Reviewerly/exHarmony huggingface dataset:
Joint Face Super-Resolution and Deblurring Using Generative Adversarial Network

Facial image super-resolution (SR) is an important aspect of facial analysis, and it can contribute significantly to tasks such as face alignment, face recognition, and image-based 3D reconstruction.Recent convolutional neural network (CNN) based models have exhibited significant advancements by learning mapping relations using pairs of low-resolution (LR) and high-resolution (HR) facial images.However, because these methods are conventionally aimed at increasing the PSNR and SSIM metrics, the reconstructed HR images might be blurry and have an overall unsatisfactory perceptual quality even when state-of-the-art quantitative results are achieved.In this study, we address this limitation by proposing an adversarial framework intended to reconstruct perceptually high-quality HR facial images while simultaneously removing blur.To this end, a simple five-layer CNN is employed to extract feature maps from LR facial images, and this feature information is provided to two-branch encoder-decoder networks that generate HR facial images with and without blur.In addition, local and global discriminators are combined to focus on the reconstruction of HR facial structures.Both qualitative and quantitative results demonstrate the effectiveness of the proposed method for generating photorealistic HR facial images from a variety of LR inputs.Moreover, it was also verified, through a use case scenario that the proposed method can contribute more to the field of face recognition than existing approaches.
Conducting Information Systems Research in the Midst of the COVID-19 Pandemic: Opportunities and Challenges

In the midst of the COVID-19 pandemic, as the world is migrating from physical to virtual in an unprecedented pace, information systems (IS) researchers endeavor to adapt their research agendas to the opportunities and challenges presented by the pandemic. The purpose of this article is to discuss these opportunities and challenges for IS research areas and methodologies, as well as to offer some thoughts about the current landscape of the research community.
Performance Analysis of Object Detection Algorithm for Intelligent Traffic Surveillance System

Object detection algorithm such as convolution neural networks (CNN) is implemented for traffic surveillance applications. A neural network consists of input minimum one hidden and an output layer. Urban vehicle dataset, which consists of four classes of images such as Heavy, Auto, Light, and Two-wheeler captured during day, evening and night, which includes blur images. The dataset is composed of images of varying illumination. Performance parameters such as accuracy, precession, recall and f1 score calculated for night and blur image dataset. Obtained results shows that the algorithm effectively detects objects with an accuracy of 91% for night images and 88 % for blur images.
The Algorithmic University: On-Line Education, Learning Management Systems, and the Struggle over Academic Labor

The use of on-line education (OLE) to deliver higher education using learning management systems (LMS) has received growing critical attention for its reliance on precarious faculty, high dropout and failure rates, and as a form of privatization. While these critiques are well grounded, they overlook the role of OLE as a strategy for rationalizing teaching and deskilling academic labor in order to produce more self-disciplined precarious “platform” workers who can labor remotely under the control of algorithmic management. To recompose the power of academic workers, new tactics, strategies, and objectives based on an analysis of the new technical composition of capital in higher education are needed.
Hierarchical Fuzzy Neural Networks With Privacy Preservation for Heterogeneous Big Data

Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning difficult, from providing enough processing power to maintaining model accuracy to protecting privacy. However, perhaps the most imposing problem is that big data is often interspersed with sensitive personal data. Hence, we propose a privacy-preserving hierarchical fuzzy neural network (PP-HFNN) to address these technical challenges while also alleviating privacy concerns. The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents. Coordination at high levels of the hierarchy is handled by the alternating optimization method, which converges very quickly. The entire training procedure is scalable, fast and does not suffer from gradient vanishing problems like the methods based on back-propagation. Comprehensive simulations conducted on both regression and classification tasks demonstrate the effectiveness of the proposed model.
Accident Detection System for Bicycle Riders

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders' injuries is higher compared to the risk of injuries for motorists. Crashes, riders' errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (${X}$ , ${Y}$ , and ${Z}$ ). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system.
Applying Genetic Programming to Improve Interpretability in Machine Learning Models

Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, name Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.
EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization

This paper presents the main characteristics of the evolutionary optimization code named EOS, Evolutionary Optimization at Sapienza, and its successful application to challenging, real-world space trajectory optimization problems. EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm, namely, a self-adaptation of the control parameters, an epidemic mechanism, a clustering technique, an ε-constrained method to deal with nonlinear constraints, and a synchronous island-model to handle multiple populations in parallel. The results reported prove that EOS is capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms when applied to high-dimensional or highly-constrained space trajectory optimization problems.
Leveraging IoT Wearable Technology Towards Early Diagnosis of Neurological Diseases

The leading trends in the framework of the Internet of Things are driving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. One of the major opportunities offered by IoT lies in the ubiquitous connectivity, thus enabling smart services such as remote patient monitoring, in-home therapy/rehabilitation, and assisted living platforms. In this paper we present a prototype of wearable smart glasses able to monitor the Eye Blinks (EBs) through ElectroOculoGram (EOG) signal in a transparent way with respect to the final user. We propose a novel pre-filtering scheme to reduce EOG noise along with an analytical derivation of a matched filter to detect and count EBs. We have carried out an in-depth experimental campaign in order to validate the robustness of our approach with respect to the main solutions available in the literature. Furthermore, we have compared the performances obtained with out wearable prototype versus the results achievable with professional medical equipments. Results show that our solution is able to achieve very high accuracy in EB detection, obtaining comparable performance with respect to professional medical desktop equipment, with the additional benefit of portability, comfort and easiness of use for the patients.
Multi-view clustering based on generalized low rank approximation

