Reviewerly/exHarmony huggingface dataset:
Development of Electronic Student Worksheets Using Nearpod and the RADEC Learning Model in Social Science Subjects in Elementary Schools

The fact that LKPD is still presented to students at school in the form of handouts printed by teachers is the inspiration for this research. The aim of this research is to create an E-LKPD product using the Nearpod application built on the RADEC platform. Development research using the ADDIE development model is this type of research. Research tools include questionnaires, tests, interviews, and documentation. The results of the elementary school level E-LKPD supported by the RADEC-based Nearpod application are classified as very valid. In the very practical field on the practicality questionnaire, both teacher response scores and student scores are very practical. The N-Gain score % test is applied to the efficacy category. So the resulting E-LKPD can be said to meet the criteria of being correct, useful and effective in improving student learning outcomes. The percentage test of the N-Gain score in the effective category is used to measure effectiveness. Therefore, it can be said that the E-LKPD implemented has fulfilled all valid, practical and effective criteria for improving student learning outcomes in science and science subjects using the Nearpod application based on radec as well as interest and enthusiasm for learning
Stereo3DMOT: Stereo Vision Based 3D Multi-object Tracking with Multimodal ReID

3D Multi-Object Tracking (MOT) is a key component in numerous applications, such as autonomous driving and intelligent robotics, playing a crucial role in the perception and decision-making processes of intelligent systems. In this paper, we propose a 3D MOT system based on a cost-effective stereo camera pair, which includes a 3D multimodal re-identification (ReID) model capable of multi-task learning. The ReID model obtains the multimodal features of objects, including RGB and point cloud information. We design data association and trajectory management algorithms. The data association computes an affinity matrix for the object feature embeddings and motion information, while the trajectory management controls the lifecycle of the trajectories. In addition, we create a ReID dataset based on the KITTI Tracking dataset, used for training and validating ReID models. Results demonstrate that our method can achieve accurate object tracking solely with a stereo camera pair, maintaining high reliability even in cases of occlusion and missed detections. Experimental evaluation shows that our approach outperforms competitive results on the KITTI MOT leaderboard. Our code, dataset, and model are available at https://github.com/maomao279/Stereo3DMOT .
A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.
Salient Feature Enhanced Multi-object Tracking with Soft-Sparse Attention in Transformer

Most existing transformer-based Multi-object tracking (MOT) methods use Convolutional Neural Network (CNN) to extract features and then use a transformer to detect and track objects. However, feature extract networks in existing MOT methods cannot pay more attention to the salient regional features and capture their consecutive contextual information, resulting in the neglect of potential object areas during detection. And self-attention in the transformer generates extensive redundant attention areas, resulting in a weak correlation between detected and tracking objects during the tracking. In this paper, we propose a salient regional feature enhancement module (SFEM) to focus more on salient regional features and enhance the continuity of contextual features, it effectively avoids the neglect of some potential object areas due to occlusion and background interference. We further propose soft-sparse attention (SSA) in the transformer to strengthen the correlation between detected and tracking objects, it establishes an exact association between objects to reduce the object’s ID switch. Experimental results on the datasets of MOT17 and MOT20 show that our model significantly outperforms the state-of-the-art metrics of MOTA, IDF1, and IDSw.
Utilizing Testing-Based Formal Verification in Neural Networks: A Theoretical Approach <sup>*</sup>

Neural networks are widely applied across various computer applications, including many safety-critical systems. However, understanding the decision-making behavior of neural networks and verifying their reliability through formal specifications continues to be a topic of ongoing discussion. The first difficulty is to give the precondition and post-condition for neural networks, and the second difficulty also exists in traditional software, i.e., the problem of exploding execution paths. Fortunately, the output range of a neural network is easier to give and when an input is given, we can obtain the activation order of neurons in the neural network. Based on the above facts, we propose DeepTBFV, a method for pre-trained neural networks, which uses a testing-based formal verification algorithm to derive the precondition of the neural network on a specified path by post-condition. Preconditions can be used to verify that a neural network meets a given formal specification, and the behavior of the neural network can also be interpreted by analyzing its preconditions.
A Deep Learning-Based Flight Turnaround Record System

