Reviewerly/exHarmony huggingface dataset:
Novel reconciliation protocol based on spinal code for continuous-variable quantum key distribution

Reconciliation is a crucial procedure in post-processing of continuous-variable quantum key distribution (CV-QKD) system, which is used to make two distant legitimate parties share identical corrected keys. The adaptive reconciliation is necessary for practical systems to cope with the variable channel. Many researchers adopt fixed-rate error correction codes to implement adaptive reconciliation, such as the punctured LDPC code; however such protocol can achieve high efficiency only in a small range of signal-to-noise ratios (SNRs). In this paper, the spinal-based rateless reconciliation protocol for CV-QKD is first proposed. The proposed adaptive reconciliation protocol can achieve high efficiency in a much larger range of SNRs. Because the protocol we propose uses the code with short length and simple encoding/decoding structure, the design and implementation complexities of our protocol are greatly decreased. Meanwhile, its parallel attribute makes it suitable for hardware implementation, which means it has the potential of achieving high-throughput reconciliation. Moreover, the security of proposed protocol is investigated and proved. Experimental results show that the reconciliation efficiency of the spinal-based protocol maintains around $$95\%$$ in a larger SNR range (0,0.5), and even exceeds $$96.5\%$$ at extremely low SNR ( $$\le 0.032$$ ) with quite low frame error rate (FER) ( $$\le 5.2\%$$ ). Both the theoretical analysis and experimental results demonstrate that the proposed adaptive reconciliation protocol is a suitable candidate for the long-distance CV-QKD systems.
Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation

Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction.
Can Open Science Change the World?

Computer hosts a virtual roundtable with six experts to discuss the potential and effects of open science.
Secret Sharing, Zero Sum Sets, and Hamming Codes

A (t,n)-secret sharing scheme is a method of distribution of information among n participants such that any t&gt;1 of them can reconstruct the secret but any t−1 cannot. A ramp secret sharing scheme is a relaxation of that protocol that allows that some (t−1)-coalitions could reconstruct the secret. In this work, we explore some ramp secret sharing schemes based on quotients of polynomial rings. The security analysis depends on the distribution of zero-sum sets in abelian groups. We characterize all finite commutative rings for which the sum of all elements is zero, a result of independent interest. When the quotient is a finite field, we are led to study the weight distribution of a coset of shortened Hamming codes.
Nanometer precise red blood cell sizing using a cost-effective quantitative dark field imaging system

Because of the bulk, complexity, calibration requirements, and need for operator training, most current flow-based blood counting devices are not appropriate for field use. Standard imaging methods could be much more compact, inexpensive, and with minimal calibration requirements. However, due to the diffraction limit, imaging lacks the nanometer precision required to measure red blood cell volumes. To address this challenge, we utilize Mie scattering, which can measure nanometer-scale morphological information from cells, in a dark-field imaging geometry. The approach consists of a custom-built dark-field scattering microscope with symmetrically oblique illumination at a precisely defined angle to record wide-field images of diluted and sphered blood samples. Scattering intensities of each cell under three wavelengths are obtained by segmenting images via digital image processing. These scattering intensities are then used to determine size and hemoglobin information via Mie theory and machine learning. Validation on 90 clinical blood samples confirmed the ability to obtain mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC), and red cell distribution width (RDW) with high accuracy. Simulations based on historical data suggest that an instrument with the accuracy achieved in this study could be used for widespread anemia screening.
Sharp Processing of Blur Image Based on Generative Adversarial Network

Aiming at the very challenging problem of motion blur caused by camera shake, object movement, etc. the traditional method using blur kernel estimation easily leads to estimation errors and makes the image restoration effect poor. We propose a deep convolutional neural network solution to restore blurred images. It is based on DeblurGAN to directly obtain deblurred images from end-to-end motion blurred images. and improves the residual network to effectively restore the detailed information of the image, Finally, through the training and testing of the deep convolution neural network model, it is proved that the method can achieve state-of-the-art performance in several commonly used indexes.
Joint Multi-Patch and Multi-Task CNNs for Robust Face Recognition

