Reviewerly/exHarmony huggingface dataset:
Cavity-enhanced single-shot readout of a quantum dot spin within 3 nanoseconds

Rapid, high-fidelity single-shot readout of quantum states is a ubiquitous requirement in quantum information technologies, playing a crucial role in quantum computation, quantum error correction, and fundamental tests of non-locality. Readout of the spin state of an optically active emitter can be achieved by driving a spin-preserving optical transition and detecting the emitted photons. The speed and fidelity of this approach is typically limited by a combination of low photon collection rates and measurement back-action. Here, we demonstrate single-shot optical readout of a semiconductor quantum dot spin state, achieving a readout time of only a few nanoseconds. In our approach, gated semiconductor quantum dots are embedded in an open microcavity. The Purcell enhancement generated by the microcavity increases the photon creation rate from one spin state but not from the other, as well as efficiently channelling the photons into a well-defined detection mode. We achieve single-shot readout of an electron spin state in 3 nanoseconds with a fidelity of (95.2$\pm$0.7)%, and observe quantum jumps using repeated single-shot measurements. Owing to the speed of our readout, errors resulting from measurement-induced back-action have minimal impact. Our work reduces the spin readout-time to values well below both the achievable spin relaxation and dephasing times in semiconductor quantum dots, opening up new possibilities for their use in quantum technologies.
A dual-embedded tamper detection framework based on block truncation coding for intelligent multimedia systems

The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia services, such as tampering, hijacking, and adversarial attacks. Therefore, this paper proposes a dual-embedding framework based on block truncation coding to improve the security of intelligent multimedia systems. First, the signal is decomposed in frequency domain by using approximate translation invariance to obtain multi-layer frequency-domain parameters; then, this paper hides the encrypted data in two layers of low-frequency coefficients through fragile and robust embedding algorithms, respectively. In addition, in order to further improve the security performance, this paper adopts the method of block truncation coding to encrypt the embedded data. On the basis of performance analysis, the superiority of this method is illustrated by comparing with the existing methods.
Individual modelling of haematotoxicity with NARX neural networks: A knowledge transfer approach

Cytotoxic cancer therapy often results in dose-limiting haematotoxic side effects. Predicting an individual's risk is a major objective in precision medicine of cancer treatment. In this regard, patient heterogeneity presents a significant challenge. In this paper, we explore the use of hypothesis-free machine learning models based on recurrent nonlinear auto-regressive networks with exogenous inputs (NARX) as an approach to achieve this goal. Also, we propose a knowledge transfer approach to ameliorate the issue of sparse individual data, which typically hampers learning of individual networks. We demonstrate the feasibility of our approach based on a virtual patient population generated using a semi-mechanistic model of haematopoiesis and imposing different cytotoxic therapy scenarios on it. Employing different techniques of model optimisation, we derive robust and parsimonious individual networks with good generalisation performances. Moreover, we analyse in detail possible factors influencing the generalisation performance. Results suggest that our transfer learning approach using NARX networks can provide robust predictions of individual patient's response to treatment. As a practical perspective, we apply our approach to individual time series data of two patients with non-Hodgkin's lymphoma.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacypreserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We rst review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a uni ed and comprehensive secure graph machine learning system.
Specifying a principle of cryptographic justice as a response to the problem of going dark

Abstract Over the past decade, the Five Eyes Intelligence community has argued cryptosystems with end-to-end encryption (E2EE) are disrupting the acquisition and analysis of digital evidence. They have labelled this phenomenon the ‘problem of going dark’. Consequently, several jurisdictions have passed ‘responsible encryption’ laws that limit access to E2EE. Based upon a rhetorical analysis (Cunningham in Understanding rhetoric: a guide to critical reading and argumentation, BrownWalker Press, Boca Raton, 2018) of official statements about ‘going dark’, it is argued there is a need for a domain-specific principle of cryptographic justice to reorient the debate away from competing technocratic claims about the necessity, proportionality, and accountability of digital surveillance programs. This article therefore specifies a principle of cryptographic justice by adapting more general norms of information justice to decision-making about encryption law and policy. The resulting principle is that encryption laws and policies should be designed to empower the comparatively powerless to protect themselves from domination (i.e., morally arbitrary forms of surveillance). It is argued this principle can reorient decision-making about encryption law and policy towards consideration of how cryptography impacts systems-level power dynamics within information societies.
Using Mask-Based Enhancement and Feature Aggregation for Single Image Deraining

