Reviewerly/exHarmony huggingface dataset:
Image denoising based on the adaptive weighted TV regularization

Image denoising problem still remains an active research field in the image processing. To improve the denoising quality, it is very important to describe the local structure of the image in the proposed model. This fact motivates us to introduce an adaptive weighted TVp regularization-based denoising model, where the rotation matrix and the weighted matrix depend on the local structure of the image. Specially, these two matrices can enhance the diffusion of the responding Euler-Lagrangian equation along with the tangential direction of the edge. This procedure offers more control over the regularization and then allows more denoising in smooth regions and less denoising when processing edge regions. In addition, since the proposed model is nonsmooth and non-Lipschitz, we employ the alternating direction method of multipliers (ADMM) to solve it with the help of using the half-quadratic scheme to solve the related ℓ2−ℓp subproblem. In particular, we also provide the convergence analysis of the used numerical methods. Some numerical comparisons show that the proposed model leads to considerable performance gains when tested on several denoising tasks.
Learning Low-Rank and Sparse Discriminative Correlation Filters for Coarse-to-Fine Visual Object Tracking

Discriminative correlation filter (DCF) has achieved advanced performance in visual object tracking with remarkable efficiency guaranteed by its implementation in the frequency domain.However, the effect of the structural relationship of DCF and object features has not been adequately explored in the context of the filter design.To remedy this deficiency, this paper proposes a Low-rank and Sparse DCF (LSDCF) that improves the relevance of features used by discriminative filters.To be more specific, we extend the classical DCF paradigm from ridge regression to lasso regression, and constrain the estimate to be of low-rank across frames, thus identifying and retaining the informative filters distributed on a low-dimensional manifold.To this end, specific temporal-spatial-channel configurations are adaptively learned to achieve enhanced discrimination and interpretability.In addition, we analyse the complementary characteristics between hand-crafted features and deep features, and propose a coarse-to-fine heuristic tracking strategy to further improve the performance of our LSDCF.Last, the augmented Lagrange multiplier optimisation method is used to achieve efficient optimisation.The experimental results obtained on a number of well-known benchmarking datasets, including OTB2013, OTB50, OTB100, TC128, UAV123, VOT2016 and VOT2018, demonstrate the effectiveness and robustness of the proposed method, delivering outstanding performance compared to the state-of-the-art trackers.
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model

This paper proposes the divergence triangle as a framework for joint training of a generator model, energy-based model and inference model. The divergence triangle is a compact and symmetric (anti-symmetric) objective function that seamlessly integrates variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence in a unified probabilistic formulation. This unification makes the processes of sampling, inference, and energy evaluation readily available without the need for costly Markov chain Monte Carlo methods. Our experiments demonstrate that the divergence triangle is capable of learning (1) an energy-based model with well-formed energy landscape, (2) direct sampling in the form of a generator network, and (3) feed-forward inference that faithfully reconstructs observed as well as synthesized data.
Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data association uncertainty, i.e., the associations between measurements and target tracks are unknown. Existing CS and tracking algorithms either make the assumption of no data association uncertainty or employ a hard-decision rule for measurement-to-target associations. We propose a novel decentralized SCS-MTT method for an unknown and time-varying number of targets under association uncertainty. Marginal posterior densities for agents and targets are obtained by an efficient belief propagation (BP) based scheme while data association is handled by marginalizing over all target-to-measurement association probabilities. Decentralized single Gaussian and Gaussian mixture implementations are provided based on average consensus schemes, which require communication only with one-hop neighbors. An additional novelty is a decentralized Gibbs mechanism for efficient evaluation of the product of Gaussian mixtures. Numerical experiments show the improved CS and MTT performance compared to the conventional approach of separate localization and target tracking.
Multiple-image encryption based on chaotic phase mask and equal modulus decomposition in quaternion gyrator domain

