Reviewerly/exHarmony huggingface dataset:
Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p &lt; 0.05.
Provably Secure Symmetric Private Information Retrieval with Quantum Cryptography

Private information retrieval (PIR) is a database query protocol that provides user privacy, in that the user can learn a particular entry of the database of his interest but his query would be hidden from the data centre. Symmetric private information retrieval (SPIR) takes PIR further by additionally offering database privacy, where the user cannot learn any additional entries of the database. Unconditionally secure SPIR solutions with multiple databases are known classically, but are unrealistic because they require long shared secret keys between the parties for secure communication and shared randomness in the protocol. Here, we propose using quantum key distribution (QKD) instead for a practical implementation, which can realise both the secure communication and shared randomness requirements. We prove that QKD maintains the security of the SPIR protocol and that it is also secure against any external eavesdropper. We also show how such a classical-quantum system could be implemented practically, using the example of a two-database SPIR protocol with keys generated by measurement device-independent QKD. Through key rate calculations, we show that such an implementation is feasible at the metropolitan level with current QKD technology.
WD3: Taming the Estimation Bias in Deep Reinforcement Learning

The overestimation phenomenon caused by function approximation is a well-known issue in value-based reinforcement learning algorithms such as deep Q-networks and DDPG, which could lead to suboptimal policies.To address this issue, TD3 takes the minimum value between a pair of critics.In this paper, we show that the TD3 algorithm introduces underestimation bias in mild assumptions.To obtain a more precise estimation for value function, we unify these two opposites and propose a novel algorithm Weighted Delayed Deep Deterministic Policy Gradient (WD3), which can eliminate the estimation bias and further improve the performance by weighting a pair of critics.To demonstrate the effectiveness of WD3, we compare the learning process of value function between DDPG, TD3, and WD3.The results verify that our algorithm does eliminate the estimation error of value functions.Furthermore, we evaluate our algorithm on the continuous control tasks.We observe that in each test task, the performance of WD3 consistently outperforms, or at the very least matches, that of the state-of-the-art algorithms 1 .
A Blockchain-enabled Architecture for IoMT Device Authentication

The healthcare sector is rapidly integrating the Internet of Medical Things (IoMT) technologies and adopting new practices regarding how patient data are collected, processed, and stored. Although various medical device authentication mechanisms have been proposed so far, most of them are power consuming, not cost-effective, or face scalability issues. We propose a novel IoMT authentication framework incorporating new concepts and technologies such as self-sovereign identity, zero-knowledge proofs, and blockchain technology to cover this gap. The architecture is built upon key elements of the healthcare ecosystem (hospital entities, device manufacturers, medical devices) and provides multientity IoMT authentication functionalities with revocation. We demonstrate the proposed architecture's efficiency through a proof of concept implementation based on Hyperledger Aries, Indy, and Ursa.
Fabric Retrieval Based on Multi-Task Learning

Due to the potential values in many areas such as e-commerce and inventory management, fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has recently become a research hotspot. It is also a challenging issue with serval obstacles: variety and complexity of fabric appearance, high requirements for retrieval accuracy. To address this issue, this paper proposes a novel approach for fabric image retrieval based on multi-task learning and deep hashing. According to the cognitive system of fabric, a multi-classification-task learning model with uncertainty loss and constraint is presented to learn fabric image representation. Then we adopt an unsupervised deep network to encode the extracted features into 128-bits hashing codes. Further, the hashing codes are regarded as the index of fabrics image for image retrieval. To evaluate the proposed approach, we expanded and upgraded the dataset WFID, which was built in our previous research specifically for fabric image retrieval. The experimental results show that the proposed approach outperforms the state-of-the-art.
MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.
Dangerous games: A literature review on cybersecurity investments

