Reviewerly/exHarmony huggingface dataset:
Analysis of parallel reading literacy instruments: Application of item response theory to statistical parallelism

This research aimed to: (1) investigate the statistical parallelism of reading literacy instruments at the level of the overall test sets; and (2) examine whether the characteristic curves and information functions as the statistical item response theory (IRT) methods for parallelism yield the comparable scores at the levels of test forms as well as individual items.The population was ninth graders in secondary schools under the Secondary Education Service Area Office 1.Samples were examined in a total of 525 students.The instruments were three parallel multiple-choice test sets designed to elicit reading literacy skills.Each test set consisted of two 20-multiple choice test forms.Parallelism was analyzed with respect to means, standard deviations, reliabilities, item parameter estimates, characteristic curves, and information functions.It was found that there was statistical parallelism among the three test sets in terms of means, standard deviations, reliabilities, and item parameter estimates.Cronbach's alpha coefficients were .81,.84,and .83,respectively.The one-way analysis of variance results presented similar mean scores (F = 0.325, p = .723),equal level of discrimination (F = 0.194, p = .824),and equal level of difficulty (F = 0.761, p = .470).Moreover, both characteristic curves and test information functions yielded similar degrees of parallelism at the test form and item-byitem levels.Interesting findings with respect to statistical parallelism are presented and discussed.
Digital Transformation and Hybrid Model in Engineering Education

Digitalisation is also transforming research in ways that are common to other domains of social interaction but are very pronounced since the scientific community is highly interconnected globally. The survey show that the use of digital productivity and collaboration tools is more intense within Higher education institutions and small economies, and it is associated with higher rates of personal scientific collaboration and productivity. Online identity is important for UG, PG and researchers because a significant but incomplete part of their science- related activities leave a digital trace. In turn, this digital footprint supports the production of metrics that researchers use to inform decisions that impact on their teaching and learning careers, such as hiring and promotion decisions in universities. Keywords – Digitalization of Engineering Science, for Scientific and Technological Policy (CSTP), Organization for Economic Co-operation and Development (OECD), Information Systems Security Association (ISSA).
Application of teaching-learning-based optimization algorithm in designing 100 kW power plant using pyrolysis of oil palm empty fruit bunches

Importance of the work: Empty fruit bunches (EFBs) from the palm oil milling process have the potential to be used as a source of energy.Objectives: To optimize the environmental impact and economic profitability of utilization of biofuel production from the EFB fast pyrolysis process. Materials & Methods:The biodiesel power plant from the EFB fast pyrolysis process was modelled using the Aspen Plus program and optimized using the MATLAB program.The multi-objective teaching-learning-based optimization algorithm was used to maximize the net present value (NPV) while minimizing the CO 2 emission of a biodiesel power plant in each case.Results: The optimization results showed that the best case from the economic aspect was an NPV of USD million 14.233 with 59,755 t CO 2 /yr, while the best case in terms of minimizing environmental impact was with the lowest CO 2 emission equivalent of 58,770 t CO 2 /yr and an NPV of USD million 12.008.The difference between these two scenarios regarding the CO 2 emission was slight (1.65%), while for the NPV, it was high (15.63%).Main finding: Both cases produced higher CO 2 emissions than from direct combustion.However, they were still environmentally friendly since they reduced the CO 2 emission equivalent compared to using the EFB as landfill and produced valuable products, while enhancing energy conservation by up to 100 kW.
A Distributed RBF-Assisted Differential Evolution for Distributed Expensive Constrained Optimization

With the development of Internet of things and distributed computing techniques, distributed and expensive constrained optimization problems (DECOPs) have emerged in the industry. DECOPs refer to optimization problems with objective and constraint functions that are computationally expensive and can only be evaluated on multiple agents of distributed networks. In DECOPs, the raw data of each agent cannot be transmitted to other agents, but only objective or constraint value of a solution can be evaluated, resulting in the incomplete data on each agent. This paper proposes a distributed RBF-assisted differential evolution (DRADE) algorithm for solving DECOPs. In DRADE, we added a master agent to the distributed networks of DECOPs, connecting work agents that can evaluate objective or constraint values of candidate solutions to the master agent in a star topology. The proposed algorithm is composed of candidate generation and selection on master agent and radial basis function (RBF) management on work agents. In candidate generation and selection, differential evolution serves as an optimizer to generate candidate solutions assisted by RBF models received from work agents to replace expensive evaluations of candidate solutions in the master agent. In RBF management, each work agent constructs and updates a RBF model with its own data, which are updated by samples selected from candidate solutions received from the master agent and their expensively evaluated values. Statistical results and analysis of experiments carried out on benchmark test functions and engineering problems show that DRADE has superior performance than compared state-of-the-art SAEAs.
An Efficient Compression Coding Method for Multimedia Video Data Based on CNN

