Reviewerly/exHarmony huggingface dataset:
Few-Shot Quality-Diversity Optimization

In the past few years,a considerable amount of research has been dedicated to the exploitation of previous learning experiences and the design of Few-shot and Meta Learning approaches, in problem domains ranging from Computer Vision to Reinforcement Learning based control. A notable exception, where to the best of our knowledge, little to no effort has been made in this direction is Quality-Diversity (QD) optimization. QD methods have been shown to be effective tools in dealing with deceptive minima and sparse rewards in Reinforcement Learning. However, they remain costly due to their reliance on inherently sample inefficient evolutionary processes. We show that, given examples from a task distribution, information about the paths taken by optimization in parameter space can be leveraged to build a prior population, which when used to initialize QD methods in unseen environments, allows for few-shot adaptation. Our proposed method does not require backpropagation. It is simple to implement and scale, and furthermore, it is agnostic to the underlying models that are being trained. Experiments carried in both sparse and dense reward settings using robotic manipulation and navigation benchmarks show that it considerably reduces the number of generations that are required for QD optimization in these environments.
Distributed and Mobile Message Level Relaying/Replaying of GNSS Signals

With the introduction of Navigation Message Authentication (NMA), future Global Navigation Satellite Systems (GNSSs) prevent spoofing by simulation, i.e., the generation of forged satellite signals based on publicly known information. However, authentication does not prevent record-and-replay attacks, commonly termed as meaconing. Meaconing attacks are less powerful in terms of adversarial control over the victim receiver location and time, but by acting at the signal level, they are not thwarted by NMA. This makes replaying/relaying attacks a significant threat for current and future GNSS. While there are numerous investigations on meaconing attacks, the vast majority does not rely on actual implementation and experimental evaluation in real-world settings. In this work, we contribute to the improvement of the experimental understanding of meaconing attacks. We design and implement a system capable of real-time, distributed, and mobile meaconing, built with off-the-shelf hardware. We extend from basic distributed meaconing attacks, with signals from different locations relayed over the Internet and replayed within range of the victim receiver(s). This basic attack form has high bandwidth requirements and thus depends on the quality of service of the available network to work. To overcome this limitation, we propose to replay on message level, i.e., to demodulate and re-generate signals before and after the transmission respectively (including the authentication part of the payload). The resultant reduced bandwidth enables the attacker to operate in mobile scenarios, as well as to replay signals from multiple GNSS constellations and/or bands simultaneously. Additionally, the attacker can delay individually selected satellite signals to potentially influence the victim position and time solution in a more fine-grained manner. Our versatile test-bench, enabling different types of replaying/relaying attacks, facilitates testing realistic scenarios towards new and improved replaying/relaying-focused countermeasures in GNSS receivers.
Stroboscopic quantum nondemolition measurements for enhanced entanglement generation between atomic ensembles

We develop a measurement operator formalism to handle quantum nondemolition (QND) measurement induced entanglement generation between two atomic gases. We first derive how the QND entangling scheme reduces to a positive operator-valued measure, and consider its limiting case when it can be used to construct a projection operator that collapses the state to a total spin projection state. We then analyze how a stroboscopic sequence of such projections made in the $x$ and $z$ basis evolves the initial wave function. Such a sequence of QND projections can enhance the entanglement between the atomic ensembles and makes the state converge towards a highly entangled state. We show several mathematical identities which greatly simplify the state evolution in the projection sequence and allow one to derive the exact state in a highly efficient manner. Our formalism does not use the Holstein-Primakoff approximation as is conventionally done, and treats the spins of the atomic gases in an exact way.
Characterizing Chinese online public opinions towards the COVID-19 recovery policy

Purpose The online users’ characteristic information can provide decision support for policy-designing and construction of public strategies. Hence, this paper aims to conduct online public opinion mining on the recovery policy stimulating the economies stroked by COVID-19 epidemic. Also, sentimental analysis is performed to uncover the posters’ emotion towards the target policy. Design/methodology/approach This paper adopts bidirectional encoder representations from transformers (BERT) as classifier in classification tasks, including misinformation detection, subject analysis and sentimental analysis. Meanwhile, latent Dirichlet allocation method and sentiment formulations are implemented in topic modelling and sentiment analysis. Findings The experimental results indicate that public opinion is mainly non-negative to the target policy. The positive emotions mainly focus on the benefits that the recovery policy might bring to stimulate economy. On the other hand, some negative opinions concerned about the shortcomings and inconvenience of the target policy. Originality/value The authors figured out the key factors focused by the public opinion on the target recovery policy. Also, the authors indicated pros and cons of the recovery policy by analysing the emotion and the corresponding topics of the public opinion on social media. The findings of the paper can be generalized in other countries theoretically to help them design recovery policy against COVID-19.
Consumer reactions to different robotic servers in theme parks

