Reviewerly/exHarmony huggingface dataset:
Understanding the Determinants of Blockchain Adoption in the Engineering-Construction Industry: Multi-Stakeholders’ Analyses

Blockchain technology is promising toward transforming conventional construction practices to improve collaboration and integration management in engineering-construction projects. The factors affecting the adoption of blockchain technology from the different stakeholder perspectives, however, have not been thoroughly investigated. Following the theory of planned behavior (TPB) and regulatory focus theory (RFT), this study explores the formation mechanisms underlying blockchain technology adoption from a multi-stakeholder perspective. The model was tested through partial least squares-structural equation modeling (PLS-SEM) and the fuzzy-set qualitative comparative analysis (fsQCA) method using data collected from Chinese construction practitioners. PLS-SEM results indicate that institutional pressures, perceived functional benefits, and behavioral control can directly drive blockchain adoption. Although perceived symbolic benefits do not directly affect blockchain adoption, their indirect effects on adoption are fully mediated by adoption intention. The PLS multi-group analysis found multiple path differences among stakeholders. The fsQCA results show that no single factor or its negation is the necessary condition to trigger blockchain technology adoption. The configuration analysis results show four new configurations that trigger the adoption of blockchain technology by owners, contractors, and consultants. This study not only enriches the related research on TPB and blockchains but also helps technology promoters to use strategies tailored to different stakeholders in theory and practice to improve blockchain technology adoption.
Violence Detection and Recognition from Diverse Video Sources

The world is witnessing a vast increase in the crime rate, accompanied by severe losses of lives and property. Surveillance cameras are present everywhere. However, human supervision is becoming inefficient and incapable of early detection of violent acts, due to the diversity and the unexpected scenarios of violence. Accordingly, contributing to automatic violence prevention is becoming exceedingly important. Fulfilling this urgent demand, we turn to Machine Learning to help detect and classify violent events from video streams. In this paper, we propose a framework for detecting violence in video captures and streams, followed by categorizing the violent act in case the video clip is classified as violent. Supervised Learning is applied for both, the binary classification problem and the multi-class violence classification problem. The detection model relies on the usage of 3D Convolutional Neural Networks. The classification model utilizes the pre-trained Inception-v3 model for feature extraction, followed by Gated Recurrent Units (GRUs) for temporal processing. The models are trained on multiple datasets, whose frame-level annotations are available. We meant to use videos from various sources such as surveillance cameras, human recordings, movies, and public websites such as YouTube, to demonstrate the effectiveness of our models on different data sources. Furthermore, Transfer Learning from pre-trained models is applied, where each model trained on one of the datasets, is lightly re-trained on a different one, mostly demonstrating better performance than the original model, in terms of computational resources demands and accuracy.
Privacy-Preserving Image Classification Using Vision Transformer

In this paper, we propose a privacy-preserving image classification method that is based on the combined use of encrypted images and the vision transformer (ViT). The proposed method allows us not only to apply images without visual information to ViT models for both training and testing but to also maintain a high classification accuracy. ViT utilizes patch embedding and position embedding for image patches, so this architecture is shown to reduce the influence of block-wise image transformation. In an experiment, the proposed method for privacy-preserving image classification is demonstrated to outperform state-of-the-art methods in terms of classification accuracy and robustness against various attacks.
Stable Long-Term Recurrent Video Super-Resolution

Recurrent models have gained popularity in deep learning (DL) based video super-resolution (VSR), due to their increased computational efficiency, temporal receptive field and temporal consistency compared to sliding-window based models. However, when inferring on long video sequences presenting low motion (i.e. in which some parts of the scene barely move), recurrent models diverge through recurrent processing, generating high frequency artifacts. To the best of our knowledge, no study about VSR pointed out this instability problem, which can be critical for some real-world applications. Video surveillance is a typical example where such artifacts would occur, as both the camera and the scene stay static for a long time. In this work, we expose instabilities of existing recurrent VSR networks on long sequences with low motion. We demonstrate it on a new long sequence dataset Quasi-Static Video Set, that we have created. Finally, we introduce a new framework of recurrent VSR networks that is both stable and competitive, based on Lipschitz stability theory. We propose a new recurrent VSR network, coined Middle Recurrent Video Super-Resolution (MRVSR), based on this framework. We empirically show its competitive performance on long sequences with low motion.
Human Intention-Aware Motion Planning and Adaptive Fuzzy Control for a Collaborative Robot With Flexible Joints

