Reviewerly/exHarmony huggingface dataset:
Text Preprocessing and Enrichment of Large Text Corpus-Based Keyphrase Generation for Goal-Oriented Dialogue Systems

Pre-Trained Language Models (PLM) have recently been trained on large corpus and achieved remarkable success in various Natural Language Generation (NLG) applications, including task-oriented Dialogue (TOD) systems. In TOD systems, NLG is key to generating a natural language utterance to the sequential natural words and user’s questions. Recent works on TOD generation have focused on enhancing PLM’s performance by utilizing external knowledge. Incorporating graph-structured data is not considered while encoding the knowledge information into a language model. In addition, PLM suffers from detrimental catastrophic forgetting issues while multiple knowledge is injected to enhance the performance on target-specific tasks. This chapter overcomes these limitations by designing a text generation model for a Goal oriented Dialog (GoD) system called GoD-BERT by fine-tuning the BERT on various GoD subtasks. The design of the GoD generation contains three major phases: Goal-specific text preprocessing and representation, Goal-Knowledge Graph Construction, and Graph Neural Network (GNN) and Adapter-based BERT Fine-tuning. The initial phase enriches the dialogue feature representation by considering the Goal-context aware dialogue information extraction. The next is constructing the goal-knowledge graph from the goal-context-aware dialogue information obtained in the first phase. The final phase enhances the BERT model performance based on the Factual Knowledge Structure-Aware Learning module, which involves an attention-based learning process in GNN with graph-structured knowledge representation, and the Continual Linguistic Knowledge-aware Infusion Learning module, which involves continual learning and knowledge updating in the adapter model to generate the dialogue responses effectively.
A Gap Between Automated Service Composition Research and Software Engineering Development Practice: Service Descriptions

In research, automatic service composition (ASC) has been a widely studied academic subject for many years. However, this field still contains topics and issues that remain unidentified or uninvestigated. In this paper, we focus on one such unsolved problem of the ASC, elaborating on a current effort and future plan. This recognized problem is caused by the difference between the formation of service descriptions used by human composers and that used by ASC approaches. In practice, engineers are used to write various development-related documents in natural language, such as their requirement specifications and software component descriptions. However, an exhaustive survey found that most existing ASC studies are assumed to take their required service descriptions in a tuple-based format. Although this difference and problem in theory can be sufficiently addressed using manual processing techniques (e.g., human transformation or extraction), we consider such human intervention to be inefficient, costly, and, most importantly, harmful to the level of automation of ASC. Thus, this study develops an automated solution to this problem. We first introduce some necessary background knowledge and foundation so that the targeted problem can be fully understood and motivated. Then, the specific problem to be studied is clearly defined and exemplified along with a detailed explanation. Finally, the three key components to explore and address this research problem (dataset, approach, and evaluation) are discussed in detail, including the current work of the authors and proposals for future research.
A Survey on Applications of Particle Swarm Optimization Algorithms for Software Effort Estimation

Particle swarm optimization is a metaheuristic, evolutionary global optimization method. The PSO algorithm is inspired from swarm behavior and categorized as swarm intelligence algorithm. It is a robust, simple, easy to understand, easy to implement, efficient, and popular algorithm. From the inception of the particle swarm optimization algorithm, it has undergone multiple changes. It has a remarkable ability to select significant data from the pool of infinite and inconsistent information. The variants of particle swarm optimization methods have applied on various academic, scientific, and industrial applications. The uses of swarm intelligence methods increase effectiveness of the application in a simple way. It improves the large application domain of engineering and scientific activities. Mainly, the paper analyzes particle swarm optimization algorithms that are used for software development related activity. Software effort is a process of calculating the amount of effort required to develop a software project. The software effort estimation activity includes analytical calculation for size, cost, time, and effort required to accomplish a software project.
Real-Time Drowning Detection at the Edge