Purpose This study aims to understand the theme park visitors’ perceived robotic qualities featured in four different robots, assess their robotic functions and consumers’ loyalty toward four different robotic server types. Design/methodology/approach A structured online survey of 399 respondents measured and compared consumer perceptions related to qualities and functions of robots in a theme park context and consumers’ behavioral intentions in four different robotic scenarios – anime, cartoon-like, human-like and animal-like robots in theme parks. Survey data were collected on an online platform and analyzed by SPSS. Findings The results showed some expected differences in robotic qualities among the different robot-type scenarios, but similar reactions overall. The findings also provided insight into the type of robots that consumers prefer and showed a little more preference toward human-like robotic servers. Research limitations/implications The study was restricted to several robotic scenarios in North American theme parks. Future qualitative and quantitative studies should look in more detail at theme park visitors’ participation in the robotic service delivery process, the robots’ mobility and interactions with fellow visitors. Practical implications The findings can guide practitioners on robots’ looks, qualities and functionalities to consider for introducing to their patrons to create more interactive environments and experiences. Originality/value The study revealed some new knowledge about consumer expectations for robotic servers in theme parks. To the best of the authors’ knowledge, this is the first study that compares different robot types on their perceived appearance, qualities and functionality, or consumers’ behavioral tendencies in the context of theme parks.
A Dialogue-Based Interface for Active Learning of Activities of Daily Living

While Human Activity Recognition (HAR) systems may benefit from Active Learning (AL) by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in HAR systems, which utilises a dataset of natural language descriptions of common activities (which we make publicly available) and semantic similarity measures. Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply our work to an existing CASAS dataset in an active learning scenario, to demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) technically, as an effective way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to use our approach and an established method, and to subsequently compare the two. Results show the potential of our approach as a user-friendly mechanism for annotation of sensor data as part of an active learning system.
Clustering high‐dimensional data via feature selection

Abstract High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data. In this paper, we propose a new clustering procedure called spectral clustering with feature selection (SC-FS), where we first obtain an initial estimate of labels via spectral clustering, then select a small fraction of features with the largest R-squared with these labels, that is, the proportion of variation explained by group labels, and conduct clustering again using selected features. Under mild conditions, we prove that the proposed method identifies all informative features with high probability and achieves the minimax optimal clustering error rate for the sparse Gaussian mixture model. Applications of SC-FS to four real-world datasets demonstrate its usefulness in clustering high-dimensional data.
Knowledge-based tensor subspace analysis system for kinship verification

Most existing automatic kinship verification methods focus on learning the optimal distance metrics between family members. However, learning facial features and kinship features simultaneously may cause the proposed models to be too weak. In this work, we explore the possibility of bridging this gap by developing knowledge-based tensor models based on pre-trained multi-view models. We propose an effective knowledge-based tensor similarity extraction framework for automatic facial kinship verification using four pre-trained networks (i.e., VGG-Face, VGG-F, VGG-M, and VGG-S). Therefore, knowledge-based deep face and general features (such as identity, age, gender, ethnicity, expression, lighting, pose, contour, edges, corners, shape, etc.) were successfully fused by our tensor design to understand the kinship cue. Multiple effective representations are learned for kinship verification statements (children and parents) using a margin maximization learning scheme based on Tensor Cross-view Quadratic Exponential Discriminant Analysis. Through the exponential learning process, the large gap between distributions of the same family can be reduced to the maximum, while the small gap between distributions of different families is simultaneously increased. The WCCN metric successfully reduces the intra-class variability problem caused by deep features. The explanation of black-box models and the problems of ubiquitous face recognition are considered in our system. The extensive experiments on four challenging datasets show that our system performs very well compared to state-of-the-art approaches.
An efficient real-time target tracking algorithm using adaptive feature fusion

