Reviewerly/exHarmony huggingface dataset:
Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images

The real-time sharing and retrieval of medical data, such as medical imaging data, via cloud systems can facilitate timely medical/disease diagnosis, for example during pandemics (e.g., COVID-19). While encryption can be used to ensure that patients’ private and medical information are not accessible by unauthorised individuals, it is challenging for cloud servers to search for and locate encrypted medical images (e.g. those relating to similar medical conditions). In this paper, we propose a novel and practical classification and retrieval method to search for and locate relevant cases over encrypted images. Specifically, we construct a privacy-preserving Convolutional Neural Network (CNN) framework that allows the classification and searching of secure, content-based, large-scale encrypted images (including large-size medical images) with homomorphic encryption. We analyze the security of our proposed method to ensure that no sensitive information from the encrypted images is leaked. Using four real-world datasets (i.e., chest X-Ray images, retinal OCT images, blood cell images, and Caltech101 image set), we evaluate and demonstrate the utility of our privacy-preserving method for searching images performed as well as CNN-based classification and searching of original images. This is an important step towards practical automated clinical diagnoses.
MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.
Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Background Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. Objective This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). Methods Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. Results After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. Conclusions With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID) RR2-10.2196/14019
Web phishing detection techniques: a survey on the state‐of‐the‐art, taxonomy and future directions

Internet dragged more than half of the world's population into the cyber world. Unfortunately, with the increase in internet transactions, cybercrimes also increase rapidly. With the anonymous structure of the internet, attackers attempt to deceive the end-users through different forms namely phishing, malware, SQL injection, man-in-the-middle, domain name system tunnelling, ransomware, web trojan, and so on. Amongst them, phishing is the most deceiving attack, which exploits the vulnerabilities in the end-users. Phishing is often done through emails and malicious websites to lure the user by posing themselves as a trusted entity. Security experts have been proposing many anti-phishing techniques. Till today there is no single solution that is capable of mitigating all the vulnerabilities. A systematic review of current trends in web phishing detection techniques is carried out and a taxonomy of automated web phishing detection is presented. The objective of this study is to acknowledge the status of current research in automated web phishing detection and evaluate their performance. This study also discusses the research avenues for future investigation.
Few-shot Font Generation with Localized Style Representations and Factorization

Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot font generation methods aim to learn to disentangle the style and content element from a few reference glyphs, and mainly focus on a universal style representation for each font style. However, such approach limits the model in representing diverse local styles, and thus makes it unsuitable to the most complicated letter system, e.g., Chinese, whose characters consist of a varying number of components (often called ``radical'') with a highly complex structure. In this paper, we propose a novel font generation method by learning localized styles, namely component-wise style representations, instead of universal styles. The proposed style representations enable us to synthesize complex local details in text designs. However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e.g., over 200 for Chinese. To reduce the number of reference glyphs, we simplify component-wise styles by a product of component factor and style factor, inspired by low-rank matrix factorization. Thanks to the combination of strong representation and a compact factorization strategy, our method shows remarkably better few-shot font generation results (with only 8 reference glyph images) than other state-of-the-arts, without utilizing strong locality supervision, e.g., location of each component, skeleton, or strokes. The source code is available at https://github.com/clovaai/lffont.
Demystify official API usage directives with crowdsourced API misuse scenarios, erroneous code examples and patches

API usage directives in official API documentation describe the contracts, constraints and guidelines for using APIs in natural language. Through the investigation of API misuse scenarios on Stack Overflow, we identify three barriers that hinder the understanding of the API usage directives, i.e., lack of specific usage context, indirect relationships to cooperative APIs, and confusing APIs with subtle differences. To overcome these barriers, we develop a text mining approach to discover the crowdsourced API misuse scenarios on Stack Overflow and extract from these scenarios erroneous code examples and patches, as well as related API and confusing APIs to construct demystification reports to help developers understand the official API usage directives described in natural language. We apply our approach to API usage directives in official Android API documentation and android-tagged discussion threads on Stack Overflow. We extract 159,116 API misuse scenarios for 23,969 API usage directives of 3138 classes and 7471 methods, from which we generate the demystification reports. Our manual examination confirms that the extracted information in the generated demystification reports are of high accuracy. By a user study of 14 developers on 8 API-misuse related error scenarios, we show that our demystification reports help developer understand and debug API-misuse related program errors faster and more accurately, compared with reading only plain API usage-directive sentences.
Pynq-YOLO-Net: An Embedded Quantized Convolutional Neural Network for Face Mask Detection in COVID-19 Pandemic Era

