Reviewerly/exHarmony huggingface dataset:
Body Weight Perception of Females using Photorealistic Avatars in Virtual and Augmented Reality

The appearance of avatars can potentially alter changes in their users’ perception and behavior. Based on this finding, approaches to support the therapy of body perception disturbances in eating or body weight disorders by mixed reality (MR) systems gain in importance. However, the methodological heterogeneity of previous research has made it difficult to assess the suitability of different MR systems for therapeutic use in these areas. The effects of MR system properties and related psychometric factors on body-related perceptions have so far remained unclear. We developed an interactive virtual mirror embodiment application to investigate the differences between an augmented reality see-through head-mounted-display (HMD) and a virtual reality HMD on the before-mentioned factors. Additionally, we considered the influence of the participant’s body-mass-index (BMI) and the BMI difference between participants and their avatars on the estimations. The 54 normal-weight female participants significantly underestimated the weight of their photorealistic, generic avatar in both conditions. Body weight estimations were significantly predicted by the participants’ BMI and the BMI difference. We also observed partially significant differences in presence and tendencies for differences in virtual body ownership between the systems. Our results offer new insights into the relationships of body weight perception in different MR environments and provide new perspectives for the development of therapeutic applications.
Efficient and secure routing protocol based on Blockchain approach for wireless sensor networks

Abstract Embedded systems and wireless sensor networks (WSN) are found today in increasingly critical areas of applications. They have become integrated and embedded in nearly all aspects of everyday life, including manufacturing, healthcare, education, critical infrastructure, and entertainment. The number of connected devices continues to grow, and due to the insecure nature of these devices, the amount of risk continues to grow as well. These risks, however, can be mitigated with the creation and adoption of WSN security standards developed to create an environment of safety, security, and confidence in the technology. Designing the security policy for WSNs requires asking some preliminary questions. These questions are particularly important in the case of WSNs because their use is highly decentralized. Blockchain's ability on governing decentralized networks makes it especially suitable for designing a self‐managing system on WSN devices. This article proposes a routing protocol that uses Blockchain technology to offer a shared memory between the network's nodes. The simulation results have shown that this solution could be applicable and could resolve the issues cited above.
Geofence Definition and Deconfliction for UAS Traffic Management

Before Unmanned Aircraft Systems (UAS) can be widely deployed over complex terrain and urban regions, a methodology is needed to deconflict and track UAS flights. This paper presents a formal geofence definition and a methodology to use geofences to temporally and spatially organize the airspace in support of a UAS Traffic Management (UTM) system. Algorithms are presented to temporally and spatially deconflict requested geofences from existing approved geofences in the UTM system. A case study shows how deconflicted geofences impact UAS flight.
Tangi: Tangible Proxies For Embodied Object Exploration And Manipulation In Virtual Reality

Exploring and manipulating complex virtual objects is challenging due to limitations of conventional controllers and free-hand interaction techniques. We present the TanGi toolkit which enables novices to rapidly build physical proxy objects using Composable Shape Primitives. TanGi also provides Manipulators allowing users to build objects including movable parts, making them suitable for rich object exploration and manipulation in VR. With a set of different use cases and applications we show the capabilities of the TanGi toolkit and evaluate its use. In a study with 16 participants, we demonstrate that novices can quickly build physical proxy objects using the Composable Shape Primitives and explore how different levels of object embodiment affect virtual object exploration. In a second study with 12 participants we evaluate TanGi's Manipulators and investigate the effectiveness of embodied interaction. Findings from this study show that TanGi's proxies outperform traditional controllers and were generally favored by participants.
Pen-based Interaction with Spreadsheets in Mobile Virtual Reality