Visual-based target tracking is easily influenced by multiple factors, such as background clutter, targets’ fast-moving, illumination variation, object shape change, occlusion, etc. These factors influence the tracking accuracy of a target tracking task. To address this issue, an efficient real-time target tracking method based on a low-dimension adaptive feature fusion is proposed to allow us the simultaneous implementation of the high-accuracy and real-time target tracking. First, the adaptive fusion of a histogram of oriented gradient (HOG) feature and color feature is utilized to improve the tracking accuracy. Second, a convolution dimension reduction method applies to the fusion between the HOG feature and color feature to reduce the over-fitting caused by their high-dimension fusions. Third, an average correlation energy estimation method is used to extract the relative confidence adaptive coefficients to ensure tracking accuracy. We experimentally confirm the proposed method on an OTB100 data set. Compared with nine popular target tracking algorithms, the proposed algorithm gains the highest tracking accuracy and success tracking rate. Compared with the traditional Sum of Template and Pixel-wise LEarners (STAPLE) algorithm, the proposed algorithm can obtain a higher success rate and accuracy, improving by 2.3% and 1.9%, respectively. The experimental results also demonstrate that the proposed algorithm can reach the real-time target tracking with 50+fps. The proposed method paves a more promising way for real-time target tracking tasks under a complex environment, such as appearance deformation, illumination change, motion blur, background, similarity, scale change, and occlusion.
SKG4EOSC - Scholarly Knowledge Graphs for EOSC: Establishing a backbone of knowledge graphs for FAIR Scholarly Information in EOSC

In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as article narratives in document form. Despite being a well-established tradition in scholarly communication, PDF-based text publishing is hindering scientific progress as it buries scholarly information into non-machine-readable formats. The key objective of SKG4EOSC is to improve science productivity through development and implementation of services for text and data conversion, and production, curation, and re-use of FAIR scholarly information. This will be achieved by (1) establishing the Open Research Knowledge Graph (ORKG, orkg.org), a service operated by the SKG4EOSC coordinator, as a Hub for access to FAIR scholarly information in the EOSC; (2) lifting to EOSC of numerous and heterogeneous domain-specific research infrastructures through the ORKG Hub’s harmonized access facilities; and (3) leverage the Hub to support cross-disciplinary research and policy decisions addressing societal challenges. SKG4EOSC will pilot the devised approaches and technologies in four research domains: biodiversity crisis, precision oncology, circular processes, and human cooperation. With the aim to improve machine-based scholarly information use, SKG4EOSC addresses an important current and future need of researchers. It extends the application of the FAIR data principles to scholarly communication practices, hence a more comprehensive coverage of the entire research lifecycle. Through explicit, machine actionable provenance links between FAIR scholarly information, primary data and contextual entities, it will substantially contribute to reproducibility, validation and trust in science. The resulting advanced machine support will catalyse new discoveries in basic research and solutions in key application areas.
Validation of the Accuracy of NASA Solar Irradiation Data for Four African Regions

Unfortunately, most developing countries lack meteorological data from weather stations due to a lack of measurement instruments or the absence of these stations.Currently, a variety of spatial databases, such as NASA/Power, Meteonorm, Solargis, and others, provide solar irradiation data for various places and time intervals.The goal of this study is to verify the accuracy of solar irradiation data from the National Aeronautics and Space Administration (NASA) database (free estimated data) by comparing them with ground-based measurements taken at four distinct locations in Algeria and Mauritania.Using six statistical parameters, data downloaded from the NASA site for 34 years, from 1984 to 2017, were averaged and compared to ground-based data recorded at M'Sila, Saida, Tamanrasset, and Nouakchott for one year (365 days) on daily (n = 365), monthly (n = 30 or 31), and annual (n = 12) time scales.It has been found that in the absence of daily measured global horizontal irradiation data for one year, the estimated multi-year average NASA data can be used instead.The use of estimated data over a long period is recommended.In the absence of actual solar irradiation data, solar energy projects in Algeria and Mauritania can rely on NASA's estimates.
Smoothing Linear Multi-Target Tracking Using Integrated Track Splitting Filter

