Reviewerly/exHarmony huggingface dataset:
Paragraph Organization Errors in the Writing of Pakistani College-Going Students: An Error Analysis Study

Writing something worthy of reading in a foreign language is a challenging task. Students learning English as a foreign or a second language suffer most in writing especially in paragraph writing. Although studies have been conducted to explore language errors in English composition of Pakistani students, paragraph organization errors have received little attention. Therefore, this study investigated errors in the unity, development, and coherence in the paragraphs written by Pakistani college students. Besides, the study also explored the factors that cause errors in the paragraphs. In this regard, writing samples of purposively selected 20 college participants were analyzed using error analysis classification by Liu and Wang (2011). Thereafter, five participants were interviewed individually through semi-structured interviews to explore factors that cause errors in the paragraphs of college students. The findings revealed that the students made most errors in paragraph unity, followed by paragraph development, and then paragraph coherence. Besides, the thematic analysis of the interviews revealed that cramming culture, excessive attention given to grammar and lack of quality feedback were the key factors that had affected the paragraph organization skills of these students. The study discusses implications for students and teachers.
The Effect of Childcare Teachers' Communication Ability and Self-reflection Intelligence on Parent-teacher Communication

본 연구의 목적은 보육교사를 대상으로 의사소통능력과 자기성찰지능이 부모-교사 의사소통에 어떠한 영향을 미치는지 분석하는데 있다. 이를 위하여 I시에 소재한 유치원 및 어린이집에 재직 중인 보육교사 215명을 대상으로 설문 조사를 실시하였다. 수집된 자료는 SPSS 28.0 통계 프로그램을 사용하여 통계 처리 하였다. 측정도구의 신뢰도 검증을 위해서 Cronbach’s α 계수를 산출하였다. 또한 Pearson의 상관분석과 다중회귀분석을 실시하였다. 연구결과는 다음과 같다. 첫째, 보육교사의 의사소통능력, 자기성찰지능은 모두 부모-교사 의사소통에 통계적으로 유의미한 정적 상관관계가 있는 것으로 나타났다. 둘째, 보육교사의 의사소통능력, 자기성찰지능 모두 부모-교사 의사소통에 영향을 미치는 변수로 나타났으며, 의사소통능력보다 자기성찰지능이 부모-교사 의사소통능력에 상대적으로 더 큰 영향력을 미치는 변인으로 밝혀졌다.The purpose of this study is to analyze how communication skills and self-reflection intelligence affect parent-teacher communication for early childhood teachers. To this end, a survey was conducted on 215 childcare teachers working in kindergartens and daycare centers located in I-city. Cronbach's α coefficient was calculated to verify the reliability of the research instruments, and pearson's correlation analysis and multiple regression analysis were conducted. The results of this study are as follows. First, childcare teachers' communication skills and introspection intelligence all had a statistically significant positive correlation with parent-teacher communication. Second, both childcare teachers' communication skills and self-reflection intelligence were found to be variables that affect parent-teacher communication, and self-reflection intelligence was found to have a relatively greater influence on parent-teacher communication skills than communication skills.
Site recurrence for continuous-time open quantum walks on the line

In recent years, several properties and recurrence criteria of discrete-time open quantum walks (OQWs) have been presented. Recently, Pellegrini introduced continuous-time open quantum walks (CTOQWs) as continuous-time natural limits of discrete-time OQWs. In this work, we study semifinite CTOQWs and some of their basic properties concerning statistics, such as transition probabilities and site recurrence. The notion of SJK-recurrence for CTOQWs is introduced, and it is shown to be equivalent to the traditional concept of recurrence. This statistic arises from the definition of $δ$-skeleton of CTOQWs, which is a dynamic that allows us to obtain a discrete-time OQW in terms of a CTOQW. We present a complete criterion for site recurrence in the case of CTOQW induced by a coin of finite dimension with a set of vertices $\mathbb{Z}$ such that its auxiliary Lindblad operator has a single stationary state. Finally, we present a similar criterion that completes the case in which the internal degree of freedom of each site is of dimension 2.
Machine Learning Approach for Material Analytics and Classification – Insights Based on a Criminal Forensic Investigation Data

