Reviewerly/exHarmony huggingface dataset:
Adaptive graph convolutional collaboration networks for semi-supervised classification

Graph convolution networks (GCNs) have achieved remarkable success in processing non-Euclidean data. GCNs update the feature representations of each sample by aggregating the structure information from K-order (layer) neighborhood samples. Existing GCNs variants rely heavily on the K-th layer semantic information with K-order neighborhood information aggregating. However, semantic features from different convolution layers have distinct sample attributes. The single-layer semantic feature is only a one-sided feature representation. Besides, the semantic features of traditional GCNs will be oversmoothing with multi-layer structure information aggregates. In this paper, to solve the above-mentioned problem, we propose adaptive graph convolutional collaboration networks (AGCCNs) for the semi-supervised classification task. AGCCNs can fully use the different scales of discrimination information contained in the different convolutional layers. Specifically, AGCCNs utilize the attention mechanism to learn the relevance (contribution) coefficient of the deep semantic features from different convolution layers for the task, which aims to effectively discriminant their importance. After multiple optimizations, AGCCNs can adaptively learn the robust deep semantic features via the effective semantic fusion process between multi-layer semantic information. Compared with GCNs that only utilize the K-th layer semantic features, AGCCNs make the learned deep semantic features contain richer and more robust semantic information. What is more, our proposed AGCCNs can aggregate the appropriate K-order neighborhood information for each sample, which can relieve the oversmoothing issue of traditional GCNs and better generalize shallow GCNs to more deep layers. Abundant experimental results on several popular datasets demonstrate the superiority of our proposed AGCCNs compared with traditional GCNs.
Two-Stage Edge Reuse Network for Salient Object Detection of Strip Steel Surface Defects

Recently, researchers have paid more attention to salient object detection of strip steel surface defects, but there are lots of difficulties, such as a mass of noise, blurry defect boundaries, complex backgrounds, and various types of defects. Existing image salient object detection methods fail to overcome the above challenging scenes. To address them, in this paper, we propose a novel Two-Stage Edge Reuse Network (TSERNet), which consists of two stages, <i>i.e</i>., prediction and refinement. In the first prediction stage, we construct a primary net based on the encoder-decoder architecture. The encoder not only extracts multi-scale features but also generates an edge map. In the decoder, we propose a novel Edge-aware Foreground-Background Integration (EFBI) module to distinguish foreground and background through edge features and reverse attention mechanism, and exploit the decoder to generate an initial saliency map. In the second refinement stage, we construct a sub-net based on the same architecture as that of the first stage. Its encoder extracts features from the initial saliency map, and its decoder deploys the Edge-aware Refinement Module (ERM), which reuses the edge map generated from the first stage, to enhance these features for purifying the initial saliency map, resultting in the final saliency map. Comprehensive experiments on the public dataset show that our proposed TSERNet is consistently superior to 22 relevant state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/TSERNet.
Contrastive label correction for noisy label learning

Noisy label learning is an important process that facilitates the collection of noisy label data for training accurate deep neural networks. The latest label correction methods are effective approaches that focus on identifying label errors in datasets for training. However, existing approaches to identify noise generally rely on model predictions that are not scalable and are error-prone. In this study, a new noisy label learning framework is proposed by leveraging supervised contrastive learning for enhanced representation and improved label correction. Specifically, the proposed framework consists of a class-balanced prototype queue, a prototype-based label correction algorithm, and a supervised representation learning module. The proposed framework produces closely aligned representations and model predictions for instances from the same classes and facilitates label correction by aggregating noisy and prototype labels. Furthermore, a theoretical analysis of the proposed framework is provided from the perspective of an expectation–maximization (EM) algorithm. To demonstrate the efficacy of the proposed framework, experiments were performed on synthetic datasets with various noise patterns and levels. The experimental results demonstrate that the proposed framework achieves superior classification accuracy compared with other label correction frameworks.
Multi-Stage RGB Image Encryption

