Reviewerly/exHarmony huggingface dataset:
Encoder-Decoder Structure With the Feature Pyramid for Depth Estimation From a Single Image

We address the problem of depth estimation from a single monocular image in the paper.Depth estimation from a single image is an ill-posed and inherently ambiguous problem.In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image.More specifically, the feature pyramid is used to detect objects of different scales in the image.The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image.We adopt Res2-50 as the encoder to extract important features.The decoder section uses a novel upsampling structure to improve the output resolution.Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects.Additionally, we use Adam as our optimization function to optimize our network and speed up convergence.Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.
Ultra-Short-Term Regional PV Power Forecasting Based on Fluctuation Pattern Recognition with Satellite Images

Regional photovoltaic (PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultra-short-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. Moreover, each pattern of PV data was trained by long-short-term memory network (LSTM) for different forecasting models, separately. Finally, the FPP model based on convolutional neural network (CNN) was proposed to predict the fluctuation pattern of the future PV power with historical satellite images as input. Simulation results show that the proposed forecasting method is more accurate than other traditional models.
Self‐supervised learning with randomised layers for remote sensing

This letter presents a new self-supervised learning approach based on randomised layers for remote sensing. Our method is basically based on the Tile2Vec approach, which is one of the state-of-the-art self-supervised learning approaches for remote sensing. Unlike the original Tile2Vec algorithm, we reformulate the triplet loss as a classification loss. We use several fully connected layers with binary cross-entropy loss instead of no fully connected layers with triplet loss of the original Tile2Vec. We observe that not updating the fully connected layers is more helpful in obtaining more robust representations. The proposed algorithm is verified and evaluated by applying it to a cropland data layer classification task. The experimental results show that our approach is superior to the original Tile2Vec approach in all experiments based on random forest, logistic regression, and multi-layer classifiers.
Automatic glomerulus detection in renal histological images

Glomeruli are microscopic structures of the kidney affected in many renal diseases. The diagnosis of these diseases depends on the study by a pathologist of each glomerulus sampled by renal biopsy. To help pathologists with the image analysis, we propose a glomerulus detection method on renal histological images. For that, we evaluated two state-ofthe-art deep-learning techniques: single shot multibox detector with Inception V2 (SI2) and faster region-based convolutional neural network with Inception V2 (FRI2). As a result, we reached: 0.88 of mAP and 0.94 of F1-score, when using SI2, and 0.87 of mAP and 0.97 of F1-score, when using FRI2. On average, to process each image, FRI2 required 30.91s, while SI2 just 0.79s. In our experiments, we found that SI2 model is the best detection method for our task since it is 64% faster in the training stage and 98% faster to detect the glomeruli in each image.
Single Image Dehazing with Optimal Color Channels and Nonlinear Transformation

Image dehazing is an important problem and it is useful as a preprocessing step in various automatic image analysis systems. The goal of image dehazing is the quality improvement of digital images by removing haze across the scene. In the present work, we consider an automatic image dehazing approach that is based on optimal color channels and nonlinear transformations. The proposed dehazing approach can remove haze fast and effectively with features preservation. In our experiments, we compare the image dehazing results with related image dehazing methods from the literature. Visual assessments, as well as quantitative assessments, are also done to show the improvements obtained by the dehazing model across different natural images. Obtained experimental results indicate that the dehazing approach proposed here performs better than other dehazing models in terms of overall better visual quality and higher blind image quality metric values.
RCM: Requirement Capturing Model for Automated Requirements Formalisation

Most existing automated requirements formalisation techniques require system engineers to (re)write their requirements using a set of predefined requirement templates with a fixed structure and known semantics to simplify the formalisation process. However, these techniques require understanding and memorising requirement templates, which are usually fixed format, limit requirements captured, and do not allow capture of more diverse requirements. To address these limitations, we need a reference model that captures key requirement details regardless of their structure, format or order. Then, using NLP techniques we can transform textual requirements into the reference model. Finally, using a suite of transformation rules we can then convert these requirements into formal notations. In this paper, we introduce the first and key step in this process, a Requirement Capturing Model (RCM) - as a reference model - to model the key elements of a system requirement regardless of their format, or order. We evaluated the robustness of the RCM model compared to 15 existing requirements representation approaches and a benchmark of 162 requirements. Our evaluation shows that RCM breakdowns support a wider range of requirements formats compared to the existing approaches. We also implemented a suite of transformation rules that transforms RCM-based requirements into temporal logic(s). In the future, we will develop NLP-based RCM extraction technique to provide end-to-end solution.
Radical Placemaking: Utilizing Low-Tech AR/VR to engage in Communal Placemaking during a Pandemic

