Reviewerly/exHarmony huggingface dataset:
Impact of injection attacks on sensor-based continuous authentication for smartphones

Given the relevance of smartphones for accessing personalized services in smart cities, Continuous Authentication (CA) mechanisms are attracting attention to avoid impersonation attacks. Some of them leverage Data Stream Mining (DSM) techniques applied over sensorial information. Injection attacks can undermine the effectiveness of DSM-based CA by fabricating artificial sensorial readings.The goal of this paper is to study the impact of injection attacks in terms of accuracy and immediacy to illustrate the time the adversary remains unnoticed. Two well-known DSM techniques (K-Nearest Neighbours and Hoeffding Adaptive Trees) and three data sources (location, gyroscope and accelerometer) are considered due to their widespread usage Results show that even if the attacker does not previously know anything about the victim, a significant attack surface arises – 1.35 min are needed, in the best case, to detect the attack on gyroscope and accelerometer and 7.27 min on location data. Moreover, we show that the type of sensor at stake and configuration settings may have a dramatic effect on countering this threat.
Offline Evaluation without Gain

We propose a simple and flexible framework for offline evaluation based on a weak ordering of results (which we call "partial preferences") that define a set of ideal rankings for a query. These partial preferences can be derived from from side-by-side preference judgments, from graded judgments, from a combination of the two, or through other methods. We then measure the performance of a ranker by computing the maximum similarity between the actual ranking it generates for the query and elements of this ideal result set. We call this measure the "compatibility" of the actual ranking with the ideal result set. We demonstrate that compatibility can replace and extend current offline evaluation measures that depend on fixed relevance grades that must be mapped to gain values, such as NDCG. We examine a specific instance of compatibility based on rank biased overlap (RBO). We experimentally validate compatibility over multiple collections with different types of partial preferences, including very fine-grained preferences and partial preferences focused on the top ranks. As well as providing additional insights and flexibility, compatibility avoids shortcomings of both full preference judgments and traditional graded judgments.
Bayesian Joint Input-State Estimation for Nonlinear Systems

This work suggests a solution for joint input-state estimation for nonlinear systems. The task is to recover the internal states of a nonlinear oscillator, the displacement and velocity of the system, and the unmeasured external forces applied. To do this, a Gaussian process latent force model is developed for nonlinear systems. The model places a Gaussian process prior over the unknown input forces for the system, converts this into a state-space form and then augments the nonlinear system with these additional hidden states. To perform inference over this nonlinear state-space model a particle Gibbs approach is used combining a “Particle Gibbs with Ancestor Sampling” Markov kernel for the states and a Metropolis-Hastings update for the hyperparameters of the Gaussian process. This approach is shown to be effective in a numerical case study on a Duffing oscillator where the internal states and the unknown forcing are recovered, each with a normalised mean-squared error less than 0.5%. It is also shown how this Bayesian approach allows uncertainty quantification of the estimates of the states and inputs which can be invaluable in further engineering analyses.
Improving Deep Learning based Optical Character Recognition via Neural Architecture Search

Optical character rcecognition (OCR) is a process of converting images of typed, handwritten or printed text into machine-encoded one. In recent years, the methods represented by deep learning have greatly improved the performance of OCR systems, but the main challenges of such systems are 1) to accurately perform text detection in complex scenes and 2) to identify and set the optimal parameters to optimize the performance of the system. In this paper, we propose an OCR method based on Neural Architecture Search technique, called AutOCR. The characteristic of the proposed method is the automatic design of text detection framework using an evolutionary computation neural architecture search method. This design can not only accurately recognize the text in a complex environment, but also avoid the process of experts participating in parameter adjustment. We compared it with different methods, and the experimental results proved the effectiveness of our method.
Backtracking Gradient Descent Method and Some Applications in Large Scale Optimisation. Part 2: Algorithms and Experiments

