Reviewerly/exHarmony huggingface dataset:
Research on the Technology of Generating Parametric Continuous Images of Comic Strip in Virtual Reality Environment Based on Machine Algorithm

This thesis explores the feasibility of parametric continuity image generation for comic strips by combining virtual reality technology with machine learning algorithms. In order to promote the development and inheritance of comic strips, we propose to establish an intelligent database of continuous virtual images of comic strips, and use virtual reality technology to apply and experience real users. At the same time, the flexibility and adaptability of machine learning algorithms enable them to be deployed in the analysis, classification and discovery of comic strips by integrating differentiation and acquisition biometric methods. Through the parametric continuous image generated based on machine learning algorithm, we can adjust different parameter settings to achieve the generation of different styles and effects to meet the personalized needs of users. This study holds that the combination of virtual reality and machine learning provides an indispensable path for the modern application of excellent traditional culture, so as to protect and inherit the treasures of Chinese comic strips. Keywords: machine algorithms, Virtual reality, computer vision, parameterization, continuous images.
Deep Learning-Based Event Prediction for Text Analysis

This paper addresses automatic event prediction from unstructured text, specifically event chains. While current approaches employ LSTM for encoding full chains, learning long-range narrative orders, or learning partial orders and long-range narrative orders, none of them consider writer sentiment. To address this, we propose a deep learning-based approach that incorporates writer sentiment. We pre-process the text, extract events, compute sentiment scores using SentiWordNet, convert events to digital vectors, and feed them along with sentiment scores into a deep learning-based classifier. This classifier uses hidden states for event pair modeling, with each pair having its associated sentiment. Evaluation results show that our approach significantly surpasses state-of-the-art methods with 29.2% accuracy.
A Study on Imputation-based Online Learning in Varying Feature Spaces

In this paper, we propose a new method for online learning in varying feature spaces (VFS) where the feature space of instances continually evolves. The proposed method, termed Online Imputation-based Learning (OIL), first imputes missing values and subsequently trains a classifier within a complete feature space. A significant advantage of this approach is the potential for considerable improvements by leveraging well-established research in both imputation and classification techniques. Furthermore, it offers the flexibility to easily modify model configurations based on specific conditions. The experimental results demonstrate that OIL not only performs comparably or even better than the state-of-the-art rival method in regularly varying data streams but also in arbitrarily varying data streams. This is evidenced across 13 benchmark datasets. Following this, we perform an ablation study under diverse conditions to further investigate the efficacy and robustness of various OIL configurations.
Paste-and-Cut: Collective Image Localization and Classification for Real-Time Multi-Camera Object Detection

In recent years, object detection has emerged as a crucial task in various real-world applications, including security surveillance, autonomous vehicles, and robotics. However, traditional object detection models face numerous challenges, such as inefficient image processing, inadequate resource utilization, and a failure to consider the different criticality of input images, making it difficult to apply these models for timely inferences in practical applications. To overcome these challenges, this paper proposes a novel object detection framework, called Paste-and-Cut, that utilizes two techniques, image merging (paste) and RoI patching (cut), to optimize resource utilization and improve object detection performance. Additionally, our approach incorporates a dynamic merge size and canvas size decision mechanism to adapt to varying object detection environments. Experimental results obtained from experiments conducted with the MOT dataset demonstrate the effectiveness of our approach in achieving real-time object detection with improved detection accuracy and without generating any deadline miss. As such, Paste-and-Cut provides a promising solution for efficient and accurate real-time object detection in multi-camera scenarios.
High-level Visual Representation via Perceptual Representation Learning

Recent advancements in the field of representation learning and video prediction have demonstrated the potential for enhancing manipulation and control strategies across various applications through precise anticipation of future states. Nevertheless, the intricate dynamic nature inherent in real-world data poses a formidable challenge in acquiring these representations. Autoregressive models, which employ the generated future frame as input for the subsequent frame prediction, suffer from issues such as compounding errors, memory overload, and extended training times due to the need for reconstructing the state from the latent vector in each iteration. To address these limitations, recent studies have introduced the concept of State Space Models (SSMs) to forecast from the latent space, offering the advantage of predicting distant future states. However, these methodologies often exhibit restricted capabilities in extracting object-centric representations. More recent object-centric approaches concentrate on closely associated features from the input data, yet their ability to capture higher-level representations remains constrained. In this paper, we propose integrating a perceptual network into the slot attention mechanism to facilitate the extraction and segregation of high-level representations. Leveraging a pre-trained perceptual network, we derive elevated object-oriented representations for each perceptual layer, aligning them with corresponding slots. This elevated representation, rich in object-centric information, holds the potential to enhance comprehension of the present state and provide valuable guidance for accurate future state prediction.
Clustering Mobile Traffic Data with Autoencoder Using Time-Series Encoded as Images

