Reviewerly/exHarmony huggingface dataset:
Hybrid Syllable and Character Representations for Mandarin ASR

With the development of deep learning, End-to-End (E2E) automatic speech recognition (ASR) based on Connectionist Temporal Classification (CTC) and attention has achieved great success and become the most popular method. In speech recognition, the selection of modeling units is critical. Most of the time, the modeling units of Mandarin are Chinese characters. However, the phenomenon of homophones and polyphonic characters in Chinese is very common, which degrades ASR performance. Pinyin can be regarded as the syllables of Chinese characters, which can reflect the pronunciation information of Chinese characters. In E2E ASR, due to the sequence-to-sequence form, Chinese characters directly correspond to the acoustic features and lack intermediate-level representations. In this paper, we introduce pinyin with tones as an auxiliary modeling unit to compensate for the mismatch between Chinese characters and acoustic features. On the basis of the hybrid modeling of syllables and Chinese characters, we propose a multi-task ASR model based on syllables and characters, which introduces a syllable CTC decoder and an attention decoder from syllables to Chinese characters to the joint CTC-attention model. Furthermore, a method of syllable auxiliary attention-rescoring method is proposed. Compared with the character-based ASR model, our method achieves a relative 8.6%/9.4% character error rate (CER) drop on Aishell-1 by greedy-search/attention-rescoring.
Nonlinear Pedestrian Tracking by Unscented Particle Filter for Autonomous Vehicle Under Existence of Non-Gaussian Distributed Uncertainty

This paper proposes an Unscented Particle Filter(UPF) based pedestrian tracking technique in process and measurement systems with non-Gaussian uncertainty distribution. Accurate recognition and state estimation of pedestrian objects are required for successful Autonomous Driving (AD). Thus, we performed modeling to estimate the nonlinear relative movement of a pedestrian and designed UPF. In order to confirm the performance and effectiveness of UPF applied in this study, multiple simulation experiments were conducted under various uncertainty scenario combinations. In particular, the performance of nonlinear state estimation was compared under the non-Gaussian distribution characteristic conditions that both the object tracking model and recognition by extroverted sensors for AD can have. As a result, the proposed UPF-based tracking method for all results has the slightest error compared to other baseline nonlinear filter methods.
Image Retargeting Techniques Identification Using Supervised Deep Learning

Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques.
Towards Arabic Multimodal Dataset for Sentiment Analysis

Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising.
Application for generating re-accessible screenshots of web pages using histogram shrinkage

We developed an application that can generate a screenshot image of a website and redisplay the website with the URL extracted from the image. Since URLs contain multibyte characters and have variable length, they are converted using Base64 and QR code. The QR code allows error correction. The converted bit sequence is then embedded as a watermark. Unlike natural images, screenshot images are artificial. In many screen-shot images, luminance values tend to be distributed around 0 or 255. When embedding, many watermarks are dropped due to overflow. Therefore, we also propose a method of shrinking the luminance distribution for screenshot images. Compared with the case in which the luminance distribution was left unchanged, the proposed method had a smaller bit error rate and was able to redisplay websites with the URLs extracted from the screenshot images.
Deep Federated Learning for IoT to Improve Healthcare Operations

Recently, technological development has given rise to the Internet of Things (IoT), advanced machine learning techniques, wireless communications, and other technologies that have served as the basis for developing applications in various industries such as healthcare. Motivated by the need for a comprehensive and efficient framework that integrates these advanced technologies to take advantage of each of them, solve the problems of implementing each technology alone, and build a privacy-preserve, robust, and scalable solution. This paper presents a novel framework that applies Deep Federated Learning for healthcare IoT. This framework aims to improve healthcare operations, preserve patient privacy, and thus deliver better healthcare services. The proposed framework discusses the technical requirements required to implement such frameworks. Also, a thorough comparison with the existing literature is performed from a technical point of view. Furthermore, an experimental implementation of fetus health classification is presented, along with preliminary findings regarding the framework's ability to build high-performance predictive models considering the distributed nature of healthcare data and its limited availability. The results of improving health services by preventing potential deaths and disabilities are also presented.
The Role of Emojis in Sentiment Analysis of Financial Microblogs

