Reviewerly/exHarmony huggingface dataset:
Contour-aware anomaly detection system for autoencoder neural network

Autoencoder (AE) networks are one of the widely used techniques that effectively detect anomalies in the images/videos from the variation of reconstruction errors between the input and output images. However, reconstruction errors are sensitive to various noises, making the model inference unstable and leading to inaccurate localization of an abnormal region in the input image. To alleviate this problem, this paper presents a post-processing algorithm to classify anomaly events by localizing anomalous contour areas from the residual image of the autoencoder neural network. Furthermore, the proposed post-processing algorithm can be integrated into the training process of autoencoder network as the validation step. It, therefore, automates both the training process of the autoencoder network and hyperparameters tuning of the proposed post-processing system. Preliminary experiments on embedded devices show improved performance in simulated environments with repetitive operations involving a robot arm and conveyor belt.
An Energy-Efficient GAN Processor for Mobile Image Translation

An energy-efficient generative adversarial network (GAN) accelerator is proposed for mobile image-to-image translation. In image translation, low precision bits below 8 were not employed due to significant output image quality degradation. In addition, due to the zero injection of transposed convolution, effective PE utilization decreased up to 89%. To address these problems, this paper proposes two key features: 1) we apply layer-wise dynamic fixed-point quantization and implement bit-combined PE to increase throughput by 2×; 2) By analyzing the matching pattern of transposed convolution, data remapping for transposed convolution that simultaneously computes 4 outputs is proposed. The proposed processor is implemented on ZCU 104 FPGA, achieving energy efficiency of 76.38 GOPS/W while consuming 6.08 W of power for mobile image-to-image translation.
Data storage for efficient knowledge distillation in object detectors

While the knowledge storage distillation method has proven effective in image classification, object detection is a considerably more complex task with numerous candidate bounding boxes. In this paper, we show that only a select few of these numerous candidate bounding boxes are pivotal for effective knowledge distillation. Consequently, we introduce a novel method that stores the features of important bounding boxes, selected based on their quality scores, and employs these features for learning using quality score-based masks. Through experiments, we demonstrate that our method significantly reduces computational overhead while preserving performance.
Multi-cost loss-based disparity estimation network from light field

The spatial-angular coupling relationship of light field data is fundamental for the scene-disparity estimation. Occlusion, smoothing and noise are major challenges in disparity estimation of light field. Based on the special geometric structure of bi-plane parameterized light field data, we proposed a novel multi-cost loss light field disparity estimation network. The neural network consists of three modules. Firstly, in order to fully utilize the occlusion information contained in the light field, we divide the light field data into four subsets, and a weight shared network is designed to obtain four initial disparity maps, which embody different occlusion situations. Then, the gradient information of the center view sub-aperture image is used to pick the credible disparity from the initial disparity maps. Lastly, a convolutional neural network is designed to further improve the robustness of the merged disparity map in smooth and noisy regions, while maintaining the structural information of the scene. Experimental results on both synthetic and real datasets show that the proposed method can obtain higher-precision disparity.
Robust Learning to Learn Graph Topologies

Graph learning and revealing the relationship between agents is an essential and pivotal problem in network science and machine learning, carrying significant implications across a broad range of applications. In the literature, there are generally two approaches to graph learning: model-based and learning-based methods. Recent learning-based methods like L2G, GLAD, and uGLAD add more flexibility to model structural priors in graph learning. We propose a novel approach, RL2G, which offers robustness to the "Learning to Learn Graph Topologies" method. This allows us to recover the topologies of graphs using a pre-trained model, independently from their size and topological properties. The experiments conducted on both synthetic and real-world data provide strong evidence that our approach performs well in revealing the relationships between different data entities.
Implementation of a Tile-Grained Pipeline Architecture for CNN Accelerator

In this paper, we propose a pipelined neural network accelerator circuit. The accelerator consists of two 256MACs PE arrays, operates at a frequency of 250MHz, and achieves a theoretical throughput of 256GOPS while utilizing relatively fewer FPGA resources. We implement the hardware on the Xilinx Zynq UltraScale+ MPSoC ZCU102 platform. Experimental results demonstrate that using this architecture can reduce DRAM access by nearly 40%.
Analysis of Social Influence, Financial Risk, Benefits, Expense, and Regulation Factors Influencing the Intention to Use Non-Fungible Token (NFT)