Drowning is currently one of the most significant worries on the globe. Using technologies that track human activity in the water, such as wearable devices and cameras, and then applying basic image processing or deep learning algorithms to recognize drowning behaviour in people is the direct approach. However, most of these methods have numerous limitations and can only address a few instances of drowning. Consequently, this research presents a method for recognizing drowning occurrences early by utilizing the arm-based characteristic of drowning individuals. Technically, the arm detection will utilize the modified Yolov7-tiny model for increased FPS while deploying on an edge device (Jetson Nano). Then, we implement a grid tracker (GT) algorithm to determine whether or not the identified arm belongs to a drowning behaviour. Experimental results confirm that our modified Yolov7-tiny model achieves a high mAP accuracy of 99.1% and a real-time performance of 35.4 FPS on Jetson Nano. Additionally, the precision and recall result of grid tracker (GT) to identify the drowning behaviour are 98.34% and 100%.
STUDENTS’ READING HABIT AT THE FOURTH SEMESTER OF ENGLISH LANGUAGE EDUCATION, FKIP-UIR

During the COVID-19 pandemic, the learning process is carried out online and students spend a lot of time at home with the phone. Therefore, there are so many types of e-books that you can look for to read in your spare time. This research aims to find out the students' reading habit in the fourth semester of English Language Education, FKIP-UIR. This research used the descriptive qualitative method. The sample of this research was used purposive sampling. The research sample was 43 of class 4B at English Language Education of FKIP UIR. This study found that students' reading habits were categorized as positive. Almost all students have a good attitude toward reading. Then, most students enjoy reading ebooks and do not feel annoyed when asked to read e-books. Meanwhile, students also have positive book reading. Students have favorite types of e-books to read and read e-books quite often. Furthermore, students also have positive time spent on reading both in academic and non-academic. Finally, students also get positive motivation in reading, and students get motivation from their families and lecturers in the academic field to read e-books because reading can improve achievement. on the other hand, the researcher found one indicator of reading habits to get negative results. The indicator is reading frequency. Most students said they did not often visit the web for online reading resources as well as students did not often read e-books because they did not like reading and preferred listening to reading.
GRAMMATICAL ERROR ANALYSIS IN THESIS ABSTRACT: ENGLISH EDUCATION GRADUATE STUDENTS OF UIN SUSKA PEKANBARU

Abstract&#x0D; An abstract is an entire picture of writing in terms of the information it contained. In terms of using English for the abstract is due to the reason that the language itself known internationally, thus makes it being recognized and read by lots of people. However, writing in English has never been easy for some people- particularly for English language students- who are demanding to write perfectly in terms of grammar. It is even more complicated due to fulfilling all the formalities; composing, developing logical ideas for the reader to comprehend its content. In this study, the writer aims to find out the errors in grammar in the thesis abstract of the graduate students of English Concentration of Post Graduate Program of State Islamic University of Sultan Syarif Qasim II (UIN SUSKA) Riau. This research uses a qualitative approach with a descriptive analysis technique. Samples of this research were collected purposively from the Post Graduate Program Library of the University in 2018, and the number is 17 (seventeen) abstracts from 2015 to 2017. The result of the data analysis shows that the types of error produced in a row are punctuation at the first place by 76.47% and then singular-plural by 47.06%, preposition and subject-verb agreement each by 29.41%, verb form and conciseness (long words) by 23.53%, and the last is Capitalizing by 5.88%. In addition, the omission of the article, especially article 'the', also occurred many times in the writing. &#x0D; &#x0D; Keywords: Error Analysis, Grammatical, Thesis Abstract, Writing&#x0D;
AN INVESTIGATION OF EFL STUDENT'S LEARNING STRATEGIES BASED ON GENDER DIFFERENCES ON ONLINE TEACHING AND LEARNING