Glass is a non-crystalline chalcogenide amorphous solid that is often transparent and has widespread practical, technological, and decorative usage in, for example, windowpanes, tableware, and optoelectronics.Each type of glass has different material compositions to better suit the required application.Composition of glass has various material compositions like Si, Na, Mg, Al, Ca, Ba and so on, based of which the type of glass is classified.This research work primarily focuses on assessing the capability of Machine Learning models for predicting the type of glass left in a crime scene which can further be utilized for higher levels of criminological investigations.The proposed research process incorporates collection of data set from records of forensic investigation.Further, the data is processed for any errors and processed with the aid of popular machine learning algorithms viz.Regression, decision trees, k-means clustering and random forest classifier.The proposed data set has seven different types of glass attributes with 224 sample instances are used in this study for classification.From the results it is evident that, random forest algorithm performs well with higher magnitudes of accuracy.
An Investigation on Vulnerability Analysis of Phishing Attacks and Countermeasures

A great venue for communication amongst regular people is the internet.Many efficient communication methods are available online, such as email, mailing lists, discussion forums, chat services, online conferencing, and blogs.Social networking websites like Facebook, Instagram, and Twitter have recently entered the picture.People who want to steal personal information have discovered a technique with the least chance of getting detected without meeting the target, known as phishing.Phishing is a cybercrime that targets passwords, banking information, credit card information, and personal identification through emails, phone calls, and texts.Mostly, online identity theft takes the form of phishing.The phisher uses social engineering to obtain the victim's account and personal information.A person, a group, or a cluster within a group of people might be the target.In the modern-day, cybersecurity is a major worry to provide a realistic experience of phishing attacks.The present paper investigates and analyses various phishing tools that can simulate such attacks.In addition, the paper investigates the prevention methods and countermeasures.It also examines the kinds of phishing tools, like Zphisher, CamPhish, and PyPhisher, being used to ensure that even people apart from experts can be aware of what a phishing attack is and how to alert others about the risk they pose and how to be prepared for them associated with the recent threats of Crelan Bank and Uber.
Effect of Electronic Modules on Self-regulated Learning and Cognitive Learning Outcomes on Chemical Bonding Material

This study aims to the effect of guided inquiry-based electronic modules on self-regulated learning and cognitive learning outcomes. The research design uses a quasi-experimental post-test only. This research was conducted on students of class X Natural Science in Yogyakarta using the cluster random sampling technique. The instrument used in this research is a self-regulated learning questionnaire and cognitive learning outcomes test. The data analysis technique uses Hotelling’s T-Test to test whether there are differences in self-regulated learning and cognitive learning outcomes with the implementation of electronic modules. The results show that the significance value is 0.002 &lt; 0.05. This means that there is a difference in self-regulated learning and cognitive learning outcomes between students who use the guided inquiry-based electronic module and those who do not use the electronic module.
Research on DODAF-based operational view of cyber defence

With the continuous advancement of network attack technology, the complexity of defending networks has become more and more challenging, resulting in increasingly prominent network security issues. At the moment, research on network defence security focuses on strategy selection and technology implementation, yet there is a lack of a unified definition and a common model framework for the network defence process. This paper examines the overall process of network defense decision-making and addresses the issues of indistinct outlines of the network defense system and a lack of clear explanations of the command and decision-making process, beginning with the architecture. Furthermore, it analyzes the operational view of network defense based on the established architecture framework DODAF(Department of Defense Architecture Framework). The core operational view models, such as the Operational Concept Diagram, Organizational Relationship Diagram, Operational Activity Diagram, and Operational Node Connection Diagram, are initially constructed to describe and analyze the network defense operational system structure and operational process from a macro perspective, and to clarify the interrelationship between defense operational objectives, major activities, combat roles, and major information flow elements.
Remote Diagnosis System of Robot Intelligent Flexible Production Line Based on Convolutional Neural Network

In order to improve the intelligent control level and automatic monitoring ability of robot intelligent flexible production line, a remote diagnosis system of robot intelligent flexible production line based on convolutional neural network was designed. Firstly, the dynamic and kinematic models of intelligent flexible production line were established. Secondly, the resonance fault parameters of the robot intelligent flexible production line were detected, and the resonance fault parameters of the robot intelligent flexible production line were measured. Then, the convolutional neural network control algorithm was used for fault feedback, and the fault feedback tracking model for remote diagnosis of robot intelligent flexible production line was obtained. Finally, the resonant fault error of the robot intelligent flexible production line was compensated by the self-feedback dynamic adjustment mechanism, the remote resonant fault diagnosis of the robot intelligent flexible production line was realized, and the integrated design was realized under the embedded control platform. The simulation results show that the designed oscillation control fitting curve is basically consistent with the actual oscillation control fitting curve and has good stability. In addition, the remote diagnosis accuracy of the design system is close to 100%, and the load error range is small. At the same time, the control time of the design system is short, which can effectively realize the remote diagnosis of the robot intelligent flexible production line and improve the output stability and robustness of the robot intelligent flexible production line.
Multi-Hop Diffusion-Based Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have recently received a lot of attention, owing to their ability to handle graph-structured data. To improve the expressive power of GCNs, several recent studies has concentrated on the stacking of multiple layers, such as convolutional neural networks. However, simply stacking multiple GCN layers will lead to over-fitting and over-smoothing issues. To integrate deeper information and solve the above problems, this paper proposes Multi-Hop Diffusion-Based Graph Convolutional Networks (MD-GCNs), a method for aggregating and stacking multi-hop neighbors of varying orders into one layer, allowing for the capture of long-distance interactions between remote nodes at each layer of GCNs. In order to calculate the weight between neighbor nodes with multi-hop in the same layer, Multi-Hop Diffusion (MD) mechanism introduces the graph diffusion to spread the weight, the receptive field of each layer of GCNs is increased. On this basis, we introduce the MD-GCNs architecture that can be stacked in multiple layers and has the ability to be expressed. Experimental results on node classification tasks in both transductive and inductive learning settings demonstrate the superiority of the proposed method.
Mengenalkan Emosi dengan Emoji Menggunakan Media Flash Card pada Anak Usia Dini di UPT PPRSA Inang Matutu Makassar