This article presents a framework to enable a human and robot to perform collaborative tasks safely and efficiently. It consists of three functions. First, human motion is predicted by utilizing Gaussian mixture regression. Second, the motion planning of an online robot is performed such that the robot can appropriately react to a human coworker while executing a task. In our proposed framework, the predicted human motion is transferred to a virtual force acting on a robot's end effector. Its initial trajectory is modified so as to avoid any collisions with the human. To obtain a smooth, collision-free, and energy-minimized trajectory, a constrained optimization problem is formulated. A neural dynamics optimization algorithm is then adopted to solve it. Third, an adaptive fuzzy controller is proposed to track the robot's desired trajectory with uncertain dynamics parameters. We provide the rigorous proof of stability for the proposed methods. The physical experiments are conducted to demonstrate the effectiveness of the proposed collaborative strategy.
OblivGM: Oblivious Attributed Subgraph Matching as a Cloud Service

In recent years there has been growing popularity of leveraging cloud computing for storing and querying attributed graphs, which have been widely used to model complex structured data in various applications. Such trend of outsourced graph analytics, however, is accompanied with critical privacy concerns regarding the information-rich and proprietary attributed graph data. In light of this, we design, implement, and evaluate OblivGM, a new system aimed at oblivious graph analytics services outsourced to the cloud. OblivGM focuses on the support for attributed subgraph matching, one popular and fundamental graph query functionality aiming to retrieve from a large attributed graph subgraphs isomorphic to a small query graph. Built from a delicate synergy of insights from attributed graph modelling and advanced lightweight cryptography, OblivGM protects the confidentiality of data content associated with attributed graphs and queries, conceals the connections among vertices in attributed graphs, and hides search access patterns. Meanwhile, OblivGM flexibly supports oblivious evaluation of varying subgraph queries, which may contain equality and/or range predicates. Extensive experiments over a real-world attributed graph dataset demonstrate that while providing strong security guarantees, OblivGM achieves practically affordable performance (with query latency on the order of a few seconds).
Dual-Space Graph-Based Interaction Network for RGB-Thermal Semantic Segmentation in Electric Power Scene

Real-time scene comprehension is the basis for automatic electric power inspection. However, existing RGB-based scene comprehension methods may achieve unsatisfied performance when dealing with complex scenarios, insufficient illumination or occluded appearances. To solve this problem, by cooperating visual and thermal images, the Dual-Space Graph-based Interaction Network (DSGBINet) is proposed to achieve all-day time semantic segmentation of power equipment in high-voltage power transmission line and electric transformer substation scenes. Specifically, modality-specific features are first extracted via two separate backbone networks with the same architecture. Multi-modality high-level features are first fused via long-range relationship in coordinate space. Then, multi-modality features from regular grids are further clustered and assigned to vertices in feature space. Cross-graph and inner-graph regional relations are utilized for reasoning and enhancement, which could exploit the mutual benefits and extract rich contextual information in a semantic view. Furthermore, to overcome the huge scale difference and the inherent characteristic of thermal images, the idea of multi-task learning is integrated into the decoding process. The edge detection and semantic segmentation are achieved collaboratively, which could segment the different power equipment more accurately and completely. The comparative and ablation experiments on the proposed two RGB-T semantic segmentation datasets evaluate the effectiveness and robustness of the proposed network compared with existing state-of-the-art methods. The extended experiments on the public datasets further demonstrate the superiority of the proposed method. Our dataset and code will be released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hhujiang/DSGBINet</uri> .
HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network

Capsule networks are designed to present the objects by a set of parts and their relationships, which provide an insight into the procedure of visual perception. Although recent works have shown the success of capsule networks on simple objects like digits, the human faces with homologous structures, which are suitable for capsules to describe, have not been explored. In this paper, we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised face subpart-part discovery. When browsing large-scale face images without labels, the network first encodes the frequently observed patterns with a set of explainable subpart capsules. Then, the subpart capsules are assembled into part-level capsules through a Transformer-based Parsing Module (TPM) to learn the compositional relations between them. During training as the face hierarchy is progressively built and refined, the part capsules adaptively encode the face parts with semantic consistency. HP-Capsule extends the application of capsule networks from digits to human faces and takes a step forward to show how the neural networks understand homologous objects without human intervention. Besides, HP-Capsule gives unsupervised face segmentation results by the covered regions of part capsules, enabling qualitative and quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the effectiveness of our method.
Leukemia Detection using Computation Intelligence Techniques

