Reviewerly/exHarmony huggingface dataset:
<i>GNNLens</i>: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNLens, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.
Graph-ICF: Item-based collaborative filtering based on graph neural network

Item-based collaborative filtering (ICF) has been widely used in industrial applications due to its good interpretability and flexible composability. The main idea of ICF is to characterize users’ preferences and recommend similar items based on the items interacted by users in the history logs. As such, the key problem for ICF approaches is how to learn item–item similarity effectively. Existing ICF approaches in general obtain an item’s feature by mapping from the embedding matrix, where only the shallow relation between the target item and the user’s historical interactive items can be captured, while the deeper relations conveyed by other users similar to the current user are neglected. To tackle this problem, we propose a graph-based ICF method (Graph-ICF), which takes advantage of the information aggregation and propagation properties of graph structure to dig out the deeper item relations that have been overlooked by existing ICF models. Moreover, existing ICF models only utilize item-level attention to discriminate which item–item similarities are important for a prediction, without considering user’s personalized favor of interacting with an item. To address this limitation, we further propose a feature level attention module in Graph-ICF to distinguish which feature dimensions better reflect the user’s preferences, and thus can yield more personalized recommendation. We conduct extensive experiments on three public benchmarks, demonstrating the superior performance of Graph-ICF over several state-of-the-art ICF models and graph-based CF methods.
LiDAR-based detection, tracking, and property estimation: A contemporary review

Object detection, Person tracking, and Person property estimation (PPE) are identical innovation areas trying to improve their accuracy in different parameters to fit various real applications. For many years, so much research has been done in these fields. Many scientists also used many more techniques and algorithms. But most of the innovations were deeply based on image-based analysis, where cameras were the critical components of data acquisition. Over the years, new technologies arrived, and different types of research are happening. Rather than cameras, some other sensors, like infrared, depth cameras, and very recently LiDAR sensors, are used to estimate person properties, track them, as well as to detect them. Especially, height, age, gender, region, etc., parameters can be measured as person property. Eventually, 3D object detection by LiDAR will be a state-of-the-art research field with the advent of autonomous driving initiations. We studied many articles and found enthusiastic outcomes with these sensor setups to understand contemporary technology and its efficacy. We categorized these research articles into video camera-based studies and other sensor-based studies. So many surveys have been done on video-based analysis, even with deep learning techniques. Another sensor-based research is very recent, and we do not get enough study on it. We thought to summarize these studies in a survey article, especially LiDAR-based analysis. This article covered most of the recent possible sensor-based studies of detection, person tracking and property estimation except cameras (all, RGB, RGB-D, etc.) based learning.
Research on Pedestrian Detection and DeepSort Tracking in Front of Intelligent Vehicle Based on Deep Learning

In order to improve the tracking failure caused by small-target pedestrians and partially blocked pedestrians in dense crowds in complex environments, a pedestrian target detection and tracking method for an intelligent vehicle was proposed based on deep learning. On the basis of the YOLO detection model, the channel attention module and spatial attention module were introduced and were joined to the back of the backbone network Darknet-53 in order to achieve weight amplification of important feature information in channel and space dimensions and improve the representation ability of the model for important feature information. Based on the improved YOLO network, the flow of the DeepSort pedestrian tracking method was designed and the Kalman filter algorithm was used to estimate the pedestrian motion state. The Mahalanobis distance and apparent feature were used to calculate the similarity between the detection frame and the predicted pedestrian trajectory; the Hungarian algorithm was used to achieve the optimal matching of pedestrian targets. Finally, the improved YOLO pedestrian detection model and the DeepSort pedestrian tracking method were verified in the same experimental environment. The verification results showed that the improved model can improve the detection accuracy of small-target pedestrians, effectively deal with the problem of target occlusion, reduce the rate of missed detection and false detection of pedestrian targets, and improve the tracking failure caused by occlusion.
A Weighted Average Consensus Approach for Decentralized Federated Learning

Federated learning (FedL) is a machine learning (ML) technique utilized to train deep neural networks (DeepNNs) in a distributed way without the need to share data among the federated training clients. FedL was proposed for edge computing and Internet of things (IoT) tasks in which a centralized server was responsible for coordinating and governing the training process. To remove the design limitation implied by the centralized entity, this work proposes two different solutions to decentralize existing FedL algorithms, enabling the application of FedL on networks with arbitrary communication topologies, and thus extending the domain of application of FedL to more complex scenarios and new tasks. Of the two proposed algorithms, one, called FedLCon, is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions, as also shown by the reported validation tests.
Adaptive multistrategy ensemble particle swarm optimization with Signal-to-Noise ratio distance metric

