Reviewerly/exHarmony huggingface dataset:
Dimensionality Reduction of Social Media Application Attributes for Ubiquitous Learning Using Principal Component Analysis

Ubiquitous learning is anywhere and anytime learning using e-learning and m-learning platforms. Learning takes place regularly on mobile devices. School-based instructors and learners have capitalised on ubiquitous learning platforms in unprecedented times such as COVID-19. There has been a proliferation of social media applications for ubiquitous learning. There are a vast number of attributes of the social media applications that must be considered for it to be deemed suitable for education. Further to this, mobile and desktop accessibility criteria must be considered. The aim of this research study was to determine the high impacting and most pertinent criteria to evaluate social media applications for school-based ubiquitous learning. Data was collected from 30 experts in the field of teaching and learning who were asked to evaluate 60 criteria. Principal Component Analysis (PCA) was the method employed for the dimensionality reduction. PCA was implemented using singular value decomposition (SVD) on R-Studio. The results showed loading values from principal component one for the top 40 educational requirements and technology criteria of the 60 criteria used in the study. The implications of this research study will guide researchers in the field of Educational Data Mining (EDM) and practitioners on the most important dimensions to consider when evaluating social media applications for ubiquitous learning.
Web-Based Expert System for Diagnosing Human Eye Disease Using the Naïve Bayes Method

The eye is one of the organs of the body that is very important for humans, so it must be kept in good health, but as humans age and the unhealthy lifestyle of many people in Indonesia experience problems with their eyes. Based on the existing problems this study discusses the application of expert systems to diagnose eye diseases. The data used for the study consisted of 22 symptoms and 5 eye diseases. Expert system that was built using the Naive Bayes method. There are two stages of work from this application. First, the system asks the patient to choose the symptoms they are experiencing. Secondly, the system will automatically display the diagnosis results of the eye disease suffered by the patient through the calculation of Naive Bayes. This system has advantages compared to the existing system in the reference journal, namely in the design of the symptom page display, making it easier for users to answer according to the symptoms felt. The results of subsequent system diagnoses are compared with the results of diagnoses from actual experts. The system trial used data of 15 eye disease patients. From the experimental results, the percentage of diagnostic suitability of 86%.
High-precision video acquisition system based on three-axis linkage

Abstract The main research content of this system is to realize the linkage control of multi-axis motor through LabVIEW and single chip microcomputer. The operator can operate the upper computer software to send the speed and spatial coordinate instructions remotely. The expansion chip ch438q is used to store temporarily, and then the data is transmitted to the processor of max13487 for control. According to the RS485 serial port communication mode, the analyzed data parameter information is transmitted In the stc15 single-chip microcomputer on four single axis control cards, the motor is controlled by the driver to make the controlled object move to the specified position quickly and accurately. In the process of movement, the upper computer can display x, y in real time. In order to test and use the system to achieve certain functions, a set of high-precision multi angle video acquisition system based on three-axis linkage and four axis control is designed. The selected camera is a 120°wide-angle camera, which can make the range of scenery observed from a certain point of view much larger than that of human eyes in the same view point Monitoring. After many experiments, the accuracy of the system can reach 1μM. The system itself can also expand a variety of functions according to different needs, which has a certain practical value.
MSFM: Multi-Scale Fusion Module for Object Detection

Feature fusion is beneficial to object detection tasks in two folds. On one hand, detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused. On the other hand, objects can be detected in different scales, which improves the robustness of the framework. In this work, we present a Multi-Scale Fusion Module (MSFM) that extracts both detail and semantical information from a single input but at different scales within the same layer. Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input. The MSFM is lightweight and can be used as a drop-in layer to many existing object detection frameworks. Experiments show that MSFM can bring +2.5% mAP improvement with only 2.4M extra parameters on Faster R-CNN with ResNet-50 FPN backbone on COCO Object Detection minival set, outperforming that with ResNet-101 FPN backbone without the module which obtains +2.0% mAP with 19.0M extra parameters. The best resulting model achieves a 45.7% mAP on test-dev set. Code will be available.
Research on yaw crossing point optimization based on genetic algorithm

This paper proposes a yaw trajectory replanning method based on improved genetic algorithm, which improves the population quality by changing the confirmation method of crossover operator and mutation operator; by constructing fitness function and using the new constraints to search for new guidance Point method to achieve yaw track replanning of civil aircraft. Comparing the results obtained by the two, the improved genetic algorithm can effectively avoid the air threat zone while satisfying the Improve the efficiency of the solution. Finally, the simulation on the Matlab platform shows that the improved genetic algorithm accelerates the convergence speed, can plan a high-precision trajectory in a short time, and has good real-time performance. This method has good practicality in the civil aviation industry.
Evaluation and Analysis of Interferograms from an InSAR Radar Encrypted by an AES-Based Cryptosystem with The Five Encryption Modes

