Reviewerly/exHarmony huggingface dataset:
A Model of Social Dynamics for Social Intelligent Agents

In this article we describe a general cognitive model of human social behavior that is meant to increase the social intelligence of autonomous intelligent agents in different contexts. Despite the remarkable improvements that have been made on human-agent interaction, agents still have a limited capacity to be aware of the social reality that is present in the human mind and significantly guides human behavior. The model discussed in this paper is a step toward increasing that capacity significantly. Two different case studies are described in which the proposed model is used to better explain and predict human behavior. The first case study is the well known Ultimatum game. The second one is a variation of the “Game of Nines” played by children.
Optical Microscopy: Optical Imaging and High‐Accuracy Quantification of Intracellular Iron Contents (Small 2/2021)

In article number 2005474, Qi-Huo Wei, Ning Gu, and co-workers show that by combining the Beer-Lambert's law with digital optical microscopy, iron contents in individual cells can be imaged and quantified with high accuracy, better than femtogram per pixel. This technique can be deployed easily in research and clinical labs for studying biomedical applications of magnetic nanoparticles and disease diagnosis.
Comparison of Frequent Pattern Mining Algorithms in Internet of Things

Frequent pattern mining is an important topic that is needed for the Internet of Things (IoT) applications frequently. Many IoT applications have been developed in which contionuous streaming data is used. In this study, performance evaluation of FP-stream (Frequent Pattern-Stream) algorithm and weighted sliding window mining (WMFP-SW) algorithms, which are developed for frequent pattern analysis in IoT, is performed using a novel real world dataset. The dataset was created by collecting 6 different sensor values over a 3 month period in an established testbed. The results showed that FP-stream algorithm is more successful than WMFP-SW algorithm at execution time, whereas WMFP-SW is better at the total number of mining frequent patterns.
A Hybrid System for NLPTEA-2020 CGED Shared Task

This paper introduces our system at NLPTEA2020 shared task for CGED, which is able to detect, locate, identify and correct grammatical errors in Chinese writings. The system consists of three components: GED, GEC, and post processing. GED is an ensemble of multiple BERT-based sequence labeling models for handling GED tasks. GEC performs error correction. We exploit a collection of heterogenous models, including Seq2Seq, GECToR and a candidate generation module to obtain correction candidates. Finally in the post processing stage, results from GED and GEC are fused to form the final outputs. We tune our models to lean towards optimizing precision, which we believe is more crucial in practice. As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision. Ours are among the top 4 to 6 in other tracks, except for FPR where we rank 12. And our system achieves the highest precisions among the top 10 submissions at IDENTIFICATION and POSITION tracks.
Key Principles And Approaches In Geohazard Communication For Enhancing Disaster Resilience

Communication is an essential aspect of preparing for, avoiding or responding to the occurrence of natural geohazards. As such, it forms an integral part of any strategy to enhance resilience to geohazard events. Conversely, inadequate or lack of communication is a common factor in failing to minimise the risks involved. Communication about geohazards occurs at several different levels: between geoscientists and other professionals such as the engineers and planners; between professionals and other groups such as emergency services and insurance companies; and between all of these parties and the general public who are affected by events. Geoscientists need to be involved in all of these lines of communication. This paper examines the essential role of geoscientists in helping to reduce the risks associated with a wide range of geohazards. A series of key principles that links to a generic model of geohazard communication applicable to a wide range of scenarios is presented
Hand-held 3D Dense Map Construction System Based on Improved ICP Algorithm

In view of the problems of poor image quality and inaccurate positioning due to interference in interactive control systems, this paper proposes a non-local mean algorithm (NLM) based on adaptive median algorithm (ALM) based on the strategy of salt and pepper noise processing), and applied to non-contact interactive systems based on laser sensors. The main innovations of this paper: 1) In the image preprocessing algorithm, a noise reduction method that is effective for Gaussian noise, salt and pepper noise, and mixed noise of both of them is proposed; 2) The improved denoising algorithm is applied to lidar-based sensor's non-contact interactive system, which significantly improves the interactive performance. Experimental results show that the proposed algorithm has obvious denoising effect, accurate image positioning and good target tracking effect.
전작권 전환 대비 한·미 공보협력체계 발전방향