Based on Q3 2022 blockchain tracking data, NFT appeal is waning due to unpredictable interest rates, inflation, global conflicts, and market volatility. Previous research examined technological, economic, ethical, and social aspects to analyze the positive and negative impacts of NFT digital collections in China with target respondents of individuals engaged in the arts. While the novelty in this study focuses on financial risks, social factors, benefits, expense, and regulations on user interest in NFTs in Indonesia to see if these factors influence individuals to use NFTs that have not been discussed in previous studies with target respondents who understand or have NFTs. The quantitative methodology used in this study is SEM-PLS approach using SmartPLS, which is commonly used in information systems studies. This study reveals that the two relationships of Expenditure Factors and Social Influence Factors have no significance or influence on the intention to use. This research uses quantitative methods aimed at the Indonesian public by obtaining data through questionnaires supported by the snowball sampling technique with the aim of being more efficient and cost-effective. This study concludes that influential factors, such as benefits, financial risk, and regulation, significantly shape users' intention to adopt NFTs. While expense and social influence factors may be related to usage intentions, they do not have much impact on NFT adoption. The factors examined in this study accounted for 71.1%, leaving room for the development of additional factors (28.9%) in the future. These insights aid NFT adoption enhancement and industry application development.
PyEye: An Integrated Approach for Signal Integrity Assessment and Eye Diagram Generation

This paper introduces a package tailored for eye diagram analysis of digital signals, a critical aspect of signal integrity evaluation. The package efficiently generates precise eye diagrams, quantifying signal integrity by identifying maximum area rectangles within the eye region. Using advanced algorithms and visualization techniques, the package equips professionals with a versatile tool for analysis, anomaly detection, and optimization. Experimental validation across diverse signals underscores the efficacy of the package, highlighting its potential for comprehensive signal integrity assessment.
A Comparative Study on Implicit Neural Representation-based Arbitrary-Scale Image Super-Resolution

Arbitrary-scale image super-resolution (ASISR) is one of the challenging tasks in computer vision due to its ill-posed nature. Recent works with implicit neural representation (INR) have shown a promising ability in representing images with arbitrary resolutions. In this paper, we compare and analyze different INR methods for ASISR.
The Effect of User Attributes on QoE of Transmission and Presentation Methods for Multi-View Video and Audio IP Transmission

This paper assumes a Multi-View Video and Audio (MVV-A) system with four cameras, i.e., the system has four viewpoints. We compare the Quality of Experience (QoE) of three video transmission and presentation methods: the one-view one-stream method, the one-view four-streams method, and the four-views four-streams method. We perform a subjective experiment with two types of users: male students who major in computer science and female students who major in teacher-training programs. As a result, we show that the user attributes can affect the trade-off related to the transmission and presentation methods of MVV-A.
Enhancing Image Clarity through a Comparative Analysis of Polarizing Filters and Homomorphic Filtering Algorithms

In the context of autonomous vehicles, camera-based lane recognition can be compromised by external factors like sunlight. This study evaluated two denoising methods: software-based denoising via the homomorphic filter algorithm and hardware-based denoising by attaching a polarizing filter in front of the camera lens. Images were analyzed under four conditions based on the combination of using the homomorphic filter and the presence of a polarizing filter. A comparative analysis revealed that a combined application of both filters achieved the highest Structural Similarity Index Measure (SSIM) and the lowest Mean Square Error (MSE), establishing it as the most effective denoising approach.
Virtual Hand Model Generation Through Size Modifications Of Hand Joint Feature Points for XR Interaction

In the field of eXtended Reality (XR), Hand-based interaction, which enables immersive experiences, is a prominent medium and its importance is emerging in the realm of realistic content experience. Previous studies interacting with virtual objects typically have limitations on feeling that they are actually interacting with virtual objects , such as touching, holding, or holding them using VR Glass or a controller. In order to overcome these limitations, a hand model that can optimally interact with virtual objects while changing the size of each joint feature point of the virtual hand is proposed. The contribution of our work are, i) hand joint size variation with optimal interaction ii) virtual hand joint simulation with real environment.The usefulness of this approach has been demonstrated by testing the interaction between virtual hands and virtual objects generated based on real-time images.
Research on Interactive Dataset for Transparent Display-Based Interactive Content Development

Recent advancements in autonomous vehicles have led to a growing popularity of transparent displays. Transparent displays are ideal for augmented reality (AR) applications that overlay digital information onto the real world, offering an appealing user experience through touch, gestures, and other interactive methods. However, most existing transparent displays are limited to the role of one-way information delivery, failing to fully harness the advantages of transparent display technology. Research on interactive datasets for realistic content interaction with transparent displays, including hand gestures, gaze, voice, points of interest (POI), and touch, is currently underway, and this paper summarizes these aspects. With the development and proliferation of transparent display technology, it is essential to review research on applicable interactive dataset technology. While this paper does not cover all studies related to interactive datasets for transparent displays, it is expected to be valuable for researchers involved in such studies.
Farsi Optical Character Recognition Using a Transformer-based Model

