Reviewerly/exHarmony huggingface dataset:
REStRAIN: Conceptualization of Agile Process Tools Integration with Reference to the Aerospace Industry

This paper describes the development of a framework that integrates engineering software and agile tools based on ontologies and business rules. The aim of this integration is to help project managers specify their own rules of integration between development software and agile tools. This integration circumvents the limitations of on-the-shelf plugins that perform a very basic integration that not only fails to satisfy the complex needs of project managers but also provides insufficient customization possibilities. A formalization of the domain knowledge ontology and its extension are presented. A generic integration algorithm is presented, and some of its performance properties are detailed. To examine the application and validation of the framework, it is deployed for the integration of spacecraft data definition software with issue management systems. This deployment shows how project managers can specify the integration rules that give them the appropriate feedback information at project level after spacecraft data updates. The evaluation of the framework is conducted at two stages. At the time of design, the usability of the framework is evaluated, and it is shown that the general usability criterion is 4.15 on a Likert-type five-point response scale. Additionally, a comparison with on-the-shelf integration plugins shows that less cognitive effort is required from project managers to customize the presented framework integration rules. At run time, the execution time of the presented framework integration algorithm is evaluated. This evaluation shows that even for updates of large data files on the development software, the agile tools are updated following project managers’ integration rules in a reasonable time.
WITHDRAWN: Novel activated technique for makespan and performance tasks set

Scheduling the large number of tasks is the challenging issue in cloud computing system because its, include more number of workloads. This is change of jobs allocated in scheme as similar and distributed types and its performance. The fundamental goal of Load Scheduling concept be toward equalize workload amongst each and every node by the minimization of execution time, communication delays, improvement of resource utilization and throughput. The crucial problem to be noticed in cloud data processing is equalization and allotment of the workload to the present nodes. Machine workload is clear like total period occupied for processing of data and it needs to operate the entire tasks allotted towards machine.
Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function (pdf) to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’ rule. It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system, and thus the importance density function can be used to approximate the true posterior density distribution. In Bayesian filtering, the nonlinear filter performs well when all conditional densities are assumed Gaussian. When applied to the nonlinear/non-Gaussian distribution systems, the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle filter-based approaches, such as the extended particle filter (EPF), and unscented particle filter (UPF), and also the Kalman filter (KF)-type approaches, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF) and CKF. Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.
Deep Learning, Grammar Transfer, and Transportation Theory

Despite its widespread adoption and success, deep learning-based artificial intelligence is limited in providing an understandable decision-making process of what it does. This makes the "intelligence" part questionable since we expect real artificial intelligence to not only complete a given task but also perform in a way that is understandable. One way to approach this is to build a connection between artificial intelligence and human intelligence. Here, we use grammar transfer to demonstrate a paradigm that connects these two types of intelligence. Specifically, we define the action of transferring the knowledge learned by a recurrent neural network from one regular grammar to another grammar as grammar transfer. We are motivated by the theory that there is a natural correspondence between second-order recurrent neural networks and deterministic finite automata, which are uniquely associated with regular grammars. To study the process of grammar transfer, we propose a category based framework we denote as grammar transfer learning. Under this framework, we introduce three isomorphic categories and define ideal transfers by using transportation theory in operations research. By regarding the optimal transfer plan as a sensible operation from a human perspective, we then use it as a reference for examining whether a learning model behaves intelligently when performing the transfer task. Experiments under our framework demonstrate that this learning model can learn a grammar intelligently in general, but fails to follow the optimal way of learning.
Research on a JPEG Digital Image Encryption Algorithm

This paper designs an encryption algorithm for JPEG image files based on content-associated key technology. Based on the JPEG data stream, the algorithm divides the file data into a format data part such as metadata attributes, primary decoding tokens and secondary decoding tokens, and a compressed data part. The algorithm encryption process is divided into two stages, one is to analyze and locate the format data to form a key unit sequence, and the other is to randomly extract the compressed data to form a key unit sequence. Judging from the playback effect of the encrypted JPEG image file, the algorithm is effective.
WITHDRAWN: A seclusion technique to access superintend ammunition

Knowledge-securing has become crucial. In conjunction with useful needs, the system is planned to own the subsequent modules. User authentication, Permission Guide, Recommendation Service, Authorization server. End users are the server which initiates the flow of giving details about their registration, setting permissions, etc. A Permission Guide that guides users through the permissions requested, and shows them a set of recommendations for each of the permissions requested. The extension parses and presents the requested permissions in an incredibly simple way. The Recommendation Service returns a collection of recommendations for the client’s demanded permissions. Authorization to delineate these new credentials via an Access Token. An Access Token may be a string that denotes a precise scope of permissions granted to an application, it also denotes alternative attributes such as the Access Token period being included in the valid account. We tend to be largely fascinated by the allocation of scope among an Access Toke.
The Impact of Remittance Flow on Real Effective Exchange Rate: Empirical Evidence from The Gambia

The paper investigates the impact of remittance on real effective exchange rate in The Gambia. The Fully Modified OLS and Dynamic OLS are used on a monthly data from 2009M1 to 2019M12. FMOLS and DOLS estimations revealed that remittance has a positive significant impact on real effective exchange rate in The Gambia, implying that 1% increment in remittance leads to a real appreciation of the Gambian Dalasi (GMD) against the major currencies by 1.5%. Likewise, inflation is positively associated with REER, while the relationship amid foreign reserves and REER is inconclusive. Contrarily, money supply and monetary policy rate were found to have a depreciating impact on REER in both models.
Towards 4D Virtual Outcrops with Hyperspectral Imaging

Accurately mapping lithology and geological structures remains a challenge in rough terrain or in active mining areas. We propose that the integration of terrestrial and drone-borne multi-sensor remote sensing techniques can significantly boost the reliability, safety, and efficiency of geological activities in exploration and for the monitoring of mining activities. We have now developed a complete procedural chain to jointly and accurately process Structure-from-Motion Multi-View Stereo point clouds and hyperspectral data cubes in the visible to near-infrared (VNIR) and short-wave infrared (SWIR), as well as long-wave infrared (LWIR) ranges acquired by terrestrial sensors. Hyperspectral data are processed using spectroscopic and machine learning algorithms to generate meaningful 2.5D (i.e., surface) maps that are available to geologists on the ground shortly after data acquisition. We classify the geological information content using innovative machine learning techniques. We validate the remote sensing data with in-situ mineralogical and structural measurements. Repeated acquisitions allow then to integrate a time component.
Development of a Pets' Body Movement Recognition Technique Using Deep Neural Networks

This paper presents a deep neural network‐based technique to recognize pets' body movements. The motivation to develop the technique arises from the fact that there are an increasing number of animal lovers, say dog or cat lovers, who spend a tremendous amount of effort to take care of their beloved pets, and even to capture the pets' images in daily life and at particular memorable moments for sharing among friends. For illustrative purposes, the recognition model was trained and then tested using a number of cats' images. As it turned out, the model well recognized three body movements: eating, tail raising and yawn, with a recognition accuracy up to 99.45%. Using this recognition technique, pets' images can be captured automatically once specific movements are detected, and job as a pet photographer can be made easy accordingly. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Time-series classification with an all-optical recurrent neuron

We present an all-optical recurrent neuron comprising a novel space rotator and a sigmoid activation unit and demonstrate experimentally time-series classification. Real-time recognition of both RZ and NRZ 3-bit-long bit sequences with 100psec optical pulses is successfully demonstrated, revealing an average accuracy of >91%.
29.2: Deep Learning Based Video Super Resolution A Survey

