Reviewerly/exHarmony huggingface dataset:
Part-aware Progressive Unsupervised Domain Adaptation for Person Re-Identification

Unsupervised domain adaptation (UDA) aims to mitigate the domain shift that occurs when transferring knowledge from a labeled source domain to an unlabeled target domain. While it has been studied for application in unsupervised person re-identification (ReID), the relations of feature distribution across the source and target domains remain underexplored, as they either ignore the local relations or omit the in-depth consideration of negative transfer when two domains do not share identical label spaces. In light of the above, this paper presents an innovative part-aware progressive adaptation network (PPAN) that exploits global and local relations for UDA-based ReID across domains. A multi-branch network is developed that explicitly learns discriminative feature representation from both whole-body images and body-part images under the supervision of a labeled source domain. Within each network branch, an independent UDA constraint is designed that aligns the global and local feature distributions from a labeled source domain with those of an unlabeled target domain. In addition, a novel progressive adaptation strategy (PAS) is designed that effectively alleviates the negative influence of outlier source identities. The proposed unsupervised ReID model is evaluated on five widely used datasets (Market-1501, DukeMTMC-reID, CUHK03, VIPeR and PRID), and experimental results demonstrate its superior robustness and effectiveness relative to state-of-the-art approaches.
AdversarialNAS: Adversarial Neural Architecture Search for GANs

Neural Architecture Search (NAS) that aims to automate the procedure of architecture design has achieved promising results in many computer vision fields. In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation. The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner. During searching, the designed adversarial search algorithm does not need to comput any extra metric to evaluate the performance of the searched architecture, and the search paradigm considers the relevance between the two network architectures and improves their mutual balance. Therefore, AdversarialNAS is very efficient and only takes 1 GPU day to search for a superior generative model in the proposed large search space. Experiments demonstrate the effectiveness and superiority of our method. The discovered generative model sets a new state-of-the-art FID score of 10.87 and highly competitive Inception Score of 8.74 on CIFAR-10. Its transferability is also proven by setting new state-of-the-art FID score of 26.98 and Inception score of 9.63 on STL-10. Code is at: https://github.com/chengaopro/AdversarialNAS.
NTIRE 2020 Challenge on NonHomogeneous Dehazing

This paper reviews the NTIRE 2020 Challenge on Non-Homogeneous Dehazing of images (restoration of rich details in hazy image). We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. NH-Haze is the first realistic nonhomogeneous haze dataset that provides ground truth images. The nonhomogeneous haze has been produced using a professional haze generator that imitates the real conditions of haze scenes. 168 participants registered in the challenge and 27 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing.
Multi-label classification with weighted classifier selection and stacked ensemble

Multi-label classification has attracted increasing attention in various applications, such as medical diagnosis and semantic annotation. With such trend, a large number of ensemble approaches have been proposed for multi-label classification tasks. Most of these approaches construct the ensemble members by using bagging schemes, but few stacked ensemble approaches are developed. Existing research on stacked ensemble approaches remains active, but several issues remain such as (1) little has been done to learn the weights of classifiers for classifier selection; (2) the relationship between pairwise label correlations and multi-label classification performance has not been investigated sufficiently. To address these issues, we propose a novel stacked ensemble approach that simultaneously exploits label correlations and the process of learning weights of ensemble members. In our approach, first, a weighted stacked ensemble with sparsity regularization is developed to facilitate classifier selection and ensemble members construction for multi-label classification. Second, in order to improve the classification performance, the pairwise label correlations are further considered for determining weights of these ensemble members. Finally, we develop an optimization algorithm based on both of the accelerated proximal gradient and the block coordinate descent techniques to achieve the optimal ensemble solution efficiently. Extensive experiments on publicly available datasets and real Cardiovascular and Cerebrovascular Disease datasets demonstrate that our proposed algorithm outperforms related state-of-the-art methods from perspectives of benchmarking and real-world applications.
MetaSearch: Incremental Product Search via Deep Meta-Learning

With the advancement of image processing and computer vision technology, content-based product search is applied in a wide variety of common tasks, such as online shopping, automatic checkout systems, and intelligent logistics. Given a product image as a query, existing product search systems mainly perform the retrieval process using predefined databases with fixed product categories. However, real-world applications often require inserting new categories or updating existing products in the product database. When using existing product search methods, the image feature extraction models must be retrained and database indexes must be rebuilt to accommodate the updated data, and these operations incur high costs for data annotation and training time. To this end, we propose a few-shot incremental product search framework with meta-learning, which requires very few annotated images and has a reasonable training time. In particular, our framework contains a multipooling-based product feature extractor that learns a discriminative representation for each product, and we also design a meta-learning-based feature adapter to guarantee the robustness of the few-shot features. Furthermore, when expanding new categories in batches during a product search, we reconstruct the few-shot features by using an incremental weight combiner to accommodate the incremental search task. Through extensive experiments, we demonstrate that the proposed framework achieves excellent performance for new products while still guaranteeing the high search accuracy of the base categories after gradually expanding new product categories without forgetting.
Current and future applications of artificial intelligence in pathology: a clinical perspective

