Reviewerly/exHarmony huggingface dataset:
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution

We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths (i.e., number of channels in a layer) using different input resolutions to mutually learn multi-scale representations for each sub-network. It achieves consistently better ImageNet top-1 accuracy over the state-of-the-art adaptive network US-Net under different computation constraints, and outperforms the best compound scaled MobileNet in EfficientNet by 1.5%. The superiority of our method is also validated on COCO object detection and instance segmentation as well as transfer learning. Surprisingly, the training strategy of MutualNet can also boost the performance of a single network, which substantially outperforms the powerful AutoAugmentation in both efficiency (GPU search hours: 15000 vs. 0) and accuracy (ImageNet: 77.6% vs. 78.6%). Code is available at https://github.com/taoyang1122/MutualNet .
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder

Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping. Few-shot unsupervised image-to-image translation further attempts to generalize the model to an unseen domain by leveraging example images of the unseen domain provided at inference time. While remarkably successful, existing few-shot image-to-image translation models find it difficult to preserve the structure of the input image while emulating the appearance of the unseen domain, which we refer to as the content loss problem. This is particularly severe when the poses of the objects in the input and example images are very different. To address the issue, we propose a new few-shot image translation model, COCO-FUNIT, which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias. Through extensive experimental validations with comparison to the state-of-the-art, our model shows effectiveness in addressing the content loss problem. Code and pretrained models are available at https://nvlabs.github.io/COCO-FUNIT/ .
Blockchain for the Internet of Vehicles: How to Use Blockchain to Secure Vehicle-to-Everything (V2X) Communication and Payment?

As the Internet of Things (IoT) is evolving, one of its rapidly developing components is the transformation of standard Vehicle Ad-hoc Networks (VANETs) into the Internet of Vehicles (IoV). Due to the exceptional progress in computation and communication technologies, the IoV has attracted the attention of researchers and commercial companies. Nevertheless, the primary issue regarding the IoV, and in particular to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), is establishing secure and instant payments and communications. To respond to this challenge, this work proposes a Blockchain-based solution for establishing secure payment and communication (PSEV) in order to study the use of Blockchain as middle-ware between different participants of intelligent transportation systems. The proposed framework employs Ethereum to develop a solution aimed at facilitating Vehicle-to-Everything (V2X) communications and parking payments. Moreover, the solution includes Android auto and application modules for automating the communication process. It was experimentally tested to assess its computational costs, communication expenses and the real-time aspect (RTA). The results of computational tests revealed that the developed solution is faster and more scalable than the existing solutions.
Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer’s Disease

Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease (AD) commonly leads to improved performance than those using a single modality. However, it is still challenging to train a multi-modality model since it is difficult in clinical practice to obtain complete data that includes all modality data. Generally speaking, it is difficult to obtain both magnetic resonance images (MRI) and positron emission tomography (PET) images of a single patient. PET is expensive and requires the injection of radioactive substances into the patient's body, while MR images are cheaper, safer, and more widely used in practice. Discarding samples without PET data is a common method in previous studies, but the reduction in the number of samples will result in a decrease in model performance. To take advantage of multi-modal complementary information, we first adopt the Reversible Generative Adversarial Network (RevGAN) model to reconstruct the missing data. After that, a 3D convolutional neural network (CNN) classification model with multi-modality input was proposed to perform AD diagnosis. We have evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and compared the performance of the proposed method with those using state-of-the-art methods. The experimental results show that the structural and functional information of brain tissue can be mapped well and that the image synthesized by our method is close to the real image. In addition, the use of synthetic data is beneficial for the diagnosis and prediction of Alzheimer's disease, demonstrating the effectiveness of the proposed framework.
Smart supply chain and firm performance: the role of digital technologies

Purpose This study aims to explore the impact of digital transformation on firm performance with the help of smart technologies. Design/methodology/approach This study used a valid and well-structured survey to collect data from 460 respondents from 150 Small and medium-sized enterprises (SMEs) in Saudi Arabia. In order to analyze this data, SmartPLS software was used. Findings The empirical results of this study found that the digital transformation has no significant direct impact on firm performance. Therefore, in this situation, smart technologies play an important role in developing the link between digital transformation and firm performance. Originality/value This study contributes to the digital supply chain from SMEs' perspective by investigating the mediating role of smart technologies between digital transformation and firm performance.
Blockchain technology for agricultural supply chains during the COVID-19 pandemic: Benefits and cleaner solutions

This study aims to investigate blockchain technology for agricultural supply chains during the COVID-19 pandemic. Benefits and solutions are identified for the smooth conduction of agricultural supply chains during COVID-19 using blockchain. This study uses interviews with agricultural companies operating in Pakistan. The findings discover the seven most commonly shared benefits of applying blockchain technology, four major challenges, and promising solutions. About 100% of the respondents mentioned blockchain as a solution for tracking the shipment during COVID-19, data retrieval and data management, product and transaction frauds, and an Inflexible international supply chain. Roughly 75% of the respondents mentioned the challenge of lack of data retrieval and data management and the Inflexible international supply chain in COVID-19 besides their solutions. This study can expand existing knowledge related to agricultural supply chains. The experiences shared in this study can serve as lessons for practitioners to adopt the blockchain technology for performing agricultural supply chain during pandemic situations such as COVID-19.
Assessment of Code Smell for Predicting Class Change Proneness Using Machine Learning

Assessment of code smell for predicting software change proneness is essential to ensure its significance in the area of software quality. While multiple studies have been conducted in this regard, the number of systems studied and the methods used in this paper are quite different, thus, causing confusion for understanding the best methodology. The objective of this paper is to approve the effect of code smell on the change inclination of a specific class in a product framework. This is the novelty and surplus of this work against the others. Furthermore, this paper aims to validate code smell for predicting class change proneness to find an error in the prediction of change proneness using code smell. Six typical machine learning algorithms (Naive Bayes Classifier, Multilayer Perceptron, LogitBoost, Bagging, Random Forest, and Decision Tree) have been used to predict change proneness using code smell from a set of 8200 Java classes spanning 14 software systems. The experimental results suggest that code smell is indeed a powerful predictor of class change proneness with multilayer perceptron being the most effective technique. The sensitivity and specificity values for all the models are well over 70% with a few exceptions.
Targeted Adversarial Examples Against RF Deep Classifiers

Adversarial examples (AdExs) in machine learning for classification of radio frequency (RF) signals can be created in a targeted manner such that they go beyond general misclassification and result in the detection of a specific targeted class. Moreover, these drastic, targeted misclassifications can be achieved with minimal waveform perturbations, resulting in catastrophic impact to deep learning based spectrum sensing applications (e.g. WiFi is mistaken for Bluetooth). This work addresses targeted deep learning AdExs, specifically those obtained using the Carlini-Wagner algorithm, and analyzes previously introduced defense mechanisms that performed successfully against non-targeted FGSM-based attacks. To analyze the effects of the Carlini-Wagner attack, and the defense mechanisms, we trained neural networks on two datasets. The first dataset is a subset of the DeepSig dataset, comprised of three synthetic modulations BPSK, QPSK, 8-PSK, which we use to train a simple network for Modulation Recognition. The second dataset contains real-world, well-labeled, curated data from the 2.4 GHz Industrial, Scientific and Medical (ISM) band, that we use to train a network for wireless technology (protocol) classification using three classes: WiFi 802.11n, Bluetooth (BT) and ZigBee. We show that for attacks of limited intensity the impact of the attack in terms of percentage of misclassifications is similar for both datasets, and that the proposed defense is effective in both cases. Finally, we use our ISM data to show that the targeted attack is effective against the deep learning classifier but not against a classical demodulator.
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations

Everyone makes mistakes.So do human annotators when curating labels for named entity recognition (NER).Such label mistakes might hurt model training and interfere model comparison.In this study, we dive deep into one of the widely-adopted NER benchmark datasets, CoNLL03 NER.We are able to identify label mistakes in about 5.38% test sentences, which is a significant ratio considering that the state-of-the-art test F 1 score is already around 93%.Therefore, we manually correct these label mistakes and form a cleaner test set.Our re-evaluation of popular models on this corrected test set leads to more accurate assessments, compared to those on the original test set.More importantly, we propose a simple yet effective framework, CrossWeigh, to handle label mistakes during NER model training.Specifically, it partitions the training data into several folds and train independent NER models to identify potential mistakes in each fold.Then it adjusts the weights of training data accordingly to train the final NER model.Extensive experiments demonstrate significant improvements of plugging various NER models into our proposed framework on three datasets.All implementations and corrected test set are available at our Github repo 1 .
Defending Against Universal Perturbations With Shared Adversarial Training

Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of image classifiers against such adversarial perturbations, it leaves them sensitive to perturbations on a non-negligible fraction of the inputs. In this work, we show that adversarial training is more effective in preventing universal perturbations, where the same perturbation needs to fool a classifier on many inputs. Moreover, we investigate the trade-off between robustness against universal perturbations and performance on unperturbed data and propose an extension of adversarial training that handles this trade-off more gracefully. We present results for image classification and semantic segmentation to showcase that universal perturbations that fool a model hardened with adversarial training become clearly perceptible and show patterns of the target scene.
Privacy preserving classification on local differential privacy in data centers

With the rise of cloud service providers and the continuous virtualization of data centers, data center networks are also developing rapidly. As data centers become more and more complex, the demand for security increases dramatically. This paper discusses the privacy inherent in data centers. However, there is no general solution to the privacy problem in data centers due to the device heterogeneity. In this paper, we proposed a local differential privacy-based classification algorithm for data centers. In data mining of data centers, the differential privacy protection mechanism is added to deal with Laplace noise of sensitive information in the pattern mining process. We designed a method to quantify the quality of privacy protection through strict mathematical proof. Experiments demonstrated that the differential privacy-based classification algorithm proposed in this paper has higher iteration efficiency, better security and feasible accuracy. On the premise of ensuring availability, the algorithm has reliable privacy protection characteristics and excellent timeliness.
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting. This paper makes three contributions. First, we establish the first benchmark of controlled real-world label noise from the web. This new benchmark enables us to study the web label noise in a controlled setting for the first time. The second contribution is a simple but effective method to overcome both synthetic and real noisy labels. We show that our method achieves the best result on our dataset as well as on two public benchmarks (CIFAR and WebVision). Third, we conduct the largest study by far into understanding deep neural networks trained on noisy labels across different noise levels, noise types, network architectures, and training settings. The data and code are released at the following link: this http URL
Entanglement, coherence, and charging process of quantum batteries

Quantum devices are systems that can explore quantum phenomena, like entanglement or coherence, for example, to provide some enhancement performance concerning their classical counterparts. In particular, quantum batteries are devices that use entanglement as main element in its high performance in the charging powerful. In this paper, we explore the quantum battery performance and its relationship with the amount of entanglement that arises during the charging process. By using a general approach to a two and three-cell battery, our results suggest that entanglement is not the main resource to quantum batteries, where there is a non-trivial correlation-coherence trade-off as resource for the high efficiency of such quantum devices.
CLEVE: Contrastive Pre-training for Event Extraction

