Reviewerly/exHarmony huggingface dataset:
Filter gate network based on multi-head attention for aspect-level sentiment classification

Abstract Aspect-level sentiment classification (ASC) is an important branch of sentiment analysis. Its purpose is to identify the sentiment polarity of the context for a given aspect. In recent years, ASC has received widespread attention from more and more researchers. Most of the previous work used word embedding, which was trained from large corpora, as context representation. However, this method cannot fully express the actual meaning of the context. In addition, the attention mechanism in ASC brings noise and captures context words that are irrelevant to the current aspect. Based on the above problems, we propose a novel neural network, named Filter Gate Network based on Multi-head attention (FGNMH). First, we train the context in a domain-specific corpus and integrate the part-of-speech features of the context to enrich the representation of the context. Second, we use multi-head attention mechanism to model contextual semantic information. Finally, a filter layer is designed to remove context words that are irrelevant to current aspect. To verify the effectiveness of FGNMH, we conduct a large number of experiments on SemEval2014, Restaurant15, Restaurant16 and Twitter. Experimental results show that FGNMH can reach 83.92%, 81.24%, 84.05%, 85.37%and 74.37% on five data sets, respectively.
3-D Res-CapsNet convolutional neural network on automated breast ultrasound tumor diagnosis

Purpose We propose a 3-D tumor computer-aided diagnosis (CADx) system with U-net and a residual-capsule neural network (Res-CapsNet) for ABUS images and provide a reference for early tumor diagnosis, especially non-mass lesions. Methods A total of 396 patients with 444 tumors (226 malignant and 218 benign) were retrospectively enrolled from Sun Yat-sen University Cancer Center. In our CADx, preprocessing was performed first to crop and resize the tumor volumes of interest (VOIs). Then, a 3-D U-net and postprocessing were applied to the VOIs to obtain tumor masks. Finally, a 3-D Res-CapsNet classification model was executed with the VOIs and the corresponding masks to diagnose the tumors. Finally, the diagnostic performance, including accuracy, sensitivity, specificity, and area under the curve (AUC), was compared with other classification models and among three readers with different years of experience in ABUS review. Results For all tumors, the accuracy, sensitivity, specificity, and AUC of the proposed CADx were 84.9 %, 87.2 %, 82.6 %, and 0.9122, respectively, outperforming other models and junior reader. Next, the tumors were subdivided into mass and non-mass tumors to validate the system performance. For mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 85.2 %, 88.2 %, 82.3 %, and 0.9147, respectively, which was higher than that of other models and junior reader. For non-mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 81.6 %, 78.3 %, 86.7 %, and 0.8654, respectively, outperforming the two readers. Conclusion The proposed CADx with 3-D U-net and 3-D Res-CapsNet models has the potential to reduce misdiagnosis, especially for non-mass lesions.
Image Defogging Framework Using Segmentation and the Dark Channel Prior

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.
Graph Convolutional Networks for the Automated Production of Building Vector Maps From Aerial Images

Building footprint delineation from remote sensing imagery is a basic task in surveying and mapping and geographic information system (GIS), which benefits many engineering applications but requires an enormous amount of human delineation. In this study, we aim to find a way to replace human delineation with automated algorithms. For this purpose, we designed a novel pipeline for the automated production of building vector maps from aerial images, which consists of a bounding-box generation module, a graph convolutional network (GCN)-based polygon prediction module, and an empirical polygon regularization module. First, we introduce the bounding box generation module based on region-based object detection, which along with our overlap cropping strategy is used to generate a bounding box for each building instance. The generated bounding boxes have a horizontal rectangle version and a rotated version, which are used to initialize the next stage of our method. Second, we propose a GCN-based method, which is the core of this study, to conduct the initial accurate building polygon prediction by integrating multiresolution optimization and multilevel loss constraints. Our proposed method, to the authors’ best knowledge, is the first of its kind that introduces GCN to building extraction. The final step is to apply a regularization algorithm to translate the predicted polygons into structured and highly accurate building footprints. We validated the proposed method on two large aerial building data sets, WHU data set and Inria data set, where it was shown to significantly outperform other state-of-the-art methods more than 10%. Specifically, with the ground-truth rotated bounding boxes of buildings, our method is able to automatically delineate 91% of the buildings in the WHU data set and with the predicted rotated bounding the percent reached human-level delineation.
Covid notions: Towards formal definitions – and documented understanding – of privacy goals and claimed protection in proximity-tracing services