Optical Character Recognition (OCR) techniques have made significant advances in recent years using new approaches such as Transformers for Latin languages. However, research on low-resource languages like Farsi is still relatively scarce. One of the reasons for this is the complex nature of Farsi script, which poses unique challenges for OCR. Farsi OCR is essential for various applications, such as document management, digital archiving, and automated data entry. In the context of this investigation, we presents a deep neural network that utilizes transformer architecture for the recognition of Farsi words, yielding promising results. Additionally, we assess the effectiveness of our method through a comparison with state-of-the-art techniques on two datasets namely, Shotor and Sadri, achieving accuracy rates of 99.75% and 99.23%, respectively. Our results outperform previous methods and highlight the potential of transformer-based approaches for Farsi OCR.
A Study on the Implementation of User Web/APP Based Reading Ability Assessment Service

This paper presents the implementation results of an web/app-based service framework for evaluating fluency and comprehension through the recognition of speech voices of Korean elementary school students. User speech recognition applies a pre-trained model based on the koSpeech library to convert user speech into text, and evaluates text reading ability through CER measurement. Additionally, over a certain period of time, the speech uttered by the user is collected, transformed into text, and comprehension ability is assessed through semantic textual similarity measurement.
Conformer-Based End-to-End Speech Recognition Using Grouped Convolution and Multi-Headed Linear Self-Attention with Headdrop

This paper proposes a linear self-attention layer using a headdrop for a conformer-based automatic speech recognition (ASR) model. The proposed attention layer allows all heads to contribute to prediction with low computational complexity, compared to the self-attention-based conformer. Moreover, the convolutional module of the conformer is replaced with a grouped convolutional module to learn various representation subspaces. The proposed attention layer and grouped convolutional module are applied to a joint connectionist temporal classification attention-based encoder-decoder architecture. An ASR system employing the proposed model reduced the word error rate (WER) by 7% and 25% on test-clean of LibriSpeech, respectively, compared to those using the conformer and transformer. In addition, the ablation study reveals that the headdrop and grouped convolutional module reduce the WER.
Cardiology Disease Diagnosis by Analyzing Histological Microscopic Images Using Deep Learning

During the past decade, deep neural networks have become a popular tool in medical image processing, particularly when grading cancer or abnormal tissues. Due to the scarcity of specialized pathologists and the possibility of human error, it is necessary to improve the accuracy of histological data processing. To address this, we explore different deep neural networks tailored for tissue detection, segmentation, and classification in histological images. Our focus is on diagnosing cardiac diseases using histological images of the heart. Visualization and evaluation methods are used to assess the performance of the models on whole slide images. Our experimental results demonstrate a two percent increase in accuracy compared to previous works, achieving 98.44% for accuracy and 98.05% for precision.
Context-aware Image-to-Image Translation for Autonomous Driving

The fusion of deep learning and autonomous driving presents challenges in computer vision, particularly in image-to-image translation to simulate real driving conditions. A notable constraint in the industry is the scarcity of diverse, realistic training data, hindering progress in capturing fine-grained details like weather changes. To address this, extensive research has explored deep learning-based image-to-image translation. Our work targets a critical issue within autonomous driving datasets: the loss of important objects in image translation. We introduce context-aware image-to-image translation (CAIT), embedding image context information by utilizing features from a pre-trained segmentation map prediction network.
A Deep Multi-Object Tracking Technique in Swimming Video Scenes

There are many kinds of objects for multi-object tracking, but among them, tracking attempts for swimmers can be challenging. There are several factors that cause difficulties, and due to the nature of the swimming competition video, overhead race video, swimmer is covered by water and spectators, occlusion occurs, and a rapid change of direction or occlusion occurs to lose an object. Another special issue of swimmer tracking is the spray of water generated by swimming, which makes it difficult to extract the feature. In order to track the swimming data set with these characteristics, this paper divided and analyzed the swimming competition scene according to the ease of tracking, and proposed a hyper-parameter for kinds of features extracted in the FairMOT, which is one of multi-object tracking algorithms, to improve the performance of swimmer tracking.
Discriminative Learning for Supervised Anomaly Detection

Most unsupervised anomaly detection methods learn distributions from normal samples, which solve the class-imbalance problem with different amounts of samples. However, learning only normal samples can affect the model to have loose decision boundary and low discriminality. In this paper, we propose the novel supervised approach for detecting anomalies by exploiting known anomaly samples. Our method uses embeddings from ImageNet backbone model and transfer extracted features towards target domain using feature adaptor. Then, anomaly score is computed using GAN discriminator, which differentiates positive and negative samples to estimate normality of an image. Our approach achieves an anomaly detection AUROC of 99.2% on MVTec AD benchmark.
TrackMine: Topic Tracking in Model Mining using Genetic Algorithm