The application of sentiment analysis to the financial sector is a field that has been revamped thanks to social media, which has unleashed a trove of data to analyze. In particular, text analysis techniques have benefited from the attention that large part of data science researchers have devoted to it. However, as demographics evolve, so do the communication forms on social media. In particular, the usage of emojis to carry whole concepts is more and more diffused, though research on the topic is lacking. That is exactly the gap that we intend to cover with this contribution. In particular, after collecting more than 18.5 million posts from StockTwits, we use different supervised learning models in order to determine the role of emojis in sentiment analysis of financial posts on social media. We assess model accuracy, training/prediction speed, and sensitivity to training data set size for both emojis-only and text-only data, using logistic regression and BiLSTM models. Our main findings are staggering; we are the first to show that, when training sentiment analysis models exclusively on emojis, compared to a text-only approach: (i) achieved accuracy is competitive; (ii) training is 32 times faster; (iii) prediction times are reduced to a third; and, (iv) 40 times less data is needed to train the model. Additionally, we show some interesting patterns regarding emoji usage in financial microblogs. The cited contributions, other than being interesting on their own, also pave the way for further research in the field.
Machine Learning Approach for Nano-Magnetic $\text{Fe}_{2}\mathrm{O}_{3}$ Particle Classification

In this work is presented a machine learning approach for nano- magnetic particle classification. Method is applied by convolutional neuron network (CNN), performed over a large set of TEM image data. Classification process include <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Fe}2\mathrm{O}_{3}$</tex> particle size, shape, volume and number recognition. CNN training and classification accuracy is verified by Sequential Pattern Search method and Contour tracing method.
Generating Use Case Specification from SALT GUI

Requirement engineering plays a crucial role in the initial phase of software development. Among the commonly utilized artifacts for requirements is the Use Case Specification, which aids in identifying scenarios and resolving inconsistencies, ambiguities, and incompleteness in the requirements document. This process of scenario identification is also applicable in human-computer interaction, particularly in user interfaces where the graphical user interface (GUI) serves as the closest interface for user interaction and feedback. Recognizing this connection, using GUIs to generate a Use Case Specification emerged. This research proposes an approach for generating a Use Case Specification from the SALT GUI. The primary method employed in this study involves the use of Regular Expressions. A SALT GUI format was also formulated and integrated into an activity diagram to achieve the desired output. Consequently, the proposed approach successfully predicts tasks from SALT GUI to generate a Use Case Specification, yielding a precision score of 0.875, a recall of 0.625, and an f-score of 0.833. These results substantiate the feasibility of generating a Use Case Specification from SALT GUI.
Convolutional Multidimensional Amplitude Spectrum Nuclear Norm for Frequency-domain Robust Principal Component Analysis

In this paper, we propose convolutional multidimensional amplitude spectrum nuclear norm (CMASNN) for the task of frequency-domain robust principal component analysis (FRPCA). Principal component extraction (PCE) from observed signals, particularly in the presence of outliers, has long been recognized as a fundamental challenge in the field of signal processing. Although RPCA has demonstrated its efficacy as a technique, it suffers significant limitations when confronted with misaligned principal components due to its reliance on the low-rankness assumption. To address this issue, we initially introduced ASNN, which leverages the invariant property of amplitude spectra under (circular) shifts, enabling the robust extraction of principal components from observed signals by incorporating the ASNN into RPCA. Building upon this foundation, the CMASNN further enhances the ASNN by unifying multiple signals into a higher dimensional representation. This enhancement enables us to discriminate between true principal components and outliers more effectively. By substituting the CMASNN for the ASNN in FRPCA, our experimental results show that the proposed CMASNN-based FRPCA outperforms the performance of its conventional counterpart.
Virtual Garment Fitting Through Parsing and Context-Aware Generative Adversarial Networks with Discriminator Group

