Reviewerly/exHarmony huggingface dataset:
TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

Authors:
raw_author_name: Yuhai Li
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China
raw_author_name: Youchen Fan
raw_affiliation_string: School of Space Information, Space Engineering University, Beijing 101416, China
raw_author_name: Shunhu Hou
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China
raw_author_name: Zhaojing Xu
raw_affiliation_string: Graduate School, Space Engineering University, Beijing 101416, China
raw_author_name: Hongyan Wang
raw_affiliation_string: School of Space Information, Space Engineering University, Beijing 101416, China
raw_author_name: Shengliang Fang
raw_affiliation_string: School of Space Information, Space Engineering University, Beijing 101416, China

Abstract: Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm.
Enhancing Human Action Recognition with 3D Skeleton Data: A Comprehensive Study of Deep Learning and Data Augmentation

Authors:
raw_author_name: Chu Xin
raw_affiliation_string: Department of Artificial Intelligence Convergence, Graduate School, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si 16890, Republic of Korea
raw_author_name: Seokhwan Kim
raw_affiliation_string: Farmkit R&D Center, 502, 55, Heungdeokjooang-ro, Giheung-gu, Yongin-si 16953, Republic of Korea
raw_author_name: Yongjoo Cho
raw_affiliation_string: Department of Computer Science, Sangmyung University, 20 Hongjimoon-2gil, Jongno-gu, Seoul 03016, Republic of Korea
raw_author_name: Kyoung Shin Park
raw_affiliation_string: Department of Computer Engineering, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si 16890, Republic of Korea

Abstract: Human Action Recognition (HAR) is an important field that identifies human behavior through sensor data. Three-dimensional human skeleton data extracted from the Kinect depth sensor have emerged as a powerful alternative to mitigate the effects of lighting and occlusion of traditional 2D RGB or grayscale image-based HAR. Data augmentation is a key technique to enhance model generalization and robustness in deep learning while suppressing overfitting to training data. In this paper, we conduct a comprehensive study of various data augmentation techniques specific to skeletal data, which aim to improve the accuracy of deep learning models. These augmentation methods include spatial augmentation, which generates augmented samples from the original 3D skeleton sequence, and temporal augmentation, which is designed to capture subtle temporal changes in motion. The evaluation covers two publicly available datasets and a proprietary dataset and employs three neural network models. The results highlight the impact of temporal augmentation on model performance on the skeleton datasets, while exhibiting the nuanced impact of spatial augmentation. The findings underscore the importance of tailoring augmentation strategies to specific dataset characteristics and actions, providing novel perspectives for model selection in skeleton-based human action recognition tasks.
COVID-19 vaccine confidence index and economic uncertainty indices: empirical evidence from the payment-based system cryptocurrency market

Authors:
raw_author_name: Shinta Amalina Hazrati Havidz
raw_affiliation_string: Accounting Department, Finance Program, School of Accounting, Bina Nusantara University, Jakarta, Indonesia

Abstract: Purpose We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns. Design/methodology/approach We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022. Findings This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns. Originality/value This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.
A Multibranch Deep Neural Network for the Superresolution of Solar Magnetograms

Authors:
raw_author_name: Fengping Dou
raw_affiliation_string: State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
raw_author_name: Long Xu
raw_affiliation_string: Faculty of Electrical Engineering and Computer Science, Ningbo University, People's Republic of China; Peng Cheng Laboratory, Shenzhen 518000, People's Republic of China
raw_author_name: Dong Zhao
raw_affiliation_string: State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
raw_author_name: Zhixiang Ren
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen 518000, People's Republic of China

Abstract: Abstract The existing superresolution (SR) models for solar magnetograms are mostly borrowed from the SR models for natural images. They are less effective for processing solar magnetograms with a very large dynamic range and very rich image features. In this paper, a multibranch superresolution (MBSR) model is specially designed for solar magnetograms. First, we split a low-resolution magnetogram into a group of overlapping image patches, and classify them into three categories according to magnetic flux intensity, namely simple, medium, and complex. Then, image patches of each category are fed into the corresponding branch of the MBSR network, the lightweight branch for simple image patches and the heavyweight one for complex image patches. The advantage of such a strategy is twofold. On the one hand, active regions are allocated more computational resources to train a heavyweight branch more fully, while quiet regions are allocated fewer computational resources to train a lightweight branch for saving computational resources. On the other hand, a lightweight network with a simple nonlinear function is preferable to simple regions, while a heavyweight one may be underfitting. Additionally, to verify the effectiveness of the proposed model, a magnetic field structure similarity metric is proposed to measure the artifacts of the generated high-resolution (HR) magnetograms. Experimental results show that the proposed MBSR model generates HR magnetograms highly consistent with the HMI ones, and achieves the best performance over five objective metrics, including peak signal-to-noise ratio and structure similarity, etc.
Deep learning for cancer cell detection: do we need dedicated models?

