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2nd Advanced Imaging and Information processing Conference, AIIP 2024

作者： Jin, Nanxi Xin, Yuelan Deng, Shenghui Chen, Kuan Yang, Lan Huo, Haoyi Zhao, Jia Fang, Huiting Dept. of Physics and Electronic Information Engineering Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China State Key Laboratory of Tibetan Language Information Processing and Applications Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China Dept. of Computer Science Qinghai Normal University 38 Wusi West Road. Qinghai Xining810001 China

ISBN: (纸本)9781510683068

To enhance the accuracy of existing algorithms in the task of retinal vessel image segmentation, this paper proposes the incorporation of two modified convolutional blocks, in lieu of traditional ones, within the framework of the U-Net neural network, aiming to strengthen the extraction of detailed features. Firstly, a convolutional block equipped with local channel attention is devised for feature extraction in the shallow layers of the network. Secondly, a convolutional block incorporating global channel attention is introduced for feature extraction in the deeper layers. Lastly, skip connections are employed to feed the features extracted from the shallow layers into the deeper layers. Experimental results demonstrate that the proposed retinal vessel segmentation algorithm achieves a Dice coefficient of 88.21% and a sensitivity of 87.16%, marking improvements of 2.25% and 1.64% respectively over the original UNet network. © 2024 SPIE.

关键词： Image segmentation

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Talking detection in collaborative learning environments

arXiv

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arXiv 2021年

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Dept. of Electrical and Computer Engineering University of New Mexico United States Dept. of Language Literacy and Sociocultural Studies University of New Mexico United States

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, CC BY.

关键词： Speech recognition

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Android security using NLP techniques: A review

arXiv

引用

arXiv 2021年

作者： Sen, Sevil Can, Burcu WISE Lab. Dept. of Computer Engineering Hacettepe University Ankara Turkey Research Institute of Information and Language Processing University of Wolverhampton Wolverhampton United Kingdom

Android is among the most targeted platform by attackers. While attackers are improving their techniques, traditional solutions based on static and dynamic analysis have been also evolving. In addition to the application code, Android applications have some metadata that could be useful for security analysis of applications. Unlike traditional application distribution mechanisms, Android applications are distributed centrally in mobile markets. Therefore, beside application packages, such markets contain app information provided by app developers and app users. The availability of such useful textual data together with the advancement in Natural language processing (NLP) that is used to process and understand textual data has encouraged researchers to investigate the use of NLP techniques in Android security. Especially, security solutions based on NLP have accelerated in the last 5 years and proven to be useful. This study reviews these proposals and aim to explore possible research directions for future studies by presenting state-of-the-art in this domain. We mainly focus on NLP-based solutions under four categories: description-to-behaviour fidelity, description generation, privacy and malware detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Crossmodal language grounding in an embodied neurocognitive model

arXiv

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arXiv 2020年

作者： Heinrich, Stefan Yao, Yuan Hinz, Tobias Liu, Zhiyuan Hummel, Thomas Kerzel, Matthias Weber, Cornelius Wermter, Stefan Knowledge Technology Group Dept. of Informatics Universität Hamburg Germany Natural Language Processing Lab Dept. of Computer Science and Technology Tsinghua University China International Research Center for Neurointelligence University of Tokyo Japan

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterising the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilise the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children’s playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organise hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Privacy-preserving solutions in the Industrial Internet of Things

Privacy-preserving solutions in the Industrial Internet of T...

引用

International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： George Drosatos Konstantinos Rantos Dimitris Karampatzakis Thomas Lagkas Panagiotis Sarigiannidis Institute for Language and Speech Processing Athena Research Center Xanthi Greece Department of Computer Science International Hellenic University Kavala Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9781728143514

ISBN: (纸本)9781728198040

Industrial Internet of Things (IIoT) is a relatively new area of research that utilises multidisciplinary and holistic approaches to develop smart solutions for complex problems in industrial environments. Designing applications for the IIoT is a non trivial issue and requires to address, among many others, technology concerns, the protection of personal data, and the privacy of individuals. In this review paper, we identify privacy-preserving solutions that have been proposed in the literature to safeguard the privacy of individuals being part, or interacting with, the IIoT environment. As such, it considers two main categories of the analysed protocols, i.e., the privacy-preserving data management and processing solutions, and the privacy-preserving authentication methods.

关键词： Privacy Encryption Authentication

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge

ByoVoz Automatic Voice Condition Analysis System for the 201...

