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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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Information Science, Parallel and Distributed Systems (ISPDS), international conference on

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS.

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Implementation Challenges & Applications of 5G and Ecosystems

Implementation Challenges & Applications of 5G and Ecosystem...

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Soumyadip Das Madhavi Damle Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University) Pune India

5G is fifth-generation mobile connectivity, the fastest global wireless network after 4G, 3G, 2G, and 1G. It can be used in many fields, i.e., agriculture, healthcare, supply chain, and manufacturing. The speed of the 5G will be very high, the network’s capacity will be huge, increased availability and customer dependability will be, and the users’ experience will be more consistent. 5G uses various bandwidth technologies, such as sub-6GHz and mm-Wave. The principles of 4G LTE and 5G are similar regarding mobile connectivity. Much focus is given to cloud infrastructure in 5G. In 5G, edge locations will also gain significant capabilities. It will bring many opportunities to the market. The economic output of 5G will exceed ${\$}$10 trillion by 2035. The 5G technology is built on OFDM orthogonal frequency division multiplexing. Also, there will be many problems due to the implementation of 5G. 5G will be very costly in comparison with previous generations. Due to higher frequency losses, the range covers up to 300 meters (outside) and 2 meters (inside). The implications are for several areas of work the speed and reach it will bring but with its challenges that this article has focused on as a discussion. The 5G mobile network uses MIMO technology. So the technology behind 5G will be complicated for policymakers, providers, execution and all related ecosystems and does not need a sound framework but also implementation-related standards and discussion, which is addressed here. 5G is different from other radio signals as it has a massive job in services provided by gadgets in different geographic locations and techniques. Places worldwide demand separate or together very different techniques to establish connectivity, and the geography and climate of such places can have a significant role. A place with much rainfall will have challenges stabilising the network by commuting the radiation frequencies. A place with complex vacant arid texture or natural or anthropogenic

关键词： Meters 5G mobile communication OFDM Wireless networks Ecosystems Supply chains Medical services

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Adversarial U-Network for Predicting Blood Oxygen Level-Dependent Time Series

Adversarial U-Network for Predicting Blood Oxygen Level-Depe...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Cong Bao Weihao Zheng Qin Zhang Songyu Yang Zhijun Yao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Lanzhou China

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings. 1

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cognitive Fusion-based Path Planning for UAV Inspection of Power Towers

Cognitive Fusion-based Path Planning for UAV Inspection of P...

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IEEE international Workshop on Imaging Systems and Techniques (IST)

作者： Dimitrios Alexiou Kosmas Tsiakas Georgios Zampokas Evangelos Skartados Ioannis Tzanakis Nikolaos Roussos Ioannis Kostavelis Dimitrios Giakoumis Antonios Gasteratos Dimitrios Tzovaras Information Technologies Institute Centre for Research and Technology Hellas Thessaloniki Greece Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Imperial College London London United Kingdom Public Power Corporation S.A Innovation Hub Athens Greece International Hellenic University Thessaloniki Greece

The utilization of Unmanned Aerial Vehicles (UAV) for the inspection of critical power infrastructure has made significant strides in recent years. Hardware and software advancements enabled the transition from manual to semi-autonomous and fully autonomous UAV operations, which are capable of traversing complex environments and identifying potential flaws. This paper presents a novel path planning method that leverages robot vision derived from LiDAR (Light Detection and Ranging) and RGB data for the inspection of insulators on power towers. The proposed approach involves a projection-based fusion process that combines the semantic segmentation output from RGB data with point cloud data post-processed using a spatial filtering technique. By employing Principal Component Analysis (PCA), critical waypoints are extracted around the power tower, facilitating the calculation of a complete path that ensures the comprehensive coverage of insulators within the UAV’s field of view. The efficacy of our method is evaluated using a real UAV equipped with LiDAR and an RGB camera, which demonstrates its real-time path planning capability for the inspection of power towers. The results affirm the effectiveness of the proposed approach in accurately identifying and inspecting power tower insulators, thereby contributing to the advancement of unmanned inspection techniques for critical power infrastructure.

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Aldian Nurcahyo Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Compuer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Compuer Science Departement Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices.

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Mild cognitive Impairment Diagnosis Using Neuropsychological Tests and Agile Machine Learning 13th

Mild Cognitive Impairment Diagnosis Using Neuropsychological...

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13th international Advanced computing conference, IACC 2023

作者： Bhasin, Harsh Ohri, Ansh Kumar, Nishant Sharma, Manish Thakur, Hardeo Kumar Manav Rachna International Institute of Research and Studies Haryana Faridabad India Manav Rachna University Haryana Faridabad India The NorthCap University Haryana Gurugram India Roux Institute Northeastern University Portland United States Bennett University Greater Noida India

ISBN: (纸本)9783031567025

Alzheimer’s Disease constitutes one of the biggest portions of the diseases related to ageing. Mild cognitive Impairment may be considered the formative stage of this disease. The automated diagnosis of Mild cognitive Impairment using Machine Learning will help the clinicians in delaying its progression and will be easy, cheap, and efficient for the patient. This work uses neuropsychological data obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI), containing the results from 12 tests including Mini-Mental State Examination and ADAS-Cog. An extensive empirical analysis is carried out and the most important features are extracted using the proposed pipelines. The Feature Selection is done using both filter and wrapper methods and in total 13 features were selected. It was found that most of the selected features related to tasks associated with memory. The proposed method gives a performance of 0.9817 in terms of F1 Score. Thus, performing better vis-à-vis the state of the art. The proposed pipeline helps to reduce the number of neuropsychological tests to diagnose the disease. This work is one of the components of the projects that use multi-modality data including structural-Magnetic Resonance Imaging, functional-Magnetic Resonance Imaging, Positron Emission Tomography and Neuropsychological data to develop a system for efficient and effective diagnosis of MCI. The project management is done using Agile Methodology. The results are encouraging and pave the way for the development of such a system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Developing a Multi-site Hyperscanning Procedure for Investigating Interbrain Synchrony Underlying Remote Social Interaction 17th

Developing a Multi-site Hyperscanning Procedure for Investig...

