Reviewerly/exHarmony huggingface dataset:
Pengaruh Model Pembelajaran Kooperatif Tipe Teams Games Tournament (TGT) Terhadap Motivasi Belajar Matematika Siswa Kelas IV MI The Noor Bendunganjati Pacet Mojokerto

Teams Games Tournament Model, Learning Motivation The teaching and learning process carried out by teachers at MI The Noor school, especially in the use of learning models is still very lacking, this is evidenced by the learning outcomes of students who are still much below the KKM, besides that when the teaching and learning process is carried out, students are less enthusiastic in following the lesson. this greatly affects the desired understanding. Therefore, the learning process must be able to choose the right learning model, the Teams Games Tournament type cooperative learning model is very suitable for use in learning, because the Teams Games Tournament type cooperative learning model can arouse the enthusiasm of students in the learning process, This study aims to determine the effect of using the Teams Games Tournament type of cooperative learning model on the learning motivation of fourth grade students in the Circumference and area of square, rectangular, and triangular shapes. The research design is One Group Pretest Posttest. The research population consisted of 88 students in class I, II, III, IV, V. VI and 20 students in class IV as samples. The results of data analysis using the Paired sample t-test test obtained the value of Sig. (2-tailed) of 0.000, which means that the test results obtained a value smaller than 0.05, so it can be concluded that there is an influence in the use of the Teams Games Tournament type of cooperative learning model on students' learning motivation
IDENTIFIKASI TULANG BELULANG

Forensic identification is an attempt that made with the purpose of assisting the investigator to determine a person's identity. According to the identification of the bones process, we can obtain the information relating to a person's identity such as race, gender, age, and height estimates. In this case report, we discuss the identification of the bones found by locals in a place, which allegedly belongs to human bones. This discovery was reported to the police for investigation. After that, the bones were identified by a forensic expert based on a visa application letter from the police.. These bones are distinguished on the basis of an assessment of the similarities or differences in bone shape, bone size, bone color, bone density (bone), total bone, left and right bones and assessment of parts of the bones. From the results of this observation, it can be stated that the bones are derived from human.
APLIKASI PERSEDIAAN OBAT DAN PERLENGKAPAN ALAT MEDIS PADA KLINIK HANA KASIH

Hanna Kasih Clinic is one of the community health service centers located in Kec. Medan Timur, Pulo Brayan. As for the problems that occur in the system that is running at the moment that is for processing stock inventory and data entry and exit goods. All data is still recorded in a notebook according to how many items come in and out as well as the stock of drugs and medical devices still use stock cards to check. The process of checking the supply of drugs and medical devices must check one by one so it takes a long time in the checking process and allows for errors. The purpose of this study is to create an inventory system of inventory items, management of goods entering and leaving, data collection of drugs, medical devices, supplier data, stock reports, reports of goods entering and leaving. The information system that has been produced in this design can manage the stock of goods, management of incoming and outgoing goods, data collection of drugs, medical devices, and suppliers, stock reports, reports of goods entering and leaving.
Wireless Gesture Controlled Robot for Landmine Detection

Abstract: Most prominent problems today in society are terrorism and insurgency. Soldiers are risking their lives to keep a check on the above problems. Soldiers are facing difficulties to reach challenging places. Landmine explosions killed more and more amputations than ordnance. A lot of research is going-on in defence systems which are capable of safeguarding citizens from terrorist threats. This has motivated us to work on Unmanned Ground Vehicle (UGV) which could serve military operations. Robots are utilized to increase the warrior’s capacity in an open territory. Gestures involve motion of human hands, arms and head and are used within a certain context. This robot is based on the Gesture control mode or Hand wave mode. Gesture Controlled car is a robot which can be controlled by simple human gestures. User can interact with the robot in more friendly way due to wireless communication. The old method of detecting landmines such as direct sweeping is very risky for stepping the landmine unintentionally. In this project, the robot system is equipped with a metal detector useful to detect the metal presence based on coil induction when it's approaching the metal. When the robot detects the metal presence, the buzzer sound will be triggered. These robots can be used in the place of the spy team and also for the Investigation process. This robot will help in detecting the landmines and saving the precious lives of military. It will also help in detecting injured soldiers and send information to the base station. This robot has a potential to be incredibly useful for applications like surveillance, military operations and Industrial robotic arms for physically challenged people.
Application of Automatic Completion Algorithm of Power Professional Knowledge Graphs in View of Convolutional Neural Network

