Reviewerly/exHarmony huggingface dataset:
Hierarchical lifelong topic modeling using rules extracted from network communities

Topic models extract latent concepts from texts in the form of topics. Lifelong topic models extend topic models by learning topics continuously based on accumulated knowledge from the past which is updated continuously as new information becomes available. Hierarchical topic modeling extends topic modeling by extracting topics and organizing them into a hierarchical structure. In this study, we combine the two and introduce hierarchical lifelong topic models. Hierarchical lifelong topic models not only allow to examine the topics at different levels of granularity but also allows to continuously adjust the granularity of the topics as more information becomes available. A fundamental issue in hierarchical lifelong topic modeling is the extraction of rules that are used to preserve the hierarchical structural information among the rules and will continuously update based on new information. To address this issue, we introduce a network communities based rule mining approach for hierarchical lifelong topic models (NHLTM). The proposed approach extracts hierarchical structural information among the rules by representing textual documents as graphs and analyzing the underlying communities in the graph. Experimental results indicate improvement of the hierarchical topic structures in terms of topic coherence that increases from general to specific topics.
MMPDNet: Multi-Stage &amp; Multi-Attention Progressive Image Denoising

Deep convolutional neural networks (CNNs) have gained great success in many low-level tasks of computer vision, especially in the task of image denoising. However, there are two major drawbacks for denoising tasks, one is that it is very hard to choose a strategy to make deeper CNN convergence, other is that mainstream approaches do not do well a complex balance between spatial details and high-level features information while reconstructing clean images. In this paper, we proposed a novel multi-stage and multi-attention architecture of CNN for image denoising. In detail, our model is a multi-stage network, in each stage, we separate degraded inputs into different patches to learn the reconstruct mapping, thus, the overall denoising process is divided into several easily convergence steps. Meanwhile, a channel-spatial attention module is proposed to learn high-level features from different dimensions. Furthermore, an effectively supervised module is employed between every two-stage to refine the reconstructed results. The resulting multi-stage and multi-attention architecture, named MMPDNet, and its extensive experiment results deliver strong performance gains on some primary real-word denoising datasets, including SIDD and DND.
Research on Optimizer Algorithm of Sign Language Recognition Model

One of the most well-known tools for sign language recognition is neural network. There are many optimizer algorithms to improve the performance of the network. But previous studies of optimizer algorithms have not dealt with its convergence stability for sign language recognition. Therefore, we propose an optimizer algorithm for sign language recognition model, which has a good optimization effect on sign language recognition model based on LSTM neural network. We designed a sign language recognition model based on LSTM neural network, and carried out the experiment in the open dataset of SLR of University of science and technology of China. In the experiment, we added the classical optimizer algorithm for comparison. The experimental results show that. This paper proposes that the optimizer algorithm has better convergence stability than the classical algorithm, and has good adaptability to different input data.
A low-power HAR method for fall and high-intensity ADLs identification using wrist-worn accelerometer devices

Abstract There are many real-world applications like healthcare systems, job monitoring, well-being and personal fitness tracking, monitoring of elderly and frail people, assessment of rehabilitation and follow-up treatments, affording Fall Detection (FD) and ADL (Activity of Daily Living) identification, separately or even at a time. However, the two main drawbacks of these solutions are that most of the times, the devices deployed are obtrusive (devices worn on not quite common parts of the body like neck, waist and ankle) and the poor battery life. Thus, this work proposes a low-power classification algorithm based on an Ensemble of KNN and K-Means algorithms (EKMeans) to identify Falls and High-Intensity ADL events such as running, jogging and climbing up stairs. The input of EKMeans are triaxial accelerometer data gathered from wrist-wearable devices. The proposal will be validated on the Fall&amp;ADL publicly available datasets UMAFall, UCIFall and FallAllD, considering two kinds of activity labelling: Two-Class and Multi-Class. An exhaustive comparative study between our proposal, and the baseline algorithms KNN and a feed-forward Neural Network (NN) is deployed, where EKMeans outperformed clearly the Specificity (ADL classification) of the KNN and NN for the three datasets. Finally, a comparative battery consumption study has been included deploying the analyzed algorithms in a WearOS smartwatch, where EKMeans drains the battery from 100% to 0% in 27.45 hours, saving 5% and 21% concerning KNN and NN, respectively. Keywords: Human Activity Recognition, ADL Identification, Fall Detection TS Clustering, TS Classification, Wearable Devices, Low-Power HAR.
SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks

