Reviewerly/exHarmony huggingface dataset:
A quantitative textual analysis of three types of threat communication and subsequent maladaptive responses

Much research attention has been given to the behavioral outcomes of warning computer users about potential threats to their information security, primarily through the use of fear appeal messaging. This study explores not only the use of other types of external threat information (including vivid messaging and observational learning) but also the textual responses of computer users receiving the information, opening up the "black box" to their threat appraisal processes. Results indicate that maladaptive perceptions can appear throughout the appraisal process and that the type of external threat information can differently influence user attitudes.
An improved path-based clustering algorithm

Path-based clustering algorithms usually generate clusters by optimizing a criterion function. Most of state-of-the-art optimization methods give a solution close to the global optimum. By analyzing the minimax distance, we find that cluster centers have the minimum density in their own clusters. Inspired by this, we propose an improved path-based clustering algorithm (IPC) by mining the cluster centers of the dataset. IPC solves this problem by the process of elimination since it is difficult to mine these cluster centers directly. The algorithm can achieve the global optimum within O(n2). Experimental results on synthetic datasets show that IPC not only can recognize all kinds of clusters regardless of their shapes, sizes and densities, but also is robust against noises and outliers in the data. More importantly, IPC needs only one parameter (i.e., the number of clusters). Comparing IPC with other clustering algorithms on the real datasets, the experimental results show that IPC outperforms compared clustering algorithms.
Certifying quantum memories with coherence

Quantum memories are an important building block for quantum information processing. Ideally, these memories preserve the quantum properties of the input. We present general criteria for measures to evaluate the quality of quantum memories. Then we introduce a quality measure based on coherence satisfying these criteria, which we characterize in detail for the qubit case. The measure can be estimated from sparse experimental data and may be generalized to characterize other building blocks, such as quantum gates and teleportation schemes.
Reversible Data Hiding with Multiple Data for Multiple Users in an Encrypted Image

This article presents a new method which embeds multiple data from multiple users in an encrypted image. Here, the data from several users is embedded into an encrypted image. Initially, the image is encrypted by the owner followed by embedding phase, where the encrypted image is divided into four sets. Two of them are used to embed the secret data, while others are remain unaltered. The secret data from multiple users are embedded into Most Significant Bit (MSB) of the encrypted image using their location maps. In the extraction phase, an individual owner can extract the data from the encrypted image using the assigned private key. Subsequently, in the image decryption and recovery phase, images can be recovered using the unaltered neighbor pixels. However, the secret image can be recovered losslessly using the encryption key only. The proposed scheme allows the extraction of the embedded information only for the authorized user out of several users without knowing the cover information. Various simulations have been made related to this, which show the high embedding rate and accuracy.
Materials failure analysis utilizing rule-case based hybrid reasoning method

Materials failure problems are becoming a serious concern because there would be a variety of consequences possibly affecting public safety. Therefore, materials failure analysis is needed to find out the reason of failure and avoid similar failures' reoccurring. However, materials failure cases mostly rely on manual analysis by experts with sufficient domain knowledge, leading to the situation of time-consuming, low efficiency and difficult evaluation. This paper proposes a rule-case based hybrid reasoning approach for materials failure analysis with the aid of ontology. Hundreds of materials failure cases from different industries were collected and analyzed by rule-based reasoning, case-based reasoning and our hybrid method. It is demonstrated that the rule-case based hybrid reasoning method can obviously provide better analysis results in comparison with rule-based reasoning and case-based reasoning alone.
A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling

Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters. Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively. Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters. Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.
D3-LND: A two-stream framework with discriminant deep descriptor, linear CMDT and nonlinear KCMDT descriptors for action recognition

In order to improve recognition accuracy, a two-stream framework which incorporates deep-learned stream and hand-crafted stream is proposed. Firstly, a discriminant nonlinear feature fusion method is proposed, which introduces the category structure information and obtains the nonlinear relationships between features. Secondly, the global features and local features are extracted from deep network, both of them are fused by the proposed fusion method to obtain a discriminant deep descriptor. Thirdly, to capture the spatio-temporal characteristics of video, the temporal derivatives of gradient, optical flow and motion boundary are extracted in the space-time cube centered at trajectory and taken as low-level features. Subsequently, the covariance and kernelized covariance of low-level features are respectively computed to obtain the Covariance Matrix based on Dense Trajectory (CMDT) and Kernelized Covariance Matrix based on Dense Trajectory (KCMDT) descriptors. Finally, a two-stream framework with discriminant deep descriptor, linear CMDT and nonlinear KCMDT descriptors (D3-LND) is presented, which shares the benefits of both deep-learned and hand-crafted features, and further improves recognition accuracy. Experiments on challenging HMDB51 and UCF101 datasets verify the effectiveness of our method.
Designing an educational interactive eBook for newly diagnosed children with type 1 diabetes: Mapping a new design space