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17th international conference on Brain Informatics, BI 2024

作者： Limtragooltongchai, Sirapakit Yamtuan, Kawin Gracia, Javier O. Chunharas, Chaipat Sriravanasan, Ramesh Chaisilprungraung, Thitaporn Itthipuripat, Sirawaj Neuroscience Center for Research and Innovation King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thonburi Thailand DEVCOM Army Research Laboratory Humans in Complex Systems Division AdelphiMD United States Chula Neuroscience Center King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok10330 Thailand Cognitive Clinical and Computational Neuroscience Department of Internal Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Cognitive Sciences University of California IrvineCA United States Department of Biomedical Engineering University of California IrvineCA United States Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok10600 Thailand

ISBN: (纸本)9789819632930

EEG hyperscanning provides novel insights into inter-brain connectivity during social interactions by enabling the simultaneous recording of neural activity from multiple individuals. This research aims to develop and validate a new protocol in which dyads engage in a synchronized button-pressing task across both same-location and multi-location setups. Our findings reveal significantly higher interpersonal neural synchronization during the interaction period compared to the baseline. Notably, the increased neural synchronization is comparable across the same- and multi-location setups, spanning frontal, central, and posterior electrodes. Interestingly, the level of synchronization in the multi-location setup is greater than in the same-location setup over electrodes positioned above the right superior temporal gyrus (STG), a region associated with social cognition and the mirror neuron system (MNS). These findings validate our methodology for studies across different locations and open new avenues for research into inter-brain synchronization in remote environments. Our experimental protocol could enhance the design of experiments and applications involving social interaction, particularly in remote settings, which have gained popularity due to COVID-19. It also advances our understanding of the neural mechanisms that support social communication in the new normal era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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A review: Strategies and challenges for preventing AI cyber attacks 9

A review: Strategies and challenges for preventing AI cyber ...

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9th international conference on Digital Technologies in Education, Science and Industry, DTESI 2024

作者： Ali, Musab A.M. Askarbekova, Nessibeli Yeskendirova, Damelya Ongenbayeva, Zhadyra College of Computing and Information Sciences University of Technology and Applied Sciences Oman International Information Technology University 34/1 Manas St. Almaty050000 Kazakhstan

The increasing integration of artificial intelligence (AI) into critical sectors such as autonomous vehicles, healthcare, finance, and cybersecurity makes these systems prime targets for cyberattacks. AI systems, especially those based on machine learning (ML) and deep learning (DL), exhibit vulnerabilities due to their reliance on large datasets, complex algorithms, and "black-box"decision-making processes. This paper explores the weaknesses of AI systems, focusing on adversarial attacks, data poisoning, model inversion, and evasion attacks. Specific examples include how adversarial inputs can mislead self-driving cars or cause diagnostic errors in healthcare systems. Defense mechanisms like adversarial training, defensive distillation, feature squeezing, model assembling, and gradient masking are discussed as methods to protect AI systems. However, these defenses face limitations, such as high computational costs and susceptibility to advanced attacks. The study concludes by emphasizing the need for ongoing research into strengthening AI defenses, securing private data, and balancing performance with robust cybersecurity. Ensuring AI security is crucial as cyber threats continue to evolve in complexity and scope. © 2023 Copyright for this paper by its authors.

关键词： Deep learning

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Analyzing the Impact of Deep Learning Approaches on Real-Time data Analysis in Machine Learning

Analyzing the Impact of Deep Learning Approaches on Real-Tim...

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international conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Deepti Mounika Nalluri Chinna babu Mupparaju Aruna Sri Rongali Sohong Dhar P. Ajitha Sloan School of Management Sloan Fellow Candidate 2023 MIT Masters in Computer Information Systems Murray State University USA Masters in Computer Science university of central Missouri warrensburg USA Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Azure Microsoft library & information science Jadavpur University India School of Computing Sathyabama Institute of science and technology Chennai

Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring.

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MATNet: MRI Super-Resolution with Multiple Attention Mechanisms

MATNet: MRI Super-Resolution with Multiple Attention Mechani...

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New Trends in Computational Intelligence (NTCI), international conference on

作者： Longfeng Shen Fenglan Qin Yingjie Zhang Qiong Wang Bowen Hu Wei Zhao Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) Hefei Comprehensive National Science Center Institute of Artificial Integence Hefei China School of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Anhui Big-Data Research Center on University Management Huaibei Anhui China People's Hospital of Huaibei City Anhui China

High-resolution magnetic resonance imaging (MRI) provides a clear anatomical structure for diagnosis, however, its high cost makes it unsuitable in practice. On the contrary, low-resolution MRI cannot provide fine structural information but is resource and time efficient. Super-resolution methods are high-resolution images obtained from low-resolution MRI. However, existing MRI super-resolution methods suffer from the following defects: (1) CNN-based super-resolution methods lack the ability to build relationships for remote features. (2) While models using transformer based on self-attentive mechanism have achieved a breakthrough in super-resolution, the single self-attentive model fails to fully utilize the useful features of the input image. Thus, in this paper, an MRI super-resolution network (MATNet) based on a multi-attention mechanism is proposed that can utilize multiple useful image features from different dimensions for image super-resolution, improved super-resolution performance. To enable the network to obtain noise-free image features before sampling the image, we also added a denoising module to it. Finally, we prove the effectiveness of the model using the IXI public dataset.

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