Reviewerly/exHarmony huggingface dataset:
Vision-Enhanced and Consensus-Aware Transformer for Image Captioning

Image captioning generates descriptions in a natural language for a given image. Due to its great potential for a wide range of applications, many deep learning based-methods have been proposed. The co-occurrence of words such as mouse and keyboard, constitutes commonsense knowledge, which is referred to as consensus. However, it is challenging to consider commonsense knowledge in producing captions that have rich, natural, and meaningful semantics. In this paper, a Vision-enhanced and Consensus-aware Transformer (VCT) is proposed to exploit both visual information and consensus knowledge for image captioning with three key components: a vision-enhanced encoder, consensus-aware knowledge representation generator, and consensus-aware decoder. The vision-enhanced encoder extends the vanilla self-attention module with a memory-based attention module and a visual perception module for learning better visual representation of an image. Specifically, the relationships between regions in an image and the image’s global context are leveraged with scene memory in the memory-based attention module. The visual perception module further enhances the correlation among neighboring tokens in both the spatial and channel-wise dimensions. To learn consensus-aware representations, a word correlation graph is constructed by computing the statistical co-occurrence between semantic concepts. Then consensus knowledge can be acquired using a graph convolutional network in the consensus-aware knowledge representation generator. Finally, such consensus knowledge is integrated into the consensus-aware decoder through consensus memory and a knowledge-based control module to produce a caption. Experimental results on two popular benchmark datasets (MSCOCO and Flickr30k) demonstrate that our proposed model achieves state-of-the-art performance. Extensive ablation studies also validate the effectiveness of each component.
Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models

Recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.
Statistical Similarity Measure-Based Adaptive Outlier-Robust State Estimator With Applications

This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. Two SSMs are first proposed to evaluate the similarities between a pair of positive definite random matrices and between a pair of weighted random vectors, respectively. The AORSE is developed by maximizing a hybrid SSMs based cost function, wherein the posterior density function of the hidden state is assumed as a Gaussian distribution with the posterior covariance being approximately determined in a heuristic way. Simulation and experimental examples of moving-target tracking demonstrate the effectiveness of the proposed algorithm.
SEGRESS: Software Engineering Guidelines for REporting Secondary Studies

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> : Several tertiary studies have criticized the reporting of software engineering secondary studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : Our objective is to identify guidelines for reporting software engineering (SE) secondary studies which would address problems observed in the reporting of software engineering systematic reviews (SRs). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We review the criticisms of SE secondary studies and identify the major areas of concern. We assess the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement as a possible solution to the need for SR reporting guidelines, based on its status as the reporting guideline recommended by the Cochrane Collaboration whose SR guidelines were a major input to the guidelines developed for SE. We report its advantages and limitations in the context of SE secondary studies. We also assess reporting guidelines for mapping studies and qualitative reviews, and compare their structure and content with that of PRISMA 2020. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : Previous tertiary studies confirm that reports of secondary studies are of variable quality. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> recommendations that amend reporting standards may result in unnecessary duplication of text. We confirm that the PRISMA 2020 statement addresses SE reporting problems, but is mainly oriented to quantitative reviews, mixed-methods reviews and meta-analyses. However, we show that the PRISMA 2020 item definitions can be extended to cover the information needed to report mapping studies and qualitative reviews. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions</i> : In this paper and its Supplementary Material, we present and illustrate an integrated set of guidelines called SEGRESS (Software Engineering Guidelines for REporting Secondary Studies), suitable for quantitative systematic reviews (building upon PRISMA 2020), mapping studies (PRISMA-ScR), and qualitative reviews (ENTREQ and RAMESES), that addresses reporting problems found in current SE SRs.
Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more.Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased.Mobile robot navigation encompasses the following four requirements: perception, localization, pathplanning and motion control.Among those, path-planning is a vital part of a fast, secure operation.During the last couple of decades, many path-planning algorithms were developed.Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited.This paper presents a qualitative comparative study of the up-to-date mobile robot pathplanning methods capable of navigating robots in dynamic environments.The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm.The general advantages and disadvantages of each method are discussed.Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation.This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field.
Fuzzy Mutual Information-Based Multilabel Feature Selection With Label Dependency and Streaming Labels

Multilabel feature selection (MFS) has received widespread attention in various big data applications. However, most of the existing methods either explicitly or implicitly assume that all labels are given in advance before feature selection starts; or that all labels are independent. In fact, in many practical applications, the available labels usually arrive dynamically, and they may be interdependent with each other. Moreover, labels may be generated dynamically in a minibatch manner, which makes it more difficult to explore label dependency. In this article, we propose a novel fuzzy mutual information-based multilabel feature selection approach MSDS, which is able to solve single streaming label, minibatch streaming labels, and exploit label dependency simultaneously. In specific, we first promote fuzzy mutual information to be suitable for multilabel learning. This model can effectively consider the relationship between two labels, and has good applicability for measuring the relationship between multiple labels. Then, we analyze feature relevance and feature redundancy based on the combination of label dependency and streaming labels, which helps to facilitate the selection of high-quality feature subsets. Finally, a feature conversion is designed to fuse the representative features of new arrival streaming labels. Comprehensive experiments on twelve multilabel datasets clearly reveal the superiority of the proposed method against two streaming labels based algorithms and five state-of-the-art static label space based algorithms.
Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions

Many data-driven remaining useful life (RUL) prediction methods usually assume that the training and test data are independent and identically distributed. However, the different degradation trends of machines under variable working conditions can lead to problems with disparate distribution of degradation features and difficulties in obtaining the corresponding labels. To address the above problems, this paper proposed a RUL prediction method based on a transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions. Firstly, a shrinkage attention module is designed by using the attention mechanism and shrinkage operation to eliminate the interference of irrelevant information and increase the focus on critical features. Secondly, a multi-stage shrinkage attention temporal convolution block based on a hybrid attention subnetwork and soft thresholding subnetwork is designed to efficiently learn the manifold structure of the input data to capture the degenerate information-rich deep features. Finally, an unsupervised domain adaptation strategy based on representation subspace distance and bases mismatch penalization is proposed to enhance the learning of cross-domain invariant features. The proposed method is experimentally studied on XJTU-SY and FEMTO datasets. The experimental results demonstrate that the effectiveness and accuracy of the proposed method in RUL prediction are higher than other methods.
Autonomous Transportation Systems and Services Enabled by the Next-Generation Network

The vast development of the next-generation network (NGN) impels its integration with emerging technologies, such as big data, artificial intelligence, and federated learning, to deliver autonomous and intelligent services in various areas. Notably, in modern transportation systems (TSs), the advances of NGN enable a transformation toward an autonomous transportation system (ATS), which can bridge the demand and supply through a self-actuating cycle (sensing, learning, rearranging, and reacting). Since NGN-enabled ATS is still in its infancy, a concrete vision is missing to forge a common research ground. To fill the gap, this article is intended to elucidate NGN-enabled ATS by first discussing its intrinsic difference against the conventional TSs (CTSs) and then depicting its service blueprint in fostering more intelligent and autonomous mobility services. After that, a full-scale ATS service design reference is proposed to ensure the generality, adaptivity, compatibility, interoperability, and scal-ability of services in and across its development stages, representing the levels of autonomy from partial to high to full automation. Furthermore, its superiority is discussed through a preliminary evaluation of personal mobility service based on centralized and federated learning. Finally, open questions and future research directions of this emerging topic are also discussed.
LoMar: A Local Defense Against Poisoning Attack on Federated Learning

Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL enables a privacy-preserving mobile edge computing framework using IoT devices, recent studies have shown that this approach is susceptible to poisoning attacks from the side of remote clients. To address the poisoning attacks on FL, we provide a <i>two-phase</i> defense algorithm called <inline-formula><tex-math notation="LaTeX">${\underline{Lo}cal\ \underline{Ma}licious\ Facto\underline{r}}$</tex-math></inline-formula> (LoMar). In phase I, LoMar scores model updates from each remote client by measuring the relative distribution over their neighbors using a kernel density estimation method. In phase II, an optimal threshold is approximated to distinguish malicious and clean updates from a statistical perspective. Comprehensive experiments on four real-world datasets have been conducted, and the experimental results show that our defense strategy can effectively protect the FL system. Specifically, the defense performance on Amazon dataset under a label-flipping attack indicates that, compared with FG+Krum, LoMar increases the target label testing accuracy from <inline-formula><tex-math notation="LaTeX">$96.0\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$98.8\%$</tex-math></inline-formula> , and the overall averaged testing accuracy from <inline-formula><tex-math notation="LaTeX">$90.1\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$97.0\%$</tex-math></inline-formula> .
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection

Accurate and efficient pedestrian detection is crucial for the intelligent transportation system regarding pedestrian safety and mobility, e.g., Advanced Driver Assistance Systems, and smart pedestrian crosswalk systems. Among all pedestrian detection methods, the vision-based detection method is demonstrated to be the most effective in previous studies. However, the existing vision-based pedestrian detection algorithms still have two limitations that restrict their implementations, those being real-time performance as well as the resistance to the impacts of environmental factors, e.g., low illumination conditions. To address these issues, this study proposes a lightweight Illumination and Temperature-aware Multispectral Network (IT-MN) for accurate and efficient pedestrian detection. The proposed IT-MN is an efficient one-stage detector. For accommodating the impacts of environmental factors and enhancing the sensing accuracy, thermal image data is fused by the proposed IT-MN with visual images to enrich useful information when visual image quality is limited. In addition, an innovative and effective late fusion strategy is also developed to optimize the image fusion performance. To make the proposed model implementable for edge computing, the model quantization is applied to reduce the model size by 75% while shortening the inference time significantly. The proposed algorithm is evaluated by comparing it with the selected state-of-the-art algorithms using a public dataset collected by in-vehicle cameras. The results show that the proposed algorithm achieves a low miss rate and inference time at 14.19% and 0.03 seconds per image pair on GPU. Besides, the quantized IT-MN achieves an inference time of 0.21 seconds per image pair on the edge device, demonstrating the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.
Pattern Mining: Current Challenges and Opportunities

