Reviewerly/exHarmony huggingface dataset:
Applying Best Practices for Improving the Performance of Software Project Manager

This paper focuses on proposing a quality assurance model for Software Projects Management. This paper presents some important concepts related to this field. It presents the phases and activities of software project management. This paper presents a set of proposed steps for achieving each activity of planning ISs projects. Depending on the proposed steps and a combination of statistical techniques, this paper introduces a proposed quality assurance model for software project management activities. This proposed model can be used to build an automated software tool.
DocXClassifier: Towards a Robust and Interpretable Deep Neural Network for Document Image Classification

Abstract Model interpretability and robustness are becoming increasingly critical today for the safe and practical deployment of deep learning (DL) models in industrial settings. As DL-backed automated document processing systems become increasingly common in business workflows, there is a pressing need today to enhance interpretability and robustness for the task of document image classification, an integral component of such systems. Surprisingly, while much research has been devoted to improving the performance of deep models for this task, little attention has been given to their interpretability and robustness. In this paper, we aim to improve upon both aspects and introduce DocXClassifier, an inherently interpretable deep document classifier that not only achieves significant performance improvements over existing approaches in image-based document classification, but also holds the capability to simultaneously generate feature importance maps while making its predictions. Our approach attains state-of-the-art performance in image-based classification on two popular document datasets, RVL-CDIP and Tobacco3482, with top-1 classification accuracies of 94.17% and 95.57%, respectively. Additionally, it sets a new record for the highest image-based classification accuracy on Tobacco3482 without transfer learning from RVL-CDIP, at 90.14%. In addition, our proposed training strategy demonstrates superior robustness compared to existing approaches, significantly outperforming them on 19 out of 21 different types of novel data distortions, while achieving comparable results on the remaining two. By combining robustness with interpretability, DocXClassifier presents a promising step towards the practical deployment of DL models for document classification tasks.
Analisis Sistem Informasi Akuntansi Pendapatan Penerimaan Kas Listrik Prabayar Pada PT Ajamu Faadhilah Agung

This research aims to determine the accounting information system process regarding cash receipts from new prepaid electricity connections and meter replacement from postpaid to prepaid as implemented by PT. AJAMU FADHILAH AGUNG which is located at Pasar VII Street No. 2 Gg. Karya Rotan Sei Rotan Village. PT AJAMU FAADHILAH AGUNG is a company operating in the electrical and mechanical sector as a provider of electrical power installation services in Indonesia, so it is hoped that it can play an active role in supporting government policies together with PT. PLN (Persero). This research is limited to new connections to prepaid electricity and replacing meters from postpaid to prepaid. Problem formulation: 1. What is the accounting information system regarding cash receipts? 2. What are the advantages and disadvantages in implementing information systems. Research objectives: 1. To find out the application of accounting information systems regarding cash receipts. 2. To find out the advantages and disadvantages of applying accounting information systems regarding cash receipts. The data collection method used in this research is decision technique and direct research in the field. The technical analysis used is the description method, namely a method carried out by analyzing the cash receipts related to new prepaid electricity connections and meter replacement from postpaid - prepaid.
Teacher's Speech Acts as Interaction in Learning Language Indonesia

The research was conducted with the aim of describing teacher's speeches in Indonesian learning in class when learning Indonesian. The type of this research is qualitative research with descriptive methods. Data collection is done by listening activities. The data that is observed is obtained from stress devices and notes when the research was examined. Teacher's speeches classified by the researchers is divided into three parts, namely utterance when giving lessons, when delivering lessons and lessons that are lessons related. Based on research conducted, it can be concluded that teachers often use assertive requirements and use regional language as the languages of instruction
Obstacles in The Bugis Language Learning Process in Pitu Riawa District, Sidenreng Rappang Regency

This research aims to explain the problems in regional language learning in schools. This type of research uses a qualitative descriptive approach in the form of a case study using data collection methods, observation, interviews and distributing questionnaires.The results of the research show that there are learning problems in the teaching and learning process, namely teacher problems, learning equipment problems, curriculum problems, and student problems. Teacher problems, namely teachers who teach not in accordance with their educational background, curriculum problems, namely the absence of a regional language curriculum, learning device problems, namely the absence of adequate learning devices, student problems, namely the emergence of student ideologies that consider the Bugis regional language as a backward subject, and consider regional languages as a frightening specter
Fostering Critical Thinking through Integrating Edpuzzle in Blended Learning

