Reviewerly/exHarmony huggingface dataset:
Real-time Fitness Activity Recognition and Correction using Deep Neural Networks

Fitness activities are beneficial to one's health and well-being. During the Covid-19 pandemic, demand for virtual trainers increased. There are current systems that can classify different exercises, and there are other systems that provide feedback on a specific exercise. We propose a system that can simultaneously recognize a pose as well as provide real-time corrective feedback on the performed exercise with the least latency between recognition and correction. In all computer vision techniques implemented so far, occlusion and a lack of labeled data are the most significant problems in correctly detecting and providing helpful feedback. Vector geometry is employed to calculate the angles between key points detected on the body to provide the user with corrective feedback and count the repetitions of each exercise. Three different architectures-GAN, Conv-LSTM, and LSTM-RNN are experimented with, for exercise recognition. A custom dataset of Jumping Jacks, Squats, and Lunges is used to train the models. GAN achieved a 92% testing accuracy but struggled in real-time performance. The LSTM-RNN architecture yielded a 95% testing accuracy and ConvLSTM obtained an accuracy of 97% on real-time sequences.
Federated Learning via Indirect Server-Client Communications

Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recently. Despite the different forms of FL algorithms (e.g., synchronous FL, asynchronous FL) and the underlying optimization methods, nearly all existing works implicitly assumed the existence of a communication infrastructure that facilitates the direct communication between the server and the clients for the model data exchange. This assumption, however, does not hold in many real-world applications that can benefit from distributed learning but lack a proper communication infrastructure (e.g., smart sensing in remote areas). In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e.g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients. Two algorithms, called FedEx-Sync and FedEx-Async, are developed depending on whether the transporters adopt a synchronized or an asynchronized schedule. Even though the indirect communications introduce heterogeneous delays to clients for both the global model dissemination and the local model collection, we prove the convergence of both versions of FedEx. The convergence analysis subsequently sheds lights on how to assign clients to different transporters and design the routes among the clients. The performance of FedEx is evaluated through experiments in a simulated network on two public datasets.
Research on Security Protection of Space-Earth Integrated Network Wireless Link Based on Consortium Blockchain Technology and Application

The space-earth integrated network is an important strategic resource of China, but its wireless link is inherently vulnerable to cyber threats. Meanwhile, the traditional security protection technology is not suitable for its special network characteristics and lightweight deployment requirements. At the same time, the centralized security management model is prone to systemic risks of single-point breakthroughs. In order to effectively ensure the efficient operation, security and credibility of the space-earth integrated network, the consortium blockchain technology system is applied to the wireless link security protection of the space-earth integrated network to meet the current security requirements such as trusted access of space-earth integrated network terminals, secure interconnection of nodes and network attack supervision, and provides an exploration direction for the construction of the space-earth integrated network security protection system.
An Ansible-based Distributed Application Architecture Rapid Deployment Scheme

This article proposes a platform of containers based on high availability Kubernetes cluster. The platform let containerized applications work continuously. The solution can be used by increasing master nodes or based on the original Kubernetes cluster in order to reduce cost. The article also providers installation scripts for quickly deployment.
Knowledge Encapsulation and Application Based on Domain Knowledge Graph

In order to speed up the training of high-tech equipment R&D talents, promote the auxiliary of knowledge reuse for product design and development, we take the liquid oxygen and liquid hydrogen engine domain as the application scenario, and proposes a knowledge encapsulation and application technology based on the domain knowledge graph. According to the complexity of knowledge application scenarios, a knowledge encapsulation technology is proposed, and reusable professional knowledge services are provided to designers through a standardized knowledge service platform. According to the intelligent application scenarios of professional technical materials and knowledge, we proposed a label clustering method and an intelligent information recommendation technology, for improving the efficiency of information and knowledge acquisition in scientific research and production. The practical application results show that based on domain knowledge graph can effectively accelerate the transformation of aerospace product design platform to openness, standardization and intelligence.
Prediction from Breast Cancer Images by Logistic Regression and AlexNet Models

