Reviewerly/exHarmony huggingface dataset:
A Blockchain Secured Pharmaceutical Distribution System to Fight Counterfeiting

Counterfeiting drugs has been a global concern for years. Considering the lack of transparency within the current pharmaceutical distribution system, research has shown that blockchain technology is a promising solution for an improved supply chain system. This study aims to explore the current solution proposals for distribution systems using blockchain technology. Based on a literature review on currently proposed solutions, it is identified that the secrecy of the data within the system and nodes' reputation in decision making has not been considered. The proposed prototype uses a zero-knowledge proof protocol to ensure the integrity of the distributed data. It uses the Markov model to track each node's 'reputation score' based on their interactions to predict the reliability of the nodes in consensus decision making. Analysis of the prototype demonstrates a reliable method in decision making, which concludes with overall improvements in the system's confidentiality, integrity, and availability. The result indicates that the decision protocol must be significantly considered in a reliable distribution system. It is recommended that the pharmaceutical distribution systems adopt a relevant protocol to design their blockchain solution. Continuous research is required further to increase performance and reliability within blockchain distribution systems.
Safety Helmet Wearing Detection Based on YOLOv5 of Attention Mechanism

Abstract Aiming at problems of low accuracy and strong detection interference of the existing safety helmet wearing detection algorithms, an object detection algorithm by adding the squeeze-and-excitation block based on the YOLOv5 algorithm is proposed in this paper. The proposed method can not only obtain the weight of picture channel, but also accurately separate the foreground and background of the picture. Keeping all parameters unchanged, the proposed method and the YOLOv5 algorithm are applied to detect the safety helmet data set in the experiment. The result shows that the YOLOv5 algorithm with the squeeze-and-excitation block has an average detection accuracy of 94.5% for safety helmets and an average detection accuracy of 92.7% for human heads. The mAP value detected by the proposed method is 2% ∼2.5% higher than using YOLOv5 algorithm directly.
Cross-Domain Few-Shot Learning Approach for Lithium-Ion Battery Surface Defects Classification Using an Improved Siamese Network

It is difficult to detect the surface defects of a lithium battery with an aluminum/steel shell. The reflectivity, lack of 3D information on the battery surface, and the shortage of many datasets make the 2D detection method hard to apply in this field. In this paper, a cross-domain few-shot learning (FSL) approach for lithium-ion battery defect classification using an improved siamese network (BSR-SNet) is proposed. To obtain the critical 3D surface of the lithium-ion battery, a multiexposure-based structured light method is utilized. Then, the heights of the 3D cloud points are transferred to grayscale information and are saved as 8-bit 2D images. For the FSL task, the DAGM 2007 datasets are used as the source domain to pre-train the improved siamese model. To avoid negative mitigation in the target domain, batch spectral regularization (BSR) is added as a penalizer in the loss function. The accuracies of the experimental results are 93.3% for 10-shot batteries and 91.0% for 5-shot batteries, which means that our method can be used to classify the surface defects of lithium batteries well.
A Study of the Impact of River Improvement and Greening on Public Reassurance and the Urban Well-Being Index during the COVID-19 Pandemic

This study aims to investigate the effect of river improvement and greening projects on people and the urban happiness index. First, the quantitative method was adopted, and data collected from 734 questionnaires were analyzed using SPSS 26.0 software. Then, the qualitative method was used, and semi-structured interviews were conducted to collect the opinions of 12 interviewees, including scholars, government employees, citizens, and practitioners. Finally, we discuss multiple comparison analysis testing. The survey results indicate that river improvement and greening projects could be conducted to take advantage of diverse ecological environments, urban transportation planning, and geographical location. Such projects can help people relieve stress, even during the COVID-19 pandemic; improve their physical and mental health; and enhance their environmental awareness. However, due to poor traffic flow, a low space utilization rate, and inflexible management practices, visiting these environments has posed a risk of infection. As a result, most respondents indicated that the leisure benefits of green fields are limited and not helpful for improving their physical and mental health or having fun. Additionally, survey responses by people from different backgrounds (p < 0.01) varied. Therefore, we believe that by providing a safe living environment, strengthening disaster prevention skills and cooperation against epidemics, reducing accident risks, improving leisure safety and fluency, and planning diverse leisure activities, we can improve people’s perception of environmental experiences, promote leisure participation, improve leisure satisfaction, and enhance well-being.
Geospatial Transformer Is What You Need for Aircraft Detection in SAR Imagery

