Reviewerly/exHarmony huggingface dataset:
DIM: long-tailed object detection and instance segmentation via dynamic instance memory

Abstract Object detection and instance segmentation have been successful on benchmarks with relatively balanced category distribution (e.g. MSCOCO). However, state-of-the-art object detection and segmentation methods still struggle to generalize on long-tailed datasets (e.g. LVIS), where a few classes (head classes) dominate the instance samples, while most classes (tailed classes) have only a few samples. To address this challenge, we propose a plug-and-play module within the Mask R-CNN framework called dynamic instance memory (DIM). Specifically, we augment Mask R-CNN with an auxiliary branch for training. It maintains a dynamic memory bank storing an instance-level prototype representation for each category , and shares the classifier with the existing instance branch. With a simple metric loss, the representations in DIM can be dynamically updated by the instance proposals in the mini-batch during training. Our DIM introduces a bias toward tailed classes to the classifier learning along with a class frequency reversed sampler, which learns generalizable representations from the original data distribution, complementing the existing instance branch. Comprehensive experiments on LVIS demonstrate the effectiveness of DIM, as well as the significant advantages of DIM over the baseline Mask R-CNN.
Impersonation Attacks Detection in Online Exams Through Static Photo Analysis with Similarity Score

Academic integrity has become a major concern in online education, with impersonation attacks being a primary threat. Automatic proctoring has been developed to mitigate this issue, but security vulnerabilities related to impersonation detection using virtual webcams still exist. To address this issue and improve the reliability and accuracy of face verification technology, we propose a novel method that focuses on similarity scores generated from face recognition. Our proposed algorithm captures student activity during an exam every 5 seconds and compares it with the profile picture on the Learning Management System (LMS). The comparison results are converted into quantitative data and represented in a table and a graph. Each repetition of the similarity score values is compared with subsequent iterations over 15 iterations, automatically detecting potential impersonation cases and upholding the integrity of student assessments. The proposed algorithm ensures that a user is a valid user before activating the cheating detection pipeline, providing a comprehensive and efficient solution for detecting potential cheating incidents in online exams. This method represents a significant advancement in face recognition technology, with the capacity to confirm a user’s identity before activating the cheating detection pipeline. In conclusion, our proposed method provides a promising approach to combat impersonation attacks in online exams, ensuring academic integrity and fairness. By automating the process of comparing similarity score values, the proposed algorithm offers a rapid and reliable method for detecting potential impersonation cases and improving the effectiveness of automatic proctoring systems. This method can be integrated into existing systems to improve their effectiveness and ensure academic integrity in online education.
Multi-Human Pose Detection Based on EELAN-Blazepose Model

The human pose estimation system is an exciting topic for developing a collaborative robot. The robot can interpret human poses and autonomously perform collaborative action using various sensors such as cameras and lidars as input devices and use the pose data to interact like giving and receiving objects from humans. There are multiple models for detecting human poses such as Openpose and Mediapipe, but only a few models can detect poses in three dimensions for cases where multiple humans are detected. In this study, we aimed to develop a sstacked model for detecting three-dimensional human poses for cases where multiple humans are detected by combining the EELAN and Blazepose models as our first step in enabling human-robot interactions like giving and taking objects. In the static image tests, our model successfully detected multiple humans and estimated their three-dimensional models separately. On the other hand, the real-time test results showed that our model successfully estimated the three-dimensional human pose for this case with a mean processing time of 354.30 milliseconds(ms) to process 20 frames per batch.
NLP K-Means Algorithm Incorporated into a Proactive Chatbot to Assist Failing Students

Predicting failure and individually assisting failing students is an ongoing challenge for most universities. This paper focuses on natural language processing and clustering the k-means algorithm applied to active chatbots. It aims to help students, and specifically to identify and predict failing students and proactively help them. Furthermore, it suggests an intervention to help students based on controllable academic factors that affect their academic performance. First, the authors outlined the research context for achieving this goal and created a predictive model of students' academic performance. The research results indicate a correlation between the variables with an accuracy of 0.935 and a precision of 0.76. Next, the k-means algorithm was used to cluster the students' problems or different factors that affect the students' academic performance. Finally, the k-means algorithm was integrated into an active chatbot to help students according to their problem groups.
Handwriting Classification based on Hand Movement using ConvLSTM

