Reviewerly/exHarmony huggingface dataset:
PERAN GURU DALAM PEMANFAATAN PERPUSTAKAAN SEBAGAI SUMBER BELAJAR UNTUK MENDUKUNG BUDAYA LITERASI MEMBACA DI SEKOLAH DASAR

This research is based on the government's efforts to foster a culture of literacy with the establishment of the School Literacy Movement "Gerakan Literasi Sekolah" (GLS) program where this program apart from cultivating a literacy culture also utilizes the library as a learning resource. Therefore this study aims to determine the teacher's role in the use of the library as a learning resource to support literacy culture at SDN Pancanegara schools for fifth grade students which is carried out by teachers, especially fifth grade teachers at Pancanegara Elementary School. This research uses a qualitative type, where the researcher as the main instrument through direct research with a naturalistic approach. This research consists of two stages, namely the preliminary stage and activity development/preliminary stage consisting of reference and field studies to obtain accurate information about the research problem. The development stage includes development activities and validation of research data collection. Data collection has been validated to be used for data collection. The results of the study show that the most important thing is the teacher's role in the process of learning activities that support the literacy culture shown by the teacher in the elementary school.
Gaze Tracking Control System for Wheelchair and Smart Home Automation

In recent times, eye-controlled interfaces have revolutionized the lives of numerous individuals, particularly those with disabilities or severe motor impairments that hinder their use of alternative computer peripherals. The focus of this research paper is an eye-tracking system that goes beyond enabling independent mobility—it empowers users to control various smart home devices in their surroundings. It is comparatively easier to use than an EMG-controlled wheelchair. Additionally, the system incorporates an audio output feature to cater to the user's daily needs effectively. Our research objective is to design an innovative eye-tracking system with a user-friendly multi-screen interface. Each screen represents a smart home device, including a wheelchair, allowing users to effortlessly navigate and select devices using different eye-blinking patterns. This intuitive interface enhances user control and accessibility. Moreover, the system prioritizes user safety by integrating various sensors. These sensors ensure secure and reliable operation, providing peace of mind for individuals using the wheelchair. Overall, the eye tracking system is equipped with advanced features, offering a reliable and safe solution for individuals with mobility limitations. This work highlights the importance of user-centric design and personalized customization to ensure the system meets the specific needs and preferences of the wheelchair user.
Activity State Tracking Under Non-Restricted Ambulatory Condition

Human Activity Recognition (HAR) is one important digital health applications to track fitness or to avoid sedentary behavior. Due to the growing popularity of consumer wearable devices, smartwatches and earbuds are being widely adopted for HAR applications. However, using just one of the devices may not be sufficient to track all activities properly. Additionally, handling motion noise becomes more challenging when a single device is used. This paper proposes a multi-modal approach to HAR by using both buds and watch. Using a large dataset of 53 subjects collected from both controlled and uncontrolled noisy environments, we demonstrate the limitations of using a single modality activity classification. We identify various noise sources imposed in uncontrolled environment and propose two novel noise handling methods to ensure the robustness of activity state tracking. We build on top of a previous activity tracking effort and demonstrate a 7.8% sensitivity improvement against current state of the art in uncontrolled noisy environment.
Real-Time Drone Detection and Tracking in Distorted Infrared Images

With the increasing use of drones for various applications, their detection and tracking have become critical for ensuring safety and security. In this paper, we propose an algorithm for detecting and tracking drones from infrared (IR) images in challenging conditions such as noise and distortion. Our algorithm involves YOLOv7 for drone detection and utilizes the SORT algorithm for real-time tracking. To detect distortion in the drone images, we employed a vision transformer in parallel with a customized CNN. The experimental results demonstrate the effectiveness of our approach in challenging conditions and highlight the potential for future developments in drone detection and tracking using deep learning techniques. We achieve a precision of 94.2%, a recall of 92.64%, and a mean average precision (mAP) of 92.6% on the provided test data. The implementation code can be found at: https://github.com/a-bentamou/Drone-detection-and-tracking.
Predicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams

Electronic Health Record (EHR) data provide a tremendous opportunity for enhancing the quality of care by delivering personalized treatments [1]. EHR data are rich and tracked at high frequency over time, and can be integrated into a real-time, continuous warning system. One problem that can benefit from real-time monitoring of EHR is the occurrence of invasive-ventilation (iV) for Intensive Care Unit (ICU) patients. The prognostication of a future event falls in the domain of survival analysis. However, iV can occur multiple times during an ICU stay, while most survival analysis tools work only with non-recurring events. Moreover, most survival analysis models are restricted to time-static data, while EHR data changes over time, e.g. heart rate. Therefore, to model the iV problem we turn to BoXHED2.0 [2], a fully nonparametric survival machine learning method that is grounded in theory [3]. Importantly, BoXHED2.0 can handle recurring events data with time-dependent covariates, and we apply it to the MIMIC IV data set [4] to develop a real-time ICU iV warning indicator. Our ICU iV model achieves an AUCPR of 0.34 (versus 0.13 for the benchmarks) and an AUROC of 0.85 (versus 0.73 for the benchmarks) out of sample, speaking to its effectiveness in real-time risk monitoring.
SYSTEMATIC MAPPING STUDY: THE USE OF TRANSPILERS

A systematic mapping of the literature on the topic of transpiler use was conducted. Research questions addressing the research topic have been compiled. A search for publications narrating the topic of transpilers in various digital sources was conducted. The selection of unique and relevant publications was performed. A quantitative distribution of works by type of publication channel, geography, and quantitative composition of authors was performed. Based on the obtained sample of publications the answers to the research questions were obtained. The terms used in place of the term "Transpiler" were researched. The most common synonym turned out to be the term "Source-to-source compiler". The fields of application of transpiling were investigated. Most often transpilers were used for code generation, computation, and optimization purposes. The programming languages most commonly encountered in research were also examined. The most common programming languages were C/C++ and Python. It was also noted that JavaScript is never the source language of transpilation. Correlations between research questions were examined. A correlation was found between research questions of synonyms and fields of application, where the names transpiler and transcompiler are more often used in the field of code generation. A connection was also found between research questions of synonyms and programming languages, where transpilers used within the same programming language are more often referred to as source-to-source compiler. A growing trend in the number of studies on transpiler topics was demonstrated. The prospects of the work were also identified – the classification of programming languages by their level (low, high) and the study of correlation with other classifications.
METRIC BINARY CLASSIFIER WITH SELECTION OF FEATURE WEIGHTS

The family of metric algorithms based on determining the distance from one observation to another has a number of advantages, such as their suitability for many types of problems and results have a clear interpretation. Therefore, metric algorithms are widely used in credit risk modeling, non-destructive quality control of products, medical diagnostics, geology, and many other practical areas. The most common metric algorithm in practice is the k-nearest neighbors method. At the same time, one of the key problems of metric algorithms is the problem of dimension, since the decision is made on the basis of all observations of the training sample. In addition, usually all variables have the same weight when calculating the distance, which leads to a drop in the quality of the algorithm with an increase in the number of features. The article discusses a new machine learning method for solving classification problems – a metric classifier with the selection of feature weights, which allows to solve these problems to a large extent. Nine algorithms were used to optimize the function. Classification quality based on them is checked on 3 problems from the UCI repository. As a result of the comparison, the truncated Newton method was chosen to build a new metric classifier. The quality of the new classifier was tested on 8 datasets from the same repository and compared with the quality of the classical nearest neighbor method. This classifier has a higher quality for problems with a large number of features in comparison to the classical approach. Data set characteristics and calculation results are presented in the corresponding tables.
BDNet: A Method-based Forward and Backward Convolutional Networks for Action Recognition in Videos

Human action recognition analyses the behaviour in a scene according to the spatial-temporal features carried in a series of image sequences. The critical challenge is to extract informative spatial-temporal features in a limited-length video that frequently constrains the receptive field of the 3D Convolutional Neural Network(CNN). However, present methods mainly consider modeling the action’s spatial-temporal features along a single direction and ignore the information in the opposite. Moreover, the fixed-weight fusion of spatial and temporal features does not distinguish their importance for each action sequence. To attack the problems above, we propose a bi-directional network (BDNet) to combinate the features from both directions of action for recognizing action. Two CNNs are set up to extract spatial-temporal features along the forward and backward action, respectively. Then, a dynamic fusion strategy is adopted to measure the importance of spatial and temporal features for each action. We conducted many experiments on the commonly used action recognition dataset UCF101. Compared with other work, the proposed method achieves promising performance in accuracy and efficiency.
Covariance Matrix Adaptation for Multi-Agent Proximal Policy Optimization