To ensure the smooth progression of the flight turnaround, airports need to record the start and end times of each node in the flight turnaround process, compare them with the desired schedule, and guide the progress of subsequent flight tasks. Therefore, the timeliness and accuracy of data collection at each turnaround node become crucial. Traditionally, some airports manually record flight turnaround node times, which is inefficient and unable to realize data structuring and visualization. To address the above issues, we propose a flight turnaround record system based on object detection. The system builds upon the YOLOv5s-based detection network by introducing attention mechanisms with FasterNet as the backbone to enhance model performance and inference speed. Furthermore, we introduce dual-perspective coordination for aircraft positions, adaptive masking, and other methods to assist the system in making logical determinations about the status of nodes. Experiments have shown that our proposed detection network outperforms the baseline YOLOv5s method on our self-built apron dataset, with a 20fps inference speed increase and a 0.08 mAP@.5:.95 detection accuracy improvement. Compared to manual methods, our system reduces the average time error for each node by 60–120 seconds, significantly improving the efficiency of flight turnaround.
Monocular 3D Ray-Aware RPN For Roadside View Object Detection

3D perception is one of the most important tasks of autonomous vehicles. Both methods based on expensive LiDAR and stereo cameras, and methods based on monocular cameras, have achieved great success in 3D object detection from vehicle view. The roadside view, as an important component of the entire intelligent transportation system, has distinct features from the vehicle's forward view. The 3D object detection from the roadside view has enormous research and application value. However, current research on 3D object detection from roadside view lags far behind the research on 3D object detection from vehicle view. Based on the work of M3D-RPN, we analyze the differences in sample space between roadside view and vehicle view. We find that although the post-optimization based on 2D-3D geometric consistency can improve 3D detection performance in the front view of the vehicle, it can reduce the performance of 3D detection in the roadside view. At the same time, to adapt to the characteristics of the roadside view, we propose a novel ray-aware convolution to replace the depth-aware convolution for the vehicle view. Compared to the M3D-RPN, our proposed M3D-RA-RPN improves the performance of monocular 3D object detection and BEV object detection on the Rope3d dataset.
ANALISIS PRAANGGAPAN DALAM IKLAN BCA REALITA RAMADAN DARI MATA CHRISTINE HAKIM #TIBATIBATENANG DI YOUTUBE DI YOUTUBE

This study aims to describe the forms of presuppositions and to describe the types of presuppositions based on George Yule's theory in BCA advertisements on the reality of Ramadan from the eyes of Christine Hakim #tibatibatenang on YouTube. This research is a qualitative descriptive research using document analysis method. The data sources used are words, phrases, and sentences that have presupposition indicators in BCA advertisements on the reality of Ramadan from the eyes of Christine Hakim #tibatibatenang on YouTube. The data was analyzed through 5 stages, namely identifying the data, classifying the data, describing the data, concluding the data, and presenting the results of data analysis. The researcher concluded that the results of this study contained 12 research data that had been collected, there were 4 existential presuppositions, 3 inactive presuppositions, 1 lexical presupposition, 2 non-factive presuppositions, 1 counterfactual presupposition, and 1 structural presupposition.
Effectful Semantics in 2-Dimensional Categories: Premonoidal and Freyd Bicategories

Premonoidal categories and Freyd categories provide an encompassing framework for the semantics of call-by-value programming languages. Premonoidal categories are a weakening of monoidal categories in which the interchange law for the tensor product may not hold, modelling the fact that effectful programs cannot generally be re-ordered. A Freyd category is a pair of categories with the same objects: a premonoidal category of general programs, and a monoidal category of 'effect-free' programs which do admit re-ordering. Certain recent innovations in semantics, however, have produced models which are not categories but bicategories. Here we develop the theory to capture such examples by introducing premonoidal and Freyd structure in a bicategorical setting. The second dimension introduces new subtleties, so we verify our definitions with several examples and a correspondence theorem (between Freyd bicategories and certain actions of monoidal bicategories) which parallels the categorical framework.
PROJECT-BASED LEARNING AS AN EFFECTIVE APPROACH TO TEACHING BIOLOGY IN INSTITUTIONS OF HIGHER EDUCATION IN PODILLIA