The demand for efficient picture encryption techniques is growing all the time. Meanwhile, recent research has demonstrated that using chaotic functions and PRNG to administrate security produces excellent outcomes. This paper presents a 3-stage image encryption technique. The Mersenne Twister PRNG is used in the first stage, followed by an S-box in the second stage, and finally the tan Logistic map in the third stage. The suggested encryption scheme’s performance evaluation reveals high resistance to many types of attacks at a low computing cost. The suggested image encryption is therefore appropriate for real-time applications. A comparison of the proposed scheme to the state-of-the-art also shows that it is superior.
Automated detection of microfossil fish teeth from slide images using combined deep learning models

Microfossil fish teeth, known as ichthyoliths, provide a key constraint on the depositional age and environment of deep-sea sediments, especially pelagic clays where siliceous and calcareous microfossils are rarely observed. However, traditional methods for the observation of ichthyoliths require considerable time and manual labor, which can hinder their wider application. In this study, we constructed a system to automatically detect ichthyoliths in microscopic images by combining two open source deep learning models. First, the regions for ichthyoliths within the microscopic images are predicted by the instance segmentation model Mask R–CNN. All the detected regions are then re-classified using the image classification model EfficientNet-V2 to determine the classes more accurately. Compared with only using the Mask R–CNN model, the combined system offers significantly higher performance (89.0% precision, 78.6% recall, and an F1 score of 83.5%), demonstrating the utility of the system. Our system can also predict the lengths of the teeth that have been detected, with more than 90% of the predicted lengths being within ±20% of measured length. This system provides a novel, automated, and reliable approach for the detection and length measurement of ichthyoliths from microscope images that can be applied in a range of paleoceanographic and paleoecological contexts.
Blockchain financial geographies: Disrupting space, agency and scale

The potential of blockchain-technology to disrupt the financial and related sectors by making many intermediaries superfluous is subject of frequent discussions. We analyze the current and potential structural effects of blockchain-based business models. In order to make “disruption” more traceable, we define three dimensions of it: space, agency and scale. Using a combination of Crunchbase data, interviews and participant observation at workshops and conferences, and case studies we outline areas in which companies are seeking to implement blockchain in financial functions and assess the extent to which this represents structural changes in finance and associated advanced producer services (APS). We find that while the expectations for blockchain as a transformative force in finance/APS are high, the actual structural effects are much less clear as we see established industry players (e.g., banks) capturing these efforts and/or new entrants essentially recreating the existing structures and functions of the current financial sector. We outline a number of possibilities as to why to date blockchain has not met these expectations of disruption. We explore how scale emerges as a theoretically fruitful avenue for understanding which phenomena are actually well placed to fundamentally alter the structure of the financial sector including related advanced services. Three case studies on initial coin offerings, real estate investments and the “money memory” associated with blockchain-based currencies show that potentially transformative effects derive from blockchain technology being able to shift scale.
Adaptive Re-Ranking with a Corpus Graph

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores. The process enables the use of highly-effective but expensive scoring functions that are not suitable for use directly in structures like inverted indices or approximate nearest neighbour indices. However, re-ranking pipelines are inherently limited by the recall of the initial candidate pool; documents that are not identified as candidates for re-ranking by the initial retrieval function cannot be identified. We propose a novel approach for overcoming the recall limitation based on the well-established clustering hypothesis. Throughout the re-ranking process, our approach adds documents to the pool that are most similar to the highest-scoring documents up to that point. This feedback process adapts the pool of candidates to those that may also yield high ranking scores, even if they were not present in the initial pool. It can also increase the score of documents that appear deeper in the pool that would have otherwise been skipped due to a limited re-ranking budget. We find that our Graph-based Adaptive Re-ranking (GAR) approach significantly improves the performance of re-ranking pipelines in terms of precision- and recall-oriented measures, is complementary to a variety of existing techniques (e.g., dense retrieval), is robust to its hyperparameters, and contributes minimally to computational and storage costs. For instance, on the MS MARCO passage ranking dataset, GAR can improve the nDCG of a BM25 candidate pool by up to 8% when applying a monoT5 ranker.
Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data

Big Tech companies operating in a data-driven economy offer services that rely on their users' personal data and usually store this personal information in "data silos" that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation.
Data governance through a multi-DLT architecture in view of the GDPR

