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4th Multidisciplinary International Social Networks Conference, MISNC 2017

作者： García, Cristian González Meana-Llorián, Daniel García-Díaz, Vicente Núñez-Valdez, Edward Rolando Model-Driven Engineering Research Group Department of Computer Science Oviedo University of Oviedo Asturias Spain

ISBN: (纸本)9781450348812

Social networks are an inexhaustible source of knowledge. One of its main uses is to express opinions about a particular product or service. Thus, users usually write opinions about places or so-called points of interest. That is a valuable knowledge for other users and especially for users who know and trust the opinions of the writers. In this work, we present an overview of a platform to capture opinions related to places and generate a positive or a negative recommendation for users based on both the global use of Twitter as well as the comments made by trusted people on the network. © 2017 ACM.

关键词： Social networking (online)

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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Multi-Dimensional research Software Categorization

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Computing in Science and engineering 2025年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Hermann, Ben Lamprecht, Anna-Lena Linxweiler, Jan Prat, Arnau Rumpe, Bernhard Schoening-Stierand, Katrin Yang, Shinhyung Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Helmholtz Centre for Infection Research Department for Epidemiology Brunswick Germany University of Cologne Cologne51147 Germany Carl von Ossietzky Universität Oldenburg Department of Computer Science Oldenburg26129 Germany Secure Software Engineering Group TU Dortmund Dortmund44227 Germany TU Braunschweig Braunschweig38106 Germany Braunschweig38108 Germany RWTH Aachen University Germany University of Hamburg Hub of Computing and Data Science Hamburg22761 Germany

research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, readiness-based, developer-based, and dissemination-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. We report on the multi-dimensional categorization of selected research software examples. © 1999-2011 IEEE.

关键词： Aerospace engineering Analytical models data collection data models data visualization Monitoring Numerical models Software Software development management Software engineering

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern

Classification of Awareness in Disorders of Consciousness Us...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Chaewon Lee Sunho Lee Jitka Annen Ji-Hoon Jeong Marcello Massimini Silvia Casarotto Melanie Boly Olivier Bodart Aurore Thibaut Mario Rosanova Steven Laureys Olivia Gosseries Minji Lee Dept. Data Science The Catholic University of Korea Bucheon Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Republic of Korea Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent Ghent Belgium Dept. Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical and Clinical Sciences “L. Sacco” University of Milan Milan Italy Dept. Psychiatry Wisconsin Institute for Sleep and Consciousness University of Wisconsin Madison USA Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future.

关键词： Deep learning Accuracy Biological system modeling Pattern classification Focusing Signal processing Brain modeling Particle measurements Electroencephalography Robustness

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Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving

arXiv

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arXiv 2024年

作者： Manzoor, Anam Singh, Aryan Sistu, Ganesh Mohandas, Reenu Grua, Eoin Scanlan, Anthony Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose unique challenges for extracting spatial and geometric information due to dynamic changes in object attributes. Our experiments focus on segmenting the WoodScape fisheye image dataset into ten distinct classes, assessing the Deformable Networks’ ability to capture intricate spatial relationships and improve segmentation accuracy. Additionally, we explore different loss functions to address class imbalance issues and compare the performance of conventional CNN architectures with Deformable Convolution-based CNNs, including Vanilla U-Net and Residual U-Net architectures. The significant improvement in mIoU score resulting from integrating Deformable CNNs demonstrates their effectiveness in handling the geometric distortions present in fisheye imagery, exceeding the performance of traditional CNN architectures. This underscores the significant role of Deformable convolution in enhancing semantic segmentation performance for fisheye imagery. © 2024, CC BY.

关键词： Semantic Segmentation

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Neural End-to-End Coreference Resolution using Morphological Information 6

Neural End-to-End Coreference Resolution using Morphological...

