Reviewerly/exHarmony huggingface dataset:
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.
Multi-motion Segmentation via Co-attention-induced Heterogeneous Model Fitting

Motion segmentation is an essential task in artificial intelligence and computer vision. However, scene motion in real-world intelligent systems usually integrates multiple types of models, so specifying only one type of basic model may lead to the failure of scene-motion segmentation tasks. In this paper, we propose a novel and efficient heterogeneous model-fitting-based motion segmentation method (HMFMS) to accurately segment moving objects. HMFMS includes a new co-attention-induced heterogeneous model construction algorithm (HMC), an adaptive heterogeneous model refinement algorithm (HMR), and a heterogeneous model segmentation algorithm (HMS). First, we propose HMC to generate high-quality accumulated correlation matrices, by evaluating the quality of heterogeneous model hypotheses, based on the density estimation technique. Next, we propose HMR to construct sparse affinity matrices from the accumulated correlation matrices by applying information theory, effectively suppressing the values of correlations between different objects. Finally, we fuse the sparse affinity matrices and perform motion segmentation by using HMS, to obtain more accurate segmentation results. Experimental results show that HMFMS obtains superior performance on four challenging datasets (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hopkins155</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hopkins12</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTPV62</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KT3DMoSeg</i> ), compared with several subspace-based and model-fitting-based motion segmentation methods. More remarkably, HMFMS outperforms the state-of-the-art MCMS method by 57.1% and 1.8 times in terms of accuracy and computational efficiency on the representative <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KT3DMoSeg</i> , respectively.
Multisensor Temporal Unsupervised Domain Adaptation for Land Cover Mapping With Spatial Pseudo-Labeling and Adversarial Learning

With the huge variety of earth observation satellite missions available nowadays, the collection of multi-sensor remote sensing information depicting the same geographical area has become systematic in practice, paving the way to the further breakthroughs in automatic land cover mapping with the aim to support decision makers in a variety of land management applications. In this context, along with the increase in the volume of data available, the availability of ground truth data to train supervised models, which is usually time-consuming and costly, may even be more critical. In this scenario, the possibility to transfer a model learnt on a particular time span ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source domain</i> ) to a different period of time ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target domain</i> ), over the same geographical area, can be advantageous in terms of both cost and time efforts. However, such model transfer is challenging due to different climate, weather or environmental conditions affecting remote sensing data collected at different time periods, resulting in possible distribution shifts between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">target</i> domains. With the aim to cope with the multi-sensor temporal transfer scenario in the context of land cover mapping, where multi-temporal and multi-scale information are used jointly, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M<sup>3</sup>SPADA</i> (Multi-sensor, Multi-temporal and Multi-scale SPatially-Aware Domain Adaptation framework), a deep learning methodology that jointly exploits self-training and adversarial learning to transfer a multi-sensor land cover classifier from a time period (year) to a different one on the same geographical area. Here, we consider the case in which each domain (source and target) is described by a pair of remote sensing data sets: a satellite image time series (SITS) of optical images and a single Very High spatial Resolution (VHR) scene. Experimental evaluation on a real-world study case located in Burkina Faso and characterized by operational constraints shows the quality of our proposal to deal with the temporal multi-sensor transfer in the context of land cover mapping.
GuRuChain: Guarantee and Reputation-based Blockchain Service Trading Platform

Blockchain (BC) is a promising Distributed Ledger Technology (DLT) that has attracted attention in recent years due to its characteristics of maintaining privacy and security. This technology, which removes the need for a trusted third party, can be applied in various trust-related domains, such as energy trading, crowdsourcing, and the Internet of Things. This paper presents GuRuChain, a platform framework for online service trading. It combines on-chain and off-chain trust management to provide a blockchain system suitable for trust-related applications. The platform does not only ensure the recording of online transactions; but also the deliverance of offline services. In GuRuChain, we propose a mathematical model to assess the trustworthiness of each participant. We also introduce an incentive mechanism based on reputation and guarantee to monitor the participants' behavior. Finally, we maintain the consistency of the BC through the proposed consensus scheme called Proof Guarantee and Reputation (PoGR). PoGR selects nodes based on a scoring formula that uses three parameters, reputation score, guarantee balance, and risk taken to ensure fairness. The results of the performance evaluation demonstrate the feasibility, efficiency, and scalability of GuRuChain.
Highly dynamic X-ray image enhancement based on generative adversarial network

Abstract When using X-ray to detect industrial workpieces, the images obtained frequently have low contrast, making it difficult to detect defects. This paper proposes a GAN-based X-ray image enhancement network to address this issue. In detail, the X-ray image is concatenated with its antiphase image (as an exposure mask) as the input image, and a trainable Sobel operator is used to extract the edge features of the input image. The input image and edge features are then concatenated and fed into the U-Net generator to be enhanced. The spatial and channel attention models are used to adjust feature weights in U-Net, and a detail extraction network is designed to extract detail features from the input X-ray image. Furthermore, the extracted detail features are fused with the image by the generator after contrast stretching to produce the final enhanced image. Finally, a global-local discriminator is used to discriminate the authenticity of the image so that the contrast of the final obtained image is improved and the details are highlighted. Following experimental validation, the method proposed in this paper has a significant enhancement effect on industrial X-ray images and performs well in terms of enhancing image contrast and highlighting image details.
Performance Study of Variational Quantum Algorithms for Solving the Poisson Equation on a Quantum Computer

