Reviewerly/exHarmony huggingface dataset:
The Mediating Role of Psychological Empowerment in Information Security Compliance Intentions

The issue of employee noncompliance with information security policies is universal.Noncompliance increases the possibility of invasive information security threats, which can result in compromised organizational assets.Although research has empirically revealed a relationship between structural empowerment and employee intention to comply with information security policies, the mediating role of psychological empowerment in the relationship has received limited attention.This study conceptualizes the role of psychological empowerment as a mediator between structural empowerment and the intention to comply with information security policy.It suggests that empowerment work structures, which include information security education, training, and awareness (SETA), access to information security strategic goals, and participation in information security decision-making all increase employees' feelings of being psychologically empowered, which consequently leads to positive intentions to comply with information security policy.
MRI image harmonization using cycle-consistent generative adversarial network

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a valuable modality for evaluating breast abnormalities found in mammography and performing early disease detection in high-risk patients. However, images produced by various MRI scanners (e.g., GE Healthcare &amp; Siemens) differ in terms of intensity and other image characteristics such as noise distribution. This is a challenge both for the evaluation of images by radiologists and for the computational analysis of images using radiomics or deep learning. For example, an algorithm trained on a set of images acquired by one MRI scanner may perform poorly on a dataset produced by a different scanner. Therefore, there is an urgent need for image harmonization. Traditional image to image translation algorithms can be used to solve this problem, but they require paired data (i.e. the same object imaged using different scanners). In this study, we utilize a deep learning algorithm that uses unpaired data to solve this problem through a bi-directional translation between MRI images. The proposed method is based on a cycle-consistent adversarial network (CycleGAN) that uses two generator-discriminator pairs. The original CycleGAN struggles in preserving the structure (i.e. breast tissue characteristics and shape) during the translation. To overcome this, we modified the discriminator architecture and forced the penalization based on the structure at the scale of smaller patches. This allows the network to focus more on features pertaining to breast tissue. The results demonstrate that the transformed images are visually realistic, preserve the structure and harmonize intensity across images from different scanners.
An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]

Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning

In this paper, we present a novel regularization mechanism for training deep networks by minimizing the {\em Worse-Case Perturbation} (WCP). It is based on the idea that a robust model is least likely to be affected by small perturbations, such that its output decisions should be as stable as possible on both labeled and unlabeled examples. We will consider two forms of WCP regularizations -- additive and DropConnect perturbations, which impose additive noises on network weights, and make structural changes by dropping the network connections, respectively. We will show that the worse cases of both perturbations can be derived by solving respective optimization problems with spectral methods. The WCP can be minimized on both labeled and unlabeled data so that networks can be trained in a semi-supervised fashion. This leads to a novel paradigm of semi-supervised classifiers by stabilizing the predicted outputs in presence of the worse-case perturbations imposed on the network weights and structures.
IPG-Net: Image Pyramid Guidance Network for Small Object Detection

For Convolutional Neural Network-based object detection, there is a typical dilemma: the spatial information is well kept in the shallow layers which unfortunately do not have enough semantic information, while the deep layers have a high semantic concept but lost a lot of spatial information, resulting in serious information imbalance. To acquire enough semantic information for shallow layers, Feature Pyramid Networks (FPN) is used to build a top- down propagated path. In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer. Our IPG-Net has two main parts: the image pyramid guidance transformation module and the image pyramid guidance fusion module. Our main idea is to introduce the image pyramid guidance into the backbone stream to solve the information imbalance problem, which alleviates the vanishment of the small object features. This IPG transformation module promises even in the deepest stage of the backbone, there is enough spatial information for bounding box regression and classification. Furthermore, we designed an effective fusion module to fuse the features from the image pyramid and features from the backbone stream. We have tried to apply this novel network to both one-stage and two-stage detection models, state of the art results are obtained on the most popular benchmark data sets, i.e. MS COCO and Pascal VOC.
Implicit Skills Extraction Using Document Embedding and Its Use in Job Recommendation

This paper presents a job recommender system to match resumes to job descriptions (JD), both of which are non-standard and unstructured/semi-structured in form. First, the paper proposes a combination of natural language processing (NLP) techniques for the task of skill extraction. The performance of the combined techniques on an industrial scale dataset yielded a precision and recall of 0.78 and 0.88 respectively. The paper then introduces the concept of extracting implicit skills – the skills which are not explicitly mentioned in a JD but may be implicit in the context of geography, industry or role. To mine and infer implicit skills for a JD, we find the other JDs similar to this JD. This similarity match is done in the semantic space. A Doc2Vec model is trained on 1.1 Million JDs covering several domains crawled from the web, and all the JDs are projected onto this semantic space. The skills absent in the JD but present in similar JDs are obtained, and the obtained skills are weighted using several techniques to obtain the set of final implicit skills. Finally, several similarity measures are explored to match the skills extracted from a candidate's resume to explicit and implicit skills of JDs. Empirical results for matching resumes and JDs demonstrate that the proposed approach gives a mean reciprocal rank of 0.88, an improvement of 29.4% when compared to the performance of a baseline method that uses only explicit skills.
Quantum Computing: An Introduction for Microwave Engineers