The recent SARS-CoV-2 pandemic gave rise to management approaches using mobile apps for contact tracing. The corresponding apps track individuals and their interactions, to facilitate alerting users of potential infections well before they become infectious themselves. Naïve implementation obviously jeopardizes the privacy of health conditions, location, activities, and social interaction of its users. A number of protocol designs for colocation tracking have already been developed, most of which claim to function in a privacy preserving manner. However, despite claims such as "GDPR compliance", "anonymity", "pseudonymity" or other forms of "privacy", the authors of these designs usually neglect to precisely define what they (aim to) protect. We make a first step towards formally defining the privacy notions of proximity tracing services, especially with regards to the health, (co-)location, and social interaction of their users. We also give a high-level intuition of which protection the most prominent proposals likely can and cannot achieve. This initial overview indicates that all proposals include some centralized services, and none protects identity and (co-)locations of infected users perfectly from both other users and the service provider.
The "Active Search" Hypothesis

While research shows that web search plays a role throughout the creative process, less is known about about how people use web search to learn and frame their thinking about an open problem. People need web search to gather information about a problem area, but this can also influence the rest of the creative process. To understand how web search affects early-stage design, we collected and analyzed search log and self-report data from 34 students in a project-based design class. Participants reported struggling with scoping broad, ill-defined information goals into queries, learning domain-specific language, and assessing the usefulness of information. Analysis found that more active and diverse search behavior (i.e. issuing more frequent and diverse queries, and opening more webpages) related to more progress in early-stage design (i.e. gathering more facts, articulating more insights, and developing better problem frames). Based on these findings, we discuss implications for designing search tools to support peoples' creative processes.
Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.
Deep Residual Dense Network for Single Image Super-Resolution

In this paper, we propose a deep residual dense network (DRDN) for single image super- resolution. Based on human perceptual characteristics, the residual in residual dense block strategy (RRDB) is exploited to implement various depths in network architectures. The proposed model exhibits a simple sequential structure comprising residual and dense blocks with skip connections. It improves the stability and computational complexity of the network, as well as the perceptual quality. We adopt a perceptual metric to learn and assess the quality of the reconstructed images. The proposed model is trained with the Diverse2k dataset, and the performance is evaluated using standard datasets. The experimental results confirm that the proposed model exhibits superior performance, with better reconstruction results and perceptual quality than conventional methods.
Quantum Cryptanalysis on a Multivariate Cryptosystem Based on Clipped Hopfield Neural Network

Shor’s quantum algorithm and other efficient quantum algorithms can break many public-key cryptographic schemes in polynomial time on a quantum computer. In response, researchers proposed postquantum cryptography to resist quantum computers. The multivariate cryptosystem (MVC) is one of a few options of postquantum cryptography. It is based on the NP-hardness of the computational problem to solve nonlinear equations over a finite field. Recently, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2018) proposed a MVC based on extended clipped hopfield neural networks (eCHNN). Its main security assumption is backed by the discrete logarithm (DL) problem over Matrics. In this brief, we present quantum cryptanalysis of Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s eCHNN-based MVC. We first show that Shor’s quantum algorithm can be modified to solve the DL problem over Matrics. Then we show that Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s construction of eCHNN-based MVC is not secure against quantum computers; this against the original intention of that multivariate cryptography is one of a few options of postquantum cryptography.
Secure Data Sharing With Flexible User Access Privilege Update in Cloud-Assisted IoMT

