Reviewerly/exHarmony huggingface dataset:
Ship Detection based on SAR Imaging Supervised by Noisy Ship Direction

Synthetic aperture radar (SAR) is an active imaging microwave sensor. Since SAR imaging is robust against weather, light, and other external conditions, Ship Detection based on SAR Imaging has high research and application values. However, the variety in size, direction, background and arrangement poses great challenges to SAR image ship detection. Due to the lack of effective constrains, plenty of boxes predicted by the existing methods are quite different from groundtruth, resulting in IOU between predicted boxes and groundtruth being smaller than the threshold and generates false alarms. Therefore, in this letter we propose a method of noisy ship direction classification, which uses the ship pixel distribution in SAR images to classify ship objects into different directions, so as to reduce the shape difference between boxes and groundtruth. Experimental results show that by combining with YOLOv5 framework, our method can significantly improve the ship's detection performance, and reach state-of-the-art.
CALDA: Improving Multi-Source Time Series Domain Adaptation With Contrastive Adversarial Learning

Unsupervised domain adaptation (UDA) provides a strategy for improving machine learning performance in data-rich (target) domains where ground truth labels are inaccessible but can be found in related (source) domains. In cases where meta-domain information such as label distributions is available, weak supervision can further boost performance. We propose a novel framework, CALDA, to tackle these two problems. CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data. Similar to prior methods, CALDA utilizes adversarial learning to align source and target feature representations. Unlike prior approaches, CALDA additionally leverages cross-source label information across domains. CALDA pulls examples with the same label close to each other, while pushing apart examples with different labels, reshaping the space through contrastive learning. Unlike prior contrastive adaptation methods, CALDA requires neither data augmentation nor pseudo labeling, which may be more challenging for time series. We empirically validate our proposed approach. Based on results from human activity recognition, electromyography, and synthetic datasets, we find utilizing cross-source information improves performance over prior time series and contrastive methods. Weak supervision further improves performance, even in the presence of noise, allowing CALDA to offer generalizable strategies for MS-UDA.
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation

Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to translate images to multiple visual domains remains challenging. Existing works do not often preserve the domain-invariant part of the image (e.g., the identity in human face translations), or they do not usually handle multiple domains or allow for multi-modal translations. This work proposes an implicit style function (ISF) to straightforwardly achieve multi-modal and multi-domain image-to-image translation from pre-trained unconditional generators. The ISF manipulates the semantics of a latent code to ensure that the image generated from the manipulated code lies in the desired visual domain. Our human faces and animal image manipulations show significantly improved results over the baselines. Our model enables cost-effective multi-modal unsupervised image-to-image translations at high resolution using pre-trained unconditional GANs. The code and data are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yhlleo/stylegan-mmuit</uri> .
Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks

Nowadays, deep learning models play an important role in a variety of scenarios, such as image classification, natural language processing, and speech recognition. However, deep learning models are shown to be vulnerable; a small change to the original data may affect the output of the model, which may incur severe consequences such as misrecognition and privacy leakage. The intentionally modified data is referred to as adversarial examples. In this paper, we explore the security vulnerabilities of deep learning models designed for textual analysis. Specifically, we propose a visual similar word replacement (VSWR) algorithm to generate adversarial examples against textual analysis models. By using adversarial examples as the input of deep learning models, we verified that deep learning models are vulnerable to such adversarial attacks. We have conducted experiments on several sentiment analysis deep learning models to evaluate the performance. The results also confirmed that the generated adversarial examples could successfully attack deep learning models. As the number of modified words increases, the model prediction accuracy becomes lower. This kind of adversarial attack implies security vulnerabilities of deep learning models.
TW-TGNN: Two Windows Graph-Based Model for Text Classification