Pertinent works have shown that the on-policy reinforcement learning algorithm Multi-Agent Proximal Policy Optimization(MAPPO) can perform similarly to other off-policy learning algorithms in multi-agent systems through minimal hyperparameter tuning. However, previous literature has indicated that in multi-agent tasks, although refined agent features improve the utility of the algorithm, they inevitably affect efficiency. Meanwhile, the policy generated from sampled advantage value may lead to overfitting and the algorithm reduces the exploration variance prematurely which leading the policy falling into the local optimal solution. Drawing inspiration from Proximal Policy Optimization With Covariance Matrix Adaptation(PPO-CMA), we improve the update of the loss function in continuous action space. Experimental results show that our method is superior to the original MAPPO and QMIX algorithms.
Innovative Application of Computer Game Algorithms of Surakarta Based on Reinforcement Learning

The traditional methods to solve the computer games problem are to use various search algorithms on the game search tree and to combine the situation evaluation to generate the corresponding methods. The paper discusses the principle of MCTS based reinforcement learning. Based on this principle, BP algorithm is combined, it is designed a self-learning game playing program of Surakarta. The experiment shows that the methods can improve the performance of the agent, avoiding design static evaluating function by manual.
Attention based Reinforcement Learning for Efficient Communication under Constraint in Multi-Agent Systems

Efficient communication is a key element of team collaboration in the real-world, and in multi-agent reinforcement learning (MARL) as well. When to communicate with whom and what is the main issue in MARL communication. We propose Graph-attention-Actor Attention-Critic (GA3C) on the basis of actor-critic in which we use graph generator consist of a graph attention networks (GAT) Encoder and multi-layer perceptron (MLP) Decoder to construct directed communication graph determining when to communicate with whom, based on which message processor quickly aggregates and processes messages from agents to achieve communication process. During the training process, critic with attention mechanism makes a more reasonable evaluation of the actor's strategy by dynamically selecting the agents that need attention. Empirical evaluations on the predator-prey environment demonstrate that our method has high-performance compared to other baselines, while having better scalability in more complex environments.
Efficient combined algorithm of Transformer and U-Net for 3D medical image segmentation

In recent years, using convolutional neural network (CNN) to segment medical images has achieved good results already. The CNN model with U-shaped structure of encoder and decoder and jump connection mechanism has been widely used in various medical tasks. However, CNN is unable to learn global information and conduct remote semantic information interaction due to its inductive bias. With the application of Transformer in Computer Vision field, the global remote dependency modeling capability brought by Transformer can make up for the locality of CNN to some extent. However, in recent studies, most proposed methods only use Transformer or combinations of Transformer and CNN at both encoder and decoder. In this paper, we propose a new U-shaped network framework with incomplete symmetry, using Transformer module for image feature extraction, and using CNN for image recovery, to seek more possibilities for the combination of Transformer and CNN in the medical segmentation field. Experiments on cardiac MRI segmentation ACDC data sets show that giving up Transformer in the decoding layer will not reduce the overall segmentation performance, and half-transformer and half-CNN network structure can give a good balance between computation and segmentation performance.
Target Identity Probability Analysis for Multi-Target Tracking

This paper proposes a novel target identity probability analysis algorithm for multi-target tracking (MTT) problems. By introducing a target identity probability matrix, finding the identity probability is converted into the problem of target-to-target association. The issue in solving the equivalent problem is that the computational complexity increases exponentially with the increase of the problem size. We propose a Gibbs sampling approach to find approximate polynomial-time solutions. Theoretical analysis reveals that the proposed algorithm provides a performance-guaranteed approximation. Numerical simulations are conducted to support the analytical findings.
Salient Object Detection based on Adaptive deep Differential Pyramid

In the initial stage of salient object detection, the main structure of the saliency model is based calculation of Center-Surround Contrast, which is introduced into a few CNN-based saliency detection model. However, the receptive field of feature layers could not be dynamically adjusted in these methods. Therefore, an Adaptive depth Difference Pyramid (ADP) module is constructed, in which receptive fields could be dynamically adjusted to calculate the difference between features of different levels, sensing the local characteristics of saliency objects. Subsequently, an ADP-based method for salient object detection is proposed. Experiments was conducted on 4 public datasets, and the mean absolute error (MAE) and maximum F-measure was selected for the evaluation metric, which achieved MAE of 87.1%, 86.7%, 88.9% and 82.3% respectively.
Single Image Dehazing Using CycleGAN Based on Feature Fusion

