Reviewerly/exHarmony huggingface dataset:
Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search

Authors:
raw_author_name: Dian Islamiaty Puteri
raw_affiliation_string: Universitas Padjadjaran
raw_author_name: Gumgum Darmawan
raw_affiliation_string: Universitas Padjadjaran
raw_author_name: Budi Nurani Ruchjana
raw_affiliation_string: Universitas Padjadjaran

Abstract: Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.
Intervention to Enhance Reading Comprehension Skills of Grade 7 Students through Background Knowledge Activation, Repeated Readings, Reading Aloud, and Asking Questions

Authors:
raw_author_name: Louela Martinez
raw_affiliation_string: Caponayan National High School

Abstract: This study sought to assess the reading proficiency of 23 purposively selected seventh-grade students at Caponayan National High School during the academic year 2023-2024, focusing on their reading comprehension levels. These assessments were the foundation for developing an effective reading program tailored to students’ needs. Over four weeks, data collection involved repeated reading-aloud sessions and immediate questioning post-reading. In the educational process carried out in the Philippines, the Informal Reading Inventory standard is used. The analysis revealed that the respondents demonstrated mastery of essential sight words, with only a few identified miscues. Significantly, the number of students experiencing frustration decreased from 16 to 4 after the intervention, accompanied by notable improvements in reading comprehension. The instructional level increased from 7 to 13, while the highest comprehension level, classified as independent, rose from 0 to 6. Assessing the students’ overall performance before and after the intervention indicated substantial progress. Statistical analysis demonstrated a significant difference before and after the intervention. Based on these findings, the study concludes that even a brief intervention can significantly improve students’ reading abilities.
The Contribution of EFL Students’ Vocabulary Mastery and Motivation to Writing Ability of Eighth-Grade Students at Smpn 5 Denpasar

Authors:
raw_author_name: Ni Made Ratminingsih
raw_affiliation_string: Universitas Pendidikan Ganesha
raw_author_name: Made Hery Santosa
raw_affiliation_string: Universitas Pendidikan Ganesha

Abstract: The ability to compose written messages in words, sentences, texts, and coherent words that readers can understand is known as writing ability. The objectives of this research were first to investigate how much the contribution of vocabulary mastery to writing ability, the second was to investigate how much the contribution of motivation to writing ability, and the third was to investigate how much the contribution of vocabulary mastery and motivation simultaneously to writing ability. This study was designed in the form of correlational research design. The population of this study was the eighth-grade students at SMPN 5 Denpasar. In this study, 149 students served as samples. They were chosen using a proportionate random sampling technique. Test of Multiple choice was for vocabulary mastery, questionnaire was for motivation and test of writing assignment was for writing ability, those were utilized to gather the data for this study. With the use of SPSS 26.0, linear and multiple regression analyses were performed on the data. The findings showed that there was high and significant contribution of vocabulary mastery to writing ability. The contribution of vocabulary mastery to writing ability was 69%, and there was high and significant contribution of motivation to writing ability. The contribution of motivation to writing ability was 70.5%. Moreover, the contribution of vocabulary mastery and motivation simultaneously to writing ability was 79.9%. It indicates that there was high and significant contribution of vocabulary mastery and motivation simultaneously to writing ability of eighth grade students of SMPN 5 Denpasar.
User-Centric Security Frameworks in Cloud Computing

Authors:
raw_author_name: Kavitha R

Abstract: The rapid proliferation of cloud computing has revolutionized data management, yet the integration of user-centric security frameworks within cloud service models remains a persistent challenge.This research endeavors to address this gap by proposing a Dynamic Resolution Allocation Approach aimed at optimizing the synergy between robust security measures and an enhanced user experience.The study begins with an exploration of the diverse challenges inherent in achieving a harmonious balance between security and usability, including issues related to cloud environment diversity, user privacy concerns, the dynamic cybersecurity landscape, and interoperability challenges.Leveraging principles of standardization, user-centric design, and continuous research adaptation, the Dynamic Resolution Allocation Approach seeks to offer a comprehensive solution.This paper delineates the methodology, challenges faced in user-centric cloud service models, the proposed dynamic resolution allocation framework, and its implementation and testing.It further discusses the implications of the proposed approach in optimizing user experience through transparency, user-friendly authentication, personalized security settings, educational resources, efficient incident response, clear communication, and continuous monitoring.The findings contribute to the ongoing discourse on user-centric security in cloud computing, presenting a novel approach to bridge existing gaps and enhance the overall security and usability of cloud service models.
Impact of person-job fit and person-organization fit on employee engagement: Moderating role of supervisor support

