Reviewerly/exHarmony huggingface dataset:
Reliability of response region: A novel mechanism in visual tracking by edge computing for IIoT environments

Reliability has been widely used in industrial IoT (IIoT) applications. Since maintaining fast and accurate tracking of targets with fast move and motion blur in industrial applications is still a major challenge, this paper proposes a novel mechanism based on reliability for target matching, which is the basic problem in computer vision. Then, by using the proposed reliability-based mechanism, a novel visual tracking method with edge computing is proposed to achieve accurate and rapid tracking with high reliability. Experimental results on Object Tracking Benchmark (OTB) dataset showed effectiveness of the proposed mechanism by comparing reliability-based and original algorithm. Results also showed that tracking performance of the proposed method has been increased, especially effected greatly on fast-moving, background clutter and motion blur. Therefore, the proposed method is validated to play an important role in real industrial applications with edge computing, which is more suitable for IIoT environments and automotive industry.
A Lightweight Optical Flow CNN —Revisiting Data Fidelity and Regularization

Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have showed promising results. FlowNet2 [1] , the state-of-the-art CNN, requires over 160M parameters to achieve accurate flow estimation. Our LiteFlowNet2 outperforms FlowNet2 on Sintel and KITTI benchmarks, while being 25.3 times smaller in the model size and 3.1 times faster in the running speed. LiteFlowNet2 is built on the foundation laid by conventional methods and resembles the corresponding roles as data fidelity and regularization in variational methods. We compute optical flow in a spatial-pyramid formulation as SPyNet [2] but through a novel lightweight cascaded flow inference. It provides high flow estimation accuracy through early correction with seamless incorporation of descriptor matching. Flow regularization is used to ameliorate the issue of outliers and vague flow boundaries through feature-driven local convolutions. Our network also owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2 and SPyNet. Comparing to LiteFlowNet [3] , LiteFlowNet2 improves the optical flow accuracy on Sintel Clean by 23.3 percent, Sintel Final by 12.8 percent, KITTI 2012 by 19.6 percent, and KITTI 2015 by 18.8 percent, while being 2.2 times faster. Our network protocol and trained models are made publicly available on https://github.com/twhui/LiteFlowNet2.
Attentional Neural Fields for Crowd Counting

Crowd counting has recently generated huge popularity in computer vision, and is extremely challenging due to the huge scale variations of objects. In this paper, we propose the Attentional Neural Field (ANF) for crowd counting via density estimation. Within the encoder-decoder network, we introduce conditional random fields (CRFs) to aggregate multi-scale features, which can build more informative representations. To better model pair-wise potentials in CRFs, we incorperate non-local attention mechanism implemented as inter- and intra-layer attentions to expand the receptive field to the entire image respectively within the same layer and across different layers, which captures long-range dependencies to conquer huge scale variations. The CRFs coupled with the attention mechanism are seamlessly integrated into the encoder-decoder network, establishing an ANF that can be optimized end-to-end by back propagation. We conduct extensive experiments on four public datasets, including ShanghaiTech, WorldEXPO 10, UCF-CC-50 and UCF-QNRF. The results show that our ANF achieves high counting performance, surpassing most previous methods.
Liver Tumor Segmentation in CT Scans Using Modified SegNet

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients' death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder-decoder is named SegNet, and consists of a hierarchical correspondence of encode-decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.
Blockchain Meets Edge Computing: Stackelberg Game and Double Auction Based Task Offloading for Mobile Blockchain

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can't afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices' resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average.
A Secure Framework for Authentication and Encryption Using Improved ECC for IoT-Based Medical Sensor Data

Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors.This has attracted attention for a secure communication framework to access e-health data on mobile devices.The wearable sensor device is attached to the patient's body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc.In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server.The patient's biometrics information has been added as a parameter in addition to the user name and password.The authentication scheme is coined with the SHA-512 algorithm that ensures integrity.To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC).Whereas in improved ECC, an additional key (secret key) is generated to enhance the system's security.In this way, the intricacy of the two phases is augmented.The computational cost of the scheme in the proposed framework is 4H + Ec + Dc which is less than the existing schemes.The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm.The obtained encryption and decryption time are 1.032 µs and 1.004µs respectively.The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest-Shamir-Adleman (RSA)and ECC algorithms.
LVI: Hijacking Transient Execution through Microarchitectural Load Value Injection

