Reviewerly/exHarmony huggingface dataset:
CVANet: Cascaded visual attention network for single image super-resolution

Authors:
raw_author_name: Weidong Zhang
raw_affiliation_string: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, 453003, China
raw_author_name: Wenyi Zhao
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Jia Li
raw_affiliation_string: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, 453003, China
raw_author_name: Peixian Zhuang
raw_affiliation_string: School of Automation and Electrical Engineering, University of Science and Technology Beijing, 100084, China
raw_author_name: Haihan Sun
raw_affiliation_string: School of Engineering, University of Tasmania, Tasmania, 7005, Australia
raw_author_name: Yibo Xu
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Chongyi Li
raw_affiliation_string: School of Computer Science, Nankai University, Tianjing, 300073, China

Abstract: Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.
A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

Authors:
raw_author_name: Naiyu Wang
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, China
raw_author_name: Wenti Yang
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, China
raw_author_name: Xiaodong Wang
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, China
raw_author_name: Longfei Wu
raw_affiliation_string: Department of Mathematics & Computer Science, Fayetteville State University, Fayetteville, NC, USA
raw_author_name: Zhitao Guan
raw_affiliation_string: School of Control and Computer Engineering, North China Electric Power University, China
raw_author_name: Xiaojiang Du
raw_affiliation_string: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
raw_author_name: Mohsen Guizani
raw_affiliation_string: Department of Computer Science and Engineering, Qatar University, Doha, Qatar

Abstract: The application of artificial intelligence technology in Internet of Vehicles (IoV) has attracted great research interests with the goal of enabling smart transportation and traffic management. Meanwhile, concerns have been raised over the security and privacy of the tons of traffic and vehicle data. In this regard, Federated Learning (FL) with privacy protection features is considered a highly promising solution. However, in the FL process, the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering, which can enable the verifiability of the local models while achieving privacy-preservation. Additionally, we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty. The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.
Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction

Authors:
raw_author_name: Qin Li
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Yong Wang
raw_affiliation_string: School of Automation, Central South University, Changsha, China

Abstract: Human motion prediction is to forecast future human motions based on the observed ones. Currently, a prediction model always includes an encoder to extract the features of the observed motions and a decoder for motion prediction with the extracted features. However, existing encoders have limited abilities of anti-interference and semantic attribute learning; thus, the prediction results are not fully satisfactory. To solve this problem, we propose two novel pretext tasks, i.e., noise-free motion reconstruction and semantic-aware contrastive learning, to implement self-supervised pretraining on the encoder. The former extracts the features of a noise-added motion to reconstruct a noise-free motion. This pretext task can improve the ability of anti-interference of the encoder. The latter shortens the distance between two motions with the same motion category while widening the distance between two motions from different motion categories. This pretext task enables the encoder to capture the semantic commonality of the motions with the same motion category, thereby enhancing the encoder's ability of semantic attribute learning. After implementing the self-supervised pretraining, the pretrained encoder is connected with a decoder to build a prediction model called ReLe-GCN, which is fine-tuned for human motion prediction. The results on public datasets show the high accuracies of ReLe-GCN in both short-term and long-term motion predictions.
$\log$-Sigmoid Activation-Based Long Short-Term Memory for Time-Series Data Classification

Authors:
raw_author_name: Priyesh Ranjan
raw_affiliation_string: Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA
raw_author_name: Pritam Khan
raw_affiliation_string: Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India
raw_author_name: Sudhir Kumar
raw_affiliation_string: Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, India
raw_author_name: Sajal K. Das
raw_affiliation_string: Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA

Abstract: With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tanh$</tex-math></inline-formula> are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid, inside the LSTM cell for time series data classification, and analyze its properties. We also present the use of a linear transformation (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log \tanh$</tex-math></inline-formula> ) of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid activation as a replacement of the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tanh$</tex-math></inline-formula> function in the LSTM cell. Both the cell activation as well as recurrent activation functions inside the LSTM cell are modified with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid activation family while tuning the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> bases. Further, we report a comparative performance analysis of the LSTM model using the proposed and the state-of-the-art activation functions on multiple public time-series databases.
A Radial Basis Function-Based Graph Attention Network With Squeeze Loss Optimization for Link Prediction

