Reviewerly/exHarmony huggingface dataset:
Estimation of the number of components of nonparametric multivariate finite mixture models

We propose a novel estimator for the number of mixture components (denoted by M) in a nonparametric finite mixture model. The setting that we consider is one where the analyst has repeated observations of K≥2 variables that are conditionally independent given a finitely supported latent variable with M support points. Under a mild assumption on the joint distribution of the observed and latent variables, we show that an integral operator T that is identified from the data has rank equal to M. We use this observation, in conjunction with the fact that singular values of operators are stable under perturbations, to propose an estimator of M, which essentially consists of a thresholding rule that counts the number of singular values of a consistent estimator of T that are greater than a data-driven threshold. We prove that our estimator of M is consistent, and establish nonasymptotic results, which provide finite sample performance guarantees for our estimator. We present a Monte Carlo study, which shows that our estimator performs well for samples of moderate size.
Progressive Feature Enhancement for Person Re-Identification

Most of person Re-Identification (ReID) works extract features from the top CNN layer for person image matching. The top CNN layer commonly corresponds to large receptive fields, thus is not effective in depicting visual cues at multiple scales, e.g., both global appearance and local details. This work proposes a Progressive Feature Enhancement (PFE) algorithm to spot and fuse multi-scale discriminative cues from different CNN layers into a single feature vector. The basic idea is to progressively learn complementary features with a layer-specific supervision from deep to shallow layers. The layer-specific supervision is inferred by the proposed Masked Feature Augmentation (MFA) module. For each CNN layer, MFA indicates cues that have been captured in its deeper layers. MFA hence supervises each layer to depict additional visual cues missed by its deeper layers. This framework effectively learns multi-scale features without requiring extra part annotations or dividing body parts. To further facilitate the layer-specific feature generation, a Two-Stage Attention Module (TSAM) is proposed to filter pixel-wise and channel-wise noises on intermediate feature maps. Extensive experiments on four ReID datasets show that our approach achieves competitive performance, e.g., with ResNet50 backbone, it achieves rank1 accuracy of 95.1%, 88.2%, 79.1% and 71.6% on Market-1501, DukeMTMC-ReID, MSMT17 and CUHK03 Detected, respectively, outperforming many state-of-the-art works.
Achieving Efficient and Privacy-Preserving Dynamic Skyline Query in Online Medical Diagnosis

Wireless body area network (WBAN) and big data techniques indubitably enable the online medical diagnosis system to be more practical. In the system, to make a more accurate diagnosis, doctors wish to obtain some archived medical data records, which are similar to the sensed patient data, to learn from the prior diagnoses. As a practically useful similarity search, the dynamic skyline query can provide doctors with similar data records having all possible relative weights of attributes. Driven by the powerful cloud, the data owner often outsources encrypted data and the corresponding services, e.g., dynamic skyline query services here, to a third-party cloud. As a result, it is required to perform the dynamic skyline query over encrypted data. However, existing schemes are either insecure or inefficient. To address the issue, in this article, we propose an efficient and privacy-preserving dynamic skyline query scheme and use it in an online medical diagnosis system. Specifically, based on symmetric homomorphic encryption (SHE), we present a set of efficient and secure protocols to achieve various operations, such as less than comparison, equality test, and dominance determination, without leaking any sensitive information to the cloud. With these secure protocols, we carefully design our dynamic skyline query scheme to attain full security and high efficiency at the same time. Detailed security analysis shows that our proposed scheme is indeed privacy-preserving. With extensive experimental evaluations, we show that our proposed scheme outperforms the alternative scheme by two orders of magnitude in the computational cost and at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.1\times $ </tex-math></inline-formula> in the communication cost.
Discriminative learning of imaginary data for few-shot classification

