Reviewerly/exHarmony huggingface dataset:
Monocular depth estimation using self-supervised learning with more effective geometric constraints

Authors:
raw_author_name: Mingkang Xiong
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Sensing and Recognition, Institute for Sensing and Navigation, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Zhenghong Zhang
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Sensing and Recognition, Institute for Sensing and Navigation, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Jiyuan Liu
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Sensing and Recognition, Institute for Sensing and Navigation, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Tao Zhang
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Sensing and Recognition, Institute for Sensing and Navigation, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Huilin Xiong
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Sensing and Recognition, Institute for Sensing and Navigation, Shanghai Jiao Tong University, Shanghai, 200240, China

Abstract: Self-supervised learning-based depth estimation from monocular videos is a challenging yet promising way for 3D environment perception. Existing methods that use photometric consistency as supervision are often fragile in the case of textureless environments or illumination variations. In this paper, we adopt geometric constraints to improve the reliability and accuracy of monocular depth estimation. We introduce an equation derived from the two-view imaging geometry, from which we also develop a novel geometric loss function that is shown to be more effective than the epipolar constraint in self-supervised learning. In addition, we design a depth reconstruction loss by explicitly aligning the depth scale of the consecutive estimated depth maps to keep the depth scale consistent. Finally, a new self-supervised learning framework is formulated by incorporating the proposed geometric constraints into depth, pose, and optical flow estimation models. Extensive experiments on KITTI, Make3D, and NYU Depth v2 datasets show that, by adding our geometric constraints, the depth estimation model compares favorably with state-of-the-art self-supervised learning methods, and each sub-task has a significant improvement.
Dual attention and channel transformer based generative adversarial network for restoration of the damaged artwork

Authors:
raw_author_name: Praveen Kumar
raw_affiliation_string: Dept. of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
raw_author_name: Varun Gupta
raw_affiliation_string: Dept. of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India

Abstract: Artworks are treasures of valuable cultural and historical heritage. Artworks get damaged due to environmental and other factors. The artificial intelligence-based restoration of digitized artwork images can guide the artists in physically restoring the damaged artworks. Previous methods have not been able to restore artwork images well. This paper proposes a dual (spatial and channel) attention and channel transformer-based generative adversarial network to restore damaged artwork images digitally. The proposed generative adversarial network has spatial and channel attention layers in the encoder part of the generator and a channel transformer between skip connections from the encoder to the decoder part of the generator. Spatial and channel attention helps learn inter-spatial and inter-channel global relationships among image features. Channel transformer ensures multiscale feature fusion and reduces the semantic gap between encoder and decoder layer features. Moreover, the proposed network has been trained using a linear combination of perceptual, adversarial, and structured similarity index measure loss, which helps better train the network. Further, the proposed network has been validated on two different datasets, and the results indicate that the proposed method outperforms state-of-the-art artwork restoration methods.
Deep learning-based object detection in maritime unmanned aerial vehicle imagery: Review and experimental comparisons

Authors:
raw_author_name: Ryan Wen Liu
raw_affiliation_string: Chongqing Research Institute, Wuhan University of Technology, Chongqing, China; State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China
raw_author_name: Jingxiang Qu
raw_affiliation_string: Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China; State Key Laboratory of Maritime Technology and Safety, Wuhan 430063, China
raw_author_name: Ruobin Gao
raw_affiliation_string: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

Abstract: With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at https://github.com/zcj234/MS2ship.
Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction

Authors:
raw_author_name: Samar Bouazizi
raw_affiliation_string: National Engineering School of Sfax, REGIM-Lab.: Research Groups in Intelligent Machines, University of Sfax, LR11ES48, 3038, Sfax, Tunisia
raw_author_name: Hela Ltifi
raw_affiliation_string: Computer Science Department, Faculty of sciences and technologies of Sidi Bouzid, University of Kairouan, Tunisia; National Engineering School of Sfax, REGIM-Lab.: Research Groups in Intelligent Machines, University of Sfax, LR11ES48, 3038, Sfax, Tunisia

Abstract: Echo State Networks (ESNs) are a powerful machine learning technique that can be used for EEG-based stroke prediction. However, conventional ESNs suffer from two main limitations: they are not always accurate, and they are not always interpretable. This paper presents a novel multi-level framework that addresses these limitations. The framework consists of three main components: optimized feature extraction, ensemble learning, and output refinement for improved interpretability. The optimized feature extraction component uses a novel algorithm to extract features from EEG data that are more relevant to stroke prediction. The ensemble learning component uses a diversified Echo State Networks (D-ESN) to combine the predictions of multiple ESNs, which improves the accuracy of the predictions. The output improvement component uses two Explainability techniques, LIME and ELI5, to gain insight into the decision-making of the D-ESN model. These techniques allow users to see how each feature in the dataset contributed to the model's prediction. The framework was evaluated on a well-known EEG dataset from stroke patients. The experimental results showed that the framework significantly outperformed baseline approaches in terms of both accuracy with 95 % and interpretability. These results suggest that the proposed framework has the potential to advance the field of stroke prediction and enable informed decision-making in clinical settings.
Reinforcement learning to achieve real-time control of triple inverted pendulum

