Reviewerly/exHarmony huggingface dataset:
Impact of Classical Modulation Signals on Quantum Key Distribution Over Multicore Fiber

In this paper, the influence of noise induced by classical signals on independent core transmitting quantum key distribution (QKD) is studied over multicore fiber (MCF). We propose a theoretical model of the cascaded noise of spontaneous Raman scattering and inter-core crosstalk (SpRS-ICXT), and the cascaded noise of four-wave mixing and inter-core crosstalk (FWM-ICXT) considering modulation frequency over MCF. To analyze the effect of modulation frequency on QKD, we define and simulate the average power difference and power fluctuation difference of SpRS-ICXT and FWM-ICXT. Then, SpRS-ICXT and FWM-ICXT noises are simulated in two cases: (1) 1 or 3 classical channels and 1 km MCF; (2) 8 or 16 classical channels and 45 km MCF. For the latter case, SpRS-ICXT and FWM-ICXT noises have a serious impact on QKD. Therefore, the impact of noise on QKD is studied. A higher modulation frequency can improve QKD performance, and the secure transmission distance can be further extended by 15.2% when the modulation frequency is 10 GHz relative to ignoring the modulation frequency. However, inter-core crosstalk (ICXT), SpRS-ICXT and FWM-ICXT noises will shorten 52.2% secure transmission distance compared to only transmitting QKD. Finally, the noise of quantum channel is experimentally measured at different symbol rates and modulation formats of classical signals, and it is shown that increasing the symbol rate of data signals at the same optical power degrades the secure key rate of QKD system.
Positioning Digital Tracing Applications in the Management of the COVID-19 Pandemic in France

To combat the COVID-19 pandemic, many European countries have developed a public health strategy involving the use of digital contact tracing (DCT) applications to improve timely tracking and contact tracing of COVID-19 cases. France's independent COVID-19 Control and Society Connection Council (CCL) was established by law in May 2020 to issue advice and recommendations on the national epidemic digital systems. In this paper, we present the recommendations by the CCL, with the objective to increase the uptake and utility of French DCT applications. As the country's most vulnerable population has been subjected to greater virus exposure, a stronger impact of the lockdown, and less access to preventive and health care services, the CCL is particularly aware of health inequalities. The French DCT app TousAntiCovid had been downloaded by 13.6 million users (ie, 20% of the French population) in March 2021. To promote the use of DCT apps, the CCL has recommended that communication about the app's individual and collective objectives be increased. The CCL has also recommended the introduction of clear, simple, accessible, incentivizing, noncoercive information within the digital tools. In addition, the CCL has recommended improving public health policies to address the needs of the underprivileged. The CCL calls for promoting population empowerment with the use of digital tools, improving public health culture for decision-makers dealing with health determinants, taking social considerations into account, and incorporating community participation.
Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

Reinforcement Learning (RL) is an area of machine learning concerned with enabling an agent to navigate an environment with uncertainty in order to maximize some notion of cumulative long-term reward. In this paper, we implement and analyze two different RL techniques, Sarsa and Deep Q-Learning, on OpenAI Gym's LunarLander-v2 environment. We then introduce additional uncertainty to the original problem to test the robustness of the mentioned techniques. With our best models, we are able to achieve average rewards of 170+ with the Sarsa agent and 200+ with the Deep Q-Learning agent on the original problem. We also show that these techniques are able to overcome the additional uncertainities and achieve positive average rewards of 100+ with both agents. We then perform a comparative analysis of the two techniques to conclude which agent performs better.
Resolving counterintuitive consequences in law using legal debugging

There are cases in which the literal interpretation of statutes may lead to counterintuitive consequences. When such cases go to high courts, judges may handle these counterintuitive consequences by identifying problematic rule conditions. Given that the law consists of a large number of rule conditions, it is demanding and exhaustive to figure out which condition is problematic. For solving this problem, our work aims to assist judges in civil law systems to resolve counterintuitive consequences using logic program representation of statutes and Legal Debugging. The core principle of Legal Debugging is to cooperate with a user to find a culprit, a root cause of counterintuitive consequences. This article proposes an algorithm to resolve a culprit. Since the statutes are represented by logic rules but changes in law are initiated by cases, we adopt a prototypical case with judgement specified by a set of rules. Then, to resolve a culprit, we reconstruct a program so that it provides reasons as if we applied case-based reasoning to a new set of prototypical cases with judgement, which include a new set of facts relevant to a considering case.
Predistorting Projected Fringes for High-Accuracy 3-D Phase Mapping in Fringe Projection Profilometry