This paper proposes an adaptive multistrategy ensemble particle swarm optimization (PSO) with signal-to-noise ratio (SNR) distance metric called AMSEPSO, which aims to solve the problems of a single learning mode of PSO and easy premature convergence when solving complex problems. In AMSEPSO, an evolutionary state estimation (ESE) strategy selection framework is proposed based on the SNR distance metric, named ESE-SNR. The appropriate learning strategy is adaptively selected through the ESE-SNR framework. To balances diversity and convergence better, nonlinear acceleration coefficient based on Singer mapping is adopted. Finally, a global best perturbation mechanism is employed to help the population escape from the local optimum. On the CEC2017 benchmarks, comparison with other advanced PSO variants and meta-heuristic algorithms show that AMSEPSO achieves remarkable performance in solving functions with different characteristics, ranking first in the results. The results show that the ESE-SNR framework can effectively evaluate the search state of the population and can greatly save the computational time of the evaluation. The ESE-SNR framework proposed in this paper provides an innovative idea for the development of multistrategy ensemble learning, and the introduction of metric learning into the PSO community helps further promote the organic integration of machine learning and swarm intelligence.
3LPR: A three-stage label propagation and reassignment framework for class-imbalanced semi-supervised learning

Semi-supervised learning (SSL) has been studied widely in standard benchmark datasets; however, real-world data often exhibit class-imbalanced distributions, which pose significant challenges for deep semi-supervised models. To address this issue, we design a three-stage learning framework, 3LPR, by combining unsupervised feature extraction, graph-based Label Propagation, and mixed data augmentation (MDA)-based label Reassignment. Specifically, we first explore the performance of supervised and unsupervised learning for feature extraction of class-imbalanced data and then establish our first stage of feature extraction through unsupervised learning. Then, we adopt graph network-based offline label propagation and sieving to effectively expand the labeled set to overcome the excessive label bias in the classifier during the training process. Finally, we propose a label reassignment (LRA) algorithm for class-imbalanced semi-supervised learning (CISSL) to train the expanded dataset, where the MDA strategy is adopted but with the label reassigned. The experimental results demonstrate that the proposed 3LPR framework for CISSL outperforms other state-of-the-art methods on various datasets.
Energy Block-Based Peer-to-Peer Contract Trading With Secure Multi-Party Computation in Nanogrid

The emergence of nanogrid has led to active local energy markets. Renewable energy sources and flexible energy storages in nanogrids provide favorable conditions for establishing a sustainable bilateral trading market. However, due to the diversity of energy sources in nanogrids, it is difficult to establish a uniform scheduling scheme. To this end, this paper first establishes an effective peer-to-peer energy block contract market to provide a scheme for users to exchange energy at any time interval. The goal is to maximize contract volume while ensuring the economic profitability of all users. Beyond that, a garbled circuit-based price comparison mechanism is proposed based on a secure multi-party computation to achieve price comparison without revealing any individual user’s data to other users for privacy preserving. Finally, a fully distributed algorithm is designed by constructing a local tracker to track the inequality amount in iteration. The algorithm also subtly parses the input data, which weakens the intertwined relationship in peer-to-peer transactions and improves computation efficiency. Case studies verify the privacy of the described method and the effectiveness of the energy block contract market.
Bio-Strings: A Relational Database Data-Type for Dealing with Large Biosequences

DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users.
(Online) public denunciation, public incivilities and offence

Public shaming of private figures in everyday settings is amplified through posting denunciations on social media platforms. The aim of this paper is to understand how the conduct of individuals targeted for online public shaming is not only construed as transgressive, but as warranting public denunciation in the first place through posting denunciations of their conduct on social media platforms. Public incivilities in encounters between strangers in public settings are discursively positioned as denounceable through framing that conduct as morally transgressive, which thereby legitimises public denunciation. However, because framing public conduct as transgressive necessarily involves selecting elements from a particular scene for public inspection and evaluation, public denunciations of public incivilities are always open to contestation. • Public incivilities occur in encounters between strangers in public settings. • Public denunciation of public incivilities can occur in situ or online. • Conduct is framed as morally transgressive through online public denunciations. • Online public denunciations of public incivilities are always open to contestation.
Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.
Focal Dice Loss-Based V-Net for Liver Segments Classification

