Reviewerly/exHarmony huggingface dataset:
BAND: Biomedical Alert News Dataset

Infectious disease outbreaks continue to pose a significant threat to human health and well-being. To improve disease surveillance and understanding of disease spread, several surveillance systems have been developed to monitor daily news alerts and social media. However, existing systems lack thorough epidemiological analysis in relation to corresponding alerts or news, largely due to the scarcity of well-annotated reports data. To address this gap, we introduce the Biomedical Alert News Dataset (BAND), which includes 1,508 samples from existing reported news articles, open emails, and alerts, as well as 30 epidemiology-related questions. These questions necessitate the model's expert reasoning abilities, thereby offering valuable insights into the outbreak of the disease. The BAND dataset brings new challenges to the NLP world, requiring better disguise capability of the content and the ability to infer important information. We provide several benchmark tasks, including Named Entity Recognition (NER), Question Answering (QA), and Event Extraction (EE), to show how existing models are capable of handling these tasks in the epidemiology domain. To the best of our knowledge, the BAND corpus is the largest corpus of well-annotated biomedical outbreak alert news with elaborately designed questions, making it a valuable resource for epidemiologists and NLP researchers alike.
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models

Using model weights pretrained on a high-resource language as a warm start can reduce the need for data and compute to obtain high-quality language models for other, especially low-resource, languages. However, if we want to use a new tokenizer specialized for the target language, we cannot transfer the source model's embedding matrix. In this paper, we propose FOCUS - Fast Overlapping Token Combinations Using Sparsemax, a novel embedding initialization method that initializes the embedding matrix effectively for a new tokenizer based on information in the source model's embedding matrix. FOCUS represents newly added tokens as combinations of tokens in the overlap of the source and target vocabularies. The overlapping tokens are selected based on semantic similarity in an auxiliary static token embedding space. We focus our study on using the multilingual XLM-R as a source model and empirically show that FOCUS outperforms random initialization and previous work in language modeling and on a range of downstream tasks (NLI, QA, and NER).
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment

We present a systematic study and comprehensive evaluation of large language models for automatic multilingual readability assessment. In particular, we construct ReadMe++, a multilingual multi-domain dataset with human annotations of 9757 sentences in Arabic, English, French, Hindi, and Russian collected from 112 different data sources. ReadMe++ offers more domain and language diversity than existing readability datasets, making it ideal for benchmarking multilingual and non-English language models (including mBERT, XLM-R, mT5, Llama-2, GPT-4, etc.) in the supervised, unsupervised, and few-shot prompting settings. Our experiments reveal that models fine-tuned on ReadMe++ outperform those trained on single-domain datasets, showcasing superior performance on multi-domain readability assessment and cross-lingual transfer capabilities. We also compare to traditional readability metrics (such as Flesch-Kincaid Grade Level and Open Source Metric for Measuring Arabic Narratives), as well as the state-of-the-art unsupervised metric RSRS (Martinc et al., 2021). We will make our data and code publicly available at: https://github.com/tareknaous/readme.
Evaluation of a teleoperated vehicle for educational purposes

The use of robotics vehicles in the education of children and teenagers constitutes a modern tendency of great interest in Cuba, due to the quantity of knowledge that provides and the variety of disciplines that it integrates. The present work has as objective to evaluate the use of a teleoperated and robotic vehicle based on Arduino, in the education of children and adolescents from Cuba. It includes the study of the state of the art about educational robotics in the world and in Cuba, who allows to define the main educational activities permitted by robots and the parameters to evaluate in the vehicle of analysis. Afterwards, it carries out a description of the technical specifications of the device, which influences in the benefits obtained by the use of the vehicle. Finally, it carries out the evaluation taking into account the defined parameters in the study of the state of the art. It obtains as conclusions that the use of the vehicle with educational purposes would be very fruitful, in spite of minimum limitations that do not put directly in risk the quality of educational activities.
EXPLORING PRE-SERVICE TEACHERS’ EXPERIENCE WITH THE USE OF SMARTPHONES DURING THE INTERNSHIP PERIOD - CASE STUDY: CRMEF OF RABAT, MOROCCO