The article explores the problem of the limited effectiveness of traditional teaching methods in biology within the context of modern educational environments for the students of Podillia. The research aims to determine the effectiveness of project-based learning as an alternative approach in teaching biology at the higher education level for the students of Podillia. The study involves an analysis of recent research and publications related to the identified problem. Research methods: theoretical (analysis and synthesis of literary sources, generalization and comparison of data, prediction of further actions) and empirical (observation, pedagogical experiment, pedagogical reflection). The research findings confirm that project-based learning facilitates active student participation, fosters the development of creative thinking, and enhances communicative, soft skills for the students of Podillia. The scientific novelty of this article lies in conducting a research that addresses the problem of insufficient effectiveness of traditional teaching methods in the context of modern educational environment for students in the Podillia region. Additionally, the article explores project-based learning as an alternative approach in teaching biology at the higher education level for Podillia students. Therefore, this article contributes significantly to the field of biological education by proposing a new approach to teaching biology at the higher education level, which can enhance the learning outcomes for students in the Podillia region. The conclusions of the study emphasize the significance of project-based learning in the context of teaching biology in higher education and suggest prospects for further research in this approach for the students of Podillia and other regions.
Track-Before-Detect Adaptive Birth Using Generic Observation Model Labeled Random Finite Sets

Labeled random finite set filters are a popular option for multi-target tracking in challenging high-clutter, low probability of detection scenarios. Recent advances have shown that the labeled random finite set formalism can be extended to the track-before-detect setting, but lack a thorough discussion of measurement-driven track initialization. This paper presents an approach for generating a labeled multi-Bernoulli prior density on target states from radar power measurements. The prior density is generated adaptively from radar measurements and the multi-object density of previously persisting targets via a thresholding procedure that assumes a constant backscatter model (i.e., Swerling 0), Gaussian point spread function, and complex Gaussian receiver noise. The threshold per cell is constructed using a fixed false alarm rate that accounts for the contribution of energy from existing targets in the scene. This procedure improves upon existing approaches by enabling tracks to be initialized in the proximity of existing tracks. The proposed approach is demonstrated within a generic observation model labeled random finite set filter over a challenging scenario containing multiple overlapping target returns and target spawning dynamics.
Graph Data and GCN Based Maritime Target Detection of Multi-frame Scanning Radar

The research on sea target signal detection methods is valuable in many fields. Affected by the complex sea clutter environment and target features, it is difficult for conventional target detection methods to achieve high-performance detection. When deep learning methods such as convolutional neural networks are applied for radar signal processing, each signal sample is independent of each other, and the spatial-temporal relationship information cannot be effectively utilized. In recent years, Graph Convolutional Networks (GCN) have shown great advantages in graph data processing and have been applied in the field of signal processing. This paper proposes a maritime target detection method based on radar signal graph data and GCN, which converts radar multi-frame scanning signals into data with a graph structure, and uses GCN to perform binary classification on the nodes in the graph data to realize maritime target detection. Experimental results show that the proposed method has better performances that conventional 2D-CACFAR method with lower false alarm rate and higher detection probability.
Multitask Learning for Radar-Based Characterization of Drones

For the effective deployment of countermeasures against drones, information on their intent is crucial. There are several indicators for a drone’s intent, e.g., its size, payload, and behavior. Within the current study, the focus was on estimating subsets of the following four indicators: a drone’s wing type, its number of rotors, the presence of a payload and its mean rotor rotation rate. Three Multitask Learning (MTL) approaches were analyzed for the simultaneous estimation of subsets of these indicators based on radar micro-Doppler spectrograms. MTL refers to training neural networks simultaneously for multiple related tasks. The assumption is that if tasks share features between them, an MTL model is easier to train and has improved generalization capabilities as compared to separately trained single-task neural networks. The results of this initial study show that MTL provides overall better performance than the single-task learning approach, given the available data set of measured drone spectrograms.
Design and application of data-driven LESN-DHP controller