During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.
Privacy-preserving quality prediction for edge-based IoT services

Quality of Service (QoS) prediction and privacy preservation are two key challenges in service recommendation. Nevertheless, the existing QoS prediction methods cannot be directly utilized in edge computing networks (ECNs) due to the user mobility and distribution nature in these networks. To address this problem, the DEQP2 model, a distributed edge QoS prediction model with privacy-preserving, is proposed in this paper. In the DEQP2 model, the Laplace vector mechanism is first employed for distributed privacy protection processing at the edge. Then, the distributed edge differential privacy (DEDP) QoS prediction algorithm is proposed, to enable a comprehensive consideration of the influence of user preferences and the edge environment. Finally, we conduct experiments on the EdgeQoS dataset, which is an integrated dataset derived from the WSDream and Telecom real-world datasets. The experimental results show that the proposed DEQP2 model provides measurable privacy preservation without significantly reducing the accuracy.
Qutrit Randomized Benchmarking

Ternary quantum processors offer significant potential computational advantages over conventional qubit technologies, leveraging the encoding and processing of quantum information in qutrits (three-level systems). To evaluate and compare the performance of such emerging quantum hardware it is essential to have robust benchmarking methods suitable for a higher-dimensional Hilbert space. We demonstrate extensions of industry standard randomized benchmarking (RB) protocols, developed and used extensively for qubits, suitable for ternary quantum logic. Using a superconducting five-qutrit processor, we find an average single-qutrit process infidelity of $3.8\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$. Through interleaved RB, we characterize a few relevant gates, and employ simultaneous RB to fully characterize crosstalk errors. Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.85. Our results present and demonstrate RB-based tools to characterize the performance of a qutrit processor, and a general approach to diagnose control errors in future qudit hardware.
Masked reconstruction based self-supervision for human activity recognition

The ubiquitous availability of wearable sensing devices has rendered large scale collection of movement data a straightforward endeavor. Yet, annotation of these data remains a challenge and as such, publicly available datasets for human activity recognition (HAR) are typically limited in size as well as in variability, which constrains HAR model training and effectiveness. We introduce masked reconstruction as a viable self-supervised pre-training objective for human activity recognition and explore its effectiveness in comparison to state-of-the-art unsupervised learning techniques. In scenarios with small labeled datasets, the pre-training results in improvements over end-to-end learning on two of the four benchmark datasets. This is promising because the pre-training objective can be integrated "as is" into state-of-the-art recognition pipelines to effectively facilitate improved model robustness, and thus, ultimately, leading to better recognition performance.
Attribute and instance weighted naive Bayes

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms, but its conditional independence assumption rarely holds true in real-world applications. Therefore, many different categories of improved approaches, including attribute weighting and instance weighting, have been proposed to alleviate this assumption. However, few of these approaches simultaneously pay attention to attribute weighting and instance weighting. In this study, we propose a new improved model called attribute and instance weighted naive Bayes (AIWNB), which combines attribute weighting with instance weighting into one uniform framework. In AIWNB, the attribute weights are incorporated into the naive Bayesian classification formula, and then the prior and conditional probabilities are estimated using instance weighted training data. To learn instance weights, we single out an eager approach and a lazy approach, and thus two different versions are created, which we denote as AIWNBE and AIWNBL, respectively. Extensive experimental results show that both AIWNBE and AIWNBL significantly outperform NB and all the other existing state-of-the-art competitors.
PIRATE: A Blockchain-Based Secure Framework of Distributed Machine Learning in 5G Networks

In fifth-generation (5G) networks and beyond, communication latency and network bandwidth will be no longer be bottlenecks to mobile users. Thus, almost every mobile device can participate in distributed learning. That is, the availability issue of distributed learning can be eliminated. However, model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients among multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in the 5G era, this article proposes a secure computing framework based on the sharding technique of blockchain, namely PiRATE. To prove the feasibility of the proposed PiRATE, we implemented a prototype. A case study shows how the proposed PiRATE contributes to distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine- resilient learning framework.
What is the Vocabulary of Flaky Tests?

Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites.
Blockchain for supply chain quality management: challenges and opportunities in context of open manufacturing and industrial internet of things

In global Supply Chain Quality Management (SCQM), there are methods to ensure continuity of supply, at appropriate prices while meeting quality standards. But they are limited by the large and fallible role of humans, especially in manufacturing and quality control. Open Manufacturing aims to establish an open network of manufacturers that enables product information sharing among others in the production chain. Realizing the goals of open manufacturing requires processing the large datasets inherent to the production chain partners. But it is difficult to comply with the trust and privacy requirements of the suppliers in a supply chain. This paper presents a comprehensive survey of blockchain and its current and potential use in the industrial context including Industrial Internet of Things (IIoT)-based sensor networks, and the impact of the blockchain technology in manufacturing related areas such as traceability. The focus of this paper is on a potential application of blockchain in SCQM with detailed analysis of its technical feasibility and the methods of transitioning between the current approaches to a blockchain-oriented platform. An innovative use of blockchain to store referral information is also discussed. The survey concludes that blockchain along with IIoT can improve SCQM in an Open Manufacturing model.
Cervical cell classification with graph convolutional network