Abstract Cybersecurity has gained prominence in the decision‐making of firms. Due to the increasing occurrences of threats in the cyberspace, investments in cybersecurity have become critical to mitigate the operational disruption of businesses. This paper surveys the theoretical literature on the firms' incentives to invest in cybersecurity. A taxonomy of the existing contributions is provided to frame them in a common reference scheme and a model is developed to encompass such contributions and discuss their main findings. Papers that investigate the investment problem of an isolated firm are distinguished from those that consider interdependent firms. In turn, interdependent cybersecurity is analyzed in three different contexts: (i) firms that operate their business via a common computer network, but are not competitors in the product market; (ii) firms that are competitors in the product market, but run their business using non‐interconnected computer systems; (iii) firms that are competitors and rely on a common computer network. Finally, promising avenues for future research and policy implications are discussed.
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

It is often critical for prediction models to be robust to distributional shifts between training and testing data. From a causal perspective, the challenge is to distinguish the stable causal relationships from the unstable spurious correlations across shifts. We describe a causal transfer random forest (CTRF) that combines existing training data with a small amount of data from a randomized experiment to train a model which is robust to the feature shifts and therefore transfers to a new targeting distribution. Theoretically, we justify the robustness of the approach against feature shifts with the knowledge from causal learning. Empirically, we evaluate the CTRF using both synthetic data experiments and real-world experiments in the Bing Ads platform, including a click prediction task and in the context of an end-to-end counterfactual optimization system. The proposed CTRF produces robust predictions and outperforms most baseline methods compared in the presence of feature shifts.
User discomfort while using a virtual reality headset as a personal viewing system for text-intensive office tasks

Ergonomics issues while using virtual reality (VR) headsets for text-intensive applications have not been studied. Measures of neck and shoulder discomfort and simulator sickness symptoms were quantified while participants were performing a document creation task for 60 min using a VR headset and a desktop monitor. During the task with the headset, participants rotated the head 2.7 times more frequently and used the neck extensor muscles 25.9% more, in average. They also rated the neck and shoulder discomfort 60% and 17.5% higher after the task. The simulator sickness symptoms were also rated significantly higher (p < .05) for the headset condition, with more pronounced differences in the symptoms related to visual discomfort. Results indicate that the physical discomforts due to the frequent head rotations and the headset weight, and visual discomforts due to difficulty in reading texts were the main issues of the VR headset for common office tasks. Practitioner summary: Ergonomics issues associated with the use of a VR headset for conducting office productivity work tasks have been evaluated in an experiment. Study results indicate that the development in the neck physical discomfort and visual discomfort may be the main barriers to the use of current VR headsets for office works. Abbreviations: VR: virtual reality; VDT: video display terminal; EMG: electromyography; MVC: maximum voluntary contraction; SSQ: simulator sickness questionnaire; ECG: electrocardiogram; NEMG: normalised electromyography
Understanding the full cost of cyber security breaches

Media reports regularly highlight organisations that have experienced costly cyber attacks or other cyber security breaches. Such breaches can have far-reaching and long-running impacts, compromising systems and data, impacting relationships with customers, suppliers and partners, and in extreme situations becoming existential threats. As an example of the potentially significant costs involved, the cyber attack on TalkTalk, the UK telecommunications company, in 2016, is estimated to have cost up to £60m. 1 When organisations are breached, many will recognise that they have been impacted in some way. But new research suggests that organisations rarely understand the full cost. Steven Furnell of the University of Nottingham and Harry Heyburn, Andrew Whitehead and Jayesh Navin Shah at Ipsos MORI examine the range of potential direct and indirect costs organisations can incur, and the varying timeframes over which these costs come to light. And they offer a data collection tool that can support organisations to fully capture the cost of breaches.
Brain MRI artefact detection and correction using convolutional neural networks

