Reviewerly/exHarmony huggingface dataset:
Refractive three-dimensional reconstruction for underwater stereo digital image correlation

Measuring the three-dimensional (3D) deformation of submerged objects through different media with the stereo digital image correlation (stereo-DIC) method involves the refractive optical imaging problem where the non-linear transmission of light is induced by a change of medium density. The problem invalidates the underlying single viewpoint assumption of the perspective model in regular stereo-DIC, thereby resulting in erroneous measurements of 3D shape and deformation. In this work, we propose a refractive stereo-DIC method that overcomes the problem by considering light refraction in 3D reconstruction. We formulate a full refractive reconstruction geometry description based on Snell’s law of flat refraction and the regular triangulation. This allows the true shape to be effectively reconstructed by tracing and establishing the refracted ray-paths based on the regular 3D reconstruction, without reformulating the camera model and image formation. The refractive stereo-DIC is finally established by integrating the refractive 3D reconstruction into the regular DIC framework for measuring accurate 3D shape and deformation of submerged objects. We experiment the proposed approach with underwater 3D shape and deformation measurements. Both results prove its feasibility and correctness, further heralding our approach as a flexible solution that could readily extend the stereo-DIC to fluid-immersed 3D deformation characterization.
Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.
Feature selection using hybrid poor and rich optimization algorithm for text classification

In order to reduce the high dimensional feature space in the text classification, feature selection plays a significant role. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of an optimal combination of features is an essential task in text classification. In this paper, the proposed work introduces a novel hybrid feature selection method based on binary poor and rich optimization algorithm (HBPRO) to obtain the appropriate subset of optimal features. The optimal feature subset which is selected by our proposed work is evaluated using Nave Bayes classifier with two popular benchmark text corpus datasets. The experimental results confirm that the proposed feature selection scheme (HBPRO) produces higher accuracy with a reduced number of features when compared with other feature selection techniques.
Design of Secure Decentralized Car-Sharing System Using Blockchain

Car-sharing systems can solve various urban problems by providing shared vehicles to people and reducing the operation of personal vehicles. With the development of the Internet of Things, people can easily use a shared car through simple operations on their mobile devices. However, the car-sharing system has security problems. Sensitive information, such as the user’s identity, location information, and access code, is transmitted through a public channel for car-sharing. Hence, an attacker can access this information for illegal purposes, making the establishment of a secure authentication protocol essential. Furthermore, the traditional car-sharing system is established on the centralized structure, so there is a single point of failure. Thus, the design of a decentralized car-sharing scheme is vital for solving the centralized problem. This study designed a decentralized car-sharing scheme using blockchain. Specifically, blockchain technology was used to provide a decentralization car-sharing service and ensure data integrity. The participant entities of the proposed system can be authenticated anonymously. The proposed car-sharing system can be secured against various attacks and provide mutual authentication using informal analysis, automated validation of internet security protocols and applications (AVISPA) simulation, and BAN logic analysis. The computation costs and communication costs of the proposed scheme were also analyzed.
Trust-aware recommendation based on heterogeneous multi-relational graphs fusion

Users' trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users' social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user–item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user–user trust relation graph, user–item interaction graph and item–item knowledge graph, and the user feature and item feature, which have been obtained from the user–item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.
SiamCDA: Complementarity- and Distractor-Aware RGB-T Tracking Based on Siamese Network