Pattern mining is a key subfield of data mining that aims at developing algorithms to discover interesting patterns in databases. The discovered patterns can be used to help understanding the data and also to perform other tasks such as classification and prediction. After more than two decades of research in this field, great advances have been achieved in terms of theory, algorithms, and applications. However, there still remains many important challenges to be solved and also many unexplored areas. Based on this observations, this paper provides an overview of six key challenges that are promising topics for research and describe some interesting opportunities. Those challenges were identified by researchers from the field, and are: (1) mining patterns in complex graph data, (2) targeted pattern mining, (3) repetitive sequential pattern mining, (4) incremental, stream, and interactive pattern mining, (5) heuristic pattern mining, and (6) mining interesting patterns.
Convergence of deep convolutional neural networks

Convergence of deep neural networks as the depth of the networks tends to infinity is fundamental in building the mathematical foundation for deep learning. In a previous study, we investigated this question for deep networks with the Rectified Linear Unit (ReLU) activation function and with a fixed width. This does not cover the important convolutional neural networks where the widths are increased from layer to layer. For this reason, we first study convergence of general ReLU networks with increased widths and then apply the results obtained to deep convolutional neural networks. It turns out the convergence reduces to convergence of infinite products of matrices with increased sizes, which has not been considered in the literature. We establish sufficient conditions for convergence of such infinite products of matrices. Based on the conditions, we present sufficient conditions for pointwise convergence of general deep ReLU networks with increasing widths, and as well as pointwise convergence of deep ReLU convolutional neural networks.
Assessing the impact of data augmentation and a combination of CNNs on leukemia classification

An accurate early-stage leukemia diagnosis plays a critical role in treating and saving patients' lives. The two primary forms of leukemia are acute and chronic leukemia, which is subdivided into myeloid and lymphoid leukemia. Deep learning models have been increasingly used in computer-aided medical diagnosis (CAD) systems developed to detect leukemia. This article assesses the impact of widely applied techniques, mainly data augmentation and multilevel and ensemble configurations, in deep learning-based CAD systems. Our assessment included five scenarios: three binary classification problems and two multiclass classification problems. The evaluation was performed using 3,536 images from 18 datasets, and it was possible to conclude that data augmentation techniques improve the performance of convolutional neural networks (CNNs). Furthermore, there is an improvement in the classification results using a combination of CNNs. For the binary problems, the performance of the ensemble configuration was superior to that of the multilevel configuration. However, the results were statistically similar in multiclass scenarios. The results were promising, with accuracies of 94.73% and 94.59% obtained using multilevel and ensemble configurations in a scenario with four classes. The combination of methods helps to reduce the error or variance of the predictions, which improves the accuracy of the used deep learning-based model.
UAV trajectory planning in cluttered environments based on PF-RRT* algorithm with goal-biased strategy

In recent decades, Rapidly-exploring Random Tree star(RRT*) with asymptotic optimality has attracted much attention in path planning algorithm, but it suffers from slow convergence. Hence to solve the drawback, this paper proposes a novel Unmanned Aerial Vehicle(UAV) trajectory planning in cluttered environments based on PF-RRT* algorithm with goal-biased strategy. It creates a novel parent node for the new node near the obstacle by dichotomy method, instead of updating the parent node in the existing random tree nodes, which considerably decreases the path cost. The improved artificial potential field(APF) is proposed to guide the growth of the random tree towards the target point by adding random point attraction, target point attraction and obstacle repulsion, which not only addresses the local minimum problem, but also boosts the search rate of the random tree. The algorithm proposed in this paper combines with goal-biased strategy to obtain higher quality sampling points during the sampling process. Finally, the simulation verifies that the proposed algorithm is greatly optimized in terms of the number of iterations, convergence rate and path cost.
A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions

• Classical approaches to taxonomy-based adversarial attacks are extensively discussed. • Based on the extended taxonomy, some recent popular adversarial attack methods are introduced and analyzed. • A knowledge graph is established, and based on this, the hotspots of related work are visualized and analyzed. • Future research directions are proposed to further improve adversarial attacks in the field of AI security. Deep learning has been widely applied in various fields such as computer vision, natural language processing, and data mining. Although deep learning has achieved significant success in solving complex problems, it has been shown that deep neural networks are vulnerable to adversarial attacks, resulting in models that fail to perform their tasks properly, which limits the application of deep learning in security-critical areas. In this paper, we first review some of the classical and latest representative adversarial attacks based on a reasonable taxonomy of adversarial attacks. Then, we construct a knowledge graph based on the citation relationship relying on the software VOSviewer, visualize and analyze the subject development in this field based on the information of 5923 articles from Scopus. In the end, possible research directions for the development about adversarial attacks are proposed based on the trends deduced by keywords detection analysis. All the data used for visualization are available at: https://github.com/NanyunLengmu/Adversarial-Attack-Visualization .