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Multiscale Convolutional Feature Aggregation for Fine-Grained Image Retrieval 7th

Multiscale Convolutional Feature Aggregation for Fine-Graine...

引用

8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yang, Caolin Ding, Kaifeng Yang, Chengzhuan Zhejiang Normal Univ Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Fine-grained image retrieval aims to retrieve images from the same subclass as the query images from a database containing a specific class object. To solve the problem of large inter-class differences and small intra-class differences, we propose the Multiscale Convolutional Feature Aggregation (MsCFA) method accomplished in an unsupervised environment. This method comprises an object localization submodule and a multiscale feature fusion module. In the object localization module, we perform a coarse localization of the object based on the response levels within the feature map. Specifically, we crop the high-response regions of the feature map to ensure that the resulting cropped feature map contains more object features and less background noise. Subsequently, we employ the regional maximum activation of convolutions method to extract features from this cropped region. The cropped object feature is fused with the uncropped original feature to obtain the feature representation on a single scale. In the feature fusion module, we input images of different scales into the network to obtain feature representations at various scales and then fuse these different scale features to obtain our multiscale convolutional feature aggregation descriptor. In experiments on five classical fine-grained datasets, our method outperforms other fine-grained retrieval tasks in the same type of unsupervised environment and is comparable to those supervised tasks with end-to-end training.

关键词： Fine-grained Image Retrieval Convolutional Feature Feature Extraction Feature Fusion Multiscale Images

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Approaching Interoperability and Data-Related Processing Issues in a Human-Centric Industrial Scenario 1st

Approaching Interoperability and Data-Related Processing Iss...

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1st Global Internet of Things and Edge Computing Summit

作者： Syed, Danish Abbas Quadrini, Walter Choubeh, Nima Rahmani Pinzone, Marta Gusmeroli, Sergio Politecn Milan Dept Management Econ & Ind Engn Milan Italy

ISBN: (纸本)9783031785719;9783031785726

Industry 4.0 industrial automation paradigm and the related new Operator 4.0 role and pool of competencies are playing a critical role in bringing forth the Digital Transformation to manufacturing industry and SMEs in particular. The human-centric aspect of Industry 4.0 in combination with resilience, sustainability and circularity of manufacturing processes is gaining wider acceptance in Europe and across the globe while the transition towards Industry 5.0 starts to gain momentum as well as the integration of human centric solutions in Industry 4.0 automation systems. The current work uses a three-pronged approach to wearable sensors integrated with existing Industry 4.0 automation systems, by addressing sensor heterogeneity, data interoperability and network latency issues under the umbrella of a single unified and harmonised solution. Such a solution is realised in a realistic industrial scenario showcasing adaptive Human-Robot collaboration and leverages open-source software and open reference architectures.

关键词： Human in the loop Operator 5.0 5G IIoT NGSI-LD CPPS

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Fast Genomic Data Compression on Multicore Machines 12th

Fast Genomic Data Compression on Multicore Machines

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Sanz, Victoria Pousa, Adrian Naiouf, Marcelo De Giusti, Armando Natl Univ Plata Sch Comp Sci III LIDI La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783031708060;9783031708077

Nowadays, Genomics has gained relevance since it allows preventing, diagnosing and treating diseases in a personalized way. The reduction in sequencing time and cost has increased the demand and, thus, the amount of genomic data that must be stored or transferred. Consequently, it becomes necessary to develop genome compression algorithms that help to reduce storage usage without consuming too much time. This is now possible thanks to modern multicore machines. This paper improves MtHRCM, a multi-threaded compression algorithm for large collections of genomes, by reducing its sequential component in order to enhance performance and scalability. Experimental results show that our optimized version is faster than MtHRCM and achieves the same compression ratio. Also, they reveal that this new version scales well when increasing the number of threads/cores for smaller test collections, while the high amount of simultaneous I/O requests to disk limits the scalability for larger test collections.

