Reviewerly/exHarmony huggingface dataset:
Research on Dynamic Heading Calculation of Complex Magnetic Disturbance

The rapid development of micro/nano-technology in recent years, an inertial navigation system (INS) composed of MEMS devices is widely used in various fields such as detection, machinery, transportation, and military affairs. The trend of using MEMS devices for navigation in the field of transportation is increasing. The three-dimensional MEMS electronic compass mainly includes a magnetometer and accelerometer. It mainly uses the earth's magnetic field, gravity acceleration and other parameters to provide the bearing and attitude of the carrier for the navigation system. However, the current inertial navigation system is easy to get lost when it encounters magnetic disturbance, and the irregular movement process is easy to cause errors, even in the static environment is not accurate. In order to solve this problem, in this paper, a dynamic heading filtering algorithm based on Extended Kalman Filter is proposed. The two sets of sensors are used for the positive phase installation, and an extended Kalman filter algorithm is designed. The heading solution can be self-adaption according to different magnetic disturbances. Finally, the experiment verified that the heading error of the carrier after filtering into the dynamic condition was ± 1°, which has met the actual requirements.
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation

English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related.We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causativeinchoative alternation, relying on count-based and vector-based features as well as perplexitybased language model features, which are intended to reflect each alternation's felicity by simulating it.Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation.This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive.Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs.Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification.Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.
Evolutionary Algorithm Based Residual Block Search for Compression Artifact Removal

Lossy image compression is ubiquitously used for storage and transmission at lower rates. Among the existing lossy image compression methods, the JPEG standard is the most widely used technique in the multimedia world. Over the years, numerous methods have been proposed to suppress the compression artifacts introduced in JPEG-compressed images. However, all current learning-based methods include deep convolutional neural networks (CNNs) that are manually-designed by researchers. The network design process requires extensive computational resources and expertise. Focusing on this issue, we investigate evolutionary search for finding the optimal residual block based architecture for artifact removal. We first define a residual network structure and its corresponding genotype representation used in the search. Then, we provide details of the evolutionary algorithm and the multi-objective function used to find the optimal residual block architecture. Finally, we present experimental results to indicate the effectiveness of our approach and compare performance with existing artifact removal networks. The proposed approach is scalable and portable to numerous low-level vision tasks.
Successful Strategies For Teaching Students With Learning Disabilities

in this paper the researcher discussed about the various strategies  of teaching students with learning disabilities. Learning disabilities (LD) is relatively recently identified, proposed & defined area of exceptionally by Kirk (1963). Learning disability is a term which is now being used to describe such characteristics as severe reading, spelling & writing delays & reversals of symbols. It is a term which is also used to describe learning problems such as dyslexia. Learning disabled tend to experience severe difficulty in writing & find symbols of language very frustrating. Learning disabilities are a heterogeneous group of disorders & individuals suffering from these exhibits many different kinds of problems. Different strategies can help educators strike the proper balance as well as ensure faithful and sustained implementation designed to maximize instructional impact by the use of performance assessments to provide opportunities to help students understand the demands of tasks. Share the criteria that will be used to judge the product or performance. In this paper the researcher pointed towards the progress of learning disabled children by encouraging assessing their growth to increase their self-awareness as learners.
Two-Dimensional Barcodes Usage in Plastic Pipes Blockchain-Based Supply-Chain

The use of counterfeit products causes considerable financial damage to the states and companies. Product labeling and tracking in a blockchain-enabled system can overcome this problem. One- and two-dimensional barcodes are accessible and have cheap labeling technologies, but it can be challenging to read the labels on products with curved shapes. This paper presents the plastic pipe labeling problem and its solution with two-dimensional barcodes, integrated with a blockchain-enabled supply chain. The labeling technique choice is based on QR and Aztec barcodes readability empirical study for different barcode parameters, reading software libraries for flat and curved surfaces.
User Expectations and Preferences to How Social Robots Render Text Messages with Emojis

