Reviewerly/exHarmony huggingface dataset:
Numerical Verification on Collision Avoidance of Multiple Quadrotors Using Voronoi Division

In this paper, we assume a situation that multiple quadrotors are flying autonomously in the same space for the purpose of aerial photographing, surveillance, home delivery and so on. In such a situation, an algorithm to avoid mutual collision is needed. D. Zhou et al. proposed an algorithm based on the buffered Voronoi cells (BVC) to reach each target position without mutual collision when the multiple quadrotors fly and confirmed its operation by some simulations. In this research, we verified the performance of the method by numerical simulations using MATLAB. In addition, we confirmed the algorithm by dynamic simulations with four quadrotors using the dynamic simulator V-REP.
Privacy Protection and Authorization Control System based on Android

At present, lots of software don’t have an authorization mechanism for the user’s private data, especially the user’s private information is often leaked by the application on mobile terminal. This project intends to allow users to manage their own private information. It can remind users when application call sensitive information. It can inform users the kind of information which is called by the manufacturer of the application. It shows the dangerous level of the application which is rendered from our algorithm by chart. And users can control the privacy-access permission of application.
A kind of quaternary sequences of period $2p^mq^n$ and their linear complexity

Sequences with high linear complexity have wide applications in cryptography. In this paper, a new class of quaternary sequences over $\mathbb{F}_4$ with period $2p^mq^n$ is constructed using generalized cyclotomic classes. Results show that the linear complexity of these sequences attains the maximum.
C-DLinkNet: considering multi-level semantic features for human parsing

Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human parsing is to extract effective semantic features to resolve deformation and multi-scale variations. In this work, we proposed an end-to-end model called C-DLinkNet based on LinkNet, which contains a new module named Smooth Module to combine the multi-level features in Decoder part. C-DLinkNet is capable of producing competitive parsing performance compared with the state-of-the-art methods with smaller input sizes and no additional information, i.e., achiving mIoU=53.05 on the validation set of LIP dataset.
A novel method for quantifying periodicity and time delay in dynamic neural networks using unstable subaction potential threshold depolarizations

Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multisite recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straightforward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveforms are unstable (e.g., fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability. NEW &amp; NOTEWORTHY The determination of frequency(s) at which two sources are synchronized, and relative time delay between them, is a fundamental problem for a wide a range of signal-processing applications. In this methodology paper, we present novel procedures for periodicity estimation for single time series and joint periodicity and time delay estimation for two time series. The methods use properties of the cross-correlation function rather than the cross-correlation function explicitly.
SMChain: A Scalable Blockchain Protocol for Secure Metering Systems in Distributed Industrial Plants.

Metering is a critical process in large-scale distributed industrial plants, which enables multiple plants to collaborate to offer mutual services without outside interference. When distributed plants measure the data from a shared common source, e.g., flow metering in an oil pipeline, trustworthiness and immutability must be guaranteed among them. In this paper, we propose a hierarchical and scalable blockchain-based secure metering system, SMChain, to provide strong security, trustworthy guarantee, and immutable services. SMChain adopts a two-layer blockchain structure, consisting of independent local blockchains stored at individual plants and one state blockchain stored in the cloud. To deal with the scalability issues within each plant, we propose a novel scalable Byzantine Fault Tolerance (BFT) consensus protocol based on (k, n)-threshold signature scheme to deal with the Byzantine faults and reduce the intra-plant communication complexity from O(n2) to O(n). For the state blockchain, we use a cloud-based service to synchronize and integrate the local blockchains into one state blockchain, which can further be distributed back to each plant.
Extraction of Scores and Average From Algerian High-School Degree Transcripts

A system for extracting scores and average from Algerian High School Degree Transcripts is proposed. The system extracts the scores and the average based on the localization of the tables gathering this information and it consists of several stages. After preprocessing, the system locates the tables using ruling-lines information as well as other text information. Therefore, the adopted localization approach can work even in the absence of certain ruling-lines or the erasure and discontinuity of lines. After that, the localized tables are segmented into columns and the columns into information cells. Finally, cells labeling is done based on the prior knowledge of the tables structure allowing to identify the scores and the average. Experiments have been conducted on a local dataset in order to evaluate the performances of our system and compare it with three public systems at three levels, and the obtained results show the effectiveness of our system.
The stimulating directions of higher education internationalization in the process of integration into the european higher education and research area

Verbytska, A. The stimulating directions of higher education internationalization in the process of integration into the european higher education and research area / A. Verbytska // International Scientific Journal “Economics & Education”. - 2019. - Vol. 4. - Issue 1. - P. 34-39.
PERFORMANCE MONITORING ON PORTFOLIO OF STRUCTURES IN LARGE-SCALE EARTHQUAKE DISASTERS

In large-scale earthquake disasters, urban monitoring system would be effective in decision-making for emergency responses. For this purpose, we have proposed a data-driven technique for portfolio-monitoring of structures that adopts a machine-learning method called Correlation Anomaly Detection (CAD). This technique detects damaged structures in a portfolio by detecting a change in correlation property between dynamic characteristics of different structures. The key features of our proposed technique are summarized as follows.
Binary Classification from Positive Data with Skewed Confidence

Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence. However, in practice, the confidence may be skewed by bias arising in an annotation process. The Pconf classifier cannot be properly learned with skewed confidence, and consequently, the classification performance might be deteriorated. In this paper, we introduce the parameterized model of the skewed confidence, and propose the method for selecting the hyperparameter which cancels out the negative impact of skewed confidence under the assumption that we have the misclassification rate of positive samples as a prior knowledge. We demonstrate the effectiveness of the proposed method through a synthetic experiment with simple linear models and benchmark problems with neural network models. We also apply our method to drivers' drowsiness prediction to show that it works well with a real-world problem where confidence is obtained based on manual annotation.
The effectiveness of mobile learning in the teaching and learning of probability

This study investigates the effectiveness of mobile learning in the teaching and learning of Probability. The context of mobile here is not just restrained to gadgets such as smartphones but also the fact that teaching and learning can occur beyond boundaries and anywhere according to the convenience and personal preference of the students. This study made use of the pretest – posttest quasi experimental design and the students chosen for the study were from a pre-university college located in the northern region of the country. A total of 92 students made up the sample of the study. There were 46 students each in the experimental and control groups respectively. Cluster random sampling was employed as the sampling method here. The instrument used to collect data with the aim of strengthening the outcome of the study was the achievement test. A quantitative approach was undertaken specifically to analyse the obtained data. The paired sample t-test and independent sample t-test were executed in the data analysis process. Initially, every student involved in the study regardless of the group they were in possessed equal strength in their understanding of the content being covered as indicated by the results of their pre-test. The paired sample t-test yielded p &lt; 0.05 which meant that there was a significant difference between the mean score of the pre-test and post-test in the experimental group and control group respectively. The outcome of the independent sample t-test showed that there was a significant difference between the mean score of the experimental group and that of the control group in the post-test (p &lt; 0.05). This indicated that the treatment via mobile learning had indeed played a role in the improved performance of students in Probability, hence proving the effectiveness of mobile learning in the teaching and learning of Probability.
The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records

Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.
Estimating the Volume of Land-Use Electric Power Produced by Generators on Coastal Cargo Ships at Tokyo Port in Case of Natural Disasters

Japan is a country prone to many natural disasters. There has been a further demand for Japan to improve the reliability of power supply than before the Great East Japan Earthquake. Given the topography of the country, the authors think that the Earthquake taught us how effective ship-to-shore power supply systems are in improving the reliability. This study is to estimate how much power can be supplied from coastal cargo ships moored at Tokyo Port. For calculation purpose, the authors used ship specification data listed in the annual "Register of Ships" published by the Japan Shipping Exchange, Inc. and port mooring facility specification data on the Tokyo Port Facilities List. The result of the calculation showed that 230 moored ships at the port can provide up to 197,000kW, which accounts for 23% of the total amount of power that can be supplied from 2,314 registered coastal cargo ships in Japan.
Symmetrical Synthesis for Deep Metric Learning

Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the proposed method firstly generates synthetic points with each other as an axis of symmetry. Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss. Our proposed method is hyper-parameter free and plug-and-play for existing metric learning losses without network modification. We demonstrate the superiority of our proposed method over existing methods for a variety of loss functions on clustering and image retrieval tasks. Our implementations is publicly available.
Coupled Three-Mode Squeezed Vacuum

Multipartite entanglement is a key resource for various quantum information tasks. Here, we present a scheme for generating genuine tripartite entanglement via nonlinear optical processes. We derive, in the Fock basis, the corresponding output state which we termed the coupled three-mode squeezed vacuum. We find unintuitive behaviors arise in intensity squeezing between two of the three output modes due to the coupling present. We also show that this state can be genuinely tripartite entangled.
Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data

Abstract Background Gray and white matter volume difference and change are important imaging markers of pathology and disease progression in neurology and psychiatry. Such measures are usually estimated from tissue segmentation maps produced by publicly available image processing pipelines. However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different MR scanners and sites. Methods We used T1-weighted images of a single individual, scanned in 73 sessions across 27 different sites to assess the robustness of the tissue classification tools. Variability in Dice Kappa values and tissue volumes was assessed for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12. We also estimated the sample size necessary to detect a significant 1% volume reduction based on the variability of the estimates from each method within and across scanner models. Results BISON had the lowest overall variability in its volumetric estimates, followed by FreeSurfer, and SPM12. All methods also had significant differences between some of their estimates across different scanner manufacturers (e.g. BISON had significantly higher GM estimates and correspondingly lower WM estimates for GE scans compared to Philips and SIEMENs), and different signal-to-noise ratio (SNR) levels (e.g. FAST and FreeSurfer had significantly higher WM volume estimates for high versus medium and low SNR tertiles as well as correspondingly lower GM volume estimates). BISON also had the smallest sample size requirement across all scanners and tissue types, followed by FreeSurfer, and SPM12. Conclusions Our comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center datasets and multi-scanner databases.
Zheghalkin-Boolean Calculus

