Reviewerly/exHarmony huggingface dataset:
Residual Stack-Aided Hybrid CNN-LSTM-Based Automatic Modulation Classification for Orthogonal Time-Frequency Space System

In this letter, for the first time, we propose an automatic modulation classification (AMC) method for orthogonal time-frequency space (OTFS) signal modulation using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) network with a residual stack. The proposed method uses in-phase and quadrature-phase (IQ) of the received OTFS modulated signal to classify the received modulation accurately. We consider the six digital modulation schemes such as binary phase shift keying (BPSK), quadrature PSK (QPSK), minimum-shift keying (MSK), on-off keying (OOK), 4-amplitude shift keying (4ASK), and 8ASK for orthogonal time-frequency space (OTFS) in the delay-Doppler domain. Results depict that the proposed method achieves a high classification performance even at a low signal-to-noise ratio (SNR).
On the Depth of Convolutional Neural Networks for Image Classification Problems

In this work, we studied the performance changes by increasing the depth of convolutional neural network with four models and four different image datasets. The datasets include MNIST handwritten digits, AUTNT scripts, AUTNT text versus non-text, and Dogs versus Cats. We started with a pair of convolutional layer and max pool layer and gradually expanded the depth by adding pair(s) of convolutional layer and max pool layer. Initially, the accuracy is found to improve with increasing depth up to three pairs for all the four datasets. After that, the accuracy of the models decreases for all datasets. The outcome is, therefore, given using three sets of convolutional and max pool layers. Such study will be useful in designing suitable CNN models for different visual object recognition problems.
Text classification of traditional and national songs using naïve bayes algorithm

In this research, we investigate the effectiveness of the multinomial Naïve Bayes algorithm in the context of text classification, with a particular focus on distinguishing between folk songs and national songs. The rationale for choosing the Naïve Bayes method lies in its unique ability to evaluate word frequencies not only within individual documents but across the entire dataset, leading to significant improvements in accuracy and stability. Our dataset includes 480 folk songs and 90 national songs, categorized into six distinct scenarios, encompassing two, four, and 31 labels, with and without the application of Synthetic Minority Over-sampling Technique (SMOTE). The research journey involves several essential stages, beginning with pre-processing tasks such as case folding, punctuation removal, tokenization, and TF-IDF transformation. Subsequently, the text classification is executed using the multinomial Naïve Bayes algorithm, followed by rigorous testing through k-fold cross-validation and SMOTE resampling techniques. Notably, our findings reveal that the most favorable scenario unfolds when SMOTE is applied to two labels, resulting in a remarkable accuracy rate of 93.75%. These findings underscore the prowess of the multinomial Naïve Bayes algorithm in effectively classifying small data label categories.
Design of a Village Newspaper Application (Kordes) in Realizing a Digital Village in the Village of Dusun Baru V koto, Mukomuko Regency, Based on Android

KorDes is a village newspaper application that contains the latest information that occurs in the village, information on village developments and information on village data as a form of realizing a digital village. This KorDes application was formed as a solution for the people of Dusun Baru V Koto Village, Mukomuko Regency who have difficulty in accessing valid and growing information in the village. In designing the KorDes application, it will be implemented based on Android. Android-based will make it easier for people to access via their smartphones which can be accessed at any time. The design of the application uses the technique of developing UML (unified modeling language) diagrams which makes it easier to document and define applications.
SPEECH RECOGNITION APPLICATION AS AN ANIMATED OBJECT MOVEMENT CONTROLLER SYSTEM

Technological developments in the world have no boundaries. One of them is Speech Recognition. At first, words spoken by humans cannot be recognized by computers. To be recognizable, the word is processed using a specific method. Linear Predictive Coding Method (LPC) is a method used in this research to extract the characteristics of speech. The result of the LPC method is the LPC coefficient which is the number of LPC orders plus 1. The LPC coefficient is processed using Fast Fourier Transform (FFT) 512 to simplify the process of speech recognition. The results are then trained using Backpropagation Neural Network (BPNN) to recognize the spoken word. Speech recognition on the program is implemented as an animated object motion controller on the computer. The end result of this research is animated objects move in accordance with the spoken word. The optimal BPNN structure in this research is to use traingda training function, number of nodes 3, learning rate 0.05, epoch 1000, performance goal 0,00001. This structure can produce the smallest MSE value that is 0,000009957. So, this structure can recognize new words with 100% accuracy for trained data, 80% for the same respondents with trained data and reach 67.5% for new respondents.
SketchingRelatedWork: Finding and Organizing Papers through Inking a Node-Link Diagram

