Reviewerly/exHarmony huggingface dataset:
Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis

Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results.
Curricular Guidance for Associate-Degree Cybersecurity Programs

As projections of the shortage of cybersecurity workers grow, the ACM CCECC (Committee for Computing Education in Community Colleges) has led creation of a set of guidelines for two-year cybersecurity programs at the associate-degree level, called Cyber2yr2020. The Cyber2yr2020 guidelines underwent two rounds of public review and comment with the StrawDog and IronDog drafts, and the final version will be released in early 2020. This poster presents the curriculum guidelines, provides perspectives on the use of the guidelines, and offers opportunities to highlight two-year cybersecurity programs by aligning with the guidelines in a program example.
COMPARISON OF K-MEANS CLUSTERING METHOD AND K-MEDOIDS ON TWITTER DATA

The presidential election is one of the political events that occur in Indonesia once in five years. Public satisfaction and dissatisfaction with political issues have led to an increase in the number of political opinion tweets. The purpose of this study is to examine the performance of the k-means and k-medoids method in the Twitter data and to tweet about the presidential election in 2019. The data used in this study are primary data taken from Muhyi's research, then mining the text against data obtained. Because this data has been processed by Muhyi to analyze the electability of the 2019 presidential candidate pairs, for this journal needs a preprocessing was carried out to analyze the tendency of tweets to side with the candidate pairs of one or two. The difference in the pre-processing of this research with previous research is that there is a cleaning of duplicate data and normalizing. The results of this study indicate that the optimal number of clusters resulting from the k-means method and the k-medoid method are different.
BiometricAccessFilter: A Web Control Access System Based on Human Auditory Perception for Children Protection

Along with internet growth, security issues come into existence. Efficient tools to control access and to filter undesirable web content are needed all the time. In this paper, a control access method for web security based on age estimation is proposed, where the correlation between human age and auditory perception is taken into account. In particular, access is denied if a person’s age is not appropriate for the given web content. Unlike existing web access filters, our biometric approach offers greater security and protection to individual privacy. From a technical point of view, the machine-learning regression model is used to estimate the person’s age. The primary contributions of this paper include an age estimation module based on human auditory perception and provision of an open-source web filter to prevent adults from accessing children web applications. The proposed system can also be used to limit the access of children to a webpage specially designed for adults. Our system is evaluated with a dataset collected from 201 persons with different ages from 06 to 60 years old, where it considered 109 male and 82 female volunteers. Results indicate that our system can estimate the age of a person with an accuracy of 97.04% and a root mean square error (RMSE) of 4.2 years. It presents significant performances in the verification scenario with an Equal Error Rate (EER) of 1.4%.
Efficient scheme for remote preparation of arbitrary n-qubit equatorial states

Recently, a scheme for deterministic remote preparation of arbitrary multi-qubit equatorial states was proposed by Wei et al. [ Quantum Inf. Process. 17 70 (2018)]. It is worth mentioning that the construction of mutual orthogonal measurement basis plays a key role in quantum remote state preparation. In this paper, a simple and feasible remote preparation of arbitrary n -qubit equatorial states scheme is proposed. In our scheme, the success probability will reach unit. Moreover, there are no coefficient constraint and auxiliary qubits in this scheme. It means that the success probabilities are independent of the coefficients of the entangled channel. The advantage of our scheme is that the mutual orthogonal measurement basis is devised. To accomplish the quantum remote state preparation (RSP) schemes, some new sets of mutually orthogonal measurement basis are introduced.
Micro-Ring-Resonator Based Passive Photonic Spike-Time-Dependent-Plasticity Scheme for Unsupervised Learning in Optical Neural Networks

In this work, a photonic spike-time-dependent-plasticity scheme based on high-order passive ring resonators is demonstrated. Numerical simulations confirmed the validity of the approach assuming post and pre-synaptic quantum dot laser neurons.
Post-training Quantization Methods for Deep Learning Models

In this work, we perform a comprehensive study on post-training quantization methods for convolutional neural networks in two challenging tasks: classification and object detection. Furthermore, we introduce a novel method that quantizes every single layer to the smallest bit width, which does not introduce accuracy degradation. As a result, the model layers are compressed to a variable number of bit widths preserving the quality of the model. We provide experiments on object detection and classification task and show, that our method compresses convolutional neural networks up to 87% and 49% in comparison to 32 bits floating-point and naively quantized INT8 baselines respectively while maintaining desired accuracy level.
Collaborative Routing in Partially-Trusted Relay based Quantum Key Distribution Optical Networks

This paper proposes a collaborative routing scheme in partially-trusted relay based quantum key distribution optical networks. Simulation results show it achieves good performance in terms of key distribution success rate.
Universal Differentiable Renderer for Implicit Neural Representations.

