Reviewerly/exHarmony huggingface dataset:
A Survey on Python Libraries Used for Social Media Content Scraping

Python has a rich set of libraries available for extracting the digital contents that are spread across the internet. Among the available libraries, the following three libraries are popularly deployed for the purpose: they are BeautifulSoup, LXml and RegEx. A statistical study carried out over the scattered available data set shows that RegEx is capable of delivering the answer on an average of 153.7 ms. Still, RegEx has the inherent drawback of haiing limited rule extraction when it comes for the web page with more inner tags. Because of this demerit RegEx is termed as capable of performing only moderately complex contexts. Nevertheless the other libraries BeautifulS oup and LXml are capable of extracting web content under critical environment yielding the response rate of 458.68 ms and 202.96 ms respectively. Also, these two libraries are based on the DOM model proving to be the scalable libraries. The modern content grading system [1] specifically developed for the regional languages available in social media are mostly influenced by the web scrappers. This survey justifies the overwhelming performance of RegEx under differing scenarios.
Domain Aware Medical Image Classifier Interpretation by Counterfactual Impact Analysis

The success of machine learning methods for computer vision tasks has driven a surge in computer assisted prediction for medicine and biology. Based on a data-driven relationship between input image and pathological classification, these predictors deliver unprecedented accuracy. Yet, the numerous approaches trying to explain the causality of this learned relationship have fallen short: time constraints, coarse, diffuse and at times misleading results, caused by the employment of heuristic techniques like Gaussian noise and blurring, have hindered their clinical adoption.
Reversible data hiding method based on pixel expansion and homomorphic encryption

Digital image steganography algorithms usually suffer from a lossy restoration of the cover content after extraction of a secret message. When a cover object and confidential information are both utilised, the reversible property of the cover is inevitable. With this objective, several reversible data hiding (RDH) algorithms are available in the literature. Conversely, because both are diametrically related parameters, existing RDH algorithms focus on either a good embedding capacity (EC) or better stego-image quality. In this paper, a pixel expansion reversible data hiding (PE-RDH) method with a high EC and good stego-image quality are proposed. The proposed PE-RDH method was based on three typical RDH schemes, namely difference expansion, histogram shifting, and pixel value ordering. The PE-RDH method has an average EC of 0.75 bpp, with an average peak signal-to-noise ratio (PSNR) of 30.89 dB. It offers 100% recovery of the original image and confidential hidden messages. To protect secret as well as cover the proposed PE-RDH is also implemented on the encrypted image by using homomorphic encryption. The strength of the proposed method on the encrypted image was verified based on a comparison with several existing methods, and the approach achieved better results than these methods in terms of its EC, location map size and imperceptibility of directly decrypted images.
An Efficient Privacy-Aware Authentication Scheme With Hierarchical Access Control for Mobile Cloud Computing Services

In the last few years, mobile cloud computing (MCC) gains a huge development because of the popularity of mobile applications and cloud computing. User authentication and access control are two indispensable security components in the MCC environment. To the best of our knowledge, they are generally designed in different procedures. Access control can be executed after the authentication completes successfully. In order to improve efficiency, this article constructs an integrated scheme of authentication and hierarchical access control using self-certified public key cryptography (SCPKC) and the Chinese remainder theorem (CRT) for MCC environment. The proposed scheme can achieve mutual authentication while determining the access rights of mobile users without storing any access control list in the MCC service provider side. Besides, we also give a dynamic adding or deletion of MCC service provider to efficiently address potential changes in the hierarchy. The security of our proposed scheme is proved by the random oracle model. Compared with recently related multi-server authentication schemes for the MCC environment, the proposed scheme not only adds a new function of hierarchical access control but also has better computation and communication efficiencies. Therefore, the proposed scheme is more suitable for real-life MCC applications.
EGC: Entropy-based gradient compression for distributed deep learning