关键词： Genomic Data Compression Multi-threaded Hybrid Referential Compression Method Multicore Performance DNA

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Towards Efficient Federated Learning via Vehicle Selection and Resource Optimization in IoV 5th

Towards Efficient Federated Learning via Vehicle Selection a...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Gong, Nan Yan, Guozhi Zhang, Hao Xiao, Ke Yang, Zuoxiu Li, Chuzhao Liu, Kai Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Normal Univ Sch Comp & Informat Sci Chongqing Peoples R China Chongqing Univ Natl Elite Inst Engn Chongqing Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Federated Learning (FL) is a novel distributed machine learning paradigm that holds great potential for collaborative model training in Internet of Vehicles (IoV). However, employing FL in IoV presents several challenges. First of all, the heterogeneous vehicular resources may lead to the bias of vehicle selection for model training and result in poor performance with respect to data diversity. Moreover, executing FL tasks with limited resources and energy is also a non-trivial problem. In view of this, we investigate an efficient FL framework in IoV, and then formulate the Vehicle Selection and Resource Optimization (VSRO) problem, which aims at minimizing the energy consumption and training latency without jeopardizing model accuracy. On this basis, we first propose a vehicle selection algorithm based on Lyapunov optimization method that dynamically selects vehicles to participate in each round of FL, taking into account the computational and transmission capabilities of vehicles, as well as their participation frequency in FL tasks. Furthermore, a resource scheduling algorithm is proposed for the selected vehicles, which utilizes the Lagrange dual problem and sub-gradient projection to iteratively determine the optimal training process. The simulation results demonstrate that the proposed algorithms can achieve best performance on enhancing model accuracy and simultaneously reducing system energy consumption and latency.

关键词： Internet of Vehicles Federated learning Efficient training Vehicle selection Resource optimization Lyapunov optimization

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Deep Learning-Based Network Intrusion Detection Systems: A Systematic Literature Review 5th

Deep Learning-Based Network Intrusion Detection Systems: A S...

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5th Southern African Conference for Artificial Intelligence Research

作者： Mutembei, Leonard L. Senekane, Makhamisa C. van Zyl, Terence Univ Johannesburg Acad Comp Sci & Software Engn Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa Natl Inst Theoret & Computat Syst Stellenbosch South Africa

ISBN: (纸本)9783031782541;9783031782558

Machine Learning algorithms have been used to develop models in different fields like banking, healthcare, transportation, cybersecurity, and others. Many studies have shown the use of Machine Learning in network security by detecting network intrusions. However, the increase in user devices increases the data size and hence increases the complexity of the network. Cyber attackers continue to create advanced cyber attacks, and identifying them becomes extremely challenging. On the other hand, traditional Machine Learning models cannot efficiently handle large amounts of data and complexity. Therefore, this study examines how Deep Learning methods can be implemented for the Network Intrusion Detection Systems. The Network Intrusion Detection System (NIDS) helps to secure businesses within companies' networks from bad actors. As Deep Learning advances, network security experts must incorporate the techniques within the NIDS to minimize the effects of cyber attacks. For the investigation of the Deep Learning techniques in implementing NIDS, a study used Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) framework to conduct a systematic literature review and summarizes 111 studies published between 2021 and November 2023. Articles were analyzed by categorizing them into Deep Learning algorithms, architectures and datasets. The performance comparisons have been evaluated based on available articles' results on various datasets. Methods have been compared based on precision, recall and F1 score metrics. The results revealed that the most commonly used datasets are CICIDS2017, CSE-CIC-IDS2018, CICDDoS2019, NSL-KDD and UNSW-NB15. Furthermore, it was observed that hybrid implementation approaches have been shown to produce accurate and robust models compared with traditional methods. Finally, the results further demonstrated the need to explore using Machine Learning in NIDS.