Social robots are increasingly entering our households, being able to interact with humans in various ways. One functionality of social robots may be to connect to a user's mobile phone and to read text messages out loud. Such a technology and communication platform should therefore be able to support emojis properly. We therefore address emoji usage in computer-mediated communication in order to develop appropriate emoji conveyance in social robot behavior. Our research explores how participants feel about the behavior of a tabletop robot prototype named Nina that reads text messages to user and to what extent different renderings correspond to user expectations and preferences of how text messages and emoji combinations should be delivered. Based on online animated videos and questionnaires, respondents evaluated the behavior of Nina based on different renderings of text messages with emojis in them. The experiment results and data analysis show that respondents liked the social robot to display emojis with or without sound effect and to "act out" emojis in text messages almost equally well, but rated it less useful, less fun and more confusing to replace the emojis by words.
Research on Medical Image Registration Based on Graphic Neural Network Reinforcement Learning

Abstract On the basis of traditional medical image registration methods, aiming at the shortcomings of rigid body images, sequential rigid body images and non-rigid body images registration methods, this paper proposes a new method of medical image registration application based on graphic neural network reinforcement learning. Reinforcement learning adjusts the strategy from the actual system learning experience, and it is a process of gradually approaching the optimal strategy, mapping the image from high-dimensional gray space to low-dimensional feature category space; Then, the elastic registration of mutual information images is carried out in the feature class space. The advantage of this method is that it does not need extra supervision information, which effectively saves manpower and material resources. So as to make an accurate diagnosis or make a proper treatment plan.
Video Object Detection Using Motion Context and Feature Aggregation

The deep learning technique has recently led to significant improvement in object-detection accuracy. Numerous object detection schemes have been designed to process each frame independently. However, in many applications, object detection is performed using video data, which consists of a sequence of image frames. Thus, the object detection accuracy can be improved by exploiting the temporal context of the video sequence. In this paper, we propose a novel video object detection method that exploits both the motion context of the object and spatio-temporal aggregated features to enhance the video object detection performance. First, the motion context of the object is extracted by the correlation operator between the feature maps of two adjacent frames. In addition to generating the motion context, the spatial feature maps for N adjacent frames are aggregated to boost the quality of the feature map with gated attention network.
HuTrain: a Framework for Fast Creation of Real Human Pose Datasets

Image-based body tracking algorithms are useful in several scenarios, such as avatar animations and gesture interaction for VR applications. In the last few years, the best-ranked solutions presented on the state of the art of body tracking (according to the most popular datasets in the field) are intensively based on Convolutional Neural Networks (CNNs) algorithms and use large datasets for training and validation. Although these solutions achieve high precision scores while evaluated with some of these datasets, there are particular tracking challenges (for example, upside-down cases) that are not well-modeled and, therefore, not correctly tracked. Instead of lurking an all-in-one solution for all cases, we propose HuTrain, a framework for creating datasets quickly and easily. HuTrain comprises a series of steps, including automatic camera calibration, refined human pose estimation, and known dataset formats conversion. We show that, with our system, the user can generate human pose datasets, targeting specific tracking challenges for the desired application context, with no need to annotate human pose instances manually.
Towards understanding and harnessing the potential of Africa in digitalization