Abstract In this paper, we provide new results and algorithms (including backtracking versions of Nesterov accelerated gradient and Momentum) which are more applicable to large scale optimisation as in Deep Neural Networks. We also demonstrate that Backtracking Gradient Descent (Backtracking GD) can obtain good upper bound estimates for local Lipschitz constants for the gradient, and that the convergence rate of Backtracking GD is similar to that in classical work of Armijo. Experiments with datasets CIFAR10 and CIFAR100 on various popular architectures verify a heuristic argument that Backtracking GD stabilises to a finite union of sequences constructed from Standard GD for the mini-batch practice, and show that our new algorithms (while automatically fine tuning learning rates) perform better than current state-of-the-art methods such as Adam, Adagrad, Adadelta, RMSProp, Momentum and Nesterov accelerated gradient. To help readers avoiding the confusion between heuristics and more rigorously justified algorithms, we also provide a review of the current state of convergence results for gradient descent methods. Accompanying source codes are available on GitHub.
Changing perspectives on goal-directed attention control: The past, present, and future of modeling fixations during visual search

People make eye movements while interacting with objects, and these behaviors are rich with information about how visual goals are represented in the brain and used to prioritize sequential motor behavior. Here we adopt a real-world perspective and define goal-directed attention control as the guidance (or biasing) of gaze to target-object goals that have uncertain visual appearance. Specifically, we review models of goal-directed attention control that have attempted to predict the behavioral fixations made in the search for target-category goals in images. We will show how modeling perspectives on this question changed over the decades. Using the year 2020 as a reference, we will critically review the recent past of the categorical search modeling literature (~ 2000–2010), the literature defining our present (~ 2010–2020), and speculate about the future of search models and the directions that the literature may turn in the next decade (~ 2020–2030).
Video frame interpolation via optical flow estimation with image inpainting

As we all know, video frame rate determines the quality of the video. The higher the frame rate, the smoother the movements in the picture, the clearer the information expressed, and the better the viewing experience for people. Video interpolation aims to increase the video frame rate by generating a new frame image using the relevant information between two consecutive frames, which is essential in the field of computer vision. The traditional motion compensation interpolation method will cause holes and overlaps in the reconstructed frame, and is easily affected by the quality of optical flow. Therefore, this paper proposes a video frame interpolation method via optical flow estimation with image inpainting. First, the optical flow between the input frames is estimated via combined local and global-total variation (CLG-TV) optical flow estimation model. Then, the intermediate frames are synthesized under the guidance of the optical flow. Finally, the nonlocal self-similarity between the video frames is used to solve the optimization problem, to fix the pixel loss area in the interpolated frame. Quantitative and qualitative experimental results show that this method can effectively improve the quality of optical flow estimation, generate realistic and smooth video frames, and effectively increase the video frame rate.
Silver Bullet for All Trust Issues? Blockchain-Based Trust Patterns for Collaborative Business Processes

In recent years, the advancing digitization and internationalization of business processes led to increasing inter-organizational collaboration. In such collaborative processes, different organizations work together towards a single objective. Usually, subprocesses carried out by one collaborator are beyond the domain of influence of all other collaborators. This leads to uncertainty regarding the execution of the collaborative business process. If collaborators still want to engage in the process, trust is needed. Several studies identified blockchain and distributed ledger technologies as a promising tool to enhance trust in business processes. Therefore, this paper proposes and analyzes a taxonomy of blockchain-based trust design patterns from a process-centric perspective. Process engineers can utilize the taxonomy as an overview of how the blockchain technology can enhance trust in collaborative processes.
Skew Gaussian processes for classification

Abstract Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that the mean and the median coincide, while the mean and median in an asymmetric (skewed) distribution can be different numbers. In this paper, we propose skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions. A SkewGP extends the multivariate unified skew-normal distribution over finite dimensional vectors to a stochastic processes. The SkewGP class of distributions includes GPs and, therefore, SkewGPs inherit all good properties of GPs and increase their flexibility by allowing asymmetry in the probabilistic model. By exploiting the fact that SkewGP and probit likelihood are conjugate model, we derive closed form expressions for the marginal likelihood and predictive distribution of this new nonparametric classifier. We verify empirically that the proposed SkewGP classifier provides a better performance than a GP classifier based on either Laplace’s method or expectation propagation.
Deep Residual Split Directed Graph Convolutional Neural Networks for Action Recognition

