Reviewerly/exHarmony huggingface dataset:
Threats to Federated Learning

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising solution under this new reality. Existing FL protocol design has been shown to exhibit vulnerabilities which can be exploited by adversaries both within and outside of the system to compromise data privacy. It is thus of paramount importance to make FL system designers aware of the implications of future FL algorithm design on privacy-preservation. Currently, there is no survey on this topic. In this chapter, we bridge this important gap in FL literature. By providing a concise introduction to the concept of FL, and a unique taxonomy covering threat models and two major attacks on FL: 1) poisoning attacks and 2) inference attacks, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks, and discuss promising future research directions towards more robust privacy preservation in FL.
DACS: Domain Adaptation via Cross-domain Mixed Sampling

Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going from synthetic to real data. In this paper we address the problem of unsupervised do-main adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain). Existing methods have seen success by training on pseudo-labels for these unlabelled images. Multiple techniques have been proposed to mitigate low-quality pseudo-labels arising from the domain shift, with varying degrees of success. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. These mixed samples are then trained on, in addition to the labelled data itself. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes, a common synthetic-to-real semantic segmentation benchmark for UDA.
PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery

In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy.
Online continual learning in image classification: An empirical survey

Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence of more recent tasks. Over the past few years, a large range of methods and tricks have been introduced to address the continual learning problem, but many have not been fairly and systematically compared under a variety of realistic and practical settings. To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as Maximally Interfered Retrieval (MIR), iCARL, and GDumb (a very strong baseline) and determine which works best at different memory and data settings as well as better understand the key source of CF; (2) determine if the best online class incremental methods are also competitive in the domain incremental setting; and (3) evaluate the performance of 7 simple but effective tricks such as the ”review” trick and the nearest class mean (NCM) classifier to assess their relative impact. Regarding (1), we observe that iCaRL remains competitive when the memory buffer is small; GDumb outperforms many recently proposed methods in medium-size datasets and MIR performs the best in larger-scale datasets. For (2), we note that GDumb performs quite poorly while MIR – already competitive for (1) – is also strongly competitive in this very different (but important) continual learning setting. Overall, this allows us to conclude that MIR is overall a strong and versatile online continual learning method across a wide variety of settings. Finally for (3), we find that all tricks are beneficial, and when augmented with the “review” trick and NCM classifier, MIR produces performance levels that bring online continual learning much closer to its ultimate goal of matching offline training. Our codes are available at https://github.com/RaptorMai/online-continual-learning.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos. RTFM also adapts dilated convolutions and self-attention mechanisms to capture long- and short-range temporal dependencies to learn the feature magnitude more faithfully. Extensive experiments show that the RTFM-enabled MIL model (i) outperforms several state-of-the-art methods by a large margin on four benchmark data sets (ShanghaiTech, UCF-Crime, XD-Violence and UCSD-Peds) and (ii) achieves significantly improved subtle anomaly discriminability and sample efficiency.
Reproducibility in machine learning for health research: Still a ways to go

Machine learning applied to health falls short on several reproducibility metrics compared to other machine learning subfields.
Communication-efficient federated learning

Significance Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate in training machine learning (ML) models without having to share their possibly private data. FL requires a multitude of devices to frequently exchange their learned model updates, thus introducing significant communication overhead, which imposes a major challenge in FL over realistic networks that are limited in computational and communication resources. In this article, we propose a communication-efficient FL framework that enables edge devices to efficiently train and transmit model parameters, thus significantly improving FL performance and convergence speed. Our proposed FL framework paves the way to collaborative ML in large-scale networking systems such as Internet of Things networks.
Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model