Text classification is the most fundamental and classical task in the natural language processing (NLP). Recently, graph neural network (GNN) methods, especially the graph-based model, have been applied for solving this issue because of their superior capacity of capturing the global co-occurrence information. However, some existing GNN-based methods adopt a corpus-level graph structure which causes a high memory consumption. In addition, these methods have not taken account of the global co-occurrence information and local semantic information at the same time. To address these problems, we propose a new GNN-based model, namely two windows text gnn model (TW-TGNN), for text classification. More specifically, we build text-level graph for each text with a local sliding window and a dynamic global window. For one thing, the local window sliding inside the text will acquire enough local semantic features. For another, the dynamic global window sliding betweent texts can generate dynamic shared weight matrix, which overcomes the limitation of the fixed corpus level co-occurrence and provides richer dynamic global information. Our experimental results on four benchmark datasets illustrate the improvement of the proposed method over state-of-the-art text classification methods. Moreover, we find that our method captures adequate global information for the short text which is beneficial for overcoming the insufficient contextual information in the process of the short text classification.
UThwigl — An R package for closed- and open-system uranium–thorium dating

For several decades, uranium–thorium (U–Th) dating has allowed geochronologists to precisely date geological materials, providing invaluable geochronological constraints on Quaternary processes. Open-system dating of bones and teeth has also provided ages of human and faunal remains of archaeological significance. To facilitate access to closed- and open-system U–Th dating to the broad scientific community, here we provide an R package, named UThwigl . Description of input and output parameters is given, as well as a guide for running the model. The package can be used three different ways: (i) as a web application, (ii) through a web browser with an internet connection, or (iii) in R (most efficiently with RStudio). Examples of application of the model are also provided, showing that it yields ages within error of previously published values.
Deep Learning Based Thermal Object Recognition under Different Illumination Conditions

Object recognition has recently a tremendous development because of the utilization of deep learning network. The recognition accuracy frequently experiences some sources of variation that can be found in images. Probably the most difficult variations are actuated by changing illumination conditions. Numerous researchers are targeting improving performance of object recognition frameworks towards various illumination conditions. The better model understanding and contributions of deep network in the object recognition motivate to utilize the deep networks for object recognition in different illumination conditions. This paper presents an accurate and proficient approach for recognizing the objects under varying illumination conditions. Hence, this proposed technique presents deep network-based system for illumination invariant object recognition in thermal image dataset. The thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is utilized to adequately recognize objects in thermal images under various illumination conditions. The results exhibit that the proposed work can significantly upgrade the recognition performance, outperforming other state-of-the-art strategies. This work can be an incredible assistance in driver assistance system and surveillance frameworks.
Visual Data Processing Framework for a Skin-Based Human Detection

For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed using the Human-Parts dataset, the Simulated disaster victim dataset, and the Servosila Engineer robot dataset. Random, Liner SVM, Quadratic SVM, AdaBoost, and Random Forest approaches were compared using the Human body dataset. Experimental evaluation demonstrated an average precision of 90.4% for the Quadratic SVM model and showed the efficiency of RIHOG features as a descriptor for human detection tasks.
Quality Assurance Challenges For Machine Learning Software Applications During Software Development Life Cycle Phases

In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.
Training Differentially Private Neural Networks with Lottery Tickets

We propose the differentially private lottery ticket hypothesis (DPLTH). An end-to-end differentially private training paradigm based on the lottery ticket hypothesis, designed specifically to improve the privacy-utility trade-off in differentially private neural networks. DPLTH, using high-quality winners privately selected via our custom score function outperforms current methods by a margin greater than 20%. We further show that DPLTH converges faster, allowing for early stopping with reduced privacy budget consumption and that a single publicly available dataset for ticket generation is enough for enhancing the utility on multiple datasets of varying properties and from varying domains. Our extensive evaluation on six public datasets provides evidence to our claims.
Optimal resource allocation with deep reinforcement learning and greedy adaptive firefly algorithm in cloud computing

