Reviewerly/exHarmony huggingface dataset:
OPLAN TOKHANG 30 MINUTES CLASSROOM- BASED TUTORIAL PROGRAM: INTERVENTION FOR IMPROVING READING PERFORMANCE LEVEL OF GRADE – FIVE COPPER PUPILS STRUGGLING READERS

This action research was conducted to determine the level of reading performance of pupils of Grade-Five Copper struggling readers of Maasim Central Elementary School using the oplan tokhang 30 minutes classroom-based tutorial program. A descriptive-correlational method was used. An adopted Validity and Reliability- VERY HIGH VALIDITY test and Robert Ebels Criterion of Mastery was used to determine the level of performance of Grade-Five Copper pupils struggling readers. The teacher-made pre-test and the PHIL-IRI tool was used to determine the struggling readers and there were 10 pupils who were found to be very slow readers from the 27 pupils who were identified as struggling reader based on the result of the PHIL-IRI conducted. Gathered data were treated, analyzed and interpreted using frequency weighted mean. Result showed that the Pre- Test mean percentage was 18% described as LOW PERFORMANCE while Post-Test revealed 82%, HIGH PERFORMANCE. The increase in the pupils reading performance shows that using oplan tokhang 30 minutes classroom is effective in improving the reading performance of Grade V-Copper.
Using metareasoning to improve autonomous robot planning

An autonomous robot’s onboard computational resources are limited due to size, weight, and power limits. Thus, optimizing the use of those resources is essential so that the robot can complete its mission reliably and efficiently. Metareasoning, a branch of artificial intelligence, enables a robot to monitor and control its perception, mapping, planning, and other reasoning processes in response to changes in the robot and its environment. Metareasoning is implemented in a meta-level that is logically separate from the object level (which performs the reasoning processes). Previous work has developed metareasoning approaches for specific robotic systems, but these are not easy to generalize. This paper describes our implementation of a metareasoning approach in the Army Research Laboratory (ARL) ground autonomy stack, which is deployable on a variety of robotic platforms. This paper describes the general approach and our implementation of a metareasoning node that can switch the global and local path planners when a planning failure occurs. The results of simulated experiments show that adding metareasoning increases the likelihood of mission success in some cases. More research is needed to optimize the metareasoning approach. Ultimately comprehensive metareasoning that can control the most important aspects of object level reasoning will enable an autonomous robot to deploy its limited computational resources more effectively and complete its mission more reliably.
Joint Reference and Relation Extraction from Legal Documents with Enhanced Decoder Input

Abstract This paper deals with an important task in legal text processing, namely reference and relation extraction from legal documents, which includes two subtasks: 1) reference extraction; 2) relation determination. Motivated by the fact that two subtasks are related and share common information, we propose a joint learning model that solves simultaneously both subtasks. Our model employs a Transformer-based encoder-decoder architecture with non-autoregressive decoding that allows relaxing the sequentiality of traditional seq2seq models and extracting references and relations in one inference step. We also propose a method to enrich the decoder input with learnable meaningful information and therefore, improve the model accuracy. Experimental results on a dataset consisting of 5031 legal documents in Vietnamese with 61,446 references show that our proposed model performs better results than several strong baselines and achieves an F 1 score of 99.4% for the joint reference and relation extraction task.
An OCBA-Based Method for Efficient Sample Collection in Reinforcement Learning

This work focuses on the sample collection in reinforcement learning (RL), where the interaction with the environment is typically time-consuming and extravagantly expensive. In order to collect samples in a more valuable way, we propose a confidence-based sampling strategy based on the optimal computing budget allocation algorithm (OCBA), which actively allocates the computing efforts to actions with different predictive uncertainties. We estimate the uncertainty with ensembles and generalize them from tabular representations to function approximations. The OCBA-based sampling strategy could be easily integrated into various off-policy RL algorithms, where we take Q-learning, DQN, and SAC as examples to show the incorporation. Besides, we provide the theoretical analysis towards convergence and evaluate the algorithms experimentally. According to the experiments, the incorporated algorithms obtain remarkable gains compared with modern ensemble-based RL algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners—</i> Reinforcement learning is a powerful tool for handling sequential decision-making problems, e.g., autonomous driving and robotics control, where the behaviors typically have a long-term effect on future events. However, although RL achieves human-level control in some tasks, it severely suffers from low sample efficiency. Therefore, implementing RL in some practical areas, e.g., healthcare and rescue, is extremely hard due to the requirement of massive samples. This work aims to enhance the exploration of RL by incorporating OCBA, which provides an asymptotically optimal data-collection strategy for simulation-based optimization. Based on ensemble-based uncertainty estimation and OCBA-based action selection, the incorporated RL algorithms show competitive performance on many benchmarks and significantly reduce the sampling efforts during iterations.
Leveraging synthetic data for AI bias mitigation