The discipline of molecular biology is the focus of the application of statistics and computer science that is known as bioinformatics. The term "bioinformatics" refers to the study of how mathematics, statistics, and information technology might be used to the study of biological sciences. The bioinformatics of microarrays is the field’s response to the challenge posed by this definition. Genes are units of information that are responsible for encoding the genetic material found in all creatures that are alive. The term "genome" refers to the whole collection of genes that are included inside an organism. For the purpose of collecting, analyzing, storing, and exchanging genetic information with other researchers, the design of studies using microarrays is required. Because they are an organized collection of thousands of distinct Deoxyribonucleic Acid (DNA) sequences that can be used to measure variations in both DNA and Ribonucleic Acid (RNA), micro-arrays have acquired a special significance in the field of bioinformatics. This is due to the fact that micro-arrays can be used to measure both DNA and RNA. In recent years, machine learning and artificial intelligence have seen fast advancements, which has led to the methodology being one of the most widely used methods. Major corporations and educational institutions have begun investing in research in the healthcare industry with the goal of improving the accuracy with which diseases can be predicted as a result of its growing popularity and the powerful techniques it employs in pattern recognition and classification. However, while putting these strategies into practice, there are a lot of obstacles to overcome. The absence of a huge data collection including medical photographs is one of the most significant challenges that must be overcome. Because they make it possible to do quantitative research on thousands of genes all at once using a single sample of cells, microarrays in particular are being hailed as a major technological advancement in the field of biology. In the first model of an artificial neural network, two different classification algorithms—feed forward backpropagation and cas-cade correlation feedforward—were compared with different sets of neurons using two different training algorithms—Levenberg Marquardt (lm) and Resilient backpropagation (rp), in order to differentiate between benign and malignant patients. According to the findings, cascade correlation with the train (rp) produced superior output outcomes when compared to feedforward back-propagation with the train (lm).
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in end-to-end driving tasks. In addition, we design a new expert to enhance the model performance by considering multi-road rules. Experimental results prove that both proposed improvements enable our agent to achieve superior performance compared with other methods.
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection

Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which demands considerable computational power and storage space. To that end, we propose a new end-to-end compressed video representation learning for event boundary detection that leverages the rich information in the compressed domain, i.e., RGB, motion vectors, residuals, and the internal group of pictures (GOP) structure, withoutfully decoding the video. Specifically, we first use the Con-vNets to extract features of the I-frames in the Gaps. After that, a light-weight spatial-channel compressed encoder is designed to compute the feature representations of the P-frames based on the motion vectors, residuals and representations of their dependent I-frames. A temporal contrastive module is proposed to determine the event boundaries of video sequences. To remedy the ambiguities of annotations and speed up the training process, we use the Gaussian kernel to preprocess the ground-truth event boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset demonstrate that the proposed method achieves comparable results to the state-of-the-art methods with 4.5 x faster running speed.
The Impact of Digital Marketing Success on Customer Loyalty

Customer loyalty is often seen as one of the decisive factors in determining the fate of organizations. In the age of digitization, organizations should think of advanced strategies to increase their competitiveness and market share by employing the potential of digital content and enhancing their digital capabilities. Recently, many digital media have appeared available to organizations to build their brand, reach their target customers and retain them, and promote their products. Digital media represented a real revolution that made it difficult for organizations to survive in the business environment without investing in these means. Therefore, the aim of the study is twofold: 1) to explore the effects of digital content marketing and digital marketing capabilities on digital marketing success; 2) to investigate the impact of digital marketing success on customer loyalty. This research was conducted on a sample of online restaurant customers through a questionnaire distributed to 187 customers. IBM SPSS 25.0 and AMOS 22.0 were used as the main programs for analysing the collected data from 165 online restaurant customers. Structural equation modelling was applied to test the research’s hypothesized effects. The results indicated that digital content marketing and digital marketing capabilities had significant effects on digital marketing success based on the perspectives of online restaurant customers in Jordan. Moreover, digital marketing success substantially impacted customer loyalty of online restaurant customers in Jordan. It was concluded that for organizations to ensure a positive effect of digital marketing on customer loyalty, more attention should be paid to critical factors such as digital content and digital capabilities. The study calls for researchers to explore more factors affecting the success of digital marketing. It informs marketing managers that customer loyalty in the digitization era depends on the ability to create, communicate and deliver digital content using digital technologies and the quality of digital content and capabilities that deliver customer value.
GAMa: Cross-View Video Geo-Localization