Due to the phenomenon of object occlusion and inconsistent motion of different objects in video, high-efficiency compression and coding of video data will generate prediction residuals related to texture structure. To solve this problem, this study proposes an efficient compression coding method for multimedia video data based on CNN. First, the multimedia video coding unit is divided, and the coded frames are arranged in POC order for coding. Then, the coding structure adjustment parameters are calculated, and after coding, the determined reference frame and the bit consumption caused by using the reference frame can be obtained. Finally, an intra-prediction algorithm for video data is established based on CNN. CNN encoder uses a series of down-sampling convolution and ReLU nonlinear mapping to extract and fuse global information, and conducts analysis on areas with low human visual sensitivity on the HEVC transform domain. Frequency coefficient suppression. The experimental results show that the method has good coding performance for test sequences with different contents and different resolutions.
Anomaly Monitoring System of Enterprise Financial and Economic Information Based on Entropy Clustering

The currently used information anomaly monitoring system has problems of low accuracy and efficiency. In this regard, an abnormal monitoring system of enterprise financial and economic information based on entropy clustering is designed. On the basis of the design of the hardware and control module of the economic information abnormal monitoring system; the crawler tool is used to collect the financial and economic information of the enterprise; the collected data information is cleaned by the mapping operation; the processed data is processed by the abnormal knowledge discovery principle. In feature extraction, abnormal information features are obtained through decision tree; the abnormal information monitoring is realized by using the k-means algorithm of information entropy. The experimental results show that the designed system has an average alarm correct rate of 92.55% and a short response time, which is of practical value.
Distance Teaching Method of Welding Process Course for Mobile Learning Platform

The current distance teaching method is mainly to complete the explanation of basic knowledge by recording course video, students’ autonomous learning or with the help of remote communication function. It is difficult for students to fully mobilize their enthusiasm, resulting in poor teaching effect. In order to alleviate the problems of inapplicability and poor teaching effect when the current distance teaching method is applied to welding processing course, this research designs a distance teaching method of welding processing course for mobile learning platform. According to the characteristics of mobile learning platform, the communication path of distance teaching of welding processing course is determined. Then formulate the 5E Teaching Mode and design the teaching process of the course distance teaching platform. Finally, collaborative filtering algorithm and firefly algorithm are used to recommend personalized mobile learning resources for students and generate course learning paths. The experiment shows that after applying this method, the average score of students is improved by about 9.2%, and their learning enthusiasm is significantly enhanced.
Design of Mobile Monitoring System for Tower Crane in Assembly Construction Based on Internet of Things Technology

High data loss rate exists in the mobile monitoring system of assembly tower crane in construction. Therefore, a mobile monitoring system of assembly tower crane based on Internet of things technology is designed. Hardware part: adopt 32-bit data bus, integrate common high-definition multimedia interface; Software part: make use of space geometry principle to construct anti-collision model of tower group, transmit terminal parameters of tower crane safety monitoring system, optimize remote communication protocol of assembly building construction by using internet of things technology, and set up function of mobile monitoring system of tower crane. The experimental results show that the average loss rate of the two systems is 27.871%, 37.807% and 37.452% respectively, which shows that the higher loss rate is improved after the combination of IOT technology.
Research on Automatic Storage of Remote Mobile Surveillance Video Information Based on Cloud Terminal

Cloud storage technology has become a trend of future storage development. Users can no longer buy hardware storage devices, and fully realize remote networked storage, which greatly reduces the user’s use cost. At the same time, it can also provide faster and larger capacity storage and sharing functions. Therefore, in order to achieve the goal of effective storage of massive video, this paper proposes a remote mobile surveillance video information automatic storage method based on cloud terminals, constructs a remote mobile surveillance video information management model, optimizes the remote mobile surveillance video information automatic storage algorithm, and realizes the design goal of remote mobile surveillance video information automatic storage. Finally, the experiment proves that the research method of remote mobile surveillance video information automatic storage based on cloud terminals is highly effective and fully meets the research requirements.
Separation Algorithm of Fixed Wing UAV Positioning Signal Based on AI

Unmanned aerial vehicle (UAV) is an unmanned aircraft remotely controlled by radio, which is widely used in reconnaissance. However, during the operation of UAV, the positioning signal is easily disturbed by noise, which leads to low separation accuracy and poor positioning effect of fixed wing UAV. To this end, a fixed wing UAV positioning signal separation algorithm based on artificial intelligence is proposed. The fixed-wing UAV positioning signal denoising algorithm is constructed by collecting the feature information of fixed-wing UAV, and the denoising of fixed-wing UAV positioning signal is completed. In order to reduce the signal separation error and realize the fixed wing UAV positioning signal separation, signal separation is processed according to the positioning signal algorithm. Experimental results show that the proposed algorithm can effectively separate the UAV location signal from the noise, and has high accuracy and good location effect under serious multipath interference.
Update on U.S. Geological Survey Fundamental Science Practices

First posted March 21, 2023 For additional information, contact: Fundamental Science Practices Advisory Council The U.S. Geological Survey (USGS) Fundamental Science Practices (FSP) are a set of standard principles fundamental to how USGS conducts and carries out its science activities and how resulting information products and data are reviewed, approved, and released. These policies, practices, philosophical premises, and operational principles serve as the foundation for all USGS research and monitoring activities and apply to all levels of the organization. The strength and future of the USGS depend on following these practices. USGS FSP were initiated in 2006 and fully implemented in 2009 to consolidate and standardize science practices across multiple scientific mission areas and science disciplines within the USGS.
Preliminary Review of Phishing Attacks and Countermeasures on the Internet of Things (IoT) Environment

Cybercriminals now have a perfect target thanks to the growing popularity of the Internet of Things (IoT), which has increased the number of gadgets connected to the internet.In conjunction with the usage of Internet Protocol version 6 (IPv6), every device can get connected to the Internet.With this tremendous usage of devices along with Internet connection, one of the most prevalent forms of cyberattacks that target IoT devices is the phishing attack, which aims to gain owner's privileges and can make data loss.This study offers a basic analysis of phishing attacks and defenses in the context of the Internet of Things.It discusses the fundamental ideas behind phishing assaults, their types, and characteristics, as well as the most recent trends and methods that cybercriminals are using to carry out these attacks.It also examines the current countermeasures for phishing attacks on IoT devices.This paper summarizes the present state of knowledge regarding phishing attacks in the IoT environment and emphasizes the need for establishing strong defenses against these attacks for IoT devices.
The effect: An introduction to research design and causality By NickHuntington‐Klein (2022). New York. Chapman and Hall. https://doi.org/10.1201/9781003226055

BiometricsVolume 79, Issue 1 p. 531-532 BOOK REVIEWS The effect: An introduction to research design and causality By Nick Huntington-Klein (2022). New York. Chapman and Hall. https://doi.org/10.1201/9781003226055 Hung-Ching Chang, Corresponding Author Hung-Ching Chang [email protected] orcid.org/0000-0002-4847-2547 Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Correspondence Hung-Ching Chang, Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA. Email: [email protected]Search for more papers by this authorMichael T. Gorczyca, Michael T. Gorczyca Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this author Hung-Ching Chang, Corresponding Author Hung-Ching Chang [email protected] orcid.org/0000-0002-4847-2547 Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Correspondence Hung-Ching Chang, Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA. Email: [email protected]Search for more papers by this authorMichael T. Gorczyca, Michael T. Gorczyca Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this author First published: 22 March 2023 https://doi.org/10.1111/biom.13835Read 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 No abstract is available for this article. REFERENCE Robins, J. (2022) Interview with Jamie Robins. Observational Studies, 8(2), 37– 76. Volume79, Issue1March 2023Pages 531-532 ReferencesRelatedInformation
CNN-based Model for Face Expression Recognition

Face recognition is a biometric technique that uses data on facial features to identify individuals. It is also a key area of study for computer vision researchers. CNN is a subclass of feedforward neural networks with convolutional processing and depth structure and one of the illustrative deep learning techniques. Since the deep learning theory was put forth and computational power increased, CNN has rapidly advanced and is now utilized in computer vision, natural language processing, and other fields. Our research is focused on face recognition, and because the mini-Xception model has a condensed volume and few parameters, it is used in this study. The dataset we used is fer2013, which is a classical dataset among CNN algorithms and is used in many studies. We also used data augmentation methods, and Keras’ ImageDataGenerator image generator was the optimal data augmentation method we came up with after reading the paper. Finally, we came up with a final model with 61% accuracy, which we are satisfied with and within the error results of the papers we reviewed.
A Novel Method for Recognizing Readings in Images of Electric Circuit Inspection Meters Based on Deep Learning