The COVID-19 pandemic has made the struggles of the excluded louder and has also left them socially isolated. The article documents the implementation of one instance of Radical Placemaking, an “intangible”, community-driven and participatory placemaking process, in Kelvin Grove Urban Village (KGUV), Brisbane, Australia to tackle social isolation during the COVID-19 pandemic. KGUV community members were engaged in storytelling and interactive fiction online workshops to create experiential, place-based and mobile low-tech AR digital artefacts. The article expands on the methodology which involved a series of online workshops to design low-tech AR digital artefacts using digital collaboration tools (Google Classroom, Slack, Zoom) and VR environments (Mozilla Hubs). The study’s findings confirm the role of accessible AR/VR technology in enabling marginalised communities to create connectedness and community by co-creating their own authentic and diverse urban imaginaries of place and cities.
Live Twitter Sentiment Analysis

Sentiment analysis One of the progressive field of natural language processing is sentiment analysis. It is a way to detect the attitude, state of mind, or emotions of the person towards a product, service, movie, etc. by analysing the opinions and reviews shared on social media, blogs and so on. Social media platforms such as Facebook, Twitter and so on allow people to share their views with other people. Twitter become the one of the popular platform that allows users to share information by way of the short messages called tweets on a real-time basis. To make good use of this data, we develop a Real-time twitter sentiment analysis and visualization system. It is a web application and its purpose is to employ an open source approach for sentiment analysis and its visualization using a set of packages supported by python language to mine the real-time data from Twitter through application programming interfaces (APIs) using hashtags and keywords. This system will analyse the sentiments as positive and negative for a particular product and service that helps organizations, political parties, and common people to understand the effectiveness of their efforts and better decision making.
Measure the effectiveness of information systems with the naïve bayes classifier method

&lt;span id="docs-internal-guid-a31a7709-7fff-65df-d360-f52175b11d47"&gt;&lt;span&gt;Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.&lt;/span&gt;&lt;/span&gt;
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing facial recognition systems. However, existing methods fail on full-scale systems and commercial APIs. We develop our own adversarial filter that accounts for the entire image processing pipeline and is demonstrably effective against industrial-grade pipelines that include face detection and large scale databases. Additionally, we release an easy-to-use webtool that significantly degrades the accuracy of Amazon Rekognition and the Microsoft Azure Face Recognition API, reducing the accuracy of each to below 1%.
A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science

Recent review papers have discussed the opportunities and challenges of applying machine learning (ML) techniques to Earth science data. A common challenge cited in these papers is the lack of labeled training data. A literature review of Earth science papers over the last 10 years demonstrates that while there is rapid adoption of ML, particularly in biogeoscience and land surface research, the training datasets typically contain only hundreds of samples. This lack of training data limits the use of deep learning algorithms, which require larger volumes of labeled data. In situ training data are most frequently used in almost all domains, followed by model output and satellite data. The atmosphere and solid Earth domains use the largest training datasets, an order of magnitude larger than in biogeoscience papers. Random forest is the most commonly applied ML algorithm in all domains except atmospheric science and biogeoscience, which more frequently use fully connected neural networks.
IsarStep: a Benchmark for High-level Mathematical Reasoning

A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. We build a non-synthetic dataset from the largest repository of proofs written by human experts in a theorem prover. The dataset has a broad coverage of undergraduate and research-level mathematical and computer science theorems. In our defined task, a model is required to fill in a missing intermediate proposition given surrounding proofs. This task provides a starting point for the long-term goal of having machines generate human-readable proofs automatically. Our experiments and analysis reveal that while the task is challenging, neural models can capture non-trivial mathematical reasoning. We further design a hierarchical transformer that outperforms the transformer baseline. We will make the dataset and models publicly available.
User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic-Based Approach

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise.
Endoscopic Image Deblurring and Super-Resolution Reconstruction Based on Deep Learning

There are two main reasons for the degradation of endoscopic image quality: 1) Motion blur; 2) Low imaging resolution. Since the blur kernels are highly nonlinear in real scenes, the restoration effect of the method of restoring motion blur by estimating the blur kernels is often not accurate enough. This paper proposes an end-to-end image blind deblurring algorithm based on convolutional neural network. This algorithm uses the architecture of combining image deblurring and super-resolution reconstruction of convolutional neural network, which divided into 3 parts: deblurring network, super-resolution network and feature fusion network. On the super-resolution task, this paper is based on densely connected convolutional networks (Dense-Net) [1], Res2Net [2] and segmentation channel method to improve network performance, and proposes different solutions for different types of image. Experimental results show that, compared with the previous method, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the restored image obtained by the algorithm are improved. The experimental results show that the restoration index and visual perception effect of the image obtained by this algorithm are improved compared with the previous method, and the algorithm greatly saves the computational cost during the training of the neural network.
Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment

Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to improve tumour prognostication. Pathologists are the gold standard for database development, however, labelling errors may hamper development of accurate algorithms. In the present work we evaluated the benefit of multi-expert consensus (n = 3, 5, 7, 9, 11) on algorithmic performance. While training with individual databases resulted in highly variable F$_1$ scores, performance was notably increased and more consistent when using the consensus of three annotators. Adding more annotators only resulted in minor improvements. We conclude that databases by few pathologists and high label accuracy may be the best compromise between high algorithmic performance and time investment.
Asking the Right Questions: Learning Interpretable Action Models Through Query Answering

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.
Expected Value of Communication for Planning in Ad Hoc Teamwork

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as “ad hoc teamwork”, enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.
ETCC: Encrypted Two-Label Classification Using CNN

Due to the increasing variety of encryption protocols and services in the network, the characteristics of the application are very different under different protocols. However, there are very few existing studies on encrypted application classification considering the type of encryption protocols. In order to achieve the refined classification of encrypted applications, this paper proposes an Encrypted Two-Label Classification using CNN (ETCC) method, which can identify both the protocols and the applications. ETCC is a two-stage two-label classification method. The first stage classifies the protocol used for encrypted traffic. The second stage uses the corresponding classifier to classify applications according to the protocol used by the traffic. Experimental results show that the ETCC achieves 97.65% accuracy on a public dataset (CICDarknet2020).
Reactive scheduling of multiple EOSs under cloud uncertainties: Model and algorithms

Most earth observation satellites (EOSs) are low-orbit satellites equipped with optical sensors that cannot see through clouds.Hence, cloud coverage, high dynamics, and cloud uncertainties are important issues in the scheduling of EOSs.The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed.Numerous studies have been conducted on methods for the proactive scheduling of EOSs, including expectation, chance-constrained, and robust optimization models and the relevant solution algorithms.This study focuses on the reactive scheduling of EOSs under cloud uncertainties.First, using an example, we describe the reactive scheduling problem in detail, clarifying its significance and key issues.Considering the two key objectives of observation profits and scheduling stability, we construct a multi-objective optimization mathematical model.Then, we obtain the possible disruptions of EOS scheduling during execution under cloud uncertainties, adopting an event-driven policy for the reactive scheduling.For the different disruptions, different reactive scheduling algorithms are designed.Finally, numerous simulation experiments are conducted to verify the feasibility and effectiveness of the proposed reactive scheduling algorithms.The experimental results show that the reactive scheduling algorithms can both improve observation profits and reduce system perturbations.
The lower bounds on the second-order nonlinearity of three classes of Boolean functions

<p style='text-indent:20px;'>In this paper, by calculating the lower bounds on the nonlinearity of the derivatives of the following three classes of Boolean functions, we provide the tight lower bounds on the second-order nonlinearity of these Boolean functions: (1) <inline-formula><tex-math id="M1">\begin{document}$ f_1(x) = Tr_1^n(x^{2^{r+1}+2^r+1}) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M2">\begin{document}$ n = 2r+2 $\end{document}</tex-math></inline-formula> with even <inline-formula><tex-math id="M3">\begin{document}$ r $\end{document}</tex-math></inline-formula>; (2) <inline-formula><tex-math id="M4">\begin{document}$ f_2(x) = Tr_1^n(\lambda x^{2^{2r}+2^{r+1}+1}) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M5">\begin{document}$ \lambda \in \mathbb{F}_{2^r}^* $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ n = 4r $\end{document}</tex-math></inline-formula> with even <inline-formula><tex-math id="M7">\begin{document}$ r $\end{document}</tex-math></inline-formula>; (3) <inline-formula><tex-math id="M8">\begin{document}$ f_3(x,y) = yTr_1^n(x^{2^r+1})+Tr_1^n(x^{2^r+3}) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M9">\begin{document}$ (x, y)\in \mathbb{F}_{2^n}\times \mathbb{F}_2 $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M10">\begin{document}$ n = 2r $\end{document}</tex-math></inline-formula> with odd <inline-formula><tex-math id="M11">\begin{document}$ r $\end{document}</tex-math></inline-formula>. The results show that our bounds are better than previously known lower bounds in some cases.