Recent advances in quantum computing and their increased availability has led to a growing interest in possible applications. Among those is the solution of partial differential equations (PDEs) for, e.g., material or flow simulation. Currently, the most promising route to useful deployment of quantum processors in the short to near term are so-called hybrid variational quantum algorithms (VQAs). Thus, variational methods for PDEs have been proposed as a candidate for quantum advantage in the noisy intermediate-scale quantum (NISQ) era. In this work, we conduct an extensive study of utilizing VQAs on real quantum devices to solve the simplest prototype of a PDE---the Poisson equation. Although results on noiseless simulators for small problem sizes may seem deceivingly promising, the performance on quantum computers is very poor. We argue that direct resolution of PDEs via an amplitude encoding of the solution is not a good use case within reach of today's quantum devices---especially when considering large system sizes and more complicated nonlinear PDEs that are required in order to be competitive with classical high-end solvers.
It’s More Than Just Money: The Real-World Harms from Ransomware Attacks

As cyber-attacks continue to increase in frequency and sophistication, organisations must be better prepared to face the reality of an incident. Any organisational plan that intends to be successful at managing security risks must clearly understand the harm (i.e., negative impact) and the various parties affected in the aftermath of an attack. To this end, this article conducts a novel exploration into the multitude of real-world harms that can arise from cyber-attacks, with a particular focus on ransomware incidents given their current prominence. This exploration also leads to the proposal of a new, robust methodology for modelling harms from such incidents. We draw on publicly-available case data on high-profile ransomware incidents to examine the types of harm that emerge at various stages after a ransomware attack and how harms (e.g., an offline enterprise server) may trigger other negative, potentially more substantial impacts for stakeholders (e.g., the inability for a customer to access their social welfare benefits or bank account). Prominent findings from our analysis include the identification of a notable set of social/human harms beyond the business itself (and beyond the financial payment of a ransom) and a complex web of harms that emerge after attacks regardless of the industry sector. We also observed that deciphering the full extent and sequence of harms can be a challenging undertaking because of the lack of complete data available. This paper consequently argues for more transparency on ransomware harms, as it would lead to a better understanding of the realities of these incidents to the benefit of organisations and society more generally.
Hard water can be softened by atomization

Hard water is softened because its use in everyday life can cause various problems in the living environment. However, existing water-softening methods have a number of drawbacks. Here we examined a new water-softening method involving atomization. Three concentrations of calcium-based synthetic hard water and a commercial natural hard water were prepared. Each hard water was sprayed into the atmosphere at 1 MPa from an atomization nozzle and then collected. Part of the collected water was recirculated, and the atomization treatment was continued. After the treatment, a white precipitate and fine bubbles were generated, the Ca2+ concentration and electrical conductivity decreased, and the pH increased. Then over time, the white precipitate increased slowly and the Ca2+ concentration decreased until finally it almost fell within the recommended range. This water-softening phenomenon was thought to be due to expansion of the gas-liquid interfacial area and generation of fine bubbles with atomization. This method is extremely simple and expected to show high versatility.
Multi-Target Tracking of Person Based on Deep Learning

To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware Global Attention (RGA-S) and Channel Relation-aware Global Attention (RGA-C) attention mechanisms into the network structure. The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03. To enhance the discrimination performance of the network, we proposed a new loss function design method, which introduces the Hard-Negative-Mining way into the benchmark triplet loss. To improve the classification accuracy of the network, we introduced a Label-Smoothing regularization method to the cross-entropy loss. To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training, this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay. The experimental results on CUHK03 show that the mean Average Precision (mAP) of the improved ReID network is 76.5%. The Top 1 is 42.5%, the Top 5 is 65.4%, and the Top 10 is 74.3% in Cumulative Matching Characteristics (CMC); Compared with the original algorithm, the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%, the tracking precision is improved by 3.8%. The number of identity switching is reduced by 25%. The algorithm effectively alleviates the IDSwitch problem, improves the tracking accuracy of persons, and has a high practical value.
Analysis of multimedia technology and mobile learning in English teaching in colleges and universities

