Reviewerly/exHarmony huggingface dataset:
GraphMI: Extracting Private Graph Data from Graph Neural Networks

As machine learning becomes more widely used for critical applications, the need to study its implications in privacy becomes urgent. Given access to the target model and auxiliary information, model inversion attack aims to infer sensitive features of the training dataset, which leads to great privacy concerns. Despite its success in the grid domain, directly applying model inversion techniques on non grid domains such as graph achieves poor attack performance due to the difficulty to fully exploit the intrinsic properties of graphs and attributes of graph nodes used in GNN models. To bridge this gap, we present Graph Model Inversion attack, which aims to infer edges of the training graph by inverting Graph Neural Networks, one of the most popular graph analysis tools. Specifically, the projected gradient module in our method can tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. Moreover, a well designed graph autoencoder module can efficiently exploit graph topology, node attributes, and target model parameters. With the proposed method, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our method. We also show that differential privacy in its canonical form can hardly defend our attack while preserving decent utility.
Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice

Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the kagome lattice is well-known to be a prohibitively difficult problem for classical computers. Here, we give a detailed proposal for a variational quantum eigensolver (VQE) intending to solve this physical problem on a quantum computer. At the same time, this VQE constitutes an explicit experimental proposal for showing a useful quantum advantage on noisy intermediate-scale quantum devices because of its natural hardware compatibility. We classically emulate noiseless and noisy quantum computers with either 2D-grid or all-to-all connectivity and simulate patches of the kagome HAFM of up to 20 sites. In the noiseless case, the ground-state energy, as found by the VQE, approaches the true ground-state energy exponentially as a function of the circuit depth. Furthermore, VQEs for the HAFM on any graph can inherently perform their quantum computations in a decoherence-free subspace that protects against collective longitudinal and collective transversal noise, adding to the noise resilience of these algorithms. Nevertheless, the extent of the effects of other noise types suggests the need for error mitigation and performance targets alternative to high-fidelity ground-state preparation, even for essentially hardware-native VQEs.
Secure Deep Graph Generation with Link Differential Privacy

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting designated noise to the gradients of a link reconstruction based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.
Medical Image Watermarking for Telemedicine Application Security

In this work, we proposed a robust and blind watermarking approach to adequately secure medical images exchanged in telemedicine. This approach ensures the traceability and integrity of the medical and essential image for data security in the field of telemedicine. In this paper, a blind watermarking method is proposed to adequately secure the electronic patient records. The integration of the watermark will be carefully performed by combining the parity of the successive values. This innovative approach will be typically implemented in the three insertion domains: spatial, frequency and multi-resolution. For the spatial domain, the watermark will be integrated into the colorimetric values of the image. In the frequency domain, the watermark bits will be substituted to the DCT coefficient’s least significant bit. For the multi-resolution domain insertion, after calculating a DWT, the obtained LL sub-band coefficients will be used for the integration process. After comparing our approaches to the various recent works in the three domains, the obtained results demonstrate that our proposed approach offers a good imperceptibility for the frequency and spatial domains insertion.
Multi-Keyword Searchable and Verifiable Attribute-Based Encryption Over Cloud Data

In cloud data sharing systems, Searchable Encryption (SE) schemes ensure data confidentiality with retrieving, but it faces several issues in practice. First, most of the previous Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) systems enable users to initiate search requests with a single keyword, which results in many inaccurate results to be returned, thereby wasting computing and bandwidth resources. Second, untrusted cloud servers may return a small portion of incomplete search results to compress communication overhead. Besides, most CP-ABKS schemes only support an unshared multi-owner setting, which incurs a large amount of computational and storage overhead. Furthermore, when the keyword space is a polynomial, most of the previous schemes suffer from offline keyword guessing attacks. To address these issues, we focus on a multi-keyword search scheme which supports the verification of search results without losing efficiency by combining Ciphertext Policy Attribute-Based Encryption (CP-ABE) technology under the shared multi-owner mechanism. We show the security of our scheme, which achieves selective security against offline keyword guessing attacks and guarantees the unforgeability of signatures. The comparison of experimental results illustrates that our scheme is effective and enjoys superior functionalities than the most relevant solutions.
Learning Robust Recommenders through Cross-Model Agreement

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of user unawareness could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendations.
RGB image classification with quantum convolutional ansatz

With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information is extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum–classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments
Single Model for Organic and Inorganic Chemical Named Entity Recognition in ChemDataExtractor