Although deep learning techniques have achieved noticeable success in aircraft detection, the scale heterogeneity, position difference, complex background interference, and speckle noise keep aircraft detection in large-scale synthetic aperture radar (SAR) images challenging. To solve these problems, we propose the geospatial transformer framework and implement it as a three-step target detection neural network, namely, the image decomposition, the multiscale geospatial contextual attention network (MGCAN), and result recomposition. First, the given large-scale SAR image is decomposed into slices via sliding windows according to the image characteristics of the aircraft. Second, slices are input into the MGCAN network for feature extraction, and the cluster distance nonmaximum suppression (CD-NMS) is utilized to determine the bounding boxes of aircraft. Finally, the detection results are produced via recomposition. Two innovative geospatial attention modules are proposed within MGCAN, namely, the efficient pyramid convolution attention fusion (EPCAF) module and the parallel residual spatial attention (PRSA) module, to extract multiscale features of the aircraft and suppress background noise. In the experiment, four large-scale SAR images with 1-m resolution from the Gaofen-3 system are tested, which are not included in the dataset. The results indicate that the detection performance of our geospatial transformer is better than Faster R-CNN, SSD, Efficientdet-D0, and YOLOV5s. The geospatial transformer integrates deep learning with SAR target characteristics to fully capture the multiscale contextual information and geospatial information of aircraft, effectively reduces complex background interference, and tackles the position difference of targets. It greatly improves the detection performance of aircraft and offers an effective approach to merge SAR domain knowledge with deep learning techniques.
Blind Attention Geometric Restraint Neural Network for Single Image Dynamic/Defocus Deblurring

Based on the information loss analysis of the blur accumulation model, a novel single-image deblurring method is proposed. We apply the recurrent neural network architecture to capture the attention perception map and the generative adversarial network (GAN) architecture to yield the deblurring image. Considering that the attention mechanism has to make hard decisions about specific parts of the input image to be focused on since blurry regions are not given, we propose a new adaptive attention disentanglement model based on the variation blind source separation, which provides the global geometric restraint to reduce the large solution space, so that the generator can realistically restore details on blurry regions, and the discriminator can accurately assess the content consistency of the restored regions. Since we combine blind source separation, attention geometric restraint with GANs, we name the proposed method BAGdeblur. Extensive evaluations on quantitative and qualitative experiments show that the proposed method achieves the state-of-the-art performance on both synthetic datasets and real-world blurry images.
Augmented Graph Neural Network with hierarchical global-based residual connections

Graph Neural Networks (GNNs) are powerful architectures for learning on graphs. They are efficient for predicting nodes, links and graphs properties. Standard GNN variants follow a message passing schema to update nodes representations using information from higher-order neighborhoods iteratively. Consequently, deeper GNNs make it possible to define high-level nodes representations generated based on local as well as distant neighborhoods. However, deeper networks are prone to suffer from over-smoothing. To build deeper GNN architectures and avoid losing the dependency between lower (the layers closer to the input) and higher (the layers closer to the output) layers, networks can integrate residual connections to connect intermediate layers. We propose the Augmented Graph Neural Network (AGNN) model with hierarchical global-based residual connections. Using the proposed residual connections, the model generates high-level nodes representations without the need for a deeper architecture. We disclose that the nodes representations generated through our proposed AGNN model are able to define an expressive all-encompassing representation of the entire graph. As such, the graph predictions generated through the AGNN model surpass considerably state-of-the-art results. Moreover, we carry out extensive experiments to identify the best global pooling strategy and attention weights to define the adequate hierarchical and global-based residual connections for different graph property prediction tasks. Furthermore, we propose a reversible variant of the AGNN model to address the extensive memory consumption problem that typically arises from training networks on large and dense graph datasets. The proposed Reversible Augmented Graph Neural Network (R-AGNN) only stores the nodes representations acquired from the output layer as opposed to saving all representations from intermediate layers as it is conventionally done when optimizing the parameters of other GNNs. We further refine the definition of the backpropagation algorithm to fit the R-AGNN model. We evaluate the proposed models AGNN and R-AGNN on benchmark Molecular, Bioinformatics and Social Networks datasets for graph classification and achieve state-of-the-art results. For instance the AGNN model realizes improvements of +39% on IMDB-MULTI reaching 91.7% accuracy and +16% on COLLAB reaching 96.8% accuracy compared to other GNN variants.
Trace elements in sulfide minerals from the Huangshaping copper-polymetallic deposit, Hunan, China: Ore genesis and element occurrence