This paper explores the use of multi-conditional adversarial networks for SAR-to-EO image translation. Previous methods condition adversarial networks only on the input SAR. We show that by incorporating multiple complementary modalities such as Google maps and IR can further improve SAR-to-EO image translation especially on preserving sharp edges of manmade objects. We demonstrate effectiveness of our approach on a diverse set of datasets including SEN12MS, DFC2020, and SpaceNet6. Our experimental results suggest that additional information provided by complementary modalities improves the performance of SAR-to-EO image translation compared to the models trained on paired SAR and EO data only. To best of our knowledge, our approach is the first to leverage multiple modalities for improving SAR-to-EO image translation performance.
Container ISO Code Recognition System Using Multiple View Based on Google LSTM Tesseract

Optical Character Recognition (OCR) system is vastly used to identify license plates, street signs, and other applications. However, it faces difficulties to recognize ISO code from natural images of shipping containers due to rough weather condition, varying color, illumination, etc. In this paper, these challenges were overcome by integrating deep learning-based OCR recognition from multiple view which increases both accuracy and reliability. Images are taken from three different views and based on the proposed algorithm it analyses all the images to detect ISO code format using sequence matching algorithm. Next, confidence level is calculated for each recognized code using Google LSTM neural-net based Tesseract engine model and identifies the one which has highest confidence level for the proposed OCR system to be robust in delivering high level accuracy in actual application.
A White-Box Generator Membership Inference Attack Against Generative Models

Using generative models to generate unlimited number of synthetic samples is a popular replacement of database sharing. When these models are built using sensitive data, the developers should ensure that the training dataset is appropriately protected. Hence, quantifying the privacy risk of these models is important. In this paper, we focus on evaluating privacy risk of publishing generator in generative adversarial network (GAN) models. Specially, we conduct a white box membership inference attack against GAN models. The proposed attack is applicable to various kinds of GANs. We evaluate our attack accuracy with respect to various model types and training configurations. The results demonstrate superior performance of the proposed attack compared to previous attacks in white box generator access.
Spatiotemporal Anomaly Detection for Large-Scale Sensor Data

In the Internet of Things era, thousands of sensors are used to monitor machine health conditions in modern factories. Such sensing data are often analyzed to detect machinery failures and diagnose the suspicious causes to prevent loss. However, failures rarely happen and are highly diverse in the real world, which brings difficulties in collecting abnormal data for supervised models. Furthermore, large numbers of sensors would lead to the problem of high dimensionality that would decrease the accuracy of anomaly detection. To address these issues, this paper proposes the COrrelation-based SpatioTemporal Anomaly Detection (COSTAD) framework to analyze large-scale sensing data. Temporal anomalies are detected in an unsupervised manner using only normal data. Spatial anomalies are analyzed while reducing the dimensions of input features for machine learning models. Our experimental results indicate that the temporal detection approach outperforms some existing methods, and spatial detection can help analyze the suspicious causes of machinery failures.
Shaping Resilient AI Hardware Through DNN Computational Feature Exploitation

This article presents a method that allows deep neural networks (DNNs) to learn and construct self-checking mechanisms so as to detect and suppress large magnitude hardware errors. —Fei Su, Intel Corporation
CleverCart: An Eco-Friendly Shopping Self-Checkout System with IoT sensor

