Reviewerly/exHarmony huggingface dataset:
Concept Wikification for COVID-19

Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine.Given the very large and evergrowing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles.In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles.We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links.We also develop an end-to-end system for concept wikification for COVID-19.Preliminary experiments show very encouraging results.Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19_wikification.
WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.
Paired Examples as Indirect Supervision in Latent Decision Models

Compositional, structured models are appealing because they explicitly decompose problems and provide interpretable intermediate outputs that give confidence that the model is not simply latching onto data artifacts.Learning these models is challenging, however, because end-task supervision only provides a weak indirect signal on what values the latent decisions should take.This often results in the model failing to learn to perform the intermediate tasks correctly.In this work, we introduce a way to leverage paired examples that provide stronger cues for learning latent decisions.When two related training examples share internal substructure, we add an additional training objective to encourage consistency between their latent decisions.Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.We apply our method to improve compositional question answering using neural module networks on the DROP dataset.We explore three ways to acquire paired questions in DROP: (a) discovering naturally occurring paired examples within the dataset, (b) constructing paired examples using templates, and (c) generating paired examples using a question generation model.We empirically demonstrate that our proposed approach improves both in-and outof-distribution generalization and leads to correct latent decision predictions.
Research and Application of Machine Learning in Automatic Program Generation

With the development of artificial intelligence, machine learning has been applied in more and more domains. In order to improve the quality and efficiency of software, automatic program generation is becoming a research hotspot. In recent years, machine learning has also been gradually applied in automatic program generation. Decision trees, language models, and cyclic neural networks have been applied in code generation, code completion and code knowledge mining. The efficiency of software development has been improved to a certain extent using machine learning. Aimed at the automatic program generation, this paper analyzes and summarizes the models of machine learning, the modifications involved in the models and the application effects. The research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.
PSD2 influence on digital banking transformation: Banks' perspective

The European Union government has forced banks to open and make their customer information accessible with their permission, for other players in the financial market to use and take part of that business, in order to make the banking industry more innovative, stimulate new ideas in the payments market and better regulate existing legal frameworks for payment and banking services across the EU. This paper analyzes the impact that the revised PSD2 Directive has on the digital transformation of banking across the European Union.
Analysis of College Students’ Cybersecurity Awareness In Indonesia

Internet-based attacks have become common and are expected to happen continuously with the development of technology. Therefore, cybersecurity emerged as an important concept in everyday life. It is defined as the protection of cyberspace. Cybersecurity Awareness (CSA) exists as a major defense key in protecting users and systems from internet-based attacks. The research presented in this study aims to assess the level of CSA among college students in Indonesia. This study uses the Analytic Hierarchy Process (AHP) method to test students in three dimensions, including attitudes, knowledge, and behavior. To measure this dimension, six focus areas in the topic of cybersecurity were taken and developed from previous studies on the same topic. The six focus areas are password security, cyberbullying, phishing, malware, identity theft, and the last is downloading, sharing and use of pirated content. The results showed that the total level of CSA for college students in Indonesia was in the good criteria. This is indicated by a total percentage of awareness around 80%. Nevertheless there are some focus areas that can be improved to increase the percentage.
SLA‐Aware and Energy‐Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd‐Order Markov Chain Model

Chinese Journal of ElectronicsVolume 29, Issue 6 p. 1207-1217 ArticleFree Access SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd-Order Markov Chain Model Lianpeng Li, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorJian Dong, Corresponding Author dan@hit.edu.cn Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorDecheng Zuo, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorSongyan Ji, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this author Lianpeng Li, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorJian Dong, Corresponding Author dan@hit.edu.cn Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorDecheng Zuo, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this authorSongyan Ji, Harbin Institute of Technology, Harbin, 150001 ChinaSearch for more papers by this author First published: 01 November 2020 https://doi.org/10.1049/cje.2020.10.008AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Virtual machine (VM) consolidation offers a promising approach to saving energy and improving resource utilization in cloud data centers. However, the aggressive consolidation of VMs may lead to Servicelevel agreement (SLA) violations, which are essential for data centers and their users. Therefore, it is essential to find a tradeoff between the reduction in SLA violation level and energy costs. In this paper, we improve and expand our Host state 3rd-order Markov chain (HS3MC) model proposed in our previous research comprehensively: we propose a new VM selection algorithm, an improved VM placement algorithm, and an HS3MC-based VM consolidation algorithm based on our improved HS3MC model for the SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We evaluate our proposed algorithms on an extended Cloudsim simulator using the PlanetLab workload and a random workload. Specifically, we improve and expand the OpenStack Neat experimental platform, and evaluate the performance of our algorithms in a real cloud environment based on OpenStack. The experimental results show that our proposed model can significantly reduce the SLA violation rates while maintaining energy efficiency. Volume29, Issue6November 2020Pages 1207-1217 RelatedInformation
A Convolutional Neural Network Reaches Optimal Sensitivity for Detecting Some, but Not All, Patterns

