Reviewerly/exHarmony huggingface dataset:
A trusted energy trading framework by marrying blockchain and optimization

Trading based on distributed optimization is becoming a world-wide trend in energy markets. Without proper trading mechanism design, however, participants might act dishonestly, which further leads to potential trust crisis and even market failures. This trust issue has been overlooked in state-of-the-art trading mechanism design. Blockchain, known as the enabler of trust, is promising to address this challenge. However, current studies have not detailed how blockchain can disable dishonest participants in energy trading, and primarily give qualitative rather than quantitative analysis of blockchain value in energy trading. Here, we propose an energy trading framework by marrying blockchain and distributed optimization, where blockchain enables check and balance among participants and disables dishonesty. Our results on a multi-energy district demonstrate in a quantitative way how our proposed framework can help prevent energy market failures caused by dishonest participants.
A Co-Interactive Transformer for Joint Slot Filling and Intent Detection

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely related and the information of one task can benefit the other. Previous studies either implicitly model the two tasks with multi-task framework or only explicitly consider the single information flow from intent to slot. None of the prior approaches model the bidirectional connection between the two tasks simultaneously in a unified framework. In this paper, we propose a Co-Interactive Transformer which considers the cross-impact between the two tasks. Instead of adopting the self-attention mechanism in vanilla Transformer, we propose a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks, where slot and intent can be able to attend on the corresponding mutual information. The experimental results on two public datasets show that our model achieves the state-of-the-art performance.
nn-Meter

With the recent trend of on-device deep learning, inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN model inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly, such as searching for efficient DNN models with latency constraints from a huge model-design space. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices.
How virtual reality influences travel intentions: The role of mental imagery and happiness forecasting

Virtual reality can influence consumer behavior in powerful ways, but in the domain of tourism marketing, less is known about the psychological process. One possibility is that VR has stronger impact on mental imagery than traditional channels, creating vivid simulations of what it would be like to visit destinations. This process might create hedonic expectations of future happiness, which should increase the willingness to actually seek out the destination in real life. The results from a laboratory experiment provided support to this hypothesis. Participants were randomly assigned to view a nature destination either in traditional still images or virtual reality, and then reported their initial responses before making an actual consumer choice. VR exposure led to higher levels of mental imagery and happiness predictions, which in turn was associated with stronger travel intentions and purchasing decisions. However, VR effects on consumer choice were moderated by previous experience with the destination.
Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey

Deep learning has emerged as a powerful machine learning technique to employ in multimodal sentiment analysis tasks. In the recent years, many deep learning models and various algorithms have been proposed in the field of multimodal sentiment analysis which urges the need to have survey papers that summarize the recent research trends and directions. This survey paper tackles a comprehensive overview of the latest updates in this field. We present a sophisticated categorization of thirty-five state-of-the-art models, which have recently been proposed in video sentiment analysis field, into eight categories based on the architecture used in each model. The effectiveness and efficiency of these models have been evaluated on the most two widely used datasets in the field, CMU-MOSI and CMU-MOSEI. After carrying out an intensive analysis of the results, we eventually conclude that the most powerful architecture in multimodal sentiment analysis task is the Multi-Modal Multi-Utterance based architecture, which exploits both the information from all modalities and the contextual information from the neighbouring utterances in a video in order to classify the target utterance. This architecture mainly consists of two modules whose order may vary from one model to another. The first module is the Context Extraction Module that is used to model the contextual relationship among the neighbouring utterances in the video and highlight which of the relevant contextual utterances are more important to predict the sentiment of the target one. In most recent models, this module is usually a bidirectional recurrent neural network based module. The second module is an Attention-Based Module that is responsible for fusing the three modalities (text, audio and video) and prioritizing only the important ones. Furthermore, this paper provides a brief summary of the most popular approaches that have been used to extract features from multimodal videos in addition to a comparative analysis between the most popular benchmark datasets in the field. We expect that these findings can help newcomers to have a panoramic view of the entire field and get quick experience from the provided helpful insights. This will guide them easily to the development of more effective models.
Regularizing Generative Adversarial Networks under Limited Data

