Reviewerly/exHarmony huggingface dataset:
Hierarchical GAN-Tree and Bi-Directional Capsules for multi-label image classification

Compared with the flat multi-label image classification, the hierarchical structure reserves a richer source of structural information to represent complicated relationships between labels in the real world. However, existing multi-label image classification methods focus on the accuracy of label prediction, ignoring the structural information embedded in the hierarchical label space. Furthermore, they hardly form the relevant visual feature space corresponding to the hierarchical label structure. In this paper, we propose a novel hierarchical framework based on the feature and label structural information named Hierarchical GAN-Tree and Bi-Directional Capsules (HGT&BC) to address these problems. We conduct Hierarchical GAN-Tree for feature space representation and Hierarchical Bi-Directional Capsules for label space classification, respectively. Hierarchical GAN-Tree generates hierarchical feature space using the unsupervised divisive clustering pattern according to the hierarchical structure, alleviating the mode-collapse of generators and the overfitting manifestation of conventional GANs. Hierarchical Bi-Directional Capsules utilize the hierarchical label structure in iterations of top-down and bottom-up processes: the top-down process integrates hierarchical relationships into the probability computation to enhance partial hierarchical relationships; the bottom-up process modifies the dynamic routing mechanism between capsules to represent semantic objects for the comprehensive global hierarchical classifiers. Owing to the two components, HGT&BC successfully expresses the hierarchical relationships in both feature and label space and improves the performance of multi-label image classification. Extensive experimental results on four benchmark datasets demonstrate the effectiveness and efficiency of our hierarchical framework in practice.
Flexible Multiview Spectral Clustering With Self-Adaptation

Multiview spectral clustering (MVSC) has achieved state-of-the-art clustering performance on multiview data. Most existing approaches first simply concatenate multiview features or combine multiple view-specific graphs to construct a unified fusion graph and then perform spectral embedding and cluster label discretization with k -means to obtain the final clustering results. They suffer from an important drawback: all views are treated as fixed when fusing multiple graphs and equal when handling the out-of-sample extension. They cannot adaptively differentiate the discriminative capabilities of multiview features. To alleviate these problems, we propose a flexible MVSC with self-adaptation (FMSCS) method in this article. A self-adaptive learning scheme is designed for structured graph construction, multiview graph fusion, and out-of-sample extension. Specifically, we learn a fusion graph with a desirable clustering structure by adaptively exploiting the complementarity of different view features under the guidance of a proper rank constraint. Meanwhile, we flexibly learn multiple projection matrices to handle the out-of-sample extension by adaptively adjusting the view combination weights according to the specific contents of unseen data. Finally, we derive an alternate optimization strategy that guarantees desirable convergence to iteratively solve the formulated unified learning model. Extensive experiments demonstrate the superiority of our proposed method compared with state-of-the-art MVSC approaches. For the purpose of reproducibility, we provide the code and testing datasets at https://github.com/shidan0122/FMICS.
Urban scene based Semantical Modulation for Pedestrian Detection

Despite recent progress, pedestrian detection still suffers from the troublesome problems of small objects, occlusions, and numerous false positives. Intuitively, the rich context information available from urban scenes could help determine the presence and location of pedestrians. For example, roads and sidewalks are good cues for potential pedestrians, while detections on buildings and trees are often false positives. However, most existing pedestrian detectors ignore or inadequately utilize semantic context. In this paper, in order to make full use of the urban-scene semantics to facilitate pedestrian detection, we propose a new method called Semantical Modulation based Pedestrian Detector (SMPD). First, for efficiency, a semantic prediction module is jointly learned with a baseline detector for semantic predictions. Second, a semantic integration module is designed to exploit the urban-scene semantic context for detection. Specifically, we force it to be an independent detection branch based solely on semantic information. In this way, together with the baseline detector, the fused detection results explicitly depend on both the learned appearance features and the scene context around pedestrians. In addition, while existing methods cannot be applied to the datasets where semantic annotations are not available for training, we introduce a semi-supervised transfer learning approach to make our method suitable for more scenarios. We demonstrate experimentally that, thanks to the integration of semantic context from urban scenes, SMPD can accurately detect small and occluded pedestrians, as well as effectively remove false positives. As a result, SMPD achieves the new state of the art on the Citypersons and Caltech datasets.
Lightweight Network Design Based on ResNet Structure for Modulation Recognition