Since the breast cancer makes big influence of human’s health, the prevention and treatment of breast cancer is very important. Artificial Intelligence is widely used in prediction based on database. In that paper, Artificial Intelligence is used for prevention and treatment of breast cancer based on patients’ cell images. Logistic Regression and AlexNet models are used as examples. Matrix calculation and rotation are used for data pretreatment. In the experiment, 5547 images with shape 50 × 50 × 3 are used as training and testing datasets. Comparing to the CNN Tensorflow model from Kaggle, AlexNet has higher test accuracy with longer training time, and Logistic Regression well reflects the relationship between specific part of cell and the breast cancer.
Research and Application of Archive Data Management System Based on Blockchain

Blockchain is an accounting technology that is jointly maintained by multiple parties, uses cryptography to ensure transmission and access security, and can achieve consistent data storage, hard to tamper with, and prevent repudiation. It is characterized by strong confidentiality, non tampering, and decentralization. Use blockchain HyperLedger technology to store archive data, extract the hash value of the original file and other key information and record it in the blockchain ledger, to ensure that the data can not be tampered with and the credibility of the stored data.
A regulated identity management system based on blockchain platform

The objective of this study is to propose a new regulable blockchain trusted management identity system, which can solve the fundamental problem of blockchain node identity management with large gaps in application requirements, trust models, and regulatory compliance. To achieve this goal, firstly, a hierarchical CA system framework is established, using cross-domain nodes to store hash lists of certificate chains, and achieving node identity authentication and authority management through the division of certificate functions. Secondly, the certificate designed in this study is used for cross-domain authentication and interaction of business terminals. This study designs dynamic authority access of terminals and authority-based authentication mechanism to realize identity access and cross-domain authentication of terminal entities between domains. While ensuring security and certificate validity, it reduces the number of hash operations and verification signatures and improves the efficiency of cross-domain authentication compared with existing blockchain cross-domain authentication schemes. It provides a systematic solution for identity security and privacy protection when various facilities are connected to the blockchain system.
AureNet: A Real-Time Arbitrary-oriented and Ship-based Object Detection

In recent years, due to the bursting computing power, neural-network-based object detection algorithms are emerging in large numbers, which is very promising in monitoring the transportation, especially in remoting sensing of the sea where it is relatively easier to distinguish the background and foreground. But because of the arbitrary angles of the ships, the impacts of illuminations and the too large aspect ratio of the length with respect to width for the ships, ship detection has faced challenging. To improve the accuracy of ship detection, in this paper, we present a new network based on RetinaNet named AureNet where Yolox-HSV algorithm is adopted to simulate the different light intensities for data augmentation. We used RegNet as the backbone of AureNet to extract the features more efficiently. What’s more, we developed a client using C# and Python to lower the threshold to do ship detection for non-professionals. On ship detection dataset HRSC2016, AureNet achieves mAP of 0.5286, which is 1.2% higher than that of the original network RetinaNet, demonstrating the effectiveness of the proposed network.
Missing Data Imputation in Bridge Monitoring System Based on the Prediction Algorithms

In this paper, combined with the popular prediction model in the field of artificial intelligence, experimental simulation and prediction of missing data of bridge health monitoring are carried out. The prediction models involved include support vector regression model SVM, neural network prediction model, Gaussian process regression model and time series prediction model. Firstly, by introducing the background and research status of BSHMS, the existing problems in the literature and the core work of this paper are proposed. Secondly, this paper introduces the basic principles of the related algorithms, which lays a foundation for the later model building. Third, the three groups of data were tested for normal distribution by the KPSS test method, and it was decided to use 3σ rule in order to identify abnormal data,while box chart rule is used for S2 and S3 measuring points. Then, the super parameters of the four models are optimized through several experiments, and the time series prediction model is simplified to AR model by using the characteristics of ACF and PACF images. Finally, the four models are simulated and the results are compared. The results show that SVR, BP and GPR all have good prediction effects, especially BP neural network, but the relative deviation of the prediction results of AR model is large, which may be due to the fact that only the impact of time series is considered in this case, and the limitation of reference data is not considered.
Research on voiceprint Recognition system based on ECAPA-TDNN-GRU architecture