This paper falls within the framework of the security of satellite images, in particular interferograms from an Interferometric Synthetic Aperture Radar (inSAR) system. The innovation of this work consists in the application of a cryptosystem based on two algorithms Advanced Encryption Standard (AES) and the Rivest, Shamir and Adleman (RSA) encryption algorithm for securing interferograms of inSAR systems. AES employs five encryption modes Electronic Code Book (ECB), Cipher Bloc Chaining (CBC), Cipher FeedBack (CFB), Output FeedBack (OFB), and counter-mode encryption (CTR). The use of the AES algorithm alone can only ensure the confidentiality function. In the proposed cryptosystem confidentiality is ensured by the AES algorithm, authenticity is guaranteed by the RSA algorithm, and integrity is ensured by two parameters; the correlation function between the adjacent pixels and the SSIM parameters (structural similarity index SSIM). For evaluation and analysis of security performance for interferogram encryption, several test metrics are employed. These metrics are: Analysis of histograms of the encrypted interferograms, correlation between the adjacent pixels, between the original interferogram and the encrypted interferogram, SSIM between the original interferogram and the decrypted one. Moreover, we exploit the analysis of resistance to error propagation for the five modes. The obtained results show a superiority of the OFB and CTR modes for the encryption of inSAR interferograms compared to ECB, CFB, and CBC modes. It is noteworthy, that the main criteria that can be used to choose between OFB and CTR for encryption of satellite images are propagation of errors and the complexity material for their locations on the edges of the satellites propagation of errors and the complexity material for their locations on the edges of the satellites. OFB mode is employed in satellites to minimize the number of on-board circuits, which is decisive for satellites. CTR mode is recommended by the CCSDS (Consultative Committee for Space Data Systems) for telemetry (TM) and remote control (TC) encryption.
Prediction and Comparison of Daily Global Solar Radiation using Meteorological data at Lalibela, North Wollo, Ethiopia.

The air temperature and sunshine duration data were obtained from Kombolcha Meteorological Agency to predict daily global solar radiation at Lalibela, Ethiopia. The mean daily clearness index and mean daily global solar radiation was calculated based on sunshine and air temperature model for the study sites. The validation of the models for predicting mean daily global solar radiation was done using statistical tests such as: MBE, RMSE, MPE and R. The result of these statistical tests for temperature based model: (MBE tem =. -0.50, RMSE tem =0.67, MPE tem =-2.9 and R tem =0.06) and for sunshine based model: (MBE sun =. 0.08, RMSE sun = 0.49, MPE sun = 1.50 and R sun = 0.05). From overall, it was concluded that the better value of the mean daily global solar radiation was observed with sunshine based model (Angstrom-Prescott) than the temperature based model (Hargreaves and Semani) in almost all of the days of the study and be applied to locations with comparable weather condition with Lalibela sites. The maximum average daily global solar radiation that predicted from sunshine hours was 5.86 KWh/m 2 and the maximum average daily global solar radiation that predicted from air temperature was 5.28 KWh/m 2 over a period of 3 years (2014-2016). The result of this study was between the minimum and maximum value of [14] and therefore, the region has high potential for solar energy application.
Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

The canonical book reference for understanding Gaussian processes (GPs) as surrogate models in the computer modeling field has been Santner, Williams, and Notz (2018). Surrogates (Gramacy 2020) is ...
Data Physicalization—Part II

The articles in this special section focus on data physicalization. This is a continuation of the IEEE Computer Graphics and Applications (CG&A) November Special Issue on Data Physicalization1 . As we noted in our previous introduction “The practise of representing data in physical form has existed for thousands of years, yet it has only become an area of investigation and exploration for scientists, designers, and artists much more recently. Advances in areas such as digital fabrication, actuated tangible interfaces, and shape-changing displays have spurred an emerging area of research now called Data Physicalization. In the previous CG&A Special Issue on Data Physicalization (November/December 2020), we presented four articles that discussed the role of data as part of physical data-driven representations,the use of physicalizations to materialize qualitative data related to human experiences and knowledge domains, the design and construction of Data Badges—customizable physical conference badges, and “Move&Find” a multisensory data representation where people pedaled on a bicycle to exert the energy required to conduct a search.
Ctrl + Alt + Repeat: Strategies for Regaining Authority Control after a Migration

