Reviewerly/exHarmony huggingface dataset:
HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge

This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of five primary body signals including gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cameras are used to capture 772 distinctive subjects across gender, ethnicity, age, and style. With the multiview image streams, we reconstruct the geometry of body expressions using 3D mesh models, which allows representing view-specific appearance. We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complementary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets. Based on HUMBI, we formulate a new benchmark challenge of a pose-guided appearance rendering task that aims to substantially extend photorealism in modeling diverse human expressions in 3D, which is the key enabling factor of authentic social tele-presence. HUMBI is publicly available at http://humbi-data.net.
Risk Assessments of Social Engineering Attacks and Set Controls in an Online Education Environment

The cybersecurity attacks for the educational field is not too highlighted in today's time. There is an incoming threat in the educational field that if not look into can result into a dangerous attack than can victimized students and educational institutions, especially those who are from third world countries. This study is to alert and to inform everyone about the vulnerability in gathering data using education as a facade that may result in a serious breach of privacy and security. In this study, a very simple method of getting data was used - survey. The study conducted an experiment where surveys were given to many students and see if they will give their information if they were told that it was for an academic purpose. Plenty of students have answered the survey which aims to get their email address, name, social media accounts and more. From there, the researchers sent the students phishing emails to test if they are mindful of these cyberattacks. The researchers also use the information from the survey to do reconnaissance. This study will show how vulnerable the educational field is to basic cyberattacks and will give some informative recommendations on what to do.
A Hybrid Dehazing Method and its Hardware Implementation for Image Sensors

The demand for image dehazing is ever-increasing in image sensor based outdoor systems such as self-driving vehicles, automatic driver assistance, and in highway monitoring and analytics. These applications require dedicated hardware solution to meet high frame-rate and low power constraints. Previously, a few prior based hardware dehazing methods have been presented. However, they produce artifacts in the restored images due to the failure of the underlying assumptions. To address this problem and to meet the stringent requirements, a data-driven image dehazing approach based on convolutional neural network (CNN) and dark channel prior (DCP) is proposed that automatically learns the important features and produces better results. The proposed method is hardware friendly and its hardware implementation is also presented. The design employs few line buffers to store activations and eliminates the requirement of off-chip memory like dynamic random-access memory (DRAM). Field programmable gate array (FPGA) and application specific integrated circuit (AISC) implementations show that the architecture is highly suitable for application scenarios with constrained computational resources, low memory, and tight power budget. The quantitative analysis shows more than 11% average PSNR improvement in image quality on standard datasets as compared to the state-of-the-art hardware methods while consuming comparable hardware resources and power.
MuSe: a multi-level storage scheme for big RDF data using MapReduce

Abstract Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.
Self-supervised Multimodal Generalized Zero Shot Learning for Gleason Grading

Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non-interventional and easily acquired. We solve the problem in a generalized zero-shot learning (GZSL) setting since we may not access training images of every disease grade. Synthetic MRI feature vectors of unseen grades (classes) are generated by exploiting Gleason grades’ ordered nature through a conditional variational autoencoder (CVAE) incorporating self-supervised learning. Corresponding histopathology features are generated using cycle GANs, and combined with MR features to predict Gleason grades of test images. Experimental results show our method outperforms competing feature generating approaches for GZSL, and comes close to performance of fully supervised methods.
Affective Support Mediated by an On-Line Constructivist Environment in Times of Covid-19

The main objective is to know the level of influence of the factors that make up the Constructivist On-Line Learning Environment Survey (COLLES) questionnaire, on affective support, mediated through the LMS Blackboard Learn, in engineering students at the level of undergraduate degree at a private university in Peru in times of Covid-19, using the PLS-SEM methodology, which is a multivariate method called Modeling of Structural Equations with Partial Least Squares. The results obtained from the exploratory and confirmatory factorial analysis, has allowed to corroborate that the most important factor in collaboration as a basis for meaningful learning is affective support, in which affective/cognitive mediations are established between students.
Free Space Measurement Device Independent Quantum Key Distribution with Modulating Retro-Reflectors under Correlated Turbulent Channel

