Reviewerly/exHarmony huggingface dataset:
Analysis of Students' Difficulties in Determining the Main Idea in Reading Texts in Class V

This research is motivated by students' problems when learning to determine the main idea. The aim of this research is to find out the problems experienced by students in learning to determine the main idea in a text. The type of research used in this research is a case study. This research uses a qualitative approach with the type of case study research chosen using a purposive sampling technique. This research was conducted on class V teachers and class V students at SD N 20 Paninjauan. The instrument of this research is the researcher himself. Data collection in this research is interviews and documentation, the data and information that has been obtained is then analyzed using the Milles and Huberman model. The results of the research, this research shows that the implementation of class V Indonesian language content learning with the material of determining the main idea only uses the lecture method and in implementing the learning the media is not used enough to support the learning process, and the students' ability to read quickly is very low. And when reading texts that are too long, students do not understand the vocabulary in the text.
Digital Literacy of Students at PTKIN in Aceh

From 2019 to 2023, digital use is increasingly being applied in carrying out activities of daily life, especially in the learning process in the field of education. Therefore, this study examines the extent to which students' abilities in digital use. Apart from being a symbol of the progress of a society, the ability to use digital can also prevent its users from spreading or preventing the absorption of hoax information. Therefore, this study aims to look at students' digital literacy skills in PTKIN in Aceh. This study used a qualitative descriptive method with data collection using interview techniques and analyzed using qualitative data analysis techniques. Based on the results of the study, it was found that the level of digital literacy skills possessed by students tended to be quite good, namely as much as 82%. With details in terms of internet searching aspects of 89%, in terms of hypertext navigation aspects of 72%. Then, from the aspect of content evaluation, it is 88% and from the aspect of knowledge assimilation, it is 78%.
43 Mood and Everyday Function in Older Adults: Analyses of Self-report and Performance-based Measures of Everyday Function

Objective: The relation between depressed mood and functional difficulties in older adults has been demonstrated in studies using self-report measures and has been interpreted as evidence for low mood negatively impacting everyday functional abilities. However, few studies have directly examined the relation between mood and everyday function using performance-based tests. This study included a standardized, performance-based measure of everyday action (Naturalistic Action Task, NAT) to test the prediction that report of depression symptoms are associated with self-report and performance-based tests of everyday function. Associations with anxiety symptoms and motivation/grit and everyday function also were explored. Participants and Methods: 68 older adults without dementia were screened and recruited (n = 55, M age = 74.21, SD= 6.80, age range = 65 to 98) from the community and completed self-report measures of depression symptoms (GDS), anxiety (GAI), motivation (Short Grit-S), and everyday functioning (FAQ). Participants also performed the NAT, which requires completion of a breakfast and lunch task and is scored for task accomplishment, errors (micro-errors, overt, motor), and total time. Additionally, an informant also reported on the participant’s everyday function. Spearman correlations were performed and results showing a medium effect size or greater are reported. Results: Participant mood (GDS) was associated with self-reported function (FAQ; r =.45) but not performance-based measures of everyday function (NAT). Self-reported anxiety and motivation were not meaningfully associated with either self-reported or performance-based everyday function. Participant self-report (FAQ) and informant report of participant’s function (IFAQ) supported the validity of performance-based assessment as both were meaningfully associated with NAT performance (FAQ x NAT overt errors r = .34; I-FAQ x NAT micro-errors r = .34; I-FAQ x NAT motor errors r = .49). Conclusions: Mood, but not anxiety or motivation, was associated with self-reported everyday function but not performance-based function. When considered alongside the meaningful relations between self/informant-report of function and everyday task performance, results suggest mood does not impact everyday function abilities in community-dwelling older adults without dementia. We suggest that frameworks to be reconceptualized to consider the potential for mild functional difficulties to negatively impact mood in older adults without dementia. Additionally, interventions and compensatory strategies designed to improve everyday function should examine the impact on mood outcomes.
Study on the Effectiveness of Picture Series Method in Improving the Writing Ability of Recount Text for Junior High School Students

This article discusses the importance of developing recount text writing skills for junior high school students in English language learning. In the 2013 curriculum, English language skills are very important in preparing a generation that can compete globally. However, writing skills often receive less attention, and students tend to write only because the teacher commands them. The picture series method is an effective approach to improving the ability to write recount texts. This method involves a series of pictures to help students string past events into a structured narrative. The results showed that the picture series method can increase students' learning motivation, overcome barriers in writing, and strengthen teacher-student relationships. This research uses a literature review approach and field research at SMP Negri 4 Pematang Siantar to collect data on the effectiveness of the serial image method in improving the ability to write recount texts. Through literature analysis and observation of students, it was found that most students showed interest and enthusiasm in using the serial image method. A total of 81% of students succeeded in writing recount texts with the help of this method. In conclusion, the picture series method is effective in overcoming the problem of students' writing ability and improving learning interaction. This article suggests that teachers use the series picture method to learn to recount texts to create a more interesting and creative learning environment for junior high school students. This method can help students develop better thinking skills, emotional responses, and writing skills.
Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification

Multiple Instance Learning (MIL) is commonly utilized in weakly supervised whole slide image (WSI) classification. MIL techniques typically involve a feature embedding step using a pretrained feature extractor, then an aggregator that aggregates the embedded instances into predictions. Current efforts aim to enhance these sections by refining feature embeddings through self-supervised pretraining and modeling correlations between instances. In this paper, we propose a convolutional sparsely coded MIL (CSCMIL) that utilizes convolutional sparse dictionary learning to simultaneously address these two aspects. Sparse dictionary learning consists of filters or kernels that are applied with convolutional operations and utilizes an overly comprehensive dictionary to represent instances as sparse linear combinations of atoms, thereby capturing their similarities. Straightforwardly built into existing MIL frameworks, the suggested CSC module has an affordable computation cost. Experiments on various datasets showed that the suggested CSC module improved performance by 3.85% in AUC and 4.50% in accuracy, equivalent to the SimCLR pretraining (4.21% and 4.98%) significantly of current MIL approaches.
PENGEMBANGAN BAHAN AJAR KOMIK BERBASIS CERITA RAKYAT DI SEKOLAH DASAR NEGERI KOTA BENGKULU

This research aims to develop comicmaterials based onin Bengkulu City Elementary Schools. This type of research is the Research and Development (R &amp; D) with moedel ADDIE: Analyisis (Analysis), Design (design), Development (development), Implementation (Implementation), Evaluation (Evaluation). However, this research and development only reaches thestage Development . The validation was carried out by two material experts and one design expert. The teaching materials developed were rated by two grade IV teachers of 68 Elementary Schools in Bengkulu City. The data analysis technique used is descriptive statistical analysis of the results of expert validation assessments and teacher responses, and qualitative descriptive analysis of comments and suggestions from expert validation and teacher responses. From the results of the material expert validation, it was obtained a value of 89.3% with the very valid category, the design expert validation obtained a value of 96% with the very valid category. The recapitulation of values from the validation of material experts and design experts obtained an average of 92.95%. Teacher I's response obtained a value of 86.15% and teacher II's response obtained a value of 89.23% with a very valid category. The recapitulation of the value of the teacher's responses obtained an average of 87.69%, so that the product of Comic Learning Materials based on Folklore is very valid and very feasible to use. Keywords: Development, Teaching Materials, Comics, Folklore
Construções marcadoras discursivas formadas por olhar, no português, e guardare, no italiano: uma análise constrastivo-funcional

"Discourse markers formed by olhar in Portuguese and guardare in Italian: a contrastive-functional analysis. Discourse markers (DM) make up a hybrid category and little distinct from the others. As elements outside grammar, these constituents are difficult to delimit and, therefore, difficult to analyze. Based on this consideration, adopting a functionalist theoretical framework combined with the constructional approach to grammar, in the terms of Traugott and Trousdale (2013), Hilpert (2014) and Traugott (2021, 2022), among others, we proceeded to an analysis of a qualitative and contrastive nature of MD formed by the verbs of visual perception (Scheibman 2000) olhar, in Portuguese, and guardare, in Italian, in the detection of their correspondences and distinctions. We assume such MDs as specific pairs of form and meaning in contemporary uses of Portuguese and Italian, as procedural constructions, such as [olha só] and [guarda un po'], respectively. We found that these MDs act in calling attention, in monitoring interaction via manipulation of the virtually idealized attentional space, due to communicative purposes, as defended by Sambrana (2021, 2023), for Portuguese, and Oliveira and Lazzarotto (2022), for Italian. We found that they contribute to this functionality of the MDs researched cognitive processes of general domain, according to Bybee (2010), such as chunking and analogization, and also social cognition, according to Diessel (2017). Keywords: discourse markers; contrastive analysis; contexts of use; contemporary Portuguese; contemporary Italian."
Decision Support System for Determining Character Education Values

