Reviewerly/exHarmony huggingface dataset:
Spatial-Temporal Hybrid Feature Extraction Network for Few-Shot Automatic Modulation Classification

Automatic modulation classification (AMC) plays an important role in wireless spectrum monitoring. Motivated by the success of deep learning in various informatics domains, many AMC methods based on deep learning have been proposed. However, they usually require a large amount of labeled training samples for each category of modulation, which is hardly applicable in real-world AMC tasks. To tackle this issue, this paper proposes a novel few-shot learning framework, namely the spatial-temporal hybrid feature extraction network (STHFEN). In STHFEN, two feature extraction networks are designed to map the wireless communication signals into the spatial feature space and the temporal feature space, respectively. Then, a hybrid inference classifier is designed to combine the classification results in the two feature spaces. To train STHFEN more effectively, a hybrid loss function which promotes better inter-class separability of signals in the two feature spaces is proposed. Experimental results have demonstrated the effectiveness and robustness of STHFEN in few-shot AMC tasks.
A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System

Aiming for minimizing passengers waiting time and vehicles vacancy rate, online car-hailing service systems with fog computing has been deployed in various scenarios. In this paper, we focus on addressing the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) private, fine-grained and bilateral order matching between passengers and drivers; (2) authenticity verification of passengers orders in the form of ciphertext, and (3) temporal assurance of passengers&#x2019; ciphertext orders. To the best of our knowledge, no previous system has been designed to meet all three requirements. Existing cryptographic primitives (including forward/puncturable encryption (FE/PE) and attribute based matchmaking encryption (AB-ME)) may be leveraged to partially address some of challenges, but there lacks a comprehensive solution. Moreover, the integration of existing works is hampered by the heterogeneity and the weak coupling between distinct cryptographic primitives. As a result, it is infeasible to directly exploit them for the online car-hailing service. To tackle that, we put forward a new cryptographic primitive called Fine-grained Puncturable Matchmaking Encryption (FP-ME) by modifying AB-ME and incorporating PE technology. FP-ME can simultaneously implement fine-grained and bilateral order matching, the authenticity of passengers orders, and meeting the time constraint of passengers orders. We formalize the adversarial models for the proposed FP-ME and then present rigorous security analysis to prove the security of the proposed system. Additionally, we study performance of the system via simulations to demonstrate its practicability and effectiveness in the real-world applications.
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, and recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, and signal interference). This position article presents GNNs as a fundamental tool for modeling, control, and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.
An Improved Mayfly Optimization Algorithm Based on Median Position and Its Application in the Optimization of PID Parameters of Hydro-Turbine Governor

Mayfly algorithm is a new intelligent optimization algorithm with unique optimization capabilities recently proposed. It has strong research value, but there are also insufficient explorations, and it is easy to fall into the problem of local optimization. This paper aims to improve the optimization performance of the mayfly algorithm and explore its application value in practical engineering optimization problems. An improved mayfly algorithm based on the median position of the group is proposed. In its velocity update, the median position of the group is introduced with emphasis, and a non-linear gravity coefficient is introduced at the same time. Through the benchmark test function, its superiority in exploitation, convergence speed and accuracy and the improvement of exploration are verified. At the same time, the simulation model of the hydro-turbine governor using MATLAB/Simulink is established, and 10&#x0025; frequency disturbance experiments of this model are carried out separately in two typical working conditions. The experiments results show that the optimal ITAE index value of the system obtained by the improved mayfly algorithm is smaller, and 16.5 and 18.1 iterations to complete on average. In addition, the experiments results reveal that the PID parameters optimized by the improved mayfly algorithm can make the dynamic performance of the regulation system better than other popular swarm intelligence algorithms, where the overshoot decreased by more than 3.1&#x0025;, and the adjustment time also decreased in different degrees. The proposal of the median position of the group provides a new idea for the improvement of the swarm intelligence optimization algorithm. Meanwhile, a new effective method for optimizing the PID parameters of the hydro-turbine governor has been found.
Towards roadmap to implement blockchain in healthcare systems based on a maturity model