With the continuous development of electric power informatization, a large amount of electric power data information has been produced. The reasonable application of electric power database is of great significance. Building the automatic completion optimization algorithm of knowledge graphs (KGs) in power professional field provides a method to extract structured knowledge from a large number of power information and images, which has broad application value. The automatic completion algorithm of power professional KGs in view of convolutional neural network (CNN) is conducive to completing the analysis and management of power data, enabling the flexible use of data information generated by the power grid, and bringing ideas for the in-depth exploration and innovation of power grid data information application.
Research on Siamese Object Tracking Algorithm Based on Knowledge Distillation in Marine Environment

Siamese networks have gained considerable attention for object tracking due to their balance of speed and accuracy. However, existing Siamese tracking algorithms have been too rigid in their predictions of bounding box tags and lack uncertainty estimation, resulting in poor tracking performance in marine environments, particularly those with waves. To improve the effectiveness of trackers in marine environments, this study proposes a Siamese distillation network. First, to address the issue that the presence of waves and other disturbances may result in target loss or inaccuracy when tracking the target, the concept of a probability distribution of the bounding box is introduced in this study, which transforms the standard Dirac delta distribution of the bounding box into a probability distribution of the bounding box, effectively reducing the impact of interference on tracking performance and improving target location accuracy. Second, we chose ResNet100 as the backbone network to obtain richer features for localization. Finally, this work offers a knowledge distillation approach to further enhance the tracking accuracy and model performance, while considering the impact of the model’s number of parameters and computational amount on tracking performance. This network outperforms most trackers in terms of accuracy, according to extensive experimental results, and performs well on the target tracking benchmark and marine dataset annotated in this study. Specifically, this network achieved the highest accuracy value of 0.612 compared to other Siamese networks, resulting in a 2.5% increase compared to original baseline network. This suggests that the proposed algorithm is practical.
Visual AI for Satellite Imagery Perspective: A Visual Question Answering Framework in the Geospatial Domain

This study presents a task of visual question answering (VQA) for satellite imaging datasets. Satellite images provide a large-scale dataset that can be used for various purposes such as land cover classification, item counting, and detection. This project investigates the use of human intent questions for effective method for obtaining data from satellite imaging data. Users can query the system to retrieve meaningful data about regarding image entities or relationships among items depicted in the images. Developing high-performing and reliable VQA systems is a challenging task, particularly in the context of disaster management and infrastructure. In this work, we examine traditional combination methods such as addition, concatenation, and feature-wise multiplication using state-of-the-art image and text feature extraction models. We present simple and efficient system that outperforms existing techniques on the FloodNet dataset and achieves state-of-the-art performance. Our streamlined solution requires significantly less training and inference time than traditional VQA systems. We also evaluate the performance of several backbones and present their aggregated results. We propose to use a visual model to extract context information, translate it to text, and inject it into a language model. The language model then processes the question in conjunction with the visual context and extracts the information needed to generate a response.
Construction and Application of Power Data Operation Monitoring Platform Based on Knowledge Map Reasoning

Due to the gradual increase in daily power consumption by businesses and individuals, the power industry has seen an increased need to deploy more power equipment. This has led to a significant rise in the data generated by electrical equipment, a trend noted by the International Journal of Emerging Electrical Power Systems. In order to process, analyze, and manage such large amounts of data, it is necessary to introduce knowledge mapping technology into the power field. This technology allows the power data operation monitoring platform to obtain useful data from a large amount of information. In light of this phenomenon, based on an analysis of the requirements for platform construction and design principles, combined with the knowledge map reasoning method, this paper has effectively studied the construction of the power data operation monitoring platform and tested the performance of the experimental platform by assessing the response time of each functional module, data correctness verification, and data standard management.
Research on the Application of XML in Fault Diagnosis IETM

