Reviewerly/exHarmony huggingface dataset:
Understanding Analytical Exposition Text Writing with an SFL Approach

Authors:
raw_author_name: Wawan Gunawan
raw_affiliation_string: Universitas Pendidikan Indonesia

Abstract: This research aims to explore the ability of eleventh-grade students to create analytical exposition texts based on their generic structure and language features, using a descriptive qualitative research approach. The study focuses on comparing model texts with students' writing to describe the types of Systemic Functional Linguistics (SFL) approaches employed and analyze the transitivity in writing analytical exposition. The participants of this study are eleventh-grade students selected based on a teacher's recommendation. The researcher reports the natural phenomena without applying any treatments that could influence the results. The primary objective is to understand the student’s ability to produce analytical exposition texts without intervention. The data collection involves analyzing sentences in the form of students' analytical exposition text assignments provided by the teacher. The researcher examines the clarity of ideas and evaluates the grammatical and generic structure present in the students' writing. This research utilizes a descriptive qualitative approach to capture and describe the natural phenomenon of students' analytical exposition text writing. The findings contribute to understanding the students' proficiency in creating analytical exposition student texts, as well as identifying areas that require further attention in instructional practices.
Code Smell Detection and Software Refactoring Research: A Systematic Literature Review

Authors:
raw_author_name: Israr Ali
raw_affiliation_string: Dept. of Computer Science  Iqra University  Karachi, Pakistan
raw_author_name: Syed Hasan Adil
raw_affiliation_string: Department of Computer Science Iqra University Karachi, Pakistan. Karachi, Pakistan.
raw_author_name: Abdul Ahad Abro
raw_affiliation_string: Department of Computer Science Iqra University Karachi, Pakistan.

Abstract: Today we are experiencing rapid enhancements in software systems and their development. The software industry's demand for tools and techniques for software development, especially automatic and less time-consuming, is increasing daily. Software refactoring and code smell detection are now expanded from code-level changes to the architecture, model, and requirements restructuring. We are moving from an object-oriented paradigm towards cloud computing, web development and mobile application development and so much more. Therefore, code smell and refactoring techniques are talk of the town in various research communities in their objectives. In this paper we will study the existing tools and techniques, research progress by doing a systematic literature review in the field of code smell detection and software refactoring’s. We will also classify the existing research techniques, identify the trends in code smell detection and refactoring and try to highlight the gaps in the area for researchers.
Web-Based Tourism And Culture Information System Using Google Api At The Blora Regency Tourism And Culture Office

Authors:
raw_author_name: None Didik Sofian Haryadi
raw_affiliation_string: Universitas Sains dan Teknologi Komputer
raw_author_name: None Bambang Suhartono
raw_affiliation_string: Universitas Sains dan Teknologi Komputer

Abstract: With the increasingly rapid development of computer technology, internet media is a very effective means of conveying information. Through the internet, various kinds of information from various fields can be obtained quickly. The internet has become an important part as a forum or promotional medium. With the advantages of the internet, the author tries to provide information about the existence of tourism and culture in Blora so that the people of Blora city in particular and people outside the city of Blora in general know it easily and save time. The design of this system was made through several stages. The supporting software used is Apache, Macromedia Dreamweaver MX and PHP as server side-scripting and the final result of this research is a website that provides information about Tourism and Culture of Blora Regency.
Optimizing Treatment of Herbal Plant Using SOPHERBAL Android Application Fordward Chaining Method

Authors:
raw_author_name: Muhamad Azwar
raw_affiliation_string: Universitas Bumigora, Mataram, Indonesia