The existing work in cross-view geo-localization is based on images where a ground panorama is matched to an aerial image. In this work, we focus on ground videos instead of images which provides additional contextual cues which are important for this task. There are no existing datasets for this problem, therefore we propose GAMa dataset, a large-scale dataset with ground videos and corresponding aerial images. We also propose a novel approach to solve this problem. At clip-level, a short video clip is matched with corresponding aerial image and is later used to get video-level geo-localization of a long video. Moreover, we propose a hierarchical approach to further improve the clip-level geo-localization. On this challenging dataset, with unaligned images and limited field of view, our proposed method achieves a Top-1 recall rate of 19.4% and 45.1% @1.0mile. Code & dataset are available at this https://github.com/svyas23/GAMa .
Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real

Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task that remains largely unresolved. Multi-Agent Reinforcement Learning has arisen as a powerful method to accomplish this task because it considers the interaction between agents and also allows for decentralized training—which makes it highly scalable. However, transferring policies from simulation to the real world is a big challenge, even for single-agent applications. Multi-agent systems add additional complexities to the Sim-to-Real gap due to agent collaboration and environment synchronization. In this paper, we propose a method to transfer multi-agent autonomous driving policies to the real world. For this, we create a multi-agent environment that imitates the dynamics of the Duckietown multi-robot testbed, and train multi-agent policies using the MAPPO algorithm with different levels of domain randomization. We then transfer the trained policies to the Duckietown testbed and show that when using our method, domain randomization can reduce the reality gap by 90%. Moreover, we show that different levels of parameter randomization have a substantial impact on the Sim-to-Real gap. Finally, our approach achieves significantly better results than a rule-based benchmark.
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection

Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video parsing. Still at an early development stage, existing GEBD solvers are simple extensions of relevant video understanding tasks, disregarding GEBD's distinctive characteristics. In this paper, we propose a novel framework for unsupervised/supervised GEBD, by using the Temporal Self-similarity Matrix (TSM) as the video representation. The new Recursive TSM Parsing (RTP) algorithm exploits local diagonal patterns in TSM to detect boundaries, and it is combined with the Boundary Contrastive (BoCo) loss to train our encoder to generate more informative TSMs. Our framework can be applied to both unsupervised and supervised settings, with both achieving state-of-the-art performance by a huge margin in GEBD benchmark. Especially, our unsupervised method outperforms the previous state-of-the-art “supervised” model, implying its exceptional efficacy.
Arbitrary Shape Text Detection using Transformers

Recent text detection frameworks require several handcrafted components such as anchor generation, non-maximum suppression (NMS), or multiple processing stages (e.g. label generation) to detect arbitrarily shaped text images. In contrast, we propose an end-to-end trainable architecture based on Detection using Transformers (DETR), that outperforms previous state-of-the-art methods in arbitrary-shaped text detection.At its core, our proposed method leverages a bounding box loss function that accurately measures the arbitrary detected text regions’ changes in scale and aspect ratio. This is possible due to a hybrid shape representation made from Bezier curves, that are further split into piece-wise polygons. The proposed loss function is then a combination of a generalized-split-intersection-over-union loss defined over the piece-wise polygons, and regularized by a Smooth-ln regression over the Bezier curve’s control points.We evaluate our proposed model using Total-Text and CTW-1500 datasets for curved text, and MSRA-TD500 and ICDAR15 datasets for multi-oriented text, and show that the proposed method outperforms the previous state-of-the-art methods in arbitrary-shape text detection tasks.