Reviewerly/exHarmony huggingface dataset:
Feature Extraction-based Medical Image Watermarking Using Fuzzy-based Median Filter

In recent days, securing the clinical images with high-level attacks is still a great challenge. The proposed algorithm focuses on developing an algorithm, which starts with pre-processing clinical image by Switched Mode Fuzzy Median Filter (SMFMF) for extracting the noise affected pixel thereby replacing the noisy pixel with the value of median pixel. Clinical image features, such as colour, shape, and texture, are extracted for selecting appropriate region to watermark, which increases the process of watermarking and decreases the algorithm’s computational time. At watermark, calculating the key component can increase the security level and source clinical image singular values and singular values of watermark images are modified to review the robustness of clinical image. At the watermark extraction point, watermark validation for permanent checks is achieved via the logistic map technique to unravel the watermark. The peak noise signal ratio (PSNR) and normalized cross correlation values (NCC) measure the clinical image’s multi-target wellness predictions. In addition to watermarking validation, the proposed algorithm offers improved clinical strength and protection.
Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.
Deep Learning-Aided 5G Channel Estimation

Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for 5G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.
<scp>DHS‐CapsNet</scp>: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Abstract This paper proposes a new dual horizontal squash capsule network (DHS‐CapsNet) to classify the lung and colon cancers on histopathological images. DHS‐CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2‐lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS‐CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS‐CapsNet provides the top‐1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer‐aided diagnostic method to support doctors in lung and colon cancer diagnostics.
Combining Dynamic Passive Haptics and Haptic Retargeting for Enhanced Haptic Feedback in Virtual Reality

To provide immersive haptic experiences, proxy-based haptic feedback systems for virtual reality (VR) face two central challenges: (1) similarity, and (2) colocation. While to solve challenge (1), physical proxy objects need to be sufficiently similar to their virtual counterparts in terms of haptic properties, for challenge (2), proxies and virtual counterparts need to be sufficiently colocated to allow for seamless interactions. To solve these challenges, past research introduced, among others, two successful techniques: (a) Dynamic Passive Haptic Feedback (DPHF), a hardware-based technique that leverages actuated props adapting their physical state during the VR experience, and (b) Haptic Retargeting, a software-based technique leveraging hand redirection to bridge spatial offsets between real and virtual objects. Both concepts have, up to now, not ever been studied in combination. This paper proposes to combine both techniques and reports on the results of a perceptual and a psychophysical experiment situated in a proof-of-concept scenario focused on the perception of virtual weight distribution. We show that users in VR overestimate weight shifts and that, when DPHF and HR are combined, significantly greater shifts can be rendered, compared to using only a weight-shifting prop or unnoticeable hand redirection. Moreover, we find the combination of DPHF and HR to let significantly larger spatial dislocations of proxy and virtual counterpart go unnoticed by users. Our investigation is the first to show the value of combining DPHF and HR in practice, validating that their combination can better solve the challenges of similarity and colocation than the individual techniques can do alone.
Robot path planning by leveraging the graph-encoded Floyd algorithm

Path planning is an important field in the research of mobile robot technology. The so-called path planning of mobile robot means that each robot plans a path of obstacle avoidance and navigation in the same workspace to ensure that there is no collision between the robot and the robot at any time. There is no collision between robots and environmental obstacles. There are many algorithms for path planning from single robot to multi robot. However, the current robot path planning algorithms are still far from satisfactory. Thus, in this work, we propose a novel graph-based algorithm to optimize robot paths, wherein the key is a Floyd algorithm to dynamically assign weights to different paths. Firstly, we introduce the multi robot path planning methods from three aspects of multi robot cooperation, environment information and robot structure, including global path planning method, local path planning method, centralized path planning method and distributed path planning method. Next, a deep graph model by encoding the Floyd algorithm is proposed for optimizing robot paths. Finally, the development trend of multi mobile robot path planning technology is prospected and the experimental results are analyzed. Experimental results on lots of robot path datasets have shown the effectiveness and robustness of our method. Moreover, the recognized robot path sequence can guide the operation of robots in industrial area.
An Analysis of Blockchain Adoption in Supply Chains Between 2010 and 2020

In this research, the evolution of blockchain applied to supply chains has been mapped from the inception of the technology until June 2020, utilizing primarily public data sources. We have analyzed 271 blockchain projects on parameters such as their inception dates, types of blockchain, status, sectors applied to and type of organization that founded the project. We confirm generally understood trends in the blockchain market with new projects following the industry’s general hype and funding levels. We observe most activity in the Agriculture/Grocery sector and the Freight/Logistics sector. We see the shift of market interest from private companies (startups) to public companies and consortia and the change in blockchain adoption from Ethereum to Hyperledger. Finally, we observe more market-ready solutions and fewer inactive projects for Hyperledger-based projects than Ethereum-based projects.
A Novel Double-Image Encryption Algorithm Based on Rossler Hyperchaotic System and Compressive Sensing