Multi-target tracking (MTT) is a challenging issue due to an unknown number of real targets, motion uncertainties, and coalescence behavior of sensor (such as radar) measurements. The conventional MTT systems deal with intractable computational complexities because they enumerate all feasible joint measurement-to-track association hypotheses and recursively calculate the a posteriori probabilities of each of these joint hypotheses. Therefore, the state-of-art MTT system demands bypassing the entire joint data association procedure. This research work utilizes linear multi-target (LM) tracking to treat feasible target detections followed by neighbored tracks as clutters. The LM integrated track splitting (LMITS) algorithm was developed without a smoothing application that produces substantial estimation errors. Smoothing refines the state estimation in order to reduce estimation errors for an efficient MTT. Therefore, we propose a novel Fixed Interval Smoothing LMITS (FIsLMITS) algorithm in the existing LMITS algorithm framework to improve MTT performance. This algorithm initializes forward and backward tracks employing LMITS separately using measurements collected from the sensor in each scan. The forward track recursion starts after the smoothing. Therefore, each forward track acquires backward multi-tracks that arrived from upcoming scans (future scans) while simultaneously associating them in a forward track for fusion and smoothing. Thus, forward tracks become more reliable for multi-target state estimation in difficult cluttered environments. Monte Carlo simulations are carried out to demonstrate FIsLMITS with improved state estimation accuracy and false track discrimination (FTD) in comparison to the existing MTT algorithms.
BiFSMN: Binary Neural Network for Keyword Spotting

The deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. However, computational resources for these networks are significantly constrained since they usually run on-call on edge devices. In this paper, we present BiFSMN, an accurate and extreme-efficient binary neural network for KWS. We first construct a High-frequency Enhancement Distillation scheme for the binarization-aware training, which emphasizes the high-frequency information from the full-precision network's representation that is more crucial for the optimization of the binarized network. Then, to allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we also propose a Thinnable Binarization Architecture to further liberate the acceleration potential of the binarized network from the topology perspective. Moreover, we implement a Fast Bitwise Computation Kernel for BiFSMN on ARMv8 devices which fully utilizes registers and increases instruction throughput to push the limit of deployment efficiency. Extensive experiments show that BiFSMN outperforms existing binarization methods by convincing margins on various datasets and is even comparable with the full-precision counterpart (e.g., less than 3% drop on Speech Commands V1-12). We highlight that benefiting from the thinnable architecture and the optimized 1-bit implementation, BiFSMN can achieve an impressive 22.3x speedup and 15.5x storage-saving on real-world edge hardware. Our code is released at https://github.com/htqin/BiFSMN.
Bayesian Neural Networks for Reversible Steganography

Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks. However, non-trivial errors exist in inferences about some out-of-distribution and noisy data. In view of this issue, we propose to consider uncertainty in predictive models based upon a theoretical framework of Bayesian deep learning, thereby creating an adaptive steganographic system. Most modern deep-learning models are regarded as deterministic because they only offer predictions while failing to provide uncertainty measurement. Bayesian neural networks bring a probabilistic perspective to deep learning and can be regarded as self-aware intelligent machinery; that is, a machine that knows its own limitations. To quantify uncertainty, we apply Bayesian statistics to model the predictive distribution and approximate it through Monte Carlo sampling with stochastic forward passes. We further show that predictive uncertainty can be disentangled into aleatoric and epistemic uncertainties and these quantities can be learnt unsupervised. Experimental results demonstrate an improvement delivered by Bayesian uncertainty analysis upon steganographic rate-distortion performance.
Qubit-Compatible Substrates With Superconducting Through-Silicon Vias

We fabricate and characterize superconducting through-silicon vias and electrodes suitable for superconducting quantum processors.We measure internal quality factors of a million for test resonators excited at single-photon levels, on chips with superconducting vias used to stitch ground planes on the front and back sides of the chips.This resonator performance is on par with the state of the art for siliconbased planar solutions, despite the presence of vias.Via stitching of ground planes is an important enabling technology for increasing the physical size of quantum processor chips, and is a first step toward more complex quantum devices with three-dimensional integration.
State-dependent error bound for digital quantum simulation of driven systems

Digital quantum simulation is a promising application of quantum computers, where quantum dynamics is simulated by using quantum gate operations. Many techniques for decomposing a time-evolution operator of quantum dynamics into simulatable quantum gate operations have been proposed, while these methods cause some errors. To evaluate these errors, we derive a lower bound for overlap between true dynamics and digital simulated dynamics at the final time. Our result enables us to guarantee how obtained digital simulated dynamics is close to unknown true dynamics. We also extend our formalism to error evaluation of digital quantum simulation on noisy quantum computers.