During the past decade, quantum computing has grown from a field known mostly for generating scientific papers to one that is poised to reshape computing as we know it [1]. Major industrial research efforts in quantum computing are currently underway at many companies, including IBM [2], Microsoft [3], Google [4], [5], Alibaba [6], and Intel [7], to name a few. The European Union [8], Australia [9], China [10], Japan [11], Canada [12], Russia [13], and the United States [14] are each funding large national research initiatives focused on the quantum information sciences. And, recently, tens of start-up companies have emerged with goals ranging from the development of software for use on quantum computers [15] to the implementation of full-fledged quantum computers (e.g., Rigetti [16], ION-Q [17], Psi-Quantum [18], and so on). However, despite this rapid growth, because quantum computing as a field brings together many different disciplines, there is currently a shortage of engineers who understand both the engineering aspects (e.g., microwave design) and the quantum aspects required to build a quantum computer [19]. The aim of this article is to introduce microwave engineers to quantum computing and demonstrate how the microwave community's expertise could contribute to that field.
Arduino Advances in Web of Science. A Scientific Mapping of Literary Production

Technology and computer learning have acquired a great projection in the field of education.Arduino arises from current technological innovations with the intention of promoting a new approach to learning through machines.The objective of this research is to analyze the evolution of the Arduino concept in the scientific literature.To achieve this aim, a bibliometric methodology based on scientific mapping and an analysis of co-words has been used.The scientific production of Arduino indexed in Web of Science has been analyzed.We have worked with an analysis unit of 346 documents.The results of this research show that the scientific production on Arduino in the field of education starts in 2010 until today.The communications in the congresses to present the results of the investigations developed are the most used means of diffusion.The National University of Distance Education and the Lucian Blaga University of Sibiu are particularly noteworthy.The authors with more scientific production in this field of study are Bogdan, M. and Castro, M. It is significant that the collection of studies on this subject is carried out, above all, by EDULEARN Proceedings and INTED Proceedings.It can be concluded that the field of study on Arduino in the educational field is relatively recent, so that today, the basis for scientific research is still being established.However, the most relevant aspects in this field of study are physic experiments, computational thinking and computer-based learning.
Unsupervised-Learning-Based Continuous Depth and Motion Estimation With Monocular Endoscopy for Virtual Reality Minimally Invasive Surgery

Three-dimensional display and virtual reality technology have been applied in minimally invasive surgery to provide doctors with a more immersive surgical experience. One of the most popular systems based on this technology is the Da Vinci surgical robot system. The key to build the in vivo 3-D virtual reality model with a monocular endoscope is an accurate estimation of depth and motion. In this article, a fully unsupervised learning method for depth and motion estimation using the continuous monocular endoscopic video is proposed. After the detection of highlighted regions, EndoMotionNet and EndoDepthNet are designed to estimate ego-motion and depth, respectively. The timing information between consecutive frames is considered with a long short-term memory layer by EndoMotionNet to enhance the accuracy of ego-motion estimation. The estimated depth value of the previous frame is used to estimate the depth of the next frame by EndoDepthNet with a multimode fusion mechanism. The custom loss function is defined to improve the robustness and accuracy of the proposed unsupervised-learning-based method. Experiments with the public datasets verify that the proposed unsupervised-learning-based continuous depth and motion estimation method can effectively improve the accuracy of depth and motion estimation, especially after processing the frame.
Neural networks model based on an automated multi-scale method for mammogram classification

Breast cancer is the most commonly diagnosed cancer among women. Convolutional neural networks (CNN)-based mammogram classification plays a vital role in early breast cancer detection. However, it pays too much attention to the lesions of mammograms and ignores the global characteristics of the breast. In the process of diagnosis, doctors not only pay attention to the features of local lesions but also combine with the comparison to the global characteristics of breasts. Mammogram images have a visible characteristic, which is that the original image is large, while the lesions are relatively small. It means that the lesions are easy to overlook. This paper proposes an automated multi-scale end-to-end deep neural networks model for mammogram classification, that only requires mammogram images and class labels (without ROI annotations). The proposed model generated three scales of feature maps that make the classifier combine global information with the local lesions for classification. Moreover, the images processed by our method contain fewer non-breast pixels and retain the small lesions information as much as possible, which is helpful for the model to focus on the small lesions. The performance of our method is verified on the INbreast dataset. Compared to other state-of-the-art mammogram classification algorithms, our model performs the best. Moreover, the multi-scale method is applied to the networks with fewer parameters that can achieve comparable performance, while saving 60% of the computing resources. It shows that the multi-scale method can work for both performance and computational efficiency.
An Autonomous Path Planning Method for Unmanned Aerial Vehicle Based on a Tangent Intersection and Target Guidance Strategy

Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this article proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tangent graph method is used to generate two sub-paths, one of which is selected based on heuristic rules when confronting an obstacle. The UAV flies along the selected sub-path and repeatedly adjusts its flight path to avoid obstacles through this way until the collision-free path extends to the target. Considering the UAV kinematic constraints, the cubic B-spline curve is employed to smooth the waypoints for obtaining a feasible path. Compared with A*, PRM, RRT and VFH, the experimental results show that APPATT can generate the shortest collision-free path within 0.05 seconds for each instance under static environments. Moreover, compared with VFH and RRTRW, APPATT can generate satisfactory collision-free paths under uncertain environments in a nearly real-time manner. It is worth noting that APPATT has the capability of escaping from simple traps within a reasonable time.
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition

Although attention based end-to-end models have achieved promising performance in speech recognition, the multi-pass forward computation in beam-search increases inference time cost, which limits their practical applications. To address this issue, we propose a non-autoregressive end-to-end speech recognition system called LASO (listen attentively, and spell once). Because of the non-autoregressive property, LASO predicts a textual token in the sequence without the dependence on other tokens. Without beam-search, the one-pass propagation much reduces inference time cost of LASO. And because the model is based on the attention based feedforward structure, the computation can be implemented in parallel efficiently. We conduct experiments on publicly available Chinese dataset AISHELL-1. LASO achieves a character error rate of 6.4%, which outperforms the state-of-the-art autoregressive transformer model (6.7%). The average inference latency is 21 ms, which is 1/50 of the autoregressive transformer model.
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Multi-document summarization is a challenging task for which there exists little largescale datasets.We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles.Multi-XScience introduces a challenging multidocument summarization task: writing the related-work section of a paper based on its abstract and the articles it references.Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches.Descriptive statistics and empirical results-using several state-of-the-art models trained on the Multi-XScience dataset-reveal that Multi-XScience is well suited for abstractive models. 1
Machine Learning for Integrated Quantum Photonics

Realization of integrated quantum photonics is a key step toward scalable quantum applications such as quantum computing, sensing, information processing, and quantum material metrology. To enable practical quantum photonic systems, several challenges should be addressed, including (i) the realization of deterministic, bright, and stable single-photon emission operating at THz rates and at room temperatures, (ii) on-chip integration of efficient single-photon sources, and (iii) the development of deterministic and scalable nanoassembly of quantum circuitry elements. In this Perspective, we focus on the emerging field of physics-informed machine learning (ML) quantum photonics that is envisioned to play a decisive role in addressing the above challenges. Specifically, three directions of ML-assisted quantum research are discussed: (i) rapid preselection of single single-photon sources via ML-assisted quantum measurements, (ii) hybrid ML-optimization approach for developing efficient quantum circuits elements, and (iii) ML-based frameworks for developing novel deterministic assembly of on-chip quantum emitters.
Spherical Deformable U-Net: Application to Cortical Surface Parcellation and Development Prediction

Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by triangular meshes. There is no consistent neighborhood definition and thus no straightforward convolution/pooling operations for such cortical surface data. In this paper, leveraging the regular and hierarchical geometric structure of the resampled spherical cortical surfaces, we create the 1-ring filter on spherical cortical triangular meshes and accordingly develop convolution/pooling operations for constructing Spherical U-Net for cortical surface data. However, the regular nature of the 1-ring filter makes it inherently limited to model fixed geometric transformations. To further enhance the transformation modeling capability of Spherical U-Net, we introduce the deformable convolution and deformable pooling to cortical surface data and accordingly propose the Spherical Deformable U-Net (SDU-Net). Specifically, spherical offsets are learned to freely deform the 1-ring filter on the sphere to adaptively localize cortical structures with different sizes and shapes. We then apply the SDU-Net to two challenging and scientifically important tasks in neuroimaging: cortical surface parcellation and cortical attribute map prediction. Both applications validate the competitive performance of our approach in accuracy and computational efficiency in comparison with state-of-the-art methods.
Anomalies Detection Using Isolation in Concept-Drifting Data Streams

Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency.
TPE2: Three-Pixel Exact Thumbnail-Preserving Image Encryption

Facing a large number of personal images and local devices with limited resources, the cloud service plays an increasingly important role in image storage. However, there is no doubt that this will raise a lot of privacy concerns if the image is not under the direct control of the owner. Simply encrypting images utilizing traditional encryption schemes protects the privacy of them, but it sacrifices the usability of image content. Resolving the tension between the usability and privacy risks of images is a critical issue for users. Recently, Tajik et al. proposed the exact thumbnail preserving image encryption (TPE) scheme as a group of two pixels to balance image privacy and usability in the cloud. However, the scheme utilizes the Markov chain to prove the security. The fewer pixels in the group, the worse the connectivity of the chain state. Motivated by this, we further extend the TPE scheme to propose a three-pixel exact TPE scheme, called TPE2. The scheme achieves exact TPE as a group of three pixels. The experimental evaluations show that the image encrypted by the scheme has a good image quality and a satisfactory balance usability and privacy.