Abstract: The utilization of traditional herbal medicine among the inhabitants of Lombok is notably prevalent yet frequently hindered by a lack of comprehension regarding the efficacy of herbal remedies for specific ailments. Addressing this challenge, this study proposes the development of an Android application called ”sopherbal,” aimed at delivering personalized herbal plant recommendations via easily accessible mobile devices. Employing forward chaining methodology, the application identifies optimal herbal remedies based on ailment type, processing techniques, usage instructions, and recommended dosage and treatment duration. Notably, while effective in this context, the forward chaining approach entails certain trade-offs and hurdles. Previous research indicates that forward chaining facilitatesaccurate recommendation generation, and it may be constrained by its reliance on predefined rules and limited adaptability to complex, evolving scenarios. Despite these challenges, the ”sopherbal” application, featuring 50 Sasak medicinal plants curated for 15 common ailments, achieved an 86% validation rate, affirming its efficacy in bridging the gap between traditional herbal knowledge and modern healthcare needs.
Exploratory Analysis of Different Types of Adjectives for Sentiment Classification

Authors:
raw_author_name: Lubna Zafar
raw_affiliation_string: University of Poonch, Rawalakot, Azad Kashmir.
raw_author_name: Nayyer Masood
raw_affiliation_string: Capital University of Science and Technology, Islamabad.

Abstract: Online websites available on the internet, Amazon, provide a platform for users to share their valuable opinions. Users’ reviews are available in thousands, and extracting useful information from these reviews manually is a crucial task for managers, companies, and users. Reviews are written in natural language, and to extract useful information from the reviews, there is a need for an automatic technique known as sentiment analysis. Researchers have used polarity features like nouns, adjectives, verbs, and adverbs to mine sentiment using one feature or combination. The studies showed that adjectives remain the most prominent feature. However, previous research has not evaluated many types on a comprehensive dataset. This research focuses on the identification of different kinds of adjectives in a given text, the identification of the best type and the classification of different kinds as positive, negative, and neutral. Understanding the nuanced impact of various adjectives in sentiment classification is crucial for developing more accurate and context-aware natural language processing models, essential for applications ranging from sentiment analysis to customer reviews. We comprehensively evaluate different types of adjectives on machine learning algorithms. The experimental results are performed on an annotated dataset of 58,258 office product reviews collected from Amazon. Out of these evaluated adjectives, opinion adjectives have the highest precision of 0.931 on the Naïve Bayes classifier and show the best sentiments.
Nexus Between Technology, Education, Income, and Social Capital in Top 10 Terrorism Affected Countries: The Leading Role of Corruption

Authors:
raw_author_name: Muhammad Sibt e Ali
raw_affiliation_string: Zhengzhou University, Henan.
raw_author_name: Muhammad Ramzan Sheikh
raw_affiliation_string: Bahauddin Zakariya University Multan.

Abstract: There is a strong correlation between the expansion of institutions in nations with advanced educational systems and an increase in the frequency and severity of terrorist attacks. This trend remains substantial even after accounting for factors like per capita income, political stability, unemployment, inequality, and urbanization. This is when the frequency and severity of terrorist attacks appear to be at their highest. Therefore, this study analyzes the impacts of terrorism on education in the top 10 terrorist countries between 2010 and 2020. This study considers the determinants of education (EDU): GDP per capita, terrorism, social capital, technology and physical capital. The study employs FMOLS estimation and the DOLS test to check the results of variables in this study. The effect of GDP per capita, social capital, and physical capital significantly positively impact education in this model. However, terrorism and technology have a negative impact on education. Moreover, the moderate effect of terrorism on social capital, technology and physical capital is negatively related to education. On behalf of the results, this study proposes how a country can maximize education.
DocFlow: A Visual Analytics System for Question-Based Document Retrieval and Categorization

Authors:
raw_author_name: Rui Qiu
raw_affiliation_string: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
raw_author_name: Yamei Tu
raw_affiliation_string: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
raw_author_name: Yu-Shuen Wang
raw_affiliation_string: Department of Computer Science, National Yang Ming Chiao Tung University, HsinChu, Taiwan
raw_author_name: Po-Yin Yen
raw_affiliation_string: Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA
raw_author_name: Han-Wei Shen
raw_affiliation_string: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