Chemical Named Entity Recognition (NER) forms the basis of information extraction tasks in the chemical domain. However, while such tasks can involve multiple domains of chemistry at the same time, currently available named entity recognizers are specialized in one part of chemistry, resulting in such workflows failing for a biased subset of mentions. This paper presents a single model that performs at close to the state-of-the-art for both organic (CHEMDNER, 89.7 F1 score) and inorganic (Matscholar, 88.0 F1 score) NER tasks at the same time. Our NER system utilizing the Bert architecture is available as part of ChemDataExtractor 2.1, along with the data sets and scripts used to train the model.
A bibliometric analysis of digital literacy research and emerging themes pre-during COVID-19 pandemic

Purpose Digital literacy is not the mere ability to use a digital device or know to use various software. It is a domain of exploration for sociology, psychology, education and, of course, technology. This study aims to present a quantitative analysis of the literature on digital literacy using a bibliometric approach. Design/methodology/approach Using data from the Web of Science database, the importance of the research is evaluated by reviewing 2307 publications and examining the yearly publication, field category productivity, citation structure, most cited resources, documents, most-cited authors, most productive authors, and country in the field of digital literacy. Further, a cluster analysis is conducted to see the most recurrent keywords and emerging trends in this field. At last, the authors analyzed the thematic progression of keywords over these five years based on the normalized citations. Additionally, a graphical representation of the bibliometric data using VOSviewer is presented in the paper. Findings The results suggest a steady rate of publication in this field, with most of the research published in education and library fields and the USA leading the country in this realm. The emerging themes in this field are ‘Fake News’, ‘Competence’, ‘Educational Technology’, ‘Health Literacy’, ‘Self-Efficacy’ and, interestingly, ‘COVID-19’. The results also revealed that COVID-19 has been examined and associated with fake news, higher education, social media and information literacy. Originality/value This paper provides an overall summary of the most recent research work published from 2017 to 2021 on digital literacy in the backdrop of COVID-19. The study presents the thematic progression over the years and particularly the new keywords that emerged in the limelight of the pandemic. It contributes by updating the existing body of knowledge in the field of digital literacy and presents preliminary results related to COVID-19.
High-dimensional discrete Fourier transform gates with a quantum frequency processor

The discrete Fourier transform (DFT) is of fundamental interest in photonic quantum information, yet the ability to scale it to high dimensions depends heavily on the physical encoding, with practical recipes lacking in emerging platforms such as frequency bins. In this article, we show that d-point frequency-bin DFTs can be realized with a fixed three-component quantum frequency processor (QFP), simply by adding to the electro-optic modulation signals one radio-frequency harmonic per each incremental increase in d. We verify gate fidelity FW>0.9997 and success probability PW>0.965 up to d = 10 in numerical simulations, and experimentally implement the solution for d = 3, utilizing measurements with parallel DFTs to quantify entanglement and perform tomography of multiple two-photon frequency-bin states. Our results furnish new opportunities for high-dimensional frequency-bin protocols in quantum communications and networking.
End-to-end snapshot compressed super-resolution imaging with deep optics

Capturing both fine structure and high dynamics of the scene is demanded in many applications. However, such high throughput recording requires significant transmission bandwidth and large storage. Off-the-shelf super-resolution and temporal compressive sensing can partially address this challenge, but directly concatenating the two techniques fails to boost throughput because of artifact accumulation and magnification in sequential reconstruction. In this Letter, we propose an encoded capturing approach to simultaneously increase spatial and temporal resolvability with a low-bandwidth camera sensor. Specifically, we introduce point-spread-function (PSF) engineering via deep optics to encode fine spatial details and temporal compressive sensing to encode fast motions into a low-resolution snapshot. Furthermore, we develop an end-to-end deep neural network to optimize PSF and retrieve high-throughput videos from a compactly compressed measurement. Trained on simulation data and fine-tuned to fit system settings, our end-to-end system offers a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>128</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:math> data throughput compared to conventional imaging.
Error correction algorithms in non-null aspheric testing next generation sequencing data