The Huangshaping copper-polymetallic deposit, which is located in southern Hunan Province, China, is an important resource base for Pb–Zn minerals. This study used the major sulfides (sphalerite, pyrite, pyrrhotite), which formed during the Pb–Zn mineralization stage of the Huangshaping deposit, to determine the genesis of the Pb–Zn ore minerals and element occurrence. In-situ LA-ICP-MS analysis of minerals was conducted to determine their trace element concentrations and reveal the distribution and occurrence of trace elements in different sulfides. The formation temperature and genesis of the Pb–Zn ore minerals were also constrained based on the deposit geology and trace element data. Trace elements were significantly different among different sulfides in the Pb–Zn mineralization stage of the deposit. Sphalerite primarily enriches Mn, Cu, Ag, Sn, and Pb and is the main carrier mineral of Ga, In, and Cd. Pyrite relatively enriches Tl, Sb, W, and Se, and pyrrhotite primarily enriches Ge. In sphalerite, Fe, Mn, Cd, Ga, In, Cu, Sn, Ag, and Ge exist by substituting for other elements (such as Zn). The replacement method of Ge is Ge4++2Cu+↔3Zn2+, whereas that of In (Ga) is In3+(Ga3+) + Cu+↔2Zn2+. In pyrrhotite, Ge is incorporated through coupling substitution, and the abnormal concentrations of Pb, Ag, and Se are caused by galena that has replaced pyrrhotite. In pyrite, W occurs as a solid solution. When galena replaces pyrite, it causes abnormal concentrations of Pb, Tl, Sb, and Ag. The sphalerite is characterized by high concentrations of high-temperature ore-forming elements (e.g., Fe, Mn, In, and Sn) and relatively low concentrations of Cd, Ge, Ga, and other elements. Thus, the sphalerite was formed at a medium–high temperature (288–341 °C). The trace element characteristics of sphalerite are consistent with those of high-temperature hydrothermal deposits. The deposit geology, pyrite Co/Ni ratio (averaging 0.13, n = 29), S/Se ratio (averaging 173,905, n = 19), pyrrhotite Co/Ni ratio (averaging 0.10, n = 26), and binary diagram of trace elements in sphalerite indicate that Pb–Zn mineralization is related to the late-stage hydrothermal activity of skarn mineralization. The Pb–Zn mineralization and W–Mo(Fe) mineralization together constitute a skarn-type mineralization system.
SF-TL54: A Thermal Facial Landmark Dataset with Visual Pairs

Facial landmark detection is a cornerstone in many facial analysis tasks such as face recognition, drowsiness detection, and facial expression recognition. Numerous methodologies were introduced to achieve accurate and efficient facial landmark localization in visual images. However, there are only several works that address facial landmark detection in thermal images. The main challenge is the limited number of annotated datasets. In this work, we present a thermal face dataset with annotated face bounding boxes and facial landmarks. The dataset contains 2,556 thermal images of 142 individuals, where each thermal image is paired with the corresponding visual image. To the best of our knowledge, our dataset is the largest in terms of the number of individuals. In addition, our dataset can be employed for tasks such as thermal-to-visual image translation, thermal-visual face recognition, and others. We trained two models for the facial landmark detection task to show the efficacy of our dataset. The first model is a classic machine learning model based on an ensemble of regression trees. The second model is a deep learning model based on the U-net architecture. The dataset, annotations, source code, and pre-trained models are publicly available to advance research in thermal face analysis.
Deep Reinforcement Learning for UAV Intelligent Mission Planning

Rapid and precise air operation mission planning is a key technology in unmanned aerial vehicles (UAVs) autonomous combat in battles. In this paper, an end-to-end UAV intelligent mission planning method based on deep reinforcement learning (DRL) is proposed to solve the shortcomings of the traditional intelligent optimization algorithm, such as relying on simple, static, low-dimensional scenarios, and poor scalability. Specifically, the suppression of enemy air defense (SEAD) mission planning is described as a sequential decision-making problem and formalized as a Markov decision process (MDP). Then, the SEAD intelligent planning model based on the proximal policy optimization (PPO) algorithm is established and a general intelligent planning architecture is proposed. Furthermore, three policy training tricks, i.e., domain randomization, maximizing policy entropy, and underlying network parameter sharing, are introduced to improve the learning performance and generalizability of PPO. Experiments results show that the model in this work is efficient and stable, and can be adapted to the unknown continuous high-dimensional environment. It can be concluded that the UAV intelligent mission planning model based on DRL has powerful intelligent planning performance, and provides a new idea for researching UAV autonomy.
Multiple-instance domain adaptation for cost-effective sensor-based human activity recognition