Humans can quickly learn new visual categories, because they can easily visualize or imagine what we need to recognize in an image with a complex background, and how it differs from other images. Incorporating this ability to hallucinate discriminative instances of novel classes might help machine vision systems perform better few-shot classification. In this paper, we propose a Discriminative Hallucination Learning Network based on attentional mechanism, and unify the GNN-based classifier with a “hallucinator” for few-shot classification. Firstly, we use a pre-trained saliency network to hallucinate the foreground image. Then, the hallucination feature network (FNet) and zoom network (ZNet) are designed to extract more fine-grained local images adaptively with intra-cluster similarity and the inter-cluster dissimilarity. The embedding network (Enet) initialize the node representation of the graph structure from a jointly trained convolutional neural network. Finally, the proposed method is evaluated extensively on three challenging ZSL benchmark datasets. It significantly outperforms state-of-the-art methods in both supervised and semi-supervised few-shot image classification experiments.
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation. Such domain shift is particularly noticeable along class boundaries, invalidating one of the main goals of semantic segmentation that consists in obtaining sharp segmentation masks. In this work, we specifically address this core problem in the context of Unsupervised Domain Adaptation and present a novel low-level adaptation strategy that allows us to obtain sharp predictions. Moreover, inspired by recent self-training techniques, we introduce an effective data augmentation that alleviates the noise typically present at semantic boundaries when employing pseudo-labels for selftraining. Our contributions can be easily integrated into other popular adaptation frameworks, and extensive experiments show that they effectively improve performance along class boundaries.
Destroying robust steganography in online social networks

In recent years, some robust steganography approaches for covert transmission in online social networks (OSNs) have been proposed. Some existing image algorithms, such as watermarking removal methods, have been developed to remove the hidden messages that might exist in an uploaded image. However, these methods may result in poor image quality after processing, which is not practical for OSN platforms. In this paper, we propose a general method for destroying robust steganography in OSNs that is able to preserve the image quality. In particular, we design a deep learning network including an attack module and an optimization module, and a new loss function is proposed to consider both. The experimental results indicate that the proposed method can be universally applied tocounter multiple robust steganography algorithms. Furthermore, the resulting processed image can be good quality, and the time cost is sufficiently low to meet most real-time requirements.
Smart Contracts and NFTs: Non-Fungible Tokens as a Core Component of Blockchain to Be Used as Collectibles

Non-fungible tokens are one of the most important future application domains for smart contracts. Ethereum is the pioneer of a blockchain-based decentralized computing platform that has ultimately standardized these types of tokens into a well-defined interface, now known as ERC-721. Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permissionless blockchains. This paper aims to analyze blockchain and cryptocurrencies' technical underpinnings, specifically non-fungible tokens or "crypto-collectibles," with the help of a blockchain-based image matching game. While outlining the theoretical implications and use cases of NFTs, this paper also gives a glimpse into their possible use in the domain of human user verification to prevent misuse of public data by automated scripts. This demonstrates the interaction of the ERC-721 token with the Ethereum-based decentralized application. Further, we aim to reach a definitive conclusion on the benefits and challenges of NFTs and thus reach a solution that would be beneficial to both researchers and practitioners.
On Private Data Collection of Hyperledger Fabric

Hyperledger Fabric is a popular permissioned Blockchain framework for a consortium of organizations to develop Blockchain based applications and transact within the consortium. Hyperledger Fabric introduces a fine-grained access control mechanism called the private data collection (PDC), which allows private data to be shared by only a subset of participants. In this paper, we analyze PDC and show three classes of use cases in which misuse of Hyperledger Fabric features may endanger implemented Hyperledger Fabric systems. We present two groups of potential attacks including fake PDC results injection and PDC leakage against the misuse of the policy based consensus protocol. We use prototype systems to validate the discovered attacks. We also collected 6392 Hyprledger Fabric projects on GitHub and built a tool to statically analyse them. We find that 86.51% of the PDC related projects are potentially vulnerable to the fake PDC results injection attacks, and 91.67% have PDC leakage issues. We design new features for the Hyper-ledger Fabric framework to mitigate the attacks and show that the new features have minor impact on the system performance.
Visual Graph Memory with Unsupervised Representation for Visual Navigation

We present a novel graph-structured memory for visual navigation, called visual graph memory (VGM), which consists of unsupervised image representations obtained from navigation history. The proposed VGM is constructed incrementally based on the similarities among the unsupervised representations of observed images, and these representations are learned from an unlabeled image dataset. We also propose a navigation framework that can utilize the proposed VGM to tackle visual navigation problems. By incorporating a graph convolutional network and the attention mechanism, the proposed agent refers to the VGM to navigate the environment while simultaneously building the VGM. Using the VGM, the agent can embed its navigation history and other useful task-related information. We validate our approach on the visual navigation tasks using the Habitat simulator with the Gibson dataset, which provides a photo-realistic simulation environment. The extensive experimental results show that the proposed navigation agent with VGM surpasses the state-of-the-art approaches on image-goal navigation tasks. Project Page: https://sites.google.com/view/iccv2021vgm
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation

In this work, we study the ambiguity problem in the task of unsupervised 3D human pose estimation from 2D counterpart. On one hand, without explicit annotation, the scale of 3D pose is difficult to be accurately captured (scale ambiguity). On the other hand, one 2D pose might correspond to multiple 3D gestures, where the lifting procedure is inherently ambiguous (pose ambiguity). Previous methods generally use temporal constraints (e.g., constant bone length and motion smoothness) to alleviate the above issues. However, these methods commonly enforce the outputs to fulfill multiple training objectives simultaneously, which often lead to sub-optimal results. In contrast to the majority of previous works, we propose to split the whole problem into two sub-tasks, i.e., optimizing 2D input poses via a scale estimation module and then mapping optimized 2D pose to 3D counterpart via a pose lifting module. Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively. These two modules are optimized via a iterative training scheme with corresponding temporal constraints, which effectively reduce the learning difficulty and lead to better performance. Results on the Human3.6M dataset demonstrate that our approach improves upon the prior art by 23.1% and also outperforms several weakly supervised approaches that rely on 3D annotations. Our project is available at https://sites.google.com/view/ambiguity-aware-hpe.
Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images

Recent deep learning techniques have shown promising performance on survival prediction from Whole Slide Images (WSIs). These methods are often based on multiple-step frameworks including patch sampling, feature extraction, and feature aggregation. However, feature extraction typically relies on handcrafted features or Convolutional Neural Networks (CNNs) pretrained on ImageNet without fine-tuning, thus leading to suboptimal performance. Besides, to aggregate features, previous studies focus on WSI-level survival prediction but ignore the heterogeneous information that is present in multiple WSIs acquired for the same patient. To address the above challenges, we propose a survival prediction model that exploits heterogeneous features at the patient-level. Specifically, we introduce colorization as the pretext task to train the CNNs which are tailored for extracting features from patches of WSIs. In addition, we develop a patient-level framework integrating multiple WSIs for survival prediction with consistency and ranking losses. Extensive experiments show that our model achieves state-of-the-art performance on two large-scale public datasets.
Blockchain as a frontier in biotechnology and bioenergy applications

The fourth Industrial Revolution is stimulating a fast-paced and resilient industrial internet of things (IIoT) ecosystem. Blockchain, a decentralized digital ledger technology, plays a crucial role in improvising, securing, and streamlining traditional biotechnology-related industrial processes with IoT and creates a sustainable nexus between social, economic, and environmental aspects.
Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring

Machine Learning Algorithms (MLAs) have recently introduced considerable lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network (ANN), Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical (Sentinel 2, ASTER, Landsat OLI and Earth-observing 1 Advanced Land Imager (ALI)), radar (Sentinel 1 and ALOS PALSAR), DEMs and their derivatives (Slope, and Aspect). The study aims to (1) monitor the effect of data dimensionality in enhancing categorization accuracy. (2) disclose the most efficient MLA and most powerful dataset in labeling rock units accurately. (3) highlight the impact of embedding topographical and radar data in lithologic classification. (4) outline the best relation between the number of training pixels and number of utilized bands, in delivering reliable allocation. To achieve these aims, we selected training and testing pixels meticulously, in concordance with a recently published geological map of the study area. We adopted a stacked vector approach for handling the implemented multi-sensor data. Results show that diversifying information sources raised the classification accuracy by approximately 10% for each classifier. SVM and MLC are much better than ANN. Slope is better than aspect and both are less qualified when compared to DEM. Sentinel 1 (C-band) and ALOS PALSAR (L-band) effects are not so different whatever the implemented polarization. Landsat OLI is less qualified in lithologic classification when compared to Sentinel 2, ASTER and ALI. The utilized training pixels should be at least 30N for (N) channels submitted to the classifiers.
Forerunner

Ethereum is an emerging distributed computing platform that supports a decentralized replicated virtual machine at a large scale. Transactions in Ethereum are specified in smart contracts, disseminated through broadcast, accepted into the chain of blocks, and then executed on each node. In this new Dissemination-Consensus-Execution (DiCE) paradigm, the time interval between when a transaction is known (during the dissemination phase) to when the transaction is executed (after the consensus phase) offers a window of opportunity to accelerate transaction processing through speculative execution. However, the traditional speculative execution, which hinges on the ability to predict the future accurately, is inadequate because of DiCE's many-future nature.
Enabling Low Latency Edge Intelligence based on Multi-exit DNNs in the Wild

