Reviewerly/exHarmony huggingface dataset:
Aerial Monocular 3D Object Detection

Drones equipped with cameras can significantly enhance human's ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, existing 3D object detection methods developed for autonomous driving cannot be directly applied to drones due to the lack of deformation modeling, which is essential for the distant aerial perspective with sensitive distortion and small objects. To fill the gap, this work proposes a dual-view detection system named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DVDET</b> to achieve aerial monocular object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module that can properly warp information from the drone's perspective to the birds' eye view (BEV). Compared to the monocular methods for cars, our transformation includes a learnable deformable network for explicitly revising the severe deviation. To address the dataset challenge, we propose a new large-scale simulation dataset named AM3D-Sim, and a new real-world aerial dataset named AM3D-Real with high-quality annotations for 3D object detection. Extensive experiments show that i) aerial monocular 3D object detection is feasible; ii) the model pre-trained on the simulation dataset helps real-world performance; and iii) DVDET also helps monocular 3D object detection for cars. To encourage more researchers to investigate this area, we released the dataset and related code.
Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation

Recently, knowledge-grounded dialogue systems have gained increasing attention. Great efforts have been made to build response matching models where all dialogue content and knowledge sentences are leveraged. However, knowledge redundancy and distraction of irrelevant dialogue content often exist in knowledge-grounded conversations, which may affect the matching process and lead to inferior performance. In addition, irrelevant dialogue history and excessive knowledge also hinder the exploitation of popular pre-trained language models (PLMs) due to the limitation of input length. To address these challenges, we propose a new knowledge-grounded dialogue model based on PLMs, where a knowledge selector and a context selector are designed for filtering out irrelevant knowledge sentences and redundant dialogue history, respectively. Considering the lack of labeled data for the learning of two selectors, we pre-train them with weakly-supervised tasks and then jointly conduct the optimization of knowledge and context selection and fine-tuning of PLMs for response ranking with reinforcement learning (RL). By this means, the dialogue model can distill more accurate and concise knowledge and dialogue content for subsequent response ranking module, and the overall model can converge and perform better. We conduct experiments on two benchmarks and evaluation results indicate that our model can significantly outperform the state-of-the-art methods.
Multiscale quantum algorithms for quantum chemistry

Exploring the potential applications of quantum computers in material design and drug discovery is attracting more and more attention after quantum advantage has been demonstrated using Gaussian boson sampling. However, quantum resource requirements in material and (bio)molecular simulations are far beyond the capacity of near-term quantum devices. In this work, multiscale quantum computing is proposed for quantum simulations of complex systems by integrating multiple computational methods at different scales of resolution. In this framework, most computational methods can be implemented in an efficient way on classical computers, leaving the critical portion of the computation to quantum computers. The simulation scale of quantum computing strongly depends on available quantum resources. As a near-term scheme, we integrate adaptive variational quantum eigensolver algorithms, second-order Møller-Plesset perturbation theory and Hartree-Fock theory within the framework of the many-body expansion fragmentation approach. This new algorithm is applied to model systems consisting of hundreds of orbitals with decent accuracy on the classical simulator. This work should encourage further studies on quantum computing for solving practical material and biochemistry problems.
Differential Reinforcement and Global Collaboration Network for RGBT Tracking

RGB-thermal (RGBT) tracking has received extensive attention as a research hotspot, benefiting from the complementarity of visible (RGB) and thermal (T) modalities, which can better handle tracking tasks in harsh environments. Existing studies typically deploy the same structure to extract features of heterogeneous modalities with different properties, which may be difficult to simultaneously search for mining strategies suitable for each modality, resulting in insufficient feature representation. With the success of transformer, how to jointly consider the heterogeneity and global mining to facilitate better integration of heterogeneous modalities is also a crucial issue worth exploring. To solve these problems, we propose a unique differential reinforcement and global collaboration network for RGBT tracking, which includes a parallel backbone, modal differential reinforcement (MDR), and global mining collaboration module (GMCM). Specifically, MDR introduces raw data to control adaptive differential reinforcement for both the modalities via a modality-sharing augmentation unit and a modality-aware gating unit. The data-driven design makes it easier for the two modalities to search for the respective appropriate mining strategies, further effectively enhancing the respective feature representations. GMCM first mines intra-modal and cross-modal global information separately, and then performs an adaptive ensemble operation to balance the two. This operation learns intra-modal and cross-modal global weights in RGB and T modalities to better integrate heterogeneous modalities based on heterogeneity and global cues. Extensive experiments on three datasets demonstrate that the performance of the proposed network is comparable to state-of-the-art RGBT trackers.
Close or distant? The impacts of robot services and spatial distance on service satisfaction