With the increase of volume of training data and scale of network models, distributed deep learning is becoming more and more popular, which employs multiple workers to train a single model. However, communication among workers has been always a major challenge, because it may cause large time latency and bandwidth consumption. In this paper, we propose an entropy-based gradient compression (EGC) mechanism to reduce communication overhead. EGC selects the gradients communicated based on the entropy of the gradient items, which can achieve a high compression ratio without sacrificing accuracy. More importantly, EGC is a general and flexible mechanism that can be adopted in different distributed training algorithms. Accordingly, we propose three EGC-based training algorithms for different scenarios, i.e., EGC-DSGD for decentralized training, EGC-PS for centralized training, and EGC-FL for federated training. To improve the accuracy of these algorithms, we also adopt associated mechanisms, including automatic learning rate correction, momentum correction and residuals accumulation. We prove the convergence of EGC by analysis and evaluate its performance by experiments. Eight models are trained using popular public datasets (including MNIST, CIFAR-10, Tiny ImageNet and Penn Treebank) for the tasks of image classification and language modeling. The experimental results show that, compared with existing works, the EGC based algorithms can achieve roughly 1000 times gradient compression ratio while keeping the accuracy similar or even higher.
f1tenth.dev - An Open-source ROS based F1/10 Autonomous Racing Simulator

This paper presents the ROS F1/10 simulator - a ROS and Gazebo based autonomous racing simulator designed to mirror the behaviour and performance of the physical F1/10 platform. The simulator can be used to verify the performance of autonomous racing algorithms before testing on the real F1/10 racecar. The simulator supports most major ROS SLAM and navigation packages with tutorials on how to get started available on www.f1tenth.dev under open-source license and we continue to add support for more algorithms and features. The paper presents details on the simulator architecture, design, and features and presents several research use-cases including an example of developing in the sim and transferring the performance to the race racecar.
Regularized Loss for Weakly Supervised Single Class Semantic Segmentation

Fully supervised semantic segmentation is highly successful, but obtaining dense ground truth is expensive. Thus there is an increasing interest in weakly supervised approaches. We propose a new weakly supervised method for training CNNs to segment an object of a single class of interest. Instead of ground truth, we guide training with a regularized loss function. Regularized loss models prior knowledge about the likely object shape properties and thus guides segmentation towards the more plausible shapes. Training CNNs with regularized loss is difficult. We develop an annealing strategy that is crucial for successful training. The advantage of our method is simplicity: we use standard CNN architectures and intuitive and computationally efficient loss function. Furthermore, we apply the same loss function for any task/dataset, without any tailoring. We first evaluate our approach for salient object segmentation and co-segmentation. These tasks naturally involve one object class of interest. In some cases, our results are only a few points of standard performance measure behind those obtained training the same CNN with full supervision, and state-of-the art results in weakly supervised setting. Then we adapt our approach to weakly supervised multi-class semantic segmentation and obtain state-of-the-art results.
STC: Significance-aware Transform-based Codec Framework for External Memory Access Reduction

Deep convolutional neural networks (DCNNs), with extensive computation, require considerable external memory bandwidth and storage for intermediate feature maps. External memory accesses for feature maps become a significant energy bottleneck for DCNN accelerators. Many works have been done on quantizing feature maps into low precision to decrease the costs for computation and storage. There is an opportunity that the large amount of correlation among channels in feature maps can be exploited to further reduce external memory access. Towards this end, we propose a novel compression framework called Significance-aware Transform-based Codec (STC). In its compression process, significance-aware transform is introduced to obtain low-correlated feature maps in an orthogonal space, as the intrinsic representations of original feature maps. The transformed feature maps are quantized and encoded to compress external data transmission. For the next layer computation, the data will be reloaded with STC's reconstruction process. The STC framework can be supported with a small set of extensions to current DCNN accelerators. We implement STC extensions to the baseline TPU architecture for hardware evaluation. The strengthened TPU achieves average reduction of 2.57x in external memory access, 1.95x~2.78x improvement of system-level energy efficiency, with a negligible accuracy loss of only 0.5%.
Insights From Experiments With Rigor in an EvoBio Design Study