Image rain removal is an essential and challenging low-level task in computer vision. In recent years, although great progress has been achieved in the field of image and video deraining, the restored details may be incomplete in many cases, making it difficult to recover the clear images from diverse rain forms. Moreover, the features of layers may not be fully exploited, which may reduce the rain removal performance to some degree. To address the above issues, we propose a novel Mask-based Enhancement and Feature Aggregation Network (MEFA-Net). First, we develop a novel structure with enhancement branch, which can greatly improve the feature representation capability. The enhancement branch is implemented based on the random masking technique, which can generate complementary features and improve the robustness. Specially, we design a novel adaptive weighting block in the MEFA-Net, which can adaptively assign weights to the enhanced features for each specific image, effectively improving the generalization ability of MEFA-Net. Second, we propose a feature aggregation sub-module (FAS) for comprehensive representation by integrating the features from different layers. Experimental results demonstrate that the proposed MEFA-Net can achieve better performance than most state-of-the-art image deraining methods on four synthetic and one real-world datasets.
Universum-Inspired Supervised Contrastive Learning

As an effective data augmentation method, Mixup synthesizes an extra amount of samples through linear interpolations. Despite its theoretical dependency on data properties, Mixup reportedly performs well as a regularizer and calibrator contributing reliable robustness and generalization to deep model training. In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum. We find that in the framework of supervised contrastive learning, Mixup-induced universum can serve as surprisingly high-quality hard negatives, greatly relieving the need for large batch sizes in contrastive learning. With these findings, we propose Universum-inspired supervised Contrastive learning (UniCon), which incorporates Mixup strategy to generate Mixup-induced universum as universum negatives and pushes them apart from anchor samples of the target classes. We extend our method to the unsupervised setting, proposing Unsupervised Universum-inspired contrastive model (Un-Uni). Our approach not only improves Mixup with hard labels, but also innovates a novel measure to generate universum data. With a linear classifier on the learned representations, UniCon shows state-of-the-art performance on various datasets. Specially, UniCon achieves 81.7% top-1 accuracy on CIFAR-100, surpassing the state of art by a significant margin of 5.2% with a much smaller batch size, typically, 256 in UniCon vs. 1024 in SupCon (Khosla et al., 2020) using ResNet-50. Un-Uni also outperforms SOTA methods on CIFAR-100. The code of this paper is released on https://github.com/hannaiiyanggit/UniCon.
Fault-Tolerant Data Aggregation Scheme Supporting Fine-Grained Linear Operation in Smart Grid

Smart grid is a combination of traditional power system engineering and information and communication technology. Smart grid provides users with convenient services through real-time data updates. Multi-dimensional data aggregation can be more flexible for statistical analysis of electricity information. However, most of the existing multi-dimensional data aggregation schemes require the participation of a trusted third party and do not support fault tolerance. In this paper, we propose a fault-tolerant data aggregation scheme supporting fine-grained linear operations in smart grid. Firstly, we used the Chinese remainder theorem to encode the user’s multi-dimensional data and the corresponding weights. Secondly, we construct a privacy-preserving data aggregation scheme without a trusted third party, by combining paillier homomorphic encryption scheme and a secure key agreement protocol. Finally, we use the extended Shamir secret sharing scheme to construct a fault-tolerant data aggregation scheme that supports the reuse of shared key shares. Security analysis results show that our scheme satisfies semantic security and user data privacy protection. Experimental results show that compared with the existing multidimensional data aggregation schemes that require a trusted third party, our scheme does not increase additional computation and communication overhead.
The role of e-infrastructure in supporting scientific activities: challenges and perspectives