Abstract: A systematic review (SR) is essential with up-to-date research evidence to support clinical decisions and practices. However, the growing literature volume makes it challenging for SR reviewers and clinicians to discover useful information efficiently. Many human-in-the-loop information retrieval approaches (HIR) have been proposed to rank documents semantically similar to users' queries and provide interactive visualizations to facilitate document retrieval. Given that the queries are mainly composed of keywords and keyphrases retrieving documents that are semantically similar to a query does not necessarily respond to the clinician's need. Clinicians still have to review many documents to find the solution. The problem motivates us to develop a visual analytics system, DocFlow, to facilitate information-seeking. One of the features of our DocFlow is accepting natural language questions. The detailed description enables retrieving documents that can answer users' questions. Additionally, clinicians often categorize documents based on their backgrounds and with different purposes (e.g., populations, treatments). Since the criteria are unknown and cannot be pre-defined in advance, existing methods can only achieve categorization by considering the entire information in documents. In contrast, by locating answers in each document, our DocFlow can intelligently categorize documents based on users' questions. The second feature of our DocFlow is a flexible interface where users can arrange a sequence of questions to customize their rules for document retrieval and categorization. The two features of this visual analytics system support a flexible information-seeking process. The case studies and the feedback from domain experts demonstrate the usefulness and effectiveness of our DocFlow.
WRIST: Wideband, Real-Time, Spectro-Temporal RF Identification System Using Deep Learning

Authors:
raw_author_name: Hai N. Nguyen
raw_affiliation_string: Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
raw_author_name: Marinos Vomvas
raw_affiliation_string: Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
raw_author_name: Triet D. Vo-Huu
raw_affiliation_string: Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
raw_author_name: Guevara Noubir
raw_affiliation_string: Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

Abstract: RF emissions' detection, classification, and spectro-temporal localization are essential not only for understanding, managing, and protecting the radio frequency resources, but also for countering today's security threats such as jammers. Achieving this goal for wideband, real-time operation remains challenging. In this paper, we present WRIST, a Wideband, Real-time, Spectro-Temporal RF Identification system. WRIST can detect, classify, and precisely locate RF emissions in time and frequency using RF samples of 100 MHz spectrum in real-time. The system leverages an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one-stage object detection</i> Deep Learning framework, and transfer learning to a multi-channel visual-based spectral representation. Towards developing WRIST, we devised an iterative training approach which leverages synthesized and augmented RF data to efficiently build a large dataset with high-quality labels. WRIST achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$99 \%$</tex-math></inline-formula> class detection accuracy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$94 \%$</tex-math></inline-formula> emission precision and recall, with less than 0.08 bandwidth and time offset ratios in a large anechoic chamber over-the-air environment. In the extremely congested in-the-wild environment, WRIST still achieves over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$80 \%$</tex-math></inline-formula> precision and recall. WRIST currently supports five 2.4 GHz technologies (Bluetooth, Lightbridge, Wi-Fi, XPD, and ZigBee) and is easily extendable to others. We are making our curated dataset available to the whole community. It comprises over 10 million labelled RF emissions from off-the-shelf wireless radios spanning the five classes of technologies.
The importance Markov chain

Authors:
raw_author_name: Randal Douc
raw_affiliation_string: SAMOVAR, Telecom Sudparis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91820, Evry, France
raw_author_name: Hugo Marival
raw_affiliation_string: SAMOVAR, Telecom Sudparis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91820, Evry, France
raw_author_name: Christian P. Robert
raw_affiliation_string: CEREMADE, CNRS, UMR 7534, Université Paris-Dauphine, PSL University, 75016, Paris, France; Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK

Abstract: The Importance Markov chain is a novel algorithm bridging the gap between rejection sampling and importance sampling, moving from one to the other through a tuning parameter. Based on a modified sample of an instrumental Markov chain targeting an instrumental distribution (typically via a MCMC kernel), the Importance Markov chain produces an extended Markov chain where the marginal distribution of the first component converges to the target distribution. For example, when targeting a multimodal distribution, the instrumental distribution can be chosen as a tempered version of the target which allows the algorithm to explore its modes more efficiently. We obtain a Law of Large Numbers and a Central Limit Theorem as well as geometric ergodicity for this extended kernel under mild assumptions on the instrumental kernel. Computationally, the algorithm is easy to implement and preexisting librairies can be used to sample from the instrumental distribution.
A Scalable Method for Readable Tree Layouts