A predistortion-based 3-D phase mapping method with linear epipolar constraints for fringe projection system calibration and measurement is reported. Hybrid system calibration methods have been developed recently to provide accurate measurement, high speed, and flexibility for fringe projection profilometry (FPP). However, these methods still have significant limitations. With projector distortion, for example, rigorous post-undistortion often results in low efficiency in computation. Conversely, simple, approximated undistortion is only effective for low levels of distortion. As a result, projector undistortion is usually not prioritized in practice. Aiming to achieve high-accuracy 3-D reconstruction with FPP, an easily implementable projector undistortion method is proposed in this article. Theoretical analyses and simulations confirm that the proposed method is rigorous. Experiments validate that the pixelwise root-mean-square reconstruction error is 0.042 mm, a 24% reduction from that of a simple post-undistortion method. The proposed method is also efficient computationally. Finally, a 3-D phase mapping lookup table is built. The results show that the proposed method is effective for achieving high accuracy and speed in 3-D surface measurement.
Leveraging the serverless paradigm for realizing machine learning pipelines across the edge-cloud continuum

The exceedingly exponential-growing data rate highlighted numerous requirements and several approaches have been released to maximize the added-value of cloud and edge resources. Whereas data scientists utilize algorithmic models in order to transform datasets and extract actionable knowledge, a key challenge is oriented towards abstracting the underline layers: the ones enabling the management of infrastructure resources and the ones responsible to provide frameworks and components as services. In this sense, the serverless approach features as the novel paradigm of new cloud-related technology, enabling the agile implementation of applications and services. The concept of Function as a Service (FaaS) is introduced as a revolutionary model that offers the means to exploit serverless offerings. Developers have the potential to design their applications with the necessary scalability in the form of nanoservices without addressing themselves the way the infrastructure resources should be deployed and managed. By abstracting away the underlying hardware allocations, the data scientist concentrates on the business logic and critical problems of Machine Learning (ML) algorithms. This paper introduces an approach to realize the provision of ML Functions as a Service (i.e., ML-FaaS), by exploiting the Apache OpenWhisk event-driven, distributed serverless platform. The presented approach tackles also composite services that consist of single ones i.e., workflows of ML tasks including processes such as aggregation, cleaning, feature extraction, and analytics; thus, reflecting the complete data path. We also illustrate the operation of the approach mentioned above and assess its performance and effectiveness exploiting a holistic, end-toend anti-fraud detection machine learning pipeline.
MSEC: Multi-Scale Erasure and Confusion for fine-grained image classification

With the rapid development of deep learning, the performance of fine-grained image classification has experienced unprecedented improvement. However, for fine-grained image classification, quickly and effectively focusing on subtle discriminative details that make the sub-classes different from each other has always been challenging. In this paper, we propose a novel Multi-Scale Erasure and Confusion (MSEC) method to tackle the challenge of fine-grained image classification. Firstly, the input image is divided into several sub-regions, and the confidence scores of those sub-regions are calculated by the confidence function. The sub-regions with lower confidence scores are then erased by the Region Erasure Module (REM) and the erased image is confused once by the Multi-scale Region Confusion Module (Multi-scale RCM). Secondly, the sub-regions with higher confidence scores are divided and confused again by the Multi-scale RCM, and then generate an image with multi-scale information. Finally, features in the erased image and the "destructed" image are extracted by the backbone network, and the whole network is optimized by the multi-loss function to realize classification tasks. Extensive experiments on three standard fine-grained benchmark datasets, including Stanford Dogs, CUB-200-2011 and FGVC-Aircraft, show that MSEC can improve the accuracy of fine-grained image classification.
Can Fiat‐backed Stablecoins Be Considered Cash or Cash Equivalents Under International Financial Reporting Standards Rules?