Abstract Healthcare systems face various issues related to complex networks of intermediaries and a lack of transaction traceability. The most critical issues are the fragmentation of healthcare data, obstacles in providing efficient research and services, lack of clinical trial reporting, high cost and mismanagement of the drug supply chain, patient data security, and fake drugs. Blockchain technology has the potential to address these criticalities as it has in build traceability mechanisms and promises new business models by enabling incentive structures. This potential of blockchain gathers a high interest in the health industry. However, the implementation of blockchain in healthcare faces various issues as well. Currently, there are no practice‐oriented maturity models to improve such an implementation. In this paper, we present a roadmap to develop a maturity model for blockchain in healthcare (MMBH) based on critical barriers (CBs), critical success factors (CSFs), and the best practices for blockchain implementation in healthcare systems. As a first step to develop the MMBH, in this paper, we present the initial results of a systematic literature review (SLR) to identify critical success factors for implementing blockchain in healthcare systems. We also applied fuzzy technique order preference by similarity to ideal solution (TOPSIS) to prioritize the identified CSFs.
Primitively visually meaningful image encryption: A new paradigm

Traditional image encryption schemes turn images with rich visual contents into meaningless noise-like ones. This assures privacy security at the expense of visual meaning. To cope with this issue, an encryption method called visually meaningful image encryption (VMIE), by which the encrypted image can have visual meaning, is proposed to protect image privacy and visual observability. This visual meaning, however, is unrelated to the original image, which, like traditional encryption, compromises the primitively visual meaning. Motivated by this, we investigate the term ‘visual meaning’ in depth and suggest a new VMIE paradigm, namely, primitively visually meaningful image encryption, as well as accompanying goals. A novel permutation-diffusion architecture is specifically designed to match the paradigm, which can preserve the primitively visual meaning in the encrypted image. Furthermore, the prototype of this paradigm is constructed using a chaotic system and DNA coding. It is then applied to do experiments to demonstrate the superiority of this work. It should be observed that the primitively visual meaning is preserved and of good quality in the encrypted image.
FedMSplit

With the advancement of data collection techniques, end users are interested in how different types of data can collaborate to improve our life experiences. Multimodal Federated Learning (MFL) is an emerging area allowing many distributed clients, each of which can collect data from multiple types of sensors, to participate in the training of some multimodal data-related models without sharing their data. In this paper, we address a novel challenging issue in MFL, the modality incongruity, where clients may have heterogeneous setups of sensors and their local data consists of different combinations of modalities. With the modality incongruity, clients may solve different tasks on different parameter spaces, which escalates the difficulties in dealing with the statistical heterogeneity problem of federated learning; also, it would be hard to perform accurate model aggregation across different types of clients. To tackle these challenges, in this work, we propose the FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. More specifically, we split client models into smaller shareable blocks and allow each type of blocks to provide a specific view on client relationships. With the graph representation, the underlying correlations between clients can be captured as the edge features in the multi-view graph, and then be utilized to promote local model relations through the neighborhood message passing in the graph. Our experimental results demonstrate the effectiveness of our method under different sensor setups with statistical heterogeneity.
Cloud Computing, Big Data, and Blockchain Technology Adoption in ERP Implementation Methodology