The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.
AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images

The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.
Towards more effective PRM-based crowd counting via a multi-resolution fusion and attention network

The paper focuses on improving the recent plug-and-play patch rescaling module (PRM) based approaches for crowd counting. In order to make full use of the PRM potential and obtain more reliable and accurate results for challenging images with crowd-variation, large perspective, extreme occlusions, and cluttered background regions, we propose a new PRM based multi-resolution and multi-task crowd counting network by exploiting the PRM module with more effectiveness and potency. The proposed model consists of three deep-layered branches with each branch generating feature maps of different resolutions. These branches perform a feature-level fusion across each other to build the vital collective knowledge to be used for the final crowd estimate. Additionally, early-stage feature maps undergo visual attention to strengthen the later-stage channel’s understanding of the foreground regions. The integration of these deep branches with the PRM module and the early-attended blocks proves to be more effective than the original PRM based schemes through extensive numerical and visual evaluations on four benchmark datasets. The proposed approach yields a significant improvement by a margin of 12.6% in terms of the RMSE evaluation criterion. It also outperforms state-of-the-art methods in cross-dataset evaluations.
Comparative study between a neural network, approach metaheuristic and exact method for solving Traveling salesman Problem

optimization problems currently occupy an important place in the scientific community. Intuitively, an optimization problem can be seen as a search problem that consists in exploring a space containing the set of all feasible solutions, in order to find the optimal solution. The traveling salesman problem (TSP), considered as a classical example of combinatorial optimization problem, is considered as an NP-complete problem. In this work we will divide the solution of combinatorial optimization problems into three classes: continuous Hopfield network (CHN), ant colony optimization (ACO) and exact methods programmed in Cplex. The solution of a CHN optimization problem is based on a certain energy or Lyapunov function, which decreases as the system evolves until it reaches a local minimum value. Ant colony optimization to solve the traveling salesman problem (TSP) is inspired by the foraging behavior of ants. As a special case, and in order to test these methods, some computational experiments solving the TSP are also included.
3D Layout Estimation via Weakly Supervised Learning of Plane Parameters From 2D Segmentation

The task of 3D layout estimation in an indoor scene is to predict the holistic 3D structural information of the scene from an RGB image. It is costly to obtain the ground truth 3D layout, and this issue severely restricts the learning based 3D layout estimation approaches. In this paper, we present a novel weakly supervised learning framework that is able to learn the 3D layout effectively with 2D layout segmentation mask as supervision. We employ a deep neural network to predict the plane parameters and camera intrinsic parameters in the image. Based on the predicted plane instances, the 3D layout as well as the corresponding depth map and 2D segmentation can be generated. The key objectives for learning meaningful plane parameters are the label consistency of layout segmentation and depth consistency of border pixels from adjacent planes, with which the ground truth 2D layout segmentation is able to supervise the learning of the 3D layout. We further incorporate 3D geometric reasoning and prior knowledge in the learning process to ensure that the learned 3D layout is realistic and reasonable. Experimental results show that our method can produce accurate 3D layout estimates by weakly supervised learning.
Sentence Augmentation for Language Translation Using GPT-2

Data augmentation has recently become an important method for improving performance in deep learning. It is also a significant issue in machine translation, and various innovations such as back-translation and noising have been made. In particular, current state-of-the-art model architectures such as BERT-fused or efficient data generation using the GPT model provide good inspiration to improve the translation performance. In this study, we propose the generation of additional data for neural machine translation (NMT) using a sentence generator by GPT-2 that produces similar characteristics to the original. BERT-fused architecture and back-translation are employed for the translation architecture. In our experiments, the model produced BLEU scores of 27.50 for tatoebaEn-Ja, 30.14 for WMT14En-De, and 24.12 for WMT18En-Ch.
Multi-core parallel algorithms for hiding high-utility sequential patterns