Boolean calculus has been studied extensively in the past in the context of switching circuits, error-correcting codes etc. This work generalizes several approaches to defining a differential calculus for Boolean functions. A unified theory of Boolean calculus, complete with k-forms and integration, is presented through the use of Zhegalkin algebras (i.e., algebraic normal forms), culminating in a Stokes-like theorem for Boolean functions.
Axial Age-2 in the Aspect of Electronic Virtual Reality

The article examines the problem of Axial Age-2 as logical continuation of Axial Age-1 whose conception was proposed by K. Jaspers. The coming of new Axial Age-2 is associated with the appearance of electronic virtual reality. Electronic virtual reality is considered as a cardinally new type of artificial reality created by human beings for human beings taking into account a human’s freedom. To conduct a comparative analysis of different types of realities, the authors introduce new philosophical notions: “existential plasticity” of the world, “rigid world”, “tender world”. The paper shows that coming Axial Age-2 symbolizes the beginning of a cardinally new stage of the mankind’s development.
Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions

Text classification models, especially neural networks based models, have reached very high accuracy on many popular benchmark datasets. Yet, such models when deployed in real world applications, tend to perform badly. The primary reason is that these models are not tested against sufficient real world natural data. Based on the application users, the vocabulary and the style of the model's input may greatly vary. This emphasizes the need for a model agnostic test dataset, which consists of various corruptions that are natural to appear in the wild. Models trained and tested on such benchmark datasets, will be more robust against real world data. However, such data sets are not easily available. In this work, we address this problem, by extending the benchmark datasets along naturally occurring corruptions such as Spelling Errors, Text Noise and Synonyms and making them publicly available. Through extensive experiments, we compare random and targeted corruption strategies using Local Interpretable Model-Agnostic Explanations(LIME). We report the vulnerabilities in two popular text classification models along these corruptions and also find that targeted corruptions can expose vulnerabilities of a model better than random choices in most cases.
A New Reversible Data Hiding Technique Combined with Interpolation and Pixel Value Differencing Method

Steganography is the popular security method that provides complete security for communicating secret details. Image steganography is a very interesting field because of the imperceptible way of hiding data in images, since small distortion in the images cannot be identified by a human eye. This is the main idea to develop image steganography algorithms to improve visual quality. Pixel Value Differencing is able to provide a high quality stego image in spite of the high capacity of the concealed information. This paper proposes the first the interpolation techniques with the pixel block then applying then the Pixel Value Differencing method. Here in the first phase the original image is portioned into 2X2 block then applying the nearest neighbour interpolation technique after that in the second phase PVD is used to embed the secret data. Then the new pixel value of the neighbouring pixel also calculated. In this paper one variant are proposed by using single range table. We observed that for both the variant PSNR value and the hiding capacity are increased.
Membangun Aplikasi Sistem Informasi Komunitas Motor Berbasis Android (Studi Kasus : GSX Community Boyolali-Klaten)

Research titled utilization of android application to support the Information System and Registration of new members of the GSX community who are secretariat in Boyolali-Semarang Street Dusun I, Winong, Boyolali, Boyolali. The research began from March 18, 2019 until June 18, 2019, with the aim of a) Building an Android-based application in accordance with the problems that have been analyzed, b) Designing an Android-based application program that can help facilitate prospective members who wish to register GSX Community Boyolali-Klaten members, c) Creating an android-based application so that members can more easily access and find the latest information about the community. The research methodology used is the PIECES Method, object modeling using UML. The results achieved are an android application to facilitate the registration of new members and providing information.
Ch. Palahniuk’s Creative Process: From Abstract Representation to Material Implementation

The article examines Ch. Palahniuk’s creative activity. Creative process is considered in the wide context: as a creator’s intra-personal and interpersonal communication with the world and society. The researcher analyses the stages of Ch. Palahniuk’s creative intention objectivation: from subjective choice of a life situation associated with certain sociocultural and historical reality to its subsequent implementation in a literary text and perception by a reader. The conclusion is made about the mechanism of Ch. Palahniuk’s artistic creation.
Analysis of the Experimental Result Underwater Imaging

For finding the changing law that the interaction between the photons and the water, the experiment has been finish and the result has been analyzed. There are a circle with five colors those are red, white, black, cyan, and yellow under the bottom of the wastepaper basket. The height of the wastepaper basket is 0.5m. The circle is shot by move phone. The image that shot underwater 0.01m is original image. The image that shot under water is distortion image. The distortion images are enhanced by Advanced Multiscale Retinex with different time’s standard deviation cutting out. The enhancement method is based on the Gauss function with k time’s standard deviation. For the every image of underwater imaging, the k is between 0.2 and 3.0 and the step is 0.2. The enhanced images are different from the original image for every pixel. The curve that is between the root-mean-square with the k time’s standard deviation cutting out has minimum value. The root-mean-square is between the enhanced images and the original image for every pixel. For the sequence the minimum value is in different k. The first result shows the deeper the water depth is, the bigger the k is. The depth of water is from 0.02m to 0.36m, the k is from 1.2 to 3.0. Another result shows the deeper the water depth is, the flatter the curve is when k is bigger.