The article outlines the role of e-infrastructures in supporting scientific activities and discusses the challenges and prospects associated with their utilization. In the digital world, e-infrastructures play a crucial role in facilitating access to scientific information, enabling data sharing, and fostering collaboration among research communities. An overview of the main types of e-infrastructures in Ukraine, such as the Ukrainian National Grid (UNG), Ukrainian Research and Academic Network (URAN), GÉANT, and the National Repository of Academic Texts (NRAT), is provided, along with their key characteristics. The main advantages of utilizing e-infrastructures to support and conduct scientific research can be summarized as follows: access to infrastructure and resources, support for various scientific fields, collaboration and cooperation, high productivity and efficiency, integration with international initiatives, ensuring security and confidentiality, and fostering the development of the national scientific and technological community. The primary functions of e-infrastructures are described. Emphasis is placed on the significant impact of e-infrastructures on the scientific community, including notable changes in the organization and communication among researchers facilitated by e-infrastructures. Currently, interaction between scientific fields and communities takes place through e-infrastructures platforms. The experience of the article’s authors in popularizing digital services and e-infrastructures through the organization of scientific mass events for Ukrainian scientists is described. Emphasis is placed on the increased utilization of e-infrastructures by researchers and students, as well as their understanding of the potential for enhancing the efficiency and quality of scientific research.
Global Composite Compression of Deep Neural Network in Wireless Sensor Networks for Edge Intelligent Fault Diagnosis

To address the problem of large-scale complex deep neural network (DNN) models that cannot be deployed at the edge of wireless sensor networks (WSNs) with limited storage and computing resources, resulting in a lack of real-time intelligent data-processing capability, global composite compression of DNN in WSNs for edge intelligent fault diagnosis is proposed. First, the criteria importance through intercriteria correlation (CRITIC) weighted Euclidean–Pearson distance similarity (CEPDS) algorithm is proposed to perform coarse and fine-grained composite pruning on the DNN model to remove redundant parameters and kernels. The Wasserstein distance (WD) method and the mini-batch <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering algorithm were used to globally quantify the output features and weight parameters of the pruned model, further reducing model storage and improving model inference speed. The experimental results show that the proposed method can compress the DNN model by approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> , maintain a high diagnostic accuracy of approximately 99%, reduce the power consumption of nodes by approximately 10%, and reduce the monitoring system time by 42%, indicating that the method has reached an advanced level in DNN model compression, node power consumption, and data transmission delay.
Augmented Robotic Telepresence (ART): A Prototype for Enhancing Remote Interaction and Participation

Mobile robotic telepresence (MRP) allows remote users’ access and mobility in a range of local environments. MRP devices have been adopted in societally significant domains such as workplaces, museums, commerce, education, and healthcare, especially during the Covid-19 pandemic to provide accessibility to these spaces when physical attendance was precluded. Although telepresence robots have autonomous systems features such as collision avoidance, they do not typically allow for physical manipulation of the environment, so they have been found to engender limited trustworthiness and have yet to achieve widespread adoption. This work presents a prototype exploring the potential of Augmented Robotic Telepresence (ART) to improve on inclusion, accessibility, and independence provided to remote users of MRP, broadening the space for interaction and participation, by augmenting affordances in the local environment via techniques such as Augmented Reality (AR), Internet of Things (IoT), and remote actuation. Herein we describe the ART prototype developed thus far, which is built on top of a commercial MRP robot, the Double 3 by Double Robotics; we elaborate on the ART prototype design, its implementation, and current capabilities. Lastly, we describe our research plans, including design workshops with museum stakeholders, and point towards directions for future work.
A statistical method for predicting quantitative variables in association rule mining

Association rules encode common patterns and structures identified in datasets. They can be derived by association rule mining (ARM) algorithms. The association rules are human-readable and allow comprehensible predictions, unlike many other types of prediction algorithms. Classical ARM algorithms, like Apriori or FP-growth, cannot process interval or ratio scaled data (quantitative variables) which limits their applicability. We address this restriction in classical ARM algorithms, making it possible to process quantitative variables on the right side of a rule. Our approach is based on applying the Kullback–Leibler divergence (KLD) to identify a rule which holistically considers complete data distributions instead of using only summary statistics. We demonstrate the new approach by using, among others, the example of predicting the length of stay of intensive care patients. The length of stay describes the number of days a patient spends in the intensive care unit. In addition, we further demonstrate our approach by predicting the credit score of bank customers and the contract duration of customers of a fictional telco company based on two publically available datasets. This paper shows a new approach for predicting quantitative variables in ARM. We demonstrate the new approach using the FP-growth algorithm.
Low-rank discrete multi-view spectral clustering

