Reviewerly/exHarmony huggingface dataset:
ArtHDR-Net: Perceptually Realistic and Accurate HDR Content Creation

High Dynamic Range (HDR) content creation has become an important topic for modern media and entertainment sectors, gaming and Augmented/Virtual Reality industries. Many methods have been proposed to recreate the HDR counterparts of input Low Dynamic Range (LDR) images/videos given a single exposure or multi-exposure LDRs. The state-of-the-art methods focus primarily on the preservation of the reconstruction’s structural similarity and the pixel-wise accuracy. However, these conventional approaches do not emphasize preserving the artistic intent of the images in terms of human visual perception, which is an essential element in media, entertainment and gaming. In this paper, we attempt to study and fill this gap. We propose an architecture called ArtHDR-Net based on a Convolutional Neural Network that uses multi-exposed LDR features as input. Experimental results show that ArtHDR-Net can achieve state-of-the-art performance in terms of the HDR-VDP-2 score (i.e., mean opinion score index) while reaching competitive performance in terms of PSNR and SSIM.
Dual-Path Residual Attention Convolution Networks for Color-Embedded-Grayscale Image

The reversible grayscale to color recovery is a challenging task due to its ill-posed inverse nature. The recovered color image produced by the color-embedded-greyscale process commonly does not have mesmerizing quality because of color distortion and checkerboard artifacts. To tackle this problem, we propose a Dual-Path Residual Attention Convolution Network (DPRACNet) to improve the quality of recovered color image. This method implements dual-path convolution network and residual attention as the building block. As reported in the Experiment Section, the DPRACNet provides best performance among the previous methods with Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) is 31.62 and 0.934, respectively.
A Social Analysis of Thailand's 2023 Election Through Twitter Feeds

Nowadays, it cannot be denied that social media has been in many parts of people's lives, especially in entertainment, business and education. Public figures including politicians frequently use social platforms to connect with their audience, for example Facebook, Instagram, Tiktok and etc. One of the most commonly used is a platform called Twitter or also known as X. It is a platform that people can share their opinions in short messages. Since there is an alphabet limitation, people can deliver and receive message rapidly. It is a perfect platform that we could use to update the real-time situations for example election, emergency alert, natural disasters and etc. According to its characteristic, it can be used as a data source for social media analytics, which involves collecting and analyzing data to perform business decisions. The social media analytics has become commonplace because the number of people using social media tends to increase every second. Specifically, Twitter sentiment analysis offers a quick and inexpensive tool for monitoring and predictions. In this work, Twitter is used as a data source to monitor and predict Thailand Election in 2023 because this election highly attracts attention from Thais and international. Moreover, Twitter is a significant platform that is used to debate about political events in Thailand. The result of this election potentially indicates Thailand's future, including economics, public health, environment, international relations and etc. Our work performs sentiment analysis to categorize the data and mainly focuses on extracting sentiment from text without considering semantic word relations and co-occurrences because it is appropriate and functional for Thai sentence analysis. Based on the result, it can be seen that Twitter plays a significant role in politics, with politicians wielding considerable influence over public perception. Comparing of the actual election result and the predicted result, this analysis demonstrates the potential for prediction precisely. The candidates with high positive sentiments have higher possibility to win the election while those with low sentiments has lower possibility.
CH-MEAD: A Chinese Multimodal Conversational Emotion Analysis Dataset with Fine-Grained Emotion Taxonomy

Emotion recognition in conversations (ERC) is important for building human-like chatbots. Much research work has been devoted to the English community, however, there is still missing large-scale multimodal ERC dataset for the Chinese community. On the other hand, emotion states in the dynamic flow of conversations are typically subtle and complex. But currently most adopted emotion taxonomy for emotion recognition in conversations is based on Ekman’s six basic emotion categories [1], which cannot cover the diverse set of emotions occurring in multi-turn dialogues, such as the emerging states of anxiety, puzzled and so on. In this paper, we present a Chinese multimodal conversational emotion analysis dataset (CH-MEAD), which contains 25, 292 video segments (utterances) labeled for 26 emotion categories (including neutral) and are collected from more than 400 Chinese TV series, movies and show. To our best knowledge, the CH-MEAD is the first large-scale Chinese multimodal conversational dataset with fine-grained emotion taxonomy, which is suitable for studying the speaker’s subtle and complex emotion states in dialogues and will also promote the development of multimodal ERC in Chinese research community. Based on the CH-MEAD dataset, we propose a speaker-aware contextual multimodal fusion (SCMF) network and demonstrate its efficiency over the current SOTA models in our experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Maximization of 2D Cross-Correlation Based on Auxiliary Function Method for Image Alignment