Owing to the rapid growth of the e-commerce industry, image-based virtual try-on has emerged as a popular research topic in recent years. Despite the introduction of multiple approaches to achieve this concept, there remains ample scope for research and improvement. In this regard, Generative Adversarial Networks (GANs) demonstrate a framework possessing immense potential for subsequent development. Nonetheless, the generated images reported in the literature often manifest blurred edges between semantic regions, thereby diminishing the credibility of results. Furthermore, the generation of try-on images may retain the original shape of the upper body clothing on the model mistakenly, such as the length and tightness of the torso, rather than adapting to the shape of the target clothing. In this paper, we propose a more comprehensive architecture to overcome the limitations of GAN-based approaches, which includes the following contributions. First, we introduce a new parsing and context generator that takes into account the warped binary mask of the geometric matching image of the target clothing. The outputs of this generator incorporate the generation of human parsing images that correspond to the generated try-on images. Moreover, we have designed a novel discriminator group that is specifically focused on judging whether the generated image is a reasonable representation of the specific clothing being worn. According to the experimental results, our method effectively exhibits better synthesis quality and remedies the common challenges encountered while using GANs for virtual try-on.
Ultimatelink Between Characters Having a Certain Meaning in Physical Space to URL in Cyberspace with Robust Print and Scan

We propose a method that links character string having a certain semantic meaning on papers in real world to the digital information in cyberspace, and call it Ultimatelink. Ultimatelink adds additional information to characters by superimposing a circular color mark on the characters without changing the shape of the characters. A URL (Universal Resource Locator) can be generated by connecting additional information of semantically organized characters which are come from captured images by digital devices. In the experiment, we investigated the behavior of Ultimatelink for the five type of Japanese characters. The target characters are multiple katakana characters, multiple kanji characters only, multiple kanji and hiragana characters, kanji with a few strokes, and kanji with many strokes. As a result of experiments, it was confirmed that information superimposition and information extraction are possible in most cases, but there are problems such as failure to extract a single character or the ability to extract color markers appropriately depending on the character shape.
Learning to Enhance the Position Embedding and Coherence

Transformer is known as a powerful learning machine for sequence-to-sequence learning. One weakness of this attention-based machine is that the attention mechanism is not sensitive to position information. The absolute position embedding lacks of the generalization to an unknown test sequence where the length is longer than those of training sequences. It becomes important to adopt the relative position embedding in transformer, but the computation load is significantly increased. This paper deals with this issue through a new relation-aware position embedding which properly characterizes the position information with the limited computation overhead where the property of position coherence is held. The merit of the proposed method is illustrated by the experiments on neural machine translation in terms of BLEU and computation time,
Enhancing Genetic Algorithm Performance with Hybrid Strategy for Solving Optimization Problems

This study presents a novel algorithm called hybrid-GA, which combines genetic algorithm (GA) with the Harris Hawks Optimization (HHO) algorithm to address the challenge of enhancing GA performance in solving optimization problems. While GA is known for its strong exploration capabilities, it often faces challenges in exploitation, limiting its ability to find global optimal solutions. The hybrid-GA algorithm aims to surpass existing methods by achieving a better balance between exploration and exploitation, resulting in improved solution quality, faster convergence, and enhanced exploration-exploitation ability. The algorithm effectiveness is demonstrated through experiments on six benchmark functions from CEC2017, where the hybrid-GA outperforms compared algorithms on five of six functions, showcasing its potential for enhancing GA performance in optimization problem-solving. These findings contribute to advancing the field by providing a promising solution to address the exploration-exploitation challenge in GA-based optimization.
Transformer-based Automatic Speech Recognition of Simultaneous Interpretation with Auxiliary Input of Source Language Text