The recent Coronavirus COVID-19 is a very infectious disease that is transmitted through droplets generated when an infected person coughs, sneezes, or exhales.So, people must wear a face mask to reduce the power of the transition of this virus.Governments around the world have imposed the use of face masks in public spaces and supermarkets.In this paper, we propose to build a face mask detection system based on a lightweight Convolutional Neural Network (CNN) and the YOLO object detection framework to implement it on an embedded low power device.The object detection framework was designed using a single Convolutional Neural Network for object detection in real-time.To make the YOLO framework suitable for embedded implementation, we propose to build a lightweight Convolutional Neural Network and quantize it by using a single bit for weight and 2 bits for activations.The proposed network called Pynq-YOLO-Net was implemented on the Pynq Z1 platform.The computation was divided between the software and the hardware.The features extraction part was executed on the hardware device and the output part was executed on the software.This configuration has allowed reaching real-time processing with a very good detection accuracy of 97% when tested on the combination of collected datasets.
Tighter Proofs for the SIGMA and TLS 1.3 Key Exchange Protocols

We give new, fully-quantitative and concrete bounds that justify the SIGMA and TLS 1.3 key exchange protocols not just in principle, but in practice. By this we mean that, for standardized elliptic curve group sizes, the overall protocol actually achieves the intended security level. Prior work gave reductions of both protocols' security to the underlying building blocks that were loose (in the number of users and/or sessions), so loose that they gave no guarantees for practical parameters. Adapting techniques by Cohn-Gordon et al. (Crypto 2019), we give reductions for SIGMA and TLS 1.3 to the strong Diffie–Hellman problem which are tight, and prove that this problem is as hard as solving discrete logarithms in the generic group model. Leveraging our tighter bounds, we meet the protocols' targeted security levels when instantiated with standardized curves and improve over prior bounds by up to over 90 bits of security across a range of real-world parameters.
Speckle Noise Reduction in Sonar Image Based on Adaptive Redundant Dictionary

As acoustic waves are affected by the channel characteristics, such as scattering and reverberation when propagating in water, sonar images often exhibit speckle noise which will cause visual quality of the image to decrease. Therefore, denoising is a crucial preprocessing technique in sonar image applications. However, speckle noise is mainly caused by the sediment echo signals which are related to the background of seafloor sediment and can be obtained by prior modeling. Although deep learning-based denoising algorithms represent a research hotspot now, they are not suitable for such applications due to the high calculation amount and the large requirement of original images considering that sonar is carried by Autonomous Underwater Vehicles (AUVs) for collecting sonar images and performing calculation. In contrast, dictionary learning-based denoising method is more suitable and easier to be modeled. Compared with deep learning, it can greatly reduce the calculation amount and is more easily integrated into AUV systems. In addition, dictionary learning method based on image sparse representation can effectively achieve image denoising similarly. In order to solve the above problems, we propose a new adaptive dictionary learning method based on multi-resolution characteristics, which combines K-SVD dictionary learning with wavelet transform. Our method has the characteristics of dictionary learning and inherits the features of wavelet analysis as well. Compared with several classical methods, the proposed method is better at speckle noise reduction and edge detail preservation. At the same time, the calculation time is greatly reduced and the efficiency is significantly improved.
Few-shot Learning for Multi-label Intent Detection

In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we first learn universal thresholding experience on data-rich domains, and then adapt the thresholds to certain few-shot domains with a calibration based on nonparametric learning. For better calculation of label-instance relevance score, we introduce label name embedding as anchor points in representation space, which refines representations of different classes to be well-separated from each other. Experiments on two datasets show that the proposed model significantly outperforms strong baselines in both one-shot and five-shot settings.
GEBCO GRIDDED BATHYMETRIC DATASETS FOR MAPPING JAPAN TRENCH GEOMORPHOLOGY BY MEANS OF GMT SCRIPTING TOOLSET