The core of most existing graph-based multi-view clustering methods is to learn a rigid consistent spectral embedding from multiple graphs. In practice, however, such a consistency over spectral embedding may be rigorous to limit the final clustering result, since the quality and structure of different graphs are generally different. To this end, we propose to learn a relaxed consistent spectral embedding via a generalized low rank approximation model. Particularly, the proposed model introduces an adaptively weighted system which can further improve the robustness of algorithm by assigning a specific weight for each view. For the involved objective function is non-convex and non-smooth, a Relaxed MM (Majorization-Minimization) approach is developed to solve it. And, we show that Relaxed MM can reduce the computation complexity from O(n3), required by most existing graph-based methods, to O(nc2), where c and n are the number of clusters and samples, respectively, and c≪n. Numerical experiments performed on real world data demonstrate that our algorithm generally achieves comparable or better clustering results compared to eight state-of-the-art multi-view clustering methods.
Multiple graph kernel learning based on GMDH-type neural network

Multiple kernel learning (MKL), as a principled classification method, selects and combines base kernels to increase the categorization accuracy of Support Vector Machines (SVMs). The group method of data handling neural network (GMDH-NN) has been applied in many fields of optimization, data mining, and pattern recognition. It can automatically seek interrelatedness in data, select an optimal structure for the model or network, and enhance the accuracy of existing algorithms. We can utilize the advantages of the GMDH-NN to build a multiple graph kernel learning (MGKL) method and enhance the categorization performance of graph kernel SVMs. In this paper, we first define a unitized symmetric regularity criterion (USRC) to improve the symmetric regularity criterion of GMDH-NN. Second, a novel structure for the initial model of the GMDH-NN is defined, which uses the posterior probability output of graph kernel SVMs. We then use a hybrid graph kernel in the H1-space for MGKL in combination with the GMDH-NN. This way, we can obtain a pool of optimal graph kernels with different kernel parameters. Our experiments on standard graph datasets show that this new MGKL method is highly effective.
Recent Advancements in Microarchitectural Security: Review of Machine Learning Countermeasures

Recent microarchitectural security countermeasures by employing applications' low-level features collected from Hardware Performance Counters (HPCs) registers have emerged as a promising solution to address the inefficiency of traditional software-based methods. Furthermore, recent studies have shown that malicious activities at the hardware level ranging from application-based malware to microarchitecture-based Side-Channel Attacks (SCAs) can be accurately distinguished from normal traces using Machine Learning (ML) algorithms. Such ML-based countermeasures reduce the latency of attack detection process as well as the hardware and resource utilization overheads. This paper provides an in-depth analysis of recent advancements in machine learning countermeasures for microarchitectural security to detect malicious software and emerging side-channel attacks.
Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks

Abstract Entanglement is a key resource in many quantum information applications and achieving high values independently of the initial conditions is an important task. Here we address the problem of generating highly entangled states in a discrete time quantum walk irrespective of the initial state using two different approaches. First, we present and analyze a deterministic sequence of coin operators which produces high values of entanglement in a universal manner for a class of localized initial states. In a second approach, we optimize the discrete sequence of coin operators using a reinforcement learning algorithm. While the amount of entanglement produced by the deterministic sequence is fully independent of the initial states considered, the optimized sequences achieve in general higher average values of entanglement that do however depend on the initial state parameters. Our proposed sequence and optimization algorithm are especially useful in cases where the initial state is not fully known or entanglement has to be generated in a universal manner for a range of initial states.
High Performance and Efficient Real-Time Face Detector on Central Processing Unit Based on Convolutional Neural Network

Face detection is crucial in the development of face recognition, expression, tracking, and classification. Conventional methods have accuracy constraints on several challenging conditions, including nonfrontal faces, occlusions, and complex backgrounds. However, the convolutional neural network (CNN) methods produce high performances despite a large amount of computation. Therefore, CNN requires expensive hardware and is not suitable for low-cost central processing units (CPUs). This article develops a light architecture for a CNN-based real-time face detector. The proposed architecture consists of two main modules, the backbone to extract distinctive facial features and multilevel detection to perform prediction at multiple scales. Furthermore, it utilizes several approaches to enhance the training result, including balancing loss and tweaks on the training configuration. The proposed detector has one stage and is trained using the input of images from WIDER FACE with challenges, which contains more challenging images than other datasets. As a result, the detector achieves state-of-the-art performance on several benchmark datasets compared with the other CPU-based models. Then, its efficiency is superior to that of competitors, as it runs at 53 frames per second on a CPU for video graphics array resolution images.
SVM-based global vision system of sports competition and action recognition