This study intends to explore Moroccan English pre-service teachers’ experience with using cell phones during their internships. Nineteen instructors were picked at random from the Rabat CRMEF (Regional Center for Education and Training Professions). Interviews were conducted with all nineteen participants. The results displayed that trainees’ preconception on the complexity of using smartphones may affect their tendency on employing this mobile tool as an instructional medium. The findings also revealed that pre-service teachers are not ready to integrate smartphones in EFL classrooms due to the lack of ICT training, distractions that smartphones bring about, and schools’ policies banning the use of this gadget.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0029/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Prediksi Kategori Kelulusan Mahasiswa Menggunakan Metode Regresi Logistik Multinomial

Students must meet certain goals to earn a degree but can extend their time at university or drop out (DO). The problem of dropping out of students has become an important issue for tertiary institutions to ensure the success or graduation of students and reduce dropouts. DO can affect the accreditation of the tertiary institution. The quality of higher education institutions in Indonesia is measured based on accreditation from the National Accreditation Board for Higher Education or BAN-PT. One of the main standards measured is the Quality of Students and Graduates. The quality of educational accreditation is measured by the percentage of student graduation and the university's strategy to retain students. To predict student graduation based on graduation time categories, researchers collected academic data from students in 2012-2018 at the Informatics Engineering Study Program, State Islamic University of Maulana Malik Ibrahim Malang. The variables used as predictors are gender, type of entry pathway, and grade point average from semesters one to six. The resulting model was evaluated to obtain an accuracy value of 85.5%, a precision of 78.5%, a recall of 93.9%, and a micro f1-score of 89.8%. An accuracy value of 85.5% indicates that the system can classify properly using the logistic regression model.
Online Learning Quality Measurement Application for Higher Education: Development and Analysis Using ISO 9126

This study aims to develop an application for measuring the quality of online learning in higher education using the analytical hierarchy process (AHP) model and analyze the quality of the application using the ISO 9126 standard. This is a Research and Development study. The application development process uses the software development life cycle (SDLC) method with the waterfall method. Furthermore, the application quality analysis process uses the ISO 9126 standard, consisting of functionality, reliability, efficiency, maintainability, usability, and portability. The results showed that the application for measuring the quality of higher education online learning based on the AHP model had been successfully developed using the SDLC method with the waterfall model. Furthermore, the results of the analysis of application quality using the ISO 9126 standard show that the average results are excellent and suitable for assessing the quality of online learning in higher education.&#x0D; Keywords: online learning, higher education, analytical hierarchy process (AHP), ISO 9126
Real Time Secure Clickbait and Biometric ATM User Authentication and Multiple Bank Transaction System

Nowadays, people use ATMs widely.Withdrawals from ATMs are increasing day by day.The current traditional ATM is vulnerable to crime due to the rapid development of technology.With a total of 270,000 reports of bank card fraud, it was the most reported form of identity theft in 2021.This project proposes an automated teller security model that uses electronic facial recognition using a deep convolutional neural network.Face verification Click bait link is generated and sent to the bank account holder to verify the identity of the unauthorized user through some special AI agent for remote authentication.However, it is clear that human biometric characteristics cannot be imitated.. Experimental results on real-time datasets show superior performance of the proposed approach compared to state-ofthe-art deep learning techniques in terms of both learning efficiency and equal accuracy..Using this real-time dataset, the proposed system achieves the highest accuracy of 97.93%.
Entanglement Distribution in Quantum Repeater with Purification and Optimized Buffer Time

Quantum repeater networks that allow long-distance entanglement distribution will be the backbone of distributed quantum information processing. In this paper we explore entanglement distribution using quantum repeaters with optimized buffer time, equipped with noisy quantum memories and performing imperfect entanglement purification and swapping. We observe that increasing the number of memories on end nodes leads to a higher entanglement distribution rate per memory and higher probability of high-fidelity entanglement distribution, at least for the case with perfect operations. When imperfect operations are considered, however, we make the surprising observation that the per-memory entanglement rate decreases with increasing number of memories. Our results suggest that building quantum repeaters that perform well under realistic conditions requires careful modeling and design that takes into consideration the operations and resources that are finite and imperfect.
Comparison Approaches of the Fuzzy C-Means and Gaussian Mixture Model in Clustering the Welfare of the Indonesian People

We compared the previous study about clustering the welfare of the Indonesian people using the Fuzzy C-Means (FCM) approach to a recent study, the Gaussian mixture model (GMM). Both of which were soft clustering. The case analyzes by classifying 34 provincial data in Indonesia, based on eight welfare of people indicator variables in 2017, which the Central Statistics Agency had issued. We compared the FCM and the GMM approaches to determine a better level of accuracy in clustering data using the Silhouette index, the Davies-Bouldin index, and the Calinski-Harabasz index values as a validity test method. The FCM and GMM methods found that the optimal clusters were 2 and 6. When we observed the consistency of the three tests’ validity results, the GMM method was preferable to the FCM clustering method.&#x0D; Keywords: fuzzy, Gaussian mixture model, clustering
Memory Efficient Neural Processes via Constant Memory Attention Block

Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, however, leverage expensive attention mechanisms, limiting their applications, particularly in low-resource settings. In this work, we propose Constant Memory Attention Block (CMAB), a novel general-purpose attention block that (1) is permutation invariant, (2) computes its output in constant memory, and (3) performs updates in constant computation. Building on CMAB, we propose Constant Memory Attentive Neural Processes (CMANPs), an NP variant which only requires \textbf{constant} memory. Empirically, we show CMANPs achieve state-of-the-art results on popular NP benchmarks (meta-regression and image completion) while being significantly more memory efficient than prior methods.
torchgfn: A PyTorch GFlowNet library

The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library which facilitates the testing of new features such as training losses that can be easily compared to standard benchmark implementations, or on a set of common environments. torchgfn is a PyTorch library that aims to address this need. It provides users with a simple API for environments and useful abstractions for samplers and losses. Multiple examples are provided, replicating and unifying published results. The code is available in https://github.com/saleml/torchgfn.
ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers

Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm. Moreover, LLM-generated programs lack guaranteed correctness and require human verification. To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness. ALGO first generates a reference oracle by prompting an LLM to exhaustively enumerate all the combinations of relevant variables. This oracle is then utilized to guide an arbitrary search strategy in exploring the algorithm space and to verify the synthesized algorithms. Our study shows that the LLM-generated oracles are correct for 88% of the cases. With the oracles as verifiers, ALGO can be integrated with any existing code generation model in a model-agnostic manner to enhance its performance. Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT, the current state-of-the-art model on CodeContests. We can also get 1.3x better pass rate over the ChatGPT Code Interpreter on unseen problems. The problem set we used for testing, the prompts we used, the verifier and solution programs, and the test cases generated by ALGO are available at https://github.com/zkx06111/ALGO.
K-Means Method for Clustering Average Length of School (ALS) in Central Java

Average School Length (ALS) represents the level of performance of every person in a school region. The more years of education, the better the education obtained by the population; therefore, it becomes more essential as it can demonstrate the quality of the human resources in a given region. In addition, extensive research implies that the average length of schooling influences economic growth considerably. If the average length of education improves, then the number of unemployed and poor in an area decreases and affects economic growth positively and significantly. The study’s goal was to carry out an investigation utilizing artificial intelligence in the form of a cluster mapping on the averages of regeneration in central Java. This needs to be done to get a macro view of the level of development of the average school years through regional mapping over the last few years. The data set used can be found on the website of the Central Java Provincial Statistics Agency, which is the subject of the 2017-2019 average school year by sex. The solution approach is k-means, which is part of the data collection process. High and low clusters are the number used in this investigation. Prior to the k-means approach, pre-processing is conducted out by taking from 2017-2019 the average RLS number based on gender. The results of the computed average value were analyzed with k-means. The study found that out of 35 provinces, eight (23%) provinces were of the high cluster (cluster 1) and 27 of the low cluster (cluster 0) (77%). More than 70% of Central Java regions have remained common ALS, according to results.&#x0D; Keywords: K-Means, clustering, ALS
RE$^2$: Region-Aware Relation Extraction from Visually Rich Documents

Current research in form understanding predominantly relies on large pre-trained language models, necessitating extensive data for pre-training. However, the importance of layout structure (i.e., the spatial relationship between the entity blocks in the visually rich document) to relation extraction has been overlooked. In this paper, we propose REgion-Aware Relation Extraction (RE$^2$) that leverages region-level spatial structure among the entity blocks to improve their relation prediction. We design an edge-aware graph attention network to learn the interaction between entities while considering their spatial relationship defined by their region-level representations. We also introduce a constraint objective to regularize the model towards consistency with the inherent constraints of the relation extraction task. Extensive experiments across various datasets, languages and domains demonstrate the superiority of our proposed approach.
PENENTUAN AREA PRIORITAS PENANGANAN PASCA BANJIR BERBASIS ANALISIS SPASIAL DI KECAMATAN KARANG INTAN