Modulating retro-reflector (MRR), originally introduced to support laser communication, relieves most of the weight, power, and pointing requirements to the ground station. In this paper, a plug-and-play measurement device independent quantum key distribution (MDI-QKD) scheme with MRR is proposed not only to eliminate detector side channels and allow an untrusted satellite relay between two users, but also to simplify the requirements set-ups in practical flexible moving scenarios. The plug-and-play architecture compensates for the polarization drift during the transmission to provide superior performance in implementing the MDI-QKD on a free-space channel, and the MRR device is adopted to relax the requirements on both communication terminals. A double-pass correlated turbulent channel model is presented to investigate the complex and unstable channel characteristics caused by the atmospheric turbulence. Furthermore, the security of the modified MDI-QKD scheme is analyzed under some classical attacks and the simulation results indicate the feasibility under the situation that the system performance deteriorates with the increase of fading correlation coefficient and the turbulence intensity, which provides a meaningful step towards an MDI-QKD based on the moving platforms to join a dynamic quantum network with untrusted relays.
It's Hard for Neural Networks to Learn the Game of Life

Efforts to improve the learning abilities of neural networks have focused mostly on the role of optimization methods rather than on weight initializations. Recent findings, however, suggest that neural networks rely on lucky random initial weights of subnetworks called “lottery tickets” that converge quickly to a solution [1]. To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a small CNN. We find that networks of this architecture trained on this task rarely converge. Rather, networks require substantially more parameters to consistently converge. Furthermore, we find that the initialization parameters that gradient descent converges to a solution are sensitive to small perturbations, such as a single sign change. Finally, we observe a critical value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{0}$</tex> such that training minimal networks with examples in which cells are alive with probability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d_{0}$</tex> dramatically increases the chance of convergence to a solution. Our results are consistent with the lottery ticket hypothesis [1].
A three-stage approach for modeling multiple time series applied to symbolic quartile data

Data science concerns to look for ways for extracting knowledge from extraordinary volume of data in any domain. This paper introduces a new three-stage approach for time series forecasting based on quartile data that combines aggregation, prototype selection and forecasting in the Symbolic Data Analysis (SDA) framework. Initially, time series are summarized and classes of entities are obtained by representing new time series. A prototype selection process based on mutual information is applied in order to obtain a more representative class data set. Each class is described by a list of three continues values, called quartile symbolic data. Boxplots time series are displayed, as a special case. In this framework, symbolic data are obtained to take into account variability between the entities of the classes. Approaches for boxplots forecasting are constructed from multivariate statistical modeling. Synthetic and real symbolic time series are considered in the experiments to demonstrate the usefulness of the proposed approach.
An improved group theory-based optimization algorithm for discounted 0-1 knapsack problem

Discounted 0-1 knapsack problem (D0-1KP) has been proved to be NP-hard, thus a lot of researches focus on designing non-deterministic algorithms to solve it. Group theory-based optimization algorithm (GTOA), as a recently proposed evolutionary algorithm (EA), can provide satisfactory results to D0-1KP. GTOA introduces important theories of algebra, i.e., group theory, to describe combinatorial optimization problems, and applies the classic operations in group theory to design operators for EA. In order to generate a better solution according to a set of existing solutions during each evolutionary iteration, an important operator called random linear combination operator (RLCO) is designed. However, the practical meaning of applying the operations in group theory is hard to explain, and the proposed RLCO is lack of interpretability, causing difficulties in analyzing and improving the algorithm. In this paper, to improve the interpretability and further enhance the performance, we propose a new operator named random xor operator (RXO), and interpret it from the view point of bitwise operation. By replacing RLCO with RXO, a new GTOA algorithm is realized for D0-1KP. Experimental results demonstrate that it can provide very competitive performance.
A comparative study of machine learning and deep learning algorithms for recognizing facial emotions

