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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Peter, Robinroy Ratnabala, Lavanya Andrew Charles, Eugene Yugarajah Tsetserukou, Dzmitry Ltd Jaffna Sri Lanka Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Moscow121205 Russia University of Jaffna Department of Computer Science Jaffna Sri Lanka

ISBN: (纸本)9781665410205

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A ∗ algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance. © 2024 IEEE.

关键词： Traffic congestion

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MHSDB: A Comprehensive Benchmark for Multimodal Humor and Sarcasm Detection Leveraging Foundation Models

MHSDB: A Comprehensive Benchmark for Multimodal Humor and Sa...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Zhongren Wang, Donghao Chen, Ciqiang Huang, Dong-Yan Zhang, Zixing College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China BTECH Robotics Corp China

ISBN: (纸本)9798350368741

Understanding multimodal humor and sarcasm detection remains a key challenge in artificial intelligence. Despite recent advances, inconsistencies in feature extraction, evaluation methods, and experimental setups have hindered fair comparisons across different approaches. To address this issue, we propose the Multimodal Humor and Sarcasm Detection Benchmark (MHSDB), the first unified evaluation platform specifically designed for these tasks. MHSDB combines four datasets in English and Hindi and standardizes feature extraction and evaluation processes to facilitate consistent comparisons. We systematically evaluate mainstream foundation models across audio, video, and text modalities. Unimodal representations are assessed using self-attention mechanisms, while multimodal representations are evaluated through mainstream fusion strategies, including utterance-level and sequence-level approaches. Our experimental results reveal that multimodal approaches outperform unimodal ones in capturing complex contexts and multi-layered semantics. Additionally, specific fusion strategies excel at integrating cross-modal information, achieving state-of-the-art performance, and paving the way for future research on optimizing feature representation and multimodal fusion. © 2025 IEEE.

关键词： Benchmark Foundation Models Multimodal Fusion Multimodal Humor Detection Sarcasm Detection

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

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computer Modeling in Engineering & sciences 2023年第5期135卷 1571-1587页

作者： Jun Liu Geng Yuan Changdi Yang Houbing Song Liang Luo Robotics Institute School of Computer ScienceCarnegie Mellon UniversityPittsburghPA15217USA Department of Electrical&Computer Engineering College of EngineeringNortheastern UniversityBostonMA02115USA Security and Optimization for Networked Globe Laboratory(SONG Lab) Embry-Riddle Aeronautical UniversityDaytona BeachFL32114USA School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

关键词： Interpretable graphics training visualization image segmentation left ventricle CNNs global average pooling

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DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning 38

DiffTORI: Differentiable Trajectory Optimization for Deep Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wan, Weikang Wang, Ziyu Wang, Yufei Erickson, Zackory Held, David Computer Science and Engineering Department University of California San Diego United States Institute for Interdisciplinary Information Sciences Tsinghua University China Robotics Institute Carnegie Mellon University United States

This paper introduces DiffTORI, which utilizes Differentiable Trajectory Optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function. The key to our approach is to leverage the recent progress in differentiable trajectory optimization, which enables computing the gradients of the loss with respect to the parameters of trajectory optimization. As a result, the cost and dynamics functions of trajectory optimization can be learned end-to-end. DiffTORI addresses the "objective mismatch" issue of prior model-based RL algorithms, as the dynamics model in DiffTORI is learned to directly maximize task performance by differentiating the policy gradient loss through the trajectory optimization process. We further benchmark DiffTORI for imitation learning on standard robotic manipulation task suites with high-dimensional sensory observations and compare our method to feed-forward policy classes as well as Energy-Based Models (EBM) and Diffusion. Across 15 model-based RL tasks and 35 imitation learning tasks with high-dimensional image and point cloud inputs, DiffTORI outperforms prior state-of-the-art methods in both domains. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Reinforcement Learning for Solving the Vehicle Routing Problem in Practical Logistics 13