Authors:
raw_author_name: Alamgir Hossain
raw_affiliation_string: Khulna University, Khulna, Bangladesh
raw_author_name: Mahfuja Khatun
raw_affiliation_string: James Cook University, Townsville, Australia & Human Resource Management Discipline, Khulna University, Khulna, Bangladesh

Abstract: Purpose: This study aimed to examine the impact of two fit concepts, namely, person-job fit (P-J) and person-organization fit (P-O), on employee engagement while taking supervisor support as the moderating variable. Research Methodology: The relationships among constructs were analyzed using correlation, and the study hypotheses were tested using regression. All employees of different mobile telecommunication companies in Bangladesh comprised the study population. The sample size was 100, which was taken using the judgmental sampling technique. Primary data were collected from a field-level survey using a structured questionnaire. Results: The results revealed a positive association between employee engagement and person-job fit, while person-organization fit had a similar association with engagement. The impact of these variables on employee engagement ultimately affects turnover intentions. This study also revealed that the association between person-organization fit and employee engagement is positively moderated by supervisor support. Limitations: The study used a common non-probability sampling technique and a small sample size, which is a limitation. Contribution: The insights will help managers to ensure a match between the job and organizational requirements and those of employees. The study will also inspire future research to connect Field Theory and social exchange theory (SET) with different concepts in different country and industry contexts. Novelty: For the first time in the relevant field, the study simultaneously connected field theory and SET theory with P-J and P-O fit with employee engagement.
A Study on the Effect of Media Literacy Competences on Responding to Fake News

Authors:
raw_author_name: Jaewoong Shim

Abstract: This study examined how individual media literacy competencies affect response behavior towards fake news, which is causing damage to individuals and society. Media literacy competencies are being emphasized as a means to minimize this damage. Therefore, the study investigated the relationship between individual demographic characteristics and media literacy competencies (critical competency, use production competency, social communication competency) concerning information distrust, information confirmation, and active response. This investigation utilized secondary data from the Korea Communications Commission's 2021 ‘Media Literacy Measurement Study to Support Media Education.’ The results of the study revealed a positive relationship between information distrust and critical competency, as well as a positive relationship between information confirmation and critical competency, social communication competency, use, and production competency. Additionally, active response exhibited a positive relationship with male participants, those with lower educational backgrounds, individuals with lower media usage times, critical competency, and social communication competency. Conversely, there was a negative relationship with use and production competency. This study found that media literacy competences which are emphasized as countermeasures against fake news, are related to fake news response behavior. Accordingly, as a countermeasure against fake news, it is proposed to strengthen individual media literacy competences through media education.
[Research on automatic generation of multimodal medical image reports based on memory driven].

Authors:
raw_author_name: Junze Fang
raw_affiliation_string: School of Artificial Intelligence, Beijng Technology and Business University, Beijng 100048, P. R. China.
raw_author_name: Zihan Ju
raw_affiliation_string: School of Artificial Intelligence, Beijng Technology and Business University, Beijng 100048, P. R. China.
raw_author_name: Zheng Guo
raw_affiliation_string: School of Artificial Intelligence, Beijng Technology and Business University, Beijng 100048, P. R. China.
raw_author_name: Yu Wang
raw_affiliation_string: School of Artificial Intelligence, Beijng Technology and Business University, Beijng 100048, P. R. China.