The recent Spectre attack first showed how to inject incorrect branch targets into a victim domain by poisoning microarchitectural branch prediction history. In this paper, we generalize injection-based methodologies to the memory hierarchy by directly injecting incorrect, attacker-controlled values into a victim's transient execution. We propose Load Value Injection (LVI) as an innovative technique to reversely exploit Meltdown-type microarchitectural data leakage. LVI abuses that faulting or assisted loads, executed by a legitimate victim program, may transiently use dummy values or poisoned data from various microarchitectural buffers, before eventually being re-issued by the processor. We show how LVI gadgets allow to expose victim secrets and hijack transient control flow. We practically demonstrate LVI in several proof-of-concept attacks against Intel SGX enclaves, and we discuss implications for traditional user process and kernel isolation. State-of-the-art Meltdown and Spectre defenses, including widespread silicon-level and microcode mitigations, are orthogonal to our novel LVI techniques. LVI drastically widens the spectrum of incorrect transient paths. Fully mitigating our attacks requires serializing the processor pipeline with lfence instructions after possibly every memory load. Additionally and even worse, due to implicit loads, certain instructions have to be blacklisted, including the ubiquitous x86 ret instruction. Intel plans compiler and assembler-based full mitigations that will allow at least SGX enclave programs to remain secure on LVI-vulnerable systems. Depending on the application and optimization strategy, we observe extensive overheads of factor 2 to 19 for prototype implementations of the full mitigation.
The Role of Virtual Reality in Improving Health Outcomes for Community-Dwelling Older Adults: Systematic Review

Background Virtual reality (VR) delivered through immersive headsets creates an opportunity to deliver interventions to improve physical, mental, and psychosocial health outcomes. VR app studies with older adults have primarily focused on rehabilitation and physical function including gait, balance, fall prevention, pain management, and cognition. Several systematic reviews have previously been conducted, but much of the extant literature is focused on rehabilitation or other institutional settings, and little is known about the effectiveness of VR apps using immersive headsets to target health outcomes among community-dwelling older adults. Objective The objective of this review was to evaluate the effectiveness of VR apps delivered using commercially available immersive headsets to improve physical, mental, or psychosocial health outcomes in community-dwelling older adults. Methods Peer-reviewed publications that included community-dwelling older adults aged ≥60 years residing in residential aged care settings and nursing homes were included. This systematic review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for systematic reviews of effectiveness evidence. The title of this review was registered with JBI, and the systematic review protocol was registered with the International Prospective Register of Systematic Reviews. Results In total, 7 studies that specifically included community-dwelling older adults were included in this review. VR apps using a head-mounted display led to improvements in a number of health outcomes, including pain management, posture, cognitive functioning specifically related to Alzheimer disease, and a decreased risk of falls. A total of 6 studies reported a statistically significant difference post VR intervention, and 1 study reported an improvement in cognitive function to reduce navigational errors. Only one study reported on the usability and acceptability of the interventions delivered through VR. While one study used a distraction mechanism for pain management, none of the studies used gaming technology to promote enjoyment. Conclusions Interventions to improve health outcomes through VR have demonstrated potential; however, the ability to synthesize findings by primary outcome for the older adult population is not possible. A number of factors, especially related to frailty, usability, and acceptability, also need to be explored before more substantial recommendations on the effectiveness of VR interventions for older adults can be made. Trial Registration PROSPERO CRD42019143504; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143504
COVID-19 Mobile Positioning Data Contact Tracing and Patient Privacy Regulations: Exploratory Search of Global Response Strategies and the Use of Digital Tools in Nigeria

Background The coronavirus disease (COVID-19) pandemic is the biggest global economic and health challenge of the century. Its effect and impact are still evolving, with deaths estimated to reach 40 million if unchecked. One effective and complementary strategy to slow the spread and reduce the impact is to trace the primary and secondary contacts of confirmed COVID-19 cases using contact tracing technology. Objective The objective of this paper is to survey strategies for digital contact tracing for the COVID-19 pandemic and to present how using mobile positioning data conforms with Nigeria’s data privacy regulations. Methods We conducted an exploratory review of current measures for COVID-19 contact tracing implemented around the world. We then analyzed how countries are using mobile positioning data technology to reduce the spread of COVID-19. We made recommendations on how Nigeria can adopt this approach while adhering to the guidelines provided by the National Data Protection Regulation (NDPR). Results Despite the potential of digital contact tracing, it always conflicts with patient data privacy regulations. We found that Nigeria’s response complies with the NDPR, and that it is possible to leverage call detail records to complement current strategies within the NDPR. Conclusions Our study shows that mobile position data contact tracing is important for epidemic control as long as it conforms to relevant data privacy regulations. Implementation guidelines will limit data misuse.
The bitter truth about gate-based quantum algorithms in the NISQ era

Abstract Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum (NISQ) device has several challenges that arise from the fact that such devices are noisy and have limited quantum resources. Thus, various factors contributing to the depth and width as well as to the noise of an implementation of a gate-based algorithm must be understood in order to assess whether an implementation will execute successfully on a given NISQ device. In this contribution, we discuss these factors and their impact on algorithm implementations. Especially, we will cover state preparation, oracle expansion, connectivity, circuit rewriting, and readout: these factors are very often ignored when presenting a gate-based algorithm but they are crucial when implementing such an algorithm on near-term quantum computers. Our contribution will help developers in charge of realizing gate-based algorithms on such machines in (i) achieving an executable implementation, and (ii) assessing the success of their implementation on a given machine.
Edge-Assisted Privacy-Preserving Raw Data Sharing Framework for Connected Autonomous Vehicles