Machine learning-based human activity recognition (HAR) is important as the means of human–computer interaction to empower the existing systems in many areas, such as healthcare, entertainment, logistics, and manufacturing. To build such a recognition tool, it is clear that sufficient labeled samples are required. Oftentimes, it is more difficult to obtain labeled samples rather than to obtain unlabeled ones due to the prohibitive conditions (e.g., financial cost, time, hazardous environment, and human labor), so we end up with incomplete or weakly labeled data. The multiple-instance learning technique (MIL) alleviates such issue by allowing us to leverage weakly labeled data by performing the classification of a bag of instances rather than a single instance. However, since multiple-instance learning is intrinsically the generalization of supervised learning, it may face the same problem as the usual supervised learning approaches: performance degradation on the data with different distribution. In fact, such distribution difference is common in sensor-based HAR which makes it difficult for a classifier model to perform predictions. For example, the difficulties happen when the current data distribution is shifted due to sensor deterioration, or when the model that is generated from a certain domain is applied to a different domain (e.g., different person with different device placement, posture, and gait). In this work, we propose a multiple-instance domain adaptation approach that handles weakly annotated data for model training, while providing adaptation mechanism to deal with data distribution difference. We incorporate "high-level adaptation" and "bag-level adaptation" to find a robust sensor data representation which minimizes distribution difference. The proposed approach is tested on standard sensor-based HAR datasets, conditioned on weak annotation and cross-domain settings. Our experimental result shows promising recognition performance improvements compared to the classical MIL and domain adaptation approaches.
DeepPlayer-Track: Player and Referee Tracking With Jersey Color Recognition in Soccer

In real-world sports video analysis, identity switching caused by inter-object interactions is still a major difficulty for multi-player tracking.Due to similar appearances of players on the same squad, existing methodologies make it difficult to correlate detections and retain identities.In this paper, a novel approach (DeepPlayer-Track) is proposed to track the players and referees, by representing the deep features to retain the tracking identity.To provide identity-coherent trajectories, a sophisticated multi-player tracker is being developed further, encompassing deep features of player and referee identification.The proposed methodology consists of two parts: (i) the You Only Look Once (YOLOv4) can detect and classify players, soccer balls, referees, and background; (ii) Applying a modified deep feature association with a simple online real-time (SORT) tracking model which connects nodes from frame to frame using cosine distance and deep appearance descriptor to correlate the coefficient of the player identity (ID) which improved tracking performance by distinct identities.The proposed model achieved a tracking accuracy of 96% and 60% on MOTA and GMOTA metrics respectively with a detection speed of 23 frames per second (FPS).
Attribute-Based Blind Signature Scheme Based on Elliptic Curve Cryptography

Blind signature is a special digital signature that allows the signer to sign a document without knowing its content. However, in many situations, multiple people need to blindly sign messages. At this time, the traditional blind signature can no longer satisfy the application requirements. To solve this problem, attribute-based cryptography has been combined with the blind signature. The concept of the attribute-based blind signature is generated. At present, all attribute-based blind signature schemes require the support of bilinear pairing technology, which involves several complex pairing and exponential operations in the signature and verification processes, and the computational efficiency is not high. In this paper, we present an attribute-based blind signature scheme based on elliptic curve cryptography (ECC), and the security of new scheme is proved under the intractability of elliptic curve discrete logarithm problem (ECDLP). Our scheme is a key policy attribute-based signature (KP-ABS). The new scheme uses linear secret sharing scheme (LSSS) matrix technology that does not require recursive operation to achieve more flexible and fine-grained access control. In addition, the scheme is based on Elliptic Curve Cryptography (ECC) using scalar multiplication on an elliptic curve instead of a bilinear pairing operation. Our scheme has significant advantages in terms of computational efficiency and storage compared with existing attribute-based blind signature schemes.
Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval

Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset-(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval-confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance.