Reviewerly/exHarmony huggingface dataset:
Attention-based Local Mean <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1490" altimg="si25.svg"><mml:mi>K</mml:mi></mml:math>-Nearest Centroid Neighbor Classifier

Among classic data mining algorithms, the K-Nearest Neighbor (KNN)-based methods are effective and straightforward solutions for the classification tasks. However, most KNN-based methods do not fully consider the impact across different training samples in classification tasks, which leads to performance decline. To address this issue, we propose a method named Attention-based Local Mean K-Nearest Centroid Neighbor Classifier (ALMKNCN), bridging the nearest centroid neighbor computing with the attention mechanism, which fully considers the influence of each training query sample. Specifically, we first calculate the local centroids of each class with the given query pattern. Then, our ALMKNCN introduces the attention mechanism to calculate the weight of pseudo-distance between the test sample to each class centroid. Finally, based on attention coefficient, the distances between the query sample and local mean vectors are weighted to predict the classes for query samples. Extensive experiments are carried out on real data sets and synthetic data sets by comparing ALMKNCN with the state-of-art KNN-based methods. The experimental results demonstrate that our proposed ALMKNCN outperforms the compared methods with large margins.
Siamese coding network and pair similarity prediction for near-duplicate image detection

Abstract Near-duplicate detection in a dataset involves finding the elements that are closest to a new query element according to a given similarity function and proximity threshold. The brute force approach is very computationally intensive as it evaluates the similarity between the queried item and all items in the dataset. The potential application domain is an image sharing website that checks for plagiarism or piracy every time a new image is uploaded. Among the various approaches, near-duplicate detection was effectively addressed by SimPair LSH (Fisichella et al., in Decker, Lhotská, Link, Spies, Wagner (eds) Database and expert systems applications, Springer, 2014). As the name suggests, SimPair LSH uses locality sensitive hashing (LSH) and computes and stores in advance a small set of near-duplicate pairs present in the dataset and uses them to reduce the candidate set returned for a given query using the Triangle inequality. We develop an algorithm that predicts how the candidate set will be reduced. We also develop a new efficient method for near-duplicate image detection using a deep Siamese coding neural network that is able to extract effective features from images useful for building LSH indices. Extensive experiments on two benchmark datasets confirm the effectiveness of our deep Siamese coding network and prediction algorithm.
Spatially adaptive multi-scale contextual attention for image inpainting

Abstract Image inpainting is the task to fill missing regions of an image. Recently, researchers have achieved a great performance by using convolutional neural networks (CNNs) with the conventional patch-matching method. Existing methods compute the attention scores, which are based on the similarity of patches between the known and missing regions. Considering that patches at different spatial positions can convey different levels of detail, we propose a spatially adaptive multi-scale attention score that uses the patches of different scales to compute scores for each pixel at different positions. Through experiments on the Paris Street View and Places datasets, our proposal shows slight improvement compared with some related methods on the quantitative evaluation metrics commonly used in the existing methods. Moreover, we found that these quantitative metrics are not appropriate enough considering the subjective impressions of the generated images. Therefore, we conducted subjective evaluation through user study for comparison, which shows that our proposal has superiority of performance generating much more detailed and subjectively plausible images.
Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.
Self-supervised Short-text Modeling through Auxiliary Context Generation

Short text is ambiguous and often relies predominantly on the domain and context at hand in order to attain semantic relevance. Existing classification models perform poorly on short text due to data sparsity and inadequate context. Auxiliary context, which can often provide sufficient background regarding the domain, is typically available in several application scenarios. While some of the existing works aim to leverage real-world knowledge to enhance short-text representations, they fail to place appropriate emphasis on the auxiliary context. Such models do not harness the full potential of the available context in auxiliary sources. To address this challenge, we reformulate short-text classification as a dual channel self-supervised learning problem (that leverages auxiliary context) with a generation network and a corresponding prediction model. We propose a self-supervised framework, Pseudo-Auxiliary Context generation network for Short-text Modeling (PACS) , to comprehensively leverage auxiliary context and it is jointly learned with a prediction network in an end-to-end manner. Our PACS model consists of two sub-networks: a Context Generation Network (CGN) that models the auxiliary context’s distribution and a Prediction Network (PN) to map the short-text features and auxiliary context distribution to the final class label. Our experimental results on diverse datasets demonstrate that PACS outperforms formidable state-of-the-art baselines. We also demonstrate the performance of our model on cold-start scenarios (where contextual information is non-existent) during prediction. Furthermore, we perform interpretability and ablation studies to analyze various representational features captured by our model and the individual contribution of its modules to the overall performance of PACS, respectively.
“Thank you for reaching out:” Brand relationship management and the conversational human voice of customer care in online service encounters