Abstract: The task of automatic generation of medical image reports faces various challenges, such as diverse types of diseases and a lack of professionalism and fluency in report descriptions. To address these issues, this paper proposes a multimodal medical imaging report based on memory drive method (mMIRmd). Firstly, a hierarchical vision transformer using shifted windows (Swin-Transformer) is utilized to extract multi-perspective visual features of patient medical images, and semantic features of textual medical history information are extracted using bidirectional encoder representations from transformers (BERT). Subsequently, the visual and semantic features are integrated to enhance the model's ability to recognize different disease types. Furthermore, a medical text pre-trained word vector dictionary is employed to encode labels of visual features, thereby enhancing the professionalism of the generated reports. Finally, a memory driven module is introduced in the decoder, addressing long-distance dependencies in medical image data. This study is validated on the chest X-ray dataset collected at Indiana University (IU X-Ray) and the medical information mart for intensive care chest x-ray (MIMIC-CXR) released by the Massachusetts Institute of Technology and Massachusetts General Hospital. Experimental results indicate that the proposed method can better focus on the affected areas, improve the accuracy and fluency of report generation, and assist radiologists in quickly completing medical image report writing.医学影像报告自动生成任务面临疾病类型多样、报告描述缺乏专业性和流畅性等多重挑战。为解决以上问题，本文提出一种基于记忆驱动的多模态医学影像报告自动生成方法（mMIRmd），首先使用基于移位窗口的层次视觉转换器（Swin-Transformer）提取患者医学影像的多视角视觉特征，通过基于转换器的双向编码模型（BERT）提取病史信息的语义特征，然后将多模态特征进行融合，提高模型对不同疾病类型的识别能力。其次，使用医学文本预训练的词向量词典对视觉特征标签进行编码，以提高生成报告的专业性。最后，在解码器中引入记忆驱动模块，解决医学影像数据中的长距离依赖关系。本研究在印第安纳大学收集的胸部X光数据集（IU X-Ray）和麻省理工学院联合马萨诸塞州总医院发布的重症监护X光医疗数据集（MIMIC-CXR）上进行验证。实验结果表明，本文所提方法能更好地关注患病区域，提高生成报告的准确性与流畅性，可以辅助放射科医生快速完成医学影像报告的撰写。.
Temporal Output Discrepancy for Loss Estimation-Based Active Learning

Authors:
raw_author_name: Siyu Huang
raw_affiliation_string: Department of Computer Science and Informa-tion Technology, Austin Peay State University, Clarksville, TN 37044 USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134 USA
raw_author_name: Haoyi Xiong
raw_affiliation_string: Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134 USA; the Big Data Laboratory, Baidu Research, Beijing 100193, China
raw_author_name: Bihan Wen
raw_affiliation_string: Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134 USA; School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Ave, Singapore
raw_author_name: Jun Huan
raw_affiliation_string: Department of Computer Science and Informa-tion Technology, Austin Peay State University, Clarksville, TN 37044 USA; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134 USA
raw_author_name: Dejing Dou
raw_affiliation_string: Big Data Laboratory, Baidu Research, Beijing, China; Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02134 USA; School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798

Abstract: While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset. Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss. The core of our approach is a measurement Temporal Output Discrepancy (TOD) that estimates the sample loss by evaluating the discrepancy of outputs given by models at different optimization steps. Our theoretical investigation shows that TOD lower-bounds the accumulated sample loss thus it can be used to select informative unlabeled samples. On basis of TOD, we further develop an effective unlabeled data sampling strategy as well as an unsupervised learning criterion for active learning. Due to the simplicity of TOD, our methods are efficient, flexible, and task-agnostic. Extensive experimental results demonstrate that our approach achieves superior performances than the state-of-the-art active learning methods on image classification and semantic segmentation tasks. In addition, we show that TOD can be utilized to select the best model of potentially the highest testing accuracy from a pool of candidate models.
OLMo: Accelerating the Science of Language Models

Authors:
raw_author_name: Groeneveld, Dirk
raw_author_name: Beltagy, Iz
raw_author_name: Walsh, Pete
raw_author_name: Bhagia, Akshita
raw_author_name: Kinney, Rodney
raw_author_name: Tafjord, Oyvind
raw_author_name: Jha, Ananya Harsh
raw_author_name: Ivison, Hamish
raw_author_name: Magnusson, Ian
raw_author_name: Wang, Yizhong
raw_author_name: Atkinson, David
raw_author_name: Authur, Russell
raw_author_name: Chandu, Khyathi Raghavi
raw_author_name: Cohan, Arman
raw_author_name: Elazar, Yanai
raw_author_name: Gu, Yuling
raw_author_name: Hessel, Jack
raw_author_name: Khot, Tushar
raw_author_name: Merrill, William
raw_author_name: Morrison, Jacob
raw_author_name: Muennighoff, Niklas
raw_author_name: Naik, Aakanksha
raw_author_name: Peters, Matthew E.
raw_author_name: Pyatkin, Valentina
raw_author_name: Ravichander, Abhilasha
raw_author_name: Schwenk, Dustin
raw_author_name: Shah, Saurabh
raw_author_name: Strubell, Emma
raw_author_name: Subramani, Nishant
raw_author_name: Wortsman, Mitchell
raw_author_name: Dasigi, Pradeep
raw_author_name: Lambert, Nathan
raw_author_name: Richardson, Kyle
raw_author_name: Zettlemoyer, Luke
raw_author_name: Dodge, Jesse
raw_author_name: Lo, Kyle
raw_author_name: Soldaini, Luca
raw_author_name: Smith, Noah A.
raw_author_name: Hajishirzi, Hannaneh

