Reviewerly/exHarmony huggingface dataset:
Multi-view stereo in the Deep Learning Era: A comprehensive review

Multi-view stereo infers the 3D geometry from a set of images captured from several known positions and viewpoints. It is one of the most important components of 3D reconstruction. Recently, deep learning has been increasingly used to solve several 3D vision problems due to the predominating performance, including the multi-view stereo problem. This paper presents a comprehensive review, covering recent deep learning methods for multi-view stereo. These methods are mainly categorized into depth map based and volumetric based methods according to the 3D representation form, and representative methods are reviewed in detail. Specifically, the plane sweep based methods leveraging depth maps are presented following the stage of approaches, i.e. feature extraction, cost volume construction, cost volume regularization, depth map regression and post-processing. This review also summarizes several widely used datasets and their corresponding metrics for evaluation. Finally, several insightful observations and challenges are put forward enlightening future research directions.
Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

As data grow rapidly on social media by users' contributions, specially with the recent coronavirus pandemic, the need to acquire knowledge of their behaviors is in high demand. The opinions behind posts on the pandemic are the scope of the tested dataset in this study. Finding the most suitable classification algorithms for this kind of data is challenging. Within this context, models of deep learning for sentiment analysis can introduce detailed representation capabilities and enhanced performance compared to existing feature-based techniques. In this paper, we focus on enhancing the performance of sentiment classification using a customized deep learning model with an advanced word embedding technique and create a long short-term memory (LSTM) network. Furthermore, we propose an ensemble model that combines our baseline classifier with other state-of-the-art classifiers used for sentiment analysis. The contributions of this paper are twofold. (1) We establish a robust framework based on word embedding and an LSTM network that learns the contextual relations among words and understands unseen or rare words in relatively emerging situations such as the coronavirus pandemic by recognizing suffixes and prefixes from training data. (2) We capture and utilize the significant differences in state-of-the-art methods by proposing a hybrid ensemble model for sentiment analysis. We conduct several experiments using our own Twitter coronavirus hashtag dataset as well as public review datasets from Amazon and Yelp. For concluding results, a statistical study is carried out indicating that the performance of these proposed models surpasses other models in terms of classification accuracy.
An Optimization-Based Path Planning Approach for Autonomous Vehicles Using the DynEFWA-Artificial Potential Field

With the rapid development of autonomous driving technology, collision avoidance has become a research hotspot since it has the potential to increase safety. To obtain a collision-free path, the artificial potential field (APF) is widely used as a path planning method. APF is capable of establishing functional relationships between the vehicle and surrounding objects. However, the function features of the traditional APF method can cause autonomous vehicles to fall into the local minimum, and the generated zigzag path may be difficult to follow. Motivated by these challenges, this paper proposes a real-time path planning method for autonomous vehicles using the dynamic enhanced firework algorithm-APF. Firstly, to improve the safety and smoothness of the planned path by the traditional APF method, the constraints of the vehicle dynamics and different types of obstacles are taken into consideration. Secondly, an optimization problem is formulated to find an optimal path with the least cost in the driving area. Finally, the proposed method is verified with both a simulation and a hardware-in-loop test environment. The results show that the studied autonomous vehicle successfully avoids obstacles and arrives at the goal position by using the proposed path-planning method, and the path smoothness is improved.
Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data

Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification, and prediction of future events, from different locations while maintaining a strict privacy constraint. Due to privacy concerns and critical communication bottlenecks, it can become impractical to send the FL updated models to a centralized server. Thus, this paper studies the potential of hierarchical FL in Internet of Things (IoT) heterogeneous systems. In particular, we propose an optimized solution for user assignment and resource allocation over hierarchical FL architecture for IoT heterogeneous systems. This work focuses on a generic class of machine learning models that are trained using gradient-descent-based schemes while considering the practical constraints of non-uniformly distributed data across different users. We evaluate the proposed system using two real-world datasets, and we show that it outperforms state-of-the-art FL solutions. Specifically, our numerical results highlight the effectiveness of our approach and its ability to provide 4–6% increase in the classification accuracy, with respect to hierarchical FL schemes that consider distance-based user assignment. Furthermore, the proposed approach could significantly accelerate FL training and reduce communication overhead by providing 75–85% reduction in the communication rounds between edge nodes and the centralized server, for the same model accuracy.
Dynamic Selective Network for RGB-D Salient Object Detection

RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, i.e. RGB images and depth maps, via various fusion strategies. However, some of them ignore the inherent difference between the two modalities, which leads to the performance degradation when handling some challenging scenes. Therefore, in this paper, we propose a novel RGB-D saliency model, namely Dynamic Selective Network (DSNet), to perform salient object detection (SOD) in RGB-D images by taking full advantage of the complementarity between the two modalities. Specifically, we first deploy a cross-modal global context module (CGCM) to acquire the high-level semantic information, which can be used to roughly locate salient objects. Then, we design a dynamic selective module (DSM) to dynamically mine the cross-modal complementary information between RGB images and depth maps, and to further optimize the multi-level and multi-scale information by executing the gated and pooling based selection, respectively. Moreover, we conduct the boundary refinement to obtain high-quality saliency maps with clear boundary details. Extensive experiments on eight public RGB-D datasets show that the proposed DSNet achieves a competitive and excellent performance against the current 17 state-of-the-art RGB-D SOD models.
When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements

Blockchain is a prominently discussed technology in operations and supply chain management and firms increasingly engage in blockchain initiatives. Yet, an understanding of the technology's financial value remains elusive. Based on 175 firm announcements between 2015 and 2019, we conduct an international event study to estimate the impact of blockchain initiatives on the market value of the firm. We empirically demonstrate that blockchain announcements are associated with a significant average abnormal return of 0.30% on the announcement day, and that there are indications of positive long‐term effects on shareholder value. We further demonstrate how blockchain use case, project, and firm characteristics affect the stock market reaction. Specifically, we find that the stock market reaction to blockchain announcements is less positive when blockchain is used to trace physical objects or to share sensitive data, providing empirical evidence for the risk associated with current challenges in the design of blockchain use cases. Our results also suggest that the involvement of an external information technology service provider in a blockchain project attenuates the positive stock market reaction. Interestingly, more innovative firms do not experience a stronger stock market reaction to blockchain announcements. Leveraging the international scope of our sample, we further shed light on how the firm's competitive (i.e., industry factors) and macro environment (i.e., country factors) affect the stock market reaction. Our findings indicate that the industry's R&amp;D intensity and the country's data restriction level play a crucial role in driving the value attributed to blockchain initiatives.
A robust drug recall supply chain management system using hyperledger blockchain ecosystem

Drug recall is a critical issue for manufacturing companies, as a manufacturer might face criticism and severe business downfall due to a defective drug. A defective drug is a highly detrimental issue, as it can cost several lives. Therefore, recalling the drug becomes one of the most sensitive issues in the pharmaceutical industry. This paper presents a blockchain-enabled network that allows manufacturers to effectively monitor a drug while in the supply chain with improved security and transparency throughout the process. The study also tries to minimize the cost and time sustained by the manufacturing company to transfer the drug to the end-user by proposing forward and backward supply chain mathematical models. Specifically, the forward chain model supports drug delivery from the manufacturer to the end-user in less time with a reliable transport mode. The backward supply chain model explicitly focuses on reducing the extra time and cost incurred to the manufacturer in pursuit of recalling the defective drug. Moreover, a real-time implementation of the proposed blockchain-enabled supply chain management system using the Hyperledger Composer is done to demonstrate the transparency of the process.
A deep learning based trust- and tag-aware recommender system

Recommender systems are popular tools used in many applications, such as e-commerce, e-learning, and social networks to help users select their desired items. Collaborative filtering is a widely used recommendation technique that employs previous ratings of users to predict their future interests. Lack of sufficient ratings often reduces the performance of collaborative filtering recommendation methods. Additional side resources, such as trust relationships and tag information can be employed to enhance the recommendation accuracy. However, trust and tag data are often heavily sparse as users mainly provide insufficient information about these side resources. Moreover, such additional resources have generally large dimensions which result in increasing the computational complexity of recommendation models in calculating similarity values. To cope with these problems, a new recommendation model is proposed that utilizes deep neural networks to model the representation of trust relationships and tag information. To this end, a sparse autoencoder is used to extract latent features from user-user trust relationships and user-tag matrices. Then, the extracted latent features are utilized to calculate similarity values between users, which are then used to form the nearest neighbors of the target user and predict unseen items. The proposed method can tackle the data sparsity problem and reduce the computational complexity of recommender systems as the extracted latent features have smaller dimensions in comparison to the original data. Experimental results on two benchmark datasets reveal the effectiveness of the proposed method and show its outperformance over state-of-the-art recommender systems.
COVID surveillance robot: Monitoring social distancing constraints in indoor scenarios