The recognition of handwritten text presents challenges due to the variability and complexity of human handwriting, making it difficult to capture subtle nuances through traditional methods. Hand gesture recognition has emerged as an alternative method for predicting handwritten text, using sensors such as Kinect, LeapMotion, gyroscopes, accelerometers, and electromyograms to extract geometric and spatial information. Continuous hand-gesture recognition using cameras is preferred due to its ease of use and low hardware costs. Researchers have proposed different methods for recognizing hand gestures, including fuzzy logic, deterministic finite automata, trajectory-based methods, and dynamic probability long short-term memory (DP-LSTM). However, the latest research has shown that using LSTM can result in spatial information being lost. Therefore, this work proposes an architecture that captures spatial information using Convolutional Neural Network (CNN) and LSTM as Conv-LSTM, achieving high recognition rates in hand gesture trajectories for letters a to e in English captured using MediaPipe. Our results show that our proposed model can achieve high accuracy in classification and attained 0.8438.
Automatic Recognition of Driving Events based on Deep Learning

In recent years, there has been a rapid development of intelligent driving assistance systems. Although most vehicles nowadays are equipped with driving assistance systems, the number of car accidents continues to rise. The main cause of car accidents is still largely attributed to human factors. Therefore, there has been an increasing focus on research related to accident detection and driver behavior analysis. This study used deep learning methods to automatically recognize driving events from recorded driving videos. In the training phase of deep learning, we cropped all the videos in the training data into multiple clips, and labeled driving event categories for each clip, including four categories: vehicle stopped, straight driving, turning, and collision. The proposed model references the architecture of the SlowFastNet model and the concepts of I3D. We expanded Inception-V3 to a 3D structure and replaced the bottom architecture of SlowFastNet with 3D-Inception-V3, making the network more applicable to the training data. After training, the model can recognize driving events in various driving environments. Through experimental comparisons, our network architecture achieved the highest recognition accuracy, with an accuracy rate of 93.3%.
Hyperledger-Operated Blockchain Integration: Writing, Deploying and Testing Custom Chaincode

Blockchain is a specific Distributed Ledger Technology (DLT) that is an emerging technology currently disrupting various fields. The aim of this paper is to explore ways to harness the advantages of blockchain while being implemented to existing systems. A permissioned blockchain can be established through Hyperledger Fabric (HLF) that utilizes ledgers that are interacted upon by a chaincode. An HLF network was established to investigate the custom chaincode. The scenario of the project was grounded on the functions performed in a web-based property inventory management system that uses a centralized database. The chaincode in the project was written using JavaScript and Node.js was used to create the whole chaincode source. A channel was built between the nodes of the blockchain where the chaincode was deployed. Subsequently, to open a gateway to the network, multiple Representational State Transfer (RST) Application Programming Interface (API) were created. Several gateway endpoints were tested through Insomnia, a cross-platform API client for RST. The tests performed employed various request methods (GET, POST, PUT, and DELETE) which resulted in evidence that the custom chaincode is fully functional and adheres to the OpenAPI specification. The paper concludes that it is highly feasible and advantageous to integrate a blockchain into an existing Web 2.0 system. Most functions and business logic in existing traditional systems can be reflected in a chaincode with proper planning and execution. In the future, other aspects of the blockchain network will be explored further.
Monitoring Method of Power Data Asset Operation Platform Based on Visualization Technology

In the power data asset operation platform, it will be affected by the data edge distribution, data correlation and data drift value. The monitoring ARL value changes greatly and the monitoring performance becomes worse. Therefore, a monitoring method of power data asset operation platform based on visualization technology is proposed. The parallel coordinate visualization method in visualization technology is adopted to visualize the data of power data asset operation platform through five steps: preprocessing data, rearranging dimensions, clustering, modifying dimension labels and interactive technology; Determine the relationship between data samples and observations, use control chart technology, give the likelihood function of control chart monitoring platform data, construct platform monitoring control chart statistics, and monitor the power data asset operation platform according to the control chart function and control line calculation function. The experimental results show that its ARL value changes little, and is basically not affected by data edge distribution, data correlation and data drift, so it has better monitoring performance.
Harmonic Elimination in Inverter Using Modified GWO Algorithm