Virtual Reality (VR) can enhance the display and interaction of mobile knowledge work and in particular, spreadsheet applications. While spreadsheets are widely used yet are challenging to interact with, especially on mobile devices, using them in VR has not been explored in depth. A special uniqueness of the domain is the contrast between the immersive and large display space afforded by VR, contrasted by the very limited interaction space that may be afforded for the information worker on the go, such as an airplane seat or a small work-space. To close this gap, we present a tool-set for enhancing spreadsheet interaction on tablets using immersive VR headsets and pen-based input. This combination opens up many possibilities for enhancing the productivity for spreadsheet interaction. We propose to use the space around and in front of the tablet for enhanced visualization of spreadsheet data and meta-data. For example, extending sheet display beyond the bounds of the physical screen, or easier debugging by uncovering hidden dependencies between sheet's cells. Combining the precise on-screen input of a pen with spatial sensing around the tablet, we propose tools for the efficient creation and editing of spreadsheets functions such as off-the-screen layered menus, visualization of sheets dependencies, and gaze-and-touch-based switching between spreadsheet tabs. We study the feasibility of the proposed tool-set using a video-based online survey and an expert-based assessment of indicative human performance potential.
Learnable Subspace Clustering

This article studies the large-scale subspace clustering (LS2C) problem with millions of data points. Many popular subspace clustering methods cannot directly handle the LS2C problem although they have been considered to be state-of-the-art methods for small-scale data points. A simple reason is that these methods often choose all data points as a large dictionary to build huge coding models, which results in high time and space complexity. In this article, we develop a learnable subspace clustering paradigm to efficiently solve the LS2C problem. The key concept is to learn a parametric function to partition the high-dimensional subspaces into their underlying low-dimensional subspaces instead of the computationally demanding classical coding models. Moreover, we propose a unified, robust, predictive coding machine (RPCM) to learn the parametric function, which can be solved by an alternating minimization algorithm. Besides, we provide a bounded contraction analysis of the parametric function. To the best of our knowledge, this article is the first work to efficiently cluster millions of data points among the subspace clustering methods. Experiments on million-scale data sets verify that our paradigm outperforms the related state-of-the-art methods in both efficiency and effectiveness.
An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

Microservice architecture (MSA) is a new software architecture, which divides a large single application and service into dozens of supporting microservices. With the increasingly popularity of MSA, the security issues of MSA get a lot of attention. In this paper, we propose an algorithm for mining causality and the root cause. Our algorithm consists of two parts: invocation chain anomaly analysis based on robust principal component analysis (RPCA) and a single indicator anomaly detection algorithm. The single indicator anomaly detection algorithm is composed of Isolation Forest (IF) algorithm, One-Class Support Vector Machine (SVM) algorithm, Local Outlier Factor (LOF) algorithm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle. For general and network time-consuming anomaly in the process of the MSA, we formulate different anomaly time-consuming detection strategies. We select a batch of sample data and three batches of test data of the 2020 International AIOps Challenge to debug our algorithm. According to the scoring criteria of the competition organizers, our algorithm has an average score of 0.8304 (The full score is 1) in the four batches of data. Our proposed algorithm has higher accuracy than some traditional machine learning algorithms in anomaly detection.
Agro-information Service and Information-seeking Behaviour of Small-scale Farmers in Rural Bangladesh

Agriculture is the mainstay of Bangladesh’s economy, accounting for 15.89% of its GDP and 45.1% of its labour employment. Efficiency of economic activities in agriculture crucially depends on the flow of information relating to farming decisions, as well as on demand for such information by farmers. This study examines the information needs and information-seeking behaviour of small-scale farmers in rural Bangladesh. Opinions and feedback of farmers were collected through mini–focus group discussions (MFGD) and in-depth interviews (IDI) in seven districts across Bangladesh. The study finds that many farmers lack awareness of where and how to obtain agro-information. The priority areas on which farmers typically seek information include seeds, fertilisers, agriculture credit, irrigation, disease and pest management. The information-seeking behaviour of the farmers depends on two variables: the nature of the crisis and the reliability of the information available. Farmers rely on personal experience and informal networks when the nature of crisis is familiar or non-critical; they rely on multiple external sources when the nature of crisis is critical and personal knowledge proves inadequate. The use of information and communications technology (ICT) (such as Union Digital Centers and Agricultural Helplines) in this regard is minimal, and the potential of utilisation of the office of agriculture extension remains untapped. The major constraints faced by the farmers in seeking information using modern ICT include farmers’ prevailing norms and perceptions (e.g., resistance to change and adoption of new technology), relatively high costs of and low awareness regarding agro-information, poor infrastructure to be able to support ICT services, spatial inconvenience, in terms of location and availability of ICT facilities, and low literacy among the farmers.
High-throughput phenotyping with temporal sequences