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning.However, existing pre-training methods have not involved modeling event characteristics, resulting in the developed EE models cannot take full advantage of large-scale unsupervised data.To this end, we propose CLEVE, a contrastive pre-training framework for EE to better learn event knowledge from large unsupervised data and their semantic structures (e.g.AMR) obtained with automatic parsers.CLEVE contains a text encoder to learn event semantics and a graph encoder to learn event structures respectively.Specifically, the text encoder learns event semantic representations by self-supervised contrastive learning to represent the words of the same events closer than those unrelated words; the graph encoder learns event structure representations by graph contrastive pre-training on parsed eventrelated semantic structures.The two complementary representations then work together to improve both the conventional supervised EE and the unsupervised "liberal" EE, which requires jointly extracting events and discovering event schemata without any annotated data.Experiments on ACE 2005 and MAVEN datasets show that CLEVE achieves significant improvements, especially in the challenging unsupervised setting.The source code and pre-trained checkpoints can be obtained from https://github.com/THU-KEG/CLEVE.
Spectral clustering in the dynamic stochastic block model

In the present paper, we have studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering. The procedure is computationally viable, adaptive to the unknown smoothness of the functional connection probabilities, to the rate $s$ of membership switching, and to the unknown number of clusters. In addition, it is accompanied by non-asymptotic guarantees for the precision of estimation and clustering.
Blockchain in logistics industry: in fizz customer trust or not

Purpose The study is an attempt to explore much talked but less understood issue of “blockchain in logistics industry” in modern perspective. The customers' acceptance of blockchain technology in logistics and supply chain is tested through “Technology Acceptance Model” by using attitude, perceived usefulness (PU), perceived ease of use (PEOU), behavioral intention and use behavior. Design/methodology/approach Data has been collected through online and offline medium, where active 240 responses have been collected finally using convenience sampling. Confirmatory factor analysis with structural equation modeling (SEM) was carried out for data analysis. Findings The customers' acceptance of blockchain technology in logistics and supply chain is tested through “Technology Acceptance Model.” The findings reveal model fit where PEOU, PU and attitude are the major constructs of the model to realize the substantial gains in logistics process efficiency. Research limitations/implications Convenience sampling has been considered for the study to collect the data of online users of various technology applications for tracking and shipment detail, whereas a more specified method sampling can be considered for the future research. The study has been conducted in the Indian context, which has been considered as the limitation pertaining to generalization across countries and industries. Practical implications The findings of this study will be helpful for market practitioners to build transparency between customers and industry to overcome the frictions in logistics. Blockchain will help in monitoring the performance history and previous commitments of logistics professionals resulting in selecting a responsible logistics solution provider. Access to critical data by the authorized member of the supply chain will reduce unsubstantiated disputes. Social implications Blockchain technology will be available to everyone on the network. This will bring transparency and help logistics professionals such as carriers, shippers and brokers to detect early frauds and prevent thefts. It will increase customer trust toward any financial transaction for tracking the ownership of titles. Originality/value Blockchain technology is envisioned to be a technology that could be a game-changer for decentralizing infrastructure, introducing transparency and building trust in the supply chain. The current study is a novel addition to the literature where blockchain technology enables the indisputable storage of verified data that was previously kept in safeguarded silos.
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification

User intent classification plays a vital role in dialogue systems.Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun.This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unknown intent detection.In particular, we model utterance embeddings with a Gaussian mixture distribution and inject dynamic class semantic information into Gaussian means, which enables learning more classconcentrated embeddings that help to facilitate downstream outlier detection.Coupled with a density-based outlier detection algorithm, SEG achieves competitive results on three real task-oriented dialogue datasets in two languages for unknown intent detection.On top of that, we propose to integrate SEG as an unknown intent identifier into existing generalized zero-shot intent classification models to improve their performance.A case study on a state-of-the-art method, ReCapsNet, shows that SEG can push the classification performance to a significantly higher level.
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structural element in both of these methods is the inclusion of an outer loop at the beginning of which a full pass over the training data is made in order to compute the exact gradient, which is then used to construct a variance-reduced estimator of the gradient. In this work we design {\em loopless variants} of both of these methods. In particular, we remove the outer loop and replace its function by a coin flip performed in each iteration designed to trigger, with a small probability, the computation of the gradient. We prove that the new methods enjoy the same superior theoretical convergence properties as the original methods. However, we demonstrate through numerical experiments that our methods have substantially superior practical behavior.