Recent years have witnessed the prevalence of using the Siamese network for RGB-T tracking because of its remarkable success in RGB object tracking. Despite their faster than real-time speeds, existing RGB-T Siamese trackers suffer from low accuracy and poor robustness, compared to other state-of-the-art RGB-T trackers. To address such issues, a new complementarity- and distractor-aware RGB-T tracker based on Siamese network (referred to as SiamCDA) is developed in this paper. To this end, several modules are presented, where the feature pyramid network (FPN) is incorporated into the Siamese network to capture the cross-level information within unimodal features extracted from the RGB or the thermal images. Next, a complementarity-aware multi-modal feature fusion module (CA-MF) is specially designed to capture the cross-modal information between RGB features and thermal features. In the final bounding box selection phase, a distractor-aware region proposal selection module (DAS) further enhances the robustness of our tracker. On top of the technical modules, we also build a large-scale, diverse synthetic RGB-T tracking dataset, containing more than 4831 pairs of synthetic RGB-T videos and 12K synthetic RGB-T images. Extensive experiments on three RGB-T tracking benchmark datasets demonstrate the outstanding performance of our proposed tracker with a tracking speed over 37 frames per second (FPS).
Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rarely solve both challenges in a unified framework. In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques. First, we analytically show that the essential issue behind the large disparity and non-Lambertian challenges is the aliasing problem. Classic LF rendering approaches typically mitigate the aliasing with a reconstruction filter in the Fourier domain, which is, however, intractable to implement within a deep learning pipeline. Instead, we introduce an alternative framework to perform anti-aliasing reconstruction in the image domain and analytically show comparable efficacy on the aliasing issue. To explore the full potential, we then embed the anti-aliasing framework into a deep neural network through the design of an integrated architecture and trainable parameters. The network is trained through end-to-end optimization using a peculiar training set, including regular LFs and unstructured LFs. The proposed deep learning pipeline shows a substantial superiority in solving both the large disparity and the non-Lambertian challenges compared with other state-of-the-art approaches. In addition to the view interpolation for an LF, we also show that the proposed pipeline also benefits light field view extrapolation.
Transparent Adaptation in Deep Medical Image Diagnosis

The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, transparent way for prediction in medical imaging. A novel methodology is presented, in which deep neural architectures that have been trained to provide highly accurate predictions over existing datasets are adapted, in a consistent way, to make predictions over different contexts and datasets. Unified prediction is then achieved over the original and the new datasets. Successful application is illustrated through a large experimental study for prediction of Parkinson's disease from MRI and DaTScans, as well as for prediction of COVID-19 from CT scans and X-rays.
Blockchain and SGX-Enabled Edge-Computing-Empowered Secure IoMT Data Analysis

The Internet of Medical Things (IoMT) is an important application of the Internet of Things (IoT) in the health field, including remote health monitoring and remote medical diagnosis. This not only brings convenience to the patient but also reduces the cost of the patient. However, the surge of data brought by mobile health monitoring equipment challenges the traditional centralized data processing model. In particular, medical data are closely related to patient privacy. Therefore, only part of the specific medical data should be provided to the medical institutions in need, rather than all the data, to ensure the confidentiality of the data to the greatest extent. But curious data processing centers can easily lead to data leakage. To tackle these challenges, we use edge computing and blockchain to build a new framework. In particular, the trusted execution environment, namely, software guard extension (SGX) technology, is introduced into edge computing to ensure the confidentiality of the data analysis process. The blockchain authenticates the IoMT devices and cloud service providers that are added to the network and provides an access policy management mechanism for IoMT data. Moreover, a prototype of the proposed framework is implemented using Hyperledger Fabric and Intel SGX, and the analysis of the blockchain and SGX performance are also presented.
Secure Password-Protected Encryption Key for Deduplicated Cloud Storage Systems

In this article, we propose SPADE, an encrypted data deduplication scheme that resists compromised key servers and frees users from the key management problem. Specifically, we propose a proactivization mechanism for the servers-aided message-locked encryption (MLE) to periodically substitute key servers with newly employed ones, which renews the security protection and retains encrypted data deduplication. We present a servers-aided password-hardening protocol to resist dictionary guessing attacks. Based on the protocol, we further propose a password-based layered encryption mechanism and a password-based authentication mechanism and integrate them into SPADE to enable users to access their data only using their passwords. Provable security and high efficiency of SPADE are demonstrated by comprehensive analyses and experimental evaluations.
Modeling quantum light-matter interactions in waveguide QED with retardation, nonlinear interactions, and a time-delayed feedback: Matrix product states versus a space-discretized waveguide model