Motivated by increased calls for research on language use in service encounters, this study examines how customer care agents working in the retail banking sector linguistically operationalize a conversational human voice in their online service encounters to support brand-customer relationships on social media. Applying a rhetorical move analysis framework to an extensive corpus of customer care agents’ responses to customers’ queries, comments, and complaints on Facebook, this study describes the discourse structure of these responses to identify the conventionalized moves performing the relational work in these brief online encounters. By examining the linguistic features that characterize these moves, my findings confirm that the linguistic elements supporting message personalization, positive politeness strategies, and invitational rhetoric collectively create the recognizable conversational voice that supports agents’ efforts to initiate and manage brand-customer relationships online. My findings also highlight the role of digital punctuation and emoji usage to support positive politeness strategies and to communicate agents’ attitudinal stance in their efforts to deliver service with a smile in an online environment. Finally, this study calls attention to and provides insights into language usage, online social interaction, and webcare discourse within one facet of the banking services industry.
A novel forget-update module for few-shot domain generalization

Existing Few-Shot Learning (FSL) methods learn and recognize new classes with the help of prior knowledge. However, they cannot handle this task well in a cross-domain scenario when training and testing sets are from different domains, since the fact that prior knowledge in different domains often varies greatly. To solve this problem, in this paper, we propose a few-shot domain generalization method, which is designed to extract relationship embeddings using Forget-Update Modules named FUM. The relationship embedding considers valuable relational information between samples in a specific task, and the forget-update module takes into account differences between domains and adjusts the distribution of relational embeddings through forgetting and updating mechanisms based on specific tasks. To evaluate the few-shot domain generalization ability of FUM, extensive experiments on eight cross-domain scenarios and six same-domain scenarios are conducted, and the results show that FUM achieves superior performances compared to recent few-shot learning methods. Visualization results also show that the distribution of the relationship embeddings extracted by FUM has stronger few-shot domain generalization ability than the feature embeddings used in the existing FSL methods.
IoT Based Low-Cost Robotic Agent Design for Covid-19 affected people

The communication field is heading towards a distant Internet of Things (IoT) that will more precisely follow the connections and quickly oversee things. Disabled and virus-affected people can be helped by these innovations with the help of a network of variable and mechanical frames. At the moment, the whole world is covered by the Covid19 pandemic. Infections are severe, and sick and old people are invulnerable. Moreover, the royal figures, logic specialists, and personalities are afraid of this contagious infection. This proposed artistic creation is an IoT-based automated system that helps to be prepared to aid disabled people with this charging system. This designed robot is specialized and can be equipped to understand the gestures of a person and study the commands with 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> movement without photo manipulation. The device is equipped with gyroscope acceleration sensors MPU 6050 for gesture recognition. The verbal radiofrequency exchange has been used to create the framework.
Unsupervised Multimodal Anomaly Detection With Missing Sources for Liquid Rocket Engine