Authors:
raw_author_name: Martin Tabakov
raw_affiliation_string: Department of Artificial Intelligence, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 2, 50-370, Wrocław, Poland
raw_author_name: Urszula Markowska-Kaczmar
raw_affiliation_string: Department of Artificial Intelligence, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 2, 50-370, Wrocław, Poland
raw_author_name: Lukasz Fulawka
raw_affiliation_string: Molecular Pathology Center Cellgen, Ul. Piwna 13, 50-353, Wrocław, Poland

Abstract: Abstract This article proposes a novel concept for a two-step Ki-67/lymphocytes classification cell detection pipeline on Ki-67 stained histopathological slides utilizing commonly available and undedicated, in terms of the medical problem considered, deep learning models. Models used vary in implementation, complexity, and applications, allowing for the use of a dedicated architecture depending on the physician’s needs. Moreover, generic models’ performance was compared with the problem-dedicated one. Experiments highlight that with relatively small training datasets, commonly used architectures for instance segmentation and object detection are competitive with a dedicated model. To ensure generalization and minimize biased sampling, experiments were performed on data derived from two unrelated histopathology laboratories.
Circumventing Stragglers and Staleness in Distributed CNN using LSTM

Authors:
raw_author_name: Aswathy Ravikumar
raw_affiliation_string: Vellore Institute of Technology University
raw_author_name: Harini Sriraman
raw_affiliation_string: Vellore Institute of Technology University
raw_author_name: Saddikuti Lokesh
raw_affiliation_string: Vellore Institute of Technology University
raw_author_name: Jitendra Sai
raw_affiliation_string: Vellore Institute of Technology University

Abstract: INTRODUCTION: Using neural networks for these inherently distributed applications is challenging and time-consuming. There is a crucial need for a framework that supports a distributed deep neural network to yield accurate results at an accelerated time.&#x0D; METHODS: In the proposed framework, any experienced novice user can utilize and execute the neural network models in a distributed manner with the automated hyperparameter tuning feature. In addition, the proposed framework is provided in AWS Sage maker for scaling the distribution and achieving exascale FLOPS. We benchmarked the framework performance by applying it to a medical dataset. &#x0D; RESULTS: The maximum performance is achieved with a speedup of 6.59 in 5 nodes. The model encourages expert/ novice neural network users to apply neural network models in the distributed platform and get enhanced results with accelerated training time. There has been a lot of research on how to improve the training time of Convolutional Neural Networks (CNNs) using distributed models, with a particular emphasis on automating the hyperparameter tweaking process. The study shows that training times may be decreased across the board by not just manually tweaking hyperparameters, but also by using L2 regularization, a dropout layer, and ConvLSTM for automatic hyperparameter modification.&#x0D; CONCLUSION: The proposed method improved the training speed for model-parallel setups by 1.4% and increased the speed for parallel data by 2.206%. Data-parallel execution achieved a high accuracy of 93.3825%, whereas model-parallel execution achieved a top accuracy of 89.59%.
Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based Recommendation

Authors:
raw_author_name: Liya Huang
raw_affiliation_string: Information Center, Guizhou Power Grid Co., Ltd., Jiefang Road, Guiyang, 550800, Guizhou, China
raw_author_name: Ran Li
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, liuhe Road, Hangzhou, 310023, Zhejiang, China
raw_author_name: Jingsheng Lei
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, liuhe Road, Hangzhou, 310023, Zhejiang, China
raw_author_name: Yuan Ji
raw_affiliation_string: Information Center, Guizhou Power Grid Co., Ltd., Jiefang Road, Guiyang, 550800, Guizhou, China
raw_author_name: Wenbing Shi
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, liuhe Road, Hangzhou, 310023, Zhejiang, China
raw_author_name: Shengying Yang
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, liuhe Road, Hangzhou, 310023, Zhejiang, China