This study deals with cooperation issues in media operation between RoK-US during wartime. In the modern warfare, media frames the war itself. It means people’s recognition about the status of war is largely dependent on the media coverages. This study sheds lights on US military’s experiences in Somalia, Balkan, Afghanistan, and Iraq and draws lessons from them. Then, this study delves into the issues of current status of RoK-US cooperation in media operation, coordination efforts, and problems in coordination. Considering the fact that OpCon transfer will be made within several years, cooperation issue should be taken seriously. Currently, the initiative of RoK-US media operation is in the hand of US military, specifically, in the hand of the commander of the Combined Forces Command. If there are problems when US military lead the cooperation in media operation, there would be more problems when RoK military control the cooperation after RoK gets the OpCon back from the US. For this, the direction of RoK-US wartime media operation cooperation is provided in the last part of article. Considering the diversity of media, this study suggests simultaneous responses toward global, domestic, and cyber media and provides sophisticate strategy for dealing with various media. Saturation strategy, loose control but one-voice principle, establishment of media strategy cooperation networks are the alternatives that this study provide.
A defense scheme of voice control system against DolphinAttack

DolphinAttack is an attack model aiming at audio frequency identification vulnerabilities in voice control system microphones. It modulates voice command on the ultrasonic carrier to achieve inaudibility, and the modulated low frequency audio command can be successfully demodulated, recorded, and interpreted by the speech recognition system. The DolphinAttack threatens the property right and privacy of smart device users. In the paper, we propose a security reinforcement method for voice control system based on disconnected loop authentication that combines software and hardware to prevent DolphinAttack. The security enhancement solution that we proposed based on secondary authentication also can mitigate some unknown vulnerabilities. When the attacker or user wakes up the speech recognition system and performs command control, it will force the voice control system at a high security level to perform secondary authentication. If the command passes the secondary authentication, it enters the execution state. Otherwise, the command is discarded and the report is sent to the user. This kind of security enhancement avoids the privacy leakage and property damage caused by the DolphinAttack at some degree.
Research on Index Scheme of Encrypted XML Data Structure

This paper proposed a B+ tree encrypted XML structure index scheme and further discussed a query model using Dewey coding. In the process of XML document encryption, the XML encrypted data is stored together with the B+ tree index based on the encrypted data on the server-side, to complete the structural index of the encrypted data on the server-side. This method improves the efficiency of the query and effectively achieves the structural index of encrypted XML data.
Back-Projection Pipeline

We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but seeks the more difficult task of learning how to enhance images. Compared to similar approaches, we propose a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow. Features are updated at multiple scales in each layer of the network. The update dynamic through these layers includes interactions between different resolutions in a way that is causal in scale, and it is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets. The system can be used as a generic multi-resolution approach to enhance images. We test it on several challenging tasks with special focus on super-resolution and raindrop removal. Our results are competitive with state-of-the-arts and show a strong ability of our system to learn both global and local image features.
Design of Multi-unmanned Platform Collaborative Demonstration Environment

In modern unmanned operations, the combat mode centered on “single-plane platform” is gradually shming to “multi-aircraft coordination” as the center. Multi-unmanned platform coordinated control includes multi-machine coordinated task planning, coordinated target allocation, coordinated task execution, coordinated target tracking, and coordinated target reconnaissance and attack. This research addresses the issues of multi-aircraft cooperative formation control, mission planning, and path planning involved in the multi-unmanned platform collaborative demonstration system. Based on the theoretical principles of multi-aircraft cooperative target allocation, route planning, and multi-aircraft cooperative reconnaissance, search and attack, we design engineering practical high-efficiency, real-time and easy-to-implement algorithms to complete the multi-machine collaborative control and its physical verification platform system, and carry out live demonstration verification.
Based on GAN Generating Chaotic Sequence

Abstract In this paper, an adversarial encryption algorithm based on generating chaotic sequence by GAN is proposed. Starting from the poor leakage resistance of the basic adversarial encryption communication model based on GAN, the network structure was improved. Secondly, this paper used the generated adversarial network to generate chaotic-like sequences as the key K and entered the improved adversarial encryption model. The addition of the chaotic model further improved the security of the key. In the subsequent training process, the encryption and decryption party and the attacker confront each other and optimize, and then obtain a more secure encryption model. Finally, this paper analyzes the security of the proposed encryption scheme through the key and overall model security. After subsequent experimental tests, this encryption method can eliminate the chaotic periodicity to a certain extent and the model’s anti-attack ability has also been greatly improved. After leaking part of the key to the attacker, the secure communication can still be maintained.
Fake news sobre COVID-19 e suas implicações à pessoa com diabetes mellitus