For electric circuit inspection, the conventional manual inspection method has a series of problems including the heavy work load of recording the readings, the low accuracy, and the hidden safety hazards.The intelligent recognization method for images of meter readings based on digital image technology has a great practical value.However, the existing recognization methods for meter readings based on deep learning generally ignore the extraction of key points such as the pointer and scale on meter dashboard, the existing algorithms are of poor robustness and anti-jamming ability, therefore, this paper aims to study a novel method for recognizing readings in the images of electric circuit inspection meters based on deep learning.At first, this paper corrects the tilt of meter dashboard, and accurately positions the dashboard center.Then, based on the YOLOv5 network model, this paper constructs the said recognization model, gives the structure of the YOLOv5 network model, and introduces its working principle.At last, experimental results are drawn to verify the validity of the proposed method for processing the images of meter readings and the constructed recognization model.
Privacy Preservation Techniques in Cloud Computing

Cloud Computing helps customers to take benefit from the variety of services, such as on-demand self-service, universal network connectivity, risk transfer, a usage-based payment and location-independent resource sharing.The privacy of cloud data is a critical concern in the cloud computing environment that necessitates unique attention.Kanonymity is a useful concept for preserving privacy in cloud computing.The concept of generalization is most commonly used because of its little information loss.However, such algorithms are often computationally expensive, they struggle to perform well when dealing with massive amounts of data.This article examines the security risks and concerns that cloud consumers and service providers confront.This research study identifies the research gaps and proposes a method to address the problem.
Automatic text summarization–A systematic literature review

Automatic summarization is the act of computationally condensing a set of data to produce a subset (a summary) that captures the key ideas or information within the original text. To do this, artificial intelligence algorithms that are tailored for diverse sorts of data are frequently created and used. Ten research articles considering databases like IEEE, Scopus, and Springer Nature have been considered. The paradigm shift that AI has created in the field of Automatic Text Summarization is discussed in detail.
Journey to SAP S/4HANA intelligent enterprise: is there a risk in transitions

This paper aims to evaluate the transition from conventional SAP to a business solution called S/4HANA, and understand the attached risks and challenges. The study starts with an introduction of the SAP ERP system and S/4HANA. Then, the implementation methodologies for the transition are analysed. The research object is an organisation transitioned to S/4HANA recently. Lastly, the business offerings of S4HANA would be discussed that make life easier on implementation. This research employs a mixed method. Statistical analysis is employed to analyse the data collected from questionnaires and assess the awareness and experience during the transition to S/4HANA. In addition, a qualitative approach based on interviews is used to identify the risks and challenges of the transition. The research finds that although a transition is not as smooth as thought by many organisations as just a migration, it is a need for organisations in this highly completive digital economy.
Analysis of University Professors’ Digital Teaching Competence

Digital competence for teaching is the set of values, abilities, skills, and attitudes that allow the proper implementation of the teaching-learning process during online education.This study analyzed digital teaching competence in professors from two different university contexts concerning sociodemographic and academic variables in June 2021.This research had a quantitative approach, classified as non-experimental, analytical, and cross-sectional.The population comprised 79 professors from the Faculty of Health Sciences, National University of Chimborazo, Ecuador, as well as 35 from the Faculty of Educational Sciences, University of Granada, Spain.The data were collected using the Online Tutor Functions and Competencies Scale.There were statistically significant differences in the specific competence related to socialization activities regarding the university where the participants were teaching (p = 0.018) and experience as a professor (p = 0.028).Results of the global competence indicated significant differences according to the time of experience in teaching and preferred teaching modality.A significant association was established between the variables related to academic components and the global competence for digital teaching.The R 2 values indicated a low level of goodness of fit, with a higher percentage favoring the forecast level.The preferred teaching modality had a significant predictability value, but its odds ratio showed that it did not condition the state of global competence for digital teaching.The study innovated using binary logistic regression to analyze the predictive capacity of specific dimensions regarding digital competence in higher education settings.