Background and Objective: Brain MRI is one of the most commonly used diagnostic imaging tools to detect neurodegenerative disease. Diagnostic image quality is a key factor to enable robust image analysis algorithms developed for downstream tasks such as segmentation. In clinical practice, one of the main challenges is the presence of image artefacts, which can lead to low diagnostic image quality. Methods: In this paper, we propose using dense convolutional neural networks to detect and a residual U-net architecture to correct motion related brain MRI artefacts. We first generate synthetic artefacts using an MR physics based corruption strategy. Then, we use a detection strategy based on dense convolutional neural network to detect artefacts. The detected artefacts are corrected using a residual U-net network trained on corrupted data. Results: Our pipeline for detection and correction of artefacts is capable of reaching not only better quality image quality, but also better segmentation accuracy of stroke segmentation. The algorithm is validated using 28 cases brain MRI stroke segmentation dataset and showed an accuracy of 97.8% for detecting artefacts in our experiments. We also illustrated the improved image quality and segmentation accuracy with the proposed correction algorithm. Conclusions: Ensuring high image quality and high segmentation quality jointly can improve the automatic image analysis pipelines and reduce the influence of low image quality on final prognosis. With this work, we illustrate a performance analysis on brain MRI stroke segmentation.
Detection of loaded and unloaded UAV using deep neural network

Unmanned aerial vehicles or drones quickly became cheaper, becoming more advanced and affordable to the general public. And the ease of control made them popular among people, who want to deliver various suspicious loads. UAV detection can be performed by different existing techniques, such as radar, radio frequency, acoustic and optical sensing techniques. Because of low-cost and low-power technology computer vision is considered as an effective method for detecting Unmanned aerial vehicles. Previous studies of UAV detection mostly have dealt with detection of UAV existence. The primary aim of this paper is to review recent research into the UAV detection and perform single stage loaded and unloaded UAV detection based on YOLOv2.
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This paper deals with M2-signals, namely multivariate (or vector-valued) signals defined over a multidimensional domain. In particular, we propose an optimization technique to solve the covariance extension problem for stationary random vector fields. The multidimensional Itakura–Saito distance is employed as an optimization criterion to select the solution among the spectra satisfying a finite number of moment constraints. In order to avoid technicalities that may happen on the boundary of the feasible set, we deal with the discrete version of the problem where the multidimensional integrals are approximated by Riemann sums. The spectrum solution is also discrete, which occurs naturally when the underlying random field is periodic. We show that a solution to the discrete problem exists, is unique and depends smoothly on the problem data. Therefore, we have a well-posed problem whose solution can be tuned in a smooth manner. Finally, we have applied our theory to the target parameter estimation problem in an integrated system of automotive modules. Simulation results show that our spectral estimator has promising performance.
OANet: Learning Two-View Correspondences and Geometry Using Order-Aware Network

Establishing correct correspondences between two images should consider both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we propose Order-Aware Network, which infers the probabilities of correspondences being inliers and regresses the relative pose encoded by the essential or fundamental matrix. Specifically, this proposed network is built hierarchically and comprises three operations. First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix. These clusters are in canonical order and invariant to input permutations. Next, the clusters are spatially correlated to encode the global context of correspondences. After that, the context-encoded clusters are interpolated back to the original size and position to build a hierarchical architecture. We intensively experiment on both outdoor and indoor datasets. The accuracy of the two-view geometry and correspondences are significantly improved over the state-of-the-arts. Besides, based on the proposed method and advanced local feature, we won the first place in CVPR 2019 image matching workshop challenge and also achieve state-of-the-art results in the Visual Localization benchmark. Code is available at https://github.com/zjhthu/OANet.
CPU and GPU Accelerated Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits their widespread applications. In this paper, our objective is to improve the performance of FHE schemes by designing efficient parallel frameworks. In particular, we choose Torus Fully Homomorphic Encryption (TFHE) [1] as it offers exact results for an infinite number of boolean gate (e.g., AND, XOR) evaluations. We first extend the gate operations to algebraic circuits such as addition, multiplication, and their vector and matrix equivalents. Secondly, we consider the multi-core CPUs to improve the efficiency of both the gate and the arithmetic operations. Finally, we port the TFHE to the Graphics Processing Units (GPU) and device novel optimizations for boolean and arithmetic circuits employing the multitude of cores. We also experimentally analyze both the CPU and GPU parallel frameworks for different numeric representations (16 to 32-bit). Our GPU implementation outperforms the existing technique [1], and it achieves a speedup of 20× for any 32-bit boolean operation and 14.5× for multiplications.
Mr NISC: Multiparty Reusable Non-Interactive Secure Computation

