Reviewerly/exHarmony huggingface dataset:
Semi-asynchronous federated learning algorithm based on clustering and training efficiency

In order to solve the problem of device heterogeneity in federated learning, FedAsync proposes asynchronous federated learning, that is, the server and the client interact in an asynchronous manner, that is, the server updates the global model immediately after receiving the local model. Communication between server and client is non-blocking. Therefore, the server and client can update the model at any time without synchronization, which is advantageous when the devices have heterogeneous conditions. However, when the gap between clients increases, the time difference of the uploaded model parameters becomes larger, which has a great impact on the accuracy. In response to the above problems, this paper first introduces the concept of training efficiency, evaluates the training ability of customers, and determines the training frequency of customers according to the training ability, thereby narrowing the gap between customers. Second, change the semi-asynchronous strategy, that is, the uploaded model is not updated at any time, but aggregated and updated after a certain amount or a certain time is satisfied. Finally, the effect is verified by simulation experiments.
Arbitrary-oriented object detection algorithm based on cross-layer integrating attention mechanism for remote images

Compared with the classical object detection algorithms with horizontal bounding box, such as Yolo and Faster-R-CNN, the oriented object detector in remote sensing images can be more robust. Considering that the challenge of oriented object detection in remote sensing images, we propose a cross-layer feature fusion improvement strategy. Specifically, to obtain multiple layer feature maps for subsequent object detection, the shallow feature map with texture information merged with the deep feature map with semantic features. The spatial attention mechanism is introduced to enhance our algorithm's attention to the non-local information in these feature maps. Extensive experiments on a public dataset, DOTA, demonstrate the effectiveness of our proposed method. Under the large difference of object scale, arbitrary orientation of object, objects with dense arrangement and complex background, the experimental results show that our method has better performance.
Region-Aware Arbitrary-Shaped Text Detection With Progressive Fusion

Segmentation-based text detectors are flexible to capture arbitrary-shaped text regions. Due to large geometry variance, it is necessary to construct effective and robust representations to identify text regions with various shapes and scales. In this paper, we focus on designing effective multi-scale contextual features for locating text instances. Specially, we develop a Region Context Module (RCM) to summarize the semantic response and adaptively extract text-region-aware information in a limited local area. To construct complementary multi-scale contextual representations, multiple RCM branches with different scales are employed and integrated via Progressive Fusion Module (PFM). Our proposed RCM and PFM serve as the plug-and-play modules which can be incorporated into existing scene text detection platforms to further boost detection performance. Extensive experiments show that our methods achieve state-of-the-art performances on Total-Text, SCUT-CTW1500 and MSRA-TD500 datasets. The code with models will become publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wqtwjt1996/RP-Text</uri> .
Use of Digital Economies Ensuring Smart and Sustainable Manufacturing Systems for Industry 5.0

Smart wearable technology is taken into account as a paradigm shift in healthcare and health insurance industry. More and more wrists are wrapped in smartwatches with advanced technology using serval applications connected to smart mobile phones and sensors to track and monitor the healthy lifestyle of the wearers. This paper aims at conducting a systematic review of literature related to smart wearables that engage people in ensuring sustainable healthy lifestyle. The increasing capability of automation with AI turned the robots to cobots and results in Industry 5.0 as a brand-new historic period. Sensing technology, big data, and AI (AI) have proven viable at automating, managing, and optimizing a good range of non-industrial processes. The study focused on impact of consumer-grade smart wearables including smartwatch, and other patient related devices monitoring heart rate, speech therapy adherence, etc. Study found that health insurance companies are already using such devices to track the healthy lifestyle of patients and determining the insurance premium based on that.
SOTF: Secure Organizational Transactions Framework Based on Bitcoin Payment Bridge

Bitcoin is a decentralized cryptocurrency where all the transactions are saved in a ledger. Blockchains have good features to be used in different finance applications by using smart contract. Also, blockchains are attractive platform for many industrial domains, including the logistics and supply chain industries. Supply chain technology contributes to record every single asset through its flow, tracking orders, receipts, and payments. There are several relay protocols proposed in previous research; these relay protocols are used to connect different types of blockchains. In this paper, we present a Secure Organizational Transactions Framework (SOTF) based on bridge chain to connect private chain or consortium with public chain which reduces interaction with public blockchain and saves the organization details privately in private chain or consortium. The proposed framework makes it possible for smart contract services to acquire means of payment in the consortium and private chain. Moreover, the proposed framework automates the process of updating the payment non-interactively. In this paper, we validate the communication between organizations blockchain and bitcoin, and find out the development cost of the proposed framework which ensures the efficiency and feasibility of the proposed framework. The code of framework implementation is publicly available at GitHub.
Action Recognition From a Single Coded Image