We present two different methods for modelling non-Markovian quantum light-matter interactions in waveguide QED systems, using matrix product states (MPSs) and a space-discretized waveguide (SDW) model. After describing the general theory and implementation of both approaches, we compare and contrast these methods directly on three topical problems of interest in waveguide-QED, including (i) a two-level system (TLS) coupled to an infinite (one-dimensional) waveguide, (ii) a TLS coupled to a terminated waveguide with a time-delayed coherent feedback, and (iii) two spatially separated TLSs coupled within an infinite waveguide. Both approaches are shown to efficiently model multi-photon nonlinear dynamics in highly non-Markovian regimes, and we highlight the advantages and disadvantages of these methods for modelling waveguide QED interactions, including their implementation in Python, computational run times, and ease of conceptual understanding. We explore both vacuum dynamics as well as regimes of strong optical pumping, where a weak excitation approximation cannot be applied. The MPS approach scales better when modelling multi-photon dynamics and long delay times, and explicitly includes non-Markovian memory effects. In contrast, the SDW model accounts for non-Markovian effects through space discretization, and solves Markovian equations of motion, yet rigorously includes the effects of retardation. The SDW model, based on an extension of recent collisional pictures in quantum optics, is solved through quantum trajectory techniques, and can more easily add in additional dissipation processes, including off-chip decay and TLS pure dephasing. The impact of these processes is shown directly on feedback-induced population trapping and TLS entanglement between spatially separated TLSs.
Trends in blockchain-based electronic voting systems

Electronic voting systems face many challenges, including authentication, privacy, data integrity, transparency and verifiability. However, the developed over 10 years ago blockchain technology provides an out-of-the-box solution for many of those challenges. Despite that, some issues are still to be solved, like remote authentication, anonymity and end-to-end verifiability. For this reason, research on new and better blockchain-based systems is still ongoing. The main goal of this study is to highlight the current trends in this research and its eventual shortcomings. This was accomplished by conducting a Systematic Literature Review (SLR) (Kitchenham and Charters, 2007), which resulted in selecting 35 publications. The performed SLR allowed to define trends in utilized blockchain technologies, intended scenarios, testing methods, main benefits and challenges faced by various systems and the most used cryptographic solutions.
Omnichannel Management in B2B. Complexity-based model. Empirical evidence from a panel of experts based on Fuzzy Cognitive Maps

In recent years, academics and professionals have proposed omnichannel management as the best approach to offering multiple channels to end customers. This approach has been reinforced by the recent crisis caused by Covid-19 and the consequent demand for digital channels. In the current literature there is an evident gap in the study of omnichannel management for manufacturing or wholesale companies and their relationships with other companies, which typically use B2B models. This article includes a model that permits the identification of causal characteristics in omnichannel management based on fuzzy cognitive maps (FCM), the simulation of possible scenarios and the impact that changes in the environment or in the organization's internal activities may have on omnichannel management. From the results of a Delphi process based on an international Panel of Experts and using complexity theory, a Fuzzy Cognitive Map (FCM) was built that can serve as a reference for B2B omnichannel management. The main value of the research is provided by the practical model that allows simulating what-if scenarios, that is, with the modification of the input conditions with respect to a base scenario and thus favors directing the omnichannel strategy to be followed in a B2B field.
Pose Estimation and Detection for Event Recognition using Sense-Aware Features and Adaboost Classifier

To examine events identification and recognition in sequential images, approaches used several parameters such as size, location or position of the human body parts along with its surrounding effects. In this paper, we estimated several body key points to monitor and track its appearance in complex events. Such detection of key body parts need several features descriptor such as optical flow, moving body parts and 0-180° intensity characteristics for event identification. While, extraction architecture model used human body key points through the R-Transform factor having intensity values. After intensity values extraction, the estimation of moveable body parts are applied for change detection system. In addition, optical flow features are also extracted. The extracted intensity and moveable body features are merged with optical flow vectors. These extracted features are injected into pre-classifier as Particle Swarm optimization and recognizer engine as Adaboost. The experimental results on a challenging videos datasets such as UCF 101 and YouTube show significant accuracy 75.33% and 76.66% of event recognition. We achieved higher performance of body-parts detection and event recognition as compare to state-of-art approaches.
Center-Boundary Dual Attention for Oriented Object Detection in Remote Sensing Images