Observing social/physical distancing norms between humans has become an indispensable precaution to slow down the transmission of COVID-19. We present a novel method to automatically detect pairs of humans in a crowded scenario who are not maintaining social distancing, i.e. about 2 meters of space between them using an autonomous mobile robot and existing CCTV (Closed-Circuit TeleVision) cameras. The robot is equipped with commodity sensors, namely an RGB-D (Red Green Blue-Depth) camera and a 2-D lidar to detect social distancing breaches within their sensing range and navigate towards the location of the breach. Moreover, it discreetly alerts the relevant people to move apart by using a mounted display. In addition, we also equip the robot with a thermal camera that transmits thermal images to security/healthcare personnel who monitors COVID symptoms such as a fever. In indoor scenarios, we integrate the mobile robot setup with a static wall-mounted CCTV camera to further improve the number of social distancing breaches detected, accurately pursuing walking groups of people etc. We highlight the performance benefits of our robot + CCTV approach in different static and dynamic indoor scenarios.
An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

Abstract Cloud computing is the on‐demand access to computer resources such as applications, servers, data storage, development tools, networking capabilities, and other resources that are held in a remote data center maintained by a cloud services provider(CSP) and accessible over the internet. The limited resources and higher time consumption are the major issues faced by cloud users. The appropriate selection of virtual machines (VMs) is used to maximize cloud resource usage and reduce task execution time for the clients (patients, physicians, etc.). In this article, we have introduced an efficient cloud‐based healthcare services paradigm (HCS). The opposition‐based Laplacian equilibrium optimizer (O‐LEO) algorithm is used to select optimal VMs in which the maximization of resource utilization and minimization of task execution time is performed. Additionally, the boosted support vector machine (SVM) effectively predicts chronic kidney disease (CKD) thereby ensuring better prediction results. The CloudSim platform is used as the implementation platform of the proposed method. The overall time taken by the O‐LEO‐based CloudSim is less than the standard Cloud Sim model to create three cloudlets which improve the system efficiency by 6%. When compared with the existing techniques, both the O‐LEO and boosted SVM classifier outperforms superior performances.
Ethical Marketing in the Blockchain-Based Sharing Economy: Theoretical Integration and Guiding Insights

Abstract Since the introduction of Ethereum in 2015, blockchain technology (BT) has been evolving, and BT has been associated with the concept of the sharing economy by business academics. Despite the marketing research on the sharing economy that has been extensively conducted in the last decade, the linkage between BT and ethical marketing in the sharing economy remains unclear. Through a systematic literature review of 163 articles and a co-citation analysis, this study identifies the key elements of blockchain capabilities, blockchain attributes, and the underlying economic theories of blockchain. It also synthesizes and proposes a shift of ethical marketing logic in the blockchain-based sharing economy that delineates the principles of stakeholder capitalism. The article concludes with a list of future research directions that underline three approaches of stakeholder theory (i.e., the descriptive, instrument, and normative approaches). These directions aim to guide marketing scholars concerning how BT enables an institutionally embedded view of ethical marketing activities and practices that enhance collaborative marketing and subsequently innovate value chains and create sustainable business models in the sharing economy, as well as to the metaverse.
Decentralized Federated Learning for Healthcare Networks: A Case Study on Tumor Segmentation