The result of previous studies on English Foreign Language (EFL) learning indicate that the students apply all of the learning strategies as follows; meta-cognitive, cognitive, social, and affective strategies (Safitri, Sumardi, Supriyadi, 2018). Individual differences are the factor that influences the Language learning strategies (LLS) and plays an influential role in the process of language learning. In this regard, this paper makes attempt to explore the online learning strategies used by Indonesian EFL students and to analyze those strategies in performance assessment during online teaching and learning concerning gender differences. This paper applies a qualitative study with a total of 30 EFL learners (15 males and 15 females) selected from the 5th-semester student at Tidar University. The participants completed the questionnaire data that consist of thirteen statements from the previous study (Safitri, Sumardi, Supriyadi, 2018). The researcher finding from this study is female students frequently used more learning strategy rather than males students in online learning.
THE IMPLEMENTATION OF USING ICT IN TEACHING ENGLISH AT EFL CLASS OF RONGGOLAWE UNIVERSITY TUBAN

Abstract&#x0D; In this time, almost everyone knows of ICT in their daily lives. In the field of education the impact of ICT could not be avoided any longer, but must be faced. This study focused to find out the implementation of using ICT by the English lecturers of EFL class at UNIROW. The researchers wants to find out what kind of ICT that used by the lecturers when they teach four skills of English in EFL class. This research is conducted using descriptive qualitative research design to answer the questions of the study. In descriptive qualitative research, the researcher use interview and questionnaire to collect the data from the subject. Moreover, to get validity of data, the interview is supported by questionnaire. This questionnaire is analyzed by using descriptive statistics where frequency counts are tabulated and converted to percentages. The finding of this research, from the questionnaire and also the interview showed that all of lecturers in EFL class using ICT when they teach four skill of English. Beside that the researchers find the kind of ICT that used by the lecturers in teaching four skills of English at EFL class.&#x0D; &#x0D; Keywords: ICT, Teacher’s Perception, EFL class.&#x0D; &#x0D; Abstrak&#x0D; Pada saat ini hamper setiap orang mengenal ICT pada kehidupan sehari-hari mereka. Pada bidang pendidikan pengaruh dari ICT tidak bisa lagi dihindari, melainkan harus dihadapi. Penelitian ini berfokus untuk mengetahui penerapan ICT di kelas oleh dosen Bahasa Inggris di Unirow. Peneliti ingin mengetahui jenis-jenis ICT yang digunakan oleh para dosen ketika mengajar empat keahlian dalam pengajaran Bahasa Inggris di kelas. Penelitian ini menerapkan desain penelitian deskriptif kualitatif untuk menjawab pertanyaan-pertanyaan penelitian ini. Pada penelitian deskriptif kualitatif, peneliti melakukan interiview dan membuat kuesioner untuk mengumpulkan data dari subyek penelitian. Dan juga untuk mendapatkan validitas data, interview ini didukung dengan pembagian kuesioner/angket. Kuesioner ini dianalisis dengan menguunakan statisti deskriptif dimana penghitungan frekuensi ditabulasi dan dikonversikan menjadi prosentase. Hasil temuan dari penelitian ini, baik dari kuesioner maupun interview menunjukkan bahwa semua dosen pada jurusan Bahasa Inggris menerapkan ICT saat mengajar keempat keahlian Bahasa Inggris. Selain itu, peneliti juga menemukan berbagai jenis ICT yang digunakan oleh para dosen dalam mengajarkan empat keahlian dalam pengajaran Bahasa Inggris.&#x0D; Kata kunci: ICT, persepsi, kelas Bahasa Inggris
Sentimental analysis &amp; Hate speech detection on English and German text collected from social media platforms using optimal feature extraction and hybrid diagonal gated recurrent neural network