High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.
Remote Sensing Image Super-Resolution via Saliency-Guided Feedback GANs

In remote sensing images (RSIs), the visual characteristics of different regions are versatile, which poses a considerable challenge to single image super-resolution (SISR). Most existing SISR methods for RSIs ignore the diverse reconstruction needs of different regions and thus face a serious contradiction between high perception quality and less spatial distortion. The mean square error (MSE) optimization-based methods produce results of unsatisfactory visual quality, while generative adversarial networks (GANs) can produce photo-realistic but severely distorted results caused by pseudotextures. In addition, increasingly deeper networks, although providing powerful feature representations, also face problems of overfitting and occupying too much storage space. In this article, we propose a new saliency-guided feedback GAN (SG-FBGAN) to address these problems. The proposed SG-FBGAN applies different reconstruction principles for areas with varying levels of saliency and uses feedback (FB) connections to improve the expressivity of the network while reducing parameters. First, we propose a saliency-guided FB generator with our carefully designed paired-feedback block (PFBB). The PFBB uses two branches, a salient and a nonsalient branch, to handle the FB information and generate powerful high-level representations for salient and nonsalient areas, respectively. Then, we measure the visual perception quality of salient areas, nonsalient areas, and the global image with a saliency-guided multidiscriminator, which can dramatically eliminate pseudotextures. Finally, we introduce a curriculum learning strategy to enable the proposed SG-FBGAN to handle complex degradation models. Comprehensive evaluations and ablation studies validate the effectiveness of our proposal.
Many-to-Many Voice Transformer Network

This paper proposes a voice conversion (VC) method based on a sequence-to-sequence (S2S) learning framework, which enables simultaneous conversion of the voice characteristics, pitch contour, and duration of input speech. We previously proposed an S2S-based VC method using a transformer network architecture called the voice transformer network (VTN). The original VTN was designed to learn only a mapping of speech feature sequences from one speaker to another. Here, the main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers. This extension, called the many-to-many VTN, enables us to fully use available training data collected from multiple speakers by capturing common latent features that can be shared across different speakers. It also allows us to introduce a training loss called the identity mapping loss to ensure that the input feature sequence will remain unchanged when the source and target speaker indices are the same. Using this particular loss for model training has been found to be extremely effective in improving the performance of the model at test time. We conducted speaker identity conversion experiments and found that our model obtained higher sound quality and speaker similarity than baseline methods. We also found that our model, with a slight modification to its architecture, can handle any-to-many conversion tasks reasonably well.
SAR-to-Optical Image Translation Using SSIM and Perceptual Loss Based Cycle-Consistent GAN

Synthetic aperture radar (SAR) and optical sensing are different earth observation methods. Compared to the optical sensors, the SAR has the imaging advantages such as all-weather, all-time, ability to traverse clouds and vegetation, etc. We propose the application of the cycle-consistent generative adversarial network (CycleGAN) for translating the SAR images and the optical images with each other by training two generator networks and two discriminator networks simultaneously. In this paper, we add the structure similarity index measure (SSIM) loss factor and perceptual loss into the basis CycleGAN's loss function for keeping rich structural information. The tentative study demonstrated the potential of the SAR and the optical image translation based on the CycleGAN. The proposed method was verified and the experimental results showed the superiority of the proposed method with the combined loss function.
Diagnostic Regions Attention Network (DRA-Net) for Histopathology WSI Recommendation and Retrieval