The unprecedented success of deep convolutional neural networks (CNN) on the task of video-based human action recognition assumes the availability of good resolution videos and resources to develop and deploy complex models. Unfortunately, certain budgetary and environmental constraints on the camera system and the recognition model may not be able to accommodate these assumptions and require reducing their complexity. To alleviate these issues, we introduce a deep sensing solution to directly recognize human actions from coded exposure images. Our deep sensing solution consists of a binary CNN-based encoder network that emulates the capturing of a coded exposure image of a dynamic scene using a coded exposure camera, followed by a 2D CNN for recognizing human action in the captured coded exposure image. Furthermore, we propose a novel knowledge distillation framework to jointly train the encoder and the action recognition model and show that the proposed training approach improves the action recognition accuracy by an absolute margin of 6.2%, 2.9%, and 7.9% on Something 2-v2, Kinetics-400, and UCF-101 datasets, respectively, in comparison to our previous approach. Finally, we built a prototype coded exposure camera using LCoS to validate the feasibility of our deep sensing solution. Our evaluation of the prototype camera show results that are consistent with the simulation results.
Neural Networks Implemented by Differential Evolutionary Algorithms to Counter Attacks

It is urgent and necessary to investigate the adversarial attacks on different models, the attack patterns and attack methods of the adversarial attacks. In this paper, three convolutional neural network models, LeNet, ResNet and DenseNet, were used to train image recognition for the Cifar-10 multispecies dataset, and a differential evolutionary algorithm was used to implement a counterattack on the neural network. Among them, the Drop-out mechanism and Batch-Nomalization layer were added to the neural network model to solve the overfitting problem and improve the gradient dispersion problem of the neural network, respectively, and finally the differential evolution algorithm was used to achieve the attack on the neural network model. The experimental results have shown that the image recognition accuracies of LeNet, ResNet, and DenseNet models reached 53.83%, 92.95%, and 93.17%, respectively. When the differential evolutionary algorithm was used to implement the adversarial attack on the three neural network models, 93%, 78%, and 69% were achieved, respectively. Comparing the attack success rate of the three network models, it can be found that the result is consistent with the image recognition rate and network structure robustness of the three models.
A Spoofing Proof Stateless Session Architecture

To restrict unauthorized access to the data of the website. Most of the web-based systems nowadays require users to verify themselves before accessing the website is authentic information. In terms of security, it is very important to take different security measures for the protection of the authentic data of the website. However, most of the authentication systems which are used on the web today have several security flaws. This document is based on the security of the previous schemes. Compared to the previous approaches, this “spoofed proof stateless session model” method offers superior security assurance in a scenario in which an attacker has unauthorized access to the data of the website. The various protocol models are being developed and implemented on the web to analyze the performance. The aim was to secure the authentic database backups of the website and prevent them from SQL injection attacks by using the read-only properties for the database. This limits potential harm and provides users with reasonable security safeguards when an attacker has an unauthorized read-only access to the website's authentic database. This scheme provides robustness to the disclosure of authentic databases. Proven experimental results show the overheads due to the modified authentication method and the insecure model.
One Novel Dynamic Entity Intelligent Inspection Technology of Bulk Commodity Trading Market

Bulk commodity trading is distinguished by a high volume of transactions, a diverse range of transactions, and high price volatility. Once the risk occurs, it will have a great impact on the trading entities and the trading market. Therefore, it is a top priority to avoid the trade risk of the commodity market from the source. However, traditional entity credit inspection methods have the following drawbacks: single data source, fixed pattern, and fixed particles. These problems lead to the difficulty of entity inspection of market transactions, low reliability, and a high risk of existence. A dynamic entity intelligent inspection service mode based on multi-source data fusion is proposed in this paper. First, for this service mode, the detection technology of multi-source data fusion is used to solve the problem of a single data source. Second, dynamic inspection techniques are used to solve the problem of modal granularity fixation. Finally, the complete process of enterprise service is proposed. Through the service mode and inspection technology proposed in this paper, the entity access qualification can be fed back in an all-round and multi-level way. Before bulk commodity trading, the trading platform can make a comprehensive, multi-directional, and reliable cognition of the trading entity information. In trading, the trading platform will have a deeper understanding of the credit change status of the trading entity.
Hiding Privacy Data in Visual Surveillance Video based on Wavelet and Flexible Function