Currently voiceprint Recognition systems are playing an increasingly important role in social life. The most popular voiceprint Recognition technology currently relies on neural networks to extract speaker’s features. The successful ECAPA-TDNN architecture is an improved time-delayed neural network based on the x-vector architecture, which explicitly models channel interdependencies by introducing the introduction of Squeeze and Excitation blocks, while the SEResNet block expands the frame layer context by adjusting the proportion of channels with global properties of the recording. In this paper, a number of improvements are proposed to the architecture based on the latest trends in the field of voiceprint Recognition-related issues. Firstly, the initial frame layer can be reset as a one-dimensional Res2Net module for skip connections. In consideration of the effect of the input data on the neural network, we pre-process the input audio data using the Melspectrogram approach. Secondly, a gated recurrent unit (GRU) network is introduced and embedded in the residual structure of the multilayer aggregation feature to further mine the temporal contextual information in the audio signal. The output of the GRU is aggregated with the output of the last SE-Res2Block module for subsequent feature extraction. Finally, the proposed ArcFace loss function is used in this paper to penalise the angle between the depth features and their corresponding weights in an additive manner, thus enhancing both intra-class tightness and inter-class variance and increasing the validation of our proposed model architecture for voiceprint Recognition.
Better Resemblance without Bigger Patterns: Making Context-sensitive Decisions in WFC

Gumin's WaveFunctionCollapse (WFC) algorithm attempts to generate output designs that resemble provided input designs. While the algorithm's constraint-solving core is able to ensure that no local patterns are adjacent in the outputs that were not adjacent in the input, it does not accurately reproduce statistical properties of the input designs. Examining the algorithm's behavior at the level of pattern adjacencies, we show that there are large gaps between the statistics of the input and output designs, even when applying Gumin's search heuristic intended to influence output statistics. By offering a very small revision to this search heuristic, we show that the resemblance of outputs to inputs can be dramatically improved. Another way of improving resemblance is to increase the size of local patterns considered by WFC, but this can easily lead to a kind of overfitting that results in the outputs plagiarizing large portions of the input design. By contrast, our alternate revision increases resemblance without increasing pattern size. The simplicity of our method, requiring a very localized change to existing WFC implementations, allows it to be immediately applied to a wide range of applications.
Dual Critic Conditional Wasserstein GAN for Height-Map Generation

Traditionally, video-game maps are either made by hand, requiring many man-hours to produce good results, or made using Procedural Content Generation (PCG) techniques, which rely on a predetermined algorithm to generate every feature of the map. More recent studies have tried an approach using Deep Learning algorithms, which have their own limitations, in particular taking away the creative freedom of the designers. To circumvent this problem we propose a system that transforms low fidelity sketches into realistic height-maps through a Deep Learning model we call the Dual Critic Conditional Wasserstein GAN (DCCWGAN), thus providing high visual quality without removing control from the user. The presented system is capable of producing images that resemble the received input, and a user study with 79 participants showed that observers are not able to distinguish between earth-based height-map images and the images generated by our system.
Sturgeon-GRAPH: Constrained Graph Generation from Examples

Procedural level generation techniques that learn local neighborhoods from example levels (such as WaveFunctionCollapse) have risen in popularity. Usually the neighborhood structure (such as a regular grid) onto which a level is generated is fixed in advance and not generated. In this work, we present a constraint-based approach for graph generation that learns local neighborhood patterns (in the form of labeled nodes and edges) from example graphs. This allows the approach to generate graphs with varying structures that are still locally similar to the examples. We demonstrate the approach on several applications, such as abstract graphs describing Legend of Zelda dungeons. Additionally, using Super Mario Bros. levels, we show how techniques that run on a grid may be considered a special case of graph generation where each tile is a node connecting to its neighboring tiles' nodes.
AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector

In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.
FMT Selector: Fourier-Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

The terminal guidance tracking always has high speed rotating and scale changing. It makes a large margin of error in current tracking algorithms, which can transmit wrong position in the wireless sensor networks of the Internet of Things. In this paper, we propose a selector based on Fourier-Mellin transformer, which can accurately track the target with high speed rotating. Firstly we use existing detector to get all proposal target per frame. Then, we use Fourier-Mellin matching to select the best target in the new frame. Our dataset is the ship video which is created by HRSC2016 in rotating. Experimental results show that the accuracy of this method is up to 89.8%. Compared with other traditional tracking algorithms, it has a leap forward. The FMT selector can be applied to the back end of the detector, and it can achieve a feasible effect in the field of terminal guidance tracking.
Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic

The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.
A machine learning approach for digital watermarking

ABSTRACTABSTRACTRecently, machine learning (ML) has been applying in almost all scientific fields to model and simulate the behaviour of complex systems. At the same time, the number of the proposed watermarking techniques have been increasing every year. Although most of the researchers in the watermarking and data hiding field put all their effort to compete each other to develop more efficient algorithm, the selection of the most efficient one is almost impossible for industries or software developers. In other words, comparison among all the watermarking techniques in order to select one watermarking technique would be cumbersome task. Moreover, the computer security is becoming more advanced which a single watermark technique cannot fulfil all the required criteria and there is a chance that an anti-watermarking is developed to remove the embedded watermark from the host data. This gap of knowledge to use the watermarking technology for a highly secure application is a real nightmare for the information security engineers and software designers. In this paper, two new approaches are proposed to train deep learning and shallow learning models based on the state-of-the-arts watermarking techniques. For these proposed approaches, several ML algorithms are applied to model various watermarked data, which are watermarked by different watermarking techniques in various spectrums and domains. An experimental setup is constructed based on several speeches, audios, images and videos beside the Amazon Sagemaker as ML modelling to implement the proposed approach. The experimental results show that apart from an overall effectiveness of the model, it would resolve some ambiguities by applying the proposed ML approach as a general watermarking workflow.KEYWORDS: Digital watermarkingmachine learningdata hidingartificial intelligencecomputer security AcknowledgmentsThe author would like to appreciate the reviewers for all useful and constructive comments on my manuscript. All comments have been considered and the paper is revised accordingly. This research was not financially supported.Disclosure statementNo potential conflict of interest was reported by the author.DeclarationsData sharing not applicable to this article as no datasets were generated or analysed during the current study.Additional informationNotes on contributorsMohammad Ali NematollahiMohammad Ali Nematollahi received his master's degree in computer engineering (software) at the Islamic Azad University (IAU), Dubai, and holds a Ph.D. in computer and embedded systems engineering from University Putra Malaysia (UPM). His research interests include digital signal/image processing and digital watermarking, Machine learning, and Artificial Intelligent.
Spectral Analysis for Comparing Bitcoin to Currencies and Assets

We present an analysis on variability Bitcoin characteristics that help to quantitatively differentiate Bitcoin from the state-owned traditional currencies and the asset Gold. We provide a detailed study on returns of exchange rates—against the Swiss Franc—of several traditional currencies together with Bitcoin and Gold; for that purpose, we define a distance between currencies by means of the spectral densities of the ARMA models of the returns of the exchange rates, and we present the computed matrix of the distances between the chosen currencies. A statistical analysis of these matrix distances is further proposed, which shows that the distance between Bitcoin and any other currency or Gold is not comparable to any of the distances between currencies or between currencies and Gold and not involving Bitcoin. This result shows that Bitcoin is essentially different from the traditional currencies and from Gold, at least in what concerns the structure of its variance and auto-covariances.