In this paper, we propose an algorithm to maximize the generalized 2D cross-correlation function for image alignment, a cornerstone task in image signal processing and computer vision. As a fast image alignment method, 2D cross-correlation-based methods are often used in real-time applications. These approaches aim to optimize the 2D cross-correlation function between two images, inferring the displacement between them. Nevertheless, image misalignment commonly necessitates a maximization of the continuous cross-correlation function, and existing iterative and approximation-based approaches exhibit limited computational speed or accuracy. In response to this challenge, this paper proposes a novel algorithm to maximize the generalized 2D cross-correlation function. Inspired by Yamaoka et al.’s work for the 1D case, our approach maximizes a quadratic auxiliary function that bounds the 2D cross-correlation from below, instead of directly maximizing the cross-correlation. The accuracy and computational speed of our method, in comparison to three existing techniques, were assessed via a simulation experiment. Our findings confirm the superiority of the proposed method over a quadratic curve fitting approach in terms of image shift estimation accuracy. Remarkably, our proposed method proved approximately 6.8 and 4 times faster than gradient ascent and golden section search per iteration, respectively, competing with the speed of the non-iterative curve fitting.
From Synthetic To Real: Enhancing Deep Learning Models With Generative Adversarial Networks For Efficient Data Utilization In Automatic Retail Stores

One of the major challenges in developing deep learning models for automatic retail stores is the availability of diverse and accurate data. Achieving high accuracy requires a large amount of data that is specifically collected and labeled for the task at hand. However, this process is time-consuming and prone to potential errors during labeling, which can affect model training. To mitigate this challenge, data augmentation methods have been widely adopted to train models when data is limited. However, the effectiveness of data augmentation is not always guaranteed, as it still relies on the original data. To address this limitation, a new approach has been developed that utilizes synthetic data generated through 3D scanning of objects. However, the challenge lies in ensuring that this synthetic data accurately represents real-world data to maintain model accuracy. In this paper, we propose an approach that integrates adversarial learning into the YOLOv8 model and trains it using synthetic data. We then apply this method to automatic retail stores to demonstrate its efficacy in real-life scenarios. Experimental results on the AIC-22 dataset reveal significant improvements compared to the original YOLOv8 version. The proposed approach achieves around 6.7% increase in precision, a 7.1% improvement in recall, and a 10.3% enhancement in mAP while maintaining the same inference speed of 127 fps on the RTX 3090Ti.
Light-weight Zero-Reference-based Image Enhancement for Low-Light Images

Images captured under low-light conditions often suffer from overall darkness and poor visual quality. In this study, we propose a light-weight zero-reference image enhancement method to improve the visual quality of low-light images. The proposed method is composed of some parts: image partition, transformation map estimation, and pixel value adjustment. To achieve transformation map estimation, a light-weight network, CSPDCE-Net, is developed. For performance evaluation, two image datasets, SICE and LOL, are selected here. For visual quality, the average PSNR and SSIM of the proposed method can achieve 17.01dB and 0.71, respectively. Compared with the existing methods, the proposed method can achieve better performance in terms of PSNR and SSIM. The experimental results show that the proposed method not only can process low-light images well but also is superior to some existing methods.
Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from Shadow

We present a self-trainable method, Mask2Hand, which learns to solve the challenging task of predicting 3D hand pose and shape from a 2D binary mask of hand silhouette/shadow without additional manually-annotated data. Given the intrinsic camera parameters and the parametric hand model in the camera space, we adopt the differentiable rendering technique to project 3D estimations onto the 2D binary silhouette space. By applying a tailored combination of losses between the rendered silhouette and the input binary mask, we are able to integrate the self-guidance mechanism into our end-to-end optimization process for constraining global mesh registration and hand pose estimation. The experiments show that our method, which takes a single binary mask as the input, can achieve comparable prediction accuracy on both unaligned and aligned settings as state-of-the-art methods that require RGB or depth inputs.
Improving Aspect Sentiment Classification via Retrieving from Training Data

Aspect sentiment classification (ASC) is an essential subtask of aspect-based sentiment analysis. Recently, pre-trained language models (PLMs) have gradually become the mainstream building block for the ASC task and retrieval-based methods are shown to be effective in helping PLMs better understand various downstream tasks. However, retrieval-based methods need to introduce external knowledge, and building an index of large-scale corpus leads to huge cost. To this end, we propose an effective method that utilizes training data to construct a retrieval corpus and retrieve instances most similar to current input to enhance semantic representations. In the proposed method, three kinds of retrieval metrics are applied, allowing us to quickly adapt to ASC tasks. Experimental results show that this simple retrieval augmented method can achieve significantly better performance on three benchmark ASC datasets (Twitter, Laptop, and Restaurant). Our code is available at https://github.com/rickltt/re-bert.
Enhanced Keystroke Dynamics Authentication Using Keystroke Vector Dissimilarity