Widespread adoption of artificial intelligence (AI) in civilian and defense government agencies requires the stakeholders to have trust in AI solutions. One of the five principles of ethical AI, identified by the Department of Defense, emphasizes that AI solutions be equitable. The AI system involves a series of choices from data selection to model definition, each of which is subject to human and algorithmic biases and can lead to unintended consequences. This paper focuses on allowing AI bias mitigation with the use of synthetic data. The proposed technique, named Fair-GAN, builds upon the recently developed Fair-SMOTE approach, which used synthesized data to fix class and other imbalances caused by protected attributes such as race and gender. Fair-GAN uses Generative Adversarial Networks (GAN) instead of the Synthetic Minority Oversampling Technique (SMOTE). While SMOTE can only synthesize tabular and numerical data, GAN can synthesize tabular data with numerical, binary, and categorical variables. GAN can also synthesize other data forms such as images, audio and text. In our experiments, we use the Synthetic Data Vault (SDV), which implements approaches such as conditional tabular GAN (CTGAN) and tabular variational autoencoders (TVAE). We show the applicability of Fair-GAN to several benchmark problems, which are used to evaluate the efficacy of AI bias mitigation algorithms. It is shown that Fair-GAN leads to significant improvements in metrics used for evaluating AI fairness such as the statistical parity difference, disparate impact, average odds difference, and equal opportunities difference.
PENGGUNAAN APLIKASI ATLAS TERHADAP KINERJA AUDITOR

The purpose of this study was to determine whether using ATLAS has an effect on auditor performance as measured by work quality, work quantity, and timeliness with several indicators. The methodology used is qualitative based on observations and interviews with senior auditors at KAP KKSP. The results of this study are ATLAS affects the quality of work on indicators of misstatement detection, compatibility with applicable general standards, and compliance with SOPs. On work quantity, ATLAS affects the indicator of the amount of work that can be completed with targets within a certain time but has no effect on the ability to utilize facilities and infrastructure where ATLAS is positioned as a tool in accommodating audit procedures. Finally, ATLAS affects the timeliness of auditors in completing their work. The limitation of this research is that it is only based on one KAP and is only limited to the offline version of ATLAS.
Ethanol–water motifs—A re-interpretation of the double-difference pair distribution functions of aqueous iron oxide nanoparticle dispersions

In dispersion, nanoparticles can interact with the surrounding dispersion medium, such that an interfacial region with a structure differing from that of the bulk exists. Distinct nanoparticulate surfaces induce specific degrees of interfacial phenomena, and the availability of surface atoms is a crucial prerequisite for interfacial restructuring. Here, we investigate the nanoparticle–water interface of 0.5–10 wt. % aqueous iron oxide nanoparticle dispersions of 6 nm diameter in the presence of 6 vol. % ethanol with x-ray absorption spectroscopy (XAS) and atomic pair distribution function (PDF) analysis. The absence of surface hydroxyl-groups in XAS spectra is in accordance with the double-difference PDF (dd-PDF) analysis, due to a fully covered surface from the capping agent. The previously observed dd-PDF signal is not stemming from a hydration shell, as postulated in Thomä et al. [Nat Commun. 10, 995 (2019)], but from the residual traces of ethanol from nanoparticle purification. With this article, we provide an insight into the arrangement of EtOH solutes in water at low concentration.
The Effect of Online Communication and Use of Information Systems on Audit Quality

During the Covid-19 pandemic it became a challenge for the Public Accounting Firm to carry out audit procedures. During the Covid-19 pandemic, physical meetings or communication between auditors or public accountants and a company's clients were limited, so these meetings, which were generally held face-to-face, became online meetings. The main obstacle for auditors or public accountants during the Covid-19 pandemic is that the audit procedures that must be carried out for the 2020 financial statements will be different from the previous year. In general, audit engagements or communication with clients to obtain audit evidence is carried out face-to-face, but to reduce the spread of the Covid-19 virus, such communication is carried out online. Of course, these communication issues must still be guided by Auditing Standard 260 regarding communication with those charged with governance. This study aims to determine the effect of online audit communication and the use of information systems on audit quality. This research is research with the aim of testing the hypothesis. Studies that engage in hypothesis testing usually explain the nature of a particular relationship or determine the independence of two or more factors in a situation. This study uses an explanatory method where the researcher wants to find the cause of one or more problems. This study aims to determine the effect of online audit communication and the use of information systems on audit quality. This research is research with the aim of testing the hypothesis. Studies that engage in hypothesis testing usually explain the nature of a particular relationship or determine the independence of two or more factors in a situation. This study uses an explanatory method where the researcher wants to find the cause of one or more problems. This study aims to determine the effect of online audit communication and the use of information systems on audit quality. This research is research with the aim of testing the hypothesis. Studies that engage in hypothesis testing usually explain the nature of a particular relationship or determine the independence of two or more factors in a situation. This study uses an explanatory method where the researcher wants to find the cause of one or more problems.
Analysis of Student Needs for Android-Based Learning Media for Biology Subjects