Design study is an established approach of conducting problem-driven visualization research. The academic visualization community has produced a large body of work for reporting on design studies, informed by a handful of theoretical frameworks, and applied to a broad range of application areas. The result is an abundance of reported insights into visualization design, with an emphasis on novel visualization techniques and systems as the primary contribution of these studies. In recent work we proposed a new, interpretivist perspective on design study and six companion criteria for rigor that highlight the opportunities for researchers to contribute knowledge that extends beyond visualization idioms and software. In this work we conducted a year-long collaboration with evolutionary biologists to develop an interactive tool for visual exploration of multivariate datasets and phylogenetic trees. During this design study we experimented with methods to support three of the rigor criteria: ABUNDANT, REFLEXIVE, and TRANSPARENT. As a result we contribute two novel visualization techniques for the analysis of multivariate phylogenetic datasets, three methodological recommendations for conducting design studies drawn from reflections over our process of experimentation, and two writing devices for reporting interpretivist design study. We offer this work as an example for implementing the rigor criteria to produce a diverse range of knowledge contributions.
Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.
BioDraw: Reliable Multi-Factor User Authentication with One Single Finger Swipe

Multi-factor user authentication (MFUA) becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing MFUAs require multiple interactions between users and different authentication components when sensing the multiple factors, leading to extra overhead and bad use experiences. In this paper, we propose a secure and user-friendly MFUA system, namely BioDraw, which utilizes four categories of biometrics (impedance, geometry, composition, and behavior) of human hand plus the pattern-based password to identify and authenticate users. A user only needs to draw a pattern on a RFID tag array, while four biometrics can be simultaneously collected. Particularly, we design a gradient-based pattern recognition algorithm for pattern recognition and then a CNN-LSTM-based classifier for user recognition. Furthermore, to guarantee the systemic security, we propose a novel anti-spoofing scheme, called Binary ALOHA, which utilizes the inhabit randomness of RFID systems. We perform extensive experiments over 21 volunteers. The experiment result demonstrates that BioDraw can achieve a high authentication accuracy (with a false reject rate less than 2%) and is effective in defending against various attacks.
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation technique: 4-class and 8-class XCAT-GANs. The 4-class technique relies on only the annotations of the heart; while the 8-class technique employs a predicted multi-tissue label map of the heart-surrounding organs and provides better guidance for our conditional image synthesis. For both techniques, we train our conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subjects of synthetic CMR images at the end-diastolic and end-systolic phases, we evaluate the usefulness of such data in the downstream cardiac cavity segmentation task under different augmentation strategies. Results demonstrate that even with only 20% of real images (40 volumes) seen during training, segmentation performance is retained with the addition of synthetic CMR images. Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
Cross-domain Medical Image Translation by Shared Latent Gaussian Mixture Model

Current deep learning based segmentation models generalize poorly to different domains due to the lack of sufficient labelled image data. An important example in radiology is generalizing from contrast enhanced CT to non-contrast CT. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are generally used to achieve precise diagnoses. For example, contrast enhanced CT at different phases are used to enhance certain pathologies or internal organs. Many existing cross-domain image-to-image translation models show impressive results on large organ segmentation by successfully preserving large structures across domains. However, such models lack the ability to preserve fine structures during the translation process, which is significant for many clinical applications, such as segmenting small calcified plaques in the aorta and pelvic arteries. In order to preserve fine structures during medical image translation, we propose a patch-based model using shared latent variables from a Gaussian mixture. We compare our image translation framework to several state-of-the-art methods on cross-domain image translation and show our model does a better job preserving fine structures. The superior performance of our model is verified by performing two tasks with the translated images - detection and segmentation of aortic plaques and pancreas segmentation. We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.
Reining in the Web's Inconsistencies with Site Policy