In order to transmit multiple-image synchronously, this paper introduces an encryption algorithm by combining equal modulus decomposition with quaternion gyrator transform. Firstly, each color image is encoded into a quaternion-valued matrix for a holistic processing. With the chaotic random phase mask, the quaternion gyrator spectrum is obtained. It is subsequently split into a complex-valued interim matrix and the equal modulus decomposition is performed to enhance the security, where the spectrum is divided into two complex-valued masks. Thereafter, the two set of phase masks are respectively superimposed and followed by gyrator transforms. Finally, a real-valued matrix is constructed as the final ciphertext image by splicing the real and imaginary parts together. The phase masks that are generated using the chaotic and real-valued ciphertext are convenient to storage and transmission. Moreover, the initial conditions are closely related with the plaintext images, which makes the cryptosystem achieve high security. Numerical simulations are made to demonstrate the reliability of the proposed cryptosystem.
Understanding digital health: Productive tensions at the intersection of sociology of health and science and technology studies

Abstract In this editorial introduction, we explore how digital health is being explored at the intersection of sociology of health and science and technology studies ( STS ). We suggest that socio‐material approaches and practice theories provide a shared space within which productive tensions between sociology of health and STS can continue. These tensions emerge around the long‐standing challenges of avoiding technological determinism while maintaining a clear focus on the materiality and agency of technologies and recognising enduring sets of relations that emerge in new digital health practices while avoiding social determinism. The papers in this Special Issue explore diverse fields of healthcare (e.g. reproductive health, primary care, diabetes management, mental health) within which heterogenous technologies (e.g. health apps, mobile platforms, smart textiles, time‐lapse imaging) are becoming increasingly embedded. By synthesising the main arguments and contributions in each paper, we elaborate on four key dimensions within which digital technologies create ambivalence and (re)configure health practices. First, promissory digital health highlights contradictory virtues within discourses that configure digital health. Second, ( re)configuring knowledge outlines ambivalences of navigating new information environments and handling quantified data. Third, (re)configuring connectivity explores the relationships that evolve through digital networks. Fourth, (re)configuring control explores how new forms of power are inscribed and handled within algorithmic decision‐making in health. We argue that these dimensions offer fruitful perspectives along which digital health can be explored across a range of technologies and health practices. We conclude by highlighting applications, methods and dimensions of digital health that require further research.
Tackling Class Imbalance Problem in Software Defect Prediction Through Cluster-Based Over-Sampling With Filtering

In practice, Software Defect Prediction (SDP) models often suffer from highly imbalanced data, which makes classifiers difficult to identify defective instances. Recently, many techniques were proposed to tackle this problem, over-sampling technique is one of the most well-known methods to address class imbalance problem. This technique balances the number of defective and non-defective instances by generating new defective instances. However, these approaches would generate non-diverse synthetic instances, and many unnecessary noise instances at the same time. Motived by this, we propose a Cluster-based Over-sampling with noise filtering (KMFOS) approach to tackle class imbalance problem in SDP. KMFOS firstly divides defective instances into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> clusters, and new defective instances are generated by interpolation between instances of each two clusters. After this, these new defective instances would diversely spread in the space of defective dataset. Then, we extend this cluster-based over-sampling through the Closest List Noise Identification (CLNI) to clean the noise instances. We do extensive experiments on 24 projects to compare KMFOS with some over-sampling approaches such as SMOTE, Borderline-SMOTE, ADASYN, random over-sampling (ROS), K-means SMOTE, SMOTE + IPF, SMOTE + ENN and SMOTE + Tomek Links using five prediction classifiers. At the same time, we also compare KMFOS with other state-of-the-art class-imbalance methods including balancebaggingclassifier, RUSboostclassifier, InstanceHardnessThreshold and cost-sensitive methods. Experimental results indicate our KMFOS can obtain better <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bal</i> values than other over-sampling methods and other compared class-imbalance methods. Hence, KMFOS is an efficient approach to generate balanced data for SDP and improves the performance of predicting models.
A Boundary Node Method for path planning of mobile robots

