Reviewerly/exHarmony huggingface dataset:
Survey: Leakage and Privacy at Inference Time

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malicious leakage, followed by description of currently available defences, assessment metrics, and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.
Data Augmentation by Guided Deep Interpolation

State-of-the-art machine learning algorithms require large amount of high quality data. In practice, however, the sample size is commonly low and data is imbalanced along different class labels. Low sample size and imbalanced class distribution can significantly deteriorate the predictive performance of machine learning models. In order to overcome data quality issues, we propose a novel data augmentation method, Guided Deep Interpolation (GDI). It is based on a convolutional auto-encoder network, which is equipped with an auxiliary linear self-expressive layer. The network is trained by minimizing a composite objective function so that to extract the underlying clustered structure of semantic similarities of data points while high reconstruction quality is also preserved. The trained network is used to define a sampling strategy and a synthetic data generation procedure. Making use of the weights of the self-expressive layer, we introduce a measure of semantic variability to quantify how similar a data point to other data points on average. Based on the proposed measure of semantic variability, a joint distribution is defined. Using the distribution we can draw pairs of similar data points so that one point is semantically underrepresented (isolated) while its pair possesses relatively high semantic variability. A sampled pair is interpolated in the deep feature space of the network so that to increase semantic variability while preserve class label of the semantically underrepresented data point. The trained decoder is used to determine pixel space representations of latent space interpolations. The resulting data augmentation procedure generates synthetic samples by increasing the semantic variability of semantically underrepresented instances in a class label preserving way. Our experimental results show that the proposed method outperforms traditional and generative model-based data augmentation methods on low sample size and imbalanced data sets.
Calibration of stereo cameras with a marked-crossed fringe pattern

Stereo cameras have been widely used for three-dimensional (3D) imaging, and their performances are highly depended on the calibration accuracy. This paper presents a marked-crossed fringe pattern for the calibration of stereo cameras. Some coded markers are also encoded into the crossed fringe pattern, and the feature points are encoded into the carried phase. Two wrapped phase maps are recovered by using the Fourier transform, and the markers are extracted and decoded for absolute phase unwrapping. Referring to the absolute phase values, the one-to-one mapping between the world points and the image points can be established to calibrate the stereo cameras. Compared with the traditional method, the proposed method adopts global coding, which can flexibly select any part of the feature points for calibration and obtain high-precision camera parameters. Simulations and experiments show the effectiveness of the method.
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images

Various combinations of cameras enrich computational photography, among which reference-based super-resolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to accomplish high-fidelity super-resolution under a large resolution gap, e.g., 8× upscaling, due to the lower consideration of the underlying scene structure. In this paper, we aim to solve the RefSR problem in actual multiscale camera systems inspired by multiplane image (MPI) representation. Specifically, we propose Cross-MPI, an end-to-end RefSR network composed of a novel plane-aware attention-based MPI mechanism, a multiscale guided upsampling module as well as a super-resolution (SR) synthesis and fusion module. Instead of using a direct and exhaustive matching between the cross-scale stereo, the proposed plane-aware attention mechanism fully utilizes the concealed scene structure for efficient attention-based correspondence searching. Further combined with a gentle coarse-to-fine guided upsampling strategy, the proposed Cross-MPI can achieve a robust and accurate detail transmission. Experimental results on both digitally synthesized and optical zoom cross-scale data show that the Cross-MPI framework can achieve superior performance against the existing RefSR methods and is a real fit for actual multiscale camera systems even with large-scale differences.
The Role of Haptic Feedback and Gamification in Virtual Museum Systems

This article reports the results of a research aimed to evaluate the ability of a haptic interface to improve the user experience (UX) with virtual museum systems. In particular, two user studies have been carried out to (1) compare the experience aroused during the manipulation of a 3D printed replica of an artifact with a pen-like stylus with that aroused during the interaction (visual and tactile) with a 3D rendering application using a haptic interface and PC monitor, and (2) compare the users’ perceived usability and UX among a traditional mouse-based desktop interface, haptic interface, and haptic gamified interface based on the SUS scale and the AttrakDiff2 questionnaire. A total of 65 people were involved. The considered haptic application is based on the haptic device Omega 6 produced by Force Dimension, and it is a permanent attraction of the Museo Archeologico Nazionale delle Marche. Results suggest that the proposed haptic interface is suitable for people who commonly use mouse-based computer interaction, but without previous experience with haptic systems, and provide some insights useful to better understand the role of haptic feedback and gamification in enhancing UX with virtual museums, and to guide the development of other similar applications in the future.
PH-GCN: Person Retrieval With Part-Based Hierarchical Graph Convolutional Network