Aiming at the problems of low accuracy and easy precocity of grey wolf optimization algorithm for solving nonlinear equations of multi-level inverter SHEPWM, a multi-strategy fusion grey wolf optimization algorithm is proposed. Firstly, chaos is used to initialize the individual positions of gray wolves for increasing the individual diversity of the population; Then, a nonlinear dynamic variation parameter based on trigonometric function and beta distribution is given to realize the good transition of the algorithm from exploration to development; Finally, by introducing the idea of particle swarm optimization, the gray wolf’s individual experience optimal value and the population optimal value are combined to update the gray wolf’s position information for accelerating the algorithm convergence speed. Simulation and experimental results verify the correctness and feasibility of the algorithm.
Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

CONTEXT: Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. OBJECTIVES: We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. METHOD: We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. RESULTS: Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. CONCLUSIONS: Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.
The Tensor Discriminant Ridge Regression Model With Extreme Learning Machine for Hyperspectral Image Classification

Multivariate Ridge regression(MR), linear discriminant analysis(LDA) and extreme learning machine(ELM) have been widely used in hyperspectral image(HSI) classification. However, these methods do not consider the influence of noise in HSIs, spatial information and the internal relationship between samples. As a result, the sample distribution is not ideal and the classification effect cannot be improved. This paper extends LDA and MR to the field of tensors, that can not only use the spatial information of the sample, but also can make the distribution of homogeneous samples more concentrated. Besides, this paper analyzes the relationship between the number of neurons in the hidden layer of ELM and the classification accuracy. Finally, singular spectral analysis(SSA) is chosen to improve classification accuracy. The tensor discriminant ridge regression model with ELM and SSA for hyperspectral image classification(TDRES) is proposed. Experiments show compared with tensor-based classifiers, ELM and other state-of-the-art methods, the proposed method is efficient and effective.
Embedding models for supervised automatic extraction and classification of named entities in scientific acknowledgements

Abstract Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgment text in scientific papers. We trained and implemented a named entity recognition (NER) task using the flair NLP framework. The training was conducted using three default Flair NER models with four differently-sized corpora and different versions of the flair NLP framework. The Flair Embeddings model trained on the medium corpus with the latest FLAIR version showed the best accuracy of 0.79. Expanding the size of a training corpus from very small to medium size massively increased the accuracy of all training algorithms, but further expansion of the training corpus did not bring further improvement. Moreover, the performance of the model slightly deteriorated. Our model is able to recognize six entity types: funding agency, grant number, individuals, university, corporation, and miscellaneous. The model works more precisely for some entity types than for others; thus, individuals and grant numbers showed a very good F1-Score over 0.9. Most of the previous works on acknowledgment analysis were limited by the manual evaluation of data and therefore by the amount of processed data. This model can be applied for the comprehensive analysis of acknowledgment texts and may potentially make a great contribution to the field of automated acknowledgment analysis.
Pragmatic evidence of cross-language link detection: A systematic literature review

There is a rising trend for heterogeneous software applications involving multilingual source code. The key focus of reverse engineers is to unravel the cross-language links (XLLs) and their dependencies. This study aims to perform a systematic literature review (SLR) to compile different approaches, tools, techniques, and shortcomings of such techniques and understand the XLLs and their dependencies while performing reverse engineering on state-of-the-art software applications. This SLR selects 76 primary studies and uses them to create a ’go-to’ literature database, where professionals from software engineering could find all the content pertinent to the analysis and XLL detection for major multilingual applications like Java enterprise applications, Android applications, etc. It has been observed that traditional source code analysis mechanisms to reverse engineer contemporary software applications face scores of problems and limitations that need to be addressed. To assist the community in the above-mentioned goal, a general schema with definitions of XLLs and associated concepts is furnished. This study provides an SLR on XLLs, comprehensive taxonomy called cross-language analysis, which incorporates all the methods for XLL detection in multilingual source code. By pursuing future directions suggested in the end, researchers and practitioners can advance the field of multilingual applications; such as Enterprise resource planning (ERP) solutions, and cross-language software corpora, leading to improved software development practices and better understanding of language interactions in multilingual environments. The research data provided in the survey presents a comprehensive analysis of the complexities involved in working with diverse programming languages and frameworks, offering valuable insights for language technology researchers, software developers, academics, and decision-makers. This integration will enable them to identify and manage dependencies across diverse languages, leading to more efficient and reliable multilingual software systems.
A new path planning method for AUV based on the Navier–Stokes equations for ocean currents