Abstract Stablecoins are a rapidly evolving subcategory of cryptocurrency that aim to reduce the price fluctuations of traditional cryptocurrencies and thus become a common digital payment instrument. This paper aims to assess the material substance of fiat‐backed stablecoins to determine whether fiat‐backed stablecoins could be considered, in accordance with IFRS, as cash or cash equivalents. We chose 11 fiat‐backed stablecoins representing 99.97% of the total market capitalisation of all fiat‐backed stablecoins. Using a three‐step approach, we performed an analysis of the legal and general terms and conditions of the selected stablecoins and of cryptocurrency exchanges, and quantitatively analysed their risk characteristics in comparison with fiat currency pairs, money market indexes and instruments, and traditional cryptocurrencies. The results show that nine of the 11 stablecoins met the objective requirements of cash equivalents according to their material substance and, using an extensive interpretation of IAS 7, could be reported as cash equivalents. This study enhances understanding of the material substance of fiat‐backed stablecoins for their financial reporting and can be used by entities when creating accounting policies under existing IFRS rules and by accounting standard setters as evidence when formulating new rules or officially interpreting existing ones to provide guidance on the financial reporting of fiat‐backed stablecoins.
Risk and protective factors for intuitive and rational judgment of cybersecurity risks in a large sample of K-12 students and teachers

K-12 students and teachers are a vulnerable population for cybersecurity risks. Identifying both risk factors and protective factors associated with intuitive and rational judgment of cybersecurity risks would help them develop strategies to tackle cyber risks. A total of 2703 K-12 students and teachers from 45 GenCyber Summer Camps participated in the survey study at the beginning of the camps, and a total of 1021 K-12 students and teachers participated in the follow-up survey at the end of the camps. The Cybersecurity Judgment Questionnaire was developed and administered to assess intuitive and rational judgments of cybersecurity risks. Two major findings of the study include: (1) three significant risk factors associated with both intuitive and rational cybersecurity judgment were Age Group, Region, and Prior GenCyber Camp Experience. That is, younger students, the campers from the West region, and participants attending the camps before tended to have a lower level of cybersecurity judgment; and (2) two significant protective factors were Cyber Use Length and Current GenCyber Camp Experience, i.e., the experiences of both using computers, Internet, cellphones and participating in the current camps having significant advantages in judging cybersecurity risks intuitively and rationally. Thus, it is critical to use both the push strategy to minimize the risk factors and the pull strategy to maximize the protective factors. It is concluded with a summary of limitations and future studies, i.e., replicating the findings in regular K-12 schools, examining more risk and protective factors, conducting longitudinal studies, and studying underlying mechanisms.
End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i.d. complex Gaussian channels and spatially-correlated channels.
Efficient Robotic Object Search Via HIEM: Hierarchical Policy Learning With Intrinsic-Extrinsic Modeling

Despite the significant success at enabling robots with autonomous behaviors makes deep reinforcement learning a promising approach for robotic object search task, the deep reinforcement learning approach severely suffers from the nature sparse reward setting of the task. To tackle this challenge, we present a novel policy learning paradigm for the object search task, based on hierarchical and interpretable modeling with an intrinsic-extrinsic reward setting. More specifically, we explore the environment efficiently through a proxy low-level policy which is driven by the intrinsic rewarding sub-goals. We further learn our hierarchical policy from the efficient exploration experience where we optimize both of our high-level and low-level policies towards the extrinsic rewarding goal to perform the object search task well. Experiments conducted on the House3D environment validate and show that the robot, trained with our model, can perform the object search task in a more optimal and interpretable way.
A Survey of Algorithmic and Hardware Optimization Techniques for Vision Convolutional Neural Networks on FPGAs

In today’s world, the applications of convolutional neural networks (CNN) are limitless and are employed in numerous fields. The CNNs get wider and deeper to achieve near-human accuracy. Implementing such networks on resource constrained hardware is a cumbersome task. CNNs need to be optimized both on hardware and algorithmic levels to compress and fit into resource limited devices. This survey aims to investigate different optimization techniques of Vision CNNs, both on algorithmic and hardware level, which would help in efficient hardware implementation, especially for FPGAs.
Boundarymix: Generating pseudo-training images for improving segmentation with scribble annotations