With each new generation of mobile communication, the mobile traffic data are increasing exponentially. However, there are limitations to the capacity of base stations. As a result, it become necessary to manage mobile traffic efficiently. It will result in the understanding of the characteristics of mobile traffic, yet it is challenging to find research based on base station traffic volume. In this paper, we encode mobile traffic data of base station from multiple locations to image, and cluster the encoded images using autoencoder. Based on the labeled locations of several base stations we directly investigated, we confirm whether the unlabeled base stations are well grouped into shopping malls, offices, residential areas, and outside areas.
A Grover-like Quantum Algorithm for Minimum Distance Decoding of Linear Block Codes

In this paper, we propose a Grover-like quantum algorithm for minimum distance decoding of linear block codes. Our algorithm is motivated by the observation that the amplitude of the solution state is amplified even if the initial state of the Grover operation is not on a Hadamard basis. We experimentally derive some parameters of our algorithm. Based on our experimental results, we expect to perform fewer Grover operations on average than algorithms based on the traditional Durr-Hoyer algorithm.
MetaHate: Text-based Hate Speech Detection for Metaverse Applications using Deep Learning

The rise of digital communication and the metaverse has revolutionized interaction paradigms, while also introducing challenges in ensuring safe engagements. Hate speech, pervasive in digital spaces, threatens inclusivity. This research introduces a tailored hate speech detection system for the metaverse. Through a comprehensive evaluation of deep learning models, effective real-time detection approaches are identified. To guarantee reliable deployment in the metaverse, models are subjected to explainability assessments using Local Interpretable Model-Agnostic Explanations (LIME). A lightweight Convolutional Neural Network (CNN) model is developed and deployed on the Roblox server, exhibiting commendable accuracy and efficiency. Remarkably, quantization reduces the CNN model size by 93.59%. This pioneering study addresses the dearth of metaverse-focused hate speech research, fostering secure and inclusive virtual spaces.
Enhancing Nighttime Vehicle Detection via Transformer-based Data Augmentation

In autonomous driving systems, vehicle detection technology typically relies on object detection models trained on driving image datasets. However, accurate vehicle detection becomes challenging during nighttime due to low-light conditions, necessitating a sufficient amount of nighttime driving images for training the model. Unfortunately, publicly available datasets lack an adequate amount of nighttime driving images, and collecting them directly is cost-ineffective. In this paper, we propose a novel augmentation method based on transformer to convert daytime driving images into realistic nighttime driving images. Our method analyzes the style case of the given daytime driving image, selects a tailored style image that corresponds to the analyzed style case, and transfers the daytime driving image into the realistic nighttime driving image using the selected style image. Our diverse range of evaluations demonstrates the effectiveness of our proposed method in augmenting realistic nighttime driving images.
On the Scalability of Parking Trajectory Optimization of Autonomous Ground Vehicles

Although the use of optimization-based algorithms for autonomous motion planning in the context of parking has been studied in the literature, most of the existing works were based on either unrealistic simulation environments or a single vehicle type or model. In order to support the deployment of such frameworks for real-world applications, the need for the scalability analysis of such optimization frameworks under realistic simulation environments as well as different vehicle types becomes important. Therefore this paper investigates the suitability of a two-stage optimization framework under a realistic simulation environment as well as using 4 different vehicle models. Specifically, the two-stage optimization process involves first the use of the A star algorithm for initial path generation, and in the second stage, Sequential Quadratic Programming (SQP) is used to optimize the results pathways. In terms of vehicle type, we employ four different vehicle types with different model parameters and evaluated the performance of the framework accordingly. The results show that also the optimization framework is capable of generating feasible parking trajectories, some vehicle types require more script run-time compared to others.
Towards Better Time-series Data Augmentation for Contrastive Learning

Contrastive learning is now a popular choice for representation learning in various domains, including image and natural language processing. However, contrastive learning for time-series data is relatively limited, due to its unrecognizable, high-dimensional temporal structures. It is still difficult to generate valid augmented views that are semantically accurate, despite the significant research advances in the field of time-series data augmentation. In this work, we survey recent works in time-series contrastive learning and propose a simple augmentation-agnostic technique that can effectively improve the fidelity of the augmented views.
Asma’ak: An Emarati Sign Language Translator

