Reviewerly/exHarmony huggingface dataset:
Quantum computing for smart grid applications

Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next-generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state-of-art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real-world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.
Few-Shot Object Detection Based on Self-Knowledge Distillation

In many fields, due to the lack of large-scale training data, the traditional object detection methods cannot complete the actual work well. The main reason is the overfitting problem and lack of the generalization ability. In this work, we propose a general method to alleviate the overfitting problem in the few-shot object detection. Our work extends Faster R-CNN with self-knowledge distillation algorithm and designs the loss function with attention mechanism, which can improve true detection in the foreground. In this way, object detector can learn an approximate mapping relationship from few samples, which makes the network possess a stronger generalization ability when tackling few images. Through numerous comparative experiments, we demonstrate that our method is general and feasible on VOC and COCO benchmarks datasets with different settings. We provide a new idea for solving the problem of few-shot object detection, and produce an excellent performance of recall rate on few-shot object detection.
Leveraging digital and relational governance mechanisms in developing trusting supply chain relationships: the interplay between blockchain and norm of solidarity

Purpose Drawing on the signaling theory and the relational exchange theory, this study investigates how buyer–supplier trust is influenced through the congruence and incongruence between blockchain and norm of solidarity. The moderating role of technology uncertainty is further examined. Design/methodology/approach Using a survey data of 110 Chinese firms, this study empirically tests not only the combined effect of blockchain and norm of solidarity on trust, but also how this combined effect is moderated by technology uncertainty. The proposed hypotheses are tested using the polynomial regression analysis and the response surface methodology. Findings The results suggest that trust increases along with an increasing congruence between blockchain and norm or solidarity, but in a diminishing rate (i.e. an inverted U-shaped relationship). Simultaneously, incongruence between blockchain and norm of solidarity can also guarantee sufficient trust (i.e. a U-shaped relationship). Moreover, technology uncertainty overturns the inverted U-shaped relationship between blockchain and norm of solidarity congruence on trust into a U-shaped relationship and nullifies the U-shaped relationship between blockchain and norm of solidarity incongruence on trust. Originality/value This study enriches supply chain governance literature by introducing the emerging blockchain governance and examining the blockchain governance's interplay with a conventional relational norm. The study emphasizes that the combined effects of these two are quite complex. Blockchain and norm of solidarity can offset each other’s limitations when both are at low to moderate levels. But simultaneous pursuit of both high blockchain and norm has only limited marginal benefits. Furthermore, the study also highlights the importance of technology uncertainty under which the combined effects between the two governance mechanisms vary. Collectively, the results provide nuanced insights into the design of supply chain governance portfolios in the digital era.
Blockchain Technology Adoption: Multinational Analysis of the Agriculture Supply Chain

Agri-produce holds an inimitable place in human-life across the globe. It is an inevitable requirement of livelihoods across the globe. Advanced technologies to improve multiple aspects of agriculture have been developed in recent years. The blockchain technology (BCT) is among these technologies that is proving to be pivotal in supporting business transactions in today&#x0027;s digitally connected environment. This article explores BCT adoption in the agriculture supply chain while focusing on comparing the trends in developed versus developing economies. The study was geared toward identifying the enabling factors and barriers to BCT adoption in the agriculture supply chain. Data was collected from The Netherlands, United States, Saudi Arabia, and India. The study used the Interpretive Structural Modeling&#x2014;Decision Making Trial and Evaluation Laboratory in identifying the factors and ranking them based on their extent of influence. While the study found differences of the enabling factors among the four economies, it also identified that policies are the most important enabler of BCT adoption in the agriculture supply chain.
Ciphertext-policy attribute-based encryption supporting policy-hiding and cloud auditing in smart health

Smart Health, with its flexibility and efficiency, has been widely deployed, especially during the COVID-19 pandemic. However, privacy protection mechanisms for Smart Health are not yet well established and still present a number of security issues. Ciphertext-Policy Attribute-Based Encryption (CP-ABE), is identified as the furthest potential approach for constructing privacy-preserving Smart Health. However, traditional CP-ABE is facing some new challenges. On the one hand, access policy is not encrypted, and the identity information of the user could be exposed. On the other hand, Smart Health Records (SHRs) are outsourced to the Cloud Service Providers (CSP) and may be at risk of being tampered with. In this article, we have built a CP-ABE solution (PHCA) that supports policy-hiding and cloud auditing to ensure privacy security for smart health, in which the decryption cost is constant. To ensure data integrity, we securely introduce an effective third-party auditor. In addition, we design and implement safe and effective outsourcing decryption algorithms, which significantly low the decryption costs for users. Performance comparisons and security analysis demonstrate that our solutions function effectively.
QuanEstimation: An open-source toolkit for quantum parameter estimation

