Reviewerly/exHarmony huggingface dataset:
Second-order multi-instance learning model for whole slide image classification

Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node metastasis of breast cancer, which usually lack fine-grade annotations of tumor regions and have large resolutions (typically 105 × 105pixels). Multi-instance learning has gradually become a dominant weakly supervised learning framework for WSI classification when only slide-level labels are available. In this paper, we develop a novel second-order multiple instances learning method (SoMIL) with an adaptive aggregator stacked by the attention mechanism and recurrent neural network (RNN) for histopathological image classification. To be specific, the proposed method applies a second-order pooling module (matrix power normalization covariance) for instance-level feature extraction of weakly supervised learning framework, attempting to explore second-order statistics of deep features for histopathological images. Additionally, we utilize an efficient channel attention mechanism to adaptively highlight the most discriminative instance features, followed by an RNN to update the final bag-level representation for the slide classification. Experimental results on the lymph node metastasis dataset of 2016 Camelyon grand challenge demonstrate the significant improvement of our proposed SoMIL framework compared with other state-of-the-art multi-instance learning methods. Moreover, in the external validation on 130 WSIs, SoMIL also achieves an impressive area under the curve performance that competitive to the fully-supervised framework.
A Domain-Guided Noise-Optimization-Based Inversion Method for Facial Image Manipulation

A style-based architecture (StyleGAN2) yields outstanding results in data-driven unconditional generative image modeling. This work proposes a Domain-guided Noise-optimization-based Inversion (DNI) method to perform facial image manipulation. It works based on an inverse code that includes: 1) a novel domain-guided encoder called Image2latent to project the image to StyleGAN2 latent space, which can reconstruct an input image with high-quality and maintain its semantic meaning well; 2) a noise optimization mechanism in which a set of noise vectors are used to capture the high-frequency details such as image edges, further improving image reconstruction quality; and 3) a mask for seamless image fusion and local style migration. We further propose a novel semantic alignment evaluation pipeline. It evaluates the semantic alignment with an inverse code by using different attribute boundaries. Extensive qualitative and quantitative comparisons show that DNI can capture rich semantic information and achieve a satisfactory image reconstruction. It can realize a variety of facial image manipulation tasks and outperform state of the art.
A generalizability measure for program synthesis with genetic programming

The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of the programs synthesized by GP. However, generating training data is usually an expensive task as the output value for every training case must be calculated manually by the user. Therefore, this work suggests an approximation of the expected generalization ability of solution candidates found by GP. To obtain candidate solutions that all solve the training cases, but are structurally different, a GP run is not stopped after the first solution is found that solves all training instances but search continues for more generations. For all found candidate solutions (solving all training cases), we calculate the behavioral vector for a set of randomly generated additional inputs. The proportion of the number of different found candidate solutions generating the same behavioral vector with highest frequency compared to all other found candidate solutions with different behavior can serve as an approximation for the generalizability of the found solutions. The paper presents experimental results for a number of standard program synthesis problems confirming the high prediction accuracy.
HSC: Leveraging horizontal shortcut connections for improving accuracy and computational efficiency of lightweight CNN

The past few years have witnessed the dramatic increase in layers of convolutional neural networks (CNN). Most studies focused on the CNN’s vertical structure design (e.g. residual structure, creating short paths architecture from early layers to later layers in vertical connections), but few people pay their attention to the process of feature generation and extraction in a single convolutional layer in CNN. In this paper, we find the non-feature suppression phenomenon in the process of extracting features. On the basis of this, we proposed an orthogonal approach named HSC (Horizontal Shortcut Connections) to improve feature representation fusion and computational efficiency for CNN. Especially, our HSC approach can effectively reduce interference overhead of non-feature areas and enhance the information fusion for depthwise convolution and group convolution which are the key blocks in lightweight neuron network. At HSC layer, the feature-maps of all preceding layer are properly connected with our strategy in horizon direction to constitute features and then produce a new representation which are used as input feature-maps passed on subsequent layers. Our HSC block can be plugged into convolution neural networks that include group convolution or depewise convolution, and can effectively improve accuracy of convolutional networks with slight additional computational cost. We evaluate our design on the popular lightweight neural networks and standard CNN structure. Compared with existing methods, we can achieve 1.63% accuracy improvement for MobileNet v2 on CIFAR-10 dataset and up to 3.70% accuracy improvement on CIFAR-100 dataset by adding HSC block after depthwise convolution, and 2.80% accuracy improvement on ImageNet dataset. For Mobilenet v3-small, we can achieve 0.8% accuracy improvement on ImageNet dataset. In order to prove the improvement effect of group convolution, the standard convolution is changed manually to group convolution and then the HSC block is added after group convolution, we can achieve 4X to 6X FLOPs improvement while maintaining the accuracy of neural networks. Notably, on ILSVRC- 2012, our method reduces more than 43% FLOPs on ResNet-50 without accuracy declines and reduces 60.1% FLOPs on ResNet-50 with 0.44% accuracy declines.We also present primary hardware experiment results when HSC framework running on special hardware platform.
Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A simplistic solution is the sequential running of Video Super Resolution and Video Frame interpolation models. However, this type of solutions are memory inefficient, have high inference time, and could not make the proper use of space-time relation property. To this extent, we first interpolate in LR space using quadratic modeling. Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps and blending mask which are used to synthesize LR interpolated frame is reused in HR space using bilinear upsampling. This leads to a coarse estimate of HR intermediate frame which often contains artifacts along motion boundaries. We use a refinement network to improve the quality of HR intermediate frame via residual learning. Our model is lightweight and performs better than current state-of-the-art models in REDS STSR Validation set.
Enhancing the efficiency and scalability of blockchain through probabilistic verification and clustering