Service robots have become an efficient service option in retail, hospitality, and tourism settings. However, customers tend to be dissatisfied with robot-provided services. This article investigates customers' satisfaction with service robots from the perspective of a neglected factor: spatial distance. COVID-19 prevention policies have brought various challenges to service providers and have highlighted the importance of spatial distance. We propose that adopting service robots during a pandemic could be a win–win strategy for customers and providers. Our three studies included experimental manipulations of service providers and spatial distance. Study 1 examined the correlation between service robots and customers' service satisfaction. Studies 2 and 3 indicated that customers were more satisfied with robot services (vs. human services) in settings featuring close spatial distance, whereas customers preferred human staff (vs. robots) under far spatial distance. We also verified approach–avoidance motivation as the underlying mechanism of the above interaction. These results excluded other factors, such as consumers' tech savviness, perceived expertise, and knowledge level. Findings can help service providers to better understand how spatial distance influences customers’ satisfaction with service robots and devise competitive marketing strategies.
Image–Text Multimodal Sentiment Analysis Framework of Assamese News Articles Using Late Fusion

Before the arrival of the web as a corpus, people detected positive and negative news based on the understanding of the textual content from physical newspaper rather than an automatic identification approach from readily available e-newspapers. Thus, the earlier sentiment analysis approach is based on unimodal data, and less effort is paid to the multimodal data. However, the presence of multimodal information helps us to get a clearer understanding of the sentiment. To the best of our knowledge, less work has been introduced on the image–text multimodal sentiment analysis framework of Assamese, a low-resource Indian language mostly spoken in the northeast part of India. We built an Assamese news articles dataset consisting of news text and associated images and one image caption to conduct an experimental study. Focusing on important words and discriminative regions of the images mostly related to sentiment, two individual unimodal such as textual and visual models are proposed. The visual model is developed using an encoder-decoder–based image caption generation system. An image–text multimodal approach is proposed to explore the internal correlation between textual and visual features for joint sentiment classification. Finally, we propose the multimodal sentiment analysis framework, i.e., Textual Visual Multimodal Fusion, by employing a late fusion scheme to merge the three different modalities for the final sentiment prediction. Experimental results conducted on the Assamese dataset built in-house demonstrate that the contextual integration of multimodal features delivers better performance than unimodal features.
Design, Hardware Implementation on FPGA and Performance Analysis of Three Chaos-Based Stream Ciphers

In this paper, we come up with three secure chaos-based stream ciphers, implemented on an FPGA board, for data confidentiality and integrity. To do so, first, we performed the statistical security and hardware metrics of certain discrete chaotic map models, such as the Logistic, Skew-Tent, PWLCM, 3D-Chebyshev map, and 32-bit LFSR, which are the main components of the proposed chaotic generators. Based on the performance analysis collected from the discrete chaotic maps, we then designed, implemented, and analyzed the performance of three proposed robust pseudo-random number generators of chaotic sequences (PRNGs-CS) and their corresponding stream ciphers. The proposed PRNGs-CS are based on the predefined coupling matrix M. The latter achieves a weak mixing of the chaotic maps and a chaotic multiplexing technique or XOR operator for the output function. Therefore, the randomness of the sequences generated is expanded as well as their lengths, and divide-and-conquer attacks on chaotic systems are avoided. In addition, the proposed PRNGs-CS contain polynomial mappings of at least degree 2 or 3 to make algebraic attacks very difficult. Various experimental results obtained and analysis of performance in opposition to different kinds of numerical and cryptographic attacks determine the high level of security and good hardware metrics achieved by the proposed chaos system. The proposed system outperformed the state-of-the-art works in terms of high-security level and a high throughput which can be considered an alternative to the standard methods.
Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study