Abstract The centralization of control over the processing of personal data threatens the privacy of individuals due to the lack of transparency and the obstruction of easy access to their data. Individuals need the tools to effectively exercise their rights, enshrined in regulations such as the European Union General Data Protection Regulation (GDPR). Having direct control over the flow of their personal data would not only favor their privacy but also a “data altruism”, as supported by the new European proposal for a Data Governance Act. In this work, we propose a multi-layered architecture for the management of personal information based on the use of distributed ledger technologies (DLTs). After an in-depth analysis of the tensions between the GDPR and DLTs, we propose the following components: (1) a personal data storage based on a (possibly decentralized) file storage (DFS) to guarantee data sovereignty to individuals, confidentiality and data portability; (2) a DLT-based authorization system to control access to data through two distributed mechanisms, i.e. secret sharing (SS) and threshold proxy re-encryption (TPRE); (3) an audit system based on a second DLT. Furthermore, we provide a prototype implementation built upon an Ethereum private blockchain, InterPlanetary File System (IPFS) and Sia and we evaluate its performance in terms of response time.
Scalable Bayesian phylogenetics

Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
Dual-stream cross-modality fusion transformer for RGB-D action recognition

RGB-D-based action recognition can achieve accurate and robust performance due to rich complementary information, and thus has many application scenarios. However, existing works combine multiple modalities by late fusion or learn multimodal representation with simple feature-level fusion methods, which fail to effectively utilize complementary semantic information and model interactions between unimodal features. In this paper, we design a self-attention-based modal enhancement module (MEM) and a cross-attention-based modal interaction module (MIM) to enhance and fuse RGB and depth features. Moreover, a novel bottleneck excitation feed-forward block (BEF) is proposed to enhance the expression ability of the model with few extra parameters and computational overhead. By integrating these two modules with BEFs, one basic fusion layer of the cross-modality fusion transformer is obtained. We apply the transformer on top of the dual-stream convolutional neural networks (ConvNets) to build a dual-stream cross-modality fusion transformer (DSCMT) for RGB-D action recognition. Extensive experiments on the NTU RGB+D 120, PKU-MMD, and THU-READ datasets verify the effectiveness and superiority of the DSCMT. Furthermore, our DSCMT can still make considerable improvements when changing convolutional backbones or when applied to different multimodal combinations, indicating its universality and scalability. The code is available at https://github.com/liuzwin98/DSCMT.
FedTMI: Knowledge aided federated transfer learning for industrial missing data imputation

Missing data are quite common in the industrial field. Since most data driven methods used in these applications rely on complete and high-quality data set, it is important to handle the missing data problem. Also, the severity of missing data varies across factories, which means that a single factory could fail to handle missing data locally. With the rapid development of cloud–edge computing, different factories could work together to handle missing data problem by federated learning without sharing their private training data. However, popular federated imputation methods assume each edge, i.e., a factory, to be an equal participant during learning a central model in the cloud, and thus are unable to handle heterogeneous data across different clients, leading to slow convergence and degraded learning performance. In this paper, a federated transfer missing data imputation method (FedTMI) is proposed to address this dilemma. Firstly, edge models are built with traditional Generative Adversarial Imputation Nets (GAIN) trained on edge data sets and edge knowledge is extracted as knowledge vectors to identify variables which could provide room for performance improvement. Secondly, for a certain target edge, with edge models and edge knowledge being accumulated in the cloud, models from non-target edges are chosen as helper models following certain rules aided by the corresponding edge knowledge. The helper models could provide effective guidance for data imputation in the target edge. Thirdly, the target edge executes federated transfer learning with the selected helper models. Model knowledge of helper models is transferred to the target edge, forming an updated target edge model with its edge data. Case studies on steam-driven water pumps in thermal power plants show the feasibility of the proposed FedTMI. It outperforms the baseline method with model averaging (FedAvg), especially when the data are not independent and identically distributed (non-i.i.d.) across different edges.
Click-through rate prediction using transfer learning with fine-tuned parameters

