, respectively network delay, sharpness improvement and device recognition, and two processing methods to non-real-time video with the mirror reflection problem or the smoke problem. Finally, the paper also summarizes two applications of generative adversarial imitation learning (GAIL) in surgical videos, which focus on surgical suture (i.e. Motion2Vec) or selective catheterization simulation. The summary indicate that GAN currently focuses on minimally invasive surgery video processing, which means the type of surgery is relatively monotony. In addition, GAIL is seldom used to simulate learning based on surgery videos. Therefore, GAN, especially GAIL, still has a broad prospect in the application of surgical videos.
Design and implementation of boat fist teaching system based on Unity3D

The development of computer technology has far-reaching implications for the transmission of the intangible cultural heritage boat fist. In the general environment of regular epidemic management, E-learning mode of teaching is becoming increasingly popular. Distinguished from ordinary video follow-through practice, it provides real-time movement accuracy feedback while not limiting the user's learning venue. Therefore, in this paper, with the background of boat fist teaching, cultural dissemination and interaction design, based on Unity3D software and assisted by human body key point recognition technology, a boat fist teaching system is developed which is conducive to boat fist dissemination, cultural transmission, reducing learning costs and improving learning efficiency. The purpose is to spread and promote boat fist, a municipal-level intangible cultural heritage listed item in Qingpu District, Shanghai, so that more people can learn and understand them.
Improving Virtual Sensor Models by Censored Online Data

Digital twins are able to bridge the physical and the virtual world, which is especially useful in industrial environments. One small, but rather essential, kind of digital twin is the so called virtual sensor model. This type of model is used to enhance or replace a physical sensor in industrial settings to reduce costs or enable information retrieval from inaccessible locations within a machine or process. The virtual sensor model is usually trained once, whereat only a predefined amount of information without any adaptation possibilities on the clients side is provided. As manufacturers want to provide the possibility of custom adaptations for their clients, the virtual sensor models require to incorporate adaptive features, which also have to handle incomplete data recordings, e.g. uncensored data. Traditional offline machine learning approaches are often insufficient for such adaptive requirements, therefore the usage of online learning approaches is gaining increased attention, to avoid high computational, storage and temporal costs. This paper covers the continued training of such adaptive virtual sensor models, focusing on the handling and integration of censored online data. Different approaches to tackle the problems of catastrophic forgetting in online learning and correction of censored data are presented as well as the handling of censored data in online learning environments. The experiments sections compares various scenarios with and without censored data using an industrial dataset and demonstrates the positive influence of different online learning approaches.
Nonlinear Secret Sharing Schemes based on Z<sub>4</sub> Linear Codes

Secret sharing scheme is a method in which a secret is divided among shares by a authorized dealer. Linear secret sharing scheme is vulnerable to “Tompa-Woll attack”. To solve the problem of Tompa woll attack we design nonlinear secret sharing scheme. In this paper we design nonlinear secret sharing scheme based on gray mapping of Z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> linear equations. Kerdock and Preparata codes are Z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> linear codes and dual of each other. We obtain linear equations over Z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> using duality property. Gray mapping is nonlinear mapping so the obtained secret sharing scheme is nonlinear. In our scheme secret as well as shares are symbols of Z <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> .
Energy Efficient Resource Scheduling in Cloud Computing Based on Task Arrival Model

High energy consumption has become a vital bottleneck restricting the development of cloud computing. Most of the current resource management frameworks focus on the scheduling module but fail to consider the burstiness of workloads adequately. In this paper, we present a resource management framework based on the arrival model switching mechanism to optimize the energy efficiency of cloud data centers. We analyze cloud task characteristics and propose two types of task arrival models. The arrival model based on the Poisson process is for common scenarios, and the grey model in traffic burst (GMTB) is for bursty scenarios. An anomaly detection module is introduced to detect the abnormal events and determine whether the model needs to be switched. Finally, we propose an integrated virtual machine scheduling policy to balance the energy consumption and service level agreement.
Multi-Object Recognition Method Inspired by Multimodal Information Processing in the Human Brain

In order to realize the Digital Twin, it is necessary to instantly understand various objects existing in the real world through various sensor devices. In recent years, the development of machine learning technologies such as a convolutional neural network has been remarkable, and in the field of video analysis, they achieve very high recognition rates. However, if all sensor data were collected and processed in the cloud, network transmission bandwidth and communication delays would become bottlenecks. The brain is a light weight system that makes decisions based on uncertain observations and We have previously proposed an object recognition method based on a mathematical model of how the brain recognizes information. In this paper, we extend this method. The features of the brain’s recognition of spatial context are modeled by a conditional random field and incorporated into it. We show that our proposal can recognize multiple objects with an accuracy of more than 83.2% even from noisy information, and can be applied to 60 fps video in the evaluation environment.