The goal of this work is to learn implicit 3D shape representation with 2Dsupervision (i.e., a collection of images). To that end we introduce theUniversal Differentiable Renderer (UDR) a neural network architecture that canprovably approximate reflected light from an implicit neural representation ofa 3D surface, under a wide set of reflectance properties and lightingconditions. Experimenting with the task of multiview 3D reconstruction, we findour model to improve upon the baselines in the accuracy of the reconstructed 3Dgeometry and rendering from unseen viewing directions.
Application of Artificial Intelligence Technology in Basketball Games

Abstract With the vigorous development of computer technology, artificial intelligence technology has also been greatly improved, and its application in sports competitions has become more and more widespread. The basketball game is a fierce rivalry under the team. In addition to testing the physical fitness of athletes, the ability to independently coordinate and plan athletes needs to be tested. Artificial intelligence has great application value in basketball games. Applying artificial intelligence technology to field decision-making in basketball games, real-time display of basketball game field data in real time, assisting coaches in field analysis and decision-making, can provide targeted training goals for daily training, and has strong application value. In this paper, an artificial intelligence assisting system for basketball game field decision-making is constructed to explore the application of artificial intelligence technology in basketball games. This paper tests the system proposed in this article through simulation experiments. The test results show that in the module test, the module error rate is 0.5%, the reliability is high, the module functions can be basically realized, the system responds quickly, and the security is high. The results of this paper will provide a good paradigm role for the application of artificial intelligence in sports competitions.
Dynamic complexity of document spanners

The present paper investigates the dynamic complexity of document spanners, a formal framework for information extraction introduced by Fagin, Kimelfeld, Reiss, and Vansummeren (JACM 2015). We first look at the class of regular spanners and prove that any regular spanner can be maintained in the dynamic complexity class DynPROP. This result follows from work done previously on the dynamic complexity of formal languages by Gelade, Marquardt, and Schwentick (TOCL 2012). To investigate core spanners we use SpLog, a concatenation logic that exactly captures core spanners. We show that the dynamic complexity class DynCQ is more expressive than SpLog and therefore can maintain any core spanner. This result is then extended to show that DynFO can maintain any generalized core spanner and that DynFO is more powerful than SpLog with negation.
Usability Study of Smart Phone Messaging for Elderly and Low-literate Users

Smartphones are electronic devices that people can carry around and install/add compatible third-party Apps to expend their functionality.Smartphones are mainly developed for calling and messaging purposes.All applications' interfaces are designed for the current trends.Therefore, Senior Citizen and Low-literate users face difficulties to use smartphones due to the perceived complicated interface and functionality.This paper analyzes Senior Citizen and Low-literate user's requirements to read and write messages from users "memory load", "navigation consistency", "consistency and standard", and "touch screen finger-based tapping" perspective.Then a framework based on "visual representation", "navigation" and "miss click avoidance" is developed.A comparison between the proposed application and other messaging applications is provided.This research work focused on the Senior Citizen and Low-literate users to improve their user experience of the smartphone messaging application.
Is Graph Structure Necessary for Multi-hop Reasoning?

Recently, many works attempt to model texts as graph structure and introduce graph neural networks to deal with it on many NLP this http URL this paper, we investigate whether graph structure is necessary for multi-hop reasoning tasks and what role it plays. Our analysis is centered on HotpotQA. We use the state-of-the-art published model, Dynamically Fused Graph Network (DFGN), as our baseline. By directly modifying the pre-trained model, our baseline model gains a large improvement and significantly surpass both published and unpublished works. Ablation experiments established that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop reasoning. We point out that both the graph structure and the adjacency matrix are task-related prior knowledge, and graph-attention can be considered as a special case of self-attention. Experiments demonstrate that graph-attention or the entire graph structure can be replaced by self-attention or Transformers, and achieve similar results to the previous state-of-the-art model achieved.
R Markdown: The Definitive Guide

Markdown is a flat text file that can have software code embedded in it. It is employed for writing reproducible research documents which can be converted into web pages, presentations, or other de...
A Machine Learning Based Strategy for Election Result Prediction

