Reviewerly/exHarmony huggingface dataset:
Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

In the stage of aging society and population aging, the social needs of the elderly are widely discussed by researchers. Especially driven by the demand of tele-medical treatment and tele-rehabilitation therapy, it is vital for the elderly to integrate into virtual communities by combining social virtual reality (VR) with different medical services and entertainment needs. In addition, affected by the COVID-19 epidemic, it is more difficult for people to have face-to-face contacts. With more remote consultation, entertainment and virtual social connectivity, the application of social VR is more urgent and valuable. However, there is little discussion on the acceptability and influencing factors of social VR among the elderly at present. Therefore, in order to get further data, we used (1) early stage semi-structured interviews and then (2) Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaires for investigation. One hundred fourteen elderly people aged 60–89 living in the metropolitan area of Taipei were taken as the subjects. To help them understand the situation and state of using social VR, these elderly people were asked to use a head-mounted display (HMD) to experience social VR games. The preliminary results showed that the elderly had obvious preference for entertainment (32.4%) and medical treatment (31.3%). The interview showed that this was related to the physiological condition or medical needs of the age range. In order to further understand how social VR would affect the social life of the elderly, we proposed the further demand structure of UTAUT Model based on the interview of both experts and the elderly. The model structures include (1) Performance Expectancy, (2) Perceived Enjoyment, (3) Social Influence, (4) User Attitude, (5) Behavioral Intention, and so on. These structures were applied to conduct interviews and questionnaires to find out the influence extent and relevance of the elderly on different structural needs, and suggestions were given accordingly. The results of the above interviews showed that (1) the elderly thought that the functions of entertaining and interacting of social VR could increase their social opportunities, and also meet medical needs (teleconference, cognitive decline, etc.), (2) the closeness of social relations (between family members, friends, doctors, and places), and also affect the relevance of Perceived Enjoyment (β = 0.77, p = 0.000 &lt; 0.05). The results of these phenomena and interviews showed the interplay between the demand structures and their special relevance. They also indicated that as to social VR technologies, various demands and functional issues of the elderly need to be considered, and these demands would appear in the subtle usage, and different social VR interfaces and functions would emerge based on their special living ways and physical and psychological demands.
Face Forgery Detection by 3D Decomposition

Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment, and decompose it in a computer graphics view. Specifically, by disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. Based on this observation, we propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns. Besides, we highlight the manipulated region with a supervised attention mechanism and introduce a two-stream structure to exploit both face image and facial detail together as a multi-modality task. Extensive experiments indicate the effectiveness of the extra features extracted from the facial detail, and our method achieves the state-of-the-art performance.
Dense Quantum Measurement Theory

Abstract Quantum measurement is a fundamental cornerstone of experimental quantum computations. The main issues in current quantum measurement strategies are the high number of measurement rounds to determine a global optimal measurement output and the low success probability of finding a global optimal measurement output. Each measurement round requires preparing the quantum system and applying quantum operations and measurements with high-precision control in the physical layer. These issues result in extremely high-cost measurements with a low probability of success at the end of the measurement rounds. Here, we define a novel measurement for quantum computations called dense quantum measurement. The dense measurement strategy aims at fixing the main drawbacks of standard quantum measurements by achieving a significant reduction in the number of necessary measurement rounds and by radically improving the success probabilities of finding global optimal outputs. We provide application scenarios for quantum circuits with arbitrary unitary sequences, and prove that dense measurement theory provides an experimentally implementable solution for gate-model quantum computer architectures.
Sentence embeddings in NLI with iterative refinement encoders

Abstract Sentence-level representations are necessary for various natural language processing tasks. Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks. We build on top of one such model and propose a hierarchy of bidirectional LSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for Stanford Natural Language Inference and Multi-Genre Natural Language Inference. We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks. Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings’ ability to capture some of the important linguistic properties of sentences.
Metrics Towards Measuring Cyber Agility