We investigate the spatial contrast-sensitivity of modern convolutional neural networks (CNNs) and a linear support vector machine (SVM). To measure performance, we compare the CNN contrast sensitivity across a range of patterns with the contrast sensitivity of a Bayesian ideal observer (IO) with the signal-known-exactly and noise-known-statistically. A ResNet-18 reaches optimal performance for harmonic patterns, as well as several classes of real world signals including faces. For these stimuli the CNN substantially outperforms the SVM. We further analyze the case in which the signal might appear in one of multiple locations and found that CNN spatial sensitivity continues to match the IO. However, the CNN sensitivity is far below optimal at detecting certain complex texture patterns. These measurements show that CNNs spatial contrast-sensitivity differs markedly between spatial patterns. The variation in spatial contrast-sensitivity may be a significant factor, influencing the performance level of an imaging system designed to detect low contrast spatial patterns.
The Effect of Mobile Learning on Students’ Reading Self-Efficacy: A Case Study of the APP “English Liulishuo”

A number of studies have been conducted regarding self-efficacy in the field of foreign language learning. Yet, with the popularity of mobile learning, research on the relationship between mobile learning and self-efficacy in this field is still limited. To bridge the gap, the study aims to investigate the effects of mobile learning on students&amp;rsquo; reading self-efficacy, i.e. whether the use of mobile learning can improve students&amp;rsquo; English reading self-efficacy. A questionnaire is employed to collect data from 294 non-English major students in universities. To survey the effect of mobile learning on students&amp;rsquo; reading self-efficacy, the data is accessed by the software SPSS 20.0. Results of independent T test demonstrate that for overall students, reading self-efficacy for students who have used the app is significantly different from those who haven&amp;rsquo;t in overall reading skills and in the four dimensions of reading skills, i.e. basic reading skills, applied reading skills, reading task skills, and advanced reading skills. As for students with relatively better reading performance, the results are consistent. However, for students with relatively weak reading performance, the reading self-efficacy of students who have used the app only shows significant differences in overall reading skills and in the two dimensions of basic reading skills and applied reading skills, but shows no difference in the dimensions of reading task skills and advanced reading skills. Finally, practical suggestions for mobile learning and students&amp;rsquo; English reading are given.
ARPNetSteg: Network Steganography Using Address Resolution Protocol

Steganography is a technique that allows hidden transfer of data using some media such as Image, Audio, Video, Network Protocol or a Document, without its existence getting noticed. Over the past few years, a lot of research has been done in the field of Image, Video and Audio Steganography but very little work has been done in Network Steganography. A Network Steganography technique hides data in a Network Data Unit, i.e., a Network Protocol Packet. In this paper we present an algorithm ARPNetSteg that implements Network Steganography using the Address resolution protocol. Our technique is a robust technique that can transfer 44 bits of covert data per ARP reply packet.
Aerial Video Trackers Review

Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.
Robust Estimation of Student Performance in Massive Open Online Course using Fuzzy Logic Approach