With the advance of Model-Driven Engineering (MDE), number of generated models has grown exponentially. From the one hand, to provide support for software development using MDE, we need to store various models in the model repositories. From the other hand, extracting a particular model of interest from model repositories is a major challenge. One of the solutions is to use topic tracking in model mining. This provides a rapid and robust exploration that aligns well with the process of finding desired models in complex and large model repositories. In this paper, we propose TrackMine, a genetic algorithm based on topic tracking, to effectively sort the arrangement of models in a repository. This can help modelers realize the tracking, usage, and evolution of the models. TrackMine creates an optimized list of models by rearranging the order of models using user-defined similarity metrics. We demonstrate the applicability of our approach through a proof of concept implementation and evaluate the benefits of the presented algorithm using three different datasets. The experimental results demonstrate the practicality and suitability of our approach for model mining.
Adversarial Robustness Evaluation with Separation Index

The paper introduces a method to assess the robustness of deep neural networks against adversarial attacks. It employs a geometric-based separation metric called the Separation Index, which measures the distance between data points with distinct labels within the latent space of variational autoencoders utilized for classification tasks. The Separation Index quantifies the degree of data separation by comparing each data point with its neighboring data points. A higher value signifies greater separation between different classes, thus ensuring enhanced robustness. This approach yields dependable results when confronted with gradient-based adversarial attacks, including FGSM, R-FGSM, MI-FGSM, and PGD, under both white-box and black-box conditions.
Decentralized Federated Learning in IoT Environments: A Hierarchical Approach

Decentralized federated learning has emerged as a promising approach to train machine learning models in Internet of Things (IoT) environments, where data privacy, communication constraints, and resource limitations are critical concerns. This article presents a hierarchical architecture for decentralized federated learning in IoT, designed to address the unique challenges of IoT devices. The proposed architecture comprises multiple levels, including the edge level, local aggregators, regional aggregators, and a global aggregator. Each level plays a distinct role in facilitating communication and coordination while considering resource constraints. Furthermore, we provide an in-depth analysis of the convergence properties of the proposed framework, leveraging gradient and derivative relations. Experimental results demonstrate the effectiveness and efficiency of the proposed approach in achieving accurate and privacy-preserving learning in IoT settings.
Color Image Encryption Based on 4D Hyperchaotic System and RSA Algorithm Combined Scrambling and Diffusion

In this paper, a color image encryption scheme based on 4D hyperchaos system and RSA algorithm is proposed. Firstly, RSA algorithm is used to generate the initial value of 4D hyperchaotic system, and the key stream is iteratively generated. Then, the color image is divided into RGB three channels. In order to change the position of image pixels, Arnold scrambling is performed on the image. Finally, simultaneous scrambling and diffusion algorithms are used to encrypt the preprocessed images for a second time. Experimental results show that the encryption scheme proposed in this paper is effective. Compared with the existing algorithms, this encryption scheme improves the encryption efficiency, has strong anti-aggression and robustness.
Low-Light Image Enhancement via Gradient Based Cross-Attention Transformer

This paper proposes a novel deep learning-based method for enhancing low-light images. The approach introduces a transformer-based attention mechanism on gradient domain. Experimental results demonstrate superior performance compared to existing techniques, making it a promising solution for improving low-light image quality.
Stereo Confidence Estimation with Pyramid Pooling Transformer

We present a transformer-based encoder-decoder structure for estimating confidence map in stereo matching. Confidence map is obtained by estimating the reliability of the estimated disparity by stereo matching. Recently, deep convolutional neural networks (CNNs) have improved stereo estimation performance. However, these approaches have weakness with global feature. To address this limitation, we propose a novel stereo estimation method using transformer network to effectively capture the global information. Additionally, we introduce a Pyramid Pooling network to capture the local information as well as CNN structure. Experimental results show that the proposed architecture effectively capture both global and local information.
3D Articulated Human Pose Recognition via Learning Deep Gaussian Mixture Models for Virtual Exercise

In this paper, a new 3D articulated human pose recognition method based on deep Gaussian mixture models is proposed for a virtual exercise game using a RGB-D camera. The proposed method acquires 3D skeleton data for each human pose with the RGB-D camera. Then, a new deep Gaussian mixture model (DGMM) is learned from 3D skeleton data for each human pose. The proposed method can recognize each 3D articulated human pose by applying the DGMM network to 3D skeleton data stream of the RGB-D camera. The performance of the proposed method is verified with real experiments.