In this paper we propose a new method for solving the path planning problem in a static environment to find an optimal collision-free path between starting and goal points. First, the grid model of the robot’s working environment is constructed, and then the potential value of the grid cells is calculated based on the new proposed potential function. This function is used to guide the robot to move toward the desired goal, it has the lowest value at the goal position and the value is increased as the robot moves further away. Second, we developed an efficient method, called the Boundary Node Method, to find the initial feasible path. In this method, the robot is simulated by a nine-node quadrilateral element, where the centroid node represents the robot’s position. The robot moves in the working environment toward the goal with eight-boundary nodes based on the potential value of the boundary nodes. The initial feasible path is generated from a sequence of waypoints that the robot has to traverse as it moves toward the goal point without colliding with any obstacles. However, the proposed method can generate the path safely and efficiently, but the path is not optimal in terms of the total path length. Therefore, in order to construct an optimal or near-optimal collision-free path, an additional method, called the Path Enhancement Method, is developed. Finally, the cubic spline interpolation is adopted to generate a continuous smooth path that connects the starting point to the goal point. The proposed method has been tested in several working environments with different degrees of complexities. The results demonstrated that the proposed method is able to generate near-optimal collision-free path efficiently. Moreover, we compared the performance of the proposed methods with the other path planning methods in terms of path length and computational time. The results revealed that the proposed method can solve the robot path planning problem more efficiently. Finally, in order to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out on the real robot.
Multi-Attention and Incorporating Background Information Model for Chest X-Ray Image Report Generation

Chest X-ray images are widely used in clinical practice such as diagnosis and treatment.The automatic radiology report generation system can effectively reduce the rate of misdiagnosis and missed diagnosis.Previous studies were focused on the long text generation problem of image paragraph, ignoring the characteristics of the image and the auxiliary role of patient background information for diagnosis.In this paper, we propose a new hierarchical model with multi-attention considering the background information.The multi-attention mechanism can focus on the image's channel and spatial information simultaneously, and map it to the sentence topic.The patient's background information will be encoded by the neural network first, then it will be aggregated into a vector representation by a multi-layer perception and added to the pretrained vanilla word embedding, which finally forms a new word embedding after fusion.Our experimental results demonstrated that the model outperforms all baselines, achieving the state-of-the-art performance in terms of accuracy.
Investigating factors affecting on medical sciences students’ intention to adopt mobile learning

Mobile learning (m-learning) provides a good opportunity for students' lifelong learning. The design and implementation of effective and successful mobile learning requires identification of factors that affect m-learning. The aim of this study was to investigate the factors that affect the intention of students of medical sciences to adopt mobile learning based on theory of planned behavior (TPB).In this cross-sectional study, 332 students of medical sciences were randomly selected. The study tool was a based a questionnaire that had been designed based on TPB model. Descriptive statistics (mean, standard deviation, frequency and percentage) were calculated. In order to determine the standardized factor loading and assess the study hypotheses, structural equation modeling was used. Composite reliability, average variance extracted, and standardized factor loading were used to determine the convergent validity.The mean of mobile learning readiness was 3.59 ± 0.83. Among the TPB structures, the structures of attitude (β = 0.525) and behavioral control (β = 0.318) had positive and significant effect on the intention to adopt m-learning (P ≤ 0.01). However, the structure of subject norm did not have a significant effect on the intention to adopt m-learning. In general, attitude, behavioral control and subject norm structures were 0.675 determinants of the intention to adopt m-learning (r2 = 0.675).In this study Mobile learning readiness of the students was at moderate level. Also the results indicated Positive and significant effect of attitude and behavioral control on the intention of students to accept m-learning. The TPB-based model was a suitable model for identifying psychological factors that affect the intention of students of medical sciences to adopt m-leaning. In order to increase the students' acceptance of mobile learning, we suggest that, other psychological, behavioral, social, and cultural factors that affect the acceptance of m-learning should be identified. Educational programs are also suggested to be introduced to students to familiarize them with the m-learning and its application in learning process.
Decision Tree SVM: An extension of linear SVM for non-linear classification

Kernel trick is widely applied to Support Vector Machine (SVM) to deal with linearly inseparable data which is known as kernel SVM. However, kernel SVM always has high computational cost in practice which makes it unsuitable to handle large scale data. Moreover, kernel SVM always brings hyper-parameters, e.g. bandwidth in Gaussian kernel. Since the hyper-parameters have a significant influence on the final performance of kernel SVM and are pretty hard to tune especially for large scale data, one may need to put lots of effort into finding good enough parameters, and improper settings of the hyper-parameters often make the classification performance even lower than that of linear SVM. Inspired by recent progresses on linear SVM for dealing with large scale data, we propose a well-designed classifier to efficiently handle large scale linearly inseparable data, i.e., Decision Tree SVM (DTSVM). DTSVM has much lower computational cost compared with kernel SVM, and it brings almost no hyper-parameters except a few thresholds which can be fixed in practice. Comprehensive experiments on large scale datasets demonstrate the superiority of the proposed method.
Multiview Uncorrelated Locality Preserving Projection