Learning media is one component of learning. The accuracy of the selection of media and learning models that will be applied is one of the factors in the success of effective learning. This study aims to obtain the results of the analysis of student needs for android-based learning media in biology subjects. This student needs analysis is focused on SMPN 16 Poleang Tengah students. The selected method in data collection is a quantitative method using the purposive sampling technique. The sample used to obtain the data is class VIII student respondents and consists of 22 respondents. Data were collected by filling out a questionnaire the form of a questionnaire. The results of this questionnaire data are then processed and made in the form of percentages and tables. Analysis of student needs for Android-based learning media shows that the use of Android-based learning media in the learning process is very much needed, and students feel interested in using Android as a learning medium. This can be seen based on the percentage of statements agreeing to manufacture android-based learning media in biology subjects, namely the percentage of 90.1% agreeing. The analysis of student needs will be used to design android-based learning media for biology subjects in class VIII of SMP Negeri 16 Poleang Tengah. Keywords: Learning media, Android,
LCIP: a retargetable framework for optimized CNN inference

Deep Neural Networks (DNNs) are adopted in numerous application areas of signal and information processing with Convolutional Neural Networks (CNNs) being a particularly popular class of DNNs. Many machine learning (ML) frameworks have evolved for design and training of CNN models, and similarly, a wide variety of target platforms, ranging from mobile and resource-constrained platforms to desktop and more powerful platforms, are used to deploy CNN-equipped applications. To help designers navigate the complex design spaces involved in deploying CNN models derived from ML frameworks on alternative processing platforms, retargetable methods for implementing CNN models are of increasing interest. In this paper, we present a novel software tool, called the Lightweight-dataflow-based CNN Inference Package (LCIP), for retargetable, optimized CNN inference on different hardware platforms (e.g., x86 and ARM CPUs, and GPUs). In LCIP, source code for CNN operators (convolution, pooling, etc.) derived from ML frameworks is wrapped within dataflow actors. The resulting coarse grain dataflow models are then optimized using the retargetable LCIP runtime engine, which employs higher- level dataflow analysis and orchestration that is complementary to the intra-operator performance optimizations provided by the ML framework and the back-end development tools of the target platform. Additionally, LCIP enables heterogeneous and distributed edge inference of CNNs by offloading part of the CNN to additional devices, such as onboard GPU or network devices. Our experimental results show that LCIP provides significant improvements in inference throughput on commonly-used CNN architectures, and the improvement is consistent across desktop and resource-constrained platforms.
INTEGRATING DIGITAL TECHNOLOGIES IN THE EARLY CHILDHOOD CLASSROOM: HOW COMPETENT ARE IN-SERVICE UNDERGRADUATE TEACHERS?

Digital technologies such as programmable toys, camera, android phones, IPad, lab tops, educational robots, industrial robots, videos, video games, VCDs, Internet, Photoshop suites, and the likes have become essential tools that support instructions at all levels of education. These technologies are used either directly or as items to support instruction. This situation places great demand on teachers’ competence in using these technologies effectively in the emerging teaching and learning ecosystem which demands the application of new knowledge and skills. This study investigated in service degree early childhood education teachers ‘competence in integrating digital technologies in implementing the early childhood education curriculum. The descriptive survey design was adopted for the study. The population for the study was 200 students in contact one to four in the 2022 session in Federal College of Education, Pankshin , Nigeria. The sample for the studies consisted of 150 students who were selected using simple random sampling. Five research questions guided the study. The instrument used was the Tech Check Questionnaire (TCQ). Data was analysed using mean and standard deviation to answer research questions raised. Findings indicate that teachers’ competence in using technology in areas of presentation of instructions, educational robotics and internet usage was low. It was recommended among others that hands on training on use of relevant digital technologies in presentation, educational robotics and internet use be mounted for teachers to improve competency for digital curriculum implementation.
Big data and cloud computing: roles and relationships, techniques and tools