This research highlights the challenges faced by individuals who are deaf in communicating with those who do not understand sign language. Artificial Intelligence (AI) has emerged as a promising solution to this problem, with deep learning enabling machines to process sequences of data and accurately recognize sign language gestures. The Asma'ak sign language recognition system was developed to detect Emirati Sign Language hand gestures and instantly translate them into text, thus promoting greater inclusivity and engagement within society. The system's reliability and validity are demonstrated through testing on various operating systems, genders, and age groups, achieving a high level of accuracy and precision. Overall, Asma’ak holds significant potential for improving communication and breaking down linguistic barriers for individuals with hearing impairments.
Automated labeling method for direction detection of objects inside X-ray image

To utilize image datasets for deep learning models, high-quality labeling data is required for good performance. In the case of acquiring a dataset, there are difficulties such as making an annotation by manpower. Many studies or software are provided to increase the convenience of such labeling. In this paper, YOLOX-based object detection, Canny edge detection, and Hough transformation-based linear component extraction techniques are applied to automatically label the position of a linear structure inside an X-ray image. To verify this method, actually acquired X-ray images of wind turbine blades were used in this experiment.
A<sub>r</sub> <sup>*</sup>: a Bounded Suboptimal Search algorithm using Conditional Node Re-expansion Policy

The A* algorithm is a well-known graph search technique widely used in various domains like robotics, games, and logistics. A* combines breadth-first search and heuristics to find optimal paths by evaluating cost and heuristic estimates. However, optimization challenges persist, leading to the need for suboptimal solutions within predefined quality bounds. In the case of Bounded Suboptimal Search, a possible strategy is to reduce unnecessary re-expansions effectively. Existing researches have explored distinct approaches across two primary domains: Never Re-expansion (NR) and Always Re-expansion (AR). In this paper, we introduce an innovative paradigm termed Conditional Re-expansion (CR). This novel algorithm selectively permits re-expansions that yield a considerable decrease in the g-cost value, contingent upon an acceptance error parameter denoted as r. Additionally, we propose an efficient anytime algorithm to rectify the outputs of the algorithm mentioned before. This involves prioritizing the repair of states that were denied reexpansion in the CR phase. Theoretical groundwork and practical demonstrations across diverse benchmarks have been conducted to validate the findings.
AI Knows Which Words Will Appear in Next Year’s Korean CSAT

A text-mining-based word class categorization method and LSTM-based vocabulary pattern prediction method are introduced in this paper. A preprocessing method based on simple text appearance frequency analysis is first described. This method was developed as a data screening tool but showed 4.35 times higher prediction accuracy compared to Word Master book. An LSTM deep learning method is also suggested for vocabulary appearance pattern prediction method. AI performs a regression with various size of data window of previous exams to predict the probabilities of word appearance in the next exam. Predicted values of AI over various data windows are processed into a single score as a weighted sum, which we call an "AI-Score", which represents the probability of word appearance in next year's exam. Suggested method showed 100% accuracy at the range 100-score area and showed only 1.7% error of prediction in the section where the scores were over 60 points. All source codes are freely available at the authors' Git Hub repository. (https://github.com/needleworm/bigdata_voca)
Detection of Vehicle Lights and Classification of Its State

The lights of a vehicle are an essential part of it. It not only helps to drive but also helps in controlling the vehicle. In this paper, a novel method is presented for the detection of vehicle headlights and taillights. This paper also classifies the state of the vehicle lights. Vehicles that are near to the camera are considered for the experiment and the size of the vehicle light had a significant impact on the detection accuracy. Indian Driving Dataset (IDD) was used for the training and testing of the model. IDD consists of the rural and urban roads of Hyderabad and Bengaluru City. It covers various light conditions like day and night and scenarios like a smaller number of vehicles and a greater number of vehicles for detection. Some data preprocessing was also performed before using the dataset for the experiment. The model first detects the vehicle light and then it also classifies the state of the vehicle light. For vehicle light detection, some of the widely used object detection models such as YOLO, Faster RCNN, and MobileNet are used. For the classification of the state of the vehicle light, a color thresholding approach was used to develop the system. The system achieved a mean average precision(mAP) of 91.1%. It classifies it as Headlight on, Taillight on, or light off. The model performance was also evaluated and tested on IDD videos and it was very promising.
Indian Sign Language Recognition with Conversion to Bilingual Text and Audio

