Reviewerly/exHarmony huggingface dataset:
STUDENTS’ MAIN PROBLEMS IN WRITING NARRARTIVE TEXT A Descriptive Study Conducted towards Students of Manajemen Pendidikan Islam at STAI AL MUHAMMAD CEPU

ABSTRACTDuring conducting my English class for 3 years, only some of MPI students succeeded in writing narrative texts, and others got the standard minimum score. The problems can be mentioned as; lack of vocabularies, error and mistake in past tense structure, and also the paragraph organization. Based on the situation above, the writer wanted to figure out the main problems (mistakes and errors) faced by MPI students in writing narrative texts. By conduction this study, the writer hoped that the writer would be able find the treatment, so that students will be able improve their skill and competence in writing narrative texts. This study, the writer used descriptive qualitative research. To gain the data, the writer analyzed the students’ work in composing narrative texts (documents) and interview section. From the documents and interview section, the writer could draw the conclusion that the main mistakes and errors should be treated were: 1) the generic structure of their writing narrative texts; 2) the use of past tense structure; simple past tense, past continuous, past perfect, and past perfect continuous tense; 3) the use of incorrect elements of sentences.Key words: students’ problems, writing, narrative-text
Outlier‐robust zonotope set‐membership filter for discrete‐time nonlinear system

Abstract Transient sensor malfunction, improper replacement measures, and large non‐Gaussian noise can cause data outliers in real‐world scenarios. In this article, an outlier‐robust zonotope set‐membership filter is proposed for uncertain nonlinear systems susceptible to outliers. In order to linearize nonlinear systems, a linearization method based on Stirling's interpolation formula is proposed. Then, an outlier detector utilizing the linearization error bounds, the prediction domain, and the output feasible domain is constructed. Based on the linearized system, a tight strip construction method is given to reduce the conservatism of the estimated enclosure. Moreover, a prediction‐correction‐backward reconstruction filter structure is designed to improve estimation accuracy and reduce conservatism. The proposed structure fuses historical data to attenuate the impact of uncertainty on the system. The robustness, guarantee, and convergence of the proposed filter are investigated. Finally, the effectiveness of the proposed approach is evaluated by two simulation examples.
Layered Sharding on Open Blockchain

Sharding can significantly improve the blockchain scalability, by dividing nodes into small groups called shards that can handle transactions in parallel. However, all existing sharding systems adopt complete sharding, i.e., shards are isolated. It raises additional overhead to guarantee the atomicity and consistency of cross-shard transactions and seriously degrades the sharding performance. In this chapter, we present Pyramid, the first layered sharding blockchain system, in which some shards can store the full records of multiple shards thus the cross-shard transactions can be processed and validated in these shards internally. When committing cross-shard transactions, to achieve consistency among the related shards, a layered sharding consensus based on the collaboration among several shards is presented. Compared with complete sharding in which each cross-shard transaction is split into multiple sub-transactions and cost multiple consensus rounds to commit, the layered sharding consensus can commit cross-shard transactions in one round. Furthermore, the security, scalability, and performance of layered sharding with different sharding structures are theoretically analyzed. Finally, we implement a prototype for Pyramid and its evaluation results illustrate that compared with the state-of-the-art complete sharding systems, Pyramid can improve the transaction throughput by 2.95 times in a system with 17 shards and 3500 nodes.
A Study on the Assessment of the Proposed Alitaptap Learning Management System Using SWOT Analysis

Most of the existing learning management systems (LMS) are designed and give students a steeper learning curve meaning, taking so much of their time in learning the system, or it would be their parents who would navigate the system for them. To address this issue, the researchers proposed Alitaptap, a learning management system (LMS) specially designed for kids at the preschool and elementary levels. It provides a child-friendly user interface, utilizing colors that would not only be attractive to them but also direct them to the right buttons. It would also utilize icons and images that would entice the user group to use the application more. It also has the functionality to download all the necessary files, presentations, and tasks for the week for the users to be able to do their tasks offline. This study tackles the use of SWOT analysis to be able to assess the Alitaptap app and identify its different strengths, weaknesses, opportunities, and threats. A questionnaire utilizing the six thinking hats strategy was utilized and distributed to different respondents and after the responses, a SWOT analysis was conducted followed by a TOWS Matrix to identify the state of the app. Overall, the Alitaptap app is a great potential product in the learning management system category aimed toward children.
Internet of Things-based Cow Body Weight Recording System

