Reviewerly/exHarmony huggingface dataset:
Quality-Aware Part Models for Occluded Person Re-Identification

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common non-occluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians; 3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Wang-pengfei/QPM</uri> .
Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound

Contrast-enhanced ultrasound (CEUS) is generally employed for focal liver lesions (FLLs) diagnosis. Among the FLLs, atypical hepatocellular carcinoma (HCC) is difficult to distinguish from focal nodular hyperplasia (FNH) in CEUS video. For this reason, we propose and evaluate a feature fusion method to resolve this problem. The proposed algorithm extracts a set of hand-crafted features and the deep features from the CEUS cine clip data. The hand-crafted features include the spatial-temporal feature based on a novel descriptor called Velocity-Similarity and Dissimilarity Matching Local Binary Pattern (V-SDMLBP), and the deep features from a 3-D convolution neural network (3D-CNN). Then the two types of features are fused. Finally, a classifier is employed to diagnose HCC or FNH. Several classifiers have achieved excellent performance, which demonstrates the superiority of the fused features. In addition, compared with general CNNs, the proposed fused features have better interpretability.
Human activity recognition of children with wearable devices using LightGBM machine learning

Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59-8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.
Cross-Attentional Spatio-Temporal Semantic Graph Networks for Video Question Answering

Due to the rich spatio-temporal visual content and complex multimodal relations, Video Question Answering (VideoQA) has become a challenging task and attracted increasing attention. Current methods usually leverage visual attention, linguistic attention, or self-attention to uncover latent correlations between video content and question semantics. Although these methods exploit interactive information between different modalities to improve comprehension ability, inter- and intra-modality correlations cannot be effectively integrated in a uniform model. To address this problem, we propose a novel VideoQA model called Cross-Attentional Spatio-Temporal Semantic Graph Networks (CASSG). Specifically, a multi-head multi-hop attention module with diversity and progressivity is first proposed to explore fine-grained interactions between different modalities in a crossing manner. Then, heterogeneous graphs are constructed from the cross-attended video frames, clips, and question words, in which the multi-stream spatio-temporal semantic graphs are designed to synchronously reasoning inter- and intra-modality correlations. Last, the global and local information fusion method is proposed to coalesce the local reasoning vector learned from multi-stream spatio-temporal semantic graphs and the global vector learned from another branch to infer the answer. Experimental results on three public VideoQA datasets confirm the effectiveness and superiority of our model compared with state-of-the-art methods.
Deep Learning and Onion Routing-Based Collaborative Intelligence Framework for Smart Homes Underlying 6G Networks

Sensor communication in the smart home environment is still in its infancy as the information exchange between sensors is vulnerable to security threats. Many traditional solutions use single-layer or multi-layer (i.e., onion routing protocol) encryption/decryption algorithms. But, in the traditional onion routing protocol, if the directory server is compromised, it may not track the malicious onion nodes within the onion network. It questioned the path anonymity of the onion routing protocol. Motivated by this, we proposed a blockchain and onion routing (OR)-based secure and trusted framework in the paper. The anonymity of the proposed OR network is maintained by storing and tracking the onion nodes threshold values through the blockchain network. A long short-term memory (LSTM) model is also utilized to classify the sensors data requests as malicious and non-malicious. The performance of the proposed system is evaluated with different performance metrics such as F1 score and accuracy. The LSTM model significantly improves the initial detection rate of malicious data requests from smart home sensors. Over these benefits, we considered the entire communication via 6G channel, reducing the overall communication latency. Additionally, the OR network is simulated over the shadow simulator to analyze the OR network’s performance considering parameters such as packet delivery ratio and malicious onion node detection rate.
Decentralized Wireless Federated Learning With Differential Privacy

This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -differential privacy and the privacy budget per worker scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{N}})$</tex-math></inline-formula> , in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\sqrt{\frac{1}{TN}})$</tex-math></inline-formula> as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.
Differentially Private Byzantine-Robust Federated Learning

Federated learning is a collaborative machine learning framework where a global model is trained by different organizations under the privacy restrictions. Promising as it is, privacy and robustness issues emerge when an adversary attempts to infer the private information from the exchanged parameters or compromise the global model. Various protocols have been proposed to counter the security risks, however, it becomes challenging when one wants to make federated learning protocols robust against Byzantine adversaries while preserving the privacy of the individual participant. In this article, we propose a differentially private Byzantine-robust federated learning scheme (DPBFL) with high computation and communication efficiency. The proposed scheme is effective in preventing adversarial attacks launched by the Byzantine participants and achieves differential privacy through a novel aggregation protocol in the shuffle model. The theoretical analysis indicates that the proposed scheme converges to the approximate optimal solution with the learning error dependent on the differential privacy budget and the number of Byzantine participants. Experimental results on MNIST, FashionMNIST and CIFAR10 demonstrate that the proposed scheme is effective and efficient.
Decoder Choice Network for Metalearning

Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space. The main challenge for implementing this idea is that a decoder with many parameters may be required. To tackle this problem, the article provides an alternative design of the decoder with a structure that shares certain weights, thereby reducing the number of required parameters. In addition, this work combines ensemble learning with the proposed approach to improve the overall learning performance. Systematic experimental studies demonstrate that the proposed approach offers results superior to the state of the art in performing the Omniglot classification and miniImageNet classification tasks.
Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection

Machine learning has provided great potential in intelligent signal processing, e.g., channel estimation and signal detection. But still, it is difficult to deploy deep neural network (DNN) on low-cost and low-power edge devices, e.g., in both training and inference phases. For one thing, the training of massive network parameters incurs a huge computational complexity and rarely becomes feasible; and for another, even a trained DNN involves still high computations and demands considerable storage size. In this work, we present a tiny machine learning (Tiny-ML) approach for hardware-efficient channel estimation and signal detection. The key innovation of our Tiny-ML is that we replace each large dense layer of DNN with three small cascading sub-layers; and therefore, the computation/storage of a large matrix is replaced with that of small ones. To enable the lightweight training of our Tiny-ML, a novel rank-restricted back-propagation algorithm is further designed. Numerical simulations demonstrate the advantages of our new method. Without sacrificing the estimation and detection accuracy, our Tiny-ML attains an acceleration of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {\mathbf {2.5\sim\!3\times }}$</tex-math></inline-formula> in model training and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {\mathbf {\sim\!4.5\times }}$</tex-math></inline-formula> in model inference, as well as a substantial storage reduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {\mathbf {\sim\!4.5\times }}$</tex-math></inline-formula> , when compared to classical fully-connected (FC) DNN. As such, our method paves the way for deploying tiny AI onto low-cost and low-power hardware devices, thereby exploiting the full potential of intelligent signal processing.
A novel quantum image encryption technique based on improved controlled alternated quantum walks and hyperchaotic system

This paper proposes a novel quantum image encryption algorithm based on a local substitution and global permutation scheme. First, a plaintext-related secret key is generated by using a hyperchaotic system; second, the plain image is scrambled employing a block-based general Arnold transform and permutation-based special linear transform; third, S-box substitution and special linear transform are iterated multiple times until certain conditions are achieved. A novel controlled alternated quantum walks scheme is also proposed to generate substitution boxes. Numerical simulation shows that the proposed encryption scheme can effectively a encrypt plain image to an incomprehensible noise image, and is capable of resisting a variety of attacks.
Unsupervised feature selection via self-paced learning and low-redundant regularization

Much more attention has been paid to unsupervised feature selection nowadays due to the emergence of massive unlabeled data. The distribution of samples and the latent effect of training a learning method using samples in more effective order need to be considered so as to improve the robustness of the method. Self-paced learning is an effective method considering the training order of samples. In this study, an unsupervised feature selection is proposed by integrating the framework of self-paced learning and subspace learning. Moreover, the local manifold structure is preserved and the redundancy of features is constrained by two regularization terms. L2,1/2-norm is applied to the projection matrix, which aims to retain discriminative features and further alleviate the effect of noise in the data. Then, an iterative method is presented to solve the optimization problem. The convergence of the method is proved theoretically and experimentally. The proposed method is compared with other state of the art algorithms on nine real-world datasets. The experimental results show that the proposed method can improve the performance of clustering methods and outperform other compared algorithms.
A novel double image encryption algorithm based on coupled chaotic system

Abstract This paper constructs a two-dimensional Logistic-Sine-Henon map (2D-LSHM) for image encryption. By comparing with some existing chaotic systems, the 2D-LSHM has better chaotic performance and is more suitable for image encryption scenarios. Using the 2D-LSHM, we propose a double image adaptive encryption algorithm consisting of bit-level combinatorial permutation and four-directional adaptive diffusion called LSHM-DIAEA. Differently from traditional image encryption algorithms, the proposed LSHM-DIAEA can encrypt a color image and a grayscale image at the same time so that the information from each other can be fused and affected by each other, which makes the encryption process more secure and efficient. In addition, to enhance plaintext sensitivity, we introduce the secure hash algorithm-512 (SHA-512) and the Histogram of Oriented Gradient (HOG) feature extraction algorithm into the LSHM-DIAEA. Simulation and security analysis results illustrate that the LSHM-DIAEA provides a high security level to resist various cryptanalytic attacks and a high execution efficiency.
UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog

Visual Dialog aims to answer multi-round, interactive questions based on the dialog history and image content. Existing methods either consider answer ranking and generating individually or only weakly capture the relation across the two tasks implicitly by two separate models. The research on a universal framework that jointly learns to rank and generate answers in a single model is seldom explored. In this paper, we propose a contrastive learning-based framework UTC to unify and facilitate both discriminative and generative tasks in visual dialog with a single model. Specifically, considering the inherent limitation of the previous learning paradigm, we devise two inter-task contrastive losses i.e., context contrastive loss and answer contrastive loss to make the discriminative and generative tasks mutually reinforce each other. These two com-plementary contrastive losses exploit dialog context and target answer as anchor points to provide representation learning signals from different perspectives. We evaluate our proposed UTC on the VisDial v1.0 dataset, where our method outperforms the state-of-the-art on both discriminative and generative tasks and surpasses previous state-of-the-art generative methods by more than 2 absolute points on Recall@1.
Network traffic classification using convolutional neural network and ant-lion optimization

Traffic identification has become a challenging task in recent years. Recently, deep learning methods have been extensively studied for network traffic classification recently. Unfortunately, these models require a large amount of training data. Another challenge with most traffic classification methods is that the features must be extracted by an expert. In these methods, finding the desired features that lead to a better classification is very tedious and time-consuming. In this regard, an excellent solution to address this challenge is to use of deep learning methods that automatically extract features. This paper is an attempt to address these issues by using a combination of the convolutional neural network (CNN), the ant-lion meta-heuristic algorithm (ALO), and the self-organizing map (SOM) to create a traffic classification model that can accurately identify traffic types. The proposed method was able to identify encrypted traffic and distinguish between VPN and non-VPN traffics. The model was evaluated using the "ISCX VPN-non-VPN" dataset, and achieved 98% accuracy.
Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering

Aiming at solving the problem of clustering in the multi-view datasets which include samples with information missing in one or more views, incomplete multi-view clustering has received considerable attention. However, most studies can not get satisfying accuracy and efficiency when dealing with datasets in which a considerable number of instances are missing in partial views. To address this problem, a method named Auto-weighted Sample-level Fusion with Anchors for Incomplete Multi-view Clustering (ASA-IC) is proposed in this paper. It designs an auto-weighted sample-level fusion strategy, which realizes the optimized conversion from the individual instance-to-anchor similarity learning to the concensus instance-to-anchor similarity matrix construction. ASA-IC can not only handle incomplete samples and effectively explore the relationship between each instance and anchors, but also deal with various incomplete clustering situations and be applied in large-scale datasets as well. Besides, experiments on 5 complete datasets and 27 incomplete ones illustrate its effectiveness quantitatively and qualitatively.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations

Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, human-interpretable model imitates the behavior of these blackbox models are often proposed to help users trust model predictions. In this work, we audit the quality of such explanations for different protected subgroups using real data from four settings in finance, healthcare, college admissions, and the US justice system. Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups. We also demonstrate that pairing explainability methods with recent advances in robust machine learning can improve explanation fairness in some settings. However, we highlight the importance of communicating details of non-zero fidelity gaps to users, since a single solution might not exist across all settings. Finally, we discuss the implications of unfair explanation models as a challenging and understudied problem facing the machine learning community.
ISANET: Non-small cell lung cancer classification and detection based on CNN and attention mechanism

Lung cancer is one of the malignant tumors with high morbidity and mortality worldwide. Among them, non-small cell lung cancer accounts for about 85% of all lung cancers. In the existing chest CT plain scan or enhanced scan, non-small cell lung cancer images may overlap significantly in imaging features, leading to misdiagnosis and the inability to give an accurate histological classification. Among the existing relevant non-small cell lung cancer classification models, few studies on the precise classification of non-small cell lung cancer types. A multi-category classification model based on CNN is proposed to tackle these problems. The proposed model ISANET embeds channel attention and spatial attention mechanisms to focus on pathological areas based on InceptionV3. The three-category lung cancer dataset provided by the Affiliated Hospital of Hebei University is used, including lung squamous cell carcinoma, lung adenocarcinoma, and normal. Comparative experiments are done between ISANET and the traditional models AlexNet, VGG16, InceptionV3, MobilenetV2, and ResNet18. Results of experiments on two public datasets verify the effectiveness of ISANET, reaching 95.24% and 98.14% respectively, which indicates that ISANET has obtained superior accuracy in classifying non-small cell lung cancer.