Blockchain is a disruptive technique that finds many applications in FinTech, IoT, and token economy. Because of the asynchrony, the competitive mining, and the indeterministic block propagation delay in networks, forks in the blockchain occur frequently, which not only waste a lot of computing resources but also result in potential security issues. This issue will greatly affect the efficiency of blockchain networks. In the meantime, when blockchain networks expand, the storage data for each node will be increasing dramatically. Participates are about to face the problem of storage limitation. Blockchain is hard to scale. This paper introduced PvScheme, a probabilistic verification scheme that could effectively reduce the block propagation delay and reduce the occurrence of blockchain forks. We further enhanced the security of PvScheme to provide reliable block delivery. We also analysed the resistance of PvScheme to fake blocks and double spending attacks. Furthermore, we proposed ICIStrategy, a multi-node collaborative storage strategy based on intra-cluster integrity. In ICIStrategy, we divided all participates into several clusters. Each cluster requires holding all data of the network, whereas a node within the cluster does not need to maintain data integrity. It aims to solve the storage pressure by reducing the amount data that each participate need to store and reduce communication overhead by collaboratively storing and verifying blocks through in-cluster nodes. We designed the experiments to study the performance of PvScheme and ICIStrategy. The experiment results illustrated that PvScheme could reduce the propagation delay of the standard protocol by 43.1% when the validation degree was 0.5. And we got the best effect when the validation degree is 0.9. It got a relatively lower forks and a relatively higher security at the same time, which achieved a compromise between the efficiency and security in the blockchain. According to the experimental results, ICIStrategy decreased effectively the storage of each node. And the larger the scale of networks, the less the storage of each node.
Image retrieval based on aggregated deep features weighted by regional significance and channel sensitivity

Deep convolutional neural networks (CNN) have demonstrated a very powerful approach for extracting discriminative local descriptors for image description. Many related works suggest that an effective aggregation representation for deep convolutional features is particularly important in forming robust and compact image representations. In this paper, a new robust global descriptor for image retrieval is proposed by creating an effective method for aggregating local deep convolutional features weighted by regional significance and channel sensitivity through sum-pooling on multiple regions. The proposed aggregation method effectively takes advantage of multiple scales, and considers both the varied significance of regional visual content and the sparsity and intensity of response values in the channel. This can improve the ability of deep features to be described and discerned. The experimental results on six benchmark datasets demonstrate that our method can achieve retrieval results comparable to those of some popular approaches for deep feature aggregation but without fine-tuning strategies and multiple image inputs.
Can Bitcoin Investors Profit from Predictions by Crypto Experts?

Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction revisions relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
Visibility enhancement of underwater images based on active polarized illumination and average filtering technology

Underwater imaging is a crucial and challenging problem. The backscattering caused by particles in turbid water can severely degrade the image. In this paper, A novel image dehazing method is devised, which combines optics with image processing technology. This method can directly remove part of the backscattering through polarization technology, and then quickly solve the dehazing image through our algorithm. Experimental results show that our method is valid and robust for targets of diverse materials, grayscale images and color images in diverse scattering environments. Furthermore, our method takes only 2 percent of the time of the fast dark channel prior (DCP) method when processing 1 K resolution images, which meets the requirements of real-time.
Evaluating Coupling Models for Cloud Datacenters and Power Grids

The rapid growth of datacenter (DC) loads can be leveraged to help meet renewable portfolio standard (RPS, renewable fraction) targets in power grids. The ability to manipulate DC loads over time (shifting) provides a mechanism to deal with temporal mismatch between non-dispatchable renewable generation (e.g. wind and solar) and overall grid loads, and this flexibility ultimately facilitates the absorption of renewables and grid decarbonization. To this end, we study DC-grid coupling models, exploring their impact on grid dispatch, renewable absorption, power prices, and carbon emissions. With a detailed model of grid dispatch, generation, topology, and loads, we consider three coupling approaches: fixed, datacenter-local optimization (online dynamic programming), and grid-wide optimization (optimal power flow).
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings

Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information.
Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.
Permissioned Blockchains: Properties, Techniques and Applications