Liver segmentation is a crucial step in surgical planning from computed tomography scans. The possibility to obtain a precise delineation of the liver boundaries with the exploitation of automatic techniques can help the radiologists, reducing the annotation time and providing more objective and repeatable results. Subsequent phases typically involve liver vessels’ segmentation and liver segments’ classification. It is especially important to recognize different segments, since each has its own vascularization, and so, hepatic segmentectomies can be performed during surgery, avoiding the unnecessary removal of healthy liver parenchyma. In this work, we focused on the liver segments’ classification task. We exploited a 2.5D Convolutional Neural Network (CNN), namely V-Net, trained with the multi-class focal Dice loss. The idea of focal loss was originally thought as the cross-entropy loss function, aiming at focusing on “hard” samples, avoiding the gradient being overwhelmed by a large number of falsenegatives. In this paper, we introduce two novel focal Dice formulations, one based on the concept of individual voxel’s probability and another related to the Dice formulation for sets. By applying multi-class focal Dice loss to the aforementioned task, we were able to obtain respectable results, with an average Dice coefficient among classes of 82.91%. Moreover, the knowledge of anatomic segments’ configurations allowed the application of a set of rules during the post-processing phase, slightly improving the final segmentation results, obtaining an average Dice coefficient of 83.38%. The average accuracy was close to 99%. The best model turned out to be the one with the focal Dice formulation based on sets. We conducted the Wilcoxon signed-rank test to check if these results were statistically significant, confirming their relevance.
Mispronunciation Detection and Diagnosis with Articulatory-Level Feedback Generation for Non-Native Arabic Speech

A high-performance versatile computer-assisted pronunciation training (CAPT) system that provides the learner immediate feedback as to whether their pronunciation is correct is very helpful in learning correct pronunciation and allows learners to practice this at any time and with unlimited repetitions, without the presence of an instructor. In this paper, we propose deep learning-based techniques to build a high-performance versatile CAPT system for mispronunciation detection and diagnosis (MDD) and articulatory feedback generation for non-native Arabic learners. The proposed system can locate the error in pronunciation, recognize the mispronounced phonemes, and detect the corresponding articulatory features (AFs), not only in words but even in sentences. We formulate the recognition of phonemes and corresponding AFs as a multi-label object recognition problem, where the objects are the phonemes and their AFs in a spectral image. Moreover, we investigate the use of cutting-edge neural text-to-speech (TTS) technology to generate a new corpus of high-quality speech from predefined text that has the most common substitution errors among Arabic learners. The proposed model and its various enhanced versions achieved excellent results. We compared the performance of the different proposed models with the state-of-the-art end-to-end technique of MDD, and our system had a better performance. In addition, we proposed using fusion between the proposed model and the end-to-end model and obtained a better performance. Our best model achieved a 3.83% phoneme error rate (PER) in the phoneme recognition task, a 70.53% F1-score in the MDD task, and a detection error rate (DER) of 2.6% for the AF detection task.
Uncertainty Estimation in Medical Image Classification: Systematic Review

Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network's uncertainty together with its prediction.In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation.Google Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms "uncertainty," "uncertainty estimation," "network calibration," and "out-of-distribution detection" were used in combination with the terms "medical images," "medical image analysis," and "medical image classification."A total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty.The applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts.RR2-10.2196/11936.
Deceptive reviews and sentiment polarity: Effective link by exploiting BERT

Today, reviews are the advertising medium par excellence through which companies are able to influence customers’ spending decisions. Although the initial purpose of reviews was to provide companies with a feedback tool to improve products and services based on customer needs, they soon became a way to climb the sales rankings, often illegally. In fact, deceptive and fake reviews have managed to evade the often non-existent means of validation of online platforms, proliferating a new business. To combat this phenomenon, several classification methods have been developed to train automated tools in the arduous task of distinguishing between genuine and misleading reviews, the most recent based on machine and deep learning techniques. This paper proposes a multi-label classification methodology based on the Google BERT neural language model to build a deceptive review detector aided by its sentiment awareness: improved modeling of the link between sentiment polarity and deceptiveness during the fine-tuning phase by exploiting the Binary Cross Entropy with Logits loss function adds to the advantages provided by pre-trained contextual models, which are able to capture word polysemy through word embeddings and benefit from pre-training on huge corpora. Tests were performed on the Deceptive Opinion Spam Corpus and Yelp New York City datasets, providing a quantitative and qualitative analysis of the results which, when compared with the state of the art available in the literature, showed an encouraging increase in performance. • Distinguish between deceptive and real reviews written by unknown users online. • Combine sentiment polarity and deceptiveness labels via multilabel classification. • Vanguard results on Deceptive Opinion Spam and Yelp corpora via BERT language model.