To better prepare students for the demands of the twenty-first century, the Ministry of Education and Culture of the Republic of Indonesia incorporates character education into a range of educational courses. English as a foreign language, teachers can select the chosen learning approach for their students—which might require using critical thinking skills—by using digital interactive media like Edpuzzle. This study's goal was to evaluate how digital interactive media like Edpuzzle were used in online English as a foreign language (EFL) classes and how it affected the students' critical thinking abilities. In order to help students enhance their critical thinking abilities in the context of problem-solving related to various learning styles, this project intends to create digital media based on character values. The 4-D approach was used in this product development study (Define, Design, Develop, and Disseminate). The digital interactive media Edpuzzle obtained a total average score of 3.60 based on the validation from the material expert and a score of 3.50 based on the evidence from the media expert, indicating that the study's findings are correct. 92% of the eighth-graders reactions were positive, per the assessments. Active learning during the teaching process also causes the students to feel positive emotions. Students can also develop their ability to think critically by speaking out in class and having meaningful debates with their classmates
Teacher’s Perceptions of Using Contextual Teaching and Learning (CTL) Method in Teaching English at SMP Negeri 4 Panca Rijang

This study was conducted to determine the teacher's perception of the use of the Contextual Teaching and Learning (CTL) method in teaching English. This research was conducted at SMP Negeri 4 Panca Rijang. This research used descriptive qualitative method. The participants are 3 English teachers who teach grades 1, 2, and 3 at SMP Negeri 4 Panca Rijang. The instruments of this research are interviews and questionnaires. The results showed 3 positive perceptions and 3 negative perceptions. As for 3 of the negative perception. Based on the data obtained, the researchers concluded that the results of interviews and questionnaires, positive perceptions where all teachers said that Contextual Teaching and Learning learning was very helpful for teachers in delivering learning material and helping students more easily understand the material presented in the learning process.
FORMATION OF CRITICAL THINKING OF PRIMARY SCHOOL STUDENTS THROUGH AN EFFECTIVE SURVEY

The article deals with the problem of the formation of critical thinking of younger schoolchildren through an effective survey. In order to determine the relevance of the topic, the concept of the development of education and science and the State Mandatory Standard of Primary Education are analyzed. A review of the research of domestic and foreign scientists who studied the concept of "critical thinking" is presented. It is confirmed that this is one of the skills that allows the student to develop freely in the educational space. &nbsp;The general pedagogical process was carried out taking into account the three stages, functions and methods of pedagogical technology for the development of critical thinking proposed by J. Steele, K. Meredith, and C. Temple. As a result of the use of pedagogical technology for the development of critical thinking, students make mistakes at the first stage, freely express their thoughts without fear of teacher correction; update their knowledge and experience; solve large-scale problems on the topic, make common decisions; at the second stage, they understand the content of new information, compare existing knowledge and experience, pose questions aimed at finding effective and solvable ways, express their own opinion about new information, argue their positions; at the third stage, the ability to independently determine tasks, make forecasts, and make decisions develops. Since the object of the study were primary school students, experimental work was carried out with students of grades 4 "B" and "D" of the Lyceum school No. 23 named after Z.Kosmodemyanskaya. Three stages of the experiment have been implemented. Tasks aimed at forming students' critical thinking at the formative stage are developed based on the educational goals of the 4th grade curriculum. The effectiveness of the developed tasks aimed at the problem of research has been proved in the course of experimental research.
METHODS AND TECHNIQUES OF REMOTE ORGANIZATION OF FIELDWORK IN GEOGRAPHY LESSONS