High-utility sequential pattern mining (HUSPM) can be applied in many applications such as retail, market basket analysis, click-stream analysis, healthcare data analysis, and bioinformatics. HUSPM algorithms discover useful information from data. However, looking at the dark side, the sensitive patterns can also be disclosed by the competitors, who use a HUSPM algorithm on the leaked data. Therefore, high-utility sequential pattern hiding (HUSPH) is used to protect the privacy information from HUSPM algorithms. This paper proposes three algorithms named High U tility S equential P attern H iding Using P ure A rray Structure (USHPA), High U tility S equential Pattern H iding Using P arallel Strategy (USHP), and High U tility S equential Pattern H iding Using R andom Distribution Strategy (USHR) for hiding high-utility sequential patterns on quantitative sequence datasets. These algorithms use a proposed data structure named P attern U tility S et for H iding (PUSH) to speed up the hiding process. We also introduce a metric called Privacy Factor to evaluate the quality of hiding results. The comparative experiments were conducted on real datasets to evaluate the performance of the proposed algorithms in terms of runtime, memory consumption, scalability, missing cost, and privacy factor. Results show that the proposed algorithms can efficiently sanitize the input datasets, and they outperform the compared algorithms for all metrics.
The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing

Introduction: The Food and Drug Administration Center for Biologics Evaluation and Research conducts post-market surveillance of biologic products to ensure their safety and effectiveness. Studies have found that common vaccine exposures may be missing from structured data elements of electronic health records (EHRs), instead being captured in clinical notes. This impacts monitoring of adverse events following immunizations (AEFIs). For example, COVID-19 vaccines have been regularly administered outside of traditional medical settings. We developed a natural language processing (NLP) algorithm to mine unstructured clinical notes for vaccinations not captured in structured EHR data. Methods: A random sample of 1,000 influenza vaccine administrations, representing 995 unique patients, was extracted from a large U.S. EHR database. NLP techniques were used to detect administrations from the clinical notes in the training dataset [80% (N = 797) of patients]. The algorithm was applied to the validation dataset [20% (N = 198) of patients] to assess performance. Full medical charts for 28 randomly selected administration events in the validation dataset were reviewed by clinicians. The NLP algorithm was then applied across the entire dataset (N = 995) to quantify the number of additional events identified. Results: A total of 3,199 administrations were identified in the structured data and clinical notes combined. Of these, 2,740 (85.7%) were identified in the structured data, while the NLP algorithm identified 1,183 (37.0%) administrations in clinical notes; 459 were not also captured in the structured data. This represents a 16.8% increase in the identification of vaccine administrations compared to using structured data alone. The validation of 28 vaccine administrations confirmed 27 (96.4%) as "definite" vaccine administrations; 18 (64.3%) had evidence of a vaccination event in the structured data, while 10 (35.7%) were found solely in the unstructured notes. Discussion: We demonstrated the utility of an NLP algorithm to identify vaccine administrations not captured in structured EHR data. NLP techniques have the potential to improve detection of vaccine administrations not otherwise reported without increasing the analysis burden on physicians or practitioners. Future applications could include refining estimates of vaccine coverage and detecting other exposures, population characteristics, and outcomes not reliably captured in structured EHR data.
Adversarial vulnerability of deep neural network-based gait event detection: A comparative study using accelerometer-based data

Gait event detection is a critical task in numerous healthcare applications to identify and recognize a user’s gait, such as based on accelerometer-based data. Many deep neural network techniques have been explored in recent years with significant achievements of accurate gait event detection. There is a need to study the adversarial vulnerability of the gait event detection in deep neural network-based solutions when they are evaluated with adversarial examples, which can be synthesized by strategically inserting minor perturbations into the original examples, in different gait scenarios and environments. In this paper, we first build a convolutional neural network (CNN)-based model and a long short-term memory (LSTM)-based model as the target gait event detection models. Then, we apply our proposed adversarial example generation approach to generate adversarial examples, which are further fed into the target CNN and LSTM networks as the testing data to evaluate their adversarial vulnerability performance. Our experimental results show that, when deceived using the generated adversarial examples, the gait event detection performance of the target CNN model is significantly reduced by as much as 0.168, 0.115, 0.197, 0.221, for the accuracy, precision, recall, and F1-score, respectively, from its original attack-free performance. The performance of the target LSTM model has also suffered from a substantial reduction as 0.136, 0.116, 0.167, 0.186, for these four metrics, respectively. This study highlights the crucial issue of the adversarial vulnerability of deep neural network-based gait event detection frameworks based on accelerometer-based data.
Blockchain humanitarianism and crypto-colonialism