In this paper, we report on a project investigating the role of Interactive Technologies (IT) and participatory design methods in supporting self-care practices in paediatric Type 1 Diabetes Mellitus (T1DM). In particular, we discuss the design of an educational interactive eBook to support newly diagnosed children and their families in learning about effective management outside the clinical–medical consultation. We use our design as an illustration of a potential new design space for type 1 diabetes learning resources. We map this space by identifying a series of oppositions that helps us to explore new design assumptions that could better support the education of newly diagnosed children and families: learning alone vs learning together, medical vs patient perspective, prescriptive language vs narratives and social stories, and static vs interactive educational contents. Through a discussion of these shifting of points of focus in the design of educational products in T1DM, we hope to open up new opportunities to rethink the design of tools to support the education of paediatric diabetes (and possibly of other chronic diseases and conditions).
Lesion focused super-resolution

Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which has not only resulted in better perceptual image quality without blurring, but also been more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks.
SparseMaps: Convolutional networks with sparse feature maps for tiny image classification

Deep convolutional models have been able to get extraordinary results in visual, speech and textual processing domains. Nevertheless, the complexity of manifold of images in data space necessitates the deep networks to have a large number of parameters and subsequently these deep models prone to redundancy and overfitting. One of the most effective methods to tackle this problem is devising special regularization methods in the context of convolutional models. In this paper, after presenting and discussing the most important models in the field, sparse feature maps are proposed and employed as a penalty term in the cost function. This conducts the learning process to construct kernels so that the feature maps at every point are sparse along the depth. A sparse representation is able to adapt to the varying level of information of natural images and can help to extract more independent and informative representations. Also, the DropMaps concept is proposed and employed in the last convolutional layer of the model. This technique applies dropout on the feature maps that causes coincidence of feature maps to be avoided. As is shown in this paper, sparse feature maps and DropMaps can handle the problem of overfitting in large models for tiny images. We have studied the effect of the sparsity rate on the accuracy of the model, and it is observed the accuracy of the test dataset reaches its maximum at a sparsity rate of 0.05. Moreover, by designing appropriate learning rate curves, we were able to obtain ensemble machines with much less cost for training. It is noticeable that the test accuracy is higher than the validation accuracy of the ensemble, indicating that the model has not overfitted. In the input of the model, a random online preprocessing layer is employed for the training phase that helps regularization of the model. Comparing input space and feature space of the model we found that the proposed network is able to successfully separate images of different classes. Finally, testing the proposed model with MNIST dataset has shown that the test set can be classified with accuracy 99.75. The same test with CIFAR 10 dataset attained an accuracy of 94.05.
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science [1].
Fusion of Multiple Person Re-id Methods With Model and Data-Aware Abilities

Person re-identification (person re-id) has attracted rapidly increasing attention in computer vision and pattern recognition research community in recent years. With the goal of providing match ranking results between each query person image and the gallery ones, the person re-id technique has been widely explored and a large number of person re-id methods have been developed. As these algorithms leverage different kinds of prior assumptions, image features, distance matching functions, et al., each of them has its own strengths and weaknesses. Inspired by these facts, this paper proposes a novel person re-id method based on the idea of inferring superior fusion results from a variety of previous base person re-id algorithms using different methodologies or features. To achieve this goal, we propose a novel framework which mainly consists of two steps: 1) a number of existing person re-id methods are implemented, and the ranking results are obtained in the test datasets. and 2) the robust fusion strategy is applied to obtain better re-ranked matching results by simultaneously considering the recognition abilities of various base re-id methods and the difficulties of different gallery person images to be correctly recognized under the generative model of labels, abilities, and difficulties framework. Comprehensive experiments show the effectiveness of our proposed method, and we have received state-of-the-art results on recent popular person re-id datasets.
Dynamic Provable Data Possession of Multiple Copies in Cloud Storage Based on Full-Node of AVL Tree

This article describes how to protect the security of cloud storage, a provable data possession scheme based on full-nodes of an AVL tree for multiple data copies in cloud storage. In the proposed scheme, a Henon chaotic map is first implemented for the node calculation of the AVL tree, and then the location of the data in the cloud is verified by AVL tree. As an AVL tree can keep the balance even with multiple dynamic operations made on the data in the cloud, it can improve the search efficiency of the data block, and reduce the length of the authentication path. Simulation results and analysis confirm that it can achieve good security and high efficiency.
Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

A deterministic privacy metric using non-stochastic information theory is developed. Particularly, minimax information is used to construct a measure of information leakage, which is inversely proportional to the measure of privacy. Anyone can submit a query to a trusted agent with access to a non-stochastic uncertain private dataset. Optimal deterministic privacy-preserving policies for responding to the submitted query are computed by maximizing the measure of privacy subject to a constraint on the worst-case quality of the response (i.e., the worst-case difference between the response by the agent and the output of the query computed on the private dataset). The optimal privacy-preserving policy is proved to be a piecewise constant function in the form of a quantization operator applied on the output of the submitted query. The measure of privacy is also used to analyze the performance of $k$-anonymity methodology (a popular deterministic mechanism for privacy-preserving release of datasets using suppression and generalization techniques), proving that it is in fact not privacy-preserving.
Understanding users' information security awareness and intentions