The article identifies a number of issues related to the organization of educational field work in general education schools in geography - the inconsistency of educational bases with the forms of educational field practice, improper performance of educational field work due to the high level of safety rules. Field work is of great importance as a cognitive activity. The practical application by students of theoretical information acquired during acquaintance with geographical objects contributes to the development of research skills. During the analysis, it was suggested that activities at all stages of the organization of hydrological training and field work can be carried out through digital platforms that are conducted remotely. In this regard, the organization of field training in a remote format requires the use of digital resources. In the course of the research work, an analysis of theoretical and practical scientific works was carried out, a survey of geography teachers of secondary schools was conducted. In accordance with the results of the survey received at the level of the republic, new opportunities for solving the identified issues will be analyzed, conclusions are given. It is clear from the results of the survey that the work of field practice is not always carried out in the traditional format. The methods of organizing hydrological field work at school and the possibilities of using digital tools are determined, methodological approaches to geography teachers are proposed.
СТУДЕНТТЕРДІҢ ИКЕМДІ ДАҒДЫЛАРЫН ДАМЫТУДЫ БАҒАЛАУ: ЖҰМЫС ТӘЖІРИБЕСІНЕН

The article raises the question of increasing emotional intelligence (soft skills), which are currently very important for achieving the competitiveness of students in the labor market. Considering that improving these skills is one of the new directions in the dynamically changing world, it is required the development of human resources, especially in the field of student-based research. However, it must be admitted that not enough attention is paid to developing this skill set in our state. Hence, this study aims to assess students' learning outcomes to improve their soft skills, including integrating innovative forms and instructional strategies. The learning outcomes of students are evaluated using both quantitative and qualitative methods. The study involved 78 students of the Higher College at Amanzholov University and Amanzholov University. The results showed that some students do not have a clear idea of the content of competencies, and several significant ones have not been mastered at the proper level. This fact confirms the need to develop additional curricula and other activities within the framework of educational work to improve all components of these skills. Finally, vocational secondary education policy experts in Kazakhstan can use innovative formats and teaching strategies to improve future specialists' soft skills.
Stereo Matching Method for Remote Sensing Images Based on Attention and Scale Fusion

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.
Encouraging Literate Students to Embrace More Literacy Behaviors

Being literate is not merely able to read and write a language. Schools and classes are calling themselves as having a literacy program so as to facilitate their students with literacy behaviors. Hence, students are mostly literate only with the materials from school. They tend to read and act like literate students at school where the teachers are around. However, whether they are truly literate through other literacy behaviors is another question to ask and will be answered in this article. As seen that the purpose of this article is to know whether students considered themselves as literate students as well as to describe their literacy behaviors. It is stated earlier that literacy behaviors are not only related to reading and writing, whereas those will be stated more clearly in this article. The target respondents of this article to fulfill the data were the English Education Study Program students of Universitas PGRI Wiranegara. They were given an online questionnaire and answered several questions related to their knowledge and self-evaluation about literate and literacy behaviors. The result shows that they are aware of being literate students and, interestingly, they have various subjects and activities referred to literacy behaviors. Through realizing the students’ literacy behaviors, teachers/lecturers will be able to design certain materials and assignments which are in the area of students’ comfort as well as not neglecting the need for challenges.
Fishing out regular polysemy with the Gener (generic) relation

Lexical Systems are formal network models of natural language lexicons. They are characterized by their graph structure that is mainly woven by paradigmatic and syntagmatic lexical relations known as lexical functions. Being organized as “social networks of words,” Lexical Systems are radically distinct from taxonomic models which organize lexical units within hierarchized classes. In spite of their apparent messiness, these structures are perfectly suited for the storage of lexical regularities. This paper is devoted to the Gener (generic) lexical function and its significance for the identification and modeling of regular polysemy in network models of the Lexical System family. We show how the systematic lexicographic encoding of Gener relations allows for the study of polysemy patterns and the uniform modeling of this phenomenon in lexicographic models. The approach is illustrated based on the lexicographic construction of a Lexical System – the French Lexical Network (fr-LN) –, using the generic term Fr. fleur ‘flower’.
Balancing act: when to flex and when to stay fixed