Abstract The allocation of resources is a foremost demanding task in cloud computing. Scholars are yet finding it difficult to allocate appropriate resources to the set of user tasks. Our objective is to provide a platform that optimizes a dynamic resource allocation scheme. Multi‐agent deep reinforcement learning‐based greedy adaptive firefly algorithm (MAD‐GAF) has been proposed herein includes both the resource management and allocation techniques. This chooses the best Quality of Service (QoS) measured host for a group of tasks efficiently and subsequently minimize the task execution time. The proposed cloud brokering architecture comprises a multi‐agent system, the cloud provider and the user. Initially, deep reinforcement learning has been built to recreate the request of cloud customers by forecasting the value of unused resources. Then the recreated customer request is forwarded to the global broker agent, which maps the virtual machine (VM) to the most appropriate cluster of physical machine (PM). The virtual machine monitor (VMM) selects VMs by managing and accessing the physical resources. The global utility agent allocates VMs using the GAF optimization algorithm, which specifies the best QoS measured host to decrease the whole tasks' average response time, thus optimizing resource allocation compared to the current approaches.
Stop Criterion in Building Decision Trees with Bagging Method for Dispersed Data

This article discusses issues related to decision making based on applying decision trees and bagging methods on dispersed knowledge. In dispersed knowledge, local decision tables possess data independently in fragments. In this study, sub-tables are further generated with bagging method for each local table, based on which the decision trees are built. These decision trees classify the test object, and a probability vector is defined over the decision classes for each local table. For each vector, decision classes with the maximum value of the coordinates are selected and final joint decisions for all local tables are made by majority voting. Quality of decision making has been observed to increase when bagging method as an ensemble method is combined with decision trees on independent dispersed data. An important criterion in building a decision tree is to know when to stop growing the tree (stop splitting). That is, at what minimum number of objects on a working node do we stop building the tree to ensure the best decision results. The contribution of the paper is to observe the influence a stop criterion (expressed in the number of objects in the node) for decision trees used in conjunction with bagging method on independent data sources. It can be concluded that in dispersed data set, the stop split criteria does not influence the classification quality much. The statistical significance of the difference in the mean classification error values was confirmed only for a very high stop criterion (0.1× number of objects in training set) and for a very low stop criterion (equal to two). There is no significant statistical difference in the classification quality obtained for the stop criterion values: 4, 6, 8 and 10. An interesting remark is that for some dispersed data sets, in the case of smaller number of local tables and larger number of bootstrap samples, better quality of classification is obtained for a small number of objects in the stop criterion (mostly for two objects). Only, at a significant increase in the minimum number of objects at which growth of trees is stopped is quality of classification affected. However, the gain in reducing the complexity for trees that we get when using the larger values of stop criterion is significant.
Developing a Prescription Recognition System Based on CRAFT and Tesseract

Optical Character Recognition (OCR) plays an essential role in nowadays life, which contributes to solving problems in terms of timing and accuracy of documents. The use of OCR in the health sector can help solve problems of drug handling or inventorying in drug banks to prevent unnecessary risks. However, if you apply existing OCR meth- ods such as Tesseract or EasyOCR, it will be challenging to find out the name of the medicine or the medicine ingredient in a prescription. In this paper, we propose a system to help find the medicine names from the prescription image. We then provide users with information on the identified medicine names. Methods are built by combining and transforming many existing identity models. In addition, we have successfully developed an application running on the Android platform to get feedback on improving the system and want to help them get more information about the drugs they are using. Experimental results show that the model recognizes drug names quite well on a given database, even with medium resolution photos.
Face Restoration Network with Feature Prior

Recent works on blind face restoration mainly focus on reference-based methods, which made great progress in recovering high-frequency details and realistic texture from the real world low-quality (LQ) images. However, the multi-scale trait of LQ images is not fully utilized with these methods. Extra face reference also takes up much resources and brings redundant model parameters. In this paper, we introduce the face restoration network with feature prior (FP-FRN) consisting of an adversarial network with a multi-scale feature extraction network which utilizes the multi-scale facial feature to preserve low-level facial characteristics and predict high-level details. Compared to other state-of-the-art approaches, i.e., DFDNet, PSFR-GAN, out FP-FRN generates more realistic texture details and better preserved the low-level feature of the LQ images such as color and shape. As demonstrated by experiments on datasets of synthesized and real LQ images, FP-FRN is superior over other methods.
Revisiting Semilattice Semantics

The operational semantics of Urquhart is a deep and important part of the development of relevant logics. In this paper, I present an overview of work on Urquhart’s operational semantics. I then present the basics of collection frames. Finally, I show how one kind of collection frame, namely, functional set frames, is equivalent to Urquhart’s semilattice semantics.
EtherH: A Hybrid Index to Support Blockchain Data Query