Human action recognition is the basis technology of human behavior understanding, and it is a research hotspot in the field of computer vision. Recently, some studies show skeleton data (i.e., joint points and edges) is naturally more conducive to mining the connotation of human action, so exploring the relationship between joints and bones is helpful to improve action recognition. In this article, regarding skeleton data as a directed graph, we design directed graph convolutional neural networks with a novel residual split structure. First, we construct a directed graph represent model to extract human behavior by two kinds of graph models. Second, we use a novel residual split block to construct graph convolution neural network. Different from the traditional residual networks, we split high-dimensional features into several shallow features with the same dimension. It can not only ensure the diversity of mining features, but also avoid the gradient disappearing. Finally, during training, we use random sampling of data to reduce the burden of network training. Experiment results show that the proposed method achieves higher recognition rate than the comparative methods on the NTU-RGBD dataset.
BC-RAN: Cloud radio access network enabled by blockchain for 5G

Cloud radio access network (C-RAN) supports the explosion of Internet of Things (IoT) applications and the demand for massive access in 5G networks. However, centralized access of trustless operators causes severe malicious insider threats, such as maliciously tamper and accidentally delete data. Blockchain, a traceable and non-tamper distributed ledger maintained by all nodes, has been considered as a feasible method to overcome these threats, while the size of the blockchain network is limited by practical byzantine fault tolerance (PBFT) consensus algorithm where the participants, the nodes who participate in consensus algorithm, bring heavy communication traffic. In this paper, we first propose a C-RAN enabled by blockchain (BC-RAN) for 5G with PeerTrust-based PBFT (Trust-PBFT) consensus algorithm to prevent the malicious insider threats and scale up the size of the blockchain network. We also propose a tri-chain structure of blockchain to divide transactions into three categories for storage to facilitate traceability. The algorithm calculates the trust value of each node, and those with high trust values will be elected to be the participants of PBFT. Simultaneously, due to the introduction of PeerTrust, another benefit is to improve the fault tolerance performance efficiently. Besides, an adjustment mechanism is designed-newly and implemented in the algorithm to impede effects produced by the malicious nodes in the blockchain network. Finally, several simulation experiments are designed to evaluate and compare the fault-tolerance performance and scalability of Trust-PBFT.
A Voxelized Fractal Descriptor for 3D Object Recognition

Currently, state-of-the-art methods for 3D object recognition rely in a deep learning-pipeline.Nonetheless, these methods require a large amount of data that is not easy to obtain.In addition to that, the majority of them exploit features of the datasets, like the fact of being CAD models to create rendered representation which will not work in real life because the 3D sensors provide point clouds.We propose a novel global descriptor for point clouds which takes advantage of the fractal dimension of the objects.Our approach introduces many benefits, such as being agnostic to the density of points of the sample, number of points in the input cloud, sensor of choice, and noise up to a level, and it works on real life point cloud data provided by commercial sensors.We tested our descriptor for 3D object recognition using ModelNet, which is a well-known dataset for that task.Our approach achieves 92.84% accuracy on the ModelNet10, and 88.74% accuracy on the ModelNet40.
Rank Based Moth Flame optimisation for Feature Selection in the Medical Application

Feature selection (FS) is a challenging data mining problem that incorporates a complex search process to find the most informative feature subset. In the brute force methods generating the entire feature space and applying an exhaustive search makes the FS NP-hard problem. Meta-heuristic algorithms are good alternative solutions that provide (near) optimal solutions through a random search process instead of a complete search. In this paper, an FS approach based on the Moth Flame optimization algorithm (MFO) and k-NN classifier are proposed. MFO is a recent meta-heuristic algorithm that has proved its effectiveness in solving different complex problems in a reasonable time. Nevertheless, the performance of MFO highly depends on achieving a balance between exploration and exploitation during the search process. To address this issue, we propose an adaptive method to update the position of a moth toward the best global solution based on the search status. The proposed MFO has been evaluated using sixteen benchmark medical data sets and the results show promising performance of the modified MFO algorithm in terms of the applied evaluation measures.
Multi-modal semantic image segmentation

