Reviewerly/exHarmony huggingface dataset:
Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm

As a severe hazard in coal mining and rock excavation, the rockburst is usually induced by the high energy tremor.Microseismic (MS) monitoring is suggested to forecast the rockburst risk to reduce its damage.The paper aims to investigate contribution of multi-MS data, including MS raw wave data and MS energy data, to prediction of the high energy tremor, using support vector machine (SVM) together with genetic algorithm (GA).MS monitoring data recorded for more than 400 days at Wudong coal mine of Xinjiang, China, were used in the paper.132 and 24 features are initially extracted from MS raw wave and energy data in the frequency domain, entropy and time-frequency domain, respectively.GA is not only used to select effective ones among initially extracted features, but also optimize hyperparameters for SVM to classify high energy tremors from general MS events.The performances of the proposed approach based on multi-MS data are evaluated by cross-validation.The results show that the classifier achieves 98% sensitivity, 88% accuracy and 87% specificity using both MS raw wave and energy data, which is better than solely utilizing MS raw wave (98% sensitivity, 84% accuracy and 83% specificity) or energy data (98% sensitivity, 86% accuracy and 85% specificity).These findings suggest that MS raw wave data makes important contribution to rockburst risk prediction as well as MS energy data, and the better performance can be achieved when utilizing two kinds of data simultaneously.
Opening Pandora’s Box: Peeking inside Psychology’s data sharing practices, and seven recommendations for change

Open data-sharing is a valuable practice that ought to enhance the impact, reach, and transparency of a research project. While widely advocated by many researchers and mandated by some journals and funding agencies, little is known about detailed practices across psychological science. In a pre-registered study, we show that overall, few research papers directly link to available data in many, though not all, journals. Most importantly, even where open data can be identified, the majority of these lacked completeness and reusability-conclusions that closely mirror those reported outside of Psychology. Exploring the reasons behind these findings, we offer seven specific recommendations for engineering and incentivizing improved practices, so that the potential of open data can be better realized across psychology and social science more generally.
A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problems

Most of the real-world black-box optimization problems are associated with multiple non-linear as well as non-convex constraints, making them difficult to solve. In this work, we introduce a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with linear timing complexity to adopt the constraints of Constrained Optimization Problems (COPs). CMA-ES is already well-known as a powerful algorithm for solving continuous, non-convex, and black-box optimization problems by fitting a second-order model to the underlying objective function (similar in spirit, to the Hessian approximation used by Quasi-Newton methods in mathematical programming). The proposed algorithm utilizes an e-constraint-based ranking and a repair method to handle the violation of the constraints. The experimental results on a group of real-world optimization problems show that the performance of the proposed algorithm is better than several other state-of-the-art algorithms in terms of constraint handling and robustness.
Face mask design to mitigate facial expression occlusion

The COVID-19 pandemic dictated that wearing face masks during public interactions was the new norm across much of the globe. As the masks naturally occlude part of the wearer's face, the part of communication that occurs through facial expressions is lost, and could reduce acceptance of mask wear. To address the issue, we created 2 face mask prototypes, incorporating simple expressive display elements and evaluated them in a user study. Aiming to explore the potential for low-cost solutions, suitable for large-scale deployment, our concepts utilized bi-state electrochromic displays. One concept Mouthy Mask aimed to reproduce the image of the wearer's mouth, whilst the Smiley Mask was symbolic in nature. The smart face masks were considered useful in public contexts to support short socially expected rituals. Generally a visualization directly representing the wearer's mouth was preferred to an emoji style visualization. As a contribution, our work presents a stepping stone towards productizable solutions for smart face masks that potentially increase the acceptability of face mask wear in public.
Integration of Next Generation IIoT with Blockchain for the Development of Smart Industries

In modern era, a wide range of smart industries is being focus on automation-based applications. Various technologies are rapidly implementing in Industrial Internet of Things (IIoT) for manufacturing sectors that helping to achieve advanced schedule production framework and on time delivery of products. The integration of IIoT platforms with the blockchain are challenging service in manufacturing system. The primary objective of this article is to characterize various issues and challenges that are implementing IIoT and blockchain in industries. The proposed work is an integration of IIoT and blockchain in industrial processes for solving the security issues in real-time. Also, identifying various enablers of blockchain and issues of IIoT from smart industries manufacturing using a survey tool is formed in the form of questionnaire. Based on these responses Decision Making Trial and Evaluation Laboratory (DEMATEL) technique has been implemented for categorizing these challenges into cause and effect. In this paper, we introduce the general layout with their key issues and challenges of IIoT and blockchain that signifies the safety requirements to design the IIoT and blockchain. Further, we describe how IIoT can be integrated to the blockchain for smart Industrial applications. Finally, various recommendations are the proposed to upcoming IIoT and blockchain developments. The proposed work will be highly beneficial for the smart industries to develop a next generation IIoT and blockchain based framework.
Tighter Constraints of Multiqubit Entanglement*