In this paper, we present a joint multi-patch and multi-task convolutional neural networks (JMM-CNNs) framework to learn more descriptive and robust face representation for face recognition. In the proposed JMM-CNNs, a set of multi-patch CNNs and a feature fusion network are constructed to learn and fuse global and local facial features, then a multi-task learning algorithm, including face recognition task and pose estimation task, is operated on the fused feature to obtain a pose-invariant face representation for the face recognition task. To further enhance the pose insensitiveness of the learned face representation, we also introduce a similarity regularization term on features of the two tasks to propose a regularization loss. Moreover, a simple but effective patch sampling strategy is applied to make the JMM-CNNs have an end-to-end network architecture. Experiments on Multi-PIE dataset demonstrate the effectiveness of the proposed method, and we achieve a competitive performance compared with state-of-the-art methods on Labeled Face in the Wild (LFW), YouTube Faces (YTF) and MegaFace Challenge.
Metrics for Cognitive Management of IT Services

The article presents metrics for managing IT services in a service-oriented architecture of the information system. Cognitive management of the effectiveness of IT services is considered on a variety of ICT infrastructure management metrics, information system management metrics, IT service management metrics. The necessary components of the formation of an analytical platform for the cognitive management of IT services in the EIM environment for SAP BI (Business Objects Business Intelligent) are considered. A model of cognitive management of IT services using key performance indicators (KPIs) for managing IT service metrics is presented.
DISSEMINATION: STEPS TOWARDS AN EFFECTIVE ACTION OF SEISMIC RISK REDUCTION FOR NON-STRUCTURAL DAMAGE

“Move, protect, secure” were the three key points that the KnowRISK project posed at the core of its communication and dissemination strategy. This three key points enable each person, professional or not, to reduce non-structural damage caused by earthquakes. Dissemination is usually the last but never least step of a communication plan, and indeed it played a crucial role in KnowRISK for conveying the three key-point message to the widest audience. Standard dissemination activities, such as open-door events, and internet allowed us to achieve a wide spreading of ideas and best practices, reaching more than 4000 non-professionals and almost 50000 page views of the KnowRISK website (in three years), respectively. As communication was recipient-targeted, the dissemination task of the project was similarly addressed to professionals, layman, and schools. In particular, schools were chosen in order to profit from the chain-reaction action that is capable to spread a message from students to the surrounding environment.
Semi-supervised Segmentation with Self-training Based on Quality Estimation and Refinement

Building a large dataset with high-quality annotations for medical image segmentation is time-consuming and highly depends on expert knowledge. Therefore semi-supervised segmentation has been investigated by utilizing a small set of labeled data and a large set of unlabeled data with generated pseudo labels, but the quality of pseudo labels is crucial since bad labels may lead to even worse segmentation. In this paper, we propose a novel semi-supervised segmentation framework which can automatically estimate and refine the quality of pseudo labels, and select only those good samples to expand the training set for self-training. Specifically the quality is automatically estimated in the view of shape and semantic confidence using variational auto-encoder (VAE) and CNN based network. And, the selected labels are refined in an adversarial way by distinguishing whether a label is the ground truth mask or not at pixel level. Our method is evaluated on the established neuroblastoma(NB) and BraTS18 dataset and outperforms other state-of-the-art semi-supervised medical image segmentation methods. We can achieve a fully supervised performance while requiring \(\sim \)4x less annotation effort.
Knitted Data Glove System for Finger Motion Classification

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.
Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs

Abstract Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus. Results Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph. Conclusion Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.
APPLICATION OF ARTIFICIAL INTELLIGENCE TO BITCOIN COURSE MODELLING

Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.
Application and Practice of Augmented Reality Technology in the Design of K-12 Education-assisted products