Cloud-assisted Internet of Medical Things (IoMT) is becoming an emerging paradigm in the healthcare domain, which involves collection, storage and usage of the medical data. Considering the confidentiality and accessibility of the outsourced data, secure and fine-grained data sharing is a crucial requirement for the patients. Attribute-based encryption (ABE) is a promising solution to deal with this issue, but considering its property of each attribute sharing with multiple users, how to flexibly and efficiently update access privileges of certain users without affecting others is still a serious challenge. In this article, we propose a secure and fine-grained data sharing scheme with flexible user access privilege update in cloud-assisted IoMT environment. Specifically, we take ABE as the basic building block, and utilize proxy re-encryption and key blinding techniques to empower the cloud server to re-encrypt the ciphertext affected by revocation and update keys for unrevoked users. In addition, adding attributes for users to extend their access rights is realized only based on few key components stored in cloud without entirely re-computing and re-issuing keys for them. As a result, the patients are able to flexibly and efficiently share their data and manage users’ privileges. Formal proof and detailed performance evaluation demonstrate the security and efficiency of the proposed scheme.
TPOT-NN: augmenting tree-based automated machine learning with neural network estimators

Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists for when to choose one approach over the other in the context of specific real-world problems. Furthermore, relatively few tools exist that allow the integration of both AutoML and DL in the same analysis to yield results combining both of their strengths. Here, we seek to address both of these issues, by (1.) providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.) evaluating a new tool for genetic programming-based AutoML that incorporates deep estimators. Our observations suggest that AutoML outperforms simple DL classifiers when trained on similar datasets for binary classification but integrating DL into AutoML improves classification performance even further. However, the substantial time needed to train AutoML+DL pipelines will likely outweigh performance advantages in many applications.
Estimation of the Hourly Global Solar Irradiation on the Tilted and Oriented Plane of Photovoltaic Solar Panels Applied to Greenhouse Production

Agrometeorological stations have horizontal solar irradiation data available, but the design and simulation of photovoltaic (PV) systems require data about the solar panel (inclined and/or oriented). Greenhouses for agricultural production, outside the large protected production areas, are usually off-grid; thus, the solar irradiation variable on the panel plane is critical for an optimal PV design. Modeling of solar radiation components (beam, diffuse, and ground-reflected) is carried out by calculating the extraterrestrial solar radiation, solar height, angle of incidence, and diffuse solar radiation. In this study, the modeling was done using Simulink-MATLAB blocks to facilitate its application, using the day of the year, the time of day, and the hourly horizontal global solar irradiation as input variables. The rest of the parameters (i.e., inclination, orientation, solar constant, albedo, latitude, and longitude) were fixed in each block. The results obtained using anisotropic models of diffuse solar irradiation of the sky in the region of Castile and León (Spain) showed improvements over the results obtained with isotropic models. This work enables the precise estimation of solar irradiation on a solar panel flexibly, for particular places, and with the best models for each of the components of solar radiation.
A Bayesian Regularized Neural Network for Analyzing Bitcoin Trends

Bitcoin is a decentralized digital currency without a central bank or single administrator sent from user to user on the peer-to-peer bitcoin blockchain network without intermediaries' need. In this Bitcoin trend analysis work, initial attributes are considered from five sectors based on financial, social, token, network, and that count to thirteen attributes. The thirteen attributes considered are price, volume, market cap, a mean dollar invested age, social volume, social dominance, development activity, transaction volume, token age consumed, token velocity, token circulation, market value to realized value, and realized cap. We apply the attribute selection and trend analysis mapped with potential seven attributes: Price, Volume, Market Cap, Social Dominance, Development Activity, Market Value to Realized Value & Realized Cap. We have conducted Nonlinear Autoregressive with External Input analysis considering seven attributes. The work employed three training algorithms to train a neural network as Levenberg-Marquard, Bayesian Regularization, and Scaled Conjugate Gradient algorithm. The Error histogram and regression plots results indicate that the Bayesian Regularized Neural Network is showing good performance and thus provides a better forecast.
Data-free knowledge distillation in neural networks for regression