Objective: To reflect the Fake News about COVID-19 and its implications to the person with Diabetes Mellitus. Method: This is a reflexive analysis carried out in discussions between teachers and students, during the meetings of the subject Topics of Philosophy of Science in Nursing and Health, in which the main principles of the philosophy and its relationship, contribution and importance in the practice of nursing were discussed. Results and discussion: It is recommended that people with diabetes seek information on official sites, because it is relevant for the population to be informed about all information related to COVID-19, in addition, the competent agencies should intensify the recommendations of social isolation and non-pharmacological measures. Conclusion: However, it is necessary to contribute to the non propagation of fake news by notifying them on the site of the Ministry of Health.
월드카페와 브레인라이팅을 활용한 영양교육이 초등학생의 채소 섭취에 대한 자아효능감과 태도에 미치는 효과

This study was conducted to develop nutrition education using world cafe and brain writing and to investigate the effects of self-efficacy and...
중국인 학습자의 한국어 폐찰음 발음에 대한 청취·음향적 연구

The purpose of this study is to analyze pronunciation errors and acoustic characteristics on Korean affricates produced by Chinese learners of...
Few-shot Network Anomaly Detection via Cross-network Meta-learning

Network anomaly detection aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis. Due to the unbearable labeling cost, existing methods are predominately developed in an unsupervised manner. Nonetheless, the anomalies they identify may turn out to be data noises or uninteresting data instances due to the lack of prior knowledge on the anomalies of interest. Hence, it is critical to investigate and develop few-shot learning for network anomaly detection. In real-world scenarios, few labeled anomalies are also easy to be accessed on similar networks from the same domain as of the target network, while most of the existing works omit to leverage them and merely focus on a single network. Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks -- Graph Deviation Networks (GDN) that can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Extensive evaluations demonstrate the efficacy of the proposed approach on few-shot or even one-shot network anomaly detection.
Water Quality Autoregression Prediction Model Based on Markov-Switching Theory Using Crow Search Algorithm

&amp;lt;p&amp;gt;Water quality prediction is an important technical means for preventing and controlling water pollution and is crucial in the formulation of reasonable water pollution prevention and control measures. The time series structure of natural water quality is complex and heteroscedastic, so it is difficult for the traditional prediction model to reflect the actual situation well. Hence, Markov-switching (MS) theory is applied to a water quality autoregression (AR) prediction model (MSAR) in this paper. Further, MSAR is improved by introducing the crow search algorithm to obtain model parameters (CSA-MSAR). Then existing water quality time series for COD&amp;lt;sub&amp;gt;Mn&amp;lt;/sub&amp;gt; was selected as the data for the CSA-MSAR model after a normality test and the Box&amp;amp;#8211;Cox normality transformation. The results show that the CSA-MSAR model for COD&amp;lt;sub&amp;gt;Mn&amp;lt;/sub&amp;gt; with (s, p) values of (3, 5) has the best performance. The improvement degree for selection criteria compared with AR model is as follows: Akaike information criterion for MSAR is 32.020% and 31.611% for CSA-MSAR; Bayesian information criterion for MSAR is 10.632% and 13.464% for CSA-MSAR; likelihood value for MSAR is 40.016% and 40.801% for CSA-MSAR; C for MSAR is 63.559% and 64.968% for CSA-MSAR. Moreover, the results show that the average prediction precision of the first- to fifth-order prediction is raised by 89.016% for MSAR and 89.340% for CSA-MSAR compared with AR, indicating that the introduction of MS makes the CSA-MSAR and MSAR models conform to the smoothness of the mean and variance in each state. The results also indicate that the introduction of CSA into the maximum likelihood estimation to obtain the parameters raise the model prediction precision (the average prediction precision of CSA-MSAR is higher than MSAR by 5.231% excluding the fifth-order prediction) and the CSA-MSAR model is scientifically valid and reasonable for water quality prediction.&amp;lt;/p&amp;gt;
Momentum Contrastive Autoencoder