关键词： Artificial Intelligence Deep Learning Machine Learning Network Intrusion Detection System PRISMA

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Automatic Candling System with Artificial Intelligence Based on Neural Networks 4th

Automatic Candling System with Artificial Intelligence Based...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Patricio Fernandez, Angel Urdaneta, Maryory Albarracin, Wilmer Univ Tecnol Israel Quito 170522 Ecuador

ISBN: (纸本)9783031832093;9783031832109

Artificial vision systems use complex algorithms to identify specific characteristics in the objects of study, but by integrating artificial intelligence, the accuracy and simplicity of the processes increases. The project focuses on a scalable, versatile and lightweight prototype, resulting in an automatic candling system. Three main characteristics of chicken eggs, used in pastry and pastillage, were identified, classifying them as normal, cracked and with excess pores. In addition, a prototype was developed with a wooden structure that houses a Smartphone in charge of capturing images, LED panels for illumination and a special perforated cuvette. The captured images are transmitted to a laptop via WiFi and processed with Python algorithms using libraries such as Qt, OpenCV, Keras and TensorFlow. Two scripts manage the training of the neural network model and the system interface, which monitors, controls and stores data in databases managed by MariaDB and phpMyAdmin. With 900 images (720 for training and 180 for validation) and 2700 additional images analyzed, an accuracy of 93.93% has been achieved with a response time of 0.021954 s per egg. The system is suitable for candling of any oviparous egg.

关键词： CNN hyperparameter IP webcam Keras OpenCV ovoscopy Python TensorFlow

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Integrating Knowledge and Data-Driven Artificial Intelligence for Decisional Enterprise Interoperability 5th

Integrating Knowledge and Data-Driven Artificial Intelligenc...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Emmanouilidis, Christos Waschull, Sabine Zotelli, Jessica Univ Groningen POB 800 NL-9700 AV Groningen Netherlands

ISBN: (纸本)9783031807749;9783031807756

Although data-driven artificial intelligence (AI) is increasingly applied in decision-making, challenges such as a lack of explainability and trust limit its integration in enterprise ***-making processes. Establishing a minimum level of common information sharing and trust among decision-making stakeholders, often termed as decisional interoperability, is needed for industry adoption. Purely data-driven approaches risk ignoring the enterprise environment and situational context of decisions and are insufficient for such interoperability to the extent that the decision-making rationale is opaque. While linked data and knowledge approaches have long been pursued in the context of data-driven machine learning, these have not been particularly well explored in the context of decisional enterprise interoperability. This paper aims to narrow this gap. It explores how the introduction of AI is changing decisional interoperability concerns. It then outlines patterns of human-AI teaming in decision-making, as well as methods, such as knowledge-infused AI and mechanisms, such as active learning, for enhancing decisional interoperability. Three diverse application domain-cases offer context for an analysis of AI decision-making considerations and decisional interoperability. The paper concludes with arguments about how integration of knowledge into data-driven AI contributes to decisional interoperability and further work needed in this area.

关键词： Decision-making Decisional interoperability Knowledge-infused artificial intelligence

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Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology 12th

Evaluating Histopathological Cancer Detection: A Comparative...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Marcillo-Vera, Ana Caceres-Benitez, Karen Almeida-Galarraga, Diego Tirado-Espin, Andres Univ Yachay Tech Sch Math & Computat Sci Urcuqui 100119 Ecuador Univ Yachay Tech Sch Biol Sci & Engn Urcuqui 100119 Ecuador