Abstract Fault diagnosis IETM is a digital technical manual which integrates editing, management, display and publication and faces the field of fault diagnosis. In its development process, there are some problems such as complex data management and difficult interaction. The successful application of XML technology becomes the key to solve these problems. In this paper, based on S1000D IETM development standard, the main characteristics of XML and its related technologies are first introduced, and then the application of XML and its related technologies in fault diagnosis IETM is analyzed and studied, including the creation of fault information data module, the display control of fault information and the comprehensive management of data. A conversion method between XML document data and database data is proposed, and the design process of fault diagnosis IETM based on XML is discussed. Finally, a fault diagnosis IETM display system based on XML is preliminarily developed by combining the related technologies mentioned above. Experiments show that XML technology can effectively manage the information in fault diagnosis IETM and display it to users in an interactive way, which has important theoretical value and certain practical significance for developing fault diagnosis IETM and exploring the deeper application of XML in IETM.
Covid-19 Data Analysis in Tarakan with Poisson Regression and Spatial Poisson Process

COVID-19 entered Indonesia in March 2020 and included North Kalimantan Province, Tarakan. COVID-19 cases have outspread in Tarakan. The cause of the outspread and the patterns were not known yet. One relevant approach was to use Generalized Linear Models. The two methods are Poisson Regression and Stochastic with Spatial Poisson Process. The variables used were rainfall, population density, and temperature in each village in Tarakan. The Poisson Regression analysis founds that only one factor affected temperature. Then, the results were refined with the Spatial Poisson Process, where in addition to the influencing factors also, the distribution patterns are obtained. The analysis showed that the pattern of case distribution was included in the non-homogeneous Poisson process criteria. Then the model of the case density intensity was obtained using regression. From the model, it was known that the covariate variables significantly influence rainfall and temperature. Compared with general Poisson regression analysis, the results showed that only the average temperature variables had a significant effect. Thus, a better method was used, namely the Spatial Poisson Process. It was also shown by the two models' AIC values, where the AIC value of the Spatial Poisson Process model was smaller than the Poisson Regression.
Contextualizing Argument Quality Assessment with Relevant Knowledge

Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world arguments are tightly anchored in context, existing computational methods analyze their quality in isolation, which affects their accuracy and generalizability. We propose SPARK: a novel method for scoring argument quality based on contextualization via relevant knowledge. We devise four augmentations that leverage large language models to provide feedback, infer hidden assumptions, supply a similar-quality argument, or give a counter-argument. SPARK uses a dual-encoder Transformer architecture to enable the original argument and its augmentation to be considered jointly. Our experiments in both in-domain and zero-shot setups show that SPARK consistently outperforms existing techniques across multiple metrics.
YOLOv3 with Spatial Pyramid Pooling for Object Detection with Unmanned Aerial Vehicles

Object detection with Unmanned Aerial Vehicles (UAVs) has attracted much attention in the research field of computer vision. However, not easy to accurately detect objects with data obtained from UAVs, which capture images from very high altitudes, making the image dominated by small object sizes, that difficult to detect. Motivated by that challenge, we aim to improve the performance of the one-stage detector YOLOv3 by adding a Spatial Pyramid Pooling (SPP) layer on the end of the backbone darknet-53 to obtain more efficient feature extraction process in object detection tasks with UAVs. We also conducted an evaluation study on different versions of YOLOv3 methods. Includes YOLOv3 with SPP, YOLOv3, and YOLOv3-tiny, which we analyzed with the VisDrone2019-Det dataset. Here we show that YOLOv3 with SPP can get results mAP 0.6% higher than YOLOv3 and 26.6% than YOLOv3-Tiny at 640x640 input scale and is even able to maintain accuracy at different input image scales than other versions of the YOLOv3 method. Those results prove that the addition of SPP layers to YOLOv3 can be an efficient solution for improving the performance of the object detection method with data obtained from UAVs.
Pruning Pre-trained Language Models with Principled Importance and Self-regularization

Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrained 0-1 Integer Linear Programming problem. The solution to this optimization problem leads to a principled importance criterion which we use to rank parameters during iterative model pruning. To mitigate the poor generalization at high sparsity levels, we propose a self-regularization scheme where model prediction is regularized by the latest checkpoint with increasing sparsity throughout pruning. Our experiments on natural language understanding, question-answering, named entity recognition, and data-to-text generation with various Transformer-based PLMs show the effectiveness of the approach at various sparsity levels.
2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression

We consider distributed convex optimization problems in the regime when the communication between the server and the workers is expensive in both uplink and downlink directions. We develop a new and provably accelerated method, which we call 2Direction, based on fast bidirectional compressed communication and a new bespoke error-feedback mechanism which may be of independent interest. Indeed, we find that the EF and EF21-P mechanisms (Seide et al., 2014; Gruntkowska et al., 2023) that have considerable success in the design of efficient non-accelerated methods are not appropriate for accelerated methods. In particular, we prove that 2Direction improves the previous state-of-the-art communication complexity $\widetilde{\Theta}\left(K \times \left(\frac{L}{\alpha \mu} + \frac{L_{\max} \omega}{n \mu} + \omega\right)\right)$ (Gruntkowska et al., 2023) to $\widetilde{\Theta}(K \times (\sqrt{\frac{L (\omega + 1)}{\alpha \mu}} + \sqrt{\frac{L_{\max} \omega^2}{n \mu}} + \frac{1}{\alpha} + \omega))$ in the $\mu$-strongly-convex setting, where $L$ and $L_{\max}$ are smoothness constants, $n$ is # of workers, $\omega$ and $\alpha$ are compression errors of the Rand$K$ and Top$K$ sparsifiers (as examples), $K$ is # of coordinates/bits that the server and workers send to each other. Moreover, our method is the first that improves upon the communication complexity of the vanilla accelerated gradient descent (AGD) method (Nesterov, 2018). We obtain similar improvements in the general convex regime as well. Finally, our theoretical findings are corroborated by experimental evidence.
Pre-trained Mixed Integer Optimization through Multi-variable Cardinality Branching

We propose a new method to accelerate online Mixed Integer Optimization with Pre-trained machine learning models (PreMIO). The key component of PreMIO is a multi-variable cardinality branching procedure that splits the feasible region with data-driven hyperplanes, which can be easily integrated into any MIP solver with two lines of code. Moreover, we incorporate learning theory and concentration inequalities to develop a straightforward and interpretable hyper-parameter selection strategy for our method. We test the performance of PreMIO by applying it to state-of-the-art MIP solvers and running numerical experiments on both classical OR benchmark datasets and real-life instances. The results validate the effectiveness of our proposed method.
Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus

We present a large-scale in-the-wild Japanese laughter corpus and a laughter synthesis method. Previous work on laughter synthesis lacks not only data but also proper ways to represent laughter. To solve these problems, we first propose an in-the-wild corpus comprising $3.5$ hours of laughter, which is to our best knowledge the largest laughter corpus designed for laughter synthesis. We then propose pseudo phonetic tokens (PPTs) to represent laughter by a sequence of discrete tokens, which are obtained by training a clustering model on features extracted from laughter by a pretrained self-supervised model. Laughter can then be synthesized by feeding PPTs into a text-to-speech system. We further show PPTs can be used to train a language model for unconditional laughter generation. Results of comprehensive subjective and objective evaluations demonstrate that the proposed method significantly outperforms a baseline method, and can generate natural laughter unconditionally.
P-NOC: Adversarial CAM Generation for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) techniques explore individual regularization strategies to refine Class Activation Maps (CAMs). In this work, we first analyze complementary WSSS techniques in the literature, their segmentation properties, and the conditions in which they are most effective. Based on these findings, we devise two new techniques: P-NOC and CCAM-H. In the first, we promote the conjoint training of two adversarial CAM generating networks: the generator, which progressively learns to erase regions containing class-specific features, and a discriminator, which is refined to gradually shift its attention to new class discriminant features. In the latter, we employ the high quality pseudo-segmentation priors produced by P-NOC to guide the learning to saliency information in a weakly supervised fashion. Finally, we employ both pseudo-segmentation priors and pseudo-saliency proposals in the random walk procedure, resulting in higher quality pseudo-semantic segmentation masks, and competitive results with the state of the art.
TheoremQA: A Theorem-driven Question Answering dataset

The recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. However, their capabilities to solve more challenging math problems which require domain-specific knowledge (i.e. theorem) have yet to be investigated. In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models' capabilities to apply theorems to solve challenging science problems. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems (e.g. Taylor's theorem, Lagrange's theorem, Huffman coding, Quantum Theorem, Elasticity Theorem, etc) from Math, Physics, EE&CS, and Finance. We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts. We found that GPT-4's capabilities to solve these problems are unparalleled, achieving an accuracy of 51% with Program-of-Thoughts Prompting. All the existing open-sourced models are below 15%, barely surpassing the random-guess baseline. Given the diversity and broad coverage of TheoremQA, we believe it can be used as a better benchmark to evaluate LLMs' capabilities to solve challenging science problems. The data and code are released in https://github.com/wenhuchen/TheoremQA.