We propose a modality invariant method to obtain high quality semantic object segmentation of human body parts, for four imaging modalities which consist of visible images, X-ray images, thermal images (heatmaps) and infrared radiation (IR) images. We first consider two modalities (i.e. visible and X-ray images) to develop an architecture suitable for multi-modal semantic segmentation. Due to the intrinsic difference between images from the two modalities, state-of-the-art approaches such as Mask R-CNN do not perform satisfactorily. Insights from analysing how the intermediate layers within Mask R-CNN work on both visible and X-ray modalities have led us to propose a new and efficient network architecture which yields highly accurate semantic segmentation results across both visible and X-ray domains. We design multi-task losses to train the network across different modalities. By conducting multiple experiments across visible and X-ray images of the human upper extremity, we validate the proposed approach, which outperforms the traditional Mask R-CNN method through better exploiting the output features of CNNs. Based on the insights gained on these images from visible and X-ray domains, we extend the proposed multi-modal semantic segmentation method to two additional modalities; (viz. heatmap and IR images). Experiments conducted on these two modalities, further confirm our architecture’s capacity to improve the segmentation by exploiting the complementary information in the different modalities of the images. Our method can also be applied to include other modalities and can be effectively utilized for several tasks including medical image analysis tasks such as image registration and 3D reconstruction across modalities.
EPRT: An Efficient Privacy-Preserving Medical Service Recommendation and Trust Discovery Scheme for eHealth System

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called EPRT , which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.
Application of wavelet transform image processing technology in financial stock analysis

Traditional mathematical models have problems in the analysis of financial stocks that are not intuitive enough. In order to improve the intuitiveness of the stock forecasting model, based on the image recognition technology, this study normalizes the image and performs feature recognition with grayscale images. At the same time, this paper ignores the small fluctuations and combines the characteristics of stock images to remove the drying process and proposes an algorithm model based on feature recognition. In addition, in order to improve the image accuracy, the model combines the edge extraction technology to extract features, which reflects the actual rise and fall of the stock. Finally, this paper designs experiments to conduct research and analysis. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Equality through Innovation: Promoting Women in the Workplace in Low‐ and Middle‐Income Countries with Health Information Technology

Abstract Technological innovation has served as a useful catalyst for development goals across sectors, from agriculture to education. Various forms of health information technology (HIT), such as mobile health and telemedicine, have been used globally to support health care delivery systems. Forms of HIT, developed primarily by men in rich countries, are touted to support all patients and providers. However, while women perform most global health delivery, especially in low‐ and middle‐income countries (LMIC), they have less access to technology. Stakeholders have been slow to recognize and resolve conflicts about how technology affects gender disparities, especially in health care. In this paper, we describe, using a social capital framework, how integrating HIT in health systems can empower women in the health care workforce. We conclude with policy recommendations that can support women in the health care workforce through HIT.
A Hybrid Chaotic Particle Swarm Optimization with Differential Evolution for feature selection

The selection of feature subsets has been broadly utilized in data mining and machine learning tasks to produce a solution with a small number of features which improves the classifier's accuracy and it also aims to reduce the dataset dimensionality while still sustaining high classification performance. Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. Particle Swarm Optimization (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, since feature selection is a challenging task with a complex search space, PSO has problems with pre-mature convergence and easily gets trapped at local optimum solutions. Hence, the need to balance the search behaviour between exploitation and exploration. In our previous work, a novel chaotic dynamic weight particle swarm optimization (CHPSO) in which a chaotic map and dynamic weight was introduced to improve the search process of PSO for feature selection. Therefore, this paper improved on CHPSO by introducing a hybrid of chaotic particle swarm optimization and differential evolution known as CHPSODE. The search accuracy and performance of the proposed (CHPSODE) algorithms was evaluated on eight commonly used classical benchmark functions. The experimental results showed that the CHPSODE achieves good results in discovering a realistic solution for solving a feature selection problem by balancing the exploration and exploitation search process and as such has proven to be a reliable and efficient metaheuristics algorithm for feature selection.
A Machine Learning-Based Model to Evaluate Readability and Assess Grade Level for the Web Pages

Abstract Evaluating readability of web documents has gained attention due to several factors such as improving the effectiveness of writing and to reach a wider spectrum of audience. Current practices in this direction follow several statistical measures in evaluating readability of the document. In this paper, we have proposed a machine learning-based model to compute readability of web pages. The minimum educational standards required (grade level) to understand the contents of a web page are also computed. The proposed model classifies the web pages into highly readable, readable or less readable using specified feature set. To classify a web page with the aforementioned categories, we have incorporated the features such as sentence count, word count, syllable count, type-token ratio and lexical ambiguity. To increase the usability of the proposed model, we have developed an accessible browser extension to perform the assessments of every web page loaded into the browser.