In 2019, a new position of Authority Metadata Librarian was created at Northwestern University Libraries. Authority work is an important process that allows for the disambiguation of subjects and names in order to provide consistent, accurate access points in our discovery systems. The Authority Metadata Librarian was charged with setting ongoing authority control workflows and cleaning up the backlog of problems that had accumulated in the four years since migrating to the Alma library services platform (LSP). While still contributing records to the Library of Congress Name Authority Cooperative Program (NACO), Northwestern had not implemented local authority control workflows in Alma since the migration. This presentation offers a review of the strategic approach the Libraries employed for reinstituting authority control processes in its catalog: first, creating a process for ongoing workflows to realistically maintain authority control when new records are added to the collection and second, dealing with a backlog of problems from years without authority control processes in place. The presentation began with an assessment of the catalog’s authority control environment. It included approaches to performing authority control, using tools such as Alma Analytics, Python, and MarcEdit. It also covered some authorities projects done while working from home during the COVID-19 pandemic.
Buscando una educación innovadora

Education in the twentieth century was characterized by its democratization: it became free and compulsory, regardless of the economic or social position of the student, and it changed to a progressive education focused on the needs and potential of the learner. It is the century of teacher education and training. The State had a leading role in education, since this was a basic obligation for its citizens. In other words, it corresponds to a period of great reforms.
Penerapan Metode SMART dalam Seleksi Penerima Bantuan Sosial Warga Masyarakat Terdampak COVID-19

The Tabita Women's Association (PW) faces obstacles in determining objective criteria in determining community members affected by Covid-19 for social assistance to be distributed so that they are right on target. This is due to the absence of a systematic and measurable system in determining which citizens are eligible as recipients of social assistance. To help PW Tabita, it is necessary to establish a system capable of providing output recommendations for the selection of the most appropriate community members as recipients of social assistance. The criteria for selecting social assistance recipients refer to the fulfillment of several elements, namely: employment status, monthly income, number of dependents, residence status, electricity tariff status, insurance participants, and PKH (Family Hope Program) participants. The Simple Multi-Attribute Rating Technique (SMART) method is a method applied in this research. The results showed that determining the appropriate weight for each criterion greatly influenced the results of the calculation of the recommendation for providing social funds for people affected by Covid-19. Then in order to obtain more accurate results, it is necessary to test the validity of the criteria to obtain more precise criteria in accordance with the eligibility needs of receiving social funds for community members affected by Covid-19 from PW Tabita.
Refractive index distribution of the ice-layer in ICF target from the interference method

In the inertial confinement fusion (ICF) system, the uniformity of the target is important to the successful ignition. A method based on the phase-shifting interferometry and ray tracing is proposed to obtain the 2D (two-dimensional) refractive index distributions of the ice-layer at three wavelengths (532 nm, 785 nm, 1064 nm), from which the 2D density distribution of the ice-layer is got. A differential method is utilized to get the reference refractive index, based on which the refractive index distribution of the layer is retrieved. For targets with uniform refractive index in the ice-layer, simulations show that the relative retrieval accuracies are better than 10 ppb. Experiments show that the refractive index distributions have good consistency at different wavelengths for the same target. The detectable normalized RMS (root mean square) of the refractive index distributions is at least 2.9E-4. The relative accuracies of the average refractive indexes of the shells are better than 1.5% for all three wavelengths. The optimal selection of the two rings on the wavefront map that yields minimal uncertainty to the reference refractive index is discussed. It is concluded that the proposed method and system poses great potential in the in-situ measurement of the refractive index and density distribution of the ICF target layers.
Large-factor single image super-resolution based on back projection and residual block

Deep learning has made great contributions to the study of single image super resolution. The recently proposed feed-forward architectures of super-resolution focus on nonlinear mapping from low-resolution inputs to high-resolution outputs. However, the feed-forward structure does not well represent the interdependencies between low- and high-resolution images. This leads to bad effect of SISR for large scaling factor. To solve this problem, this paper proposes an enhanced back-projection network that provides an up and down sampling process with error feedback to capture various spatial correlations, and introduces the residual block in sampling process to alleviate the difficulty of training deep networks and achieve better results. The results about 8x SR show that the proposed network is effective with compare to other popular methods in the large scaling factor.
FILLET - Platform for Intelligent Nutrition