In the past years, the increase in data has been accompanied by rapid growth in various fields. It is difficult to analyze large volumes of data using traditional and relational database technology. Therefore, new databases have emerged, and for this reason, big data has become one of the new topics in IT and business today. Also, the cloud environment is increasingly used to store and process big data. Cloud processing refers to processing anything, including Big Data Analytics, on the "cloud". A "cloud" is a collection of high-powered servers from providers that can often view and query large data sets much faster than a regular computer. These two topics differ from each other in various aspects, including definition, collection references, usage method, form and format, and application. In this research, the dimensions and basic concepts, characteristics, tools and techniques, classification, and communication of data are examined. Big has been dealt with cloud computing, and in addition, storage systems, opportunities and challenges, and big data design principles in the cloud environment have been analyzed.
PAHD: perception-action based human decision making using explainable graph neural networks on SAR images

Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently used to identify ground-based objects, including battle tanks, personnel carriers, and missile launchers. Determining the vehicle class, such as the BRDM2 tank, BMP2 tank, BTR60 tank, and BTR70 tank, is crucial, as it can help determine whether the target object is an ally or an enemy. While the ML algorithm provides feedback on the recognized target, the final decision is left to the commanding officers. Therefore, providing detailed information alongside the identified target can significantly impact their actions. This detailed information includes the SAR image features that contributed to the classification, the classification confidence, and the probability of the identified object being classified as a different object type or class. We propose a GNN-based ATR framework that provides the final classified class and outputs the detailed information mentioned above. This is the first study to provide a detailed analysis of the classification class, making final decisions more straightforward. Moreover, our GNN framework achieves an overall accuracy of 99.2% when evaluated on the MSTAR dataset, improving over previous state-of-the-art GNN methods.
#2775 USING A DEEP LEARNING MODEL TO EVALUATE PATHOLOGICAL INJURY OF DIABETIC KIDNEY DISEASE

Abstract Background and Aims Early diagnosis and evaluation play an important role in preventing the progression of diabetic kidney disease (DKD). Renal biopsy is the gold standard of DKD diagnosis. In 2010, the Renal Pathology Society (RPS) developed a consensus classification for DKD, which classifies DKD glomerular lesions and scores for tubulointerstitial lesion. However, as the pathologic heterogeneity of DKD patients and unparallel relationship between pathology features and clinical symptoms, it remains controversial whether is reliable to use RPS classification for renal outcomes prediction. Besides, the inconsistence between pathologists might magnify the gap between prediction and outcome. More reliable methods for DKD pathology assessment are needed. The development of machine learning (ML) algorithms especially convolutional neural networks (CNNs) enables it possible, which can provide automatic and precise analysis for complicated images. Thus, this study aimed to construct a ML-based model to assist and provide automatic decision supports to clinical doctors. Method We examined 204 DKD renal biopsied whole-slide images (WSIs) of Periodic Acid Schiff staining from Southeast University Institute of Nephrology whose database came from more than 30 hospital centers. All the WSIs were randomly annotated by three pathologists. We established a consecutive CNNs framework: DKD pathology evaluation system (DPES). It consists of three parts: 1. Based on classic UNet architecture, we developed a segmentation model whose backbone is replaced by ResNet 50 architecture for detecting glomeruli. 2. According to RPS classification, a CNN added with channel attention mechanism is built to discriminate sclerotic glomeruli, glomeruli with K-W node, and glomeruli with mesangial expansion. Through this method, the entire WSIs were divided into RPS grades I-IV. 3. For the analysis of tubulointerstitial lesion, we constructed a segmentation model as previous description to calculate the area of the atrophic tubule and its percentage of the total tubules. Results For glomerular assessment, compared with other ML networks, DPES achieved better performance in segmentation for glomeruli (Intersection over Union (IOU): 0.82, Precision: 0.89, Accuracy: 0.91) and tubulointerstitial (atrophy (TA): IOU: 0.9;Precision: 0.94; Accuracy: 0.95; non-tubular atrophy (NT): IOU: 0.84; Precision: 0.90; Accuracy: 0.93). Interstitial fibrosis and tubular atrophy (IFTA) from network compared with three pathologists didn’t show statistically difference. Conclusion Our findings demonstrated that the DPES achieved similar performance with three pathologists. It suggests that machine learning algorithms can generate reliable results and provide more supports for clinical decision.