Writing an academic paper requires significant time and effort to find, read, and organize many related papers, which is a complex knowledge task. We present a novel interactive system that allows users to perform these tasks quickly and easily on the 2D canvas with pen and multitouch inputs. Our system turns users’ sketches and handwriting into a node-link diagram of papers and citations that users can iteratively expand in situ toward constructing a coherent narrative when writing Related Work sections.
Pneunocchio: A playful nose augmentation for facilitating embodied representation

Prior research has offered a plethora of wearables centred around sensing bodily actions ranging from more explicit data, such as movement and physiological response, to implicit information, such as ocular and brain activity. Bodily augmentations that physically extend the user’s body along with altering body schema and image have been proposed recently as well, owing to factors such as accessibility and improving communication. However, these attempts have usually consisted of uncomfortable interfaces that either restrict the user’s movement or are intrusive in nature. In this work, we present Pneunocchio, a playful nose augmentation based on the lore of Pinocchio. Pneunocchio consists of a pneumatic-based inflatable that a user wears on their nose to play a game of two truths and a lie. With our work, we aim to explore expressive bodily augmentations that respond to a player’s physiological state that can alter the perception of their body while serving as an expressive match for a current part of the body.
RatiO R-CNN: An Efficient and Accurate Detection Method for Oriented Object Detection

In recent years, oriented object detection has attracted much attention as an emerging branch of computer vision. Compared with objects in natural images, the oriented objects are distributed in any direction, and their ground truth bounding boxes have an extensive range of aspect ratios. In this paper, we propose a two-stage oriented object detection framework called RatiO R-CNN, which has good accuracy and efficiency on oriented object detection datasets. Specifically, we used a new anchor box generation method, RotateGA, to adapt to the characteristics of oriented detection datasets. We design a length ratio loss function to solve the deformation problem of the predicted boxes obtained by regression so that we can generate oriented proposals efficiently and accurately in the RPN phase. In the second stage, we propose a dynamic-oriented R-CNN head, which adjusts the IoU threshold and regression loss during the training process by adapting to the quality of the samples. Our model achieved the best results on the DOTA v1.0 dataset (77.82%mAP50, 49.44%mAP75), proving the validity of our model.
Single Image Dehazing with Deep-Image-Prior Networks

Most conventional dehazing methods focus on separately estimating key parameters (e.g., the transmission map and the atmospheric light) based on the atmospheric scattering model to generate haze-free images, which may face the limitation of error accumulation. With the advance of deep learning technologies, employing deep neural networks (DNNs) to conduct haze removal becomes popular dehazing methods recently. Most DNNs-based methods automatically learn haze-free image or key parameters in the atmospheric scattering model in end-to-end manners, which heavily rely on training models on dataset. This work aims to recover haze-free images directly by DNNs without any time-consuming training process on dataset or cascading parameter estimation steps. In this paper, haze removal is achieved in Maximum-a-Posterior (MAP) framework based on an exist re-formulation of the atmospheric scattering model, which only involves one integrated variable. The proposed MAP framework is connected with DNN by two self-supervised generative networks—two deep-image-prior (DIP) networks, which are present for modeling the deep priors of the haze-free image and the integrated variable. We further investigate the statistical property of the integrated variable and propose handcrafted regularizers to better constrain the integrated variable. By iteratively updating two networks, solutions of the haze-free image and the integrated variable can be solved jointly. Experiments on both synthesized and real hazy images show that the proposed method performs competitively to state-of-the-art dehazing methods in terms of PSNR, SSIM and visual evaluations.
Video Coding for Machines Based on Motion Assisted Saliency Analysis

With an increasing amount of video content analyzed automatically by computer vision algorithms, video coding for machines has received growing attention. With saliency analysis for machine vision, video coding scheme can be designed to preserve key information for machine tasks during compression. In this paper, based on versatile video coding (VVC), we propose a video coding scheme for machines using motion assisted saliency analysis. Taking multi-object tracking as an example task, Grad-CAM, a deep learning visualization method, is used to conduct saliency analysis for key video frames. Considering the complexity of saliency analysis and to improve association performance of multi-object tracking, a motion assisted saliency analysis has been designed for non-key frames, whose saliency maps are predicted using motion vectors and saliency maps of key frames. With saliency maps of all the frames, a coding tree unit (CTU)-level QP adjustment scheme has been derived, which prioritizes salient regions concerning machine vision during bitrate allocation. Experimental results show that, compared with VVC, the proposed scheme can achieve at least 20.3% of bitrate saving with the same tracking accuracy.
Learning Sparse Neural Networks with Identity Layers