Abstract CleverCart is an automated shopping cart that uses IoT to connect to users’ phone and act as a personal cashier for customers that are shopping in a grocery store. The CleverCart shopping cart is able to connect to the phone via the CleverCart shopping app. The CleverCart mobile application would be able to connect with the cart by scanning the QR code of the cart once connected, the users are able to scan items that they want to purchase and can proceed to put it in the cart, the weighing sensors installed within the cart will cross check whether the weight of the product scanned and the weight of the product added in the cart should be the same. The app also has features such as “shopping list” that reminds users what items are needed to be purchase and once the customers are finished, they can simply choose an electronic payment method to pay. The purpose behind CleverCart is to minimize human interaction in grocery stores and to speed up payments.
A Comparative Study of Image Enhancement Methods for Human Skin Image

A person's skin serves as a primary line of protection against harmful chemical exposure. During the Covid 19 out-break, customer-provider interactions on social media increased, leading to improvements of the intelligent system for accurate skin type analysis. However, optimizing image quality before further analysis is an important step for training and testing data. As a result, image enhancement technologies contribute to the improvement of image quality. In this paper, we presets a study of four image enhancement techniques for improving the image contrast and detail of facial skin images required for further skincare analysis and treatment. The techniques of image enhancement include Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), and Min-Max Contrast Stretching. The experimental results demonstrate that the CLAHE technique delivers the highest quality of clarity and also facilitates further image processing.
An Automated Multi-scale Retinex for Dim Image Enhancement

Multi-scale Retinex with Color Restoration (MSRCR) suffers from problems such as noise amplified in the original images, especially dim and dark images, and the hue of the output images becoming greyish. Firstly, we take Singular Value Decomposition (SVD) to perform initial noise reduction to solve the problems. Secondly, images convert from RGB to HSV color space model that is more suitable for human vision, and then luminance and saturation are enhanced by automated MSRCR and Gamma stretching, respectively. Besides, to further denoise, enhanced parts are re-combined into RGB color space, and Guided Filtering denoises them once again. Experiments indicate that compared with MSRCR, our method’s denoising effect, color restoration, and image clarity improve by 2.3%, 11%, and 83.3%, respectively. Not only is our method objectively better in terms of evaluation metrics, but also subjectively, it is observed that the images processed by our method are more appropriate to the human visual system. What is more, our processing time is the most optimal among all comparison algorithms.
An Interpretable Monitoring Framework for Virtual Physics-Based Non-Interfering Robot Social Planning

A majority of collision avoidance and motion planning algorithms deployed on autonomous mobile robots tend to be reactive to the presence and motion of nearby dynamic actors. While these algorithms can produce collision free navigation, they do not necessarily follow social protocol and exhibit unnatural motions that force actors to behave very differently from their planned paths. As humans, we can reason about why and how we might <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interfere</i> with others and use this reasoning to alter our motion proactively. We also adapt our motion based on different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priorities</i> , which affect how we accommodate and interact with each other. In this letter, we propose an approach for a mobile robot to generate similar non-interfering and priority-based behaviors that pertain to how the robot accommodates dynamic actors. We augment a very efficient but reactive virtual physics-based planner with Hidden Markov Models and a Decision Tree-based monitor that: i) predicts if the robot will interfere with a nearby actor, ii) explains what factors cause the prediction, iii) plans priority-based corrective actions, and iv) updates the prediction model at runtime to improve and refine robot behaviors. Our approach is validated with simulations and extensive experiments on ground and aerial vehicles in the presence of dynamic actors.
A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman Filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman Filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle Filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the Smooth Variable Structure Filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of Smooth Variable Structure Filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the Smooth Variable Structure Filter approach compared to other filters.
Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation

Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. However, these algorithms will be infeasible when only a few labeled data exist in the source domain, thus the performance decreases significantly. To address this challenge, we propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples. Firstly, DGL introduces the Nyström method to construct a plastic graph that shares similar geometric property with the target domain. Then, DGL flexibly employs the Nyström approximation error to measure the divergence between the plastic graph and source graph to formalize the distribution mismatch from the geometric perspective. Through minimizing the approximation error, DGL learns a domain-invariant geometric graph to bridge the source and target domains. Finally, we integrate the learned domain-invariant graph with the semi-supervised learning and further propose an adaptive semi-supervised model to handle the cross-domain problems. The results of extensive experiments on popular datasets verify the superiority of DGL, especially when only a few labeled source samples are available.
Blockchain: A Compendium on Contemporary Privacy Preservation Approaches and Its Manifestation in Varied Realms