Poor dietary behaviours are commonly associated with severe chronic diseases such as cardiovascular diseases, diabetes and obesity. Personalized food recommendation systems can be an important motivation to stimulate and inform people on best dietary practices by suggesting healthy foods and nutritionally balanced meals adjusted to their preferences and daily routines. The development of such systems require the process and integration of data available from different sources with different representations. FILLET is an intelligent platform for nutrition capable of collecting and integrating data from multiple sources including recipe websites, food blogs and nutrition databases. Components were developed for web scraping, identifying ingredients, estimating nutritional content and matching ingredients with food products from retailers to support a meal recommendation and shopping list assistance services. We present for each component the challenges identified in the literature and the ones we faced in their development, describing our approach and the lessons learned that can contribute to the future improvement of the platform and the development of related platforms.
Education 4.0: A Systematic Industrial Case Based Review of Barriers and Applications of Decentralized Trust Using Blockchain

In the past couple of years, widespread technology adoption can be seen in the education sector. Accessibility of the internet and minimum data tariffs have empowered students across the globe to access information and enabled virtual learning. Education 4.0 is a new revolution that aligns learning with the needs of the fourth industrial revolution. However, this new revolution in the education sector has its own set of opportunities and barriers. The objective of this research is to systematically analyze barriers in Information Sharing in the Education Sector across key use cases of Record Keeping, IPR, and Identity Management. The research identifies barriers concerning the above use cases.
Dialogue management using reinforcement learning

Dialogue has been widely used for verbal communication between human and robot interaction, such as assistant robot in hospital. However, this robot was usually limited by predetermined dialogue, so it will be difficult to understand new words for new desired goal. In this paper, we discussed conversation in Indonesian on entertainment, motivation, emergency, and helping with knowledge growing method. We provided mp3 audio for music, fairy tale, comedy request, and motivation. The execution time for this request was 3.74 ms on average. In emergency situation, patient able to ask robot to call the nurse. Robot will record complaint of pain and inform nurse. From 7 emergency reports, all complaints were successfully saved on database. In helping conversation, robot will walk to pick up belongings of patient. Once the robot did not understand with patient’s conversation, robot will ask until it understands. From asking conversation, knowledge expands from 2 to 10, with learning execution from 1405 ms to 3490 ms. SARSA was faster towards steady state because of higher cumulative rewards. Q-learning and SARSA were achieved desired object within 200 episodes. It concludes that RL method to overcome robot knowledge limitation in achieving new dialogue goal for patient assistant were achieved.
Feature Extraction and Selection in Hidden Layer of Deep Learning Based on Graph Compressive Sensing

Faced with massive high-dimensional, multi-modal, and redundant information data sets, there are many processing methods, one of which is deep learning. Although the deep learning model is effective, there is a feature redundancy phenomenon in the hidden layer after feature extraction. This phenomenon will not only cause over-fitting problems in subsequent data mining, but also cause excessive cost loss. This paper proposes a feature extraction and selection method in hidden layer of deep learning based on graph compressive sensing, which can extract low-dimensional features from complex high-dimensional features, and eliminate the redundancy of features.
Cognitive Distributed Computing System for Intelligent Agriculture

This paper contains the obtained results in the research and design of cognitive systems with distributed computing and its applying in Intelligent Agriculture. Only some aspects of Intelligent Agriculture related to the organization of the irrigation process and the efficient use of water resources are approached. The functionality of the system is based on the development of a network of data processing nodes and local control of the water distribution process. The architecture of the distributed computing system presents a network of nodes with Mesh topology, in which the computing power is evenly distributed. Fuzzy logic models and Neural Networks are used as data processing and knowledge interpretation models. The paper presents: how to define the irrigation space for Smart Agriculture, mathematical models, the result of designing data processing and water distribution nodes, sequence diagram, and a case study that explains how the distributed computing system with cognitive properties works.
Disambiguation of Semantic Relations Using Evidence Aggregation According to a Sense Inventory

This paper describes EPROP, a novel technique requiring little prior knowledge for word sense disambiguation of semantic relations between pairs of ambiguous concepts in knowledge bases. Our method makes inferences by aggregating evidences from ambiguous word interpretations and propagating the acquired knowledge over a taxonomy to generalize or specialize this knowledge. This propagation process allows the estimation of the degree of belief for each possible word sense assignment given the available evidence. EPROP only requires a sense inventory structured as a taxonomy to disambiguate a knowledge base by combining evidence from the ambiguous facts stored in the knowledge base. We have performed different experiments that show that our method achieves good results on the disambiguation of the semantic relations included in WordNet and ConceptNet. We also show how our method can be used to improve the performance of state-of-the-art word sense disambiguation methods.