Abstract: Introducing Emotions with Emoji Using Flash Card Media in Early Childhood at UPT PPRSA Inang Matutu Makassar. This study aims to use flash card media to introduce emotions with pictures of emojis to early childhood at UPT PPRSA Inang Matutu. The method of implementing this activity is carried out in five stages, namely conducting needs analysis, preparation, making flash cards, implementing activities and evaluating the results of activities in the form of observations. The observations showed that in the first step, some of the children already knew the emotions shown from the emojis, although some were still shy and kept silent for a long time before mentioning the emotions. In the second step, all the children were able to point out the type of expression they meant, although some were still pointing the wrong way. In the third step, the children were very active in expressing their emotions and following the emotions in the emojis, although some children were still lacking in expressing their emotions.
Efektivitas Model Pembelajaran Kostruktivisme terhadap Kemampuan Menyimak Siswa Kelas III Sekolah Dasar di Kota Makassar

Abstract: The Effectiveness of the Constructivism Learning Model on the Listening Ability of Grade III Elementary School Students in Makassar City. The research aims to improve the speaking skills of grade III students in Makassar City. This study used an experimental pretest approach, postest control group. Pretest-Posttest Control Group design. The population of this study was class III A as many as 15 people and class III B as many as 15 students at SDN Pulau Kodingareng Makassar City. Its data collection techniques using; listening and observation tests. Data analysis techniques use descriptive statistical analysis and inferential statistics. The results of this study show that there are differences in learning achievement between experimental classes and control classes using a learning model of constructivism of Indonesian language materials, especially in improving students' listening skills. The difference in learning achievement is due to the difference in factors according to the use of models combined with media with appropriate techniques such as the use of constructivism models. shows that there is a constructivism model for the effectiveness and improvement of students' listening skills, This is evidenced by the increase that occurs at the time of improvement that occurs during the application of the learning model.
Evaluation of interaction methods in an Extended Reality training environment

This research paper details the assessment of four distinct interaction techniques for immersive training within the construction industry. The primary focus of this study was to evaluate the extent to which these methods were capable of replicating physical environment interactions within virtual environments. A common challenge associated with immersive training within construction is the need for external tools to facilitate interactions, which can negatively impact user experience. To mitigate this issue, we introduce four immersive training use cases that rely on natural interfaces for interaction. Additionally, we conducted a Quality of Experience (QoE) experiment to validate the efficacy of these use cases and interaction techniques, assessing acceptability, presence, and visual quality. Moreover, this paper presents a classification system for four distinct natural interaction methods, which we evaluated for differences in visual quality.
Document Analyzing Using Deep Learning

Abstract: Many businesses and large organizations have a large set of documents that need to be stored in various locations. cluster. In recent years, this task has become time consuming as the number of documents and articles has increased. Analysis of Documents are her one of the subjective research techniques analysts use to validate ideas. with some help. A visual technique that takes full advantage of layout and text formatting in a very clean output format. with the help of the model Most large and lots of architectural documents can be sorted. With the help of layout models and new interaction strategies Various layouts of any format within a single.
Federated selective aggregation for on-device knowledge amalgamation