The objective of this paper is to develop robust estimation of student performance in Massive Open Online Course (MOOC) using fuzzy logic approach. A massive open online course (MOOC) is an online course aimed at unlimited participation and open access viathe web.This evaluation for MOOC was implemented using online assessment marks and online self-learning time.Data were collected from 30 students who were participated in online course.Data for online assessment marks was represented using trapezoidal membership function.Meanwhile, data for online self-learning time was represented using triangular membership function.Output data for this analysis using final examination marks with gaussian membership function.Fuzzy logic procedure involved in this study using three procedures namely fuzzification of all inputs, fuzzy inference process using rule base and defuzzification to get output values.Results indicated higher value online assessment marks and higher value of online self-learning time contributed to higher performance in final examination.The findings of this study will help educators to forecast student performance in final examination with considering online input variables namely online assessments marks and online self-learning time.This study also will help students to adjust their self-learning time in obtaining required expected result in final examination.
Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.
Geostandards and Geoanalytical Research Bibliographic Review 2019

This bibliographic review gives an outline of publications in 2019 focusing on reference materials (RMs) used in geochemistry and related fields, such as palaeoclimate and environmental research.
Applying convolutional neural networks for limited-memory application

Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch &amp; alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
End-to-End Image Patch Quality Assessment for Image/Video With Compression Artifacts

In this paper, we present an experimental image quality assessment (IQA) method for image/video patches with compression artifacts. Using the High Efficiency Video Coding (HEVC) standard, we create a new database of image patches with compression artifacts. Then, we conduct a completed subjective testing process to obtain the `ground truth' quality scores for the mentioned database. Finally, we employ an end-to-end learning method to estimate the IQA model for the patches with HEVC compression artifacts. In such proposed method, a modified convolutional neural network (CNN) architecture is exploited for feature extraction while an adaptive moment estimation optimizer solution is used to perform the training process. Experimental results show that the proposed end-to-end IQA method significantly outperforms the relevant IQA benchmarks, especially when the compression artifacts are strongly realized in image/video patches. The proposed IQA method is expected to drive a new set of image/video compression solutions in future image/video coding and transmissions.
The Application of Improved Sparrow Search Algorithm in Sensor Networks Coverage Optimization of Bridge Monitoring

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.
Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details. Meanwhile, self-attention mechanism that is effective in modeling long-range dependencies is employed to recover harmonious images for occluded faces. Deep regression networks are used to learn a nonlinear mapping from facial appearance to facial shape. Benefited from the mutual cooperation of GAN-IA and deep regression networks, a robust facial landmark detection model is achieved for the occlusion problem and the performance of the model achieves obviously improvement on challenging datasets.
Naive Bayes Classification under Local Differential Privacy

Supervised learning techniques such as classification algorithms learn from training data to predict the correct label for newly presented input data. In many real-world scenarios, training data required by such techniques can contain personal information and data collection can be a significant problem due to privacy concerns. Cryptographic techniques have been used before to do training on encrypted data. However, such techniques are computationally expensive and they are not scalable most of the time. If a dataset in another party will be used for training, differential privacy technology can be used to preserve the privacy of the individuals in the dataset. When there is no such dataset and data needs to be collected from individuals directly for training, local differential privacy can be used. Local differential privacy is a technology to preserve privacy during data sharing with an untrusted data collector. In this work, we propose to use local differential privacy techniques to train a Naive Bayes classifier. Using the proposed solution, an untrusted party collects perturbed data from individuals that keep the relationship between the feature values and class labels. By estimating probabilities needed by the Naive Bayes classifier using the perturbed data, the untrusted party can classify new instances with high accuracy. We develop solutions that work for both discrete and continuous data. We also propose utilizing dimensionality reduction techniques to decrease communication cost and improve accuracy. We show the accuracy of the proposed Naive Bayes classifier achieving local differential privacy via experiments on several datasets. We also show how dimensionality reduction enhances the accuracy.
Combining Several Substitution Cipher Algorithms using Circular Queue Data Structure

With the revolutionized expansion of the Internet, worldwide information increases the application of communication technology, and the rapid growth of significant data volume boosts the requirement to accomplish secure, robust, and confident techniques using various effective algorithms. Lots of algorithms and techniques are available for data security. This paper presents a cryptosystem that combines several Substitution Cipher Algorithms along with the Circular queue data structure. The two different substitution techniques are; Homophonic Substitution Cipher and Polyalphabetic Substitution Cipher in which they merged in a single circular queue with four different keys for each of them, which produces eight different outputs for every single incoming letter. The present work can be applied efficiently for personal information security and network communication security as well, and the time required for ciphering and deciphering a message is less than 0.1 sec.