Deep Reinforcement Learning for Solving the Vehicle Routing ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Cai, Junchuang Zhang, Xinzhi Lin, Qiuzhen Dong, Lisha Chen, Weineng Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Institute of Information Technology College of Sino-German Robotics Shenzhen China SSouth China University of Technology College of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350308365

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPPDTW) is applicable to a wide range of practical scenarios within the domains of transportation and logistics. When addressing this complex problem, conventional heuristic algorithms have exhibited limitations in effectiveness, primarily due to the need for expert knowledge in heuristic design, which often prioritizes immediate improvements and leads to convergence towards local optima. To overcome this challenge, we propose a deep Q-network-based iterated local search algorithm (DQN-ILS). This algorithm employs a deep Q-learning network to automatically learn a policy for selecting heuristics to improve the current solution. Simultaneously, an adaptive perturbation strategy is designed to avoid becoming trapped in local optima. In this way, our algorithm can better balance the trade-off between exploration and exploitation when solving VRPPDTW. Experimental studies conducted on a real test suite of large-scale VRPPDTW instances validate the superiority of the proposed DQN-ILS over several recently proposed methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson’s Disease Using Gait Signals

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SN computer science 2023年第2期4卷 1-12页

作者： Klinton Amaladass, P. Subathra, M.S.P. Jeba Priya, S. Sivakumar, Mahima Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

One of the most common ailments, especially among the elderly, is Parkinson's disease (PD). Although previous research has demonstrated that heuristics can diagnose Parkinson's disease using decisive signs like tremor, muscular rigidity, movement disorders, and voice disorders, it has also been reported that current approaches, which rely on simple motor tasks, are limited and lack stability and accessibility. The purpose of this study is to identify a novel cost-effective and time-efficient early detection technique for the prediction of this disease using a signal processing feature extraction approach namely, Shifted Extended Local Binary Pattern (S-ELBP) using gait signals. The features extracted using the proposed methods are given as the input to an artificial neural network (ANN) to classify them as Healthy or Parkinson’s. The proposed method has quite promising results when evaluated using different performance metrics. The method has yielded accuracy: 97.6%, specificity: 95.71%, sensitivity: 99, positive predictive value (PPV): 97.2%, negative predictive value (NPV): 98.8%, Matthews correlation coefficient (MCC): 95.4%, F1-score: 97.9%, and geometric mean: 97.19%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Parkinson’s disease Parkinson’s gait Shifted extended local binary pattern

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Single-Shot VR 23

Single-Shot VR

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Special Interest Group on computer Graphics and Interactive Techniques Conference - Emerging Technologies, SIGGRAPH 2023

作者： Qin, Yingsi Chen, Wei-Yu O'Toole, Matthew Sankaranarayanan, Aswin C. Image Science Lab at ECE Department Carnegie Mellon University United States Robotics Institute & Computer Science Department Carnegie Mellon University United States

ISBN: (纸本)9798400701542

The physical world has contents at varying depths, allowing our eye to squish or relax to focus at different distances;this is commonly referred to as the accommodation cue for human eyes. To allow a realistic 3D viewing experience, it is crucial to support the accommodation cue - the 3D display needs to show contents at different depths. However, supporting the native focusing of the eye has been an immense challenge to 3D displays. Commercial near-eye VR displays, which use binocular disparity as the primary cue for inducing depth perception, fail this challenge since all contents they show arise from a fixed depth - ignoring the focusing of the eye. Many research prototypes of VR displays do account for the accommodation cue;however, supporting accommodation cues invariably comes with performance loss among other typically assessed criteria for 3D displays. To tackle these challenges, we present a novel kind of near-eye 3D display that can create 3D scenes supporting realistic accommodation cues in a single shot, i.e., without using time multiplexing or eye tracking. This display, which we present in our demo, can stream 3D content over a large depth range, at 4K spatial resolution, and in real-time. Our display offers an exciting step forward towards a truly immersive real-time 3D experience. Participants will get to enjoy 3D movies and play interactive games in their demo experience. © 2023 Owner/Author.

关键词： Three dimensional displays

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