In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker's strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a defender's strategy changes over time. These changes may or may not be in direct response to a change in the opponent's strategy. In any case, it is important to have a set of quantitative metrics to characterize and understand the effectiveness of attackers' and defenders' evolutionary strategies, which reflect their cyber agility. Despite its clear importance, few systematic metrics have been developed to quantify the cyber agility of attackers and defenders. In this paper, we propose the first metric framework for measuring cyber agility in terms of the effectiveness of the dynamic evolution of cyber attacks and defenses. The proposed framework is generic and applicable to transform any relevant, quantitative, and/or conventional static security metrics (e.g., false positives and false negatives) into dynamic metrics to capture dynamics of system behaviors. In order to validate the usefulness of the proposed framework, we conduct case studies on measuring the evolution of cyber attacks and defenses using two real-world datasets. We discuss the limitations of the current work and identify future research directions.
Optimized protocol for twin-field quantum key distribution

Twin-field quantum key distribution (TF-QKD) and its variant protocols are highly attractive due to the advantage of overcoming the rate-loss limit for secret key rates of point-to-point QKD protocols. For variations of TF-QKD, the key point to ensure security is switching randomly between a code mode and a test mode. Among all TF-QKD protocols, their code modes are very different, e.g. modulating continuous phases, modulating only two opposite phases, and sending or not sending signal pulses. Here we show that, by discretizing the number of global phases in the code mode, we can give a unified view on the first two types of TF-QKD protocols, and demonstrate that increasing the number of discrete phases extends the achievable distance, and as a trade-off, lowers the secret key rate at short distances due to the phase post-selection.
MOGPTK: The multi-output Gaussian process toolkit

We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike. MOGPTK uses a Python front-end and relies on the PyTorch suite, thus enabling GPU-accelerated training. The toolkit facilitates implementing the entire pipeline of GP modelling, including data loading, parameter initialization, model learning, parameter interpretation, up to data imputation and extrapolation. MOGPTK implements the main multi-output covariance kernels from literature, as well as spectral-based parameter initialization strategies. The source code, tutorials and examples in the form of Jupyter notebooks, together with the API documentation, can be found in this GitHub repository: https://github.com/GAMES-UChile/mogptk.
Feasibility assessment for practical continuous variable quantum key distribution over the satellite‐to‐Earth channel

Currently, quantum key distribution (QKD) using continuous variable (CV) technology has only been demonstrated over short-range terrestrial links. Here, we attempt to answer whether CV-QKD over the much longer satellite-to-Earth channel is feasible. To this end, we first review the concepts and technologies that will enable CV-QKD over the satellite-to-Earth channels. We then consider, in the infinite key limit, the simplest-to-deploy QKD protocols, the coherent state (CS) QKD protocol with homodyne detection and the CS-QKD protocol with heterodyne detection. We then focus on the CS-QKD protocol with heterodyne detection in the pragmatic setting of finite keys, where complete security against general attacks is known. We pay particular attention to the relevant noise terms in the satellite-to-Earth channel and their impact on the secret key rates. In system set-ups where diffraction dominates losses, we find that the main components of the total excess noise are the intensity fluctuations due to scintillation, and the time-of-arrival fluctuations between signal and local oscillator. We conclude that for a wide range of pragmatic system models, CS-QKD with information-theoretic security in the satellite-to-Earth channel is feasible.
TAMS: development of a multipurpose three-arm aerial manipulator system

This paper describes the concept, design and realization of a novel manipulator system for a multirotor aerial robot. The proposed manipulator system consists of three robotic arms attached to a multirotor frame. To overcome the limited payload capabilities in multirotor aerial platforms, the same set of on-board manipulators are used to realize various tasks, primarily the manipulation or grasping of objects of various sizes. Furthermore, they can provide aid in complex navigation tasks by doing physical, contact-based obstacle avoidance and are able to act as adaptive landing gear in uneven terrain. The design requirements and the analysis to realize these tasks is discussed. The proposal is supported with the successful demonstration of these tasks using the aerial multi manipulator system in an outdoor environment.
Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.
ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora

Recent studies have demonstrated that pretrained cross-lingual models achieve impressive performance in downstream cross-lingual tasks.This improvement benefits from learning a large amount of monolingual and parallel corpora.Although it is generally acknowledged that parallel corpora are critical for improving the model performance, existing methods are often constrained by the size of parallel corpora, especially for lowresource languages.In this paper, we propose ERNIE-M, a new training method that encourages the model to align the representation of multiple languages with monolingual corpora, to overcome the constraint that the parallel corpus size places on the model performance.Our key insight is to integrate back-translation into the pre-training process.We generate pseudo-parallel sentence pairs on a monolingual corpus to enable the learning of semantic alignments between different languages, thereby enhancing the semantic modeling of cross-lingual models.Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-lingual downstream tasks. 1
The theoretical research of generative adversarial networks: an overview