Abstract Multimedia and mobile learning are effective teaching tools because of their many useful characteristics. Transmission of educational material to students is facilitated by technology in both multimedia and mobile learning environments. A more interesting and engaging learning experience may be achieved by combining text, graphics, music, video, and animation. Media-rich classrooms are often referred to as “multimedia learning.” In contrast, students who use mobile learning may access their courses from any place with an internet connection and a mobile device such as a smartphone or a tablet. Whenever this involves commonplace technological needs, its cellular gadget and notably its clever telephone take the cake. Smartphone gadgets have a very wide range of potential applications and uses. Some ethical considerations are preventing media from reinforcing stereotypes or prejudice in the communities where they are utilized. Student information are gathered and used in a way that protects their privacy, with materials required for students to engage in mobile learning, and respecting intellectual property and copyright regulations while using multimedia language-learning tools. This importance of learning cannot be overstated but when such results come, mobile devices have increasingly been used in schools. This question of whether mobile devices have an impact on education remains open. Because of such “mobile gaining knowledge,” along with an overall phrase describing researching where different devices could be utilized in combination to enhance schooling, mobile devices had become more popular. Because today’s kids utilize their mobile devices frequently, studies on their effect on word learning are immediately required. This pilot research seeks to understand why school learners think via smartphone-based L2 exercises. In particular, this study collects data about several plenty applications using cell phones in linguistic research. Two hundred and ninety-four college children from one top Turkish institution participated in this research. This project uses some hybrid reporting models and relies on description analysis over its methodology. Their results show that users place one high value on obtaining ready accessibility to materials while studying some foreign language. Respondents not only remarked about their cellphone’s portability but also proposed new applications for enhancing their learning in other countries.
A biometric‐based implicit authentication protocol with privacy protection for ubiquitous communication environments

Summary Due to the proliferation of the wireless communication technologies, we are now in the era of ubiquitous computing. People can enjoy services almost in any time and any location based on the mobile intelligent devices. Meanwhile, more and more personal information are transmitted through the network. To protect these sensitive information, authentication and privacy protection are primary concerns in ubiquitous communication environments. However, traditional physical‐based biometric authentication scheme usually requires explicit cooperation of the user, which is inconvenient to the users. In order to solve this problem, a behavior biometric authentication protocol with privacy protection is designed using matrix multiplication and homomorphic encryption for ubiquitous communication environments. The correctness, security, and privacy protection properties are proved with security analysis. Performance evaluation shows that our protocol achieves stronger security and higher communication efficiency compared with other related protocols, which makes it very suitable for ubiquitous communication environments because communication consumes much more energy than computation does in mobile communication.
Class-imbalanced complementary-label learning via weighted loss

Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions. Unfortunately, existing CLL approaches have not investigate this problem. To alleviate this challenge, we propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification. To tackle this problem, we propose a novel CLL approach called Weighted Complementary-Label Learning (WCLL). The proposed method models a weighted empirical risk minimization loss by utilizing the class-imbalanced complementary labels, which is also applicable to multi-class imbalanced training samples. Furthermore, we derive an estimation error bound to provide theoretical assurance. To evaluate our approach, we conduct extensive experiments on several widely-used benchmark datasets and a real-world dataset, and compare our method with existing state-of-the-art methods. The proposed approach shows significant improvement in these datasets, even in the case of multiple class-imbalanced scenarios. Notably, the proposed method not only utilizes complementary labels to train a classifier but also solves the problem of class imbalance.
A Semantic Perception and CNN-Transformer Hybrid Network for Occluded Person Re-identification

The objective of the occluded person re-identification (ReID) task is to capture the same person from different camera angles when the pedestrian’s body is partially occluded. In this task, there are two main challenges: 1) pedestrians are often occluded by other persons or objects, and 2) pedestrians change poses. Moreover, these two issues often simultaneously occur. Although many occluded person ReID algorithms have been proposed, many existing methods can often only solve one of these issues well, and the other issue is often ignored. In this work, a novel semantic perception and CNN-transformer hybrid network (abbreviated as SPH) is proposed for occluded person ReID, which consists of a CNN-based human semantic perception stream and a transformer-based pose perception stream. In the former, a human semantic auxiliary module and a human semantic perception module are designed to obtain human semantic information where multi-granularity region features of the human body are extracted to solve the issues of occlusion. In the latter, we propose a token-based pose integration module to obtain the corresponding patch for each pose key-point and the relative position information to solve the change in pedestrian pose. Moreover, these two streams are jointly optimized in a unified framework. In addition, to further solve the issue of occlusion, the human completion strategy is proposed for the query sample where the gallery samples are used to complete the missing parts of the query. Extensive experimental results on three public occluded person ReID datasets, Occluded-DukeMTMC, P-DukeMTMC-reID, and Occluded-REID, demonstrate that the proposed method can outperform all SOTA occluded person ReID methods in terms of the mAP and Rank-1. Compared with PAT (CVPR21) on the Occluded-DukeMTMC and Occluded-REID datasets, the improvements in mAP/Rank-1 reached 10.1%/7.4%, and 10%/1%, respectively. Moreover, when TransReID (ICCV21) was used, SPH achieved improvements of 4.5% (mAP) and 5.5% (Rank-1) on the Occluded-DukeMTMC dataset.
Research on the Influence of Senior Team Heterogeneity on Enterprise Performance Based on MAR Model——The Perspective of Equity Concentration

In the new period of diversified development of Chinese enterprises, the key role of senior management team for enterprise survival is increasingly prominent.This paper constructs a multiple regression analysis model with ownership concentration as the moderating variable, and conducts linear regression analysis on the impact of demographic characteristics heterogeneity and knowledge heterogeneity of senior management team members on corporate performance of listed enterprises on small and medium-sized board of the Shenzhen Stock Exchange.This study provides further theoretical support for the construction and optimization of senior management team and the improvement of the comprehensive management level of small and medium-sized enterprises.