The development of whole slide imaging techniques and online digital pathology platforms have accelerated the popularization of telepathology for remote tumor diagnoses. During a diagnosis, the behavior information of the pathologist can be recorded by the platform and then archived with the digital case. The browsing path of the pathologist on the WSI is one of the valuable information in the digital database because the image content within the path is expected to be highly correlated with the diagnosis report of the pathologist. In this article, we proposed a novel approach for computer-assisted cancer diagnosis named session-based histopathology image recommendation (SHIR) based on the browsing paths on WSIs. To achieve the SHIR, we developed a novel diagnostic regions attention network (DRA-Net) to learn the pathology knowledge from the image content associated with the browsing paths. The DRA-Net does not rely on the pixel-level or region-level annotations of pathologists. All the data for training can be automatically collected by the digital pathology platform without interrupting the pathologists’ diagnoses. The proposed approaches were evaluated on a gastric dataset containing 983 cases within 5 categories of gastric lesions. The quantitative and qualitative assessments on the dataset have demonstrated the proposed SHIR framework with the novel DRA-Net is effective in recommending diagnostically relevant cases for auxiliary diagnosis. The MRR and MAP for the recommendation are respectively 0.816 and 0.836 on the gastric dataset. The source code of the DRA-Net is available at https://github.com/zhengyushan/dpathnet .
Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs on Edge-device

The real-time detection of traffic rule violators in a city-wide surveillance network is a highly desirable but challenging task because it needs to perform computationally complex analytics on the live video streams from large number of cameras, simultaneously. In this paper, we propose an efficient framework using edge computing to deploy a system for automatic detection of bike-riders without helmet. First, we propose a novel robust and compact method for the detection of the motorcyclists without helmet using convolutional neural networks (CNNs). Then, we scale it for the real-time performance on an edge-device by dropping redundant filters and quantizing the model weights. To reduce the network latency, we place the detector module on edge-devices in the cameras. The edge-nodes send their detected alerts to a central alert database where the end users access these alerts through a web interface. To evaluate the proposed method, we collected two datasets of real traffic videos, namely, IITH_Helmet_1 which contains sparse traffic and IITH_Helmet_2 which contains dense traffic. The experimental results show that our method achieves a high detection accuracy of ≈95% while maintaining the real-time processing speed of ≈22fps on Nvidia-TXI.
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles

In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3D object detection and obtain more explainable and convincing uncertainty for the prediction. The loss function was designed using corner transformation and uncertainty modeling. With the new loss function, the performance of our method on the vat split of KITTI dataset shows up to a 15% increase in terms of Average Precision (AP) comparing with the baseline using simple Ll Loss. In the study of the characteristics of predicted uncertainties, we find that generally more accurate prediction of the bounding box is accompanied by lower uncertainty. The distribution of corner uncertainties agrees on the distribution of the point cloud in the bounding box, which means the corner with denser observed points has lower uncertainty. Moreover, our method learns the constraint from the cuboid geometry of the bounding box in the uncertainty prediction. Finally, we propose an efficient Bayesian updating method to recover the uncertainty for the original parameters of the bounding boxes which can help provide probabilistic results for the tracking and planning module.
Efficient biometric-based identity management on the Blockchain for smart industrial applications

In this work, we propose a new Blockchain-based Identity Management system for smart industry. First, we describe an efficient biometric-based anonymous credential scheme, which supports selective disclosure, suspension/thaw and revocation of credentials/entities. Our system provides non-transferability through a freshly computed hidden biometric attribute, which is generated using a secure fuzzy extractor during each authentication. This mechanism combined with offchain storage guarantees GDPR compliance, which is required for protecting user’s data. We define blinded (Brands) DLRep scheme to provide multi-show unlinkability, which is a lacking feature in Brands’ credential based systems. For larger organizations, we re-design the system by replacing the Merkle Tree with an accumulator to improve scalability. The new system enables auditing by adapting the standard Industrial IoT (IIoT) Identity Management Lifecycle to Blockchain. Finally, we show that the new proposal outperforms BASS, i.e. the most recent blockchain-based anonymous credential scheme designed for smart industry. The computational cost at the user-side (can be a weak IoT device) of our scheme is 8-times less than that of BASS. Thus, our system is more suitable for IIoT.
Modeling Artistic Workflows for Image Generation and Editing

People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant work may be required to propagate this new decision forward to the final artwork. Motivated by the above observations, we propose a generative model that follows a given artistic workflow, enabling both multi-stage image generation as well as multi-stage image editing of an existing piece of art. Furthermore, for the editing scenario, we introduce an optimization process along with learning-based regularization to ensure the edited image produced by the model closely aligns with the originally provided image. Qualitative and quantitative results on three different artistic datasets demonstrate the effectiveness of the proposed framework on both image generation and editing tasks.