ISBN: (纸本)9783031754302;9783031754319

This study evaluates four well-known convolutional neural networks: VGG19, EfficientNetB0, ResNet50, and InceptionV3, for tumor detection in lymph node pathology images. Using a significant subset of the PCam dataset, models were trained on binary classification tasks focused on identifying tumor tissue, with data augmentation applied to enhance generalization. The methodology involved a rigorous process of data preparation, model selection, training, and evaluation under limited hardware resources, using a standard laptop. The dataset was split into training and validation sets with an 80/20 ratio, and models were trained using the Adam optimizer with a learning rate of 0.001 over multiple epochs. VGG19 achieved the highest validation accuracy at 77.38% and AUC of 85.35% but required substantial computational time and exhibited overfitting. EfficientNetB0, though faster to train (20m 40 s), showed lower validation accuracy (58.91%) and AUC (60.14%). ResNet50 performed well during training but faced generalization challenges. InceptionV3 demonstrated a balanced performance with a validation accuracy of 70.74% and AUC of 76.41%, making it a promising option across varied datasets. These findings highlight the strengths and limitations of different CNN architectures in enhancing cancer diagnosis in lymph node pathology, providing insights for future research and clinical applications.

关键词： Metastatic cancer EfficientNetB0 VGG19 ResNet50 InceptionV3 Image segmentation Tumor detection

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Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for Skin Cancer Diagnosis 4th

Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Attallah, Omneya Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Engn Coll Engn & Technol Alexandria 21937 Egypt Arab Acad Sci Technol & Maritime Transport Wearables Biosensing & Biosignal Proc Lab Alexandria 21937 Egypt

ISBN: (纸本)9783031821554;9783031821561

Skin cancer (SC) comprises approximately 33% of all reported cancer cases globally. Thus, rapid and accurate diagnosis is crucial for effective treatment and better patient outcomes. computer-aided diagnostic (CAD) systems are necessary for assisting and improving traditional diagnostic approaches, however, most of them count on only spatial information and/or individual deep-learning models to realize a diagnosis. Furthermore, current CADs retrieve deep features with substantial sizes, resulting in increased training complexity and speed. This article presents a CAD framework to classify multiple subcategories of SC based on multiple Convolutional Neural Networks (CNNs) with distinct configurations. The study explores various combinations of CNNs to select the best mixture of CNNs that impact performance. Additionally, it incorporates spatial and time-frequency demonstrations to successfully diagnose SC through the utilisation of wavelet analysis. In addition, wavelet analysis is employed to reduce the large dimensionality of deep features, which is further minimised using principal component analysis. The results indicate that the suggested CAD accomplished a rate of accuracy of 0.956 using only 159 features. It surpasses the CADs currently available. Therefore, it can serve as a powerful tool for identifying SC and could be employed to enhance the precision of medical diagnostics.

关键词： Skin Cancer Classification Feature Fusion Convolutional Neural Networks Discrete Wavelet Transform

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Leveraging JSBSim and Gymnasium: A Reinforcement Learning Approach for Air Combat Simulation 8th

Leveraging JSBSim and Gymnasium: A Reinforcement Learning Ap...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Salhi, Abderahim Jabour, Joseph E. Arnolds, Thomas L. Ross, James E. Dozier, Haley R. US Army Corps Engn 3909 Halls Ferry Rd Vicksburg MS 39180 USA

ISBN: (纸本)9783031856273;9783031856280

This paper explores the application of reinforcement learning in air combat simulations, emphasizing the use of JSBSim and Gymnasium to create realistic combat scenarios. The objective is to leverage artificial intelligence to validate military designs and enhance tactics in the field. The ongoing efforts involve testing and surveying several prominent frameworks for their suitability for cross-domain application towards designing and developing an open-source testbed for RL techniques to execute and visualize the simulations. We share insights gained from experimenting with these tools and highlight the creation of customized scenarios for specific needs. The focus is on collaborative multiaircraft air combat, aiming to improve mission success and reduce casualties. Referencing DARPA's ACE project, the paper showcases real-world AI applications in air combat intelligence. This contribution adds to the discourse on air combat, flight simulation, and reinforcement learning, emphasizing practical implications for military advancements.

关键词： Reinforcement Learning (RL) Auto Reinforcement Learning (AutoRL) Air Combat Simulation Multi Agent Reinforcement Learning

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