引用

2018 IEEE International Conference on Big Data, Big Data 2018

作者： Arias-Londoño, Julián David Gómez-García, Jorge Andrés Moro-Velazquez, Láureano Godino-Llorente, Juan Ignacio Dept. of Systems Engineering and Computer Science Universidad de Antioquia Medellín Colombia Dept. of Teoría de la Señal y Comunicaciones Universidad Politécnica de Madrid Madrid Spain Center for Language and Speech Processing Johns Hopkins University Baltimore United States

ISBN: (纸本)9781538650356

This paper presents the methods and results used by the ByoVoz team for the design of an automatic voice condition analysis system, which was submitted to the 2018 Far East Memorial Hospital voice data challenge. The proposed methodology is based on a cascading scheme that firstly discriminates between pathological and normophonic voices, and then identifies the type of disorder. By using diverse feature selection techniques, a subset of complexity, spectral/cepstral and perturbation characteristics were identified for the proposed tasks. Then, several generative classification methodologies based on Gaussian Mixture Models and Gradient Boosting were employed to provide decisions about the input voices in the binary classification, and using one-vs-one classification systems based on Random Forests for the categorization according to the type of disorder. By using a 4-folds cross-validation approach on the training partition a sensitivity=0.93 and specificity=0.74 was obtained. Similarly, an unweighted average recall of 0.63 and an accuracy of 66% was obtained for the identification task. Using the scoring metric proposed in the challenge the final resulting score is of 0.77. When the testing partition was considered, a sensitivity of 0.92, specificity of 0.54, unweighted average recall of 0.61 and a score of 0.72 were obtained. This was the fifth best result in the competition. © 2018 IEEE.

关键词： Speech recognition

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Indefinite kernel spectral learning 30

Indefinite kernel spectral learning

引用

30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Dept. of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

This paper introduces the indefinite learning in the framework of least squares support vector machines (LS-SVM). Here the analysis of the Multi-Class Semi-Supervised Kernel Spectral Clustering (MSS-KSC) model with indefinite kernels is provided. In indefinite MSS-KSC one finds the solution by solving a linear system of equations in the dual. In addition the use of the propped indefinite model for large scale data using Nyström approximation technique as well as unsupervised learning using Kernel Spectral Clustering are also discussed in the published full paper1. Experimental results on several real-life datasets are given to illustrate the efficiency of the proposed indefinite kernel spectral learning. © 2018 University of Groningen. All rights reserved.

关键词： Clustering algorithms

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What does explainable AI really mean? A new conceptualization of perspectives 1

What does explainable AI really mean? A new conceptualizatio...

引用

1st International Workshop on Comprehensibility and Explanation in AI and ML, CEX 2017

作者： Doran, Derek Schulz, Sarah Besold, Tarek R. Dept. of Computer Science and Engineering Kno.e.sis Research Center Wright State University DaytonOH United States Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Computer Science City University of London London United Kingdom

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algorithmic mechanisms;interpretable systems where users can mathematically analyze its algorithmic mechanisms;and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process. Copyright © 2018 for this paper by its authors. Copying permitted for private and academic purposes.

关键词： Artificial intelligence

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LexInfo: A Declarative Model for the Lexicon-Ontology Interface

SSRN

引用

SSRN 2018年

作者： Cimiano, Philipp Buitelaar, Paul McCrae, John Sintek, Michael University of Bielefeld Germany Unit for Natural Language Processing Digital Enterprise Research Institute National University of Ireland Galway Ireland Knowledge Management Dept. & Competence Center Semantic Web DFKI Germany

In this paper we motivate why it is crucial to associate linguistic information with ontologies and why more expressive models, beyond the label systems implemented in RDF, OWL and SKOS, are needed to capture the relation between natural language constructs and ontological structures. We argue that in the light of tasks such as ontology-based information extraction (i.e., ontology population) from text, ontology learning from text, knowledge-based question answering and ontology verbalization, currently available models are not sufficient as they only allow us to associate literals as labels to ontology elements. Using literals as labels, however, does not allow us to capture additional linguistic structure or information which is definitely needed as we argue. In this paper we thus present a model for linguistic grounding of ontologies called LexInfo. LexInfo allows us to associate linguistic information with respect to any level of linguistic description and expressivity to elements in an ontology. LexInfo has been implemented as an OWL ontology and is freely available together with an API. Our main contribution is the model itself, but even more importantly a clear motivation why more elaborate models for associating linguistic information with ontologies are needed. We also further discuss the implementation of the LexInfo API, different tools that support the creation of LexInfo lexicons as well as some preliminary applications. © 2018, The Authors. All rights reserved.

关键词： Ontology

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