• BoundaryMix is proposed for scribble-supervised semantic segmentation. • BoundaryMix supplements the missing boundary information of scribble annotations by generating pseudo training images and annotations. • Scribble are used to annotate remote sensing images and show that scribble annotation is also suitable for different scenarios. • Experiments on PASCAL VOC and POTSDAM datasets show that BoundaryMix almost closes the gap between weakly-supervised and fully-supervised semantic segmentation. Weakly-supervised semantic segmentation, as a promising solution to alleviate the burden of collecting per-pixel annotations, aims to train a segmentation model from partial weak annotations. Scribble on the object is one of the commonly used weak annotations and has shown to be sufficient for learning a decent segmentation model. Despite being effective, scribble-based weakly-supervised learning methods often lead to imprecise segmentation on object boundaries. This is mainly because the scribble annotations usually locate inside the objects and the dataset lacks annotations close to the semantic boundaries. To alleviate this issue, this paper proposes a simple-but-effective solution, i.e. , BoundaryMix, which generates pseudo training image-annotation pairs from the original images to supplement the missing semantic boundaries. Specifically, given a prediction of segmentation, we cut off the regions around the estimated boundaries, which are error-prone and replace them with the contents from another image, which in effect creates new samples with less ambiguity around semantic boundaries. With training on scribbles and the on-the-fly generated pseudo annotations, the network acquires better prediction capability around the boundary region and thus improves the overall segmentation performance. By conducting experiments on PASCAL VOC 2012 dataset and POTSDAM dataset with only scribble annotations, we demonstrate the excellent performance of the proposed method and the almost closed gap between scribble-supervised and fully-supervised image segmentation.
Falling motion detection algorithm based on deep learning

Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices, which need to be worn all the time, and the cost of the device is high. In recent years, the fall detection method based on computer vision has become a research hot spot. This paper proposes a framework for falling motion detection based on deep learning. To quickly and accurately classify human movements, a method using bone key points as the feature descriptors of human movements is proposed. The OpenPose algorithm is used to extract the human skeleton point information as the primary human body feature, and then use the deep learning method to classify further and recognise our action features. In this paper, four types of daily actions, such as falling and walking, are classified and recognised. The results show that the algorithm achieves an accuracy of 99.4% on our dataset. Simultaneously, 86.1% accuracy is reached in the public dataset fall detection dataset.
Olly: A tangible for togetherness

This research explores how tangible interactive technology might offer opportunities for socialization and sensory regulation. We present a study carried out in an educational setting during leisure activities with a small group of children with autism who like music. We introduce Όλοι (pronounced Olly), a sonic textile Tangible User Interface (TUI) designed around the observations of five minimally verbal children with autism aged between 5-10 years. The TUI was tested for an average of 24 minutes once per week, over a period of five weeks in a specialized school based in North-East London, UK. We propose a methodological approach that embraces diversity and promotes designs that support repetitive movements and self-regulation to provide the children with a favorable environment and tools to socialize with peers. The findings show positive outcomes with regards to spontaneous social interactions between peers particularly when children interacted with or around Olly. These were observed in the form of eye-contact, turn-taking, sharing (of the space, the object and experience), and more complex social play dynamics like associative and cooperative play. We illustrate how the TUI was a positive stimulus of social behaviors and discuss design implications for novel technologies that aim to foster shared experiences between children with autism.
De-identifying Spanish medical texts - named entity recognition applied to radiology reports

Abstract Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are also language-dependent. Here, we introduce a named entity recognition strategy for Spanish medical texts, translatable to other languages. Results We tested 4 neural networks on our radiology reports dataset, achieving a recall of 97.18% of the identifying entities. Alongside, we developed a randomization algorithm to substitute the detected entities with new ones from the same category, making it virtually impossible to differentiate real data from synthetic data. The three best architectures were tested with the MEDDOCAN challenge dataset of electronic health records as an external test, achieving a recall of 69.18%. Conclusions The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports. It does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records.