Compact feature representation of person image is important for person re-identification (Re-ID) task. Recently, part-based representation models have been widely studied for extracting the more compact and robust feature representation for person image to improve person Re-ID results. However, existing part-based representation models mostly extract the features of different parts independently which ignore the spatial relationship information among different parts. To address this issue, in this paper we propose a novel deep learning framework, named Part-based Hierarchical Graph Convolutional Network (PH-GCN) for person Re-ID problem. Given a person image, PH-GCN first constructs a hierarchical graph to represent the spatial relationships among different parts. Then, both local and global feature learning is achieved by the feature information passing in PH-GCN, which takes the information of other parts into account for part feature representation. Finally, a perceptron layer is adopted for the final person part label prediction and re-identification. The proposed framework provides a general solution that integrates <i>local</i>, <i>global</i> and <i>structural</i> feature learning simultaneously in a unified end-to-end network representation and learning. Extensive experiments on several widely used benchmark datasets demonstrate the effectiveness and benefits of the proposed PH-GCN approach for person Re-ID task.
Bidirectional remote state preparation in noisy environment assisted by weak measurement

By using special single-qubit measurement bases, we first proposed a bidirectional remote state preparation (BRSP) scheme to prepare two arbitrary single-qubit states in a noiseless quantum channel. Then, we modified it to a joint BRSP scheme and generalized it to prepare multi-qubit states. Meanwhile, we discussed the preparation fidelity of the scheme under the simulation of amplitude damping (AD) noise. In addition, we employed the weak measurement and its reversing measurements for the BRSP scheme and obtained higher fidelity of preparation results. Finally, through numerical simulation, we compared and analyzed the preparation fidelities under the above two conditions, the results of which showed that the weak measurement method was helpful in improving the proposed BRSP in the AD noise channel.
An unbiased ray-marching transmittance estimator

We present an in-depth analysis of the sources of variance in state-of-the-art unbiased volumetric transmittance estimators, and propose several new methods for improving their efficiency. These combine to produce a single estimator that is universally optimal relative to prior work, with up to several orders of magnitude lower variance at the same cost, and has zero variance for any ray with non-varying extinction. We first reduce the variance of truncated power-series estimators using a novel efficient application of U-statistics. We then greatly reduce the average expansion order of the power series and redistribute density evaluations to filter the optical depth estimates with an equidistant sampling comb. Combined with the use of an online control variate built from a sampled mean density estimate, the resulting estimator effectively performs ray marching most of the time while using rarely-sampled higher order terms to correct the bias.
All optical XOR logic gate formed by unsupervised optical neuron networks

We propose a design for an all-optical logic exclusive-OR (XOR) gate in terms of intensity-modulation or phase-modulation approaches. In this study, the unsupervised optical neuron networks (ONNs) are based on reservoir computing (RC) and the echo state networks (ESNs). Thanks to the optical interfering effect in the directional coupler, it provides a nonlinear function for the reservoir computing. By scanning the phase through the phase shifter in our optical neuron networks, we find the optimized results and demonstrate the relationship between the input and output signals. The simulated results match the truth logic table in XOR gate. We also demonstrate the bit error ratio (BER) of the all-optical logic XOR gate. The BER for intensity-modulation approach is 1.55% at 90 degree, and the phase-modulation approach is 1.78% at 91 degree. Thus, the simulated results also indicate that the optical neuron network has potential to achieve an optical integrated circuit. If this idea could be fabricated as an optical logic device, the processing rate in the ONN is in light frequency. It will help us to process the binary data sequence more efficiently.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network

Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsumes the target one. The key challenge of PDA is the issue of negative transfer caused by source-only classes. For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem. In this paper, we propose a novel Partial Adversarial Temporal Attentive Network (PATAN) to address the PVDA problem by utilizing both spatial and temporal features for filtering source-only classes. Besides, PATAN constructs effective overall temporal features by attending to local temporal features that contribute more toward the class filtration process. We further introduce new benchmarks to facilitate research on PVDA problems, covering a wide range of PVDA scenarios. Empirical results demonstrate the state-of-the-art performance of our proposed PATAN across the multiple PVDA benchmarks. Code will be provided at: https://github.com/xuyu0010/PATAN.
Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic

Individuals have the right to health according to the Constitution and other laws in China. Significant barriers have prevented the full realisation of the right to health in the COVID-19 era. Big data technology, which is a vital tool for COVID-19 containment, has been a central topic of discussion, as it has been used to protect the right to health through public health surveillance, contact tracing, real-time epidemic outbreak monitoring, trend forecasting, online consultations, and the allocation of medical and health resources in China. Big data technology has enabled precise and efficient epidemic prevention and control and has improved the efficiency and accuracy of the diagnosis and treatment of this new form of coronavirus pneumonia due to Chinese institutional factors. Although big data technology has successfully supported the containment of the virus and protected the right to health in the COVID-19 era, it also risks infringing on individual privacy rights. Chinese policymakers should understand the positive and negative impacts of big data technology and should prioritise the Personal Information Protection Law and other laws that are meant to protect and strengthen the right to privacy.
Passive binary defocusing for large depth 3D measurement based on deep learning

Phase-shifting profilometry (PSP) based on the binary defocusing technique has been widely used due to its high-speed capability. However, the required adjustment in projector defocus by traditional method is inaccurate, inflexible, and associated with fringe pitch. Instead of manual defocusing adjustment, a passive defocus of the binary patterns based on deep learning is proposed in this paper. Learning the corresponding binary patterns with a specifically designed convolutional neural network, high-quality three-step sinusoidal patterns can be generated. Experimental results demonstrate that the proposed method could reduce phase error by 80%–90% for different fringe pitches without projector defocus and outperform the traditional method by providing more accurate and robust results within a large measuring depth.
Comparison of Facial Landmark Detection Methods for Micro-Expressions Analysis

In the paper the problem of the facial landmark detection is investigated. This task is considered as the stage of the pipeline for micro-expression analysis. The classification of the known facial landmark detection methods is represented. The most promising methods are selected for the experimental comparison. These methods are based on the cascaded regression and on the deep learning. The known open source software implementations for them are chosen, viz. the OpenFace library and the MediaPipe framework. The experiments are carried out on the images from the Spontaneous Actions and Micro-Movements (SAMM) dataset. To evaluate and compare the obtained results for the two tested methods the reference image is constructed manually. Average deviations of the detected landmarks from the referenced are estimated. The comparison shows that a little better precision of the facial landmark detection is achieved using the deep learning based method.
The Laplace Mechanism has optimal utility for differential privacy over continuous queries

Differential Privacy protects individuals’ data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the released information less specific but, unavoidably, also decreases its utility. Yet it has been shown that for discrete data (e.g. counting queries), a mandated degree of privacy and a reasonable interpretation of loss of utility, the Geometric obfuscating mechanism is optimal: it loses as little utility as possible [Ghosh et al. [1]].For continuous query results however (e.g. real numbers) the optimality result does not hold. Our contribution here is to show that optimality is regained by using the Laplace mechanism for the obfuscation.The technical apparatus involved includes the earlier discrete result [Ghosh op. cit.], recent work on abstract channels and their geometric representation as hyper-distributions [Alvim et al. [2]], and the dual interpretations of distance between distributions provided by the Kantorovich-Rubinstein Theorem.
A novel method for removing Rician noise from MRI based on variational mode decomposition