Information security violations have negative organizational and personal impacts. Studies that examine key factors to motivate individuals to change their security-related behaviours are important to promote safe and secure computing. Drawing on protection motivation theory (PMT), this research aims to investigate the antecedents of threat and coping appraisals in relation to the individuals' information security protection intention among university students. It contributes to a better understanding of users' information security awareness through an examination of the impact of security education, training and awareness (SETA) programme on information security threat and countermeasures awareness and the role of information security awareness as an antecedent to the cognitive processes associated with coping and threat appraisals. In addition, this research contributes to extend PMT by investigating the role of fear and maladaptive rewards in explaining information security behaviours.
Trustworthy Delegation Toward Securing Mobile Healthcare Cyber-Physical Systems

Attribute-based encryption (ABE) offers a promising solution for flexible access control over sensitive personal health records in a mobile healthcare system on top of a public cloud infrastructure. However, ABE cannot be simply applied to lightweight devices due to its substantial computation cost during decryption. This problem could be alleviated by delegating significant parts of the decryption operations to computationally powerful parties, such as cloud servers, but the correctness of the delegated computation would be at stake. Thus, previous works enabled users to validate the partial decryption by employing a cryptographic commitment or message authentication code (MAC). This paper demonstrates that the previous commitment or MAC-based schemes cannot support verifiability in the presence of potentially malevolent cloud servers. We propose two concrete attacks on previous commitment or MAC-based schemes. We propose an effective countermeasure scheme for securing resource-limited mobile healthcare systems and provide a rigorous security proof in the standard model, demonstrating that the proposed scheme is secure against our attacks. The experimental analysis shows that the proposed scheme provides the similar performance compared with the previous commitment-based schemes and outperforms the MAC-based scheme.
Improving the Secret Key Rate of Coherent Quantum Key Distribution With Bayesian Inference

Laser phase noise is one of the major limitations in continuous-variable quantum key distribution, when the local oscillator is placed at the receiver site. Thus, improving phase noise mitigation can improve the secret key rate. This paper provides an experimental investigation of different Bayesian methods for that purpose. It is shown that, compared to a previously reported method, the excess noise power can be reduced by about 15%. In some scenarios, this can improve the secret key rate by a factor of nine.
Online multiple object tracking using confidence score‐based appearance model learning and hierarchical data association

The goal of multiple object tracking (MOT) is to estimate the locations of objects and maintain their identities consistently to yield their individual trajectories. MOT has been developed enormously, but it is still a challenging work due to similar appearances of different objects and occlusion by other objects or background in a complex scene. In this study, the authors propose confidence score‐based appearance model learning and hierarchical data association for MOT. First, the confidence score is used to divide associated tracklet‐detection in the first stage data association into confident and unconfident results, and in the second stage, data association is applied to unconfident tracklet‐detection to improve the performance. Furthermore, it can be employed to enhance the robustness of the appearance model and due to the fast confidence score calculation, it can balance the accuracy and processing time. The experimental results with challenging public datasets show distinct performance improvement over other state‐of‐the‐art methods and demonstrate the effect of the authors’ method for online MOT.
Accurate full-edge detection and depth measurement of internal defects using digital speckle pattern interferometry

In this paper, a new method, Moment-Stiffness Method (MSM), is proposed for measuring the edge and depth of internal defects which can be applied to any structures with known stiffness. MSM is based on the relation of the differences of displacements between flawed and flawless areas caused by the change of stiffness. Theoretical, numerical and experimental results are presented. In the method, digital speckle pattern interferometry (DSPI) is used to measure the out-of-plane displacement of a plate with an internal defect. In the detection of the first specimen, the distributions of first- and second-order radial derivatives of the out-of-plane displacement are obtained by the radial numerical differential. Then the full edge and depth of the defect is accurately determined using MSM. All the results indicate that the proposed method is an effective tool to measure internal defects precisely.
A Modified Local Binary Pattern Descriptor for SAR Image Matching

Image matching is an important step which is taken in most applications of synthetic aperture radar (SAR) images. In this letter, a method is proposed for SAR image matching which introduces a modified local binary pattern (LBP) as a descriptor. Multitextural feature LBP (MTF-LBP) uses the gray-level cooccurrence matrix to increase image texture information. MTF-LBP creates bit plane for each point candidate for matching. Then, using hamming distance, true matches are determined. Experiments are conducted on four spaceborne SAR image pairs including Radarsat-2, TerraSAR-X, ALOS-PALSAR, and Sentinel-1. The proposed method is compared with five common LBP approaches. The results indicate that the proposed method has a better performance in terms of the number of true matches.