Augmented Reality (AR) technology provides a new perspective for learners by virtue of their outstanding performance, such as virtual reality and interactivity, and enables learners to visualize complex spatial relationships and abstract concepts. It is applied to the educational environment at more times. This article introduces the application of Augmented Reality technology in the field of K-12 education at domestic and abroad, and it designs and implements a word memory teaching application for Android mobile phones based on Unity3D. Finally, it gives analysis and summary which focuses on the application and exploration of AR technology in the field of education.
Prediction of Lee filter performance for Sentinel-1 SAR images

Synthetic aperture radar (SAR) images are corrupted by a specific noise-like phenomenon called speckle that prevents efficient processing of remote sensing data. There are many denoising methods already proposed including well known (local statistic) Lee filter. Its performance in terms of different criteria depends on several factors including image complexity where it sometimes occurs useless to process complex structure images (containing texture regions). We show that performance of the Lee filter can be predicted before starting image filtering and which can be done faster than the filtering itself. For this purpose, we propose to apply a trained neural network that employs analysis of image statistics and spectral features in a limited number of scanning windows. We show that many metrics including visual quality metrics can be predicted for SAR images acquired by Sentinel-1 sensor recently put into operation.
Developing Privacy-preserving AI Systems: The Lessons learned

Advances in customers' data privacy laws create pressures and pain points across the entire lifecycle of AI products. Working figures such as data scientists and data engineers need to account for the correct use of privacy-enhancing technologies such as homomorphic encryption, secure multi-party computation, and trusted execution environment when they develop, test and deploy products embedding AI models while providing data protection guarantees. In this work, we share the lessons learned during the development of frameworks to aid data scientists and data engineers to map their optimized workloads onto privacy-enhancing technologies seamlessly and correctly.
Performance analysis of public management teaching practice training based on artificial intelligence technology

At present, it is difficult to quantify the performance of public management teaching practice. Inviewof this, based on artificial intelligence technology, this study imitates the economic performance evaluation method to introduce evaluation parameters, and adopts the analogy method to introduce the artificial intelligence economic performance evaluation system into the model proposed in this study. Moreover, this study combines with actual teaching needs to build a performance analysis model of public management teaching training based on artificial intelligence technology. In addition, this study uses a B/S structure to build the system, set functional modules based on demand analysis, and use an expert system to score. In order to study system performance and system stability in the context of big data, the system performance is studied through actual scoring and large amounts of data training. The research results show that the model proposed in this paper has a certain effect.
Fuzzy TOPSIS Method as a Decision Supporting System to Rank Malaysia Online Shopping Website Quality During COVID-19 MCO 2020

The rapid growth of Internet technology development has allowed consumers to purchase online products or services, especially during the Movement Control Order (MCO) lockdown due to the COVID-19 pandemic in Malaysia.Online shopping has become a new norm; however, the services needed frequent updates for improvements.Literature has shown that online shopping website quality influenced online shoppers' decision-making.Hencein improving the quality of online shopping websites, the criteria for the website's quality is vital.Therefore, this study aims to identify the criteria of Malaysia online shopping website quality and rank the website quality by using Fuzzy TOPSIS method.Questionnaire is developed for website usersto evaluate the online shopping website quality via google form and disseminated through social media.After data cleaning, 300 respondents' data were used for analysis.The result shows that the online shopping website quality for Shopee is ranked the first, next is Lazada, then Lelong and finally the 11-street.
Detection and Analysis of Drive-by Downloads and Malicious Websites

A drive-by download is a download that occurs without user’s action or knowledge. It usually triggers an exploit of vulnerability in a browser to downloads an unknown file. The malicious program in the downloaded file installs itself on the victim’s machine. Moreover, the downloaded file can be camouflaged as an installer that would further install malicious software. Drive-by downloads is a very good example of the exponential increase in malicious activity over the Internet and how it affects the daily use of the web. In this paper, we try to address the problem caused by drive-by downloads from different standpoints. We provide in-depth understanding of the difficulties in dealing with drive-by downloads and suggest appropriate solutions. We propose machine learning and feature selection solutions to remedy the drive-by download problem. Experimental results reported 98.2% precision, 98.2% F-Measure and 97.2% ROC area.
Exploring Cultural Inclusion in the Curriculum and Practices for Teaching Bahasa Indonesia to Speakers of Other Languages