This article compares the performance of three algorithms for Facial Emotion Recognition (FER). The algorithms chosen include one machine learning model - SupportVector Machine (SVM) and two deep learning models - Convolutional Neural Network (CNN) and VGG16. All three algorithms were implemented using Python and evaluated on FER2013 dataset. The metrics used for comparison were testing accuracy, training time and the size of the weights file. The best performing algorithm from the study is used to implement FER in real time on a live video stream. The implementation details are presented and results are discussed.
Self-supervision Based Dual-Transformation Learning for Stain Normalization, Classification andSegmentation

Stain color variation s across images are common in the medical imaging domain. However, such variations among the training and test datasets may lead to unsatisfactory performance on the latter in any desired task. This paper proposes a novel coupled-network composed of two U-Net type architectures that utilize self-supervised learning. The first subnetwork (N1) learns an identity transformation, while the second (N2) learns a transformation to perform stain normalization. We also introduce classification heads in the subnetworks, trained along with the stain normalization task. To the best of our knowledge, the proposed coupling framework, where the information from the encoders of both the subnetworks is utilized by the decoders of both subnetworks as well as trained in a coupled fashion, is introduced in this domain for the first time. Interestingly, the coupling of N1 (for identity transformation) and N2 (for stain normalization) helps N2 learn the stain normalization task while being cognizant of the features essential to reconstruct images. Similarly, N1 learns to extract relevant features for reconstruction invariant to stain color variations due to its coupling with N2. Thus, the two subnetworks help each other, leading to improved performance on the subsequent task of classification. Further, it is shown that the proposed architecture can also be used for segmentation, making it applicable for all three applications: stain normalization, classification, and segmentation. Experiments are carried out on four datasets to show the efficacy of the proposed architecture.
Twin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5544" altimg="si3.svg"><mml:mi>K</mml:mi></mml:math>-class support vector classification with pinball loss

The twin K-class support vector classification (TKSVC) is an effective and efficient algorithm for multi-class classification problem. However, due to the use of hinge loss function, it is very sensitive to feature noises and not stable for re-sampling. To avoid the above flaws, we present a novel twin K-class support vector classification with pinball loss (Pin-TKSVC) in this paper. The structure of Pin-TKSVC is analogous to that of TKSVC. Namely, it adopts "One-vs.-One-vs.-Rest" structure to do multi-class classification and solves two smaller sized quadratic programming problems to save the running time. Besides, the introduction of pinball loss does not increase the additional computational complexity. More importantly, the Pin-TKSVC maximizes the quantile distance between different categories, making it a more robust classifier. To testify its performance, we do numerical experiments on twenty datasets with different noises, and compare it with five state-of-the-art algorithms. The experimental results confirm the validity of our algorithm.
SRNet: Scale-Aware Representation Learning Network for Dense Crowd Counting

Huge variations in the scales of people in images create an extremely challenging problem in the task of crowd counting.Currently, many researchers apply multi-column structures to solve the scale variation problem.However, multi-column structures usually have complex structures with large numbers of parameters and are difficult to optimize.To this end, we propose a scale-aware representation learning network (SRNet) that uses a commonly used encoder-decoder framework.An image is converted into deep features by the first ten layers of VGG16 in the encoder.Then, the features are regressed to a crowd density map via the decoder.The decoder mainly consists of two modules: the scale-aware feature learning module (SAM) and the pixel-aware upsampling module (PAM).SAM models the multi-scale features of a crowd at each level with different sizes of receptive fields, and PAM enlarges the spatial resolution and enhances the pixel-level semantic information, thereby improving the overall counting accuracy.We conduct extensive crowd counting experiments on ShanghaiTech Part_A, UCF-QNRF, and UCF_CC_50 datasets.Furthermore, to obtain the locations of each person, we conduct crowd localization experiments on UCF-QNRF and NWPU-Crowd datasets.The qualitative and quantitative results prove the effectiveness of the SRNet in dense crowd counting and crowd localization tasks.
DKG-PIPD: A Novel Method About Building Deep Knowledge Graph