Due to the difficulty of obtaining paired data sets from the real world to train the network, most of the current dehazing networks are trained by synthetic hazy data sets, which will have drawbacks such as poor generalization ability to natural haze scenes and loss of depth details. This paper proposes an image dehazing method using CycleGAN based on improved feature fusion to solve the problem. The method is designed with an encoder-decoder structure in the generator network, enabling more feature information to be extracted at multiple scales. In order to restore the detailed information of the image, this paper introduces the residual dense block instead of the convolution module to extract and fuse the feature information under different receptive fields in each stage of the network. Aiming at the complexity of the fog distribution in the actual scene, this paper introduces an improved channel and spatial attention mechanism in the skip connection of the network to accomplish non-uniform processing of haze areas with different concentrations. At the same time, to improve the quality of the generated image, this paper introduces perceptual loss to enhance the detailed information of the output features, making the generated image more realistic. The experimental findings suggest that the proposed method can achieve better subjective visual effects and image details, and the outcomes of objective indicators are also improved.
Path Planning of Mobile Robot Based on Improved Artificial Potential Field Method

An improved artificial potential field method is proposed to address the problems of target unreachability and falling into local minima in the path planning process of the traditional artificial potential field method. Firstly, the distance factor between the robot and the target point is introduced to solve the target unreachability problem; secondly, the robot is guided out of the local minima point by setting the virtual target point through the double-circle strategy; finally, in order to satisfy the continuity of robot velocity and acceleration, the resulting path is smoothed by using three uniform B-sample curves. The experimental results show that the improved algorithm can effectively solve the problems of target unreachability and falling into local minima in the traditional algorithm, and the smoothness of the path after the spline treatment is better compared with the original path.
ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification

Vehicle re-identification is a cross-view search task by matching the same target vehicle from different perspectives. It serves an important role in road-vehicle collaboration and intelligent road control. With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows limited scalability because of the heavy reliance on large-scale annotated datasets. Therefore, the unsupervised vehicle reidentification with stronger cross-scene generalization ability has attracted more attention. Considering that Masked Autoencoder (MAE) has shown excellent performance in self-supervised learning, this work designs a Contour Guided Masked Autoencoder for Unsupervised Vehicle Re-Identification (ConMAE), which is inspired by extracting the informative contour clue to highlight the key regions for cross-view correlation. ConMAE is implemented by preserving the image blocks with contour pixels and randomly masking the blocks with smooth textures. In addition, to improve the quality of pseudo labels of vehicles for unsupervised re-identification, we design a label softening strategy and adaptively update the label with the increase of training steps. We carry out experiments on VeRi-776 and VehiclelD datasets, and a significant performance improvement is obtained by the comparison with the state-of-the-art unsupervised vehicle re-identification methods. The code is available on the website of https://github.com/JWFanggit/ConMAE-Vehicle-ReID
KPCARST: a method of weakening conflict evidence based on kernel principal component analysis and relatively similar transformation

How to deal with conflict evidence is an essential issue in Dempster-Shafer evidence theory (DST). In the Dempster combination rule, a high anomaly can produce counterintuitive phenomena, so scholars have proposed many effective methods to weaken the anomaly. This paper proposes a novel anomaly evidence detection framework based on kernel principal components analysis and relative similarity transformation (KPCARST) to address this issue further. The new method can weaken anomalies’ influence on the evidence fusion results and achieve more effective results than existing methods. A numerical example verifies the effectiveness of the new method.
Multi-Layer Features Based Self-Support Network for Few-Shot Segmentation

Few-shot semantic segmentation aims to make the network identify the object regions of new categories with only a few annotated samples. The key to the few-shot segmentation is to complete information interaction between support and query images, and avoid overfitting as much as possible. For combining the advantages of metric-based prototype learning and visual correspondence, we propose a Multi-Layer Features Based Self-Support Network (MFSNet). In MFSNet, we divide the interactive part of support and query features into two stages, multi-layer similarity feature stage and self-support prototype fusion stage. The multi-layer similarity feature stage focuses more on extracting the details of the segmented object, and the self-support prototype fusion stage refine the general feature information of the object. The two stages are linked together through self-support strategies. In addition, we propose a Fusion Block in MFSNet to establish a more robust semantic relationship between multi-layer features. MFSNet is tested on two benchmark semantic segmentation datasets, Pascal-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , and achieves new state-of-the-art performances on 1-shot segmentation tasks.