Concurrency and Computation: Practice and ExperienceVolume 33, Issue 1 e6127 EDITORIAL Towards understanding and harnessing the potential of Africa in digitalization Soumia Benkrid, Soumia Benkrid Ecole nationale Supérieure d'Informatique, Alger, AlgérieSearch for more papers by this authorRim Moussa, Rim Moussa University of Carthage, Tunis, TunisiaSearch for more papers by this authorHassan Badir, Hassan Badir Abdelmalek Essaadi University, Tanger, MoroccoSearch for more papers by this authorMoussa Lo, Moussa Lo University of Gaston Berger, Saint-Louis, SenegalSearch for more papers by this authorLadjel Bellatreche, Corresponding Author Ladjel Bellatreche bellatreche@ensma.fr orcid.org/0000-0001-9968-0066 LIAS/ISAE-ENSMA – Poitiers University, Futuroscope, France Correspondence Ladjel Bellatreche, LIAS/ISAE-ENSMA – Poitiers University, Futuroscope, France. Email: bellatreche@ensma.frSearch for more papers by this author Soumia Benkrid, Soumia Benkrid Ecole nationale Supérieure d'Informatique, Alger, AlgérieSearch for more papers by this authorRim Moussa, Rim Moussa University of Carthage, Tunis, TunisiaSearch for more papers by this authorHassan Badir, Hassan Badir Abdelmalek Essaadi University, Tanger, MoroccoSearch for more papers by this authorMoussa Lo, Moussa Lo University of Gaston Berger, Saint-Louis, SenegalSearch for more papers by this authorLadjel Bellatreche, Corresponding Author Ladjel Bellatreche bellatreche@ensma.fr orcid.org/0000-0001-9968-0066 LIAS/ISAE-ENSMA – Poitiers University, Futuroscope, France Correspondence Ladjel Bellatreche, LIAS/ISAE-ENSMA – Poitiers University, Futuroscope, France. Email: bellatreche@ensma.frSearch for more papers by this author First published: 16 December 2020 https://doi.org/10.1002/cpe.6127 Please ensure that you use the most up to date class file, available from the CPE Home Page at http://www3.interscience.wiley.com/journal/117946197/grouphome/home.html Read the full textAboutPDF 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. Volume33, Issue1Special Issue: Future Perspectives on Decentralized Applications (FPDAPP18). Towards Understanding and Harnessing the Potential of Africa in Digitalization (DigAfrica2019)10 January 2021e6127 RelatedInformation
Data of E-Resources: Moving Forward with Assessment

Academic libraries have been moving toward a culture of assessment by curating data and making future decisions based on these data. Managing all of the data collection can be a cumbersome task with heavy time commitments. For technical services departments, this culture shift presents new challenges for management of data, job descriptions, and workflows or procedures. Pennsylvania College of Technology's Madigan Library welcomes these challenges by recognizing the goals of its institution to create critical thinking students of the twenty-first century by assessing the effectiveness of library resources, especially ones that can be accessed in a digital format. The purpose of this chapter is to illustrate the various quantitative and qualitative methods a library can incorporate to collect data of e-resources, organize that data into comprehensible formats, and share results and make recommendations for future collections in an ongoing, holistic assessment format. Following the college's curriculum goals, this chapter will show how the Madigan Library collects data and assesses e-resources, online teaching resources, acquisitions workflows, and other aspects of the library with ongoing assessment and data collection. Managing data and making decisions within these departments are discussed.
Real-time Detection and Tracking Network with Feature Sharing

Multiple object tracking (MOT) systems can benefit many applications, such as autonomous driving, action recognition, and surveillance. State-of-the-art methods detect objects in an image and then use a representation model to connect these objects with existing trajectories. However, the combination of these two components to reduce computation has received minimal attention. In this study, we propose a single-shot network for simultaneously detecting objects and extracting tracking features to achieve a real-time MOT system. We also present a detection-tracking coupled method that uses temporal information to improve the accuracy of object detection and make trajectories complete. Experimentation on the KITTI driving dataset indicates that our scheme achieves an accurate and fast MOT system. In particular, the lightweight network reaches a running speed of 100 FPS.
Mining Software Repositories for the Characterization of Continuous Integration and Delivery

Continuous Integration (CI) and Delivery (CD) are software development practices increasingly adopted in industry, which aiming at ensuring quality and stability, and making the whole process more efficient and less error-prone. Software engineers that incorporate these practices may have difficulties trying to get an overview of the impact of adopting them and tracking progress. Therefore, performing analysis regarding the level of the application of CI/CD practices in a software repository can help to understand how such practices are adopted, which can result in a better characterization of their benefits and limitations. To support this analysis, we developed Garimpeiro, a web application that supports the characterization of open source repositories hosted on GitHub regarding the level of adoption of CI/CD practices. This paper describes how the tool extracts data from GitHub repositories by showing the execution workflow, high-level architecture, and involved assumptions to define metrics. Finally, this paper also includes running examples of the tool, which demonstrates its capabilities and feasibility.
THE IMPLEMENTATION OF REPETITION DRILL IN TEACHING SPEAKING SKILL