Internet of Things (IoT) can be applied to various aspects, especially it can be used as a system for recording, storing, sorting, and processing data. This application can be used in a cattle body weight recording system. Recording is all types of record activities such as identification activities, genealogical records, production and reproduction records, maintenance management records and livestock health records in certain populations. In the process of fattening cattle, recording is an essential activity, but in a survey conducted at the Sumber Sekar laboratory of the Faculty of Animal Husbandry, Universitas Brawijaya, a problem was found, namely the availability of cattle scales for the recording process. For this reason, an internet of things-based cow body weight recording system was designed. The internet of things-based cow body weight recording system uses 2 Load Cell sensors to weigh the cow's body weight and an RFID sensor to read the cow's identity, in addition to the sensor the system has a website as a user interface. It is hoped that this application can improve the efficiency of the cow body weight recording process and produce an intelligent tool system.
FTCM: Frequency-Temporal Collaborative Module for Efficient 3D Human Pose Estimation in Video

Capturing cross-pose correlation from a sequence of frame-level 2D poses is essential for 3D human pose estimation (3D-HPE) in the video. Recent studies have shown the promising potential of modeling the pose relation with feature-mixing operations on the temporal domain. However, they seldom consider the interaction across poses in the frequency domain. This paper studies a Frequency-Temporal Collaborative Module (FTCM) to explore the feasibility of encoding the cross-pose correlations in both frequency and temporal domains. FTCM aims to jointly capture the global and local cross-pose correlations with a more lightweight network model. Specifically, FTCM splits the pose features into two groups along the channel dimension and separately models the frequency and temporal interactions across poses with different feature-mixing operations in parallel. To achieve this goal, we purposely design two pose-mixing units, i.e., the frequency pose-mixing (FPM) and the temporal pose-mixing (TPM). Particularly, FPM is designed to reap the global correlations among different pose frequencies with the representation obtained by converting the original pose signals with Fast Fourier transform (FFT). Unlike the pose-mixing used by previous methods like Transformers that influences an individual pose with all other poses, TPM locally calibrates the pose with dynamics aggregated within several adjacent poses in the temporal domain, explicitly weighting neighboring poses more with respect to the far-away ones so as to enforce a strict locality constraint. Besides, the group strategy significantly reduces the model complexity. To verify the effectiveness of FTCM, we conduct extensive experiments on two benchmarks (i.e., Human3.6M and MPI-INF-3DHP). Experimental results not only exhibit favorable accuracy/complexity trade-offs of our FTCM but also show superior or comparable performance to state-of-the-art methods on both datasets. The code and model are publicly available at: https://github.com/zhenhuat/FTCM.
XAIProcessLens: A Counterfactual-Based Dashboard for Explainable AI in Process Industries

We present a novel counterfactual-based dashboard for explainable artificial intelligence (XAI) in process industries, aimed at enhancing the understanding and adoption of machine learning (ML) models by providing transparency, explainability, and performance evaluation. Our dashboard comprises two modules: a statistical analysis module for data visualization and model performance assessment, and an XAI module for exploring counterfactual explanations at varying levels of abstraction. Through a case study of an industrial batch process, we demonstrate the dashboard’s applicability and potential to increase trust in ML models among stakeholders, paving the way for confident deployment in process industries.
Exploring How Skimming and Scanning Fosters EFL Students’ Reading Comprehension at an English Club Senior High School in Indonesia