To achieve reliable and automatic anomaly detection (AD) for large equipment such as liquid rocket engine (LRE), multisource data are commonly manipulated in deep learning pipelines. However, current AD methods mainly aim at single source or single modality, whereas existing multimodal methods cannot effectively cope with a common issue, modality incompleteness. To this end, we propose an unsupervised multimodal method for AD with missing sources in LRE system. The proposed method handles intramodality fusion, intermodality fusion, and decision fusion in a unified framework composed of multiple deep autoencoders (AEs) and a skip-connected AE. Specifically, the first module restores missing sources to construct a complete modality, thus advancing the secondary reconstruction. Different from vanilla reconstruction-based methods, the proposed method minimizes reconstruction loss and meanwhile maximizes the dissimilarity of representations in two latent spaces. Utilizing reconstruction errors and latent representation discrepancy, the anomaly score is acquired. At decision level, the model performance can be further enhanced via anomaly score fusion. To demonstrate the effectiveness, extensive experiments are carried out on multivariate time-series data from static ignition of several LREs. The results indicate the superiority and potential of the proposed method for AD with missing sources for LRE.
Quantum Version of the k‐NN Classifier Based on a Quantum Sorting Algorithm

Abstract In this work a quantum sorting algorithm with adaptable requirements of memory and circuit depth is introduced, and is used to develop a new quantum version of the classical machine learning algorithm known as k‐nearest neighbors (k‐NN). Both the efficiency and performance of this new quantum version of the k‐NN algorithm are compared to those of the classical k‐NN and another quantum version proposed by Schuld et al. Results show that the efficiency of both quantum algorithms is similar to each other and superior to that of the classical algorithm. On the other hand, the performance of the proposed quantum k‐NN algorithm is superior to the one proposed by Schuld et al. and similar to that of the classical k‐NN.
Privacy-preserving mimic models for clinical named entity recognition in French

A vast amount of crucial information about patients resides solely in unstructured clinical narrative notes. There has been a growing interest in clinical Named Entity Recognition (NER) task using deep learning models. Such approaches require sufficient annotated data. However, there is little publicly available annotated corpora in the medical field due to the sensitive nature of the clinical text. In this paper, we tackle this problem by building privacy-preserving shareable models for French clinical Named Entity Recognition using the mimic learning approach to enable the knowledge transfer through a teacher model trained on a private corpus to a student model. This student model could be publicly shared without any access to the original sensitive data. We evaluated three privacy-preserving models using three medical corpora and compared the performance of our models to those of baseline models such as dictionary-based models. An overall macro F-measure of 70.6% could be achieved by a student model trained using silver annotations produced by the teacher model, compared to 85.7% for the original private teacher model. Our results revealed that these privacy-preserving mimic learning models offer a good compromise between performance and data privacy preservation.
Development of a new Light-Weight Convolutional Neural Network for acoustic-based amateur drone detection

In recent years, interest in autonomous Unmanned Aerial Vehicles (UAVs) has increased, especially due to their use in different application areas, together with their ability to operate autonomously. The widespread use of UAVs also brings serious security threats. The prevention should be taken for early detection of UAVs-based threats, especially in military areas and governmental buildings, where high-level security is required. In this study, a sound processing algorithm with a new Light-Weight Convolutional Neural Network (LWCNN) model is proposed for the detection of UAVs, by distinguishing them from air noises, animal noises, and vehicle noises in environments with high spatial noise density. The most important advantages of the proposed model are its low complexity and a small number of parameters, as well as its high classification performance. The Mel Frequency Spectrum coefficients are applied to extract the sound features required to distinguish the sounds of UAVs. The features obtained from the proposed LWCNN model are used as input to the Support Vector Machines (SVM), where different objects are classified. With the proposed method, it is possible to detect UAVs and distinguish them from different sounds such as birds, airplanes, helicopters, background noises, and storms. Experimental results show that the proposed methodology obtains better results than other methods proposed in the literature.
The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

Text Classification (TC) is the process of assigning several different categories to a set of texts. This study aims to evaluate the state of the arts of TC studies. Firstly, TC-related publications indexed in Web of Science were selected as data. In total, 3,121 TC-related publications were published in 760 journals between 2000 and 2020. Then, the bibliographic information was mined to identify the publication trends, important contributors, publication venues, and involved disciplines. Besides, a thematic analysis was performed to extract topics with increasing/decreasing popularity. The findings showed that TC has become a fast-growing interdisciplinary area, and that emerging research powers such as China are playing increasingly important roles in TC research. Moreover, the thematic analysis showed increased interest in topics concerning advanced classification algorithms, performance evaluation methods, and the practical applications of TC. This study will help researchers recognize the recent trends in the area.
Reducing the number of centers in a probabilistic neural network via applying the first neighbor means clustering algorithm