The figure presents the pre-trained CTR model, the fine-tuned CTR model, and our proposed AutoFT framework from left to right. The pre-trained CTR model with random parameters starting point is optimized based on the data from source domains or based on the data from all domains. The fine-tuned CTR model is initialized with trained parameters in source scenarios and optimized with data from the new target domain. AutoFT involves three sets of parameters: the light-blue boxes contain parameters from the pre-trained CTR model, the dark-blue boxes contain parameters initialized by traditional fine-tuning strategy will be re-optimized during training and the red boxes contain the parameters of the policy networks. • The proposed AutoFT automatically finds a route between the pre-trained network and the siamese fine-tuned network per instance in the target domain and is compatible with any deep CTR models. • The transfer policy for the embedding layer and the feature interaction layers can be trained synchronously with deep CTR models. • The result shows that the lower layers may represent more general features while higher layers need more fine-tuning to fit a specific target domain. In real business platforms, recommendation systems usually need to predict the CTR of multiple business. Since different scenarios may have common feature interactions, knowledge transferring based methods are often used by re-optimizing the pre-trained CTR model from source scenarios to a new target domain. In addition to knowledge transfer, it is noteworthy that generalizing target domain data outside of the CTR model accurately is also important when re-training all of the fine-tuned parameters. Generally, the pre-trained model trained on large source domains can represent the characteristics of different instances and capture typical feature interactions. It would be useful to directly reuse fine-tuned parameters from source domains to serve the target domain. However, different instances of the target domain may need different amounts of source information to fine-tune the model parameters, and these decisions of freezing or re-optimizing model parameters, which highly depend on the fine-tuned model and target instances, may require much manual effort. In this paper, we propose an end-to-end transfer learning framework with fine-tuned parameters for CTR prediction, called Automatic Fine-Tuning (AutoFT). The principal component of AutoFT is a set of learnable transfer policies that independently determine how the specific instance-based fine-tuning policies should be trained, which decide the routing in the embedding representations and the high-order feature representations layer by layer in deep CTR model. Extensive tests on two benchmarks and one real commercial recommender system deployed in Huawei's App Store show that AutoFT can greatly increase CTR prediction performance when compared to current transferring methodologies.
A new weakly supervised discrete discriminant hashing for robust data representation

In real applications, the label information on many data is inaccurate, or a completely reliable label needs to be obtained at a high cost. The previous supervised hashing algorithms consider only the label information in the mapping process from Euclidean space to Hamming space when learning hash codes. However, there is no doubt that these algorithms are suboptimal in maintaining the relationships between high-dimensional data spaces. To overcome this problem, this paper advances a new weakly supervised discrete discriminant hashing (WDDH) to ensure a more effective representation of data and better retrieval of information. First, we consider the nearest neighbour relationship between samples, and new neighbourhood graphs are constructed to describe the geometric relationship between samples. Second, the algorithm embeds the learning of the hash function into the model and optimises the hash codes by a one-step iterative updating algorithm. Finally, it is compared with the existing classical unsupervised hashing algorithm and supervised hashing algorithm on different databases. The results and discussion of the experiments clearly show that the proposed WDDH algorithm in this paper is more robust for data representation in learning low-quality label data, coarse-grained label data and noisy data.
A Joint Global–Local Network for Human Pose Estimation With Millimeter Wave Radar

This article proposes a two-branch learning model, namely, the joint global–local network, for human pose estimation (HPE) using millimeter wave radar. The aim of this work is to remediate the ill-posed problems in HPE arising from using the destructive observations with superimposed reflection signals. In the developed two-branch learning model, the global branch takes use of the superimposed signals from the whole human body to reconstruct the coarse pose estimation from a global perspective, and the local branch is responsible for fining the pose estimations with the decomposed signals from individual body parts in a complementary way. In doing this, two branch learning processes will be coordinated with the followed attention-based fusion module in terms of the local and global consistency. It is remarkable that the learning driven by the decomposed signals is motivated by exploiting the spatial-temporal evolution patterns of individual body parts for inferring the corresponding movements, which plays a crucial yet complementary role in the collaboration with the learning driven by the superimposed signals. With the two-branch learning architecture, the proposed method is advantageous in incorporating the local motion constraints from individual body parts into the coarse global estimation from the whole human body, which contributes to reconstructing plausible yet accurate pose estimations with the local and global kinematic consistency. Extensive experiments are presented to demonstrate the effectiveness of the proposed method.