This study aimed to examine the effect of skimming and scanning strategies on improving reading comprehension among students attending the English club at Senior High School Zainul Hasan 1 Genggong. A considerable number of students continue to struggle with reading, an area that is integral to their academic progress and language acquisition. Addressing these challenges necessitates effective teaching tactics to enhance reading comprehension skills, the exploration of which formed the core of this research. The study utilized a pre-experimental research design, focusing on a single group pre-test and post-test methodology. The sample consisted of 30 students who received targeted instruction on skimming and scanning strategies over a determined period. Skimming involves rapidly reviewing a text to get a general idea, while scanning is a technique used for quickly locating specific information within a text. Both strategies are believed to be pivotal in assisting learners to become more efficient and effective readers. Data collected from the pre and post-tests was analyzed using the Paired Sample T-Test. The alternative hypothesis, suggesting that skimming and scanning techniques have no significant effect on reading comprehension, is rejected if the Sig (2-tailed) value is more than 0.05. Conversely, if the Sig (2-tailed) is less than 0.05, the alternative hypothesis is accepted, indicating a significant effect of skimming and scanning techniques on students' reading comprehension. The study's findings revealed that the students who received training in skimming and scanning demonstrated significant improvement in their reading comprehension, substantiating the beneficial impact of these techniques. This improvement is further corroborated by the Sig (2-tailed) value of 0.000, which is less than the critical threshold of 0.05, leading to the acceptance of the alternative hypothesis. The statistically significant results from the Paired Sample T-Test provided robust evidence supporting the efficacy of skimming and scanning techniques in enhancing students' reading comprehension skills. This demonstrates the significance of these strategies in addressing reading comprehension challenges among students and informs the pedagogical approaches for teaching reading in EFL contexts. It underscores the need for their integration in curriculum planning and instruction, thereby contributing substantially to the academic discourse on reading comprehension strategies.
DIY (Do-It-Yourself) English Language Learning and Practice at Universitas Negeri Makassar

This phenomenological study explores the experiences, motivations, and challenges of students at Universitas Negeri Makassar engaging in DIY (Do-It-Yourself) English language learning and practice. The research aims to gain insights into self-directed language learning and provide valuable information for educational practices and policies in the university context. A purposeful sample of 15-20 students, representing a diverse range of experiences, was selected to participate in the study. Data collection involved a combination of semi-structured interviews, focus group discussions, and reflective journals, allowing for a comprehensive understanding of the students' perspectives. Inductive thematic analysis was employed to identify key themes and patterns in the data, with the findings presented as a rich, descriptive narrative. Strategies for establishing trustworthiness included triangulation of data sources, member checking, and peer debriefing. This study contributes to our understanding of DIY language learning by highlighting the DIY English language learning strategies, benefits and challenges, and students’ ways of assessing DIY English language learning at Universitas Negeri Makassar, ultimately informing future educational practices and policies.
Realization of high-fidelity and robust geometric gates with time-optimal control technique in superconducting quantum circuit

One of the key features required to realize fault-tolerant quantum computation is the robustness of quantum gates against errors. Since geometric quantum gate is naturally insensitivity to noise, it appears to be a promising routine to achieve high-fidelity, robust quantum gates. The implementation of geometric quantum gate however faces some troubles such as its complex interaction among multiple energy levels. Moreover, traditional geometric schemes usually take more time than equivalent dynamical ones. Here, we experimentally demonstrate a geometric gate scheme with the time-optimal control (TOC) technique in a superconducting quantum circuit. With a transmon qubit and operations restricted to two computational levels, we implement a set of geometric gates which exhibit better robustness features against control errors than the dynamical counterparts. The measured fidelities of TOC X gate and X /2 gate are 99.81% and 99.79% respectively. Our work shows a promising routine toward scalable fault-tolerant quantum computation.
Comprehensive Analysis of the Factors, Structure and Characteristics of the Teaching Behavior of Physical Education Teachers in Colleges and Universities

With the advancement of education and teaching reform, core literacy has become a new teaching concept that teachers of various subjects must pay attention to.It is mainly student-centered, focusing on students' physical and mental health and all-round development.Physical education has a natural advantage in cultivating the comprehensive quality of college students, and the teaching behavior of college physical education teachers also plays a role that cannot be underestimated.This article studies the behavioral factors of physical education teachers in ordinary colleges and universities in our city.Using the method of factor analysis, the interview and observation method combined with the literature data are used for analysis, and all the physical education teachers in ordinary colleges and universities in our city are interviewed.
Deep Calibration With Random Grids