Authors:
raw_author_name: Jiusheng Chen
raw_affiliation_string: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
raw_author_name: Xiaoyu Zhang
raw_affiliation_string: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
raw_author_name: Jun Wu
raw_affiliation_string: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin, China
raw_author_name: Runxia Guo
raw_affiliation_string: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China

Abstract: Graph attention networks is a popular method to deal with link prediction tasks, but the weight assigned to each sample is not focusing on the sample's own performance in training. Moreover, since the number of links is much larger than nodes in a graph, mapping functions are usually used to map the learned node features to link features, whose expression of node similarity determines the quality of link feature learning. To tackle the above issues, a new model graph attention networks based on Radial Basis Function (RBF) with squeeze loss is proposed, including two improvements. Firstly, RBF function with extended parameters is used to transform the node features output by attention layer into link features. The result of link feature embedding can be improved by shortening the distance of nodes with links and enlarging the distance of nodes without links in vector space. Secondly, squeeze loss is designed to adjust the loss according to the performance of samples in training and change the proportion of sample loss in the loss function to allocate training resources reasonably. The link prediction task performed on datasets shows that the performance of proposed method is better than baselines.
A progressive framework for rotary motion deblurring

Authors:
raw_author_name: Jinhui Qin
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430072, China
raw_author_name: Yong Ma
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430072, China
raw_author_name: Jun Huang
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430072, China
raw_author_name: Fan Fan
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430072, China
raw_author_name: You Du
raw_affiliation_string: School of Electronic Information, Wuhan University, Wuhan, 430072, China

Abstract: The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles. Traditional rotary motion deblurring methods suffer from ringing artifacts and noise, especially for large blur extents. To solve the above problems, we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage. In the first stage, we design an adaptive blur extents factor (BE factor) to balance noise suppression and details reconstruction. And a novel deconvolution model is proposed based on BE factor. In the second stage, a triple-scale deformable module CNN(TDM-CNN) is designed to reduce the ringing artifacts, which can exploit the 2D information of an image and adaptively adjust spatial sampling locations. To establish a standard evaluation benchmark, a real-world rotary motion blur dataset is proposed and released, which includes rotary blurred images and corresponding ground truth images with different blur angles. Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets. The code and dataset are available at https://github.com/Jinhui-Qin/RotaryDeblurring.
A Phishing Account Detection Model via Network Embedding for Ethereum

Authors:
raw_author_name: Jintao Luo
raw_affiliation_string: School of Information Science and Engineering and the Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, China
raw_author_name: Jiwei Qin
raw_affiliation_string: School of Information Science and Engineering and the Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, China
raw_author_name: Ruijin Wang
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Lu Li
raw_affiliation_string: School of Information Science and Engineering and the Xinjiang Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, China

Abstract: As the first blockchain platform to support smart contracts, Ethereum has gained popularity and breeds various cybercrimes. Many phishing accounts on Ethereum take advantage of the blockchain’s anonymity to participate in illegal acts. To this end, to solve the transaction security problem caused by phishing accounts on Ethereum, this brief proposes a network embedding-based phishing account detection model. Firstly, we crawl the history of transactions from both labeled phishing and non-phishing accounts and use a new method to build these transactions as a transaction network, where each transaction edge is accompanied by additional transaction information for all source nodes of the target node. Then we propose a new random walk-based network embedding algorithm named bias2vec to obtain the embeddings of nodes as the feature inputs. Finally, we classify the accounts into phishing and non-phishing ones by different classifiers such as lightGBM and XGBoost. Experiments show that our proposed phishing account detection model achieves effective detection performance under different classifiers.
Neural Temporal Difference and Q Learning Provably Converge to Global Optima

Authors:
raw_author_name: Qi Cai
raw_affiliation_string: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;
raw_author_name: Zhuoran Yang
raw_affiliation_string: Department of Statistics and Data Science, Yale University, New Haven, Connecticut 06511;
raw_author_name: Jason D. Lee
raw_affiliation_string: Electrical Engineering, Princeton University, Princeton, New Jersey 08544