The study investigated geomorphology of the Japan Trench located east of Japan, Pacific Ocean. A high-resolution GEBCO Gridded Bathymetric Dataset was used for modeling, mapping and visualization. The study aimed to compare and analyse variations in the geomorphic structures of the two parts of the trench and to visualize variations in the geological, geophysical and bathymetric settings. Technically, the cartographic work was performed using scripting based on the Generic Mapping Toolset (GMT). Modelled cross-sectioning orthogonal profiles transecting the trench in a perpendicular direction were automatically digitized and graphed in the two segments. The results of the bathymetric analysis shown that the southern part is shallower: with deeper values in absolute (139 samples between –7000 to –8000 m) and statistical records (the most frequent values are within –5500 to –5800 m) comparing to the northern segment (–5300 to –5500 m). The geomorphological analysis shows a more complicated relief in the northern part of the trench, which has a higher seismic activity. The southern part has a gentler slope on the Honshu island side. The geoid modeling along the trench ranges in 0–20 mGal. The highest values are recorded by the Honshu Island (&gt;40 mGal). The rest of the area has rather moderate undulations (20–40 mGal). The free-air marine gravity of the Sea of Japan is &lt;40 mGal. The results include 2D and 3D graphical models, thematic cartographic maps, spatial and statistical analysis of the Japan Trench geomorphology. Tested GMT functionality can be applied to future regional bathymetric modeling of the ocean trenches. All presented maps and graphs are made using GMT scripting toolset.
Exploring Overcomplete Representations for Single Image Deraining Using CNNs

Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. Most recent methods for deraining use a deep network following a generic “encoder-decoder” architecture which captures low-level features across the initial layers and high-level features in the deeper layers. For the task of deraining, the rain streaks which are to be removed are relatively small and focusing much on global features is not an efficient way to solve the problem. To this end, in this article, we propose using an overcomplete convolutional network architecture which gives special attention in learning local structures by restraining the receptive field of filters. We combine it with U-Net so that it does not lose out on the global structures as well while focusing more on low-level features, to compute the derained image. The proposed network called, over-and-under complete deraining network (OUCD), consists of two branches: overcomplete branch which is confined to small receptive field size in order to focus on the local structures and an undercomplete branch that has larger receptive fields to primarily focus on global structures. Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. Code is available at https://github.com/jeya-maria-jose/Derain_OUCD_Net.
Enriching the transfer learning with pre-trained lexicon embedding for low-resource neural machine translation

Most State-Of-The-Art (SOTA) Neural Machine Translation (NMT) systems today achieve outstanding results based only on large parallel corpora. The large-scale parallel corpora for high-resource languages is easily obtainable. However, the translation quality of NMT for morphologically rich languages is still unsatisfactory, mainly because of the data sparsity problem encountered in Low-Resource Languages (LRLs). In the low-resource NMT paradigm, Transfer Learning (TL) has been developed into one of the most efficient methods. It is difficult to train the model on high-resource languages to include the information in both parent and child models, as well as the initially trained model that only contains the lexicon features and word embeddings of the parent model instead of the child languages feature. In this work, we aim to address this issue by proposing the language-independent Hybrid Transfer Learning (HTL) method for LRLs by sharing lexicon embedding between parent and child languages without leveraging back translation or manually injecting noises. First, we train the High-Resource Languages (HRLs) as the parent model with its vocabularies. Then, we combine the parent and child language pairs using the oversampling method to train the hybrid model initialized by the previously parent model. Finally, we fine-tune the morphologically rich child model using a hybrid model. Besides, we explore some exciting discoveries on the original TL approach. Experimental results show that our model consistently outperforms five SOTA methods in two languages Azerbaijani (Az) and Uzbek (Uz). Meanwhile, our approach is practical and significantly better, achieving improvements of up to 4.94 and 4.84 BLEU points for low-resource child languages Az ! Zh and Uz ! Zh, respectively.
Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user's body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.
Attribute-Aware Generative Design With Generative Adversarial Networks

The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process. The shrinking time-to-market and the growing diversity of users' needs further exacerbate this gap. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation or editing of design concepts. This article explores the capabilities of generative adversarial networks (GAN) for automated, attribute-aware generative design of the visual attributes of a product. Specifically, a design attribute GAN (DA-GAN) model is developed for automated generation of fashion product images with the desired visual attributes. Experiments on a large fashion dataset signify the potentials of GAN for attribute-aware generative design, verify the ability of editing attributes with relatively higher accuracy and uncover several key challenges and research questions for future work.