SID Symposium Digest of Technical PapersVolume 52, Issue S1 p. 187-187 Technical Sessions: Session 29: Display Electronics (Display Electronics)Free to Read 29.2: Deep Learning Based Video Super Resolution A Survey Yanpeng Cao, Yanpeng Cao Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this authorFeng Yu, Feng Yu Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this authorYongming Tang, Yongming Tang Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this author Yanpeng Cao, Yanpeng Cao Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this authorFeng Yu, Feng Yu Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this authorYongming Tang, Yongming Tang Joint International Research Laboratory of Information Display and Visualization, Southeast University, Nanjing, Jiangsu, ChinaSearch for more papers by this author First published: 24 February 2021 https://doi.org/10.1002/sdtp.14428AboutPDF 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume52, IssueS1International Conference on Display Technology (ICDT 2020)February 2021Pages 187-187 RelatedInformation
C-LIME: A Consistency-Oriented LIME for Time-Series Health-Risk Predictions

Predicting health risk from electronic health records (EHRs) is increasingly demanded in the medical and health fields. Many studies have pursued prediction accuracy while ignoring the interpretability of their developed models. To encourage lifestyle changes by patients and employees, an appropriate explanation of why the model outputs high risk is as important as accurately predicting the health risk. In this study, we construct 33 predictive models (11 health-checkup items checked after one, two, and three years). We also clarify a problem in the existing Local Interpretable Model-agnostic Explanations (LIME), namely, inconsistency among the health-risk predictions of the three target years. To resolve this problem, we find and exclude an anomalous sample that deteriorate the interpretation, and output a consistent interpretation of the health-risk predictions over the three years. We evaluate proposed method using more than 10,000 medical examination data. Accuracy was improved by 16% at the maximum compared to the baseline that output the risk at year Y + 1,2,3 equaling to that at year Y. Also, proposed LIME called C-LIME improve number of employees whom we can provide consistent lifestyle advice over the years three times compared to LIME. We have released a health-risk prediction and lifestyle recommendation service using proposed method for employees of the Nippon Telegraph and Telephone Group from April of 2019.
Explore How Artificial Intelligence and VR Technology will Change the Development of Future Education

Abstract In recent years, with the rapid development of VR technology and artificial intelligence, its application scope has gradually involved the education field. VR technology can break through the limitations of the traditional curriculum environment and simulate complex environments. It can visualize complex and abstract theoretical knowledge, provide learners with scientific and precise guidance and gain more knowledge. Based on this, this article discusses how artificial intelligence and VR technology can change the development of education in the future. The research of this article is mainly divided into three parts: the first part is the research on the impact of artificial intelligence on education; the second part is the research on the application of VR technology in the classroom; the third part is the investigation and research. Through research, it is found that in the current small environment of school education, artificial intelligence has the advantages of fast and accurate, and its ability to detect students’ deficiencies and improve learning efficiency far exceeds teachers. However, the current education sector is busy improving students’ memory of knowledge, while neglecting to cultivate students’ imagination and creativity. The students trained in this way will surely be replaced by artificial intelligence. Therefore, the popularization of VR technology and artificial intelligence will inevitably promote the change of education goals, so as to cultivate talents who can meet the needs of future society.
Complex Question Decomposition Method: Based on Fine-grained Named Entity Recognition and Domain Knowledge

With the development of the intelligent question answering technology, and the decomposition of complex problems has gradually become a key technology for question answering systems. Aiming at the existing complex problem decomposition methods that cannot meet the design requirements of question answering systems in the field of weaponry. In this article, we proposed a complex question decomposition method based on fine-grained named entity recognition (FG-NER) and domain knowledge. By introducing the BERT-based Bi-LSTM- CRF model, it effectively solves the identification problem of weapon types, names and corresponding attributes in the complex question sentences with weaponry domain. Experimental results demonstrate that the model has an average Fi score of 91.54% on the test dataset. It can efficiently complete complex question decomposition tasks in the field of aircraft weaponry, and it can precisely to finish the questions-answers matching problem by combining the weaponry domain knowledge base.
PNS2E: A POS-Based Grammatical Error Correction Model with Pre-Correction and Post-Selection

English grammatical error correction has become a hot topic in natural language processing domain in recent years. Since CoNLL-2014 Shared Task, Scholars have proposed new architectures, designed optimization algorithm, and implemented models whose performance were greatly enhanced. In this paper, we introduce a novel GEC model called PNS2E. By introducing a pre-correction checker and a post-selection language model, a better evaluation result than the baseline on the test set of the CoNLL-2014 Shared Task is achieved.
Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter

Opinion numbers in social media such as Twitter are so widespread that it is not possible to read all of it sentiment (positive, negative, or neutral). Sentiment analysis is one method that can be used to overcome these problems. One of sentiment analysis approach is lexicon-based approach which is highly dependent on the completeness and diversity of sentiment lexicons. Therefore, this study conducts lexicon addition to the sentiment lexicon to improve performance. The datas used in this study were tweet data on the West Java 2018 Governor election, 2019 Presidential election, and COVID-19 pandemic. The results of classification are determined by the highest frequency of occurrence of words based on positive and negative sentiment lexicons. The result of lexicon addition thus being compared to previous work which is Lailiyah method and Saputra and Nurhadryani method. The lexicon addition has proven to improve the accuracy of both Lailiyah and Saputra and Nurhadryani methods on all data with an increase respectively: 6.09% and 5.07% on the 2018 West Java Governor election data, 9.16% and 5.9% on the 2019 Presidential election data, 15.74% and 15.48% on the COVID-19 pandemic data. The lexicon addition could improve the weighted f1-measure on both Lailiyah and Saputra and Nurhadryani methods on all data with an increase respectively: 4.85% and 2.09% on 2018 West Java Governor election, 6.89% and 2.26% on 2019 Presidential election, and 12.18% and 5.10% on COVID-19 pandemic.
Solar PV in Singapore in the Absence of Subsidies

The Republic of Singapore is a city state, located on one main island surrounded by many small islets with a total land area that has grown from 581.5 sq. km. in the 1960s to 719.1 sq. km. in 2015. Singapore has a small albeit developed economy. With a 2017 GDP of 447.3 billion dollars (Ministry of Trade and Industry 2018), Singapore’s population of 5.6 million has one of the highest Gross National Income per capita in the world. Singapore does not have any traditional energy resources domestically, and its energy sector almost entirely relies on natural gas imports. Traditionally, most of Singapore’s supply of natural gas has come from pipelines that connect from Indonesia and Malaysia. However, in a bid to diversify and improve the security of fuel supply, Singapore has moved toward Liquefied Natural Gas, with an LNG terminal opening in 2013.
Development of industry oriented cross-domain study programs in cyber-physical systems for Belarusian and Ukrainian universities

One of the targets of European COST Action Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) was crystallising contents of the Action into a suitable format for educational purposes. This encouraged to create the base for a European Master and PhD program in MPM4CPS involving European Leading Universities and setting up the respective discipline road-map facing the challenge to develop a mutually recognised cross-domain expertise-based study program in CPS. One of the challenges in the development of study programs is bridging the gap between industry needs and educational output, in terms of training the prospective researchers and engineers in the CPS field. The success of MPM4CPS encouraged EU partners to apply knowledge and methods, developed by the COST Action at an ERASMUS+ project, to validate in practice its viability aiming to develop the industry-focused curricula at partners' universities of Belarus and Ukraine. In this research we discuss how the COST team efforts towards analysis of tendencies, industry needs and acquiring best education practice have been made by the ERASMUS+ team in order to create industry-focused cross-domain study programs in CPS for the partners' universities of Belarus and Ukraine.
Extending SpArSe: Automatic Gesture Recognition Architectures for Embedded Devices

Neural Architecture Search (NAS), which allows for automatically developing neural networks, has been mostly devoted to performance on a single metric, usually accuracy. New approaches have added more objectives, such as model size, in order to find networks suitable for resource-constrained platforms. SpArSe [1] is a multi-objective Bayesian optimization framework for automatically developing image classification convolutional neural networks (CNNs) for micro-controller units (MCUs). In this work, we first implement SpArSe and modify it to reduce search time, obtaining similar results regarding accuracy, model size, and maximum working memory but in less optimization time. Moreover, we extend the search space to include recurrent neural networks (RNNs) and add an inference latency objective for time-constrained tasks. Finally, we test our implementation in a gesture recognition task obtaining better results than previous manually tuned approaches for size and performance metrics, which validates the approach and its utility.