<h2>Abstract</h2> Building automated platforms in chemical processes is a shared goal by many researchers. Herein, we provide essential insights into the decision-making process for choosing automation platforms, highlighting the suitability of fixed automation for standardized tasks and the strategic use of flexible automation in dynamic research settings. By addressing key factors, we aim to assist researchers in making informed decisions tailored to their specific automation needs.
CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation

End-to-end conversational recommendation systems (CRS) generate responses by leveraging both dialog history and a knowledge base (KB). A CRS mainly faces three key challenges: (1) at each turn, it must decide if recommending a KB entity is appropriate; if so, it must identify the most relevant KB entity to recommend; and finally, it must recommend the entity in a fluent utterance that is consistent with the conversation history. Recent CRSs do not pay sufficient attention to these desiderata, often generating unfluent responses or not recommending (relevant) entities at the right turn. We introduce a new CRS we call CoRE-CoG. CoRE-CoG addresses the limitations in prior systems by implementing (1) a recommendation trigger that decides if the system utterance should include an entity, (2) a type pruning module that improves the relevance of recommended entities, and (3) a novel constrained response generator to make recommendations while maintaining fluency. Together, these modules ensure simultaneous accurate recommendation decisions and fluent system utterances. Experiments with recent benchmarks show the superiority particularly on conditional generation sub-tasks with close to 10 F1 and 4 Recall@1 percent points gain over baselines.
Parameter-Efficient Multilingual Summarisation: An Empirical Study

Although the advancements of pre-trained Large Language Models have significantly accelerated recent progress in NLP, their ever-increasing size poses significant challenges for conventional fine-tuning, especially in memory-intensive tasks. We investigate the potential of Parameter-Efficient Fine-Tuning, focusing on Low-Rank Adaptation (LoRA), in the domain of multilingual summarization, a task that is both challenging (due to typically long inputs), and relatively unexplored. We conduct an extensive study across different data availability scenarios, including high- and low-data settings, and cross-lingual transfer, leveraging models of different sizes. Our findings reveal that LoRA is competitive with full fine-tuning when trained with high quantities of data, and excels in low-data scenarios and cross-lingual transfer. We also study different strategies for few-shot cross-lingual transfer, finding that continued LoRA tuning outperforms full fine-tuning and the dynamic composition of language-specific LoRA modules.
Applications of Computer Vision in Autonomous Vehicles: Methods, Challenges and Future Directions

Autonomous vehicle refers to a vehicle capable of perceiving its surrounding environment and driving with little or no human driver input. The perception system is a fundamental component which enables the autonomous vehicle to collect data and extract relevant information from the environment to drive safely. Benefit from the recent advances in computer vision, the perception task can be achieved by using sensors, such as camera, LiDAR, radar, and ultrasonic sensor. This paper reviews publications on computer vision and autonomous driving that are published during the last ten years. In particular, we first investigate the development of autonomous driving systems and summarize these systems that are developed by the major automotive manufacturers from different countries. Second, we investigate the sensors and benchmark data sets that are commonly utilized for autonomous driving. Then, a comprehensive overview of computer vision applications for autonomous driving such as depth estimation, object detection, lane detection, and traffic sign recognition are discussed. Additionally, we review public opinions and concerns on autonomous vehicles. Based on the discussion, we analyze the current technological challenges that autonomous vehicles meet with. Finally, we present our insights and point out some promising directions for future research. This paper will help the reader to understand autonomous vehicles from the perspectives of academia and industry.
The Perception-Robustness Tradeoff in Deterministic Image Restoration

We study the behavior of deterministic methods for solving inverse problems in imaging. These methods are commonly designed to achieve two goals: (1) attaining high perceptual quality, and (2) generating reconstructions that are consistent with the measurements. We provide a rigorous proof that the better a predictor satisfies these two requirements, the larger its Lipschitz constant must be, regardless of the nature of the degradation involved. In particular, to approach perfect perceptual quality and perfect consistency, the Lipschitz constant of the model must grow to infinity. This implies that such methods are necessarily more susceptible to adversarial attacks. We demonstrate our theory on single image super-resolution algorithms, addressing both noisy and noiseless settings. We also show how this undesired behavior can be leveraged to explore the posterior distribution, thereby allowing the deterministic model to imitate stochastic methods.