Abstract: Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, this technical report details the first release of OLMo, a state-of-the-art, truly Open Language Model and its framework to build and study the science of language modeling. Unlike most prior efforts that have only released model weights and inference code, we release OLMo and the whole framework, including training data and training and evaluation code. We hope this release will empower and strengthen the open research community and inspire a new wave of innovation.
Teachers’ Perceptions of Literacy Implementation in Elementary Schools

Authors:
raw_author_name: Syahrul Ramadhan
raw_affiliation_string: Universitas Negeri Padang
raw_author_name: Elfia Sukma
raw_affiliation_string: Universitas Negeri Padang

Abstract: The purpose of this research is to find out how teachers perceive the implementation of literacy at the Gugus 1 Elementary School, North Padang District, Padang City. The research method used is quantitative descriptive. Data collection techniques through distribution of surveys implementing literacy questionnaires. The dimensions of implementing literacy described in this research consist of positive statements and negative statements. The results of the research show that teachers have a fairly positive perception tendency, this can be seen from the percentage of scores obtained from distributing the questionnaire and the explanation from the table. A very positive perception shows that teachers also participate in literacy activities at school. These results come from an analysis of the factors that cause teacher perceptions, namely functional factors and structural factors. Apart from that, it can be said that the teacher has been successful in implementing reading literacy.
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label  Contrastive Loss for Unlabeled Clients

Authors:
raw_author_name: Qiu, Xinchi
raw_author_name: Pan, Heng
raw_author_name: Zhao, Wanru
raw_author_name: Gusmao, Pedro P. B.
raw_author_name: Alibeigi, Mina
raw_author_name: Iacob, Alex
raw_author_name: Lane, Nicholas D.

Abstract: Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized. The deployment of FL in numerous real-world applications faces delays, primarily due to the prevalent reliance on supervised tasks. Generating detailed labels at edge devices, if feasible, is demanding, given resource constraints and the imperative for continuous data updates. In addressing these challenges, solutions such as federated semi-supervised learning (FSSL), which relies on unlabeled clients' data and a limited amount of labeled data on the server, become pivotal. In this paper, we propose FedAnchor, an innovative FSSL method that introduces a unique double-head structure, called anchor head, paired with the classification head trained exclusively on labeled anchor data on the server. The anchor head is empowered with a newly designed label contrastive loss based on the cosine similarity metric. Our approach mitigates the confirmation bias and overfitting issues associated with pseudo-labeling techniques based on high-confidence model prediction samples. Extensive experiments on CIFAR10/100 and SVHN datasets demonstrate that our method outperforms the state-of-the-art method by a significant margin in terms of convergence rate and model accuracy.
A Class Specific Feature Selection Method for Improving the Performance of Text Classification

Authors:
raw_author_name: Venkatesh V
raw_affiliation_string: Department of Cybersecurity and Internet of Things, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India
raw_author_name: Sharan S B
raw_affiliation_string: Department of Cybersecurity and Internet of Things, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India
raw_author_name: Monisha S
raw_affiliation_string: Department of Cybersecurity and Internet of Things, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India
raw_author_name: Ashick Sanjey D S
raw_affiliation_string: Department of Cybersecurity and Internet of Things, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India
raw_author_name: Ashokkumar P
raw_affiliation_string: epartment of Artificial Intelligence and Machine Learning, Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, India

Abstract: Recently, a significant amount of research work has been carried out in the field of feature selection. Although these methods help to increase the accuracy of the machine learning classification, the selected subset of features considers all the classes and may not select recommendable features for a particular class. The main goal of our paper is to propose a new class-specific feature selection algorithm that is capable of selecting an appropriate subset of features for each class. In this regard, we first perform class binarization and then select the best features for each class. During the feature selection process, we deal with class imbalance problems and redundancy elimination. The Weighted Average Voting Ensemble method is used for the final classification. Finally, we carry out experiments to compare our proposed feature selection approach with the existing popular feature selection methods. The results prove that our feature selection method outperforms the existing methods with an accuracy of more than 37%.