Privacy is one of the great bases of our civilization and is important for many societal purposes. Therefore, in this paper, a data hiding technique was proposed to improve privacy in the video surveillance system and it can recover all the original scenes using a securely key for the authorized user. The original video frames that hold clear faces are embedded in a blurred version of frames using wavelet coefficients and an embedding function. That is, each blurred frame holds a secret frame that represents the non-blurred version. The proposed method was implemented using MATLAB, the computer vision toolbox and the frontal multiple faces dataset. Results captured by the PSNR metric show an acceptable visual quality for retrieved secret frames.
A Time Deterministic Dynamic Segmentation Scheduling Algorithm

This paper proposed a soft real-time scheduling algorithm “time-deterministic dynamic partitioning scheduling” for multimedia tasks in embedded devices. We established a new model based on the characteristics of embedded devices. We developed a pre-scheduling mechanism to select a suitable scheduling strategy based on the actual situation of the task set in the offline cache and to rearrange the task order so as to guarantee the reliability and real-time performance of the system while having higher flexibility and stability. The mechanism also completes the tasks assigned by the system in a stable manner with high utilization.
Balance Multi-Head Attention based on Software and Hardware Co-design

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%.
Speformer: An Efficient Hardware-Software Cooperative Solution for Sparse Spectral Transformer

Nowadays, many transformer’s model optimization schemes are based on clustering algorithms, for example, Reformer utilizes locally sensitive hashing, Routing Transformer utilizes k-means clustering, etc. All these clustering methods are based on densities. However, the clustering algorithms based on consistency perform poorly for non-Gaussian distributed data sets. Unfortunately, the connectivity-based clustering algorithms tend to have higher time complexity than the previous clustering algorithms. To address the above problems, we propose an efficient sparse transformer software and hardware cooperative solution, named Speformer, based on spectral clustering. We applied the model to the enwiki8 document generation task, and the experimental results proved that our solution outperforms other models.
Advanced Analysis of Email Sender Spoofing Attack and Related Security Problems

A mail spoofing attack is a harmful activity that modifies the source of the mail and trick users into believing that the message originated from a trusted sender whereas the actual sender is the attacker. Based on the previous work, this paper analyzes the transmission process of an email. Our work identifies new attacks suitable for bypassing SPF, DMARC, and Mail User Agent’s protection mechanisms. We can forge much more realistic emails to penetrate the famous mail service provider like Tencent by conducting the attack. By completing a large-scale experiment on these well-known mail service providers, we find some of them are affected by the related vulnerabilities. Some of the bypass methods are different from previous work. Our work found that this potential security problem can only be effectively protected when all email service providers have a standard view of security and can configure appropriate security policies for each email delivery node. In addition, we also propose a mitigate method to defend against these attacks. We hope our work can draw the attention of email service providers and users and effectively reduce the potential risk of phishing email attacks on them.
Prediction of the Impact of Mobile Payments on the Consumption of Individual Nodes on Communication Networks

With the development of communication technology, the prediction of node attributes in communication networks has become more and more important. Based on the basic theory of causal analysis, this paper investigates the effect of mobile payment on the communication consumption of nodes in communication networks. A prediction model is constructed using real data sets and a novel causal forest algorithm to predict the effect of mobile payment on node communication consumption. A least squares regression model incorporating the random forest algorithm is developed to make predictions on node communication consumption using mobile payment data, and the results further demonstrate the impact of mobile payment on user communication consumption.
Graph-based Malicious Account Groups Detection in Electronic Trading of Bulk Commodities

Bulk commodities, such as food and minerals, play a vital role in social life, but there are many risks in electronic trading of bulk commodities, especially the malicious account groups composed of a large number of malicious accounts which try to manipulate market prices through continuous trading. The key to preventing this risk lies in the timely and accurate detection of malicious account groups. However, the detection of malicious account groups is a difficult task in electronic trading of bulk commodities due to the flexibility of electronic transactions. Traditional methods rely on human review. But it has low efficiency and low accuracy. This article propose a new approach which utilizes the graph theory and graph embedding to detect the malicious account groups hidden in the market. Graph theory is used to build a comprehensive trading network with the help of transaction records. Graph embedding theory is applied to extract latent features of the accounts so as to pick out the malicious accounts and find the groups. This graph-based methodology makes it possible to find the malicious account groups lurking in the market automatically, as well as improve the accuracy and efficiency remarkably.
Functioning of Channels of Multi-Scanning Monitoring in Radars with Regular or Controlled Surveillance