As the underlying technology of cryptocurrency, blockchain is characterized by decentralization and immutability. It is considered as a new generation of disruptive technology. However, the underlying storage of blockchain only provides limited support for data query. This limits the utility of blockchain technology. To improve the efficiency of access to blockchain data, we design EtherH based on Ethereum, the most representative open blockchain system. EtherH is a hybrid index that can provide Single-V query and Range query on Ethereum blockchain. To make the index build more specific, EtherH supports elastic data building. A portion of the blockchain data is selected for index building. We connect the client to the Rinkeby network to obtain block data and conduct extensive experiments. Experimental results prove that EtherH performs well in both data insertion and data query.
A deep neural network based toddler tracking system

Summary With the recent technological developments, auxiliary smart systems have started to be used in many issues that are important in people's lives. One of these issues is that parents ensure the safety of their toddlers. Toddlers tend to reach objects that may be dangerous to them at home because of their natural curiosity. For this reason, parents' inability to pay enough attention to their toddlers while doing their daily work can cause irreversible accidents. On account of all these situations, it is clear that there is a need to develop a toddler tracking system. In this article, a toddler tracking system based on deep neural network has been proposed. For experimental studies, videos in 10 different scenarios were collected from different toddlers aged between 1.5 and 4. The system includes FaceNet for face recognition and YOLOv3 for object tracking. First, desired toddler's face is recognized, and the system tracks the movements of that toddler. If the toddler gets close to dangerous objects in the house, the parents will be warned by the system. As a result of experimental studies, the system accuracy rate was increased from 80.7% to 93.3% with the changes in the previous system.
Identification of Colorectal Cancer in pathological images Using CNN Algorithm

With the advent of machine learning-based approaches, computer-aided diagnosis (CAD) relying on histopathological imaging has advanced fast in several recent years. Traditionally, features derived from images, such as textures or morphological characteristic properties, are used to train a classifier. As of late, profound deep‐‐learning-based techniques have been applied straightforwardly to the crude (natural) information. In any case, their convenience is affected by the scarcity of explained information in the biomedical area. To use the learning abilities of profound deep Convolutional Neural Networks (CNNs) inside the bounds of restricted named information, the proposed approach examines the exchange learning approaches that intend to apply the information acquired from addressing a source (e.g., non-clinical) issue, to learn better prescient models for the objective (e.g., biomedical) task. As another option, another versatile and minimal CNN-based engineering architecture that can be prepared without any preparation even on scant and low-goal information is proposed. In addition, quantitative relative assessments are led among the conventional strategies, move learning-based techniques, and the proposed versatile methodology for the specific errand of disease recognition and recognizable proof from scant and low-goal histology pictures. The major difference between deep CNN and other conventional CNN is stratified patch convolution techniques are used which reduces the overall cost all well can obtain features even from the abstract segment also. Preposterous benchmark dataset framed, for this reason, the proposed versatile methodology accomplished a higher disease recognition exactness with a critical hole, though the deep profound CNNs with move learning accomplished a prevalent malignancy(cancer) recognizable proof.
Classical Analog of Quantum Light Cones in 1D and 2D Dipole Systems

Abstract The speed at which the magnetic perturbation or information propagates along a chain of classical dipoles is discussed. While in the quantum information counterpart for long‐range interacting spins, where the speed of propagation of the information plays a paramount role, it is not strictly clear whether a light cone exists or not, numerical evidence that interacting dipoles do posses a linear light cone shortly after a perturbation takes place in classical systems is provided. Specifically, a power‐law expansion occurs which is followed by a linear propagation of the associated perturbation. As opposed to the quantum case, and in analogy with the so‐called speed of gravity problem, it is found that the speed of propagation of information can be arbitrarily large in the classical context. While it is true that quantum information outperforms any classical treatment regarding many processes and properties, it is shown that for the case of spin buffers, classical chains of dipoles can indeed play a role as auxiliary tools in both quantum communications and processing.