The sparsity of Deep Neural Networks is well investigated to maximize the performance and reduce the size of overparameterized networks as possible. Existing methods focus on pruning parameters in the training process by using thresholds and metrics. Meanwhile, feature similarity between different layers has not been discussed sufficiently before, which could be rigorously proved to be highly correlated to the network sparsity in this paper. Inspired by interlayer feature similarity in overparameterized models, we investigate the intrinsic link between network sparsity and interlayer feature similarity. Specifically, we prove that reducing interlayer feature similarity based on Centered Kernel Alignment (CKA) improves the sparsity of the network by using information bottleneck theory. Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity. In other words, layers of our sparse network tend to have their own identity compared to each other. Experimentally, we plug the proposed CKA-SR into the training process of sparse network training methods and find that CKA-SR consistently improves the performance of several State-Of-The-Art sparse training methods, especially at extremely high sparsity. Code is included in the supplementary materials.
Product Line Management with Graphical MBSE Views

Reducing the cost and delay and improving quality are major issues for product and software development, especially in the automotive domain. Product line engineering is a wellknown approach to engineer systems with the aim to reduce costs and development time as well as to improve the product quality. Feature models enable to make logical selection of features and obtain a filtered set of assets that compose the product. We propose to use a color code in feature models to make possible decisions visual in the feature tree. The color code is explained and its use is illustrated. The completeness of the approach is discussed.
Compressed models for co-reference resolution: enhancing efficiency with debiased word embeddings

Abstract This work presents a comprehensive approach to reduce bias in word embedding vectors and evaluate the impact on various Natural Language Processing (NLP) tasks. Two GloVe variations (840B and 50) are debiased by identifying the gender direction in the word embedding space and then removing or reducing the gender component from the embeddings of target words, while preserving useful semantic information. Their gender bias is assessed through the Word Embedding Association Test. The performance of co-reference resolution and text classification models trained on both original and debiased embeddings is evaluated in terms of accuracy. A compressed co-reference resolution model is examined to gauge the effectiveness of debiasing techniques on resource-efficient models. To the best of the authors’ knowledge, this is the first attempt to apply compression techniques to debiased models. By analyzing the context preservation of debiased embeddings using a Twitter misinformation dataset, this study contributes valuable insights into the practical implications of debiasing methods for real-world applications such as person profiling.
Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact. In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost.
Sentiment Analysis and Classification on Amazon Products using Improved Support Vector Machine for Multiclass Classification

There is a huge increase in number of peoples who have been accessing many social networking sites especially user post or reviews for a specific product, company, brand, individual, forums and movies etc. These reviews are helpful in judging customer perception on certain thing. The development of algorithms that could automate the categorization of distinct comments based on feedback from consumers became an analyst project, and this automated classification process is known as sentiment analysis. This research main goal is to analyze Amazon product reviews using an approach to Machine Learning (ML) built around TF-IDF and then employ the Support Vector Machine (SVM) algorithm to categorize the sentiment scores and sentences. SVM can handle binomial classification but the customer reviews is mostly classified into positive, negative and neutral and in some applications, it is fine grained into star ratings such 1-5 or sometimes 1-10. Also, in some applications features or attributes are high in number in which some are irrelevant. Hence, this work applies feature subset algorithm and improves the existing SVM to handle multiclass classification. The Sentiment analysis, Rapidminer tool is considered for classification and the results are visualized, assessed with suitable classification metrics.
LogoNet: A Fine-Grained Network for Instance-Level Logo Sketch Retrieval