Authors:
raw_author_name: Jongchan Baek
raw_affiliation_string: Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon, 34129, South Korea
raw_author_name: Changhyeon Lee
raw_affiliation_string: Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk, 36763, South Korea
raw_author_name: Young Sam Lee
raw_affiliation_string: Department of Electrical Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, South Korea
raw_author_name: Soo Jeon
raw_affiliation_string: Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada
raw_author_name: Soohee Han
raw_affiliation_string: Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk, 36763, South Korea

Abstract: This work uses reinforcement learning (RL) to achieve the first-ever data-driven real-time control of an actual, not simulated, triple inverted pendulum (TIP) in a model-free way. A swing-up control task for the TIP is formulated as a Markov decision process with a dense reward function, then conducted in real time by using a model-free RL approach. To increase the sample efficiency of learning, a structure-aware virtual experience replay (VER) method is proposed; it works together with an off-policy actor-critic algorithm. The VER exploits the geometrically-symmetric property of TIPs to create virtual sample trajectories from measured ones, then uses the resulting multifold augmented dataset to effectively train actor and critic networks during the learning process. These structure-infused training data serve to obtain additional information and hence increase the convergence speed of network learning. We combine the proposed VER with a state-of-the-art actor-critic algorithm, and then validate its effectiveness through numerical simulations. Notably, the inclusion of VER amplifies computational efficiency, slashing the requisite trials, training steps, and overall duration by approximately 66.67%. Finally experiments demonstrate the real-time control capability of the proposed approach on an actual TIP system.
Improved coverage path planning for indoor robots based on BIM and robotic configurations

Authors:
raw_author_name: Zhengyi Chen
raw_affiliation_string: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Hao Wang
raw_affiliation_string: School of Civil Engineering, Southeast University, Nanjing, China
raw_author_name: Keyu Chen
raw_affiliation_string: College of Civil Engineering and Architecture, Hainan University, Haikou, China
raw_author_name: Changhao Song
raw_affiliation_string: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Xiao Zhang
raw_affiliation_string: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Boyu Wang
raw_affiliation_string: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
raw_author_name: Jack C.P. Cheng
raw_affiliation_string: Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

Abstract: Robotics holds great potential to improve productivity in construction, and coverage path planning (CPP) is an essential capability crucial to various applications, including floor cleaning and environmental monitoring. However, there is still a lack of a comprehensive CPP system that can handle complex indoor conditions and various robotic properties. An improved CPP (ICCP) system that leverages building information modeling (BIM) and robotic configurations for indoor robots is proposed in this study. Firstly, BIM is semantically enriched to generate semantic trapezoidal grid maps (TGMs); Next, a novel concept called "coverage bonus" is incorporated into coverage pattern analysis to enable farsighted decision making; Finally, the coverage sequence is optimized by solving the cluster generalized traveling salesman problem, resulting in routes that minimize both coverage distances and disruptions in indoor activities. Experimental validation shows that the ICPP system can not only attain optimal coverage performance with the highest coverage ratio (97.6%) but also ensure the adherence to indoor coverage rules. Future research will focus on enhancing coverage ratio through tunable hyperparameters, optimizing computation time in ICCP, and expanding the study to multi-robot scenarios.
Selection of a viable blockchain service provider for data management within the internet of medical things: An MCDM approach to Indian healthcare

Authors:
raw_author_name: Raghunathan Krishankumar
raw_affiliation_string: Information Technology Systems and Analytics Area, Indian Institute of Management Bodh Gaya, Bodh Gaya, 824234, Bihar, India
raw_author_name: Dhruva Sundararajan
raw_affiliation_string: Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India
raw_author_name: Kattur S Ravichandran
raw_affiliation_string: Department of Mathematics, Amrita School of Physical Sciences, Coimbatore, Amrita Vishwa Vidyapeetham, India
raw_author_name: Samarjit Kar
raw_affiliation_string: Department of Graphical Systems, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania; Department of Mathematics, NIT Durgapur, WB, India