The aim of the paper is to present the new factors of ERP (Enterprise Resource Planning system) implementation related to the usage of cloud-based and blockchain information technology and to discover potential possibilities of using cloud computing, big data, and blockchain technologies in the methodology of ERP implementation. The article covers the characteristics of the current implementation methodologies and also discusses how these technologies can be adopted in the implementation process. During the study, a literature review was carried out and survey-based interviews with project managers were performed. Previous studies examined in the literature review have some limitations. Most of them are focused on ERP performance in the cloud or measure success factors of ERP implementation. A research gap exists regarding the impact of technologies in the implementation process and the results of this process. The research shed some light on cloud computing and blockchain in ERP implementation. The conclusions are as follows: (1) Some methods of sustainable methods of ERP implementation with the use of cloud technologies, blockchain, and big data have been examined in research studies, but there is still a lack of publications with in-depth analyses of the impact of blockchain on ERP implementation success. (2) The practitioners recommended the new technologies based on cloud computing and big data in the process of ERP implementation, and more knowledge about blockchain would have a positive impact on the success of sustainable ERP implementation as well as on the willingness to adopt the new technologies.
Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning still has some challenges, such as unstable performance and dirty data, which are future research trends. Compared with other reviews on deep active learning, our work introduced the deep active learning from computer vision-related methodologies and corresponding applications. The expected audience of this vision-friendly survey are researchers who are working in computer vision but willing to utilize deep active learning methods to solve vision problems. Specifically, this review systematically focuses on the details of methods, applications, and challenges in vision tasks, and we also introduce the classic theories, strategies, and scenarios of active learning in brief.
Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data

We present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we show a method of generating synthetic drone images. Domain randomization is used to vary the simulation parameters such as model textures, background images, and orientation. Three common drone models are classified: DJI Phantom, DJI Mavic, and DJI Inspire. To test the performance of the neural network model, Anti-UAV, a real-life dataset of flying drones is used. The proposed method reduces the time-cost associated with manually labelling drones, and we prove that it is transferable to real-life videos. The CNN achieves an overall accuracy of 92.4%, a precision of 88.8%, a recall of 88.6%, and an f1 score of 88.7% when tested on the real-life dataset.
Real-Time Big Data Analytics for Improving Sales in the Retail Industry via the Use of Internet of Things Beacons

Various discoveries achieved by applying Apache Spark in medical administrations foundations are acceptable for a considerable data plan. A part of the educational or research-based social protection organizations are either trying new things with big data or using it in front-line research expeditions. In the medical industry, there is an almost unlimited amount of data that is being created. The Electronic Medical Record (EMR) alone gathers a vast quantity of information. The goal of using new trends and technologies such as the IoT, big data and others to analyze real-time beacon-based sensor data is to help these shopping mall-based retail shops or any other physical retail store compete with online shopping in terms of customized sales promotion, customer relations, different types of analysis such as predictive, diagnostic, and preventive using customer-sales data, and other aspects. We tested a number of beacon-based sensor systems for identifying neighboring mobile phones. The approach that has been proposed Apache Spark Streaming was utilized for various studies, with sample Amazon sales data being used as input.
Review of Offline Payment Function of CBDC Considering Security Requirements

Due to the growth of the internet and communication technologies, electronic financial systems are becoming popular. Physical cash is losing its preeminence, and digital numbers on computers represent money. However, electronic financial systems, mostly operated by private entities, have defects to be compensated for, such as high charges for using the system, security issues, and the problem of exclusion. As a solution, many countries around the world are considering central bank digital currency. For central bank digital currency to be utilized as a national legal tender, it must be universal and accessible regardless of time and place, similar to physical cash. Therefore, offline payment functions that extend the accessibility of central bank digital currency are becoming attractive. However, due to the characteristics of the electronic financial system, central bank digital currency is vulnerable to possible malicious behaviors in offline situations, such as blackouts and system shutdowns. This paper reviews research studies that deal with security matters related to the offline payment function of central bank digital currency. Offline payment solutions, including central bank digital currency and other electronic financial systems, such as electronic cash and cryptocurrency, are reviewed, and supplemental methods to improve the offline payment solutions of central bank digital currency based on trusted execution environment devices are suggested.
Understanding the Effects of Virtual Reality System Usage on Spatial Perception: The Potential Impacts of Immersive Virtual Reality on Spatial Design Decisions