The 2013 curriculum is an effort made by the government in improving the curriculum according to the needs of the current generation. This 2013 curriculum is a curriculum that does not only focus on academic competence, but also includes aspects of student character and skills. However, in its implementation, the 2013 curriculum makes teachers feel confused, especially in the assessment of attitudes and character. The assessment carried out with Rubik and observation sheets has indicators that are not appropriate so that character assessment becomes inappropriate. This research will design a system to conduct student character assessment at SDN Butuh 1 Magelang. In this research, a system will be designed to help teachers conduct student character assessment with SPK character assessment in Indonesia. The method applied in this research is the Simple Additive Weighting (SAW) method. The result of this research is a system to support the decision of students' character education value.
Rectangle Search: An Anytime Beam Search (Extended Version)

Anytime heuristic search algorithms try to find a (potentially suboptimal) solution as quickly as possible and then work to find better and better solutions until an optimal solution is obtained or time is exhausted. The most widely-known anytime search algorithms are based on best-first search. In this paper, we propose a new algorithm, rectangle search, that is instead based on beam search, a variant of breadth-first search. It repeatedly explores alternatives at all depth levels and is thus best-suited to problems featuring deep local minima. Experiments using a variety of popular search benchmarks suggest that rectangle search is competitive with fixed-width beam search and often performs better than the previous best anytime search algorithms.
Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing

Mobile sensing appears as a promising solution for health inference problem (e.g., influenza-like symptom recognition) by leveraging diverse smart sensors to capture fine-grained information about human behaviors and ambient contexts. Centralized training of machine learning models can place mobile users' sensitive information under privacy risks due to data breach and misexploitation. Federated Learning (FL) enables mobile devices to collaboratively learn global models without the exposure of local private data. However, there are challenges of on-device FL deployment using mobile sensing: 1) long-term and continuously collected mobile sensing data may exhibit domain shifts as sensing objects (e.g. humans) have varying behaviors as a result of internal and/or external stimulus; 2) model retraining using all available data may increase computation and memory burden; and 3) the sparsity of annotated crowd-sourced data causes supervised FL to lack robustness. In this work, we propose FedMobile, an incremental semi-supervised federated learning algorithm, to train models semi-supervisedly and incrementally in a decentralized online fashion. We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition. Our empirical results show that FedMobile-trained models achieve the best results in comparison to the selected baseline methods.
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets

Development of comprehensive prediction models are often of great interest in many disciplines of science, but datasets with information on all desired features typically have small sample sizes. In this article, we describe a transfer learning approach for building high-dimensional generalized linear models using data from a main study that has detailed information on all predictors, and from one or more external studies that have ascertained a more limited set of predictors. We propose using the external dataset(s) to build reduced model(s) and then transfer the information on underlying parameters for the analysis of the main study through a set of calibration equations, while accounting for the study-specific effects of certain design variables. We then use a generalized method of moment (GMM) with penalization for parameter estimation and develop highly scalable algorithms for fitting models taking advantage of the popular glmnet package. We further show that the use of adaptive-Lasso penalty leads to the oracle property of underlying parameter estimates and thus leads to convenient post-selection inference procedures. We conduct extensive simulation studies to investigate both predictive performance and post-selection inference properties of the proposed method. Finally, we illustrate a timely application of the proposed method for the development of risk prediction models for five common diseases using the UK Biobank study, combining baseline information from all study participants (500K) and recently released high-throughout proteomic data (# protein = 1500) on a subset (50K) of the participants.
Exploring ChatGPT for Toxicity Detection in GitHub

Fostering a collaborative and inclusive environment is crucial for the sustained progress of open source development. However, the prevalence of negative discourse, often manifested as toxic comments, poses significant challenges to developer well-being and productivity. To identify such negativity in project communications, especially within large projects, automated toxicity detection models are necessary. To train these models effectively, we need large software engineering-specific toxicity datasets. However, such datasets are limited in availability and often exhibit imbalance (e.g., only 6 in 1000 GitHub issues are toxic), posing challenges for training effective toxicity detection models. To address this problem, we explore a zero-shot LLM (ChatGPT) that is pre-trained on massive datasets but without being fine-tuned specifically for the task of detecting toxicity in software-related text. Our preliminary evaluation indicates that ChatGPT shows promise in detecting toxicity in GitHub, and warrants further investigation. We experimented with various prompts, including those designed for justifying model outputs, thereby enhancing model interpretability and paving the way for potential integration of ChatGPT-enabled toxicity detection into developer communication channels.
How Emoji and Explanations Influence Adherence to AI Recommendations

Emoji have become an essential part of modern communication, helping to convey emotions and tone quickly and concisely. Emoji used by humans and Intelligent Agents (IA) have been shown to affect people's decision making intentions, suggesting they could be used to manipulate users to follow their advice. We present a mixed-methods crowdsourcing study (N = 194) that shows that adherence to an IA's recommendation and user experience are not affected by emoji when used in a positive, collaborative way. However, we demonstrate that explanations provided by an IA do increase adherence to its recommendation.
Errare humanum est, perseverare autem diabolicum