In this paper, a novel double-image compression-encryption algorithm is proposed by combining Rossler hyperchaos and compressive sensing. In the proposed scheme, the sparse matrixes obtained by performing two-dimensional discrete wavelet transform on two plain images are first scrambled and compressed by index confusion and compressive sensing. Next, the compressed matrixes are linearly quantized, and then variable step length Josephus confusion and bitwise exclusive-OR operation are performed on them. The final noise-like cipher image is generated by hiding half of the encrypted image into the alpha channel of the other half of encrypted image. In order to realize “One cipher image corresponds to one key”, a novel key generation mechanism is designed. Finally, the simulation analyses indicate that the proposed double-image encryption scheme has high transmission efficiency. Meanwhile, the average local information entropy of the cipher image is about 0.902 and the decryption quality is as high as 30 dB.
Learning from COVID-19 to reimagine tuberculosis diagnosis

The ongoing COVID-19 mass testing and vaccination campaigns are the result of unprecedented financial investments, rapid research and development, collaborative science, and innovation in delivery systems. It would be a shame to not leverage these systems for other priorities in global health.
Optimal decision trees for categorical data via integer programming

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this paper, we present a mixed integer programming formulation to construct optimal decision trees of a prespecified size. We take the special structure of categorical features into account and allow combinatorial decisions (based on subsets of values of features) at each node. Our approach can also handle numerical features via thresholding. We show that very good accuracy can be achieved with small trees using moderately-sized training sets. The optimization problems we solve are tractable with modern solvers.
Tumor attention networks: Better feature selection, better tumor segmentation

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further assist physicians to make better therapeutic choices for hepatic diseases and monitoring treatment. Among current mainstream segmentation approaches, multi-layer and multi-kernel convolutional neural networks (CNNs) have attracted much attention in diverse biomedical/medical image segmentation tasks with remarkable performance. However, an arbitrary stacking of feature maps makes CNNs quite inconsistent in imitating the cognition and the visual attention of human beings for a specific visual task. To mitigate the lack of a reasonable feature selection mechanism in CNNs, we exploit a novel and effective network architecture, called Tumor Attention Networks (TA-Net), for mining adaptive features by embedding Tumor Attention layers with multi-functional modules to assist the liver tumor segmentation task. In particular, each tumor attention layer can adaptively highlight valuable tumor features and suppress unrelated ones among feature maps from 3D and 2D perspectives. Moreover, an analysis of visualization results illustrates the effectiveness of our tumor attention modules and the interpretability of CNNs for liver tumor segmentation. Furthermore, we explore different arrangements of skip connections in information fusion. A deep ablation study is also conducted to illustrate the effects of different attention strategies for hepatic tumors. The results of extensive experiments demonstrate that the proposed TA-Net increases the liver tumor segmentation performance with a lower computation cost and a small parameter overhead over the state-of-the-art methods, under various evaluation metrics on clinical benchmark data. In addition, two additional medical image datasets are used to evaluate generalization capability of TA-Net, including the comparison with general semantic segmentation methods and a non-tumor segmentation task. All the program codes have been released at https://github.com/shuchao1212/TA-Net.
Seeing like an infrastructure: avidity and difference in algorithmic recommendation

As the influence of algorithmic systems has grown, critics have come to appreciate that algorithms are not autonomous technical forces, but rather heterogeneous sociotechnical systems. The people who build and maintain these infrastructures play integral roles in their functioning: in the tight and continuous cycles of contemporary software development, the thinking of developers shapes how data drives ‘data-driven’ organizations. This article contributes to contemporary debates on infrastructural politics by describing how the vernacular social theorizing of one group of developers tangles with their technical work. Drawing on ethnographic fieldwork with developers of music recommender systems in the US, I examine how they understand the variability of music listeners. I find that the dominant frame for making sense of listener variation is avidity: a level of enthusiasm for music, which manifests as a willingness to expend effort in finding listening material. For people working in this industry, avidity displaces other ways of understanding human variety – particularly demography. While the technical communities behind these systems were predominantly white and male, they understood the difference that set them apart from most users to be their enthusiasm for music. Centreing avidity provided a way to claim elite cultural status and to avoid talking about demographic diversity. It also reflects the infrastructures through which recommender system developers know and intervene upon their users: avidity is what users look like when seen through interaction logs. Less avid users leave fewer traces, and the goal of the recommender system is to encourage them to leave more. As a result, the figure of the less avid listener serves to justify increasingly rapacious data collection practices.
Shared Spectrum Monitoring Using Deep Learning

Shared spectrum usage is inevitable due to the ongoing increase in wireless services and bandwidth requirements. Spectrum monitoring is a key enabler for efficient spectrum sharing by multiple radio access technologies (RATs). In this paper, we present signal classification using deep neural networks to identify various radio technologies and their associated interferences. We use Convolutional Neural Networks (CNN) to perform signal classification and employ six well-known CNN models to train for ten signal classes. These classes include LTE, Radar, WiFi and FBMC (Filter Bank Multicarrier) and their interference combinations, which include, LTE + Radar, LTE + WiFi, FBMC + Radar, FBMC + WiFi, WiFi + Radar and Noise. The CNN models include, AlexNet, VGG16, ResNet18, SqueezeNet, InceptionV3 and ResNet50. The radio signal data sets for training and testing of CNN-based classifiers are acquired using a USRP-based experimental setup. Extensive measurements of these radio technologies (LTE, WiFi, Radar and FBMC) are done over different locations and times to generate a robust dataset. We propose a novel (spectrogram) representation called the Quarter-spectrogram (Q-spectrogram) that squeezes temporal and frequency information for input to CNN models. While considering classification accuracy, model complexity and prediction time for a single input Q-spectrogram (image), ResNet18 (CNN model) gives the best overall performance with 98% classification accuracy. While SqueezeNet (CNN model) offers the lowest model complexity which makes it very suitable for resource-constrained radio monitoring devices and also offers the least prediction time of 110 msec. Moreover, we also propose a simple WiFi classification scheme that buffers several WiFi Q-spectrograms and then makes a decision about WiFi’s presence and also gives a quantified measure of WiFi traffic density.
JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments

We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 ° RGB video at 15 fps, 3D point clouds from two 16 planar rays Velodyne LiDARs, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 ° spherical image from a fisheye camera and encoder values from the robot's wheels. Our dataset incorporates data from traditionally underrepresented scenes such as indoor environments and pedestrian areas, all from the ego-perspective of the robot, both stationary and navigating. The dataset has been annotated with over 2.4 million bounding boxes spread over five individual cameras and 1.8 million associated 3D cuboids around all people in the scenes totaling over 3500 time consistent trajectories. Together with our dataset and the annotations, we launch a benchmark and metrics for 2D and 3D person detection and tracking. With this dataset, which we plan on extending with further types of annotation in the future, we hope to provide a new source of data and a test-bench for research in the areas of egocentric robot vision, autonomous navigation, and all perceptual tasks around social robotics in human environments.
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation

Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage - dense 3D information (stereo cameras) and highly-accurate sparse point clouds (LiDAR). However, due to their different modalities and structures, the method of aligning sensor data is the key for successful sensor fusion. To this end, we propose a geometry-aware stereo-LiDAR fusion network for long-range depth estimation, called volumetric propagation network. The key idea of our network is to exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space. Unlike existing fusion strategies, we directly embed point clouds into the volume, which enables us to propagate valid information into nearby voxels in the volume, and to reduce the uncertainty of correspondences. Thus, it allows us to fuse two different input modalities seamlessly and regress a long-range depth map. Our fusion is further enhanced by a newly proposed feature extraction layer for point clouds guided by images: FusionConv. FusionConv extracts point cloud features that consider both semantic (2D image domain) and geometric (3D domain) relations and aid fusion at the volume. Our network achieves state-of-the-art performance on KITTI and Virtual-KITTI datasets among recent stereo-LiDAR fusion methods.
Self-sovereign identity for healthcare using blockchain

Secure and reliable identity is essential for identifying a user accurately and providing services. The traditional centralized identity systems have several security weaknesses and do not support user control. Self-sovereign identity (SSI) has been proposed to provide user control and secure identity model. User record privacy and security is a critical factor for any healthcare information system. Healthcare is also a domain where SSI solutions can counter patient information privacy and security threats. This paper reviews the applicability of blockchain-based SSI solution in healthcare, their advantages and requirements. It further provides a model use case for demonstrating the SSI application in healthcare.
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

Learning a good representation for space-time correspondence is the key for various computer vision tasks, including tracking object bounding boxes and performing video object pixel segmentation. To learn generalizable representation for correspondence in large-scale, a variety of self-supervised pretext tasks are proposed to explicitly perform object-level or patch-level similarity learning. Instead of following the previous literature, we propose to learn correspondence using Video Frame-level Similarity (VFS) learning, i.e, simply learning from comparing video frames. Our work is inspired by the recent success in image-level contrastive learning and similarity learning for visual recognition. Our hypothesis is that if the representation is good for recognition, it requires the convolutional features to find correspondence between similar objects or parts. Our experiments show surprising results that VFS surpasses state-of-the-art self-supervised approaches for both OTB visual object tracking and DAVIS video object segmentation. We perform detailed analysis on what matters in VFS and reveals new properties on image and frame level similarity learning. Project page with code: https://jerryxu.net/VFS.
Time- and Space-Efficient Arguments from Groups of Unknown Order

We construct public-coin time- and space-efficient zero-knowledge arguments for $$\mathbf {NP} $$ . For every time T and space S non-deterministic RAM computation, the prover runs in time $$T \cdot {{\,\mathrm{polylog}\,}}(T)$$ and space $$S \cdot {{\,\mathrm{polylog}\,}}(T)$$ , and the verifier runs in time $$n \cdot {{\,\mathrm{polylog}\,}}(T)$$ , where n is the input length. Our protocol relies on hidden order groups, which can be instantiated with a trusted setup from the hardness of factoring (products of safe primes), or without a trusted setup using class groups. The argument-system can heuristically be made non-interactive using the Fiat-Shamir transform. Our proof builds on DARK (Bünz et al., Eurocrypt 2020), a recent succinct and efficiently verifiable polynomial commitment scheme. We show how to implement a variant of DARK in a time- and space-efficient way. Along the way we: In proving these results, we develop general-purpose techniques for working with (hidden order) groups, which may be of independent interest.