Canonical Correlation Analysis (CCA) is a popular multiview dimension reduction method, which aims to maximize the correlation between two views to find the common subspace shared by these two views. However, it can only deal with two-view data, while the number of views frequently exceeds two in many real applications. To handle data with more than two views, in the previous studies, either the pairwise correlation or the high-order correlation was employed. These two types of correlation define the relation of multiview data from different viewpoints, and both have special effects for view consistency. To obtain flexible view consistency, in this article, we propose multiview uncorrelated locality preserving projection (MULPP), which considers two types of correlation simultaneously. The MULPP also considers the complementary property of different views by preserving the local structures of all the views. To obtain multiple projections and minimize the redundancy of low-dimensional features, for each view, the MULPP makes the features extracted by different projections uncorrelated. The MULPP is solved by an iteration algorithm, and the convergence of the algorithm is proven. The experiments on Multiple Feature, Coil-100, 3Sources, and NUS-WIDE data sets demonstrate the effectiveness of MULPP.
The virtual reality value chain

Virtual reality (VR) technology is predicted to become indispensable in the business world, as it fundamentally reimagines the ways in which firms interact with customers. This technology has sparked a VR ecosystem, with multiple types of firms and other stakeholders interacting to create value. Firms need to define their roles in this ecosystem in order to capitalize on the opportunities and address the challenges they face when navigating its complexities. This article develops a VR value chain that illustrates how the use of VR technologies adds value to key stakeholders. It also provides an understanding of the current state of the VR ecosystem and serves as a source for strategic managerial decision making and future VR research.
Fooling Network Interpretation in Image Classification

Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches are highlighted using standard network interpretation algorithms, thus revealing the identity of the adversary. We show that it is possible to create adversarial patches which not only fool the prediction, but also change what we interpret regarding the cause of the prediction. Moreover, we introduce our attack as a controlled setting to measure the accuracy of interpretation algorithms. We show this using extensive experiments for Grad-CAM interpretation that transfers to occluding patch interpretation as well. We believe our algorithms can facilitate developing more robust network interpretation tools that truly explain the network's underlying decision making process.
Improving Adversarial Robustness via Guided Complement Entropy

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent methods have proposed to improve adversarial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training. In this paper, we propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving "adversarial defense for free," which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a "guided" term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective. We also show that our method can be used orthogonal to adversarial training across well-known methods with noticeable robustness gain. To the best of our knowledge, our approach is the first one that improves model robustness without compromising performance.
PAN

Despite the remarkable performance in video-based action recognition over the past several years, current state-of-the-art approaches heavily rely on the optical flow as motion representation. However, computing the optical flow in advance is computationally expensive, which restricts action recognition to be real-time. In this paper, we shed light on fast action recognition by lifting the reliance on optical flow. Inspired by Persistence of Vision in human visual system, we design a novel motion cue called Persistence of Appearance (PA), which enables the network to distill motion information directly from adjacent RGB frames. Our PA derives from optical flow and focuses on the small displacements of motion boundaries. Compared with other motion representations, our PA enables the network to achieve competitive accuracy on UCF101. Meanwhile, the inference speed reaches 1855 fps, which is over 120x faster than that of the traditional optical flow based methods. Besides, we devise a decision strategy called Various-timescale inference Pooling (VIP) to empower the network with the ability of long-range temporal modeling across various timescales. We further incorporate the proposed PA and VIP to form a unified framework called Persistent Appearance Network (PAN). Compared with methods using only RGB frames, our delicately designed PAN achieves state-of-the-art results on three benchmark datasets: UCF101, HMDB51 and Kinetics, where it reaches 96.2%, 74.8% and 82.5% accuracy respectively with the run-time speed as high as 595 fps. The code for this project is available at: https://github.com/zhang-can/PAN-PyTorch .