Predicting election results is a hot area in political science. In the last decade, social media has been widely used in political elections. Most approaches can predict the result of a national election. However, it is still challenging to predict the overall results of many local elections. This paper presents a machine learning based strategy to analyze Twitter data for predicting the overall results of many local elections. To verify the effectiveness of this strategy, we apply it for analyzing the Twitter data based on the 2018 midterm election in United States. The results suggest the predicted results are close to the actual election outcome.
Accurate and Compact Convolutional Neural Networks with Trained Binarization

Although convolutional neural networks (CNNs) are now widely used in various computer vision applications, its huge resource demanding on parameter storage and computation makes the deployment on mobile and embedded devices difficult. Recently, binary convolutional neural networks are explored to help alleviate this issue by quantizing both weights and activations with only 1 single bit. However, there may exist a noticeable accuracy degradation when compared with full-precision models. In this paper, we propose an improved training approach towards compact binary CNNs with higher accuracy. Trainable scaling factors for both weights and activations are introduced to increase the value range. These scaling factors will be trained jointly with other parameters via backpropagation. Besides, a specific training algorithm is developed including tight approximation for derivative of discontinuous binarization function and $L_2$ regularization acting on weight scaling factors. With these improvements, the binary CNN achieves 92.3% accuracy on CIFAR-10 with VGG-Small network. On ImageNet, our method also obtains 46.1% top-1 accuracy with AlexNet and 54.2% with Resnet-18 surpassing previous works.
Retracted: Convolutional neural network based on differential privacy in exponential attenuation mode for image classification

Privacy information leaks have become a major problem hindering the development of the convolutional neural network and deep learning. Differential privacy protection has been applied to deep learning by more and more scholars to protect image training sets. The differentially private SGD (DP-SGD) algorithm is adding Gaussian noise of a fixed level will cause the accuracy of the model to increase slowly with the increase of training times. To solve this problem, this study presents a convolutional neural network based on differential privacy in exponential attenuation mode. Firstly, the attenuation coefficient of Gaussian noise is linked with the training times and the accuracy of the model. Secondly, the DP-SGD algorithm in exponential attenuation mode is proposed. The calculation method of the differential privacy protection budget in exponential attenuation mode is given. Finally, experiments with the MNIST dataset and X-ray images verify the feasibility of using the original data of the Gaussian noise convolutional neural network based on differential privacy protection in exponential attenuation mode.
ICT ethics-related cognition among undergraduate students

Purpose Moor (1985) designated two major problem sources typifying the social and ethical implications of computer technologies, namely, “policy vacuum” and “conceptual muddles.” Motivated by Moor’s seminal definition and Floridi’s (2013) conceptualization of information and communication technologies (ICTs) as re-ontologizing technologies, this study aims to explore Omani undergraduates’ cognition regarding ICT ethics. Design/methodology/approach Adopting a grounded theory approach for the constant comparative thematic analysis, the constituents of ICT ethics-related cognition among undergraduates and influencing factors were scrutinized. Qualitative data were gathered via focus group discussions with undergraduates and interviews with academics and information systems professionals at Sultan Qaboos University. Findings In total, 10 thematic categories revolving around a core category, constructing conceptual perceptions of and attitudes toward the realms constituting ICT ethics using an ontological, object-oriented approach, emerged from the comparative analysis. Undergraduates were found to adopt an applied approach when defining professional ICT ethics codes and policies, with a particular focus on information privacy and integrity. Research limitations/implications This qualitative study was conducted at a single research site. This may restrict the generalizability of the findings. Postgraduates were not considered when designing this qualitative inquiry. Originality/value The findings of the study hold theoretical and methodological significance with regard to ICT ethics-related cognition in the era following the fourth industrial revolution by sustaining feminist ethics in this research. Ultimately, the study developed a substantive theory scrutinizing the constitutive elements of ICT ethics-related cognition among Generation Z.
Manipulated Face Detector: Joint Spatial and Frequency Domain Attention Network

Face manipulation methods develop rapidly in recent years, which can generate high quality manipulated face images. However, detection methods perform not well on data produced by state-of-the-art manipulation methods, and they lack of generalization ability. In this paper, we propose a novel manipulated face detector, which is based on spatial and frequency domain combination and attention mechanism. Spatial domain features are extracted by facial semantic segmentation, and frequency domain features are extracted by Discrete Fourier Transform. We use features both in spatial domain and frequency domain as inputs in proposed model. And we add attention-based layers to backbone networks, in order to improve its generalization ability. We evaluate proposed model on several datasets and compare it with other state-of-the-art manipulated face detection methods. The results show our model performs best on both seen and unseen data.
LM-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis

Recent studies have shown that contextualized word embeddings outperform other types of embeddings on a variety of tasks. However, there is little research done to evaluate their effectiveness in the biomedical domain under multi-task settings. We derive the contextualized word embeddings from the Flair framework and apply them to the task of biomedical NER on 5 benchmark datasets, yielding major improvements over the baseline and achieving competitive results over the current best systems. We analyze the sources of these improvements, reporting model performances over different combinations of word embeddings, and fine-tuning and casing modes.
STUDENTS’ DIFFICULTIES IN TRANSLATING NARRATIVE TEXT FROM ENGLISH INTO INDONESIA AT GRADE VIII OF SMP NEGERI 9 PEMATANGSIANTAR

This research was intended to find out kinds of students difficulties in translating narrative text from English into Indonesia of SMP Negeri 9 Pematangsiantar. Therefore, the researcher formulated a question as the problem. As follows: “What are the students difficulties in translating narrative text from English into Indonesia of SMP Negeri 9 Pematangsiantar?” The researcher was used qualitative design. To answer the problem, the researcher used grade eight students of SMP Negeri 9 Pematangsiantar as the population and used 31 students as the sample, they were VIII-1. The instrument of the research of this research was narrative text. The data were collected by using record video of mobile phone then to be analyzed the result of students’ translation and correction. The research finding showed that students were difficulties in translating narrative text from English into Indonesia. The highest number of students’ difficulty is tense (32%). The second most difficulties faced by students is sentence (25,4%). The third difficulty is phrase (16,6%). The fourth is clause (15,4%). The fifth difficulty is attribute (8,2%). And the last is indefinite article (2,4%). It can be conclude that students have difficulties in translating narrative text from English into Indonesia is tense, not sentence, phrase, clause, attribute, and article indefinite.
Nurse Alarming Device for Bedridden Patients Using Hand Gesture Recognition System

It has always been a challenge for hospitals to eliminate the miscommunications between healthcare providers and those they serve. Whether it is to locate a nurse or responding to a patient’s request for more pain medication, ask for any help or call in emergency—it has been outrageously time consuming and difficult to streamline and improve the workflow. Since patients need round-the-clock assistance or sometimes only need help now and then for certain tasks, there should be some solution which uses such technology that can give them peace of mind even when they encounter a situation that they cannot handle independently, or if they have a health issue and need help. Hand gesture based alarming device is a nurse calling alerting system that can keep patients safe since they will be able to call for assistance when required using their own hand gestures. This paper is about suggesting a way of interaction between nurse and patients along with real time gesture recognition. Previously, for some systems, excessive wiring in patient rooms and nurses’ areas was a must, which made installation expensive and hard quite cumbersome. This technology which is purely software based and works with very less equipment seems very promising. As it can be used in hospitals, assisted living centres, or rehabilitation centres, which helps healthcare providers serve their patients more attentively and efficiently, it improves responsiveness, levels of care, operational efficiency, and patient or client satisfaction. This paper proposes the method or algorithm for an application which would help in recognising the different gestures of patient’s hand. The images are of the palm side of right and left hand and are loaded at runtime. The method has been developed with respect to single user. The real time images will be captured first and then stored in directory and on recently captured image and feature extraction will take place to identify which gesture has been articulated by the user. The comparisons will be performed in arrears and then after comparison the result will be produced in accordance through matched key points from the input image to the image stored for a specific gesture already in the directory or the database the outputs for the following can be seen in below sections.
ATDx: Building an Architectural Technical Debt Index

Architectural technical debt (ATD) in software-intensive systems refers to the architecture design decisions which work as expedient in the short term, but later negatively impact system evolvability and maintainability.Over the years numerous approaches have been proposed to detect particular types of ATD at a refined level of granularity via source code analysis.Nevertheless, how to gain an encompassing overview of the ATD present in a software-intensive system is still an open question.In this study, we present a multi-step approach designed to build an ATD index (ATDx), which provides insights into a set of ATD dimensions building upon existing architectural rules by leveraging statistical analysis.The ATDx approach can be adopted by researchers and practitioners alike in order to gain a better understanding of the nature of the ATD present in software-intensive systems, and provides a systematic framework to implement concrete instances of ATDx according to specific project and organizational needs.