Blockchain is a distributed ledger based technology that was incipient as an elementary base of primary decentralized crypto currency namely, Bitcoin. It piles and maintains records without any centralized authority or trusted third party. The decentralized, immutable, transparent characteristics of this breakthrough innovation gained wide acceptance rapidly, since its inception in 2009 and are now applied in a variety of use cases. But still, blockchain faces issues with respect to privacy, security, and scalability that hinder its practical applications. This paper reviews the challenges, privacy threats and forefront privacy-preserving mechanisms in blockchain based systems and also makes a study of various privacy-preserving mechanisms as applied to various realms of blockchain namely IoT, VANET, smart agriculture, etc.
On the Newton polygons of twisted L-functions of binomials

Let χ be an order c multiplicative character of a finite field and f(x)=xd+λxe a binomial with (d,e)=1. We study the twisted classical and T-adic Newton polygons of f. When p>(d−e)(2d−1), we give a lower bound of Newton polygons and show that they coincide if p does not divide a certain integral constant depending on pmodcd. We conjecture that this condition holds if p is large enough with respect to c,d by combining all known results and the conjecture given by Zhang-Niu. As an example, we show that it holds for e=d−1.
Solar Radiation and Wind Speed Forecasting using Deep Learning Technique

Solar radiation and wind speed are the fundamental parameters for the design and operations of solar and wind energy systems. Renewable energy sources (RESs) are intermittent and dependent on different atmospheric parameters. Therefore, it is crucial to accurately forecast RESs, such as solar radiation and wind speed. In this study, a deep learning-based random forest technique is proposed to predict solar radiation and wind speed. A novel coot algorithm (CA) is suggested to optimize the number of decision trees of the random forest model, and the performance of the CA is compared with the existing particle swarm optimization (PSO) technique. The results show that the performance of CA is better than PSO.
Dealing With Hierarchical Types and Label Noise in Fine-Grained Entity Typing

Fine-Grained entity typing is complicated by the fact that type labels form a hierarchical structure, and those training examples usually contain noisy type labels. This paper addresses these two issues by proposing a novel framework that simultaneously models the correlation among hierarchical types and the noise within the training data. Additionally, the framework contains an innovative training approach during which the noise in the training data is progressively removed. Experiments on standard benchmarking datasets validate the proposed framework and establish it as a new state of the art for this problem.
External validation of a deep learning prediction model for in-hospital mortality among ICU patients

With increasing hospital adoption of electronic health record (EHR) systems worldwide, a massive amount of EHR data are generated in intensive care practice, and deep learning models are increasingly applied in mortality prediction. However, due to the lack of external validation, it’s difficult to generalize the deep learning models in critical care settings. Our previous work proposed a routinely collected data based deep learning model for intensive care unit (ICU) mortality prediction. This study aimed to externally validate the model using a cohort from the published MIMIC III data set so as to examine the generalizability and feasibility of the model. With little changed in the modeling, the deep learning based model achieved a high accuracy (AUROC=0.90; AUPRC=0.70), and good calibration properties which was reflected by a brier score of 0.070, in the external validation database. The model’ excellent performance in our external validation cohort provides more evidence for the application of the deep learning model in clinical practice.
SNR-Boosted Automatic Modulation Classification

Automatic modulation classification is a desired feature in many modern software-defined radios; however, classification performance degrades with decreasing signal to noise ratios. We propose employing a deep convolutional signal to noise ratio estimation model to exploit relationships within signals of similar signal to noise ratio ranges through signal to noise ratio specific modulation classifiers. We utilize a two-stage process where the signal to noise ratio is first estimated and then demultiplexed into a modulation classifier that has been tuned on signals with similar signal to noise ratios. Using the proposed method, we build upon the current state-of-the-art and increase classification performance at decreasing signal to noise ratios.