Automatic speech recognition (ASR) of simultaneous interpretation is challenging due to disfluencies such as hesitations, filled pauses, interruptions, and self-repairs. Meanwhile, the simultaneous interpretation sometimes accompanies source language text. In particular, training materials for simultaneous interpreters have the source language text. The source language text can also be considered an ideal transcription of source language speech. Previously, we proposed a Transformer-based ASR with auxiliary input of source language text for transcribing simultaneous interpretation and verified the effectiveness of the model structure by using large-scale corpora of speech translation (ST). In this study, we fine-tuned and evaluated the proposed model using a corpus of simultaneous interpretation with transcription of the source language speech as the auxiliary input. We also added a filled pause model to the ASR and compared the effect on the reduction of a word error rate (WER) with the auxiliary input. The experimental results showed significant WER reductions by both the auxiliary input and the filled pause model, though the reduction made by the filled pause model was greater than that made by the auxiliary input.
MKTformer: Fine-grained Meter Classification Based on Multi-modal Knowledge Transfer

Meter classification in converter station lays the foundation for the subsequent detection-related tasks, however, training excellent meter classification models takes a large quantity of data and computing resources. Aiming to solve this problem, a fine-grained meter classification method based on multi-modal knowledge transfer is proposed. Firstly, the multi-modal knowledge of the Contrastive Language-Image Pre-training (CLIP) model is transferred to provide a more general visual representation for fine-grained features extraction. Secondly, a Task Space Mapping Unit (TSMU) is designed to improve the transfer ability of the multi-modal knowledge. Finally, a new transfer learning strategy is proposed on this basis to achieve a better transfer performance. The experimental results show that our method can achieve higher accuracy than its counterpart in significantly less train time under both fully supervised and few-shot settings, which verifies the its superiority in capturing fine-grained features and reducing training cost.
Multitask Record for Badminton Match

A framework simplifies a novel multi-task for badminton, automating detection in tournament videos and game information prediction. The model combines EfficientNetv2 with distinct module networks for different tasks. This approach achieves a balance between efficiency and precision via a streamlined model architecture.
Realizing Nipple in Profile Recognition and Nipple Detection Using a Single Classification

Nipple in Profile (NIP) is one of the positioned criteria for mammography quality evaluation. The current evaluation work relies on manual reviewing, which is subjective and time-consuming. This paper introduces a novel NIP recognition based on a convolutional neural network (CNN) without referring to prior information, which outputs an objective recognition result in real-time. Amazingly, the added value associated with the proposed method is nipple detection which analyzes the extracted gradient-weighted class activation mapping (Grad-CAM) based on three-category classification. In this work, the defined categories are Nipple in Profile, Nipple not in Profile, and non-nipple. For the NIP recognition, we only exploit the comparison between the Grad-CAMs of Nipple in Profile and Nipple not in Profile. Our method is more creative than the typical classification approaches to realize multiple tasks with a single classification. The experiment results demonstrate that the three-category classification with Densenet-121 achieves better performance in both NIP recognition and nipple detection.
Leveraging Support Vector Machine for Sports Injury Classification

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: “Normal” and “Injury”. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports.
Transformers Based Automated Short Answer Grading with Contrastive Learning for Indonesian Language

The rapid development of technology has impacted various sectors, including education. These developments have enabled e-Learning to thrive, especially during the Covid-19 pandemic. Evaluating student performance and understanding in e-Learning is typically done through quizzes. However, these evaluations, especially in essay grading, still require manual effort. This can lead to exhaustion and introduce bias and inconsistency into the scoring process. To address this issue, one possible solution is to develop an automated short-answer grading system. This research explores large language model that has a general understanding of language. This model is then subjected to a finetuning process. Specifically, this study employs BERT model, with contrastive learning method to develop an automated short-answer scoring system and compare its performance with similar systems. The model is composed of two components, namely the model body which utilizes BERT variation and the model head which employs logistic regression. The model body is structured in a siamese architecture. The results demonstrate an improvement in model performance of BERT model with constrastive learning. When compared to the pretrained BERT and BERT with cosine similarity finetuning, the reduction in prediction MAE is 21.72% and 9.90%, while for the RMSE metric, it is 17.79% and 13.80%. The transformers-based model with contrastive learning achieves metrics of 0.191 for MAE and 0.231 for RMSE. These findings indicate the potential of using the contrastive learning method in transformers models to develop an automated short-answer scoring system.