Probabilistic neural network is a variant of feedforward neural network models and has been successfully applied for various pattern classification purposes. Unlike other feedforward neural network models, probabilistic neural network, a type of radial basis function network models, has essentially only two types of the network-parameters to choose in advance, i.e. the locations of the centers and a single value of radius; a central issue relevant to the application of probabilistic neural network is therefore to determine an appropriate number of the centers accommodated within the network. In the original probabilistic neural network framework, all the training data are allocated to the respective centroid vectors, and thus the network size generally tends to be large, resulting in demanding computational resource. To alleviate this problem, clustering algorithms are commonly employed to shrink the size of the training data. In this work, reduction in the number of centers in a probabilistic neural network is addressed, via the utility of first neighbor means clustering algorithm that is non-iterative and requires only a single algorithmic hyper-parameter; such a choice is desirable in practice. Simulation results using seven publicly available databases for pattern classification tasks show that the first neighbor means clustering algorithm can yield a relatively compact-sized network within short computation time, while exhibiting a reasonably high classification performance, in comparison with communication with local agents, k-means, orthogonal least squares, resource allocating network, and resource vector machine algorithms.
A Survey of Modern Deep Learning based Generative Adversarial Networks (GANs)

GANs (Generative Adversarial Networks) are a type of deep learning generative model that has lately gained popularity in recent years. GANs can learn patterns from high-dimensional complex data, making them useful for image, audio and video processing. Nonetheless, there are several significant obstacles in the training of GANs, such as instability, mode collapse and non-convergence. To address these issues, researchers have developed a variety of GAN variations by rethinking network topology, modifying the form of goal functions, and changing optimization to precise methods in recent years. This paper describes a thorough analysis of the progress of GAN architecture and optimization solutions to improve its efficiency in various computer vision applications and challenges that are to be faced while implementing the model towards CV (computer vision) is described. It is believed that GAN is strong model and further researches are needed to work in this area to solve a variety of computer vision real time applications.
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR

We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is space-free without any external devices' constraints and map-free without pre-built maps. Considering that IMUs can capture human poses but always drift for long-period use, while LiDAR is stable for global localization but rough for local positions and orientations, HSC4D makes both sensors complement each other by a joint optimization and achieves promising results for long-term capture. Relationships between humans and environments are also explored to make their interaction more realistic. To facilitate many down-stream tasks, like AR, VR, robots, autonomous driving, etc., we propose a dataset containing three large scenes (1k-5k m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) with accurate dynamic human motions and locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.) and challenging human activities (exercising, walking up/down stairs, climbing, etc.) demonstrate the effectiveness and the generalization ability of HSC4D. The dataset and code is available at lidarhumanmotion.net/hsc4d.
Machine Learning vs. Survival Analysis Models: a study on right censored heart failure data

Machine Learning Models are known to understand the intricacies of the data well, but native ML models cannot be used in time-to-event analysis due to censoring. In this paper, we explore the use of Machine Learning Models in the field of Survival Analysis using right censored Heart Failure Clinical Records Dataset. For this purpose, we first identify the top most important features responsible for death due to heart failure using Recursive Feature Elimination and then see how Machine Learning models can be adapted to improve the time-to-event analysis outcomes. To deal with this, Machine Learning Models are modified using the techniques Inverse Probability of Censoring Weighting (IPCW) and IPCW Bagging and are trained using the processed dataset alongside various survival analysis models. Area Under the time-dependent ROC (AUC) is used as a performance metric. The results reveal that the average AUC value for Survival Analysis Models is 0.51 while that of Machine Learning Models processed using IPCW increased to 0.80, and those processed using IPCW Bagging increased by 0.82. This reflects that Machine Learning models outperform Survival Analysis models in the case of time-to-event analysis of right censored dataset, and hence, are better indicators of risk of heart disease.