In this paper we analyze the structure of a radar with additional channels of multi-scanning surveillance. These channels are implemented on the basis of "track-before-detect" technologies. We analyze the features of the implementation of "track-before-detect" technologies based on sequential and block type algorithms in additional channels of radar with regular or controlled surveillance. We propose a new approach to evaluating the effectiveness of methods of long-term multi-scanning monitoring by analyzing the throughput of a radar. We also investigate the influence of the nature of the radar surveillance on the throughput of a radar with additional multi-scanning monitoring channels.
Perfil epidemiológico e avaliação de pacientes de Telemedicina durante a pandemia de COVId-19

Based on the care impact caused by the COVID-19 pandemic, the present study aimed to describe the profile of patients seen by teleconsultation during the pandemic. This is a cross-sectional study carried out in Bahia, Brazil. The study population consisted of 99 patients and data collection was carried out using forms applied by trained professionals. The majority of the population was composed of women aged 40 to 59 years, brown in color, married or in a stable relationship and of Catholic religion. The main reasons for seeking the service according to the chapters of CIAP2 were: skeletal, psychological, endocrine, metabolic, nutritional and dermatological muscle. Among all patients, 43.48% were obese or morbidly obese. About 48.05% were aware of the free medical consultation by telemedicine over the Internet. There was a greater demand for the service by women, as well as a higher percentage of patients with musculoskeletal symptoms and people living with chronic diseases. The mental health complaints were also quite expressive, precisely in a period of the pandemic in which there was a partial interruption of the services that offered such care. The ease of access to teleconsultation can help to expand the distribution of health care to places with contrasting supply-demand.
Application development and practice model for radiation education

Abstract Since the nuclear disaster in Fukushima, Japanese soil scientists have tackled soil contamination problems due to radioactive cesium. For the radiation education based on the research results and outreach activities, we published a comic book and developed a digital application using iOS Xcode for Apple iPhone and tablet devices. This allows students to quickly understand why clay soil absorbed cesium and how to remove contaminated soil using educational mini‐games. Through radiation education seminars and a conference, our application was evaluated and revised by school teachers.
VIRTUAL SHOWCASE OF PRODUCTS FOR PHYSICAL STORES

In an increasingly digital market, physical stores have suffered from the evasion of consumers and the high investment required to become relevant and make themselves remembered amidst the infinity of options.Conversely, consumers lack a tool to find products being offered in their region in a simple way and even with the possibility of filtering the best cost-benefit for their context (price, distance, availability, discounts, etc.).From these needs emerged the concept of the virtual shop window that would allow a greater reach with the same (or lower) cost invested in marketing, allowing even small stores to have the possibility of being noticed through a web application in which their products may be exposed.The entire basis of the project was founded using experimental research as the main methodology.In addition, the application developed relied on the use of the Javascript language and the Angular framework to develop the user interface and the Java language and the Spring framework to develop the consumed API.The relational database chosen for data persistence was MySQL.This work is part of a larger project composed of three fronts, namely: market analysis, mobile application for users and web application for retailers (understood in this article ).This work is expected to provide the foundations of an intuitive and efficient tool that enables small and medium-sized entrepreneurs to regain competitiveness in the local market, through gaining visibility in the virtual environment.
On Almost-Uniform Generation of SAT Solutions: The power of 3-wise independent hashing

Given a Boolean formula φ and a distribution parameter ε, the problem of almost-uniform generation seeks to design a randomized generator such that every solution of φ is output with probability within (1 + ε)-factor of where sol(φ) is the set of all the solutions of φ. The prior state of the art scheme due to Jerrum, Valiant, and Vazirani, makes calls to a SAT oracle and employs 2 − wise independent hash functions.
On the strength of Sherali-Adams and Nullstellensatz as propositional proof systems

We characterize the strength of the algebraic proof systems Sherali-Adams () and Nullstellensatz () in terms of Frege-style proof systems. Unlike bounded-depth Frege, has polynomial-size proofs of the pigeonhole principle (). A natural question is whether adding to bounded-depth Frege is enough to simulate .