Social networking platforms allow users to ask questions, exchange information, opinions, ideas, and promote companies. Social media's massive user-generated text is useful for study, translation, and analysis. Due of their quick spread and detrimental impact, offensive remarks, hate speech, and harassment must be discovered and deleted immediately. In non-English main language environments, code-mixed text makes hate speech detection difficult. Hate speech's thematic emphasis and target-oriented orientation are typically ignored in binary categorization methods. These methods also fail in code-mixing multilingual contexts. In this study, we propose an optimal feature extraction and hybrid diagonal gated recurrent neural network (FE-DGRNN) for hate speech detection and sentiment analysis on code-mixed texts in multiple languages. Our FE-DGRNN technique consists of three processes: preprocessing, improved seagull optimization (ISO) for feature extraction, and hybrid diagonal gated recurrent neural network (Hyb-DGRNN) for hate speech detection and sentiment analysis. We evaluate the performance of our proposed technique using the HASOC 2019 dataset, focusing on English and German. The results demonstrate high accuracy of 95%, 96%, and 92% for English tasks, with Precision and F-measure of 94%, 96%, and 91%. For German tasks, the accuracy is 92% and 93%, with Precision and F-measure of 90% and 91%.
Where to Place a Pile?

When planning missions for autonomous machines in real-world scenarios, such as open-pit mining, painting, or harvesting, it is important to consider how the machines will alter the working environment during their operations. Traditional planning methods treat such changes, like piles built during drilling, as constraints given to the planner that depend on the machine's trajectory. The goal is to find a trajectory that satisfies these constraints. However, our approach formulates the planning problem as finding optimal positions for changes, such as piles, along the machine's trajectory. We propose a heuristic solver and provide extensive experimental evaluations.
A Knowledge Graph Quality Evaluation Method for Multilevel Power Grid Dispatching Cloud

Knowledge graph is an emerging technology used to demonstrate knowledge relationships and has broad application prospects. This paper delves into the theory, methodology, and practical application of knowledge management and quality assessment in multilevel dispatching centers, utilizing the knowledge graph as a foundation. In order to comprehensively evaluate the reliability and practicality of knowledge graphs, a knowledge graph quality evaluation method was proposed, including three dimensions: data intrinsic, service response, and downstream application, the method can provide a theoretical basis for the quality evaluation of knowledge graphs.
Deontic meta-rules

Abstract The use of meta-rules in logic, i.e., rules whose content includes other rules, has recently gained attention in the setting of non-monotonic reasoning: a first logical formalisation and efficient algorithms to compute the (meta)-extensions of such theories were proposed in Olivieri et al. (2021, Computing defeasible meta-logic. In JELIA 2021, LNCS, vol. 12678, pp. 69–84. Springer.). This work extends such a logical framework by considering the deontic aspect. The resulting logic will not just be able to model policies but also tackle well-known aspects that occur in numerous legal systems. The use of Defeasible Logic to model meta-rules in the application area we just alluded to has been investigated. Within this line of research, the study mentioned above was not focusing on the general computational properties of meta-rules. This study fills this gap with two major contributions. First, we introduce and formalise two variants of Defeasible Deontic Logic (DDL) with meta-rules to represent (i) defeasible meta-theories with deontic modalities and (ii) two different types of conflicts among rules: Simple Conflict DDL and Cautious Conflict DDL. Second, we advance efficient algorithms to compute the extensions for both variants.
Vehicle Target Detection in Day-Night Mode Based on YOLOv5

With the development of computer technology, autonomous vehicle technology has been rapidly updated. Robust and efficient vehicle detection on the road is the key task of autonomous vehicle environment perception. At this stage, many scholars have done a lot of research on daytime pattern detection using machine learning and deep learning technology. However, there is still a lack of vehicle detection in night mode. The main work of this paper is to create a new data set, and use different models (n, s, m, l, x) of a single-stage target detection algorithm YOLOv5 to verify the effectiveness of detecting vehicles on the road in day and night mode. Three evaluation parameters are used to evaluate the efficiency of the model: recall, precision and mAP. The goal of this paper is to find an efficient model to improve the accuracy of day-night vehicle detection, and provide vehicle condition detection results. This can provide an effective visual perception for the auto drive system.
Generalized Minkowski Distance-Based Local Mean k-Nearest Neighbor Classifier