In this study, a method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed.Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference.Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data.In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction.In addition, DKG-PIPD used a novel end-toend model to train and predict on the crawled dataset.The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method.Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced.Finally, the improvement direction of this paper was also introduced in the discussion section.
ViewfinderVR: configurable viewfinder for selection of distant objects in VR

Selection is one of the fundamental user interactions in virtual reality (VR) and 3D user interaction, and raycasting has been one of the most popular object selection techniques in VR. However, the selection of small or distant objects through raycasting has been known to be difficult. To overcome this limitation, this study proposed a new technique called ViewfinderVR for improved selection of distant objects in VR, utilizing a virtual viewfinder panel with a modern adaptation of the through-the-lens metaphor. ViewfinderVR enables faster and more accurate target selection by allowing customization of the interaction space projected onto a virtual panel within reach, and users can select objects reflected on the panel with either ray-based or touch interaction. Experimental results of Fitts' law-based tests with 20 participants showed that ViewfinderVR outperformed traditional raycasting in terms of task performance (movement time, error rate, and throughput) and perceived workload (NASA-TLX ratings), where touch interaction was superior to ray-based interaction. The associated user behavior was also recorded and analyzed to understand the underlying reasons for the improved task performance and reduced workload. The proposed technique can be used in VR applications to enhance the selection of distant objects.
Learning-Based Noise Component Map Estimation for Image Denoising

A problem of image denoising, when images are corrupted by a non-stationary noise, is considered in this paper. Since, in practice, no a priori information on noise is available, noise statistics should be pre-estimated prior to image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise, standard deviations of noise (so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sigma-map</i> ) is proposed. It achieves the state-of-the-art performance in accuracy of estimation of sigma-map for the case of non-stationary noise, as well as estimation of a noise variance for the case of an additive white Gaussian noise. Extensive experiments on image denoising using estimated sigma-maps demonstrate that our method outperforms recent CNN-based blind image denoising methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based on sigma-map estimation by up to 0.5 dB, providing, at the same time, better usage flexibility. A comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for the most of noise levels is within 0.1-0.2 dB, and does not exceed 0.6 dB.
Online/Offline Rewritable Blockchain With Auditable Outsourced Computation

Policy-based chameleon hash (PCH) is one of the techniques used for rewriting transaction-level data stored in blockchains. This technique integrates the access policy of the attribute-based encryption (ABE) in the transactions and only allows users with attributes set satisfying the access policy to modify the transactions. However, some operations in the PCH-based rewritable blockchain solution require high computational cost which may impact the performance of user systems, especially on resource-constrained devices. To solve this problem, we propose an online/offline rewritable blockchain with auditable outsourced computation (OO-RB-AOC) scheme. We utilize the ring signature to ensure the credibility of multiple attribute authorities, and adopt the online/offline technique for generating the hash of the rewritable transaction. In addition, expensive computations (e.g., pairings) required for rewriting the transactions can be outsourced to the clouds. The users can rest assured that the computations from the clouds are correct with the audit mechanism of the proposed scheme. On the other hand, the proposed scheme offers a desirable feature for commercial application where the clouds can limit the number of outsourced requests according to the subscription of the users. We also prove the security of the proposed OO-RB-AOC scheme. Finally, we present a theoretical comparison and experimental analysis of the proposed scheme.
Reimagining Robust Distributed Systems Through Accountable MAS

Robustness is an important property of software systems; the availability of proper feedback is crucial to obtain it, especially when a system consists of distributed and interconnected components. Multiagent systems (MAS) are valuable for conceptualizing and implementing distributed systems, but they fall short in addressing robustness in a systematic way at design time. In this article, we provide a definition of accountability and show its use for designing robust MAS.