Reviewerly/exHarmony huggingface dataset:
Revisiting information security risk management challenges: a practice perspective

Purpose The study aims to revisit six previously defined challenges in information security risk management to provide insights into new challenges based on current practices. Design/methodology/approach The study is based on an empirical study consisting of in-depth interviews with representatives from public sector organisations. The data were analysed by applying a practice-based view, i.e. the lens of knowing (or knowings). The results were validated by an expert panel. Findings Managerial and organisational concerns that go beyond a technical perspective have been found, which affect the ongoing social build-up of knowledge in everyday information security work.. Research limitations/implications The study has delimitation as it consists of data from four public sector organisations, i.e. statistical analyses have not been in focus, while implying a better understanding of what and why certain actions are practised in their security work. Practical implications The new challenges that have been identified offer a refined set of actionable advice to practitioners, which, for example, can support cost-efficient decisions and avoid unnecessary security trade-offs. Originality/value Information security is increasingly relevant for organisations, yet little is still known about how related risks are handled in practice. Recent studies have indicated a gap between the espoused and the actual actions. Insights from actual, situated enactment of practice can advise on process adaption and suggest more fit approaches.
Electronic Health Records

Electronic health records (EHRs) pose unique concerns for administrators and information technology professionals with regard to cybersecurity. Due to the sensitive nature and increasing value of personal health information, cyber risks and information protection should be a high priority. A literature review was conducted to identify potential threat categories and best practices in protecting EHR information. Potential threats were identified and categorized into five areas; physical, portable devices, insider use, technical, and administrative. Government policies have created administrative, physical, and technical safeguards to keep EHR information safe. Despite these efforts, EHRs are being targeted by cyber-criminals due to flaws in personal and organizational management of protected healthcare information. This paper aims to educate, inform, and advocate for the proper handling of EHRs to alleviate the burden caused by compromised electronic documents.
Analysis of controversies in the formulation and evaluation of restoration algorithms for MR Images

There is no reliable guidance available in literature so far for the selection of a suitable technique for denoising Magnetic Resonance (MR) images. The performance of edge-preserving denoising schemes like Nonlocal Means, Bilateral, Total Variation, Anisotropic Diffusion, Kuwahara, wavelet denoising, Linear Minimum Mean Square Error, Smallest Univalue Segment Assimilating Nucleus and Beltrami filters on MR images are evaluated and compared in this paper. Performance evaluation is done on real-time MR Images, Shepp–Logan Phantom images and simulated MR images. Image Quality Analysis indices used for the evaluation are Structural Similarity Index Metric, Noise Quality Measure, Peak Signal to Noise Ratio, Edge Preservation Index, MetricQ, Anisotropic Quality Index, Blind Reference Image Quality Evaluator and computational time. It has been observed that the performance of each filter is completely different on Shepp–Logan, simulated MR and real-time MR images. It is critically sensitive to the strength of noise also. No filter which can offer good performance equally on Phantom, simulated MR image and real-time MR images, is available in the literature. Values of the objective indices are not in concordance with subjective quality ratings. Filter designs optimized on Phantom or simulated MR using maximum PSNR between denoised and ground-truth images as an objective function (minimum error sense in general) do not perform well on real-time MRI.
Compact Feature Learning for Multi-Domain Image Classification

The goal of multi-domain learning is to improve the performance over multiple domains by making full use of all training data from them. However, variations of feature distributions across different domains result in a non-trivial solution of multi-domain learning. The state-of-the-art work regarding multi-domain classification aims to extract domain-invariant features and domain-specific features independently. However, they view the distributions of features from different classes as a general distribution and try to match these distributions across domains, which lead to the mixture of features from different classes across domains and degrade the performance of classification. Additionally, existing works only force the shared features among domains to be orthogonal to the features in the domain-specific network. However, redundant features between the domain-specific networks still remain, which may shrink the discriminative ability of domain-specific features. Therefore, we propose an end-to-end network to obtain the more optimal features, which we call compact features. We propose to extract the domain-invariant features by matching the joint distributions of different domains, which have dis- tinct boundaries between different classes. Moreover, we add an orthogonal constraint between the private features across domains to ensure the discriminative ability of the domain-specific space. The proposed method is validated on three landmark datasets, and the results demonstrate the effectiveness of our method.
Towards Democratizing Relational Data Visualization