For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track 2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https://github.com/RenYang-home/LDV_dataset . The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR .
Graph convolutional network combining node similarity association and layer attention for personalized recommendation

Although current graph convolutional network (GCN) has achieved competitive performance in personalized recommendation systems, most of existing GCN based recommendation methods mainly rely on user–item interaction data and the fixed association weights of the nodes at different layers, which greatly limit them to further effectively learn the final embedding representation of nodes when interaction data is scarce. This paper proposes a GCN based recommendation model combining node similarity association and layer attention mechanism (NSAGCN) for predicting user–item interactions in personalized recommendation. The proposed NSAGCN model integrates the similarity associations of the same type nodes into a heterogeneous network based on the bipartite graph of user–item interaction to enrich semantic information of the original sparse interaction graph and more effectively learn the node embedding features. In addition, a layer attention strategy is used to aggregate the embeddings from different graph convolutional layers with various association weights according to the proximity to the target nodes. Extensive experiments on three public benchmark datasets (ML-100K, ML-1M, and Book-Crossing) show that the proposed NSAGCN model outperforms state-of-the-art models by an average improvement of 8.58%, 6.91%, and 6.33% in Recall, Precision, and Normalized Discounted Cumulative Gain (NDCG), respectively.
Improving 3D Vulnerable Road User Detection With Point Augmentation

Point clouds have been a popular representation to describe 3D environments for autonomous driving applications. Despite accurate depth information, sparsity of points results in difficulties in extracting sufficient features from vulnerable objects of small sizes. One solution is leveraging self-attention networks to build long-range connections between similar objects. Another method is using generative models to estimate the complete shape of objects. Both approaches introduce large memory consumption and extra complexity to the models while the geometric characteristics of objects are overlooked. To overcome this problem, this paper proposes Point Augmentation (PA)-RCNN, focusing on small object detection by generating efficient complementary features without trainable parameters. Specifically, 3D points are sampled with the guidance of object proposals and encoded through the 3D grid-based feature aggregation to produce localised 3D voxel properties. Such voxel attributes are fed to the pooling module with the aid of fictional points, which are transformed from sampled points considering geometric symmetry. Experimental results on Waymo Open Dataset and KITTI dataset show a superior advantage in the detection of distant and small objects in comparison with existing state-of-the-art methods.
Multi-Party Evolutionary Game Analysis of Accounts Receivable Financing under the Application of Central Bank Digital Currency

Accounts receivable financing is one of the most prominent financing approaches in supply chain finance; nevertheless, in the actual financing process, financial institutions and SMEs have credit risk and information asymmetry risk, which leads to frequent nonpayment and collaboration fraudulent loans. This paper introduces central bank digital currency into traditional accounts receivable financing and solves the credit risk and information asymmetry risk using two technologies of central bank digital currency: digital technology and blockchain technology; digital technology enables the supervision of capital flow, and blockchain technology enables for access to logistics and information flow. In the context of using central bank digital currency technology, this paper builds an evolutionary game model of whether financial institutions use central bank digital currency and whether SMEs repay the loan, compares the evolutionary stabilization strategies of financial institutions and SMEs, calculates and analyzes the model’s impact, investigates changes in the decision-making and evolutionary paths of both parties, and then conducts numerical simulation analysis using Matlab and Python to verify the model’s reliability further. According to the results, adding central bank digital currency to the traditional accounts receivable financing model can reduce the loan risk of financial institutions, increase the credibility of accounts receivable financing, expedite the implementation of accounts receivable financing, and alleviate the financing concerns of SMEs.
Deep Learning-Based Yoga Posture Recognition Using the Y_PN-MSSD Model for Yoga Practitioners