Othello, also known as Reversi, is a popular 2-players board game. Olivaw is an intelligent agent playing Othello. Compared to the most famous ones (such as Saio), it exploits limited resources by autonomously learning how to improve its gameplay by playing against itself. In previous occasions, Othello players reported the impression of a sort of human-likeness in how Olivaw plays. We designed and ran an experimental study to better investigate these impressions in a controlled setting. Participants were asked to watch the moves of pre-recorded Othello games played by a human expert player against either another agent (i.e., Olivaw, Saio) or another human. The identity of the opponent, the outcome of the game (i.e., whether the human expert or the opponent player won), and the color of the players (i.e., black or white, black always playing first) were manipulated. We then asked participants to evaluate the human-likeness of the opponent player. Results confirm that the outcome of the match affects the perception of human-likeness of the players.
The Hegemony of English Loanwords Over Kurdish Language on Facebook Authors

The global rise of the internet after the 2000s, as well as the rise of American influence in Iraq and Kurdistan, strengthened English's hegemony over all other Iraqi languages, including Kurdish. In fact, many Arabic and even Kurdish expressions are now being replaced by English ones. This study aims to identify loanwords used in selected Facebook groups, as well as instances that characterize English language hegemony in Kurdish social media. It also aims to discover the types of loanwords that are more commonly used in social media in relation to Myers-Scotten's Model. And then, relate all of this to the social class, age, or gender that uses loanwords the most on Facebook. On this basis, the researchers selected 2,776 Facebook posts and comments from two Facebook groups of teachers between January 1st 2022 and December 31st 2022 and by using discourse analysis the English loanwords were analyzed. The study's key findings were that, of the two types of loanwords, core borrowings were used more frequently, indicating the presence of the English language's hegemony. Additionally, male teachers in both groups used more English loanwords than female teachers did, whereas other social variables like social class and age showed less variation.
Blockchain-Based Nash Bargaining for Task Scheduling in IoT Edge Computing Environments

Modern IoT industry cannot operate well without task scheduling, while the merging of edge computing and blockchain can empower more secure task scheduling models in distributed IoT environments. Due to various desirable properties (self-verifying, self-executing, immutability, reliability, confidentiality, etc.) provided by blockchain, the tasks generated from IoT devices can be safely scheduled. However, merely incorporating a blockchain framework into an IoT edge computing environment cannot guarantee the most desirable rewards for task participants. So in this paper, we propose to use a nash bargaining method in blockchain smart contract designing to strive the rewards for task participants and stimulate their proactivity while extending the above properties. The proposed blockchain-based nash bargaining architecture can be applied in many distributed IoT scenarios such as smart logistics, health data exchange, etc. For a practical implementation, a configurable blockchain architecture is developed consisting of both regular and constrained IoT devices. We additionally implement mutual authentication protocol and proof of authority to maintain the privacy and security for scheduling. Further, we experimentally study the feasibility of such architecture built in static and mobile IoT devices. Evaluations demonstrate that, the proposed architecture can well support a task scheduling process with tamper-resistance, which is suitable for IoT edge computing with high-level security and creditability.
Secure OFDM Transmissions With Coset-QAM

Modulation recognition for Orthogonal Frequency Division Multiplexing (OFDM) has many applications in commercial and surveillant communications (e.g., frequency spectrum monitor, military eavesdropping). In this paper, we propose a coset quadrature amplitude modulation (coset-QAM) technique with coset selection matrix to combat modulation recognition for OFDM. Numerical simulations show that the performance of the existing modulation classifiers is degraded dramatically even when the principle and coset selection matrix are known at the classifier. As a result, our coset-QAM largely improves the security compared with the traditional QAM with marginal error performance loss.
Incentive Mechanism Design for Joint Resource Allocation in Post-Quantum Blockchain-based Federated Learning

Due to the emergence of quantum computers, most of the cryptographic algorithms we currently use can be broken by quantum computers in theory. Therefore, post-quantum cryptographic algorithms that can resist attacks by quantum computers have great promise in blockchain-based federated learning (BCFL). To encourage BCFL participants to use the kyber algorithm (mining and training), we design an incentive mechanism in which the model owner (hereafter referred to as MO) assigns each client an appropriate reward for using the kyber algorithm, a mining reward and a training reward. The clients and MO will use a two-stage Stackelberg game to allocate computational resources for each subtask based on the three rewards given by the MO. We will use lattice cryptography for the full information game.