This study explores implementations for teaching Bahasa Indonesia (BI) and the process for including culture when teaching BI to speakers of other languages (BIPA). In this research, content analysis was applied, and a qualitative approach was used to analyze the data, which were obtained from the BIPA websites of 11 universities and one BIPA website hosted by Indonesia’s Ministry of Education and Culture. This study also recruited 36 people as participants. Analysis of the data through content analysis comprised six steps: i) transforming the data into narrative text, ii) determining units of analysis in line with the research questions, iii) developing rules for the data-coding system, iv) applying the coding to all units of analysis, v) testing the consistency of each coding, and vi) drawing conclusions about the verified data. The results show how the implementations of BIPA in practice conform to curriculum planning, and they identify objectives, determine teaching materials, provide a learning experience, and evaluate the results. In these four aspects, the results are appropriate, but undesirable properties were identified in terms of the length of the study, an absence of vocabulary items, and learning outcomes for BIPA for specific academic purposes. For cultural aspects, Indonesian culture was found to be included in various topics and learning experiences, but soft diplomacy was not effectively taught through the language-teaching content and methodology.
Multi-person Pose Estimation in Complex Physical Interactions

Recent literature addressed the monocular multi-person 3D human pose estimation task very satisfactorily. In these studies, different persons are usually treated as independent pose instances to estimate. However, in many every-day situations, people are interacting, and different pose instances should be considered jointly since the pose of an individual depends on the pose of his/her interactees. This work aims to develop machine learning techniques for human pose estimation of persons involved in complex interactions, using the interaction information to improve the performance.
Level Set and Density estimation on Manifolds

Given an iid sample of a distribution supported on a smooth manifold M ⊂ Rd, which is assumed to be absolutely continuous w.r.t the Hausdorff measure inherited from the ambient space, we tackle the problem of the estimation of the level sets of the density f. A consistent estimator in both Hausdorff distance and distance in measure is proposed. The estimator is the level set of the kernel-based estimator of the density f. We prove that the kernel-based density estimator converges uniformly to the unknown density f, the consistency of the level set and the consistency of the boundary of the level set estimator. The performance of our proposal is illustrated through some simulated examples.
Retiarii: A Deep Learning Exploratory-Training Framework

Traditional deep learning frameworks such as TensorFlow and PyTorch support training on a single deep neural network(DNN) model, which involves computing the weights iteratively for the DNN model. Designing a DNN model for a task remains an experimental science and is typically a practice of deep learning model exploration, dovetailed with training and validation, aiming to find the best model among a set that yields the best result. Retrofitting such exploratory-training into the training process of a single DNN model, as supported by current deep learning frameworks, is unintuitive, cumber-some, and inefficient, because of the fundamental mismatch between exploring a set of models and training a single one. Retiarii is the first framework to support deep learning exploratory-training. In particular, Retiarii (i) provides a new programming interface to specify a DNN model space for exploration, as well as an interface to describe the exploration strategy that decides which order to instantiate and train models in, how to prioritize model training, and when to terminate training of certain models; (ii) offers a Just-In-Time (JIT)engine that instantiates models, manages the training of the instantiated models, gathers the information for the exploration strategy to consume, and executes the decisions accordingly;(iii) identifies the correlations between the instantiated models and develops a set of cross-model optimizations to improve the overall exploratory-training process. Retiarii does so by introducing a key abstraction, Mutator, that connects the specifications of DNN model spaces and exploration strategies, while exposing the correlations between models for optimization. As a result, Retiarii’s clean separation of DNN model space specification, exploration strategy, and cross-model optimizations, connected through the single mutator abstraction, leads to ease of programming, reuse of components, and vastly improved (up to 8.58x) overall exploratory-training efficiency.