Non-null tests are more versatile than null tests and could give fast, generic tests with sufficient performance. The non-null testing methods are able to measure aspheric surfaces with substantial deviations from a reference sphere. The ray will return along a different path and interfere with a separate reference ray in a non-null arrangement. The retrace error occurs because of the significant disparity between the aspheric and the reference. To correct retracing errors and replicate aspheric fabrication imperfections, complex systems modelling and simulations are required. We propose a ray-tracing-based technique for simulating the non-null interferometer that estimates the optical path differences by tracking lasers across the reference and testing paths. An error correction method is used by ray trace analysis to correct the retracing errors in non-null tests of aspheric surfaces. Experiments were conducted to demonstrate the effectiveness of this strategy. The result of the study shows that the proposed system successfully and effectively rectifies the retracing error compared to other practical findings and computer simulation methods. It is more versatile, faster, and provides adequate aspheric assessment performance.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.In this paper, we present a decomposed metalearning approach which addresses the problem of few-shot NER by sequentially tackling fewshot span detection and few-shot entity typing using meta-learning.In particular, we take the few-shot span detection as a sequence labeling problem and train the span detector by introducing the model-agnostic meta-learning (MAML) algorithm to find a good model parameter initialization that could fast adapt to new entity classes.For few-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhanced prototypical networks to find a good embedding space that can better distinguish text span representations from different entity classes.Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods. 1
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation

Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models.However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well.We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data.To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any personabased dialogue generation model to improve its performance.The original training samples will first be distilled and thus expected to be fitted more easily.Next, we show various effective ways that can diversify such easier distilled data.A given base model will then be trained via the constructed data curricula, i.e. first on augmented distilled samples and then on original ones.Experiments illustrate the superiority of our method with two strong base dialogue models (Transformer encoderdecoder and GPT2).
A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data

With the development of Internet of Medical Things, massive healthcare sensor data (HSD) are transmitted in the Internet, which faces various security problems. Healthcare data are sensitive and important for patients. Automatic classification of HSD has significant value for protecting the privacy of patients. Recently, the edge computing-based federated learning has brought new opportunities and challenges. It is difficult to develop a lightweight HSD classification system for edge computing. In particular, the classification system should consider the dynamic characteristics of HSD, e.g., the change of data distributions and the appearance of initially unknown classes. To solve these problems, the paper proposes a scalable and transferable classification system, called SCALT. It is a one-classifier-per-class system based on federated learning. It comprises a one-dimensional convolution-based network for feature extraction, and an individual mini-classifier for each class. It is easy to be scaled when new class appears since only a mini-classifier will be trained. The feature extractor is updated only when it is transferred to a new task. SCALT has a parameter protection mechanism, which can avoid catastrophic forgetting in sequential HSD classification tasks. We conduct comprehensive experiments to evaluate SCALT on three different physiological signal datasets: Electrocardiogram, Electroencephalogram and Photoplethysmograph. The accuracies on the three datasets are 98.65%, 91.10% and 89.93% respectively, which are higher than the compared state-of-the-art works. At last, an application of applying SCALT to protect the privacy of patients is presented.
XR-LIVE: Enhancing Asynchronous Shared-Space Demonstrations with Spatial-temporal Assistive Toolsets for Effective Learning in Immersive Virtual Laboratories

An immersive virtual laboratory (VL) could offer flexibility of time and space, as well as safety, for remote students to conduct laboratory activities through online experiential learning. Recording an instructor's demonstration inside a VL is an approach that allows students to learn directly from a demonstration. However, students have to learn from a recording while controlling the playback, which requires attention spent on additional spatial and temporal cues. This additional cognitive load could lead to errors during the laboratory procedure. To address these challenges, we have identified four design requirements to reduce attention load in VLs; namely, organized learning steps, improved student sense of co-presence, reduction of task-instructor split-attention, and learning independent of interpersonal distance. Based on these requirements, we have designed and implemented spatial-temporal assistive toolsets for laboratories in a virtual environment, namely XR-LIVE, to reduce cognitive load and enhance learning in an asynchronous shared-space demonstration, implemented based on the setup of a standard civil engineering laboratory. We also analyzed students' behavior in the VL demonstration to design guidelines applicable to generic VLs.
A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research.
Is Attention Explanation? An Introduction to the Debate

The performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these models becomes paramount.Attention has been seen as a solution to increase performance, while providing some explanations.However, a debate has started to cast doubt on the explanatory power of attention in neural networks.Although the debate has created a vast literature thanks to contributions from various areas, the lack of communication is becoming more and more tangible.In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas.This holistic vision can be of great interest for future works in all the communities concerned by this debate.We sum up the main challenges spotted in these areas, and we conclude by discussing the most promising future avenues on attention as an explanation.