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-theart performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://github.com/google/lecam-gan.
Soteria: Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. However, recent works have demonstrated that sharing model updates makes FL vulnerable to inference attack. In this work, we show our key observation that the data representation leakage from gradients is the essential cause of privacy leakage in FL. We also provide an analysis of this observation to explain how the data presentation is leaked. Based on this observation, we propose a defense called Soteria against model inversion attack in FL. The key idea of our defense is learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. In addition, we derive a certified robustness guarantee to FL and a convergence guarantee to FedAvg, after applying our defense. To evaluate our defense, we conduct experiments on MNIST and CIFAR10 for defending against the DLG attack and GS attack. Without sacrificing accuracy, the results demonstrate that our proposed defense can increase the mean squared error between the reconstructed data and the raw data by as much as 160× for both DLG attack and GS attack, compared with baseline defense methods. Therefore, the privacy of the FL system is significantly improved. Our code can be found at https://github.com/jeremy313/Soteria.
Generalizable Person Re-identification with Relevance-aware Mixture of Experts

Domain generalizable (DG) person re-identification (ReID) is a challenging problem because we cannot access any unseen target domain data during training. Almost all the existing DG ReID methods follow the same pipeline where they use a hybrid dataset from multiple source domains for training, and then directly apply the trained model to the unseen target domains for testing. These methods often neglect individual source domains’ discriminative characteristics and their relevances w.r.t. the unseen target domains, though both of which can be leveraged to help the model’s generalization. To handle the above two issues, we propose a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains’ diverse characteristics to improve the model’s generalization. Specifically, we propose a decorrelation loss to make the source domain networks (experts) keep the diversity and discriminability of individual domains’ characteristics. Besides, we design a voting network to adaptively integrate all the experts’ features into the more generalizable aggregated features with domain relevance. Considering the target domains’ invisibility during training, we propose a novel learning-to-learn algorithm combined with our relation alignment loss to update the voting network. Extensive experiments demonstrate that our proposed RaMoE outperforms the state-of-the-art methods.
Sequence-to-Sequence Contrastive Learning for Text Recognition

We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to contrast in a sub-word level, where from each image we extract several positive pairs and multiple negative examples. To yield effective visual representations for text recognition, we further suggest novel augmentation heuristics, different encoder architectures and custom projection heads. Experiments on hand-written text and on scene text show that when a text decoder is trained on the learned representations, our method out-performs non-sequential contrastive methods. In addition, when the amount of supervision is reduced, SeqCLR significantly improves performance compared with supervised training, and when fine-tuned with 100% of the labels, our method achieves state-of-the-art results on standard hand-written text recognition benchmarks.
Multimodal Contrastive Training for Visual Representation Learning

We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data properties within each modality and semantic information from cross-modal correlation simultaneously, hence improving the quality of learned visual representations. By including multimodal training in a unified framework with different types of contrastive losses, our method can learn more powerful and generic visual features. We first train our model on COCO and evaluate the learned visual representations on various downstream tasks including image classification, object detection, and instance segmentation. For example, the visual representations pre-trained on COCO by our method achieve state-of-the-art top-1 validation accuracy of 55.3% on ImageNet classification, under the common transfer protocol. We also evaluate our method on the large-scale Stock images dataset and show its effectiveness on multi-label image tagging, and cross-modal retrieval tasks.
TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency.
How interactivity and vividness influence consumer virtual reality shopping experience: the mediating role of telepresence

Purpose The purpose of the study is to investigate the effects of interactivity and vividness on perceived usefulness and perceived enjoyment and their consequent impacts on consumer behavioral responses in a retail furniture VR store context. Considering the lack of VR empirical research, the indirect effect of interactivity and vividness on perceived usefulness and perceived enjoyment through telepresence and the moderating effect of consumer previous experience with VR are also included. Design/methodology/approach A commercial IKEA VR store was chosen for the study. Head-mounted display (HMD) VR headsets were employed for the VR shopping experience. The study was conducted at a laboratory at a large university in the southeastern United States. A total of 146 college students participated in the study. Findings Vividness had significant positive effects on perceived usefulness and perceived enjoyment, which in turn influenced attitude towards VR and behavioral intentions. Interactivity did not have positive impacts on perceived usefulness and perceived enjoyment. However, it indirectly affected perceived usefulness and perceived enjoyment through telepresence. The findings also proved the moderating effect of consumer previous VR experience between interactivity and perceived usefulness and between interactivity and perceived enjoyment. The relationship between attitude and behavioral intentions was also positive. Originality/value Notwithstanding the benefits of VR in relation to its utilitarian, hedonic, and behavioral values, little is known about consumers' responses towards the usage of VR as a shopping tool. The present study can be considered as a starting point in understanding the usefulness of VR from consumer and managerial perspectives. The findings of VR indicated in the study will help practitioners understand the urgency of adopting VR in a retail setting.
Determining breast cancer biomarker status and associated morphological features using deep learning

Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results.We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches.The patch-level AUCs are 0.939 (95%CI 0.936-0.941), 0.938 (0.936-0.940), and 0.808 (0.802-0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84-0.87), 0.75 (0.73-0.77), and 0.60 (0.56-0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining.This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution

Reference-based image super-resolution (RefSR) has shown promising success in recovering high-frequency details by utilizing an external reference image (Ref). In this task, texture details are transferred from the Ref image to the low-resolution (LR) image according to their point- or patch-wise correspondence. Therefore, high-quality correspondence matching is critical. It is also desired to be computationally efficient. Besides, existing RefSR methods tend to ignore the potential large disparity in distributions between the LR and Ref images, which hurts the effectiveness of the information utilization. In this paper, we propose the MASA network for RefSR, where two novel modules are designed to address these problems. The proposed Match & Extraction Module significantly reduces the computational cost by a coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module learns the difference of distribution between the LR and Ref images, and remaps the distribution of Ref features to that of LR features in a spatially adaptive way. This scheme makes the network robust to handle different reference images. Extensive quantitative and qualitative experiments validate the effectiveness of our proposed model.
Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation

With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. Recently, learned image compression methods had exceeded their classical counterparts in terms of rate-distortion performance. However, continuous rate adaptation remains an open question. Some learned image compression methods use multiple networks for multiple rates, while others use one single model at the expense of computational complexity increase and performance degradation. In this paper, we propose a continuously rate adjustable learned image compression framework, Asymmetric Gained Variational Autoencoder (AG-VAE). AG-VAE utilizes a pair of gain units to achieve discrete rate adaptation in one single model with a negligible additional computation. Then, by using exponential interpolation, continuous rate adaptation is achieved without compromising performance. Besides, we propose the asymmetric Gaussian entropy model for more accurate entropy estimation. Exhaustive experiments show that our method achieves comparable quantitative performance with SOTA learned image compression methods and better qualitative performance than classical image codecs. In the ablation study, we confirm the usefulness and superiority of gain units and the asymmetric Gaussian entropy model.
The Blockchain Oracle Problem in Decentralized Finance—A Multivocal Approach

Decentralized Finance (DeFi) takes the promise of blockchain a step further and aims to transform traditional financial products into trustless and transparent protocols that run without involving intermediaries. Similar to how 2017 was the year of ICOs, 2020 was the year of DeFi, with more than fifteen billion dollars of total investments. The decentralized platforms utilize oracles to retrieve asset data from the external world, but their choice and management criteria are often unknown to the end-users. If oracles are poorly selected or managed, the funds of a rising number of investors are inevitably in danger. The issue, known as “the oracle problem”, which makes real-world applications controversial and debated due to the loss of decentralization, had recently drawn attention to DeFi, given the crescent number of related hacks that caused the loss of millions of dollars held in DeFi projects. Through a multivocal approach that considers academic papers, whitepapers, preprints, and opinion posts, this study aims to shed light on the pattern that identifies the oracle problem in DeFi and outline the most promising ways to overcome the related weaknesses. This research supports the view that the oracle problem in decentralized finance bears specific characteristics which require standardization and appropriate economic incentives to be addressed.
Learning Temporal Consistency for Low Light Video Enhancement from Single Images

Single image low light enhancement is an important task and it has many practical applications. Most existing methods adopt a single image approach. Although their performance is satisfying on a static single image, we found, however, they suffer serious temporal instability when handling low light videos. We notice the problem is because existing data-driven methods are trained from single image pairs where no temporal information is available. Unfortunately, training from real temporally consistent data is also problematic because it is impossible to collect pixel-wisely paired low and normal light videos under controlled environments in large scale and diversities with noise of identical statistics. In this paper, we propose a novel method to enforce the temporal stability in low light video enhancement with only static images. The key idea is to learn and infer motion field (optical flow) from a single image and synthesize short range video sequences. Our strategy is general and can extend to large scale datasets directly. Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency. Rigorous experiments and user study demonstrate the state-of-the-art performance of our proposed method. Our code and model will be publicly available at https://github.com/zkawfanx/StableLLVE.