Over the years, browsers have adopted an ever-increasing number of client-enforced security policies deployed by means of HTTP headers. Such mechanisms are fundamental for web application security, and usually deployed on a per-page basis. This, however, enables inconsistencies, as different pages within the same security boundaries (in form of origins or sites) can express conflicting security requirements. In this paper, we formalize inconsistencies for cookie security attributes, CSP and HSTS, and then quantify the magnitude and impact of inconsistencies at scale by crawling 15,000 popular sites. We show numerous sites endanger their own security by omission or misconfiguration of the aforementioned mechanisms, which lead to unnecessary exposure to XSS, cookie theft and HSTS deactivation. We then use our data to analyse to which extent the recent Origin Policy proposal can fix the problem of inconsistencies. Unfortunately, we conclude that the current Origin Policy design suffers from major shortcomings which limit its practical applicability to address security inconsistencies, while catering to the need of real-world sites. Based on these insights, we propose Site Policy, designed to overcome Origin Policy’s shortcomings and make any insecurity explicit. We make a prototype implementation of Site Policy publicly available, along with a support toolchain for initial policy generation, security analysis, and test deployment.
A Novel Object Re-Track Framework for 3D Point Clouds

3D point cloud data is an important data source for autonomous vehicles to perceive the surroundings. Achieving accurate object tracking of 3D point clouds has become a challenging task. In this paper, we propose a 3D object two-stage re-track framework directly utilizing point clouds as the input, without using the ground truth as the reference box. The framework consists of a coarse stage and a fine stage. By tracking back the previous T frames and expanding the search space for each frame, we add the fine stage to re-track the lost objects of the coarse stage. Moreover, we design a dense AutoEncoder to enhance the discrimination in the latent space and improve shape completion performance, thus improving tracking performance. A Sample Update Strategy is also proposed to aggregate similar model shape samples in different frames, which improves the quality of the model shape. In terms of motion models for the proposed re-track framework, we further compare Kalman Filter with PointLSTM and do an extensive analysis. Finally, we test the re-track framework on the KITTI tracking dataset and outperform the public benchmark by 17.1%/15.5% in Success and Precision, respectively. Our code and model are available at https://github.com/FengZicai/Re-Track.
Topic sensitive hybrid expertise retrieval system in community question answering services

Here, we propose a topic sensitive hybrid expertise retrieval system in community question answering services. We introduce three new expertise signatures: knowledge, reputation, and authority. These signatures consider the questions, and hence, their answerers from a topic sensitive perspective. We estimate the knowledge of an answerer on a new question based on the previously answered subset of questions with similar topic distributions to the new question. The reputation of an answerer, moreover, is derived from the qualities of previously answered questions by the answerer with similar distributions of topics. Furthermore, we propose a topic sensitive authority model. It considers some topic related information associated with questions and the relationships among their answerers. We compare the proposed method with 26 existing methods on 4 real-world datasets using 5 performance measures. It outperforms the comparing algorithms in 91.73% (477 out of 520) cases.
RETRACTED: Home intelligent sports action automation system based on bluetooth

It presents the plan of compact, innovative checking system dependent on the Bluetooth sensor; the system comprises of three fundamental subsystems. Bluetooth-based sensors, wireless communication terminals, and physical status data acquisition systems for sportsperson monitoring. Real-time data acquisition system monitors Wireless communication terminal via the physical state of main human physical parameters. Physical parameters received by Bluetooth device and monitoring human physical state identified by intelligent data will be sent to the controller. Anomalous is present in data center monitoring and communication terminal to send a warning message to the hospital. Anomalous data is sent to the hospital, after which the doctor can diagnose the condition of the person. These mechanisms require low-level language access with normal behavior, limited coverage, and Bluetooth signal quality. This model proposes an elective innovation for following the development of items in the room dependent on Bluetooth correspondence innovation. The system is designed to be low cost and scalable, enabling a variety of devices, the core of which controls minimal changes. Password protection is used because only authorized users can access the device from home.