The problem of data visualization is to transform data into a visual context such that people can easily understand the significance of data. Nowadays, data visualization becomes especially important, because it is the de facto standard for modern business intelligence and successful data science. This tutorial will cover three specific topics: visualization languages define how the users can interact with various visualization systems; efficient data visualization processes the data and produces visualizations based on well-specified user queries; smart data visualization recommends data visualizations based on underspecified user queries. In this tutorial, we will go logically through these prior art, paying particular attentions on problems that may attract the interest from the database community.
Typography With Decor: Intelligent Text Style Transfer

Text effects transfer can dramatically make the text visually pleasing. In this paper, we present a novel framework to stylize the text with exquisite decor, which is ignored by the previous text stylization methods. Decorative elements pose a challenge to spontaneously handle basal text effects and decor, which are two different styles. To address this issue, our key idea is to learn to separate, transfer and recombine the decors and the basal text effect. A novel text effect transfer network is proposed to infer the styled version of the target text. The stylized text is finally embellished with decor where the placement of the decor is carefully determined by a novel structure-aware strategy. Furthermore, we propose a domain adaptation strategy for decor detection and a one-shot training strategy for text effects transfer, which greatly enhance the robustness of our network to new styles. We base our experiments on our collected topography dataset including 59,000 professionally styled text and demonstrate the superiority of our method over other state-of-the-art style transfer methods.
Grid Based Spherical CNN for Object Detection from Panoramic Images

Recently proposed spherical convolutional neural networks (SCNNs) have shown advantages over conventional planar CNNs on classifying spherical images. However, two factors hamper their application in an objection detection task. First, a convolution in S2 (a two-dimensional sphere in three-dimensional space) or SO(3) (three-dimensional special orthogonal group) space results in the loss of an object's location. Second, overlarge bandwidth is required to preserve a small object's information on a sphere because the S2/SO(3) convolution must be performed on the whole sphere, instead of a local image patch. In this study, we propose a novel grid-based spherical CNN (G-SCNN) for detecting objects from spherical images. According to input bandwidth, a sphere image is transformed to a conformal grid map to be the input of the S2/SO3 convolution, and an object's bounding box is scaled to cover an adequate area of the grid map. This solves the second problem. For the first problem, we utilize a planar region proposal network (RPN) with a data augmentation strategy that increases rotation invariance. We have also created a dataset including 600 street view panoramic images captured from a vehicle-borne panoramic camera. The dataset contains 5636 objects of interest annotated with class and bounding box and is named as WHU (Wuhan University) panoramic dataset. Results on the dataset proved our grid-based method is extremely better than the original SCNN in detecting objects from spherical images, and it outperformed several mainstream object detection networks, such as Faster R-CNN and SSD.
Solar Irradiance and Temperature Variability and Projected Trends Analysis in Burundi

This paper assessed the variability and projected trends of solar irradiance and temperature in the East of Burundi. Observed temperature from meteorological stations and the MERRA-2 data set provided by NASA/Goddard Space Flight Center are used over the historical period 1976–2005. In addition, solar irradiance data provided by SoDa database were considered. Furthermore, projection data from eight Regional Climate Models were used over the periods 2026–2045 and 2066–2085. The variability analysis was performed using a standardized index. Projected trends and changes in the future climate were respectively detected through Mann-Kendall and t-tests. The findings over the historical period revealed increase temperature and decrease in solar irradiance over the last decades of the 20th century. At a monthly scale, the variability analysis showed that excesses in solar irradiance coincide with the dry season, which led to the conclusion that it may be a period of high production for solar energy. In the future climate, upward trends in temperature are expected over the two future periods, while no significant trends are forecasted in solar irradiance over the entire studied region. However, slight decreases and significant changes in solar irradiance have been detected over all regions.
Adopting Japanese in a popular Chinese video-sharing website: heteroglossic and multilingual communication by online users of Bilibili.com

The present study investigates the adoption of Japanese by users from a popular Chinese video-sharing website, bilibili.com. Over a two-month period, 5,808 instances of language use that involve Japanese language features were sampled from 28,579 video comments. It was found that features of Japanese were often utilized – either entirely in Japanese written script, or as part of Chinese expressions – for various communicative functions such as descriptors and honorifics. The analysis suggests that online communication on the Chinese website bilibili.com is linguistically and symbolically hybrid with Japanese language features and other semiotic signs often mixed with Chinese texts. The involved communicative practices reflect the Bakhtinian notion of heteroglossia characterized with creativity and playfulness. The study further deconstructs the structuralist view of multilingualism (as discussed in) and extends the discussion of linguistic resources beyond discrete linguistic “codes” and traditional notion of language proficiency in digital discourse.
O2O Method for Fast 2D Shape Retrieval