The mining and plantation sector has a vital role in increasing GRDP and provides a positive multiplier effect on output, added value, income, and labor. However, on the other hand, it also has a negative impact on increasing environmental damage and climate change, resulting in flood disasters in almost all areas of South Kalimantan. Disaster mitigation planning is the main key to disaster management. This research compiles a post-flood management plan, especially in Karang Intan Sub-district. This research is quantitative research. The method used is a spatial analysis by overlaying the flood water level with objects in it such as houses, roads, and people. Objects that are vulnerable to flooding are those located at flood water levels above class 2 (TMA=0.1-0.5 meters). The number of houses that are vulnerable to being affected is 2,107 units, consisting of 697 units of safe flood conditions, 170 units of safe enough, and 1,239 units of vulnerable. Based on this, the evacuation point was determined as many as 23 points with the most evacuation point coverage, namely evacuation point number 22, which can reach 340 houses in Sungai Alang Village.
An Equivalent Circuit Approach to Distributed Optimization

Distributed optimization is an essential paradigm to solve large-scale optimization problems in modern applications where big-data and high-dimensionality creates a computational bottleneck. Distributed optimization algorithms that exhibit fast convergence allow us to fully utilize computing resources and effectively scale to larger optimization problems in a myriad of areas ranging from machine learning to power systems. In this work, we introduce a new centralized distributed optimization algorithm (ECADO) inspired by an equivalent circuit model of the distributed problem. The equivalent circuit (EC) model provides a physical analogy to derive new insights to develop a fast-convergent algorithm. The main contributions of this approach are: 1) a weighting scheme based on a circuit-inspired aggregate sensitivity analysis, and 2) an adaptive step-sizing derived from a stable, Backward-Euler numerical integration. We demonstrate that ECADO exhibits faster convergence compared to state-of-the art distributed optimization methods and provably converges for nonconvex problems. We leverage the ECADO features to solve convex and nonconvex optimization problems with large datasets such as: distributing data for logistic regression, training a deep neural network model for classification, and solving a high-dimensional problem security-constrained optimal power flow problem. Compared to state-of-the-art centralized methods, including ADMM, centralized gradient descent, and DANE, this new ECADO approach is shown to converge in fewer iterations.
Connotation and Practice Path of How Intelligent Technology Improving the Education Level of the Elderly

With the development of technology, the educational ability of the elderly will be greatly improved. The improvement of the education ability of the elderly emphasizes the connotation construction and quality improvement on the basis of quantity. The value of intelligent technology to enhance the education ability of the elderly is mainly reflected in the ability of carrying, updating and sharing resources, the utilization ability of quality teachers, the supporting service ability of learning space, and the systematic evaluation ability of education for the elderly. High quality to realize elderly education development, the school management, teachers’ teaching and students learning three levels to build the intelligent technology can assign elderly education ability model, and put forward should be from the logical dimension, ethical dimension, quality dimension and evaluation degree consider intelligent technology can assign elderly education ability promotion path.
Comparison between use courier transport with pneumatic tube system on the results of routine hematology test, Prothrombin Time (PPT), Activated Partial Thromboplastin Time (APTT), and potassium

Pneumatic tube system (PTS) is a transport medium that is widely used in hospitals. The samples transported via PTS would get vibrations due to changing air velocity and pressure. This unstable pressure could cause pre-analytic errors in laboratory measurements. It happened because it damages erythrocytes and lymphocytes and causes haemolysis. It has been demonstrated that these changes can alter the quality of samples and induce haemolysis by leading primarily to increase in lactate dehydrogenase (LDH) concentrations, and potassium concentration. Haemolysis is dined as the release of intracellular components of erythrocytes and other blood cells into the extracellular space of blood. It can also affect the results of in vitro platelet function test. Incorrect test results can affect the diagnosis and the treatment for patients Every hospital that uses PTS are advised to validate and investigate that PTS has the possibility of haemolysis and impacts laboratory results of blood specimens. The purpose of the study was to validate and know comparison between use courier transport with using PTS on the results of routine haematology tests, PPT, APTT, and potassium. This research was experimental research by using posttest without control. The research used statistical test which was paired t-test. The research was conducted at the Integrated Clinical Laboratory Installation of the Gadjah Mada University Academic Hospital in August – October 2020 with 30 preoperative patients. Data were analysed with Prism GraphPad 8 software. There was no significant difference between WBC, RBC, Hb, HCT, MCV, MCH, MCHC, PPT, and APTT. Moreover, there was a significant difference between the results of PLT and potassium in the samples transported via PTS and delivered by courier transport.