Data sharing among connected and autonomous vehicles without any protection will cause private information leakage. Simply encrypting data introduces a heavy overhead; most importantly, when encrypted data (ciphertext) is decrypted on a vehicle, the receiver will be fully aware of the sender's data, implying potential data leakage. To tackle these issues, we propose an edge-assisted privacy-preserving raw data sharing framework. First, we leverage the additive secret sharing technique to encrypt raw data into two ciphertexts and construct two classes of secure functions. The functions are then used to implement a privacy-preserving convolutional neural network (P-CNN). Finally, two edge servers are deployed to cooperatively execute P-CNN to extract features from two ciphertexts to obtain the same object detection results as the original CNN. We adopt the VGG16 model as a case study to illustrate how to construct P-CNN and employ the KITTI dataset to verify our solution. Experiment results demonstrate that P-CNN offers exactly the same classification results as the VGG16 model with negligible error, and the communication overhead and computational cost on the edge servers are less than existing solutions without leaking private information.
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation

We present MSeg, a composite dataset that unifies se- mantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
Impacts of technological embodiment through virtual reality on potential guests’ emotions and engagement

ABSTRACT Virtual reality technologies have increased the integration between devices and the human senses. Despite the increased interest in embodied technologies in tourism, there has been little research into the effectiveness of virtual reality, particularly in the hospitality sector. The aim of the present work is to analyze the impact of technological embodiment on potential guests’ emotional reactions and engagement in the context of a hotel-based virtual reality experience. Results from a laboratory experiment showed that, compared to desktop computers and mobile phones, virtual reality devices evoke more positive emotional reactions and higher levels of psychological and behavioral engagement. In addition, emotions and psychological engagement mediate the impact of embodied virtual reality devices on behavioral engagement. The results underline the importance of technological embodiment in providing engaging hotel-based pre-experiences, where the hotels incorporate virtual reality into their communication strategies.
Privacy challenges and research opportunities for genomic data sharing

The sharing of genomic data holds great promise in advancing precision medicine and providing personalized treatments and other types of interventions. However, these opportunities come with privacy concerns, and data misuse could potentially lead to privacy infringement for individuals and their blood relatives. With the rapid growth and increased availability of genomic datasets, understanding the current genome privacy landscape and identifying the challenges in developing effective privacy-protecting solutions are imperative. In this work, we provide an overview of major privacy threats identified by the research community and examine the privacy challenges in the context of emerging direct-to-consumer genetic-testing applications. We additionally present general privacy-protection techniques for genomic data sharing and their potential applications in direct-to-consumer genomic testing and forensic analyses. Finally, we discuss limitations in current privacy-protection methods, highlight possible mitigation strategies and suggest future research opportunities for advancing genomic data sharing.
Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading

Deep neural networks (DNN) are the de-facto solution behind many intelligent applications of today, ranging from machine translation to autonomous driving. DNNs are accurate but resource-intensive, especially for embedded devices such as mobile phones and smart objects in the Internet of Things. To overcome the related resource constraints, DNN inference is generally offloaded to the edge or to the cloud. This is accomplished by partitioning the DNN and distributing computations at the two different ends. However, most of existing solutions simply split the DNN into two parts, one running locally or at the edge, and the other one in the cloud. In contrast, this article proposes a technique to divide a DNN in multiple partitions that can be processed locally by end devices or offloaded to one or multiple powerful nodes, such as in fog networks. The proposed scheme includes both an adaptive DNN partitioning scheme and a distributed algorithm to offload computations based on a matching game approach. Results obtained by using a self-driving car dataset and several DNN benchmarks show that the proposed solution significantly reduces the total latency for DNN inference compared to other distributed approaches and is 2.6 to 4.2 times faster than the state of the art.
Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge, and Skeleton

Salient object segmentation, edge detection, and skeleton extraction are three contrasting low-level pixel-wise vision problems, where existing works mostly focused on designing tailored methods for each individual task. However, it is inconvenient and inefficient to store a pre-trained model for each task and perform multiple different tasks in sequence. There are methods that solve specific related tasks jointly but require datasets with different types of annotations supported at the same time. In this article, we first show some similarities shared by these tasks and then demonstrate how they can be leveraged for developing a unified framework that can be trained end-to-end. In particular, we introduce a selective integration module that allows each task to dynamically choose features at different levels from the shared backbone based on its own characteristics. Furthermore, we design a task-adaptive attention module, aiming at intelligently allocating information for different tasks according to the image content priors. To evaluate the performance of our proposed network on these tasks, we conduct exhaustive experiments on multiple representative datasets. We will show that though these tasks are naturally quite different, our network can work well on all of them and even perform better than current single-purpose state-of-the-art methods. In addition, we also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed framework.