Quantum parameter estimation promises a high-precision measurement in theory; however, how to design the optimal scheme in a specific scenario, especially under a practical condition, is still a serious problem that needs to be solved case by case due to the existence of multiple mathematical bounds and optimization methods. Depending on the scenario considered, different bounds may be more or less suitable, both in terms of computational complexity and the tightness of the bound itself. At the same time, the metrological schemes provided by different optimization methods need to be tested against realization complexity, robustness, etc. Hence, a comprehensive toolkit containing various bounds and optimization methods is essential for the scheme design in quantum metrology. To fill this vacancy, here we present a Python-Julia-based open-source toolkit for quantum parameter estimation, which includes many well-used mathematical bounds and optimization methods. Utilizing this toolkit, all procedures in the scheme design, such as the optimizations of the probe state, control and measurement, can be readily and efficiently performed.
Efficient Identity-Based Data Integrity Auditing With Key-Exposure Resistance for Cloud Storage

The key exposure is a serious threat for the security of data integrity auditing.Once the user's private key for auditing is exposed, most of the existing data integrity auditing schemes would inevitably become unable to work.To deal with this problem, we construct a novel and efficient identity-based data integrity auditing scheme with key-exposure resilience for cloud storage.This is achieved by designing a novel key update technique, which is fully compatible with BLS signature used in identity-based data integrity auditing.In our design, the Third Party Auditor (TPA) is responsible for generating update information.The user can update his private key based on the private key in one previous time period and the update information from the TPA.Furthermore, the proposed scheme supports real lazy update, which greatly improves the efficiency and the feasibility of key update.Meanwhile, the proposed scheme relies on identity-based cryptography, which makes certificate management easy.The security proof and the performance analysis demonstrate that the proposed scheme achieves desirable security and efficiency.
FashionViL: Fashion-Focused Vision-and-Language Representation Learning

Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they overlook the unique characteristics of both fashion V+L data and downstream tasks. In this work, we propose a novel fashion-focused V+L representation learning framework, dubbed as FashionViL. It contains two novel fashion-specific pre-training tasks designed particularly to exploit two intrinsic attributes with fashion V+L data. First, in contrast to other domains where a V+L datum contains only a single image-text pair, there could be multiple images in the fashion domain. We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text. Second, fashion text (e.g., product description) often contains rich fine-grained concepts (attributes/noun phrases). To capitalize this, a Pseudo-Attributes Classification task is introduced to encourage the learned unimodal (visual/textual) representations of the same concept to be adjacent. Further, fashion V+L tasks uniquely include ones that do not conform to the common one-stream or two-stream architectures (e.g., text-guided image retrieval). We thus propose a flexible, versatile V+L model architecture consisting of a modality-agnostic Transformer so that it can be flexibly adapted to any downstream tasks. Extensive experiments show that our FashionViL achieves new state of the art across five downstream tasks. Code is available at https://github.com/BrandonHanx/mmf .
Multimodal Pre-Training Based on Graph Attention Network for Document Understanding

Document intelligence as a relatively new research topic supports many business applications. Its main task is to automatically read, understand, and analyze documents. However, due to the diversity of formats (invoices, reports, forms, etc.) and layouts in documents, it is difficult to make machines understand documents. In this paper, we present the GraphDoc, a multimodal graph attention-based model for various document understanding tasks. GraphDoc is pre-trained in a multimodal framework by utilizing text, layout, and image information simultaneously. In a document, a text block relies heavily on its surrounding contexts, accordingly we inject the graph structure into the attention mechanism to form a graph attention layer so that each input node can only attend to its neighborhoods. The input nodes of each graph attention layer are composed of textual, visual, and positional features from semantically meaningful regions in a document image. We do the multimodal feature fusion of each node by the gate fusion layer. The contextualization between each node is modeled by the graph attention layer. GraphDoc learns a generic representation from only 320k unlabeled documents via the Masked Sentence Modeling task. Extensive experimental results on the publicly available datasets show that GraphDoc achieves state-of-the-art performance, which demonstrates the effectiveness of our proposed method.
Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering

We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representation learning and clustering are often performed sequentially. We leverage temporal information in videos by employing temporal optimal transport. In particular, we incorporate a temporal regularization term which preserves the temporal order of the activity into the standard optimal transport module for computing pseudo-label cluster assignments. The temporal optimal transport module enables our approach to learn effective representations for unsupervised activity segmentation. Furthermore, previous methods require storing learned features for the entire dataset before clustering them in an offline manner, whereas our approach processes one mini-batch at a time in an online manner. Extensive evaluations on three public datasets, i.e. 50-Salads, YouTube Instructions, and Breakfast, and our dataset, i.e., Desktop Assembly, show that our approach performs on par with or better than previous methods, despite having significantly less memory constraints.
SwinCup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer

Transformer models have recently become the dominant architecture in many computer vision tasks, including image classification, object detection, and image segmentation. The main reason behind their success is the ability to incorporate global context information into the learning process. By utilising self-attention, recent advancements in the Transformer architecture design enable models to consider long-range dependencies. In this paper, we propose a novel transformer, named Swin Transformer with Cascaded UPsampling (SwinCup) model for the segmentation of histopathology images. We use a hierarchical Swin Transformer with shifted windows as an encoder to extract global context features. The multi-scale feature extraction in a Swin transformer enables the model to attend to different areas in the image at different scales. A cascaded up-sampling decoder is used with an encoder to improve its feature aggregation. Experiments on GLAS and CRAG histopathology colorectal cancer datasets were used to validate the model, achieving an average 0.90 (F1 score) and surpassing the state-of-the-art by (23%).
Do Gradient Inversion Attacks Make Federated Learning Unsafe?

Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in FL use-cases where the clients’ training involves updating the Batch Normalization (BN) statistics and provide a new baseline attack that works for such scenarios. Furthermore, we present new ways to measure and visualize potential data leakage in FL. Our work is a step towards establishing reproducible methods of measuring data leakage in FL and could help determine the optimal tradeoffs between privacy-preserving techniques, such as differential privacy, and model accuracy based on quantifiable metrics.
Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model.
Lightweight aerial image object detection algorithm based on improved YOLOv5s

YOLOv5 is one of the most popular object detection algorithms, which is divided into multiple series according to the control of network depth and width. To realize the deployment of mobile devices or embedded devices, the paper proposes a lightweight aerial image object detection algorithm (LAI-YOLOv5s) based on the improvement of YOLOv5s with a relatively small amount of calculation and parameter and relatively fast reasoning speed. Firstly, to better detect small objects, the paper replaces the minimum detection head with the maximum detection head and proposes a new feature fusion method, DFM-CPFN(Deep Feature Map Cross Path Fusion Network), to enrich the semantic information of deep features. Secondly, the paper designs a new module based on VoVNet to improve the feature extraction ability of the backbone network. Finally, based on the idea of ShuffleNetV2, the paper makes the network more lightweight without affecting detection accuracy. Based on the VisDrone2019 dataset, the detection accuracy of LAI-YOLOv5s on the mAP@0.5 index is 8.3% higher than that of the original algorithm. Compared with other series of YOLOv5 and YOLOv3 algorithms, LAI-YOLOv5s has the advantages of low computational cost and high detection accuracy.
Revisiting Open World Object Detection

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. Recently a few studies have introduced and explored the OWOD problem, however, the main challenges in the OWOD task that distinguishing unknown classes from the background (Unknown Objectness) or known classes (Unknown Discrimination) have not been well solved, and there is lacking systematic analysis of benchmark and metrics for evaluating the OWOD task. In this paper, we revisit the OWOD problem and rethink it from benchmark, metrics, and algorithm perspectives. First, we propose five fundamental benchmark principles in line with the OWOD definition and construct two OWOD benchmarks according to the principles for a fair evaluation. Second, we point out that existing metrics neglect the detection performance of unknown classes and further design two additional metrics specific to the OWOD problem, filling the void of evaluating from the perspective of unknown classes. Finally, we introduce a novel and effective OWOD framework with an auxiliary Proposal ADvisor (PAD) and a Class-specific Expelling Classifier (CEC). The non-parametric PAD improves Unknown Objectness by assisting RPN in identifying more accurate unknown proposals based on the class-agnostic property of the object and aggregation through spatial and appearance similarity, while CEC enhances the Unknown Discrimination by calibrating the over-confident activation boundary and suppressing confusing predictions through a class-specific expelling function. Comprehensive experiments conducted on both fair benchmarks based on our OWOD benchmark principles and the original benchmark demonstrate that our method outperforms other state-of-the-art object detection methods in terms of both existing and our new metrics.
An Object-Based Bayesian Framework for Top-Down Visual Attention

We introduce a new task-independent framework to model top-down overt visual attention based on graph-ical models for probabilistic inference and reasoning. We describe a Dynamic Bayesian Network (DBN) that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions which are fed from manual annotations of objects in video scenes or by state-of-the-art object detection models. Evaluating over ∼3 hours (appx. 315,000 eye fixations and 12,600 saccades) of observers playing 3 video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multi-modal information from gist, bottom-up saliency, physical actions, and events) to eye positions, 2) 14 state-of-the-art bottom-up saliency models, and 3) brute-force algorithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data.
Saliency Integration: An Arbitrator Model

Saliency integration has attracted much attention on unifying saliency maps from multiple saliency models. Previous offline integration methods usually face two challenges: 1) if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; and 2) an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model. To address these problems, in this paper, we propose an arbitrator model (AM) for saliency integration. First, we incorporate the consensus of multiple saliency models and the external knowledge into a reference map to effectively rectify the misleading by candidate models. Second, our quest for ways of estimating the expertise of the saliency models without ground truth labels gives rise to two distinct online model-expertise estimation methods. Finally, we derive a Bayesian integration framework to reconcile the saliency models of varying expertise and the reference map. To extensively evaluate the proposed AM model, we test 27 state-of-the-art saliency models, covering both traditional and deep learning ones, on various combinations over four datasets. The evaluation results show that the AM model improves the performance substantially compared to the existing state-of-the-art integration methods, regardless of the chosen candidate saliency models.