Generative adversarial networks (GAN) has received great attention and made great progress since its emergence in 2014. In this paper, we focus on the theoretical achievements of GAN and discuss them in detail for readers who wish to know more about GAN. Based on the number of the implemented network architectures, we category the improved methods into two groups: GAN variants, which are composed of two networks, to improve the performance by adding some regularization to the loss function; hybrid GANs, which are usually combined with other generative models to improve the training stability. For GAN variants, we discuss the theoretical results of the distribution divergence, training dynamics and various improved methods. For hybrid GANs, we introduce the improved methods of combining encoder, autoencoder or VAE. We also cover some other important issues, such as the quantify metrics of generated samples and the basic construction structure. In addition, we discuss the advantages of the GAN over other deep generative models, the future directions worthy of study, as well as the open issues that the community should further address.
Mutual information-assisted adaptive variational quantum eigensolver

Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem and ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool. In this work, we propose a way to construct entangler pools with reduced size by leveraging classical algorithms. Our method uses mutual information between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group method is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.
On the Performance of ARM TrustZone

The TrustZone technology, available in the vast majority of recent Arm processors, allows the execution of code inside a so-called secure world. It effectively provides hardware-isolated areas of the processor for sensitive data and code, i.e., a trusted execution environment (TEE). The Op-Tee framework provides a collection of toolchain, open-source libraries and secure kernel specifically geared to develop applications for TrustZone. This paper presents an in-depth performance- and energy-wise study of TrustZone using the Op-Tee framework, including secure storage and the cost of switching between secure and unsecure worlds, using emulated and hardware measurements.
An Improved Hybrid Approach for Handling Class Imbalance Problem

Class imbalance issue that presents in many real-world datasets exhibit favouritism toward the majority class and showcases poor performance for the minority class. Such misclassifications may incur dubious outcome in case of disease diagnosis and other critical applications. Hence, it is a hot topic for the researchers to tackle the class imbalance issue. We present a novel hybrid approach for handling such datasets. We utilize simulated annealing algorithm for undersampling and apply support vector machine, decision tree, k-nearest neighbor and discriminant analysis for the classification task. We validate our technique in 51 real-world datasets and compare it with other recent works. Our technique yields better efficacy than the existing techniques and hence it can be applied in imbalance datasets to mitigate the misclassification.
Language Bias in the Google Scholar Ranking Algorithm

The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.
Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection, achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off the shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
CapsField: Light Field-Based Face and Expression Recognition in the Wild Using Capsule Routing

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained facial image analysis, this technology has never been used for face and expression recognition in the wild. In this context, this paper proposes a new deep face and expression recognition solution, called CapsField, based on a convolutional neural network and an additional capsule network that utilizes dynamic routing to learn hierarchical relations between capsules. CapsField extracts the spatial features from facial images and learns the angular part-whole relations for a selected set of 2D sub-aperture images rendered from each LF image. To analyze the performance of the proposed solution in the wild, the first in the wild LF face dataset, along with a new complementary constrained face dataset captured from the same subjects recorded earlier have been captured and are made available. A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests. An extensive performance assessment study using the new datasets has been conducted for the proposed and relevant prior solutions, showing that the CapsField proposed solution achieves superior performance for both face and expression recognition tasks when compared to the state-of-the-art.
Detector-integrated on-chip QKD receiver for GHz clock rates

Abstract Quantum key distribution (QKD) can greatly benefit from photonic integration, which enables implementing low-loss, alignment-free, and scalable photonic circuitry. At the same time, superconducting nanowire single-photon detectors (SNSPD) are an ideal detector technology for QKD due to their high efficiency, low dark-count rate, and low jitter. We present a QKD receiver chip featuring the full photonic circuitry needed for different time-based protocols, including single-photon detectors. By utilizing waveguide-integrated SNSPDs we achieve low dead times together with low dark-count rates and demonstrate a QKD experiment at 2.6 GHz clock rate, yielding secret-key rates of 2.5 Mbit/s for low channel attenuations of 2.5 dB without detector saturation. Due to the broadband 3D polymer couplers the reciver chip can be operated at a wide wavelength range in the telecom band, thus paving the way for highly parallelized wavelength-division multiplexing implementations.