Cervical cell classification has important clinical significance in cervical cancer screening at early stages. In contrast with the conventional classification methods which depend on hand-crafted or engineered features, Convolutional Neural Network (CNN) generally classifies cervical cells via learned deep features. However, the latent correlations of images may be ignored during CNN feature learning and thus influence the representation ability of CNN features. We propose a novel cervical cell classification method based on Graph Convolutional Network (GCN). It aims to explore the potential relationship of cervical cell images for improving the classification performance. The CNN features of all the cervical cell images are firstly clustered and the intrinsic relationships of images can be preliminarily revealed through the clustering. To further capture the underlying correlations existed among clusters, a graph structure is constructed. GCN is then applied to propagate the node dependencies and thus yield the relation-aware feature representation. The GCN features are finally incorporated to enhance the discriminative ability of CNN features. Experiments on the public cervical cell image dataset SIPaKMeD from International Conference on Image Processing in 2018 demonstrate the feasibility and effectiveness of the proposed method. In addition, we introduce a large-scale Motic liquid-based cytology image dataset which provides the large amount of data, some novel cell types with important clinical significance and staining difference and thus presents a great challenge for cervical cell classification. We evaluate the proposed method under two conditions of the consistent staining and different staining. Experimental results show our method outperforms the existing state-of-arts methods according to the quantitative metrics (i.e. accuracy, sensitivity, specificity, F-measure and confusion matrices). The intrinsic relationship exploration of cervical cells contributes significant improvements to the cervical cell classification. The relation-aware features generated by GCN effectively strengthens the representational power of CNN features. The proposed method can achieve the better classification performance and also can be potentially used in automatic screening system of cervical cytology.
A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center

This work proposes an energy-efficient resource provisioning and allocation framework to meet dynamic demands of the future applications. The frequent variations in a cloud user’s resource demand leads to the problem of an excess power consumption, resource wastage, performance and Quality-of-Service (QoS) degradation. The proposed framework addresses these challenges by matching the application’s predicted resource requirement with resource capacity of VMs precisely and thereby consolidating entire load on the minimum number of energy-efficient physical machines (PMs). The three consecutive contributions of the proposed work are: (1) Online Multi-Resource Feed-forward Neural Network (OM-FNN) to forecast the multiple resource demands concurrently for the future applications, (2) autoscaling of VMs based on the clustering of the predicted resource requirements, (3) allocation of the scaled VMs on the energy-efficient PMs. The integrated approach successively optimizes resource utilization, saves energy and automatically adapts to the changes in future application resource demand. The proposed framework is evaluated by using real workload traces of the benchmark Google Cluster Dataset and compared against different scenarios including energy-efficient VM placement (VMP) with resource prediction only, VMP without resource prediction and autoscaling, and optimal VMP with autoscaling based on actual resource utilization. The observed results demonstrate that the proposed integrated approach achieves near-optimal performance against optimal VMP and outperforms rest of the VMPs in terms of power saving and resource utilization up to 88.5% and 21.12% respectively. In addition, OM-FNN predictor shows better accuracy, lesser time and space complexity over a traditional single-input and single-output feed-forward neural network (SISO-FNN) predictor.
Indigenous Data Sovereignty in the Era of Big Data and Open Data

Abstract Indigenous Data Sovereignty, in its proclamation of the right of Indigenous peoples to govern the collection, ownership, and application of data, recognises data as a cultural and economic asset. The impact of data is magnified by the emergence of Big Data and the associated impetus to open publicly held data (Open Data). Aboriginal and Torres Strait Islander peoples, families and communities, heavily overrepresented in social disadvantage–related data will also be overrepresented in the application of these new technologies, but in a data landscape, Indigenous peoples remain largely alienated from the use of data and its utilization within the channels of policy power. Existing data infrastructure, and the emerging Open Data infrastructure, neither recognise Indigenous agency and worldviews nor consider Indigenous data needs. This is demonstrated in the absence of any consideration of Indigenous data issues in Open Data discussions and publication. Thus, while the potential benefits of this data revolution are trumpeted, our marginalised social, cultural and political location suggests we will not share equally in these benefits. This paper discusses the unforeseen (and likely unseen) consequences of the influence of Open Data and Big Data and discusses how Indigenous Data Sovereignty can mediate risks while providing pathways to collective benefits.
Near-Online Tracking With Co-Occurrence Constraints in Blockchain-Based Edge Computing

Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.
Few-Shot Scene-Adaptive Anomaly Detection

We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method. All codes are released in https://github.com/yiweilu3/Few-shot-Scene-adaptive-Anomaly-Detection .