Authors:
raw_author_name: Mingwei Li
raw_affiliation_string: Department of Computer Science, University of Arizona, Tucson, AZ, USA
raw_author_name: Reyan Ahmed
raw_affiliation_string: Department of Computer Science, University of Arizona, Tucson, AZ, USA
raw_author_name: Md. Khaledur Rahman
raw_affiliation_string: Computer Science, Indiana University, Bloomington, USA
raw_author_name: Ariful Azad
raw_affiliation_string: Computer Science, Indiana University, Bloomington, USA
raw_author_name: Stephen Kobourov
raw_affiliation_string: Department of Computer Science, University of Arizona, Tucson, AZ, USA
raw_author_name: Katy Börner
raw_affiliation_string: SLIS, Indiana University, Bloomington, USA

Abstract: Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.
Collective Decision of One-vs-Rest Networks for Open-Set Recognition

Authors:
raw_author_name: Jaeyeon Jang
raw_affiliation_string: Department of Data Science, The Catholic University of Korea, Bucheon, Republic of Korea
raw_author_name: Chang Ouk Kim
raw_affiliation_string: Department of Industrial Engineering, Yonsei University, Seoul, Republic of Korea

Abstract: Unknown examples that are unseen during training often appear in real-world pattern recognition tasks, and an intelligent self-learning system should be able to distinguish between known examples and unknown examples. Accordingly, open-set recognition (OSR), which addresses the problem of classifying knowns and identifying unknowns, has recently been highlighted. However, conventional deep neural networks (DNNs) using a softmax layer are vulnerable to overgeneralization, producing high confidence scores for unknowns. In this article, we propose a simple OSR method that is based on the intuition that the OSR performance can be maximized by setting strict and sophisticated decision boundaries that reject unknowns while maintaining satisfactory classification performance for knowns. For this purpose, a novel network structure, in which multiple one-vs-rest networks (OVRNs) follow a convolutional neural network (CNN) feature extractor, is proposed. Here, an OVRN is a simple feedforward neural network that is designed to assign confidence scores that are lower than those in the softmax layer to unknown samples so that unknown samples can be more effectively separated from known classes. Furthermore, the collective decision score is modeled by combining the multiple decisions reached by the OVRNs to alleviate overgeneralization. Extensive experiments were conducted on various datasets, and the experimental results show that the proposed method performs significantly better than the state-of-the-art methods by effectively reducing overgeneralization. The code is available at https://github.com/JaeyeonJang/Openset-collective-decision.
ViTPose++: Vision Transformer for Generic Body Pose Estimation

Authors:
raw_author_name: Yufei Xu
raw_affiliation_string: Sydney AI Centre and the School of Computer Science, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
raw_author_name: Jing Zhang
raw_affiliation_string: Sydney AI Centre and the School of Computer Science, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
raw_author_name: Qiming Zhang
raw_affiliation_string: Sydney AI Centre and the School of Computer Science, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
raw_author_name: Dacheng Tao
raw_affiliation_string: Sydney AI Centre and the School of Computer Science, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia

Abstract: In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled to 1B parameters by taking the advantage of the scalable model capacity and high parallelism, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose++ model is proposed to deal with heterogeneous body keypoint categories via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Our largest single model ViTPose-G sets a new record on the MS COCO test set without model ensemble. Furthermore, our ViTPose++ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed.
Estimation of monthly global solar radiation over twelve major cities of Libya

Authors:
raw_author_name: Alhassan Ali Teyabeen
raw_affiliation_string: Libyan center for solar energy research and studies, Tripoli, Libya
raw_author_name: Najeya B. Elhatmi
raw_author_name: Akram A. Essnid
raw_author_name: F. Mohamed

Abstract: • 7 empirical models are compared for estimation of solar radiation over Libya. • The models are established using measured data of global solar radiation. • Statistical indicators are calculated to evaluate the performance of the models. • The best model performance for each site is determined. • The results show that there is no unique model valid for all considered sites. This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.