In today's digital world, and in light of the growing pandemic, many yoga instructors opt to teach online. However, even after learning or being trained by the best sources available, such as videos, blogs, journals, or essays, there is no live tracking available to the user to see if he or she is holding poses appropriately, which can lead to body posture issues and health issues later in life. Existing technology can assist in this regard; however, beginner-level yoga practitioners have no means of knowing whether their position is good or poor without the instructor's help. As a result, the automatic assessment of yoga postures is proposed for yoga posture recognition, which can alert practitioners by using the Y_PN-MSSD model, in which Pose-Net and Mobile-Net SSD (together named as TFlite Movenet) play a major role. The Pose-Net layer takes care of the feature point detection, while the mobile-net SSD layer performs human detection in each frame. The model is categorized into three stages. Initially, there is the data collection/preparation stage, where the yoga postures are captured from four users as well as an open-source dataset with seven yoga poses. Then, by using these collected data, the model undergoes training where the feature extraction takes place by connecting key points of the human body. Finally, the yoga posture is recognized and the model assists the user through yoga poses by live-tracking them, as well as correcting them on the fly with 99.88% accuracy. Comparatively, this model outperforms the performance of the Pose-Net CNN model. As a result, the model can be used as a starting point for creating a system that will help humans practice yoga with the help of a clever, inexpensive, and impressive virtual yoga trainer.
Healthcare-Chain: Blockchain-Enabled Decentralized Trustworthy System in Healthcare Management Industry 4.0 with Cyber Safeguard

The pervasiveness of healthcare data to create better healthcare facilities and opportunities is one of the most-imperative parts of human life that offers radical advancements in healthcare services practiced through the blockchain-based management, analysis, storage, and sharing of health-related big data. Researchers can accelerate the challenges of developing a secure, scalable, and accessible dynamic healthcare infrastructure by the extensive data exchange required through individual microservices of blockchain-based privacy-preserving health data management ledgers in Healthcare Industry 4.0. Conducting secure and privacy-preserving platforms through primitive cryptographic algorithms is risky and can be a serious concern as the need to authenticate and store sensitive health data automatically are increasingly high. To achieve interoperability, security, efficiency, scalability, availability, and accountability among healthcare providers in heterogeneous networks, this paper proposes a blockchain-enabled decentralized, trustworthy privacy-preserving platform in the healthcare industry. In the healthcare-chain system, blockchain provides an appreciated secure environment for the privacy-preserving health data management ledger through hash processing, which updates high data security, storage immutability, and authentication functionality with an integrated attribute signature in accessing prescribed health block data. This article describes a new secure data retention design, prescribed evidence collection, and evaluation mechanism with integrity–confidentiality–availability to enforce the data access control policies for transactions of healthcare microservices. This scheme revealed the optimal performance in terms of mining health data size, average response time, transaction latency, and throughput for secured block transactions in blockchain networks.
I2OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing

The oil and gas business has high operating costs and frequently has significant difficulties due to asset, process, and operational failures. Remote monitoring and management of the oil field operations are essential to ensure efficiency and safety. Oil field operations often use SCADA or wireless sensor network (WSN)-based monitoring and control systems; both have numerous drawbacks. WSN-based systems are not uniform or are incompatible. Additionally, they lack transparent communication and coordination. SCADA systems also cost a lot, are rigid, are not scalable, and deliver data slowly. Edge computing and the Industrial Internet of Things (IIoT) help to overcome SCADA’s constraints by establishing an automated monitoring and control system for oil and gas operations that is effective, secure, affordable, and transparent. The main objective of this study is to exploit the IIOT and Edge Computing (EC). This study introduces an I2OT-EC framework with flowcharts, a simulator, and system architecture. The validity of the I2OT-EC framework is demonstrated by experimental findings and implementation with an application example to verify the research results as an additional verification and testing that proves the framework results were satisfactory. The significant increase of 12.14% in the runtime for the crude well using the proposed framework, coupled with other advantages, such as reduced operational costs, decentralization, and a dependable platform, highlights the benefits of this solution and its suitability for the automatic monitoring and control of oil field operations.