Abstract: Temporal difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, because of the nonlinearity in value function approximation, such a coupling leads to nonconvexity and even divergence in optimization. As a result, the global convergence of neural TD remains unclear. In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for policy evaluation. In particular, we show how such global convergence is enabled by the overparameterization of neural networks, which also plays a vital role in the empirical success of neural TD. We establish the theory for two-layer neural networks in the main paper and extend them to multilayer neural networks in the appendix. Beyond policy evaluation, we establish the global convergence of neural (soft) Q learning. Funding: Z. Yang acknowledges the Theory of Reinforceement Learning program at Simons Institute. J. D. Lee acknowledges support of the ARO under MURI Award W911NF-11-1-0304, the Sloan Research Fellowship, NSF CCF 2002272, NSF IIS 2107304, ONR Young Investigator Award, and NSF-CAREER under award #2144994. Z. Wang acknowledges National Science Foundation [Awards 2048075, 2008827, 2015568, 1934931], Simons Institute (Theory of Reinforcement Learning), Amazon, J.P. Morgan, and Two Sigma for their supports.
Vehicle Detection in High Density Traffic Surveillance Data using YOLO.v5

Authors:
raw_author_name: Sneha Mishra
raw_affiliation_string: School of Computing Science and Engineering, Galgotias University, Greater Noida, India
raw_author_name: Dileep K. Yadav
raw_affiliation_string: School of Computing Science and Engineering, Galgotias University, Greater Noida, India

Abstract: Abstract: Computer vision is one of the prime domains that enable to derive meaningful and crisp information from digital media, such as images, videos, and other visual inputs. Background: Detection and correctly tracking the moving objects in a video streaming is still a challenging problem in India. Due to the high density of vehicles, it is difficult to identify the correct objects on the roads. Methods: In this work, we have used a YOLO.v5 (You Only Look Once) algorithm to identify the different objects on road, such as trucks, cars, trams, and vans. YOLO.v5 is the latest algorithm in the family of YOLO. To train the YOLO.v5, KITTY dataset was used having 11682 images having different objects in a traffic surveillance system. After training and validating the dataset, three different models have been constructed setting various parameters. To further validate the proposed approach, results have also been evaluated on the Indian traffic dataset DATS_2022. Results: All the models have been evaluated using three performance metrics, such as precision, recall, and mean average precision (MAP). The final model has attained the best performance on KITTY dataset as 93.5% precision, 90.7% recall, and 0.67 MAP for different objects. The results attained on the Indian traffic dataset DATS_2022 included 0.65 precision, 0.78 recall value, and 0.74 MAP for different objects. Conclusion: The results depict the proposed model to have improved results as compared to stateof-the-art approaches in terms of performance and also reduce the computation time and object loss.
Probabilistic Model-Based Reinforcement Learning Unmanned Surface Vehicles Using Local Update Sparse Spectrum Approximation

Authors:
raw_author_name: Yunduan Cui
raw_affiliation_string: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Huan Yang
raw_affiliation_string: College of Computer Science and Technology, Qingdao University, Qingdao, China
raw_author_name: Cuiping Shao
raw_affiliation_string: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Lei Peng
raw_affiliation_string: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Huiyun Li
raw_affiliation_string: CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Abstract: This article focuses on the computational efficiency of probabilistic model-based reinforcement learning (MBRL) in unmanned surface vehicles (USV) under unforeseeable and unobservable external disturbances. A novel MBRL approach, local update spectrum probabilistic model predictive control (LUSPMPC) is proposed to fully release the superiority of the probabilistic model approximated in the frequency domain in computational efficiency while mitigating its risk of overfitting during the learning procedure. It employs a local update strategy to relieve the violation of Bochner's theory, and a frequency clipping trick to encourage the approximated model to focus on the features in the low-frequency domain. Evaluated by the position-keeping task in a real USV data-driven simulation, LUSPMPC shows its significant advantages in computational efficiency while achieving better learning capability, generalization capability and control performances in a wide range of sparse scales compared with the baseline MBRL approaches that approximate their models in sample space and frequency domain, and therefore becomes an appealing solution for MBRL USV system defending against rapidly changing ocean disturbances.