Wastewater treatment processes (WWTP) involve a large number of physical, chemical and biological reactions. The concentration of Dissolved oxygen (DO) is an important water quality parameter in WWTP, so it is necessary to control it. To solve this difficult problem, a dual heuristic programming (DHP) controller based on leakage Echo State Network (LESN) is proposed, which is called LESN-DHP controller for short. Firstly, a neural network (NN) identifier based on LESN (LESN-Identifier) is designed to obtain the dynamic characteristics of the system. Then the stability analysis and convergence analysis are carried out. Finally, the feasibility of LESN-DHP controller is verified by simulation experiment. The simulation experiment proves that LESN-DHP controller has better control effect and better performance index than other controllers in WWTP.
Adult Content Detection on Indonesian Tweets by Fine-tuning Transformer-based Models

The prevalence of adult content on social media has harmful effects on the moral values of young individuals. Therefore, effectively filtering inappropriate content on social media like Twitter is essential. Researchers have utilized machine learning and natural language processing techniques to develop automated systems that can identify adult content. However, using Transformer to detect adult content in the Indonesian language has yet to be thoroughly explored. Identifying adult content in a text is relatively challenging due to its subjective and context-dependent nature. The same words can be used in explicit or non-explicit contexts depending on the context or intended meaning of the surrounding text. This study aims to explore the implementation of fine-tuned Transformer-based models for identifying adult and sexually explicit content in Indonesian Twitter texts. We fine-tuned five pre-trained Transformer-based models: IndoBERT, IndoBERTweet, mBERT, XLM-RoBERTa, and DistilmBERT. Based on our experiments, we can see that all the models showed effectiveness in accurately classifying adult and non-adult content. Among the Transformer-based models, XLM-RoBERTa and IndoBERTweet demonstrated effective adult content identification in Indonesian tweets compared to other pre-trained models. XLM-RoBERTa showed a slightly better performance, which can be attributed to its larger size and advanced training techniques.
An Analysis of Donald Trump’s Speech on National Security and Terrorism in the Wake of Orlando Massacre

Speech plays an important role in shaping public perception because it is delivered by an influential figure as a reflection of their point of view. This research aims to identify the hidden powers and ideologies used by Donald Trump through Critical Discourse Analysis (CDA). This type of research is descriptive qualitative. To explore the main phenomena of Trump's speech, the researchers applied Norman Fairclough's three-dimensional CDA framework: text analysis, discursive practices, and social practices. The results of this research show that CDA can explore the dominant power and ideology being fought for, namely fascism and hegemony, which are characterized by the values of discursive practices, social practices and several presumptions. It is hoped that this research can contribute to the understanding of CDA and its function in studying various social problems. Keywords: critical discourse analysis, ideology, power, speech
Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm

Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP.
Secure Electronic Health Records Sharing System using IoT and Blockchain

The Electronic Health Record (EHR) is used for maintaining patients’ medical records in the hospital. The EHR contains the details of clinical related data of the patients under a particular provider. The EHR contains information on a patient's demographics, medications, previous medical history, laboratory results, and reports like X-rays. The EHR system is introduced in order to share the details with other health providers such as laboratories, pharmacies, emergency facilities, and clinics so that they will have all the medical history of the patient’s health conditions. These can be accessed from anywhere via any smart device. A single record for one patient across all departments. Sharing EHR with different health care providers is a major challenge since this health record is stored on centralized servers patients cannot share this information when required. To overcome this issue we have come up with an approach of using IPFS (Inter Planetary File Systems) to store this EHR in a decentralized manner and an RSA algorithm to encrypt the Health Record. By using the combination of blockchain and cryptography a secure platform can be developed for providing the patient with full control over their health record and also maintaining data integrity.