Recently, anchor-free object detectors have shown promising performance in oriented object detection on remote sensing images. However, the objects in remote sensing images always have large variations in arbitrary orientations, sizes, and aspect ratios, which makes the existing anchor-free methods hard to obtain satisfactory results. In this article, we propose a novel anchor-free detector, center-boundary dual attention (CBDA) network (CBDA-Net), for fast and accurate oriented object detection on remote sensing images. In CBDA-Net, we construct a CBDA module, which utilizes a dual attention mechanism to extract attention features on the center and boundary regions of objects. The CBDA module can learn more essential features for rotating objects and reduce the interference from complex background. Besides, to resolve the influence of object aspect ratio on angle errors, we propose an aspect ratio weighted angle loss (arwLoss), where diffident penalties are assigned on the angle loss based on the aspect ratios of objects. This loss construction is effective in improving the detection accuracy of oriented objects, especially for slender objects. We conduct extensive experiments on two publish benchmarks, i.e., DOTA and HRSC2016. The experimental results demonstrate that our CBDA-Net achieves favorable performance against other anchor-free state of the arts with a real-time speed of 50 FPS.
Intra-Document Cascading

An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all individual passages in the document and then aggregating the outputs by pooling or additional Transformer layers. A major drawback of this approach is high query latency due to the cost of evaluating every passage in the document with BERT. To make matters worse, this high inference cost and latency varies based on the length of the document, with longer documents requiring more time and computation. To address this challenge, we adopt an intra-document cascading strategy, which prunes passages of a candidate document using a less expensive model, called ESM, before running a scoring model that is more expensive and effective, called ETM. We found it best to train ESM (short for Efficient Student Model) via knowledge distillation from the ETM (short for Effective Teacher Model) e.g., BERT. This pruning allows us to only run the ETM model on a smaller set of passages whose size does not vary by document length. Our experiments on the MS MARCO and TREC Deep Learning Track benchmarks suggest that the proposed Intra-Document Cascaded Ranking Model (IDCM) leads to over 400% lower query latency by providing essentially the same effectiveness as the state-of-the-art BERT-based document ranking models.
The Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ): Estimating Individual Susceptibility to Motion Sickness-Like Symptoms When Using Visual Devices

Two studies were conducted to develop and validate a questionnaire to estimate individual susceptibility to visually induced motion sickness (VIMS).VIMS is a common side-effect when watching dynamic visual content from various sources, such as virtual reality, movie theaters, or smartphones. A reliable questionnaire predicting individual susceptibility to VIMS is currently missing. The aim was to fill this gap by introducing the Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ).A survey and an experimental study were conducted. Survey: The VIMSSQ investigated the frequency of nausea, headache, dizziness, fatigue, and eyestrain when using different visual devices. Data were collected from a survey of 322 participants for the VIMSSQ and other related phenomena such as migraine. Experimental study: 23 participants were exposed to a VIMS-inducing visual stimulus. Participants filled out the VIMSSQ together with other questionnaires and rated their level of VIMS using the Simulator Sickness Questionnaire (SSQ).Survey: The most prominent symptom when using visual devices was eyestrain, and females reported more VIMS than males. A one-factor solution with good scale reliability was found for the VIMSSQ. Experimental study: Regression analyses suggested that the VIMSSQ can be useful in predicting VIMS (R2 = .34) as measured by the SSQ, particularly when combined with questions pertaining to the tendency to avoid visual displays and experience syncope (R2 = .59).We generated normative data for the VIMSSQ and demonstrated its validity.The VIMSSQ can become a valuable tool to estimate one's susceptibility to VIMS based on self-reports.