Knowledge distillation has been used successfully to compress a large neural network (teacher) into a smaller neural network (student) by transferring the knowledge of the teacher network with its original training dataset. However, the original training dataset is not reusable in many real-world applications. To address this issue, data-free knowledge distillation, which is knowledge distillation in the absence of the original training datasets, has been studied. However, existing methods are limited to classification problems and cannot be directly applied to regression problems. In this study, we propose a novel data-free knowledge distillation method that is applicable to regression problems. Given a teacher network, we adopt a generator network to transfer the knowledge in the teacher network to a student network. We simultaneously train the generator and student networks in an adversarial manner. The generator network is trained to create synthetic data on which the teacher and student networks make different predictions, with the student network being trained to mimic the teacher network’s predictions. We demonstrate the effectiveness of the proposed method on benchmark datasets. Our results show that the student network emulates the prediction ability of the teacher network with little performance loss.
Optical incoherent synthetic aperture imaging by superposition of phase-shifted optical transfer functions

The concept of an optical incoherent synthetic aperture is widely used in astronomical interferometric telescopes. In this Letter, we propose a new, to the best of our knowledge, method to realize optical incoherent synthetic aperture imaging. The method is based on a superposition of optical transfer functions of incoherent imaging systems. Only two small sub-apertures, out of a much larger full synthetic aperture, are open at any given time, and they transfer light from the observed object to the image sensor. During the imaging process, the two sub-apertures move over the full synthetic aperture, where the gap between them starts from zero and grows with time. For every position of the pair of sub-apertures, two images are captured. In one of the images, the sub-apertures have the same phase value, and in the other image, one of the sub-apertures is phase shifted by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> </mml:math> radian relative to the other one. The final image with the image resolution of the synthetic aperture is obtained as a superposition of the entire recorded images. Optical experiments are performed on reflective objects, and results of the synthetic aperture-based method demonstrate an imaging performance similar to that of direct imaging by a system with a single aperture of the size of the synthetic aperture.
A robust deformed image matching method for multi-source image matching

Multi-source image matching is a challenging task due to the presence of image distortion, as well as significant intensity changes between image pairs in corresponding regions. In addition, the influences of variant scales and multiplicative noises will also have an adverse effect on the matching accuracy. In this paper, a combination of feature descriptor called “histogram of angle and maximal edge orientation distribution” (HAED) is proposed for multi-source image matching. First, the contour segment feature, which extracts the image information using both the angle and edge orientation distribution, presents the accurate correspondence between multi-source images. Second, the similarity calculated by using Fréchet distance metric between curves is defined as a weight parameter of each contour segment histogram to improve the matching performance. Finally, a precise bilateral matching rule is used to perform the matching between the corresponding contour segments. Infrared–visible image data sets in different environments are used for experiments. The results demonstrate that the proposed algorithm achieves a more accurate matching performance than other multi-source image matching algorithms.
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals

Abstract Recently, deep learning has achieved great success in visual tracking tasks, particularly in single-object tracking. This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning. First, we introduce basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews. Second, we briefly review existing deep learning methods by categorizing them into data-invariant and data-adaptive methods based on whether they can dynamically change their model parameters or architectures. Then, we conclude with the general components of deep trackers. In this way, we systematically analyze the novelties of several recently proposed deep trackers. Thereafter, popular datasets such as Object Tracking Benchmark (OTB) and Visual Object Tracking (VOT) are discussed, along with the performances of several deep trackers. Finally, based on observations and experimental results, we discuss three different characteristics of deep trackers, i.e., the relationships between their general components, exploration of more effective tracking frameworks, and interpretability of their motion estimation components.
The Method of Embedding Information in the Noise Domain Based on the Modification of Elements in the Pseudo-Spatial Domain

The article presents a steganographic method based on the selection of the noise area of the image with the subsequent transition to the pseudo-spatial area. Embedding of elements is carried out on the basis of indirect modification of elements according to the formulated rule.