The main component of any Virtual Reality (VR) system is the human user. The ways in which a VR system shapes human experience can affect design outcomes. This research explores the differences in spatial perception between an immersive Virtual Reality Interactive Environment (IVRIE) and traditional Virtual Reality (also known as a desktop-based Virtual Reality system, abbreviated herein as the DT system). Spatial perception and the cognition of the spatial factors of virtual spaces were studied based on different features of the two systems, including the sense of immersion, forms of interaction, experience of human scale, and movement through virtual spaces. This study focused on determining how users’ spatial decision making and performance were affected by differences in spatial perception created by the IVRIE and DT systems. Factors examined included the differences between and within the two virtual systems, based on differences in texture, system usage sequence, and the complexity of the experiential/spatial guidelines. Descriptive and inferential statistical testing using quantitative and qualitative data were used to find differences in spatial perception and decision making. The results showed significant space size variations produced by participants between and within the two different VR systems.
High-capacity reversible data hiding in encrypted images based on pixel-value-ordering and histogram shifting

New security threats are generated with the coming of information industrial 5.0 represented by artificial intelligence and Industrial Internet of Things (IIoT). One of the most significant concerns in IIoT is information security which refers to the protection of the privacy of image owner. To this end, a new reversible data hiding in encrypted image (RDHEI) method with high capacity for IIoT security is proposed in this paper. On image owner side, image contents are protected by a block level image encryption method while the correlations of adjacent pixels inside the same block are not damaged. On receiver side, to realize reversibility, all encrypted pixel blocks are classified into overflow blocks and non-overflow blocks and only non-overflow blocks are used to carry additional data. Then, some secret data, which is used to manage encrypted images, is embedded into the largest and smallest pixels in non-overflow blocks by utilizing pixel-value-ordering and histogram shifting techniques. On the receiver side, original image and secret data can be recovered perfectly. Moreover, according the availability of decryption key and data hiding key, image recovery and data extraction are separably executed. Experimental results demonstrate that significant improvement in embedding capacity is achieved without destroying reversibility. In this way, the proposed method outperforms several state-of-the-art methods and it is quite suitable for IIoT.
On Improving Cross-dataset Generalization of Deepfake Detectors

Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a binary classification problem using a backbone convolutional neural network (CNN) architecture pretrained for the task. These CNN-based methods have demonstrated very high efficacy in deep fake detection with the Area under the Curve (AUC) as high as 0.99. However, the performance of these methods degrades significantly when evaluated across datasets. In this paper, we formulate deep fake detection as a hybrid combination of supervised and reinforcement learning (RL) to improve its cross-dataset generalization performance. The proposed method chooses the top-k augmentations for each test sample by an RL agent in an image-specific manner. The classification scores, obtained using CNN, of all the augmentations of each test image are averaged together for final real or fake classification. Through extensive experimental validation, we demonstrate the superiority of our method over existing published research in cross-dataset generalization of deep fake detectors, thus obtaining state-of-the-art performance.
An Insulator Defect Detection Model in Aerial Images Based on Multiscale Feature Pyramid Network

The faults caused by insulator defects will seriously threaten the operational safety of the power grid. Therefore, insulator defect detection play a crucial role in inspecting transmission lines. Compared with traditional methods, the network such as You Only Look Once (YOLO) family based on deep learning have high accuracy and strong robustness in insulator recognition and fault detection. However, the performance of these network are usually affected by the shooting conditions as well as aerial images with diverse types of insulators and complex backgrounds, resulting in poor detection result. In addition, the relatively small insulator fault (bunch-drop) area in aerial images will also make detection difficult. To solve these problems, this paper proposes an improved insulator defect detection model based on YOLOv4 (ID-YOLO). To create our model, we design a new backbone network structure, Cross Stage Partial and Residual Split Attention Network (CSP-ResNeSt), that can solve the interference problem of complex backgrounds in aerial images to enhance the network&#x2019;s feature extraction capability. In addition, we adopt a new multiscale Bidirectional Feature Pyramid Network with Simple Attention Module (Bi-SimAM-FPN), which can address the difficulty of identifying a small scale of insulator defects in an image for more efficient feature fusion. We experimentally demonstrate that the mean average precision (mAP) of the proposed model is 95.63%, which is 3.5% higher than that of the YOLOv4. Most importantly, the detection speed of this model can reach 63 FPS, which meets the requirements of real-time detection of insulator bunch-drop faults.