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.
Self-identification with a Virtual Experience and Its Moderating Effect on Self-efficacy and Presence

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JND-Guided Perceptually Color Image Watermarking in Spatial Domain

Watermarking is an effective solution for copyright protection and forensics tracking via hiding information into the image signal.Recently, spatial-embedding watermarking methods from different transform domain have been proposed rapidly and effectively protect the copyright of the color image.Compared to the individual frequency domain method, it has both advantages of spatial domain and frequency domain.Here, we proposed a novel spatial-embedding watermarking method based on an attended just noticeable difference (JND) model with color complexity, to achieve a good tradeoff between robustness and perceptual quality.In particular, at the spatial embedding level, the direct current (DC) coefficients are selected for embedding and the perceptual JND model is used to guide the amount of pixel modification to improve visual quality.Different from the previous JND model, we proposed an attended JND model that considers color complexity, which is more consistent of the human visual perception model.Compared with the other JND models, the proposed JND model is more suitable for the watermarking framework.Experimental results show that the proposed watermarking scheme has better performance than other existing watermarking schemes.This greatly benefits the practical implementations of the spatial-embedding watermarking methods.
Video Scene Segmentation Using Tensor-Train Faster-RCNN for Multimedia IoT Systems

Video surveillance techniques like scene segmentation are playing an increasingly important role in multimedia Internet-of-Things (IoT) systems. However, existing deep learning-based methods face challenges in both accuracy and memory when deployed on edge computing devices with limited computing resources. To address these challenges, a tensor-train video scene segmentation scheme that compares the local background information in regional scene boundary boxes in adjacent frames is proposed. Compared to the existing methods, the proposed scheme can achieve competitive performance in both segmentation accuracy and parameter compression rate. In detail, first, an improved faster region convolutional neural network (faster-RCNN) model is proposed to recognize and generate a large number of region boxes with foreground and background to achieve boundary boxes. Then, the foreground boxes with sparse objects are removed and the rest are considered as optional background boxes used to measure the similarity between two adjacent frames. Second, to accelerate the training efficiency and reduce memory size, a general and efficient training way using tensor-train decomposition to factor the input-to-hidden weight matrix is proposed. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of accuracy and model compression. Our results demonstrate that the proposed model can improve the training efficiency and save the memory space for the deep computation model with good accuracy. This work opens the potential for the use of artificial intelligence methods in edge computing devices for multimedia IoT systems.
The Effect of Co-Crystallising Sulphides and Precipitation Mechanisms on Sphalerite Geochemistry: A Case Study from the Hilton Zn-Pb (Ag) Deposit, Australia

High-tech metals including Ge, Ga and In are often sourced as by-products from a range of ore minerals, including sphalerite from Zn-Pb deposits. The Hilton Zn-Pb (Ag) deposit in the Mount Isa Inlier, Queensland, contains six textural varieties of sphalerite that have formed through a diverse range of processes with variable co-crystallising sulphides. This textural complexity provides a unique opportunity to examine the effects of co-crystallising sulphides and chemical remobilisation on the trace element geochemistry of sphalerite. Early sphalerite (sph-1) is stratabound and coeval with pyrrhotite, pyrite and galena. Disseminated sphalerite (sph-2) occurs as isolated fine-grained laths rarely associated with co-crystallising sulphides and represents an alteration selvage accompanying the precipitation of early stratabound sphalerite (sph-1). Sphalerite (sph-3) occurs in early ferroan-dolomite veins and formed from the chemical remobilisation of stratabound sphalerite (sph-1) during brittle fracturing and interstitial fluid flow. This generation of veins terminate at the interface, and occurs within clasts of the paragenetically later sphalerite-dominated breccias (sph-4). Regions of high-grade Cu (&gt;2%) mineralisation contain a late generation of sphalerite (sph-5), which formed from the recrystallisation of breccia-type sphalerite (sph-4) during the infiltration of a paragenetically late Cu- and Pb-rich fluid. Late ferroan-dolomite veins crosscut all previous stages of mineralisation and also contain chemically remobilised sphalerite (sph-6). Major and trace elements including Fe, Co, In, Sn, Sb, Ag and Tl are depleted in sphalerite associated with abundant co-crystallised neighbouring sulphides (e.g., pyrite, pyrrhotite, galena and chalcopyrite) relative to sphalerite associated with minor to no co-crystallising sulphides. This depletion is attributed to the incorporation of the trace elements into the competing sulphide minerals. Chemically remobilised sphalerite is enriched in Zn, Cd, Ge, Ga and Sn, and depleted in Fe, Tl, Co, Bi and occasionally Ag, Sb and Mn relative to the primary minerals. This is attributed to the higher mobility of Zn, Ge, Ga and Sn relative to Fe and Co during the chemical remobilisation process, coupled with the effect of co-crystallising with galena and ferroan-dolomite. Results from this study indicate that the consideration of co-crystallising sulphides and post-depositional processes are important in understanding the trace element composition of sphalerite on both a microscopic and deposit-scale, and has implications for a range of Zn-Pb deposits worldwide.