Spectral clustering has attracted intensive attention in multimedia applications due to its good performance on arbitrary shaped clusters and well-defined mathematical framework. However, most existing multi-view spectral clustering methods still have the following demerits: (1) They ignore useful complementary information embedded in indicator matrices of different views. (2) The conventional post-processing methods based on the relax and discrete strategy inevitably result in the sub-optimal discrete solution. To tackle the aforementioned drawbacks, we propose a low-rank discrete multi-view spectral clustering model. Drawing inspiration from the fact that the difference between indicator matrices of different views provides useful complementary information for clustering, our model exploits the complementary information embedded in indicator matrices with tensor Schatten p-norm constraint. Further, we integrate low-rank tensor learning and discrete label recovering into a uniform framework, which avoids the uncertainty of the relaxed and discrete strategy. Extensive experiments on benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.
Differentially private distributed online mirror descent algorithm

This paper examines a private distributed online convex optimization problem in which each agent strives to minimize the sum of objective functions while also tending to keep their individual objective functions confidential. We use differential privacy as the metric to safeguard each agent's privacy and offer a distributed online mirror descent technique that is differentially private. We demonstrate that for strongly convex objective functions, our proposed algorithm satisfies the expected regret bound of OlnT while maintaining differential privacy, where T is the number of iterations. The established expected regret bound matches the optimal theoretical regret bound with respect to T. We further illuminate the trade-off between the extent of privacy-preserving and the expected regret bound through numerical simulations.
Multiview SVBRDF capture from unified shape and illumination

This paper proposes a stable method for reconstructing spatially varying appearances (SVBRDFs) from multiview images captured under casual lighting conditions. Unlike flat surface capture methods, ours can be applied to surfaces with complex silhouettes. The proposed method takes multiview images as inputs and outputs a unified SVBRDF estimation. We generated a large-scale dataset containing the multiview images, SVBRDFs, and lighting appearance of vast synthetic objects to train a two-stream hierarchical U-Net for SVBRDF estimation that is integrated into a differentiable rendering network for surface appearance reconstruction. In comparison with state-of-the-art approaches, our method produces SVBRDFs with lower biases for more casually captured images.
A network community-based differential evolution for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to locate multiple global optima simultaneously, which requires a high diversity of solutions. As a diversity preservation strategy customized for evolutionary algorithms (EAs), niching has been extensively incorporated into many algorithms to deal with MMOPs. However, many existing niching techniques require pre-defined parameters to control the size or number of niches, making the algorithm sensitive to parameters. Taking individuals as nodes, the inverse distance of two individuals as edges, and the historical information of recent generations as node attributes, we can construct a population as a network, whose purpose is to divide niches through community detection without relying on sensitive parameters. Inspired by this idea, this paper proposes a network community-based differential evolution for multimodal optimization problems (NetCDEMMOPs). In NetCDEMMOPs, three strategies are proposed and work together. Firstly, the community detection-based niching (CDN) strategy constructs the population as an attribute network and automatically divides it into multiple niches without sensitive parameters. Secondly, the community elites-based updating (CEU) strategy allocates more evolutionary resources to elite individuals of communities, then predicts and updates these elites based on historical information. Finally, the poor individual remolding (PIR) strategy remolds the last-ranking individuals and guides them toward promising positions. After extensive experimentation with the widely adopted CEC'2013 test suit, the experimental results reveal the superiority of NetCDEMMOPs over many successful or recent algorithms for MMOPs.
Quantum algorithm for twin extreme learning machine

Abstract Twin extreme learning machine (TELM) is a powerful learning algorithm, which aims at learning two nonparallel hyperplanes for data classification. However, classical TELM algorithm becomes computationally expensive when it involves big data sets. In this paper, we devise a quantum TELM algorithm to address this issue. Specifically, we first utilize the quantum amplitude estimation algorithm to prepare the desired input states and then call the quantum linear systems of equations, which adopts block-encoding technique, to obtain the model parameters in the training process. Then we invoke the swap test to estimate the distances from a new data point to the two hyperplanes and then make a classification in the prediction stage. The final complexity analysis demonstrates that our algorithm has an exponential speedup under certain conditions over the classical counterpart.