This research focuses on comparing the classification performance of Local Mean-based K Nearest Neighbors (LMKNN) using different distance metrics. Specifically, we examine different value of close neighbors and 10 distance metrics within the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{p}$</tex> Norm ranging from 1 to 10, such as Euclidean distance, Minkowski distance and Manhattan distance. The KNN algorithm is a powerful tool for classification and regression tasks. Unlike many other algorithms, KNN is non-parametric, meaning it can adapt to complex models without making any assumptions about the data distribution. This makes it a popular choice for researchers who prefer simple classification model rather than complex one. However, calculating the distance between each test data point and all training data points can be computationally expensive, particularly when working with a large data set. To address this issue, we have consulted an improved KNN algorithm based on the local mean, which significantly reduces the calculation complexity without sacrificing accuracy. We have applied our algorithm to several commonly used data sets on UCI and observed promising classification performance. Furthermore, we have extended the Euclidean distance measurement in the original algorithm to the level of vector norm. This expands the classification index of KNN beyond just Euclidean distance and provides more accurate and robust results.
Study on Constructing Breast Cancer Prediction Model with Different Algorithms

Nowadays breast cancer had become one of the most serious cancers for women in the world. Therefore, constructing a risk prediction model for breast cancer is of great importance. This paper focuses on the goal to construct a best breast cancer prediction model based on SVM, KNN, and Naïve Bayes algorithms, which is constructed based on dataset from Wisconsin Breast Tumor dataset that has 569 samples with 30 calculated traits derived from digital photographs of the cell nuclei. For methods used in the paper, data-preprocessing, EDA, PCA are used to process the dataset. After that, prediction models are constructed and optimized based on three algorithms. Results show that SVM are the best option for the dataset in this study, according to the experiment, however KNN is better suited for datasets with more samples or samples that will be separated such that the class fields are heavily crossed or overlapped. For samples with fewer features, the Nave Bayes method is more suited.
NR-Isomap: An Incremental Approach with Gaussian Process Kernels for Denoising

The Isomap is a popular nonlinear dimensionality reduction method often used for low-dimensional embedding space. When the big data is noisy, Isomap often fails because of the graph structure’ Short-Circuit Edges (SCE) problem. In this paper, an incremental Noise-Reduction Isomap (NR-Isomap) approach is to address the SCE problem in a graph structure. The GP kernels are combined with the manifold learning approach and provide significant results. NR-Isomap is based on the Hessian Locally Linear Embedding (HLLE) algorithm with Gaussian Process (GP) kernels. The HLLE algorithm provides the optimal solution to the SCE problem caused by an overly noisy dataset. NR-Isomap provides an answer to the Isomap SCE problem in noisy big data problems that cannot be handled using existing methods. Therefore, the GP kernel significantly improves and measures the performance of the NRI-somap accuracy. Our NR-Isomap method outperforms experiments on SK-Learn datasets and shows efficient results compared to Isomap. Our NR Isomap is much more efficient and faster compared to Isomap. The efficiency and accuracy of the NR-Isomap method are demonstrated theoretically and compared with other manifold learning methods.
An Efficient Approach for Merging Multidimensional Blockchains in Mobile IoT

Graph-based or multidimensional blockchains have been proposed to improve the scalability and efficiency of existing blockchain applications. However, when implemented in mobile Internet of Things (mIoT) networks, these blockchain systems can frequently split and merge and cause the merging algorithm to process a large number of similar blocks (a block is similar or identical when it exists once in multiple blockchains). The presence of similar blocks hinders the merging process, as it consumes time and computational resources to scan, validate, and potentially merge similar blocks. This paper presents an efficient approach for merging graph-based or multidimensional blockchains in mIoT networks by avoiding similar blocks and effectively detecting and merging new blocks that were created after the split. Our proposed merging algorithm employs depth-first search and Merkle tree techniques to minimize the time and computational resources spent on identical blocks. Finally, we evaluate the performance of our method in highly mobile networks and demonstrate that it can execute the merge with a more than 72% reduction in time in comparison to merging algorithms without block similarity handling.