A novel post-processing method, online to offline (O2O), to improve the efficiency of shape retrieval is proposed in this paper. The essence of this proposed method is to move more work that requires a lot of computation to offline. Based on this approach, the O2O rerank the retrieval result online with the help of the offline analysis. The result of offline analysis can be reused indefinitely, regardless of the query shape, as long as the database is unchanged. Therefore, O2O is very efficient and suitable for real-time applications. We evaluated our method for shape retrieval and recognition on five databases including MPEG-7 CE-1 Part B, Tari 1000, Animals, Kimia 99, and Swedish Plant Leaf. Our experimental results show that as a post-processing algorithm, O2O provides highly efficient and effective shape retrieval.
An efficient location recommendation scheme based on clustering and data fusion

Recently, location-based social networks (LBSNs) have witnessed a significant development, and have attracted millions of mobile users to share their locations and location-related contents. Due to the huge data in LBSNs, it is imperative to design an efficient location recommendation scheme for abundant users. But most of existing works suffer from poor accuracy and efficiency. On one hand, the traditional user-based collaborative filtering (CF) methods only focus on user characteristics, which limit the recommendation accuracy. On the other hand, it is inefficient to traverse all LBSN data in each recommendation. To solve the above issues, this paper proposes a new location recommendation method LC–G–P, in which, Louvain method is adopted to cluster users into several communities; and meanwhile, multiple features including Geographical distance, location Popularity are incorporated into location recommendation. Experiments based on real LBSN dataset illustrate that the proposed recommendation scheme LC–G–P has better accuracy and efficiency, in comparison with the existed typical location recommendation schemes.
Quantum State Smoothing for Linear Gaussian Systems

Quantum state smoothing is a technique for assigning a valid quantum state to a partially observed dynamical system, using measurement records both prior and posterior to an estimation time. We show that the technique is greatly simplified for linear Gaussian quantum systems, which have wide physical applicability. We derive a closed-form solution for the quantum smoothed state, which is more pure than the standard filtered state, while still being described by a physical quantum state, unlike other proposed quantum smoothing techniques. We apply the theory to an on-threshold optical parametric oscillator, exploring optimal conditions for purity recovery by smoothing. The role of quantum efficiency is elucidated, in both low and high efficiency limits.
An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data

This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in the system as a fixed network topology only exists as a guideline to help users understand. Furthermore, it does not need to discover every single node to understand the characteristics of each clustering within the network. After extracting and analyzing the sentiment words from the movie review data, we designed a movie network based on the similarities between the words. The network formed in this way will appear as a multilevel sentiment network visualization after the following three steps: (1) design a heatmap visualization to effectively discover the main emotions on each movie review; (2) create a two-dimensional multidimensional scaling (MDS) map of semantic word data to facilitate semantic understanding of network and then fix the movie network topology on the map; (3) create an asterism graphic with emotions to allow users to easily interpret node groups with similar sentiment words. The research also presents a virtual scenario about how our network visualization can be used as a movie recommendation system. We next evaluated our progress to determine whether it would improve user cognition for multilevel analysis experience compared to the existing network system. Results showed that our method provided improved user experience in terms of cognition. Thus, it is appropriate as an alternative method for semantic understanding.
A new fast morphological geodesic active contour method for lung CT image segmentation

This work proposes a new adaptive approach to lung segmentation based on a non-parametric adaptive active contour method (ACM) without previous training using the new Fuzzy Border Detector, called Fast Morphological Geodesic Active Contour (FGAC). Performance was evaluated with 72 lung images of volunteers that were with fibrosis, chronic obstructive pulmonary disease or were healthy. A manual segmentation by a medical specialist was considered the gold standard. The mean time of segmentation of seventy-two the two lungs was 1.98 s. This was the best average time among all the other segmentation methods compared here: GVF (240 s), VFC(30 s), OPS Euclidean (5.86 s), SISDEP (4.90 s), and CRAD (2 s); thus showing its potential for real-time applications. The FGAC showed good results in all similarity metrics compared in this work like the Jaccard Index (92.73%), Dice coefficient (96.19%) and Matthew correlation coefficient (95.54%), and also achieved good results in sensitivity (99.21%), and accuracy (98.86%). The new approach showed quantitative indexes better than the traditional methods VFC, GVF, RHT mod, RHT multi. Moreover, we evaluated the proposed method against the supervised techniques OPS-Euclidean, SISDEP, CRISP and CRAD. Our approach achieved superior or equivalent results to these methods, however with a shorter convergence time. These results had good indexes suggesting that the proposed approach can be used to aid medical diagnosis in pulmonology.
GLEAM

Mixed reality mobile platforms attempt to co-locate virtual scenes with physical environments, towards creating immersive user experiences. However, to create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic lighting that matches the physical environment. To this end, we design GLEAM, a framework that provides robust illumination estimation in real-time by integrating physical light-probe estimation with current mobile AR systems. GLEAM visually observes reflective objects to compose a realistic estimation of physical lighting. Optionally, GLEAM can network multiple devices to sense illumination from different viewpoints and compose a richer estimation to enhance realism and fidelity. Using GLEAM, AR developers gain the freedom to use a wide range of materials, which is currently limited by the unrealistic appearance of materials that need accurate illumination, such as liquids, glass, and smooth metals. Our controlled environment user studies across 30 participants reveal the effectiveness of GLEAM in providing robust and adaptive illumination estimation over commercial status quo solutions, such as pre-baked directional lighting and ARKit 2.0 illumination estimation. Our benchmarks reveal the need for situation driven tradeoffs to optimize for quality factors in situations requiring freshness over quality and vice-versa. Optimizing for different quality factors in different situations, GLEAM can update scene illumination as fast as 30ms by sacrificing richness and fidelity in highly dynamic scenes, or prioritize quality by allowing an update interval as high as 400ms in scenes that require high-fidelity estimation.
Exact Closed-Form Multitarget Bayes Filters

The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion has inspired work by dozens of research groups in at least 20 nations; and FISST publications have been cited tens of thousands of times. This review paper addresses a recent and cutting-edge aspect of this research: exact closed-form-and, therefore, provably Bayes-optimal-approximations of the multitarget Bayes filter. The five proposed such filters-generalized labeled multi-Bernoulli (GLMB), labeled multi-Bernoulli mixture (LMBM), and three Poisson multi-Bernoulli mixture (PMBM) filter variants-are assessed in depth. This assessment includes a theoretically rigorous, but intuitive, statistical theory of "undetected targets", and concrete formulas for the posterior undetected-target densities for the "standard" multitarget measurement model.
Isolation Set-Kernel and Its Application to Multi-Instance Learning

Set-level problems are as important as instance-level problems. The core in solving set-level problems is: how to measure the similarity between two sets. This paper investigates data-dependent kernels that are derived directly from data. We introduce Isolation Set-Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning. In contrast, most current set-similarities are not dependent on the underlying data distribution. We theoretically analyze the characteristic of Isolation Set-Kernel. As the set-kernel has a finite feature map, we show that it can be used to speed up the set-kernel computation significantly. We apply Isolation Set-Kernel to Multi-Instance Learning (MIL) using SVM classifier, and demonstrate that it outperforms other set-kernels or other solutions to the MIL problem.
Unveiling Hidden Implicit Similarities for Cross-Domain Recommendation

E-commerce businesses are increasingly dependent on recommendation systems to introduce personalized services and products to targeted customers. Providing effective recommendations requires sufficient knowledge about user preferences and product (item) characteristics. Given the current abundance of available data across domains, achieving a thorough understanding of the relationship between users and items can bring in more collaborative filtering power and lead to a higher recommendation accuracy. However, how to effectively utilize different types of knowledge obtained across domains is still a challenging problem. In this paper, we propose to discover both explicit and implicit similarities from latent factors across domains based on matrix tri-factorization. In our research, common factors in a shared dimension (users or items) of two coupled matrices are discovered, while at the same time, domain-specific factors of the shared dimension are also preserved. We will show that such preservation of both common and domain-specific factors are significantly beneficial to cross-domain recommendations. Moreover, on the non-shared dimension, we propose to use the middle matrix of the tri-factorization to match the unique factors, and align the matched unique factors to transfer cross-domain implicit similarities and thus further improve the recommendation. This research is the first that proposes the transfer of knowledge across the non-shared (non-coupled) dimensions. Validated on real-world datasets, our approach outperforms existing algorithms by more than two times in terms of recommendation accuracy. These empirical results illustrate the potential of utilizing both explicit and implicit similarities for making across-domain recommendations.
Social Skill Validation at LinkedIn

The main mission of LinkedIn is to connect 610M+ members to the right opportunities. To find the right opportunities, LinkedIn needs to understand each member's skill set and their expertise levels accurately. However, estimating members' skill expertise is challenging due to lack of ground-truth. So far, the industry relied on either hand-created small scale data, or large scale social gestures containing a lot of social bias (e.g., endorsements).