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CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Arslan, Tugba Pamay Acar, Kutay Eryigit, Gulsen ITÜ NLP Research Group Department of AI&Data Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey ITÜ NLP Research Group Department of Computer Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey

ISBN: (纸本)9781955917025

In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score. © CRAC 2023. All Rights Reserved.

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Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

arXiv

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arXiv 2023年

作者： Sharma, Sushil Singh, Aryan Sistu, Ganesh Halton, Mark Eising, Ciarán Data-Driven Computer Engineering Group Dept. of Electronic and Computer Engineering University of Limerick Ireland

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the scene. To address this, we advocate for the use of Bird’s Eye View (BEV) perspectives, which offer unique advantages in capturing spatial relationships and object homogeneity. In our work, we leverage Graph Neural Networks (GNNs) and positional encoding to represent objects in a BEV, achieving competitive performance compared to traditional DNN-based methods. While the BEV-based approach loses some detailed information inherent to front-view images, we balance this by enriching the BEV data by representing it as a graph where relationships between the objects in a scene are captured effectively. © 2023, CC BY.

关键词： Deep neural networks

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Prediction of wastewater pre-precipitation variables using self-organizing networks

Prediction of wastewater pre-precipitation variables using s...

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20th IEEE International Symposium on Intelligent Control, ISIC '05 and the13th Mediterranean Conference on Control and Automation, MED '05

作者： Isaias, Padelis Nilsson, Sara Stathaki, Anna King, Robert E. IEEE Data and Knowledge Engineering Group Computer Research Institute Athens Greece Environmental Research Institute IVL Stockholm Sweden Electrical Engineering and Computer Science University of Patras Greece Computer Research Institute Athens Greece

This paper describes the derivation and design of an array of self-organizing networks trained by inductive learning for one step ahead prediction of the outputs of the pre-precipitation stage of a wastewater treatmen... 详细信息

ISBN: (纸本)0780389360

关键词： Wastewater

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Computing incoherence explanations for learned ontologies

Computing incoherence explanations for learned ontologies

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7th International Conference on Web Reasoning and Rule Systems, RR 2013

作者： Fleischhacker, Daniel Meilicke, Christian Völker, Johanna Niepert, Mathias Data and Web Science Research Group University of Mannheim Germany Computer Science and Engineering University of Washington United States

ISBN: (纸本)9783642396656

Recent developments in ontology learning research have made it possible to generate significantly more expressive ontologies. Novel approaches can support human ontology engineers in rapidly creating logically complex and richly axiomatized schemas. Although the higher complexity increases the likelihood of modeling flaws, there is currently little tool support for diagnosing and repairing ontologies produced by automated approaches. Off-the-shelf debuggers based on logical reasoning struggle with the particular characteristics of learned ontologies. They are mostly inefficient when it comes to detecting modeling flaws, or highlighting all of the logical reasons for the discovered problems. In this paper, we propose a reasoning approach for discovering unsatisfiable classes and properties that is optimized for handling automatically generated, expressive ontologies. We describe our implementation of this approach, which we evaluated by comparing it with state-of-the-art reasoners. © 2013 Springer-Verlag.

关键词： Ontology

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A decision support system with distributed agents for large-scale process control

A decision support system with distributed agents for large-...

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2007 Mediterranean Conference on Control and Automation, MED

作者： Prayati, A. Stathaki, A. Furusjo, E. King, R.E. Data and Knowledge Engineering Group Computer Technology Institute Athens Greece Environmental Research Institute IVL Stockholm Sweden

ISBN: (纸本)142441282X

As CIM systems are being developed to assist plant managers and operators with real-time monitoring and control, the need for Decision Support Systems (DSS) becomes apparent so as to investigate alternative control strategies, fault diagnosis and alarming situations. As processes become more complex the need to consider also the environmental impact, on top of the commonly used economical factors, leads to new system architectures and multi-objective optimization. In this paper, a DSS architecture is presented considering distributed agents taking into account a multiplicity of impact factors in order to improve overall process operation. ©2007 IEEE.

关键词： Decision support systems

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