A keystroke dynamics is a simple example of a biometric authentication by using a typing rhythm on a keyboard without any special device. Previous research used Euclidean distance to distinguish between an authentic user and an imposter [1]. However, the Euclidean distance has a flaw in that a distance could be the same even though the keystroke rhythms are different. Therefore, this paper proposed to enhance the keystroke dynamics authentication by using a keystroke vector and calculating a standard deviation for each key to give different weights such that the variations of each keystroke is taking into account. A dataset is similar in [1] but some data is different. The performance was measured using Equal Error Rate (EER) and the result showed that the EER was 3.33%, compared to 4% in [1].
Advancing Accurate Depth Estimation and Perception with Angled Stereo Camera

This work explores the use of angled stereo vision with low-cost cameras to increase the field of view for an improved situational awareness. While it increases the perception capabilities in computer vision applications, this work also advances the accuracy of depth estimation. Traditional stereo camera setups rely on a pair of parallel optical axes, which consists largely of overlapping regions that results in a wastage of both hardware and processing resources in capturing similar views. By reducing the overlap in the images captured, more data can be captured from the surrounding for analysis. The angled stereo camera is capable of capturing more unique information as compared to the regular stereo camera alternative with the same hardware, improving situational awareness.
Resolution-Adaptive Lossless Image Compression Using Frequency Decomposition Network

This paper introduces a new lossless image compression method that aims to effectively utilize image correlations from two distinctive perspectives. Firstly, a hierarchical approach is employed to decompose the input image into subimages, and a new encoding order is designed for efficient subimage encoding. We enable the method to fully exploit the horizontal, vertical, and diagonal components of previously encoded subimages. Secondly, each subimage is further decomposed into low and high-frequency components, where the low-frequency components are first compressed. Then the low-frequency components are used as additional input for compressing the remaining high-frequency ones. In this scenario, the low-frequency ones serve as strong prior for encoding the higher frequencies. In addition to improving the compression performance, we make our method adapted to image sizes, specifically the performance differences depending on the input resolution, by introducing resolution-specific kernels. Experiments show that the proposed method achieves state-of-the-art performance for high and low-resolution benchmarks.
Can StArtGAN withstand Image Processing Attacks?

StArtGAN is a constructive-based coverless data hiding framework, that generates various classes of art images to represent secret bits. These generated art images will be collated to form a collage image that carries the secret data. In certain data hiding applications such as watermarking applications, robustness is an important factor to be considered in such a data hiding framework. Therefore, in this paper, we will investigate the robustness level of StArtGAN. Six common image processing operations with multiple variants are applied as the robustness attacks to the collage image output from StArtGAN to examine its classification accuracy (or secret data extraction accuracy) and its perceptual qualities after these attacks. The result proves that this framework is robust to Salt & Pepper Noise, Gaussian Noise, Sharpening, Gaussian Filter, and Compression Attacks.
Exploring LSTM and CNN Architectures for Sign Language Translation

Our study explores the application of deep learning models, specifically LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), in the realm of sign language translation to address communication barriers faced by individuals with hearing disabilities. Using a dedicated dataset comprising ten frequently used American Sign Language words, we rigorously compare the performance of LSTM and CNN models, measuring precision and recall metrics. The LSTM model achieves a perfect accuracy score of 1, while the CNN model demonstrates a commendable accuracy of 0.9826. These results highlight the potential of these deep learning architectures to facilitate more inclusive and accessible communication avenues in sign language, bridging the communication divide.
Hiding patient information in medical images:A high-capacity and reversible hiding algorithm for E-healthcare