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of patient data. Since large and diverse datasets for training of Machine Learning (ML) models can rarely be found in individual medical centers, classical centralized AI requires moving privacy-sensitive data from medical institutions to data centers that process the fused information. Training on data centers thus requires higher communication resource/energy demands while violating privacy. This is considered today as a significant bottleneck in pursuing scientific collaboration across trans-national clinical medical research centers. Recently, federated learning (FL) has emerged as a distributed AI approach that enables the cooperative training of ML models, without the need of sharing patient data. This paper dives into the analysis of different FL methods and proposes a real-time distributed networking framework based on the Message Queuing Telemetry Transport (MQTT) protocol. In particular, we design a number of solutions for ML over networks, based on FL tools relying on a parameter server (PS) and fully decentralized paradigms driven by consensus methods. The proposed approach is validated in the context of brain tumor segmentation, using a modified version of the popular U-NET model with representative clinical datasets obtained from the daily clinical workflow. The FL process is implemented on multiple physically separated machines located in different countries and communicating over the Internet. The real-time test-bed is used to obtain measurements of training accuracy vs. latency trade-offs, and to highlight key operational conditions that affect the performance in real deployments.
Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring

Computer vision has always been a hot field of research by contemporary scholars due to its wide range of applications. As an important branch of this field, the visual monitoring technology has shown superior vitality in the actual monitoring environment of the Internet of Things (IoT). However, when the monitoring environment is complex, once the target monitoring fails, the important information related to the target also disappears. At this time, if the existing monitoring method is used, the target cannot be monitored again. Moreover, the current filtering monitoring algorithm also has the problem of poor interpretability. Therefore, this article combines the relevant characteristics of human inertial thinking when dealing with such problems. First, our method screens the movement information of the target and introduces a fuzzy reasoning mechanism to infer the location area of the target through fuzzy thinking. Then, an alternative selection strategy based on the thinking set is applied, which alternates between the location of thinking reasoning and the location of memory to further obtain the effective visual monitoring of the target. The filtering and monitoring algorithm fused with the new mechanism in the OTB-2015 data set, the UVA123 data set, and the TC128 data set all show that the proposed fuzzy inference mechanism has good robustness and universality. Furthermore, our results confirm that it can not only ensure the monitoring speed and overall accuracy but also improve the stability of monitoring in the IoT-assisted monitoring environment, showing its effectiveness compared to state-of-the-art methods. In addition, our results confirm that the integration of the proposed edge learning method with the IoT can be well applied to the construction of smart cities and future generation systems.
Emotion and service quality of anthropomorphic robots

Deployment of robots has become prevalent in the service sector. The advanced digital technologies, especially artificial intelligence (AI), and the current COVID-19 pandemic have accelerated the wide application of service robots to provide quality customer experience with limited in-person service encounters. Many of today's service robots perform not only complex services, but they are anthropomorphic with physical and psychological states like humans. This study investigates how the varying degree of anthropomorphism in robots affect customers’ perceived service quality. A research model with associated hypotheses was developed based on affect-as-information theory. The study collected data from 455 randomly selected customers who were served by either a mechanical robot (automatic on-demand coffee maker) or an anthropomorphic robot (travel information assistant). The results of the study showed that anthropomorphism has a significant positive impact on intimacy between robots and humans, intimacy has significant positive impact on customers’ perceived service quality, and intimacy has a mediating role on the relationship between anthropomorphism and service quality.
Parallel Vision for Long-Tail Regularization: Initial Results From IVFC Autonomous Driving Testing

Long-tail effect, characterized by highly frequent occurrence of normal scenarios and the scarce appearance of extreme “long-tail” scenarios, ubiquitously exists in the vision-related problems in the real-world applications. Though many computer vision methods to date have already achieved feasible performance for most of the normal scenarios, it is still challenging for existing vision systems to accurately perceive the long-tail scenarios. This deficiency largely hinders the practical application of computer vision systems, since long-tail problems may incur fatal consequences, such as traffic accidents, taking the vision systems of autonomous vehicles as an example. In this paper, we firstly propose a theoretical framework named Long-tail Regularization (LoTR), for analyzing and tackling the long-tail problems in the vision perception of autonomous driving. LoTR is able to regularize the scarcely occurred long-tail scenarios to be frequently encountered. Then we present a Parallel Vision Actualization System (PVAS), which consists of closed-loop optimization and virtual-real interaction, to search for challenging long-tail scenarios and produce large-scale long-tail driving scenarios for autonomous vehicles. In addition, we introduce how to perform PVAS in Intelligent Vehicle Future Challenge of China (IVFC), the most durable autonomous driving competition around the world. Results over the past decade demonstrate that PVAS can effectively guide the collection of long-tail data to diminish the cost in the real world, and thus promote the capability of vision systems to adapt to complex environments, alleviating the impact of long-tail effect.