Reducing interaction in Multiparty Computation (MPC) is a highly desirable goal in cryptography. It is known that 2-round MPC can be based on the minimal assumption of 2-round Oblivious Transfer (OT) [Benhamouda and Lin, Garg and Srinivasan, EC 2018], and 1-round MPC is impossible in general. In this work, we propose a natural "hybrid" model, called multiparty reusable Non-Interactive Secure Computation (mrNISC). In this model, parties publish encodings of their private inputs $$x_i$$ on a public bulletin board, once and for all. Later, any subset I of them can compute on-the-fly a function f on their inputs $$\varvec{x}_I = {\{x_i\}}_{i \in I}$$ by just sending a single message to a stateless evaluator, conveying the result $$f(\varvec{x}_I)$$ and nothing else. Importantly, the input encodings can be reused in any number of on-the-fly computations, and the same classical simulation security guaranteed by multi-round MPC, is achieved. In short, mrNISC has a minimal yet "tractable" interaction pattern. We initiate the study of mrNISC on several fronts. First, we formalize the model of mrNISC protocols, and present both a UC security definition and a game-based security definition. Second, we construct mrNISC protocols in the plain model with semi-honest and semi-malicious security based on pairing groups. Third, we demonstrate the power of mrNISC by showing two applications: non-interactive MPC (NIMPC) with reusable setup and a distributed version of program obfuscation. At the core of our construction of mrNISC is a witness encryption scheme for a special language that verifies Non-Interactive Zero-Knowledge (NIZK) proofs of the validity of computations over committed values, which is of independent interest.
Image annotation based on multi-view robust spectral clustering

Nowadays, image annotation has been a hot topic in the semantic retrieval field due to the abundant growth of digital images. The purpose of these methods is to realize the content of images and assign appropriate keywords to them. Extensive efforts have been conducted in this field, which effectiveness is limited between low-level image features and high-level semantic concepts. In this paper, we propose a Multi-View Robust Spectral Clustering (MVRSC) method, which tries to model the relationship between semantic and multi-features of training images based on the Maximum Correntropy Criterion. A Half-Quadratic optimization framework is used to solve the objective function. According to the constructed model, a few tags are suggested based on a novel decision-level fusion distance. The stability condition and bound calculation of MVRSC are analyzed, as well. Experimental results on real-world Flickr and 500PX datasets, and Corel5K confirm the superiority of the proposed method over other competing models.
Group-based synchronous-asynchronous Grey Wolf Optimizer

Grey Wolf Optimizer represents a relatively new metaheuristic scheme for solving continuous optimization problems. In spite of its interesting characteristics, it presents several flaws such as lack of accuracy, low diversity, premature convergence and imbalance between exploitation and exploration. In this paper, a modified version of the Grey Wolf Optimizer called Group-based Synchronous-Asynchronous Grey Wolf Optimizer is introduced. The proposed scheme incorporates a synchronous-asynchronous processing scheme, a set of different nonlinear functions and an operation to increase diversity. With such mechanisms, the proposed algorithm presents a better balance between exploration and exploitation, an increment in the accuracy and the ability to avoid the convergence in local minima. Such capacities allow its use in complex engineering problems that involve highly multimodal objective functions with a difficult localization of their global optimum. To evaluate the performance of the proposed approach, it has been tested on a representative number of functions of the well-known IEEE Congress on Evolutionary Computation 2017 benchmark set of functions and real-world engineering problems. The results of our method have been compared against those produced by other states of the art metaheuristic algorithms. The results prove the effectiveness and accuracy of the proposed technique.