Monogamy and polygamy relations characterize the distributions of entanglement in multipartite systems. We provide classes of monogamy and polygamy inequalities of multiqubit entanglement in terms of concurrence, entanglement of formation, negativity, Tsallis-$q$ entanglement and R\'{e}nyi-$\alpha$ entanglement, respectively. We show that these inequalities are tighter than the existing ones for some classes of quantum states.
Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers.The choice of predicting answers as positions is mainly due to its simplicity and effectiveness.In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT.To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling.Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.
Reusable Fuzzy Extractors for Low-Entropy Distributions

Fuzzy extractors (Dodis et al., in Advances in cryptology—EUROCRYPT 2014, Springer, Berlin, 2014, pp 93–110) convert repeated noisy readings of a secret into the same uniformly distributed key. To eliminate noise, they require an initial enrollment phase that takes the first noisy reading of the secret and produces a nonsecret helper string to be used in subsequent readings. Reusable fuzzy extractors (Boyen, in Proceedings of the 11th ACM conference on computer and communications security, CCS, ACM, New York, 2004, pp 82–91) remain secure even when this initial enrollment phase is repeated multiple times with noisy versions of the same secret, producing multiple helper strings (for example, when a single person’s biometric is enrolled with multiple unrelated organizations). We construct the first reusable fuzzy extractor that makes no assumptions about how multiple readings of the source are correlated. The extractor works for binary strings with Hamming noise; it achieves computational security under the existence of digital lockers (Canetti and Dakdouk, in Advances in cryptology—EUROCRYPT 2008, Springer, Berlin, 2008, pp 489–508). It is simple and tolerates near-linear error rates. Our reusable extractor is secure for source distributions of linear min-entropy rate. The construction is also secure for sources with much lower entropy rates—lower than those supported by prior (nonreusable) constructions—assuming that the distribution has some additional structure, namely, that random subsequences of the source have sufficient minentropy. Structure beyond entropy is necessary to support distributions with low entropy rates. We then explore further how different structural properties of a noisy source can be used to construct fuzzy extractors when the error rates are high, building a computationally secure and an information-theoretically secure construction for large-alphabet sources.
Concentric circle grids for camera calibration with considering lens distortion

The accuracy of camera calibration depends significantly on the control point detection which customarily subjects to image noise, distortion and resolution. This paper uses concentric circle grids as the calibration pattern and accordingly proposes a robust and accurate control point detection method for camera calibration. Each set of concentric circles on the grids pattern consists of multiple circles whose radii decrease progressively. The geometric structure of multi-hierarchies can not only robustly extract control points from complicated scenarios but also build a voting scheme to avoid numerical instability resulting from the estimation of projected centers. According to the common self-polar triangle of concentric circles and the local sub-pixel edge detection algorithm, we efficiently estimated sub-pixel coordinates of control points under projective transformation. Simulations and real experiments were conducted to demonstrate that the performance of calibration using concentric circle grids is superior to the popular grid patterns with respect to the image noise, distortion and resolution.
Beating the standard quantum limit under ambient conditions with solid-state spins

Beating the standard quantum limit under ambient conditions with solid-state spins is realized.
Privacy-Preserving Surveillance as an Edge Service Based on Lightweight Video Protection Schemes Using Face De-Identification and Window Masking

With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance as an Edge service (PriSE) method with a hybrid architecture comprising a lightweight foreground object scanner and a video protection scheme that operates on edge cameras and fog/cloud-based models to detect privacy attributes like windows, faces, and perpetrators. The Reversible Chaotic Masking (ReCAM) scheme is designed to ensure an end-to-end privacy while the simplified foreground-object detector helps reduce resource consumption by discarding frames containing only background-objects. A robust window-object detector was developed to prevent peeping via windows; whereas human faces are detected by using a multi-tasked cascaded convolutional neural network (MTCNN) to ensure de-identification. The extensive experimental studies and comparative analysis show that the PriSE scheme (i) can efficiently detect foreground objects, and scramble those frames that contain foreground objects at the edge cameras, and (ii) detect and denature window and face objects, and identify perpetrators at a fog/cloud server to prevent unauthorized viewing via windows, to ensure anonymity of individuals, and to deter criminal activities, respectively.
Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea

Purpose The purpose of this paper is to illustrate how big data analytics pushed the limits of individuals' accountability as South Korea tried to control and contain coronavirus disease 2019 (COVID-19). Design/methodology/approach The authors draw upon Deleuzo-Guattarian framework elaborating how a surveillant assemblage was rhizomatically created and operated to monitor a segment of the population holding them accountable. Publicly available secondary data, such as press release from the government and media coverage, were used. Findings A COVID-19 Smart Management System and a Self-Quarantine Safety Protection App constituted a surveillance assemblage operating in a “state-form”. This comprises the central government departments, local councils, policing systems, providers of telecommunication and financial services, and independent groups of people. This assemblage pushed the limits of accountability as individuals who tested positive or might bear possible future risks of the infection and transmission were held accountable for their locations and health conditions. Practical implications Policymakers may consider constructing this type of state-form for containing and controlling pandemics, such as COVID-19, while dealing with the issue of undermined privacy. Social implications The mass may consider to what extent individuals' personal information should be protected and how to hold the governments accountable for the legitimate use of such information. Originality/value While accountability studies have largely focussed on formal organisations, the authors illustrated how a broader context of a state-form, harnessing big data analytics, pushes the limits of accountability.
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation

The concept of biological age (BA), although important in clinical practice, is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study, we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA $\not \approx$ CA) from the given population. We hypothesize that the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.
Meeting you was a fake: investigating the increase in romance fraud during COVID-19

Purpose Romance fraud refers to situations where an individual is deceived for financial gain by someone with whom the victim perceives to be in a romantic relationship. With the increase in internet use, the growth in loneliness and the increasing engagement in online dating sites during COVID-19, opportunities for romance fraud may have suffered an important increase. This paper aims to analyse changes in romance fraud, loneliness and internet use during the pandemic. Design/methodology/approach Data about romance fraud reported to the police in the UK, and survey data recorded by the Understanding Society longitudinal survey, are used to address our research questions. Auto regressive integrated moving average (ARIMA) modelling is used to analyse whether temporal changes observed are an effect of social changes associated with lockdown and stay-at-home orders. Findings The analysis shows that cyber-enabled romance fraud experienced a large increase after April 2020, which is greatly above any expected crime variation considering known pre-COVID trends. The increase in romance fraud was more abrupt among young adults than older persons. The results also indicate that only young adults experienced a significant increase in loneliness, while older adults reported a large increase in internet use during COVID. Originality/value To the best of the authors’ knowledge, this is a first-of-its-kind paper analysing the effect of rapid social changes on a growing type of cyber-enabled fraud. It is likely that the growth in romance fraud during COVID is due to a combined effect of an increase in internet use and growing loneliness rates experienced by many people during the pandemic.
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets.After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text.To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder.Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.We release our code and model at https://github.com/microsoft/SpeechT5.
Pulse-level noisy quantum circuits with QuTiP

The study of the impact of noise on quantum circuits is especially relevant to guide the progress of Noisy Intermediate-Scale Quantum (NISQ) computing. In this paper, we address the pulse-level simulation of noisy quantum circuits with the Quantum Toolbox in Python (QuTiP). We introduce new tools in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">qutip-qip</mml:mtext></mml:mrow></mml:math>, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian that describes the unitary evolution of the physical qubits. Various types of noise can be introduced based on the physical model, e.g., by simulating the Lindblad density-matrix dynamics or Monte Carlo quantum trajectories. In particular, the user can define environment-induced decoherence at the processor level and include noise simulation at the level of control pulses. We illustrate how the Deutsch-Jozsa algorithm is compiled and executed on a superconducting-qubit-based processor, on a spin-chain-based processor and using control optimization algorithms. We also show how to easily reproduce experimental results on cross-talk noise in an ion-based processor, and how a Ramsey experiment can be modeled with Lindblad dynamics. Finally, we illustrate how to integrate these features with other software frameworks.
InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools

Existing approaches for embedding unobtrusive tags inside 3D objects require either complex fabrication or high-cost imaging equipment. We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras. We achieve this by printing objects from an infrared-transmitting filament, which infrared cameras can see through, and by having air gaps inside for the tag’s bits, which appear at a different intensity in the infrared image.
CenterCLIP

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, the essential visual tokenization process, which produces discrete visual token sequences, generates many homogeneous tokens due to the redundancy nature of consecutive and similar frames in videos. This significantly increases computation costs and hinders the deployment of video retrieval models in web applications. In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. As the frame redundancy occurs mostly in consecutive frames, we divide videos into multiple segments and conduct segment-level clustering. Center tokens from each segment are later concatenated into a new sequence, while their original spatial-temporal relations are well maintained. We instantiate two clustering algorithms to efficiently find deterministic medoids and iteratively partition groups in high dimensional space. Through this token clustering and center selection procedure, we successfully reduce computation costs by removing redundant visual tokens. This method further enhances segment-level semantic alignment between video and text representations, enforcing the spatio-temporal interactions of tokens from within-segment frames. Our method, coined as CenterCLIP, surpasses existing state-of-the-art by a large margin on typical text-video benchmarks, while reducing the training memory cost by 35\% and accelerating the inference speed by 14\% at the best case. The code is available at \href{{https://github.com/mzhaoshuai/CenterCLIP}}{{https://github.com/mzhaoshuai/CenterCLIP}}.