Abstract: Health 4.0 is gaining significant attention globally to support better treatment and care for people. Digital technologies such as the IoMT "("Internet of Medical Things"") and blockchain substantially promote quality health services. Literature shows that embedding blockchain in IoMT is an effective way to attain secure and quality healthcare. Selecting a viable blockchain service provider (BSP) becomes a complex task and can be considered an MCDM "("Multi-Criteria Decision-Making"") problem. Earlier studies on BSP selection indicate that complex expressions cannot be well-modelled and methodical estimation of decision parameters by capturing hesitation and interaction of entities is not adequately explored. Driven by the issues, authors put forward a novel MCDM framework with (i) a double hierarchy linguistic structure for data collection in natural expressions; (ii) regret measure for experts' reliability calculation; (iii) a weighted CRITIC approach for criteria weight determination, and (iv) CRADIS-Copeland algorithm for ranking BSPs. Finally, a case example from Recent Indian healthcare is exemplified to demonstrate the framework's applicability. Recently, the Indian healthcare sector launched initiatives/plans as part of Health 4.0 to promote data management for a better quality of treatment by seeking support from digital technologies such as IoMT and blockchains to ensure data privacy and improved customer experience. Sensitivity analysis and comparison with extant methods infer that (i) BC1, BC5, and BC3 are the top three BSPs for the considered problem; (ii) criteria such as privacy aspects, intercommunication capability, global accessibility, and total cost constitute 58% importance in the supposed decision problem; (iii) developed model reduces human intervention/biases; (iv) developed model is robust to weight alteration and yields unique rank orders with both personalised and cumulative ordering of BSPs; and (v) proposed model produces broader rank values that aid in effective backup planning.
FLAD: Adaptive Federated Learning for DDoS attack detection

Authors:
raw_author_name: Roberto Doriguzzi-Corin
raw_affiliation_string: Cybersecurity Centre, Fondazione Bruno Kessler, Italy
raw_author_name: Domenico Siracusa
raw_affiliation_string: Cybersecurity Centre, Fondazione Bruno Kessler, Italy

Abstract: Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have not been properly addressed yet. Indeed, the Federated Averaging algorithm at the core of the FL concept requires the availability of test data to control the FL process. Although this might be feasible in some domains, test network traffic of newly discovered attacks cannot be always shared without disclosing sensitive information. In this paper, we address the convergence of the FL process in dynamic cybersecurity scenarios, where the trained model must be frequently updated with new recent attack profiles to empower all members of the federation with the latest detection features. To this aim, we propose FLAD (adaptive Federated Learning Approach to DDoS attack detection), an FL solution for cybersecurity applications based on an adaptive mechanism that orchestrates the FL process by dynamically assigning more computation to those members whose attacks profiles are harder to learn, without the need of sharing any test data to monitor the performance of the trained model. Using a recent dataset of DDoS attacks, we demonstrate that FLAD outperforms state-of-the-art FL algorithms in terms of convergence time and accuracy across a range of unbalanced datasets of heterogeneous DDoS attacks. We also show the robustness of our approach in a realistic scenario, where we retrain the deep learning model multiple times to introduce the profiles of new attacks on a pre-trained model.
DHGAT: Hyperbolic representation learning on dynamic graphs via attention networks

Authors:
raw_author_name: Hao Li
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Hao Jiang
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Dongsheng Ye
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Qiang Wang
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Liang Du
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Yuanyuan Zeng
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China
raw_author_name: Yingxue Wang
raw_affiliation_string: National Engineering Laboratory for Risk Perception and Prevention (NELRPP), China Academy of Electronics and Information Technology, Beijing, 100041, China
raw_author_name: Cheng Chen
raw_affiliation_string: National Engineering Laboratory for Risk Perception and Prevention (NELRPP), China Academy of Electronics and Information Technology, Beijing, 100041, China

Abstract: Hyperbolic graph embedding has garnered significant attention in a wide range of downstream applications. This is because hyperbolic geometry provides a valuable mapping tool, whereby the scale-free or hierarchical properties of complex networks can be naturally reflected as hyperbolic metric properties. Despite this, most advancements have focused on learning hyperbolic representations of static graphs, the potential advantages of hyperbolic metrics in dynamic graphs embedding have not been fully exploited. To fully harness the properties of hyperbolic space, we propose DHGAT1, a dynamic hyperbolic graph attention network with a novel architecture that designs a spatiotemporal self-attention mechanism based on hyperbolic distance. DHGAT maps dynamic graphs into the hyperbolic space, generates spatiotemporal self-attention of nodes, and directly aggregates weighted node representations without the tangent space. This is achieved by using the Einstein gyromidpoints, which reduces distortions and preserves manifold properties. The results of experiments on real-world datasets demonstrate that DHGAT performs exceptionally well in multi-step link prediction tasks, particularly in predicting new links compared to the baselines.
Research on a double image security transmission algorithm of image encryption and hiding

Authors:
raw_author_name: Jin Shang
raw_affiliation_string: Control Technology Institute, Wuxi Institute of Technology, Wuxi, 214121, China

Abstract: Image encryption is an important research field. In some application scenarios, only encrypting can actually attract the attention of attackers. Therefore, this article provides dual protection for images that require confidentiality. The image is first encrypted and then hidden in the normally transmitted image to avoid attacks from attackers. The encryption adopts a combination of DNA encoding and chaos theory, while the hiding adopts LSB algorithm. The experimental results show that the algorithm has achieved good security effects, and the correlation coefficient analysis has also achieved ideal results.
Dynamics-aware navigation among moving obstacles with application to ground and flying robots

Authors:
raw_author_name: Spyridon G. Tarantos
raw_affiliation_string: Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, 00185 Rome, Italy
raw_author_name: Giuseppe Oriolo
raw_affiliation_string: Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, 00185 Rome, Italy

Abstract: We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot-obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.