In this paper, we explore a new knowledge amalgamation problem, termed Federated Selective Aggregation for on-device knowledge amalgamation (FedSA). FedSA aims to train an on-device student model for a new task with the help of several decentralized teachers whose pre-training tasks and data are different and agnostic. Our motivation for investigating such a problem setup stems from a recent dilemma of model sharing. Due to privacy, security, or intellectual property issues, the pre-trained models are, however, not able to be shared, and the resources of devices are usually limited. The proposed FedSA offers a solution to this dilemma and makes it one step further, again, the method can be employed on low-power and resource-limited devices. To this end, we proposed a dedicated strategy for handling knowledge amalgamation. Specifically, our student-training process is driven by a novel saliency-based approach that adaptively selects teachers as the participants and integrates their representative capabilities into the student. To evaluate the effectiveness of FedSA, we conduct experiments on both single-task and multi-task settings. Experimental results demonstrate that FedSA effectively amalgamates knowledge from decentralized models and achieves competitive performance to centralized baselines.
A Track-before-Detect Strategy for Multi-sensor Passive Acoustic Localization

Abstract This paper focuses on the multi-sensor passive acoustic localization problem. The traditional solution for the problem is to estimate the parameter of the received signal, such as the time difference of arrival (TDOA). Then the position of the target is solved by the estimated parameter. This solution is suboptimal because estimations are carried out at each sensor separately, and the fact that the received signals of different sensors originate from the same target is ignored. In this paper, we propose a track-before-detect (TBD) method. The proposed method adopts a particle filter that defines its likelihood function as the product of the output of the cross-correlation (CC) between the signal of different sensors conditioned on the particle’s state. This likelihood function is designed to ensure that the processing gain of multi-sensors can be fully obtained. Moreover, the proposed method circumvents the challenging measurement-to-track association problem faced by the method based on the estimated parameter.
Smart Traffic Management System

Abstract: An efficient management system is imperative to address the frequent traffic congestion experienced at major intersections. This paper proposes the implementation of an intelligent traffic controller that utilizes real-time image processing. By employing various edge detection algorithms and object counting methods, the camera footage is analyzed to effectively manage the traffic flow. Previously, a matching method was employed, wherein cameras were installed alongside traffic lights to capture sequential image sequences. These images were then compared to a reference image depicting an empty road. However, in my paper, a filtering method is utilized instead. This method effectively filters out unwanted objects, focusing solely on vehicles, and accurately determines the number of cars present in each image.
Ethereum Blockchain Technology for Internet of Things Authentication

The Internet of Things (IoT) technology has grown rapidly and continuously. The IoT ecosystem comprises an increasing number of smart devices that can sense, act, process, store, and communicate via the Internet. Data within such communication technology is continuously exposed to be hacked or attacked. Therefore, there should be methods to protect the issue of data authentication and increase the security of such large-scale networks. In this article, a method of using a Blockchain network is proposed. The proposed system uses the Ethereum blockchain and benefits from several critical technologies, including distributed consensus, smart contracts, digital signatures, and cryptographic hashes. The whitelist and blacklist are the two lists that are suggested. The approved users who have obtained their private keys are included on the whitelist. The blacklist, on the other hand, is intended to stop illegal users who are flagged as spammers or attacks. According to findings, the Blockchain can drastically save expenses and boost the security of IoT networks
Form Based Document Understanding Using Sequential Model

Abstract: DocFormer is a teachable transformer-based multi-modular start-to-finish model for a number Visual Report Figuring out projects. Additionally, Doc Former allows the model's capacity to hyperlink textual content and visible tokens through sharing realized spatial throughout modalities. For the learn about of quite a number algorithms and pc vision, it is a critical location of research. Some humans fabricate archives and have interaction in unlawful things to do due to the fact the cutting edge report detection gadget is ineffective. In the proposed system, pretend files can be discovered in two ways. As the recognition of deep mastering science has grown, file AI, which consists of matters like record design analysis, visible records extraction, record visible query answering, file photo classification, and so on, has made large growth in current years. We are principally focusing on the elements that contributed to crimes in preceding years when it comes to the elements that make a contribution to the prevalence of crimes, such as the identification of the culprit and different factors
Development of Electronic-Public Relations Through "Mp Mobile Apps" for Optimization of Information Systems

Good education management can certainly be carried out as effectively and efficiently as possible, which in the end is a positive response from users of educational services in the form of user satisfaction. There are several aspects to increase user satisfaction, one of which is information that is quickly received by the public and internal citizens of educational institutions. The method in this study uses the Waterfall Model from Winston Walker Royce, which has 5 stages (Analysis, Design, Implementation, Testing, and Maintenance). The purpose of this research is to emphasize the improvement of information services for department members and the general public, namely the quality of information services in a practical and integrated manner. The results of the “MP Mobile Apps” development show that the "MP Mobile Apps" application has met the eligibility standards to be implemented with an average of each. The category is as much as 85%.