The humanitarian sector has emerged as a powerful mechanism of legitimation for blockchain technology. Platform developers in the aid sector have been eager to showcase the promise of decentralization and encrypted blockchain data as the inheritance of the world's poor and developing nations. This article claims that humanitarian blockchain projects are inextricably linked to the politics of the crypto-economy, proprietary platforms, and a class of solutionists championing Silicon Valley's cultural values. Blockchain humanitarianism has emerged through a private-public partnership (PPP) model in the non-governmental organization (NGO) sector that embraces tech disruption and innovation. Ethically sound blockchain humanitarian projects are precluded by the inherent obscurantism of the technology, the inability to transpose blockchain's governance logic in the social realm, and inextricable ties to the political economy of cryptocurrencies. Projects in the developing world have thus embodied a colonial logic of techno-experimentation for platform developers and imbricate the NGO sector into the PR logic of blockchain solutionism.
Document images forgery localization using a two‐stream network

Document images often contain essential and sensitive information. With image editing software, one can easily manipulate the semantic meaning of the document image by copy-move, splicing, and removal, which causes many security issues. Hence, the document image forgery detection and localization are of great significance. In this paper, a novel two-stream network is proposed to detect and locate the forgery regions of document images. One stream captures forgery traces from the spatial information, including unnatural boundaries, contrast/brightness inconsistencies, and blurring. The other stream eliminates the image semantics by six residual filters, then capture the anomalous features between neighboring pixels introduced by upsampling, downsamping, and inpainting. Finally, the discriminant network is introduced to deeply fuse features from the two streams, and discriminates whether each image patch is forged or not using global average pooling and fully connected layer. Extensive experimental results on our constructed document image data set and the Security AI Challenger Program data set demonstrate the proposed two-stream network outperforms state-of-the-art methods. This proposed two-stream network is also better than each single stream, and is robust to some common forgery postprocessing operations.
Detecting Network Attacks using Federated Learning for IoT Devices

Billions of IoT devices are connected to networks all around us, enabling cyber-physical systems. These devices can carry and generate user-sensitive data, examples of such devices are smartwatches, medical equipment, and smart home gadgets. Individual IoT devices have some form of intrusion detection system integrated, but once they are all connected, a network threat to one device could mean a threat to many. IoT devices must have a robust intrusion detection system that would keep devices secure over a network. To aid with this, we provide a machine learning solution that adheres to Global Data Protection Regulation by keeping the user data secure locally on the IoT device itself. We propose a Federated Learning (FL) approach that capitalizes on a decentralized and collaborative way of training machine learning models. In this study, we practice federated learning technique to train and create a robust intrusion detection model for the security of IoT devices. We evaluate our proposed approach using three different use-cases to show the security enhancements that improve using the FL technique, resulting in a more reliable performance in this domain.
Design of real-time vehicle detection based on YOLOv4

Intelligent Traffic System (ITS), whose key point is vehicle detection, can significantly reduce the incidence of traffic accidents through scientific traffic control. Traditional vehicle detection technology has unsatisfactory detection speed and accuracy. In order to obtain and process object feature information more efficiently, we use the convolutional neural network (CNN) with better performance. In addition, compared with traditional computers with large volume and high computing cost, the embedded platform is more suitable for deployment model in practical application. Based on the convolutional neural network YOLOv4, we deploy it on the embedded platform, and optimizes the Class-imbalance of BDD100K dataset used in the model and the lack of real-time performance of the model. After optimization, for the BDD100K dataset, the detection accuracy of the model is improved by 2.81%, and the detection speed is improved to 28.45 FPS, which can meet the requirements of real-time vehicle detection.