The unique features of blockchains such as immutability, transparency, provenance, and authenticity have been used by many large-scale data management systems to deploy a wide range of distributed applications including supply chain management, healthcare, and crowdworking in permissioned settings. Unlike permissionless settings, e.g., Bitcoin, where the network is public, and anyone can participate without a specific identity, a permissioned blockchain system consists of a set of known, identified nodes that might not fully trust each other. While the characteristics of permissioned blockchains are appealing to a wide range of largescale data management systems, these systems, have to satisfy four main requirements: confidentiality, verifiability, performance, and scalability. Various approaches have been developed in industry and academia to satisfy these requirements with varying assumptions and costs. The focus of this tutorial is on presenting many of these techniques while highlighting the trade-offs among them. We demonstrate the practicality of such techniques in real-life by presenting three different applications, i.e., supply chain management, large-scale databases, and multi-platform crowdworking environments, and show how those techniques can be utilized to meet the requirements of such applications.
Performance Evaluation of Objective Image Quality Metrics on Conventional and Learning-Based Compression Artifacts

Lossy image compression is a popular, simple and effective solution to reduce the amount of data representing digital pictures. In most lossy compression methods, the reduced volume of data in bits is achieved at the expense of introducing visual artifacts in the picture. The perceptual quality impact of such artifacts can be assessed with expensive and time-consuming subjective image quality experiments or through objective image quality metrics. However, the faster and less resource demanding objective quality metrics are not always able to reliably predict the quality as perceived by human observers. In this paper, the performance of 14 objective image quality metrics is benchmarked against a dataset of compressed images labeled with their subjective quality scores. Moreover, the performance of the above objective quality metrics in predicting the subjective quality of images distorted by both conventional and learning-based lossy compression artifacts is assessed and conclusions are drawn.
Maximum mixture correntropy based outlier-robust nonlinear filter and smoother

In this paper, we are dedicated to studying the robust filtering and smoothing problem for a nonlinear non-Gaussian system. Considering the advantage of mixture correntropy with two kernel bandwidths in dealing with non-Gaussian noise, we propose the novel robust recursive filter and smoother based on the cost functions induced by the maximum mixture correntropy criterion, where the nonlinear dynamic model function and measurement model function are linearized by using the statistical linear regression (SLR) method. In the proposed robust recursive filter and smoother, we apply a third-order spherical cubature rule to obtain the prior estimation of the state and covariance matrix, and approximate the multi-dimensional Gaussian integrals encountered in the SLR method. Furthermore, two additional weights are introduced into the proposed robust recursive filter and smoother to modify the filtering and smoothing gains, respectively. The simulation results of manoeuvring target tracking under different non-Gaussian noise scenarios illustrate the effectiveness of the proposed robust recursive filter and smoother.
An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices

Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines.However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes over time.To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model.In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device.To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices.In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) to sequentially train a model based on recent samples and combine it with autoencoder for anomaly detection.We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices.This cooperative model update is one-shot while it can be repeatedly applied to synchronize their model.Our approach is evaluated with anomaly detection tasks generated from a driving dataset of cars, a human activity dataset, and MNIST dataset.The results demonstrate that the proposed on-device federated learning can produce a merged model by integrating trained results from multiple edge devices as accurately as traditional backpropagation based neural networks and a traditional federated learning approach with lower computation or communication cost.
Quantitative Security Risk Modeling and Analysis with RisQFLan

Domain-specific quantitative modeling and analysis approaches are fundamental in scenarios in which qualitative approaches are inappropriate or unfeasible. In this paper, we present a tool-supported approach to quantitative graph-based security risk modeling and analysis based on attack-defense trees. Our approach is based on QFLan, a successful domain-specific approach to support quantitative modeling and analysis of highly configurable systems, whose domain-specific components have been decoupled to facilitate the instantiation of the QFLan approach in the domain of graph-based security risk modeling and analysis. Our approach incorporates distinctive features from three popular kinds of attack trees, namely enhanced attack trees, capabilities-based attack trees and attack countermeasure trees, into the domain-specific modeling language. The result is a new framework, called RisQFLan, to support quantitative security risk modeling and analysis based on attack-defense diagrams. By offering either exact or statistical verification of probabilistic attack scenarios, RisQFLan constitutes a significant novel contribution to the existing toolsets in that domain. We validate our approach by highlighting the additional features offered by RisQFLan in three illustrative case studies from seminal approaches to graph-based security risk modeling analysis based on attack trees.
Federated Regularization Learning: an Accurate and Safe Method for Federated Learning

Distributed machine learning (ML) and other related techniques such as federated learning are facing a high risk of information leakage. Differential privacy (DP) is commonly used to protect privacy. However, it suffers from low accuracy due to the unbalanced data distribution in federated learning and additional noise brought by DP itself. In this paper, we propose a novel federated learning model that can protect data privacy from the gradient leakage attack and black-box membership inference attack (MIA). The proposed protection scheme makes the data hard to be reproduced and be distinguished from predictions. A small simulated attacker network is embedded as a regularization punishment to defend the malicious attacks. We further introduce a gradient modification method to secure the weight information and remedy the additional accuracy loss. The proposed privacy protection scheme is evaluated on MNIST and CIFAR-10, and compared with state-of-the-art DP-based federated learning models. Experimental results demonstrate that our model can successfully defend diverse external attacks to user-level privacy with negligible accuracy loss.