Sketch-based image retrieval, which aims to use sketches as queries to retrieve images containing the same query instance, receives increasing attention in recent years. Although dramatic progress has been made in sketch retrieval, few efforts are devoted to logo sketch retrieval which is still hindered by the following challenges: Firstly, logo sketch retrieval is more difficult than typical sketch retrieval problem, since a logo sketch usually contains much less visual contents with only irregular strokes and lines. Secondly, instance-specific sketches demonstrate dramatic appearance variances, making them less identifiable when querying the same logo instance. Thirdly, there exist several sketch retrieval benchmarking datasets nowadays, whereas an instance-level logo sketch dataset is still publicly unavailable. To address the above-mentioned limitations, we make twofold contributions in this study for instance-level logo sketch retrieval. To begin with, we construct an instance-level logo sketch dataset containing 2k logo instances and exceeding 9k sketches. To our knowledge, this is the first publicly available instance-level logo sketch dataset. Next, we develop a fine-grained triple-branch CNN architecture based on hybrid attention mechanism termed LogoNet for accurate logo sketch retrieval. More specifically, we embed the hybrid attention mechanism into the triple-branch architecture for capturing the key query-specific information from the limited visual cues in the logo sketches. Experimental evaluations both on our assembled dataset and public benchmark datasets demonstrate the effectiveness of our proposed network.
Super-Resolution Reconstruction Based on Kernel Regression Method

Kernel regression method is one of the non-parametric nonlinear regression estimation methods. The univariate regression function is estimated by one-dimensional kernel function. Based on Taylor expansion, the value of regression coefficient is obtained by optimization method. Using the same method, two-dimensional kernel regression is studied. Finally, the adaptive control kernel is summarized. On the basis of the non-parametric estimation kernel regression model, the two-dimensional kernel regression function is extended to three-dimensional and each pixel in the video sequence is represented as a three-dimensional Taylor expansion. The displayed coefficients are obtained by the locally weighted least squares method, and the weights of the kernel regression are used to capture the spatiotemporal local motion information, avoiding the explicit sub-pixel precision motion estimation. Experiments in the standard test video database show that the algorithm has better reconstruction effect and larger scope of application, and can be used for videos with local and complex motion. The 3D-SKR super-resolution reconstruction algorithm solves the sub-pixel accuracy registration problem of the previous super-resolution reconstruction algorithms and extends the application range of sequence image super-resolution reconstruction to any sequence image, which is of great significance. The new algorithm framework proposed in this paper improves the robustness of the original algorithm and eliminates the influence of outliers on the reconstruction results. The algorithm proposed in this paper can adaptively capture the structural information of the image, has a good suppression effect on noise, and can effectively eliminate the artifacts caused by inaccurate motion estimation.
TANet: Adversarial Network via Tokens Transformer for Universal Domain Adaptation

Universal Domain Adaptation (UDA) aims to transfer knowledge between two datasets. The main challenge is to distinguish “unknown” classes that do not exist in the labeled source domain but exist in the unlabeled target domain. Some existing methods have poor feature representation capability and prediction diversity. Besides, they cannot clearly discover the common label set effectively and the label sets private to each domain. In this paper, we propose an algorithm named TANet, which extracts features by a Tokens Transformer and automatically learns the classification boundaries between different classes by training a one-vs-all classifier for each class and design batch nuclear-norm maximization loss to ensure the discriminativeness of the model and the diversity of classification. Moreover, by employing adversarial and non-adversarial domain discriminators in Tokens Transformer, TANet can distinguish the source and target data in the common label set. Finally, extensive experimental results show that TANet outperforms competitors and is robust.
Cardiac Magnetic Resonance Imaging Focus Generative Adversarial Network Segmentation

Cardiovascular function analysis is crucial for illness diagnosis, risk assessment, and therapy selection in clinical cardiology. Doctors may identify cardiac disorders such as right ventricular failure, hypertrophic cardiomyopathy, and dilated cardiomyopathy using a variety of imaging modalities that allow them to spot pathological alterations. The optimum course of therapy may be chosen more quickly thanks to accurate automation of the relevant duties. Artificial neural networks and deep learning are the foundation of generative adversarial networks (GANs), which are methods for creating synthetic pictures. The potential capacity of the GANs to solve problems has attracted interest in addition to their inherent flexibility and the adaptability of deep learning, on which they are founded. This survey aims to examine the significance of medical imaging in the study and diagnosis of cardiac disease. Demonstrate the widespread adoption of GAN Network approaches in the field of magnetic resonance imaging (MRI) medical image analysis; Explain the recent segmentation application of generative adversarial networks. GANs in cardiovascular imaging additionally Identify the hurdles to the effective application of the GAN Network to medical imaging tasks and highlight particular contributions that address or get around these problems