Reviewerly/exHarmony huggingface dataset:
FP-Redemption: Studying Browser Fingerprinting Adoption for the Sake of Web Security

Browser fingerprinting has established itself as a stateless technique to identify users on the Web. In particular, it is a highly criticized technique to track users. However, we believe that this identification technique can serve more virtuous purposes, such as bot detection or multi-factor authentication. In this paper, we explore the adoption of browser fingerprinting for security-oriented purposes. More specifically, we study 4 types of web pages that require security mechanisms to process user data: sign-up, sign-in, basket and payment pages. We visited 1, 485 pages on 446 domains and we identified the acquisition of browser fingerprints from 405 pages. By using an existing classification technique, we identified 169 distinct browser fingerprinting scripts included in these pages. By investigating the origins of the browser fingerprinting scripts, we identified 12 security-oriented organizations who collect browser fingerprints on sign-up, sign-in, and payment pages. Finally, we assess the effectiveness of browser fingerprinting against two potential attacks, namely stolen credentials and cookie hijacking. We observe browser fingerprinting being successfully used to enhance web security.
Image Blind Deblurring Algorithm Based on Deep Multi-level Wavelet Transform

In recent years, convolutional neural networks are widely used in single image deblurring problems. The receptive field size and network depth of convolutional neural networks can affect the performance of image deblurring algorithms. In order to improve the performance of image deblurring algorithm by increasing the receptive field, an image blind deblurring algorithm based on deep multi-level wavelet transform is proposed. Embedding the wavelet transform into the encoder-decoder architecture enhances the sparsity of the image features while increasing the receptive field. In order to reconstruct high-quality images in the wavelet domain, the paper leverges to multi-scale dilated dense block to extract multi-scale information of images, and introduces feature fusion blocks to fuse adaptively features between encoder and decoder. In addition, due to the difference in representation of image information between the wavelet domain and the spatial domain, in order to fuse these different feature representations, the spatial domain reconstruction module is used to improve further the quality of the reconstructed image in the spatial domain. The experimental results show that the proposed method has better performance on Structural SIMilarity index (SSIM) and Peak Signal-to-Noise Ratio, and has better visual effects on real blurred images.
MULTIPARAMETER HYBRID NEURAL NETWORK METAMODEL OF EDDY CURRENT PROBES WITH VOLUMETRIC STRUCTURE OF EXCITATION SYSTEM

A multiparameter metamodel of the eddy current probe with the volumetric excitation structure is constructed. As variable parameters of the metamodel, the spatial coordinates of the testing zone, the radii of the excitation coils and the height of their location above the testing object were used. Due to the use of hybrid construction of multiple neural networks using decomposition of the search space, an acceptable metamodel’s error of the eddy current probe with volumetric excitation structure is obtained.
From Blockchain to Digital Twin Community: A Technical Framework for Smart Community Governance

The current dilemmas in community governance are widespread, such as information islands, lack of communication and cooperation between community subjects, lack of integration and distribution of community resources, and lack of awareness of autonomy among community residents. This article uses a series of emerging Internet technologies to build a technical framework for the wisdom of community governance, hoping to use technical methods and concepts to assist system reform. This article hopes to achieve the community governance system more perfect, match the supply of community services with the needs of residents, coordinate and co-governance between the subjects in the community, increase the trust and sense of belonging of community residents, and cultivate residents' sense of autonomy.
Using distance learning as a strategy for maintaining income of Physical Education professionals during the COVID-19 pandemic

The period of social distancing caused by the COVID-19 pandemic is characterized as an effective proposal to control the spread of the coronavirus (SARS-CoV-2), however, it changes the social dynamics of individuals in society. Based on this question, the present study aimed to understand how was the performance of Brazilian physical education professionals during this period. The sample consisted of 400 Physical Education professionals from Brazil, 224 women (56.0%) and 176 men (44.0%), selected at random, by filling out a questionnaire. Sports activities were predominant among professionals, followed by bodybuilding activities or functional training. Most professionals performed their activities at home (71%) and used different digital platforms as work tools, noting that distance learning was a strategy used and well regarded by professionals. However, even with the use of specific intervention strategies, professional income showed fluctuations during the pandemic, with academic degrees being one of the factors responsible for changing the workload.
“Nenhum a Menos”! A Adaptação ao Home Office em Tempos de COVID-19