Image semantic segmentation has received great attention in computer vision, whose aim is to segment different objects and provide them different semantic category labels so that the computer can fully obtain the semantic information of the scene. However, the current research mainly focuses on color image data as training, for outdoor scenes and single task semantic segmentation. This paper carries out multi-task semantic segmentation model in the complex indoor environment on joint target detection using RGB-D image information based on the improved Faster-RCNN algorithm, which can simultaneously realize the indoor scene semantic segmentation, target classification and detection multiple visual tasks. In which, in view of the influence of uneven lighting in the environment, the method of fusion of RGB images and depth images is improved. While enhancing the fusion image feature information, it also improves the efficiency of model training. Simultaneously, in order to meet the needs for operating on multi-scale target objects, the non-maximum value suppression algorithm is improved to improve the model’s performance. So as to realize the output of the model’s multi-task information, the loss function has also been redesigned and optimized. The indoor scene semantic segmentation model constructed in this paper not only has good performance and high efficiency, but also can segment the contours of different scale objects clearly and adapt to the indoor uneven lighting environment.
A novel triple-image encryption and hiding algorithm based on chaos, compressive sensing and 3D DCT

In this paper, a novel triple-image encryption and hiding algorithm is proposed by combining a 2D chaotic system, compressive sensing (CS) and the 3D discrete cosine transform (DCT). First, three grayscale plain images are sparsely represented by the 2D discrete wavelet transform (DWT). The resulting sparse matrices are scrambled twice through index sort scrambling and 3D zigzag scrambling. Then, the measurement matrix generated by the 2D chaotic system is used to compress the scrambled matrices. Finally, the compressed matrices are embedded into a color carrier image using the 3D DCT to obtain a visually meaningful cipher image. It is worth mentioning that most of the existing meaningful encryption algorithms encrypt and embed single images, which cannot meet the requirements of the era of big data. However, in our scheme, three grayscale images are simultaneously encrypted and embedded into a single color carrier image, which, when compared with existing algorithms, greatly improves the encryption efficiency. Moreover, simulation experiments and contrastive analyses show that the proposed visual encryption scheme has excellent visual security, robustness, decryption quality and running efficiency.
A federated learning system with enhanced feature extraction for human activity recognition

With the rapid growth of mobile devices, wearable sensor-based human activity recognition (HAR) has become one of the hottest topics in the Internet of Things. However, it is challenging for traditional approaches to achieving high recognition accuracy while protecting users’ privacy and sensitive information. To this end, we design a federated learning system for HAR (HARFLS). Based on the FederatedAveraging method, HARFLS enables each user to handle its activity recognition task safely and collectively. However, the recognition accuracy largely depends on the system’s feature extraction ability. To capture sufficient features from HAR data, we design a perceptive extraction network (PEN) as the feature extractor for each user. PEN is mainly composed of a feature network and a relation network. The feature network, based on a convolutional block, is responsible for discovering local features from the HAR data while the relation network, a combination of long short-term memory (LSTM) and attention mechanism, focuses on mining global relationships hidden in the data. Four widely used datasets, i.e., WISDM, UCI_HAR 2012, OPPORTUNITY, and PAMAP2, are used for performance evaluation. Experimental results demonstrate that PEN outperforms 14 existing HAR algorithms on these datasets in terms of the F1-score; HARFLS with PEN obtains better recognition results on the WISDM and PAMAP2 datasets, compared with 11 existing federated learning systems with various feature extraction structures.
Government corruption, market segmentation and renewable energy technology innovation: Evidence from China

Due to global warming and increasingly severe resource and environmental constraints, the role of renewable energy in reducing pollutant emissions and mitigating environmental degradation has gradually attracted the attention of all countries. This study examines the relationship between government corruption, market segmentation, and renewable energy technology innovation. The regression results show that government corruption can increase the degree of market segmentation, and both government corruption and market segmentation can significantly reduce regional renewable energy technology innovation. Further analysis shows that the improvement in market segmentation can lead to a negative moderating effect of corruption on renewable energy technology innovation. In addition, corruption and market segmentation have complementary effects on the impact of renewable energy technology innovation. The improvement of corruption level can increase the negative influences of market segmentation on renewable energy technology innovation. Similarly, the higher degree of market segmentation can increase the restraining effect of corruption on renewable energy technology innovation. Therefore, this study provides a valuable reference for all countries to accelerate the construction of regional market integration, break through interprovincial barriers, and improve renewable energy technology innovation.
Quantum computing: A taxonomy, systematic review and future directions

Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.
Blockchain as a sustainability-oriented innovation?: Opportunities for and resistance to Blockchain technology as a driver of sustainability in global food supply chains

Blockchain technology has been forwarded as an innovation to address pressing sustainability challenges in global food supply chains. However, limited studies have critically examined the technology's role in advancing sustainability. Drawing on the literature on sustainability-oriented innovation and innovation resistance theory, we explore the potential of blockchain technology to contribute to sustainable transformations within food supply chains. We reflect on 18 expert interviews with various actors across global food supply chains to evaluate the opportunities for, and resistance to, Blockchain technology as a driver of sustainability. The findings reveal that Blockchain is used within food supply chains as both a tool for sustainability as well as a broader philosophical mindset for addressing sustainability challenges. We reveal the opportunities for Blockchain technology as a sustainability-oriented innovation that can ensure fairer supply chains, enhance food traceability, and drive environmental sustainability. We also unpack the resistance to Blockchain that hinder its potential as a sustainability-oriented innovation which include functional and psychological barriers alongside cooperative barriers and protection of the status quo. Our study contributes to the broader literature on sustainability-oriented innovation and innovation resistance theory.
PARE: Part Attention Regressor for 3D Human Body Estimation

Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable. To address this, we introduce a soft attention mechanism, called the Part Attention REgressor (PARE), that learns to predict body-part-guided attention masks. We observe that state-of-the-art methods rely on global feature representations, making them sensitive to even small occlusions. In contrast, PARE’s part-guided attention mechanism overcomes these issues by exploiting information about the visibility of individual body parts while leveraging information from neighboring body-parts to predict occluded parts. We show qualitatively that PARE learns sensible attention masks, and quantitative evaluation confirms that PARE achieves more accurate and robust reconstruction results than existing approaches on both occlusion-specific and standard benchmarks. The code and data are available for research purposes at https://pare.is.tue.mpg.de/
Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection

The field of object detection has made great progress in recent years. Most of these improvements are derived from using a more sophisticated convolutional neural network. However, in the case of humans, the attention mechanism, global structure information, and local details of objects all play an important role for detecting an object. In this paper, we propose a novel fully convolutional network, named as Attention CoupleNet, to incorporate the attention-related information and global and local information of objects to improve the detection performance. Specifically, we first design a cascade attention structure to perceive the global scene of the image and generate class-agnostic attention maps. Then the attention maps are encoded into the network to acquire object-aware features. Next, we propose a unique fully convolutional coupling structure to couple global structure and local parts of the object to further formulate a discriminative feature representation. To fully explore the global and local properties, we also design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local information. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging data sets, i.e., a mAP of 85.7% on VOC07, 84.3% on VOC12, and 35.4% on COCO. Codes are publicly available at https://github.com/tshizys/CoupleNet.
“I am not satisfied with my body, so I like augmented reality (AR)”

Augmented reality (AR)-based virtual try-on product presentations allow consumers to assess how well the displayed products match their actual bodies, unlike traditional Web-based product presentations. This study examines the important role of the consumer's perceived body image in consumer evaluation and intention to adopt AR-based virtual try-on technology. The study compares consumer responses to AR-based and traditional Web-based product presentations. The results reveal that consumers who perceive their body image as unfavorable record more favorable evaluations about AR than about traditional Web-based product presentations, while consumers who perceive their body image as favorable record no differences in their responses to the two presentations. Moreover, the positive and negative impacts of interactivity and media irritation on adoption intention are moderated by body image for AR but not for Web-based product presentations. This study not only has significant implications for researchers but also practical implications for e-tailors.
vChain

Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications. There is an increasing demand for querying the data stored in a blockchain database. To ensure query integrity, the user can maintain the entire blockchain database and query the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs. In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases. We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity. To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes. Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification. We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously. Security analysis and empirical study validate the robustness and practicality of the proposed techniques.