The autonomous underwater vehicle (AUV), due to its flexibility, safety, and other characteristics, has been widely used in various marine applications such as underwater mapping, marine ecological monitoring and marine scientific. When navigating in complex and ever-changing ocean currents, one of the key challenges in AUV control is how to effectively avoid unknown obstacles such as sunken ships and reefs, and plan the route to reach the target point. In this paper, based on partially measured ocean current velocities, the Navier–Stokes equations (N–S equations) are used to characterize the ocean current velocities within a small-scale region. Furthermore, the improved artificial potential field method is used for path planning after integrating the velocity information. Simulation and experiments indicate that the proposed algorithm is better suited to complex marine environments.
Leveraging a combination of machine learning and formal concept analysis to locate the implementation of features in software variants

Recently, software variants are adopted to build software product lines in the industry. In this adoption, the available assets (features, source code, design documents, etc.) are reused to build a software product line rather than building it from scratch. The feature location is the first step in this adoption process. In the literature, numerous approaches were proposed to locate the implementations of features in the source code. However, these approaches are guided using feature-specific information, which is not always available, especially in legacy applications. In this study, a feature location approach is proposed without predefined feature-specific information. The proposed approach incorporates a mathematical research technique called formal concept analysis with other proposed algorithms. This combination is empirically evaluated using a benchmark case study. The obtained results demonstrate that this combination achieves promising results in terms of well-known used metrics in this area: Recall, Precision, and F-measure. Also, the results show that the approach effectively finds features implementation across software variants.
Quality Detection of Export Purple Sweet Potatoes Using Yolov4-Tiny

Purple sweet potato is used as a food staple, especially for Indonesian people. The production process for sweet potato has been produced and exported to various countries for market development. One of the businesses that processes sweet potatoes is UBI Jalar UKM in Sukun District, Malang City. Sweet Potato UKM is an agricultural group engaged in the planting of sweet potatoes. Many sweet potatoes sent to the factory were rejected. The process of selecting sweet potatoes for export still uses a manual system by selecting them directly without considering quality. Therefore it is necessary to detect the quality of superior sweet potatoes using the yolov4-tiny method. Many sweet potatoes submitted by SMEs are rejected by factories/industry because export standards do not comply. Therefore, the selection process manually can be detrimental to the SMEs themselves. So it is necessary to detect the quality of purple sweet potato using the yolov4-tiny method to reduce losses to SMEs. The results on the detection of sweet potatoes using yolov4-tiny prove that the method detects sweet potatoes according to each grade. Furthermore, the system for detecting the quality of sweet potatoes employs yolov4-tiny technology to ensure that they meet industry standards for export. The sweet potatoes are classified into five grades with an impressive 99.15% mAP score.
Performance Comparison of Support Vector Machine and Random Forest Using Delta TF-IDF on Sentiment Classification of Tiket.com Reviews

Tiket.com is one of the largest Online Travel Agent (OTA) platforms in Indonesia. Tiket.com as a pioneer of online travel agents in indonesia won an achievement during the pandemic, namely won the 2022 Top Brand Award. Tiket.com application can be downloaded on Google Play Store. Google Play Store has a review feature, which can be an important aspect for consumer decisions. Sentiment analysis can be applied to classify positive and negative reviews, which can help companies review their services. This study aims to analyze the sentiment of customer reviews on the Tiket.com app using Support Vector Machine (SVM) and Random Forest (RF) algorithms with Delta TF-IDF term weighting. The dataset used in this study consists of 1500 most relevant reviews that were scraped from the Google Play Store. The best model for SVM and Random Forest is selected by tuning hyperparameters using Gridsearchcv. The results of this study indicate that SVM produces a better classification performance on 94.33% Accuracy, 87% Recall, 95.6% Precision, 91.1% F1-Score and 86.96% Kappa-Statistic.