Speaking is one of the language skills that must be mastered by students so that the learning process can run well and smoothly, one of them is by using Repetition Drill. The purpose of this research is to find out whether there is a significant effect of using repetition exercises on students' speaking skills and how students think about repetition training techniques in teaching second grade speaking skills at SMP 8 Banda Aceh in academics. The research methodology used is the Quasy experimental design. Student learning outcomes data obtained by doing pre-test, post-test, and questionnaire. Data were analyzed manually using statistical formulas, T-tests and T-tables. Namely T-test> T-table = 12.80> 1.72. the results of the questionnaire analysis showed a positive response that 74% of students stated that they agreed the repetition exercise technique improved their ability to speak descriptive texts. Means that Ho is rejected and Ha is accepted. The conclusion of this research is the repetition drill technique increases the ability to speak descriptive texts.
Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm

Purpose Data centres evolve constantly in size, complexity and power consumption. Energy-efficient scheduling in a cloud data centre is a critical and challenging research problem. It becomes essential to minimize the overall operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the cloud data centres. Resource scheduling in cloud data centres is NP-hard and often requires substantial computational resources. Design/methodology/approach To overcome these problems, the authors propose a novel model that leads to nominal operational cost and energy consumption in cloud data centres. The authors propose an effective approach, parallel hybrid Jaya algorithm, that performs parallel processing of Jaya algorithm and genetic algorithm using multi-threading and shared memory for interchanging the information to enhance convergence premature rate and global exploration. Findings Experimental results reveal that the proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms. Originality/value Experimental results reveals that our proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.
Data Model for Registered Tangible Cultural Properties Using Linked Data

National registered tangible cultural properties complement important cultural properties and are widely protected under less strict regulations. In recent years, it is expected that cultural properties will be used for local tourism and town planning. In this case, an effective cultural property database is required. However, the current database of nationally designated cultural properties is mainly for the purpose of preserving cultural properties, and there are problems such as overlapping names and addresses. Therefore, it is not an effective means for utilizing cultural properties. In this study, we propose a method to describe the information of a registered tangible cultural property building to improve the convenience of its use. In the construction, cooperation data was carried out by utilizing Infrastructure for Multilayer Interoperability (IMI) Common Vocabulary. We applied the information of the national cultural properties database to the data model, and examined its applicability. As a result, this method solved the problem of overlapping the names and addresses of cultural properties. In addition, the possibility of utilizing cultural property buildings expanded by making the data structure into linked data.
An Elliptic Curve-based Protocol for Privacy Preserving Frequency Computation in 2-Part Fully Distributed Setting

Privacy-preserving frequency computation is critical to privacy-preserving data mining in 2-Part Fully Distributed Setting (such as association rule analysis, clustering, and classification analysis) and has been investigated in many researches. However, these solutions are based on the Elgamal Cryptosystem, making computation and communication efficiency low. Therefore, this paper proposes an improved protocol using an Elliptic Curve Cryptosystem. The theoretical and experimental analysis shows that the proposed method is effective in both computing and communication compared to other methods.
Credit Card Forgery Identification By Location Using Android Based Monitoring

Abstract In cellular network, the user’s location is probably used as an authentication aspect similar to other elements like password, PIN range combined with the use of device that the person consisting of card or a mobile. An encrypted and decrypted key is generated using RSA. The generated values are send to user’s cellular as SMS. The user’s mobile region is identified by the place of use far flung customer authentication protocol and its integrity is proven by Galileo Navigation satellite known as Local element. We proposed an LRAP based method for secure transactions.
Performance testing on the shopee website in the pandemic period of COVID-19

A competitive and contend website becomes the goal of all stakeholders. The website also becomes very influential because it involves the visitor's perception of it. When the visitor's enthusiasm for a website is getting higher, the quality of the website is also getting better. Still, if the website visitor quantity is getting higher, the stress level of web e-commerce is also getting higher, and these factors affect the performance of a website. The testing is needed as the benchmark to determine system deficiencies. The purpose of this study is to compare the performance of the Shopee website before the pandemic period and during the pandemic period. The testing tool used is Apache JMeter. The research results are obtained by doing the stress testing, and it is also evidenced by the user enthusiasm assessment of the questionnaire results. The result of the study is directly proportional, i.e. the website stress level increases as the increasing visit rate, with the error rate up to 26.