Wasserstein autoencoder (WAE) shows that matching two distributions is equivalent to minimizing a simple autoencoder (AE) loss under the constraint that the latent space of this AE matches a pre-specified prior distribution. This latent space distribution matching is a core component in WAE, and is in itself a challenging task. In this paper, we propose to use the contrastive learning framework that has been shown to be effective for self-supervised representation learning, as a means to resolve this problem. We do so by exploiting the fact that contrastive learning objectives optimize the latent space distribution to be uniform over the unit hyper-sphere, which can be easily sampled from. This results in a simple and scalable algorithm that avoids many of the optimization challenges of existing generative models, while retaining the advantage of efficient sampling. Quantitatively, we show that our algorithm achieves a new state-of-the-art FID of 54.36 on CIFAR-10, and performs competitively with existing models on CelebA in terms of FID score. We also show qualitative results on CelebA-HQ in addition to these datasets, confirming that our algorithm can generate realistic images at multiple resolutions.
Verification of Nyquist Sampling Theorem for a given Continuous-Time (CT) Signal and a given frequency in the Signal Processing Environment

In this paper, the verification of the sampling theorem for a signal of a given frequency using MATLAB is presented. In the area of digital signal processing, the Nyquist-Shannon sampling theorem is a theorem that serves as a fundamental bridge between continuous-time and discrete-time signals. Sampling is carried out using a device called a sampler, which is nothing but a switch. The sampler is a switch that is continuously opened & closed at regular intervals of time, the time intervals between the successive samples are called the sampling period and the reciprocal of the sampling period is called the sampling frequency. The sampling rate has to be very very high to preserve the contents of the original image, i.e., the switch has to open & close at a very faster rate. The sampling theorem forms the basis of all communication electronics and can use for various applications. Here, the theorem is verified using a code developed in Matlab & the simulation results show the effectiveness of the programming approach developed.
BayesARIMAX: Bayesian Estimation of ARIMAX Model

The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.
Real Time Prediction of Rebound Trajectory of Rotating Table Tennis based on Binocular Vision

With the deepening of the research on table tennis, intelligent table tennis trajectory prediction and positioning has become a hot issue in the research. The traditional rotating table tennis trajectory prediction method has poor ability to divide the background of table tennis, resulting in poor accuracy of rebound trajectory prediction. Therefore, a real-time prediction method based on binocular vision is designed. The binocular vision model is constructed to obtain the three-dimensional model of table tennis. According to the color space theory, the motion model and background subtraction method are combined to obtain the moving area of the rotating table tennis target. The rebound trajectory of the rotating table tennis ball is predicted in the target active area. So far, the design of real-time prediction of the rebound trajectory of rotating table tennis ball based on binocular vision is completed. The experimental results show that the prediction accuracy of the proposed method is high, which provides a theoretical reference for future research.
Measuring the Internet during Covid-19 to Evaluate Work-from-Home

Covid-19 has radically changed our lives, with many governments and businesses mandating work-from-home (WFH) and remote education. However, work-from-home policy is not always known globally, and even when enacted, compliance can vary. These uncertainties suggest a need to measure WFH and confirm actual policy implementation. We show new algorithms that detect WFH from changes in network use during the day. We show that change-sensitive networks reflect mobile computer use, detecting WFH from changes in network intensity, the diurnal and weekly patterns of IP address response. Our algorithm provides new analysis of existing, continuous, global scans of most of the responsive IPv4 Internet (about 5.1M /24 blocks). Reuse of existing data allows us to study the emergence of Covid-19, revealing global reactions. We demonstrate the algorithm in networks with known ground truth, evaluate the data reconstruction and algorithm design choices with studies of real-world data, and validate our approach by testing random samples against news reports. In addition to Covid-related WFH, we also find other government-mandated lockdowns. Our results show the first use of network intensity to infer-real world behavior and policies.
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning

We introduce a parameterization method called Neural Bayes which allows computing statistical quantities that are in general difficult to compute and opens avenues for formulating new objectives for unsupervised representation learning. Specifically, given an observed random variable x and a latent discrete variable z, we can express p(x|z), p(z|x) and p(z) in closed form in terms of a sufficiently expressive function (Eg. neural network) using our parameterization without restricting the class of these distributions. To demonstrate its usefulness, we develop two independent use cases for this parameterization: 1. Disjoint Manifold Separation: Neural Bayes allows us to formulate an objective which can optimally label samples from disjoint manifolds present in the support of a continuous distribution. This can be seen as a specific form of clustering where each disjoint manifold in the support is a separate cluster. We design clustering tasks that obey this formulation and empirically show that the model optimally labels the disjoint manifolds. 2. Mutual Information Maximization (MIM): MIM has become a popular means for self-supervised representation learning. Neural Bayes allows us to compute mutual information between observed random variables x and latent discrete random variables z in closed form. We use this for learning image representations and show its usefulness on downstream classification tasks.