In recent years, deep neural networks (DNNs) have witnessed a booming of artificial intelligence Internet of Things applications with stringent demands across high accuracy and low latency. A widely adopted solution is to process such computation-intensive DNNs inference tasks with edge computing. Nevertheless, existing edge-based DNN processing methods still cannot achieve acceptable performance due to the intensive transmission data and unnecessary computation. To address the above limitations, we take the advantage of Multi-exit DNNs (ME-DNNs) that allows the tasks to exit early at different depths of the DNN during inference, based on the input complexity. However, naively deploying ME-DNNs in edge still fails to deliver fast and consistent inference in the wild environment. Specifically, 1) at the model-level, unsuitable exit settings will increase additional computational overhead and will lead to excessive queuing delay; 2) at the computation-level, it is hard to sustain high performance consistently in the dynamic edge computing environment. In this paper, we present a Low Latency Edge Intelligence Scheme based on Multi-Exit DNNs (LEIME) to tackle the aforementioned problem. At the model-level, we propose an exit setting algorithm to automatically build optimal ME-DNNs with lower time complexity; At the computation-level, we present a distributed offloading mechanism to fine-tune the task dispatching at runtime to sustain high performance in the dynamic environment, which has the property of close-to-optimal performance guarantee. Finally, we implement a prototype system and extensively evaluate it through testbed and large-scale simulation experiments. Experimental results demonstrate that LEIME significantly improves applications' performance, achieving 1.1–18.7 × speedup in different situations.
Computerized history-taking improves data quality for clinical decision-making—Comparison of EHR and computer-acquired history data in patients with chest pain

Patients’ medical histories are the salient dataset for diagnosis. Prior work shows consistently, however, that medical history-taking by physicians generally is incomplete and not accurate. Such findings suggest that methods to improve the completeness and accuracy of medical history data could have clinical value. We address this issue with expert system software to enable automated history-taking by computers interacting directly with patients, i . e . computerized history-taking (CHT). Here we compare the completeness and accuracy of medical history data collected and recorded by physicians in electronic health records (EHR) with data collected by CHT for patients presenting to an emergency room with acute chest pain. Physician history-taking and CHT occurred at the same ED visit for all patients. CHT almost always preceded examination by a physician. Data fields analyzed were relevant to the differential diagnosis of chest pain and comprised information obtainable only by interviewing patients. Measures of data quality were completeness and consistency of negative and positive findings in EHR as compared with CHT datasets. Data significant for the differential of chest pain was missing randomly in all EHRs across all data items analyzed so that the dimensionality of EHR data was limited. CHT files were near complete for all data elements reviewed. Separate from the incompleteness of EHR data, there were frequent factual inconsistencies between EHR and CHT data across all data elements. EHR data did not contain representations of symptoms that were consistent with those reported by patients during CHT. Trial registration: This study is registered at https://www.clinicaltrials.gov (unique identifier: NCT03439449 ).
Towards a Human Values Dashboard for Software Development

Background: There is a growing awareness of the importance of human values (e.g., inclusiveness, privacy) in software systems. However, there are no practical tools to support the integration of human values during software development. We argue that a tool that can identify human values from software development artefacts and present them to varying software development roles can (partially) address this gap. We refer to such a tool as human values dashboard. Further to this, our understanding of such a tool is limited. Aims: This study aims to (1) investigate the possibility of using a human values dashboard to help address human values during software development, (2) identify possible benefits of using a human values dashboard, and (3) elicit practitioners' needs from a human values dashboard. Method: We conducted an exploratory study by interviewing 15 software practitioners. A dashboard prototype was developed to support the interview process. We applied thematic analysis to analyse the collected data. Results: Our study finds that a human values dashboard would be useful for the development team (e.g., project manager, developer, tester). Our participants acknowledge that development artefacts, especially requirements documents and issue discussions, are the most suitable source for identifying values for the dashboard. Our study also yields a set of high-level user requirements for a human values dashboard (e.g., it shall allow determining values priority of a project). Conclusions: Our study suggests that a values dashboard is potentially used to raise awareness of values and support values-based decision-making in software development. Future work will focus on addressing the requirements and using issue discussions as potential artefacts for the dashboard.