ABSTRACT This case describes the process of changing Chianca Softwares’ face-to-face work regime to the home office during government decrees caused by the new coronavirus pandemic. Chianca Softwares, a family company, which has been a player in the technology market in the state of Paraíba since 2003, went through a process of change in its governance model in 2012, and due to the improvements in its processes, it was awarded the Prêmio Média e Pequenas Empresas (MPE) of Sebrae PB in 2016. Given its adaptability, it faced the coronavirus crisis differently, overcame difficulties, and needed to reinvent itself to survive the ‘new normal.’ In this context, the case aims to lead students to reflect on the home office implementation process in Chianca, to identify the benefits and challenges of this teleworking regime and to think about ways to mitigate these challenges and create tools and practices to mobilize, integrate, and engage people in remote work. Thus, after restructuring its entire team to the new model of work in the home office, students are led to reflect on the dilemma that Chianca managers face due to the release of their face-to-face activities: stay in the home office regime or return to original patterns.
A benchmark for quantum optimization: the traveling salesman

We present compelling reasons for symmetric traveling salesman problems (TSPs) to be the benchmark for quantum computing of combinatorial optimization problems for all types of quantum hardware. There are seven reasons for endorsing these TSPs to be the benchmark and no shortcomings.
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?

While uncertainty estimation is a well-studied topic in deep learning, most such work focuses on marginal uncertainty estimates, i.e. the predictive mean and variance at individual input locations. But it is often more useful to estimate predictive correlations between the function values at different input locations. In this paper, we consider the problem of benchmarking how accurately Bayesian models can estimate predictive correlations. We first consider a downstream task which depends on posterior predictive correlations: transductive active learning (TAL). We find that TAL makes better use of models' uncertainty estimates than ordinary active learning, and recommend this as a benchmark for evaluating Bayesian models. Since TAL is too expensive and indirect to guide development of algorithms, we introduce two metrics which more directly evaluate the predictive correlations and which can be computed efficiently: meta-correlations (i.e. the correlations between the models correlation estimates and the true values), and cross-normalized likelihoods (XLL). We validate these metrics by demonstrating their consistency with TAL performance and obtain insights about the relative performance of current Bayesian neural net and Gaussian process models.
An energy-efficient incentive mechanism for resource recycling in multi-tenant datacenters

Multi-tenant public cloud is an important sector of cloud computing. While it has witnessed an unprecedented proliferation in the past years, its low resource utilization that incurs enormous energy waste has received great attention. Since the resource of multi-tenant datacenters is typically rented to the tenants in a reserved manner, the cloud provider is unable to reduce the energy consumption or improve resource utilization via turning down idle servers. To address this challenge, this paper proposes an efficient incentive mechanism to stimulate the recycling of reserved idle resources. Specifically, by economically compensating the tenants, the reserved resources that are idle can be recycled by the cloud providers to host other cloud applications, such as e-commerce and search engine. Taking advantage of the Lyapunov optimization method, this paper rigorously designs and analyzes a dynamic pricing scheme for the recycled reserved resources. Without requiring the future information as a priori, the proposed pricing scheme can make online decisions to optimize the long-term operational cost of the cloud provider, while still maintaining the stability of the system. Both rigorous theoretical analysis and extensive trace-driven simulations verify the efficacy of the proposed incentive mechanism.
Toward semantic image inpainting: where global context meets local geometry

Image inpainting attempts to fill the missing areas of an image with plausible content that is visually coherent with the image context. Semantic image inpainting has remained a challenging task even with the emergence of deep learning-based approaches. We propose a deep semantic inpainting model built upon a generative adversarial network and a dense U-Net network. Such a design helps achieve feature reuse while avoiding feature explosion along the upsampling path of the U-Net. The model also uses a composite loss function for the generator network to enforce a joint global and local content consistency constraint. More specifically, our new loss function combines the global reconstruction loss characterizing the semantic similarity between the missing and known image regions with the local total variation loss characterizing the natural transitions among adjacent regions. Experimental results on CelebA-HQ and Paris StreetView datasets have demonstrated encouraging performance when compared with other state-of-the-art methods in terms of both quantitative and qualitative metrics. For the CelebA-HQ dataset, the proposed method can more faithfully infer the semantics of human faces; for the StreetView dataset, our method achieves improved inpainting results in terms of more natural texture transitions, better structural consistency, and enriched textural details.
MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS

A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets. Therefore, this research modified the PFCM for clustering incomplete data sets to OCSPFCM and NPSPFCM with the performance evaluated based on three aspects, 1) accuracy percentage, 2) the number of iterations, and 3) centroid errors. The results showed that the NPSPFCM outperforms the OCSPFCM with missing values ranging from 5% − 30% for all experimental data sets. Furthermore, both algorithms provide average accuracies between 97.75%−78.98% and 98.86%−92.49%, respectively.
Lane Line detection and Vehicle Identification Using Monocular Camera Based on MATLAB

Road detection and vehicle recognition are one of the core technologies of Advanced Driving Assistance System (ADAS).The main research content of this paper is road detection and vehicle recognition under the monocular camera, and select examples to realize simulation and simulation through the computer version (CV) algorithm in MATLAB software.The recognition rate of the algorithm for single/double lanes reaches 95 %, the recognition rate of dashed/solid lines reached 97%, and the recognition rate of vehicles reached 99%.The results show that the lane detection algorithm used in this paper can better complete lane line detection and vehicle recognition, and has higher recognition accuracy and better practical value.
Digital FIR Filter Design by PSO and its v ariants Attractive and Repulsive PSO(ARPSO) &amp; Craziness based PSO(CRPSO)

Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.
Super-resolution reconstruction of MR image with self-attention based generate adversarial network algorithm

Super-resolution (SR) MRI images can provide fine-grained anatomical information, however it takes a long time to acquire data. In order to accelerate the acquisition of MR images while maintaining high-quality images, extensive research has been performed on image reconstruction through the deep learning method. In this study, a reconstruction framework by using self-attention based super-resolution generative adversarial networks (SA-SR-GAN) is proposed to generate super resolution MR image from low resolution MR image. Moreover, the self-attention mechanism is integrated into super-resolution generative adversarial networks (SR-GAN) framework, which is used to calculate the weight parameters of the input features. At the same time, spectral normalization is added to make the discriminator network training process more stable. The network was trained with 40 3D images (each 3D image contains 256 slices) and tested with 10 images. The experimental results show that the PSNR and SSIM values of the super-resolution magnetic resonance image generated by the proposed SA-SR-GAN method are higher than the state-of-the-art reconstruction methods.
A Fast-RCNN Implementation for Human Silhouette Detection in Video Sequences

The intention of this article is to implement a system of detection and segmentation of human silhouettes, the above mentioned tasks present a great challenge in security topics and innovation, in the last years and mainly on automated video surveillance systems, which require understanding the presence and human interaction in video sequences, e.g. Human Computer Interaction (HCI), Human Behaviour comprehension, Human fall detection, among others, but the most important is behavioural biometrics, this paper tackles the common step in these research areas: the Human silhouette extraction through the bounding box. To evaluate the proposed system, standardized databases where used and also proper videos are obtained trying to emulate real-world scenarios, where the quality and the distance are factors that have demonstrated challenges for the detection with computer vision and machine learning.
Effects of personal trainers" expertise on clients" intent to reenroll: Loyalty as mediator

This study examined the effect of personal trainers’ expertise on clients’ intent to reenroll and whether loyalty acted as a mediator. We collected data from 289 personal training participants, using which we conducted reliability and validity testing and frequency, descriptive statistics, correlation, hierarchical regression, and regression analyses using process macros from Hayes (2018). The results were as follows. First, personal trainers’ expertise had a positive effect on clients’ loyalty. Second, personal trainers’ expertise had a positive effect on clients’ intent to reenroll. Third, clients’ loyalty had a positive effect on their intent to reenroll. Fourth, clients’ loyalty mediated the relation between personal trainers’ expertise and clients’ intent to reenroll. This study highlighted the importance of clients’ loyalty and personal trainers’ expertise for attaining positive effects in maintaining clients and encouraging them to reenroll.
Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction

Recent neural Open Information Extraction (OpenIE) models have improved traditional rule-based systems significantly for Chinese OpenIE tasks. However, these neural models are mainly word-based, suffering from word segmentation errors in Chinese. They utilize dependency information in a shallow way, making multi-hop dependencies hard to capture. This paper proposes a Multi-Grained Dependency Graph Neural Network (MGD-GNN) model to address these problems. MGD-GNN constructs a multi-grained dependency (MGD) graph with dependency edges between words and soft-segment edges between words and characters. Our model makes predictions based on character features while still has word boundary knowledge through word-character soft-segment edges. MGD-GNN updates node representations using a deep graph neural network to fully exploit the topology structure of the MGD graph and capture multi-hop dependencies. Experiments on a large-scale Chinese OpenIE dataset SpanSAOKE shows that our model could alleviate the propagation of word segmentation errors and use dependency information more effectively, giving significant improvements over previous neural OpenIE models.
MetaSys: A Practical Open-source Metadata Management System to Implement and Evaluate Cross-layer Optimizations

This article introduces the first open-source FPGA-based infrastructure, MetaSys, with a prototype in a RISC-V system, to enable the rapid implementation and evaluation of a wide range of cross-layer techniques in real hardware. Hardware-software cooperative techniques are powerful approaches to improving the performance, quality of service, and security of general-purpose processors. They are, however, typically challenging to rapidly implement and evaluate in real hardware as they require full-stack changes to the hardware, system software, and instruction-set architecture (ISA). MetaSys implements a rich hardware-software interface and lightweight metadata support that can be used as a common basis to rapidly implement and evaluate new cross-layer techniques. We demonstrate MetaSys’s versatility and ease-of-use by implementing and evaluating three cross-layer techniques for: (i) prefetching in graph analytics; (ii) bounds checking in memory unsafe languages, and (iii) return address protection in stack frames; each technique requiring only ~100 lines of Chisel code over MetaSys. Using MetaSys, we perform the first detailed experimental study to quantify the performance overheads of using a single metadata management system to enable multiple cross-layer optimizations in CPUs. We identify the key sources of bottlenecks and system inefficiency of a general metadata management system. We design MetaSys to minimize these inefficiencies and provide increased versatility compared to previously proposed metadata systems. Using three use cases and a detailed characterization, we demonstrate that a common metadata management system can be used to efficiently support diverse cross-layer techniques in CPUs. MetaSys is completely and freely available at https://github.com/CMU-SAFARI/MetaSys .
Effects of Virtual Environments and Self-representations on Redirected Jumping

We design experiments to measure the perception (detection thresholds for gains, presence, embodiment, intrinsic motivation, and cybersickness) and physical performance (heart rate intensity, preparation time, and actual jumping distance) of redirected jumping (RDJ), under six different combinations of virtual environments (VEs) (low and high visual richness) and self-representations (SRs) (invisible, shoes, human-like). Results suggested that both VEs and SRs influence users' perception and performance in RDJ, and have to be taken into account when designing locomotion techniques.
Multi-Saliency Map and Machine Learning Based Human Detection for the Embedded Top-View Imaging System

Compared to the side view, a top-view is robust against occlusion generated by objects located indoors. It offers a better wide view angle and much visibility of a scene. However, there are still problems to be handled. The top-view image shows asymmetrical features and radially distorted scenes around the corners, such as omnidirectional view images and self-occlusion. Conventional human detection methods are suitable for finding moving objects in front view imaging systems. And there are some limitations, such as slow execution speed due to computational complexity. In this paper, we propose an efficient method. A static saliency map with low activity and a dynamic saliency map with a lot of movement are respectively detected. These two models were fused to create a multi-saliency map, and both characteristics were used simultaneously to improve detection rates. To handle problems such as asymmetry, a rotation matrix was calculated around the center, and Histogram of Oriented Gradient (HOG) features descriptor were extracted from the multi-saliency map to create an image patch (a small image region of interest containing human candidates). For the classification of image patches, we used machine learning-based supervised learning models support-vector machine (SVM) algorithm to improve performance. As a result of the proposed algorithm, it showed low resource occupancy and achieved Average Precision of 92.3% and 96.12% when Intersection over Union were 50% and 45% respectively.
Advances in Human Activity Recognition

Human activity recognition (HAR) is the matter of classifying sequences of accelerometer data recorded by specialized harnesses or smart-phones into known, well-defined movements. This is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate data to known movements. Traditional approaches involve handcrafting features from the time series data based on fixed size windows and training machine learning models, such as ensembles of decision trees. A potential challenge is that this feature engineering requires deep expertise in the field.