MRI is a highly efficient medical imaging modality that captures all the delicate structural features of human tissues and organs enabling accurate clinical diagnosis. The noise occurred during image acquisition and transmission degrades these structural features and misleads the clinical diagnosis. Rician is the thermal noise found in MRI due to the thermal agitation of electrons. The main objective of the proposed work is to eliminate the Rician noise characteristics from the MRI. The proposed method effectively utilizes the properties of an efficient decomposition technique called variational mode decomposition (VMD). The proposed denoising method is performed in two stages. The high-frequency modes from the decomposed image are discarded and the denoised image is reconstructed from the low-frequency modes. The total variation (TV) image smoothing based on non-convex optimization is performed in the second stage for removing the remnant noise details from the first stage. The effectiveness of the proposed work is confirmed based on the improved peak signal to noise ratio (PSNR), stuctural similarity index measure (SSIM), quality index based on local variance (QILV) and Bhattacharrya coefficient (BC) scores. The proposed method is compared with different existing denoising methods. The experiment is conducted on two different datasets such as simulated Brainweb database and clinical dataset. Based on the comparative experimental analysis, the effectiveness of the proposed VMD-TV is confirmed by the improved objective performance metrics. In addition to objective quality assessments, the subjective evaluations carried out by radiologist and neurologists show the relatively better visual quality of the proposed method compared to the methods such as linear minimum mean square error (LMMSE) and bilateral filtering (BF).
Globally Distributed Development during COVID-19

Due to the global pandemic, in March 2020 we in academia and industry were abruptly forced into working from home. Yet teaching never stopped, and neither did developing software, fixing software, and expanding into new markets. Demands for flexible ways of working, responding to new requirements, have never been so high. How did we manage to continue working, when we had to suddenly switch all communication to online and virtual forms of contact? In this short paper we describe how Ocuco Ltd., a medium-sized organization headquartered in Ireland, managed our software development teams - distributed throughout Ireland, Europe, Asia and America during the COVID-19 pandemic. We describe how we expanded, kept our customers happy, and our teams motivated. We made changes, some large, such as providing emergency financial support; others small, like implementing regular online social pizza evenings. Technology and process changes were minor, an advantage of working in globally distributed teams since 2016, when development activities were coordinated according to the Scaled Agile Framework (SAFe). The results of implementing the changes were satisfying; productivity went up, we gained new customers, and preliminary results from our wellness survey indicate that everyone feels extremely well-supported by management to achieve their goals. However, the anonymised survey responses did show some developers' anxiety levels were slightly raised, and many are working longer hours. Administering this survey is very beneficial, as now we know, so we can act.
On the Orthogonality of Bias and Utility in Ad hoc Retrieval

Various researchers have recently explored the impact of different types of biases on information retrieval tasks such as ad hoc retrieval and question answering. While the impact of bias needs to be controlled in order to avoid increased prejudices, the literature has often viewed the relationship between increased retrieval utility (effectiveness) and reduced bias as a tradeoff where one can suffer from the other. In this paper, we empirically study this tradeoff and explore whether it would be possible to reduce bias while maintaining similar retrieval utility. We show this would be possible by revising the input query through a bias-aware pseudo-relevance feedback framework. We report our findings based on four widely used TREC corpora namely Robust04, Gov2, ClueWeb09 and ClueWeb12 and using two classes of bias metrics. The findings of this paper are significant as they are among the first to show that decrease in bias does not necessarily need to come at the cost of reduced utility.
A fully homomorphic encryption based on magic number fragmentation and <scp>El‐Gamal</scp> encryption: Smart healthcare use case

Abstract Nowadays, cloud computing offers a digital infrastructure for smart city development. Cognitive cities are steadily automating daily urban processes. The ever expanding objective‐driven communities gather and share sensitive data that must be stored securely. Cloud computing offers a suitable platform that allows cognitive smart cities to access and re‐access data to learn from their past to adapt its current behaviour. However, the cloud is an untrusted entity that may expose data when decrypted for processing by systems. In this paper, we treat the issue of encrypted data processing. Often, the data is encrypted prior to transferring it to the cloud, where the cloud must have the data in clear to be able to make calculations which raises security and privacy threats if the cloud is considered untrusted. The scenario of asking users to make the calculations after decrypting the received cloud data and encrypting the obtained results before sending them back to the cloud is not a practical solution in distributed multi‐tenant architectures. Homomorphic encryption allows offers a solution for processing encrypted data. Many existing homomorphic encryption schemes suffer from limitations that hinder their usability. This paper presents an efficient fully homomorphic encryption scheme using twin key encryption and magic number fragmentation. The details of the scheme are presented along with cryptanalytic attacks to assess its effectiveness. The proposed scheme exhibits strong resilience against brute‐force attacks compared to its rivals from the literature. Finally, we illustrate the applicability of the proposed scheme using a cognitive smart city application.