Abstract: Abstract Session-based recommendation is an important part of many e-commerce websites. Its purpose is to make recommendations based on the interaction behavior of anonymous users in a short period of time. Graph neural network can capture complex interactions in sessions, and they are a class of methods with better performance among existing session-based recommendation models. However, most existing models only learn item features for a single session. At the same time, GNNs are not good at capturing long-distance dependencies in a session, which leads to limited performance improvements for them. To address this deficiency, we propose sequence-aware graph neural network incorporating neighborhood information, named SAN-GNN. We construct a session graph and a neighborhood graph to learn item representations. For neighborhood graph, we propose a neighborhood Information extractor for the neighborhood graph to learn the neighbor information of nodes on the neighborhood graph. For the graph model of the session layer, we propose a session graph attention(SGA) module to learn the item representation of the target session. SGA uses Ta-LSTM to learn sequential dependencies in the target session and uses GCN with an integrated attention mechanism to learn node feature relevant to the target item. Exhaustive experiments on three public real-world datasets show that SAN-GNN outperforms the most advanced existing session-based method.
Hybrid time-spatial video saliency detection method to enhance human action recognition systems

Authors:
raw_author_name: Abdorreza Alavi Gharahbagh
raw_affiliation_string: Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal
raw_author_name: Vahid Hajihashemi
raw_affiliation_string: Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal
raw_author_name: Marta Campos Ferreira
raw_affiliation_string: Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal
raw_author_name: J. J. M. Machado
raw_affiliation_string: Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal
raw_author_name: João Manuel R. S. Tavares
raw_affiliation_string: Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal

Abstract: Abstract Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system’s overall performance. Using an image registration algorithm, the proposed method first removes the camera’s motion. Subsequently, each video frame’s color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.
Bayesian active learning with model selection for spectral experiments

Authors:
raw_author_name: Masaichiro Mizumaki
raw_affiliation_string: Faculty of Science, Course for Physical Sciences, Kumamoto University, Kumamoto, Japan
raw_author_name: Shun Katakami
raw_affiliation_string: Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
raw_author_name: Masato Okada
raw_affiliation_string: Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan

Abstract: Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.
Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Authors:
raw_author_name: Jianqiang Zhang
raw_affiliation_string: College of Weaponry Engineering, Naval University of Engineering, Wuhan, China
raw_author_name: Yuanyuan Zhang
raw_affiliation_string: College of Weaponry Engineering, Naval University of Engineering, Wuhan, China

Abstract: Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.
A novel method for video enhancement under low light using BFR-SEQT technique

Authors:
raw_author_name: R. P. Anto Kumar
raw_affiliation_string: Computer Science and Engineering, St. Xavier’s Catholic College of Engineering (Autonomous), Anna University, Chennai, India

Abstract: As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light areas' boundary pixels and varying low-illuminated night videos' weather conditions for removing noise and enhancing contrast. Hence, the video's visual appearance under low-light is improved using BFR-SEQT. Primarily, a video is inputted and converted into frames. Also, colour space is converted from which static and dynamic pixels are detected regarding frame differences. Using LoG-MF and KF algorithms, noise is removed from which foreground and background are separated using SD-FCM. Motion is estimated and features are extracted to enhance contrast. Then, pixel grouping using LC-GOA is done. Lastly, enhanced outputs from both phases are reconstructed and enhanced video is obtained. The proposed model improves video quality by enhancing contrast and removing noise with high PSNR values (27.6589db and 24.5478db), thus outperforming conventional methods.
Optimizing the Educational Potential of Virtual Reality: The Effects of Virtual Reality and Pop Quizzes on Spatial Presence and Efficacy Perceptions

Authors:
raw_author_name: Jisoo Ahn
raw_affiliation_string: Department of Communication and Media, Dong-Eui University, Busan, Republic of Korea
raw_author_name: Ghee-Young Noh
raw_affiliation_string: School of Media, Hallym University, Chuncheon, Republic of Korea

Abstract: Reflecting the trend of virtual reality (VR) studies in crisis and disaster communication, this study designed VR content on fire evacuation and aimed to investigate the effects of VR for educational purposes on perceptual and cognitive factors. Using a 2 (viewing condition: video vs. VR) × 2 (quiz: presence vs. absence) between-subjects design, an experiment was conducted with 176 university students in South Korea. Findings revealed that participants who were exposed to VR (compared with video) experienced greater spatial presence and, in turn, had higher response efficacy and self-efficacy. The inclusion of pop quizzes in the material hindered the evaluation of the direct and indirect effects of VR. The findings of this study can contribute to expanding the theoretical framework of VR research and identifying learning-related factors to develop more effective educational materials related to fire evacuation.