We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-stage calibration of Bayer and Stemper (2018). Our methodology inherits robustness from the former while not suffering from the need for interpolation/extrapolation techniques, a clear advantage ensured by the pointwise approach. The crucial point to the entire procedure is the generation of implied volatility surfaces on random grids, which one dispenses to the network in the training phase. We support the validity of our calibration technique with several empirical and Monte Carlo experiments for the rough Bergomi and Heston models under a simple but effective parametrization of the forward variance curve. The approach paves the way for valuable applications in financial engineering - for instance, pricing under local stochastic volatility models - and extensions to the fast-growing field of path-dependent volatility models.
An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment (TEE) is an important part of the security architecture of modern mobile devices, but its secure interaction process brings extra computing burden to mobile devices. This paper takes open portable trusted execution environment (OP-TEE) as the research object and deploys it to Raspberry Pi 3B, designs and implements a benchmark for OP-TEE, and analyzes its program characteristics. Furthermore, the application execution time, energy consumption and energy-delay product (EDP) are taken as the optimization objectives, and the central processing unit (CPU) frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model. The experimental result shows that compared with the default strategy, the scheduling method proposed in this paper saves 21.18% on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective. The Decision Tree-Support Vector Regression (SVR) scheduling model, which takes the lowest energy consumption as the optimization goal, saves 22% energy on average. The Decision Tree-K-Nearest Neighbor (KNN) scheduling model with the lowest EDP as the optimization objective optimizes about 33.9% on average.
Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient

Distributed optimization with open collaboration is a popular field since it provides an opportunity for small groups/companies/universities, and individuals to jointly solve huge-scale problems. However, standard optimization algorithms are fragile in such settings due to the possible presence of so-called Byzantine workers – participants that can send (intentionally or not) incorrect information instead of the one prescribed by the protocol (e.g., send anti-gradient instead of stochastic gradients). Thus, the problem of designing distributed methods with provable robustness to Byzantine workers has been receiving a lot of attention recently. In particular, several works consider a very promising way to achieve Byzantine tolerance via exploiting variance reduction and robust aggregation. The existing approaches use SAGA- and SARAH-type variance reduced estimators, while another popular estimator – SVRG – is not studied in the context of Byzantine-robustness. In this work, we close this gap in the literature and propose a new method – Byzantine-Robust Loopless Stochastic Variance Reduced Gradient (BR-LSVRG). We derive non-asymptotic convergence guarantees for the new method in the strongly convex case and compare its performance with existing approaches in numerical experiments.
Abstractive Summarization with Efficient Transformer Based Approach

One of the most significant research areas is how to make a document smaller while keeping its essential information because of the rapid proliferation of online data. This information must be summarized in order to recover meaningful knowledge in an acceptable time. Text summarization is what it's called. Extractive and abstractive text summarization are the two types of summarization. In current years, the arena of abstractive text summarization has become increasingly popular. Abstractive Text Summarization (ATS) aims to extract the most vital content from a text corpus and condense it into a shorter text while maintaining its meaning and semantic and grammatical accuracy. Deep learning architectures have entered a new phase in natural language processing (NLP). Many studies have demonstrated the competitive performance of innovative architectures including recurrent neural network (RNN), Attention Mechanism and LSTM among others. Transformer, a recently presented model, relies on the attention process. In this paper, abstractive text summarization is accomplished using a basic Transformer model, a Transformer with a pointer generation network (PGN) and coverage mechanism, a Fastformer architecture and Fastformer with pointer generation network (PGN) and coverage mechanism. We compare these architectures after careful and thorough hyperparameter adjustment. In the experiment the standard CNN/DM dataset is used to test these architectures on the job of abstractive summarization.
PECULIARITIES OF ORGANIZING INDIVIDUAL WORK WITH STUDENTS USING DIGITAL TECHNOLOGIES

To organize individual work with students, it is recommended to use a combination of general purpose cloud services, specialized cloud services and video conference programs. The choice of a specialized cloud service depends on the subject taught by the teacher. The toolkit of the video conferencing program should consist of the function of video recording and text chat. These are the basic requirements that will help in organizing and conducting individual work with students.
Social Networking Platform with Secure User Interactions

In this digital age, social networking platforms have become an integral part of our daily lives.However, there is a growing concern over the lack of privacy and security on existing platforms.This has led to the development of a new social networking platform that focuses on enhancing user privacy and security.This platform includes Private Reactions features to provide users with more control over their interactions.Additionally, the Personal followers/following list feature helps prevent digital stalking and harassment.The introduction of "Ghost mode" provides users with an extra layer of privacy and control over their profile.By incorporating these features, the platform aims to prevent cyberbullying and digital blackmail, creating a safer and more user-friendly environment for social networking.