The rise of online medical-related technologies has brought great convenience to doctors and patients. However, electronic patient information may be subject to attacks and tampering during medical transmission, leading to compromised information. Therefore, it is necessary to protect the security of data in medical transmission by authenticated electronic patient information. This paper proposes a high-capacity reversible data hiding technology based on interpolated images, which can successfully protect patients’ confidential information and verify received content. By using predictors to generate high-quality cover images, and then using data foding strategy to hide electronic patient information into medical images, the entire scheme can be divided into image preprocessing, data processing, and data embedding processes. Image preprocessing includes generating predicted images and performing boundary value on them to generate high-quality covered images. Data processing encrypts the secret data, divides the encrypted data into n blocks of 3 bits, and converts each block into a decimal number. Before embedding the secret data, these numbers are subjected to data foding strategy operation, and data labeling is carried out to ensure the recovery of secret data. The embedding process divides the cover image into 2x2 blocks and embeds secret data in the non-seed pixel diagonal of each block. Last but not least, a fragile watermark is also embedded in the cover image to determine whether the transmitted information has been tampered with. Experimental results show that the method can achieve a high embedding capacity of an average of 2.25 bits per pixel, while the average peak signal-to-noise ratio of the steganographic image reaches 42dB, which is good compared to existing technologies.
CPIPS: Learning to Preserve Perceptual Distances in End-to-End Image Compression

Lossy image coding standards such as JPEG and MPEG have successfully achieved high compression rates for human consumption of multimedia data. However, with the increasing prevalence of IoT devices, drones, and self-driving cars, machines rather than humans are processing a greater portion of captured visual content. Consequently, it is crucial to pursue an efficient compressed representation that caters not only to human vision but also to image processing and machine vision tasks. Drawing inspiration from the efficient coding hypothesis in biological systems and the modeling of the sensory cortex in neural science, we repurpose the compressed latent representation to prioritize semantic relevance while preserving perceptual distance. Our proposed method, Compressed Perceptual Image Patch Similarity (CPIPS), can be derived at a minimal cost from a learned neural codec and computed significantly faster than DNN-based perceptual metrics such as LPIPS and DISTS.
Efficient Recognition of Complex Human Activities Based on Smartwatch Sensors Using Deep Pyramidal Residual Network

Human activity recognition (HAR) has become a hot topic in artificial intelligence research due to the rapid development of smart wearable technologies. The goal of HAR is to accurately identify human actions using various data sources, such as video, images, and sensor data from wearable devices. Recent research in HAR has achieved promising results using learning-based methods, especially deep learning techniques. However, achieving state-of-the-art results remains a challenge for researchers. This study proposes a new approach to HAR that uses deep learning to classify human activities from smartwatch sensor data. We propose the use of a one-dimensional deep pyramidal residual network (1D-PyramidNet) for accurate human action identification. We evaluate the performance of our model against baseline models using the DHA dataset, a benchmark dataset for HAR that includes wristwatch sensor data for 11 complex human activities. The experimental results show that our 1D-PyramidNet model outperforms the baseline models, including CNN, LSTM, BiLSTM, GRU, and BiGRU. This confirms that the use of 1D-PyramidNet can improve the identification capabilities of HAR systems, achieving a maximum accuracy of 96.64%.
Steganalysis on Dual-Layer Security of Messages Using Steganography and Cryptography

Recently, Bansal et al. proposed a dual-layer message security technique using steganography and cryptography. An index table maps each text data to an index point and an elliptic curve point. The text data are encrypted using elliptic curve cryptography, and the corresponding index values are embedded in a cover image using the Least Significant Bit Inversion technique. The proposed paper breaks the security of the key exchange technique in the Bansal et al. method and the secret text is extracted.
Autoencoder-Enhanced Federated Learning with Reduced Overhead and Lower Latency

This paper investigates the application of autoencoder (AE) in supporting the training process of federated learning by reducing communication overhead and latency. We propose a scheduling algorithm to determine when and how to use autoencoder during training. Our simulation shows that federated learning with an au-toencoder significantly reduces communication overhead without compromising testing accuracy. Moreover, the testing accuracy curve shows a more consistent increase over training rounds in federated learning with an autoen-coder than in federated learning without an autoencoder. Additionally, the latency of federated learning with an autoencoder is lower than that of federated learning without an autoencoder.
Cross-Domain adaptation in Distance Space for Speaker Verification

Significant performance degradation often occurs when a well-trained speaker verification system is applied to an unseen domain. Data augmentation and domain adaptation are two common approaches to tackling this problem. However, data augmentation would not be helpful when language mismatch rather than environment noise causes the domain shift. Domain adaptation also suffers from a label mismatch problem, making feature distribution alignment unreliable. We propose incorporating a distance metric space into model adaptation to address these issues. The idea is to align not only the embeddings across domains but also the distributions of their pairwise distances, resulting in embeddings tolerant to domain shift. To validate the idea, we used the non-Chinese utterances in VoxCeleb2 and the Chinese utterances in CN-Celeb2 as the source and target domain training data, respectively. Results show that the alignments reduce the EER on the CN-Celeb1 test set by 15.2%.