Communication is an important part of our day-to-day life as it allows us to express ourselves easily. However, speech isn’t the only way for successful communication, gestures and signs also play an equally meaningful role in communication. Indian Sign Language Recognition is a topic that is far less researched as compared to its counterparts such as American Sign Language Detection. In this paper, signs of the alphabet and digits were captured using an open cv and combined with dataset images obtained from Kaggle. The dataset was processed by resizing, flattening, and converting the images into binary. Though existing systems identify Sign Language with sufficient accuracy, this system incorporates the recognition from a very easy-to-use keyboard interface as an assistant/educator having a feature focused towards Indians that will allow getting the desired result in the text as well as audio format. For displaying the results, two languages were chosen: English & Hindi. Out of the four models trained, namely SVM, KNN, Random Forest, and Decision Tree classifiers; the Random Forest Classifier outperformed other models and it was the one to give highest accuracy of 99.6%.
“AI FIT” Virtual Gym With Real-Time Exercise Form Detection

The growing importance of health and fitness in the wake of the COVID-19 pandemic has caused many people to focus on their well-being. While the fitness industry has digitalized its services, there still exists a lack of experienced human touch and a gymnasium environment in these systems. The proposed system aims to provide an integrated solution for exercise enthusiasts with all the features that can replicate an offline gym. It utilizes the process of Skeletonization using the MediaPipe library for body key point detection. It further uses a Long-term Recurrent Convolutional Network (LRCN) model composed of the Conv 2D layer and Long Short-Term Memory (LSTM) layer to detect a person’s exercise form in real-time, analyse it, and display their progress in the Analysis section. With the integration of modules such as a Training Space (for understanding different exercises), Real-time Exercise Form Detection, Analysis and Statistics(to provide a detailed analysis of your workout), and Blog Space(for the community to interact), this system provides a solution for exercise enthusiasts with all the features that can replicate an offline gym. The LRCN model trained on skeletonized image frames showed an accuracy of 96.49% on the train set and 95.50% on the test set for real-time form detection.
American Sign Language Recognition using Video Vision Transformers

Sign language is the most natural and effective way for communication amongst the hearing/vocally challenged and the hearing abled. To bridge the social gap, this paper proposes a system that aids in translating from a video feed of American Sign Language signers to a known set of common English words. Current solutions for vision based sign language to English translation is limited to a very small set of target classes and old modeling techniques that do not take into consideration the temporal factor that caters to the sequential nature of signing. We use the WLASL dataset, which is a largescale dataset for Word-Level American Sign Language and it comprises of 2000 words from over 100 signers and develop models on WLASL100 and WLASL300 each representing the top 100 and 300 most commonly appearing glossaries respectively. For modelling, we make use of video vision transformers (ViViT) to leverage the power of multi head attention in a temporal and spatial setting and experiment with two feature extraction models like Truncated Resnet50 and MoveNet-thunder which act as a precursor to the ViViT. The final model does better than state of the art models like Pose-GRU, Pose-TGCN and VGG-GRU, which were also trained on the WLASL dataset with a top-1 accuracy of 59.44% on the WLASL100 subset and a top-1 accuracy of 48.38% on the WLASL300 subset.
Opening the Black Box: Explainable Machine Learning for Heart Disease Patients

Unfortunately, the “black box” stigma that has long plagued machine learning has caused many doctors to remain leery of its applications. A “black box” is a model that is so complicated that it is difficult, if not impossible, for a person to understand. Especially in the field of medicine, where many choices have profound consequences for patients’ lives, the inability to understand the reasoning behind prediction models might erode their credibility. New studies in the area of explainable machine learning try to allay these fears. While the benefits of explainable machine learning are vast, they are especially pertinent when deciding whether or not to admit a patient to an intensive care unit. In this article, we take a look at the fundamentals of explainable machine learning and how they might be applied to the healthcare industry. In the first step, we use four well-known boosting methods for heart disease detection and prognosis. Finally, we provide an explanation of the aspects that contribute to the best model judgments, improving clinical reasoning by enhancing our capacity for better predictions and our comprehension of the reasons behind them
Integrating Text and Emoticons for Detecting Extremist Affiliations on Twitter using Deep Learning

The objective of this research paper is to apply opinion mining on Tweets fetched from twitter that would include both emoticons and text to gauge whether the tweets support or relate to extremist affiliations in any way. The study proposes a deep learning classification model that utilizes Distil BERT algorithm. The accuracy, precision, and F1-score are used to compare the performance of the different algorithms. The proposed model combines bi-grams, tri-grams, and Distil BERT model to capture sentiment expressed in text and emoticons. The study spotlights the significance of including emoticons in sentiment analysis, as they provide valuable information that can improve classification accuracy. The dataset is extracted from Twitter. This research provides an insight into the effectiveness of Distil BERT when compared to CNN and LSTM for improving classification accuracy. The implications of the study can be applied to sentiment analysis in social media and extended to other social media platforms that use emojis to express opinions and emotions.