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arXiv 2023年

作者： Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Gadekallu, Thippa Reddy Mahmud, Mufti Brown, David J. School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Nottingham Trent University Clifton Lane Nottingham United Kingdom Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China

The development of new technologies and their expanding use in a wide range of educational environments are driving the transformation of higher education. Assistive technologies are a subset of cutting-edge technology that can help students learn more effectively and make education accessible to everyone. Assistive technology can enhance, maintain, or improve the capacities of students with learning difficulties. Students with learning difficulties will be greatly benefited from the use of assistive technologies. If these technologies are used effectively, students with learning difficulties can compete with their peers and complete their academic tasks. We aim to conduct this review to better understand the role of assistive technologies in providing inclusive higher education for students with learning difficulties. The review begins with the introduction of learning difficulties and their causes;inclusive education and the need for assistive technologies;the reasoning for conducting this review;and a summary of related reviews on assistive technologies for students with learning difficulties in inclusive higher education. Then, we discuss the preliminaries for the learning difficulties type and assistive technology. Later, we discuss the effects of assistive technology on inclusive higher education for students with learning difficulties. Additionally, we discuss related projects and support tools available in inclusive higher education for students with learning difficulties. We also explore the challenges and possible solutions related to using assistive technology in higher education to provide inclusive education for students with learning difficulties. We conclude the review with a discussion of potential promising future directions. © 2023, CC BY.

关键词： Students

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Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks

arXiv

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arXiv 2024年

作者： Morik, Marco Hashemi, Ali Müller, Klaus-Robert Haufe, Stefan Nakajima, Shinichi The Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Department of Computer Science TU Berlin Berlin Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken Germany Bernstein Center for Computational Neuroscience Berlin Germany Physikalisch-Technische Bundesanstalt Berlin Germany Berlin Center for Advanced Neuroimaging Charité-Universitätsmedizin Berlin Germany RIKEN AIP Tokyo Japan

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Enhancing Language Learning through Technology: Introducing a New English-Azerbaijani (Arabic Script) Parallel Corpus

arXiv

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arXiv 2024年

作者： Khiarak, Jalil Nourmohammadi Ahmadi, Ammar Saeed, Taher Akbari Asgari-Chenaghlu, Meysam Atabay, Toğrul Karimi, Mohammad Reza Baghban Ceferli, Ismail Hasanvand, Farzad Mousavi, Seyed Mahboub Noshad, Morteza Kartal Ol Research Group Warsaw Poland Savalan Igidlari Publishing Company Ardabil Iran Research and Development Ultimate Berlin Germany Institute of Turkic Studies Marmara University Istanbul Turkey University of Kurdistan Kurdistan Iran SumerNashr Publishing Company Tabriz Iran Department of Mathematics and Statistics Lorestan University Lorestan Iran Faculty of Engineering University of Mohaghegh Ardabili Ardabil Iran Computer Science and Engineering University of Michigan Michigan United States

This paper introduces a pioneering English-Azerbaijani (Arabic Script) parallel corpus, designed to bridge the technological gap in language learning and machine translation (MT) for under-resourced languages. Consisting of 548,000 parallel sentences and approximately 9 million words per language, this dataset is derived from diverse sources such as news articles and holy texts, aiming to enhance natural language processing (NLP) applications and language education technology. This corpus marks a significant step forward in the realm of linguistic resources, particularly for Turkic languages, which have lagged in the neural machine translation (NMT) revolution. By presenting the first comprehensive case study for the English-Azerbaijani (Arabic Script) language pair, this work underscores the transformative potential of NMT in low-resource contexts. The development and utilization of this corpus not only facilitate the advancement of machine translation systems tailored for specific linguistic needs but also promote inclusive language learning through technology. The findings demonstrate the corpus's effectiveness in training deep learning MT systems and underscore its role as an essential asset for researchers and educators aiming to foster bilingual education and multilingual communication. This research covers the way for future explorations into NMT applications for languages lacking substantial digital resources, thereby enhancing global language education frameworks. The Python package of our code is available at https://***/project/chevir-kartalol/, and we also have a website accessible at https://***/. © 2024, CC BY.

关键词： Neural machine translation

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Improvements and extensions on metaphor detection

arXiv

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arXiv 2020年

作者： Ma, Weicheng Liu, Ruibo Wang, Lili Vosoughi, Soroush Minds Machines and Society Group Department of Computer Science Dartmouth College United States

Metaphors are ubiquitous in human language. The metaphor detection task (MD) aims at detecting and interpreting metaphors from written language, which is crucial in natural language understanding (NLU) research. In this paper, we introduce a pre-trained Transformer-based model into MD. Our model outperforms the previous state-of-the-art models by large margins in our evaluations, with relative improvements on the F-1 score from 5.33% to 28.39%. Second, we extend MD to a classification task about the metaphoricity of an entire piece of text to make MD applicable in more general NLU scenes. Finally, we clean up the improper or outdated annotations in one of the MD benchmark datasets and re-benchmark it with our Transformer-based model. This approach could be applied to other existing MD datasets as well, since the metaphoricity annotations in these benchmark datasets may be outdated. Future research efforts are also necessary to build an up-to-date and well-annotated dataset consisting of longer and more complex texts. © 2020, CC BY-NC-ND.

关键词： Computational linguistics

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Supervised speaker embedding de-mixing in two-speaker environment

arXiv

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arXiv 2020年

作者： Shi, Yanpei Hain, Thomas Speech and Hearing Research Group Department of Computer Science University of Sheffield United Kingdom

Separating different speaker properties from a multi-speaker environment is challenging. Instead of separating a two-speaker signal in signal space like speech source separation, a speaker embedding de-mixing approach is proposed. The proposed approach separates different speaker properties from a two-speaker signal in embedding space. The proposed approach contains two steps. In step one, the clean speaker embeddings are learned and collected by a residual TDNN based network. In step two, the two-speaker signal and the embedding of one of the speakers are both input to a speaker embedding de-mixing network. The de-mixing network is trained to generate the embedding of the other speaker by reconstruction loss. Speaker identification accuracy and the cosine similarity score between the clean embeddings and the de-mixed embeddings are used to evaluate the quality of the obtained embeddings. Experiments are done in two kind of data: artificial augmented two-speaker data (TIMIT) and real world recording of two-speaker data (MC-WSJ). Six different speaker embedding de-mixing architectures are investigated. Comparing with the performance on the clean speaker embeddings, the obtained results show that one of the proposed architectures obtained close performance, reaching 96.9% identification accuracy and 0.89 cosine similarity. Copyright © 2020, The Authors. All rights reserved.

关键词： Loudspeakers

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Regenerable Photonic Aptasensor for Detection of Bacterial Spores with Stacks of Gaas/Algaas Nanoheterostructures

SSRN

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SSRN 2024年

作者： Dubowski, Jan J.J. Ishika, Ishika Hassen, Walid M. St-Onge, René Moteshareie, Houman Tayabali, Azam F. CNRS IRL-3463 Quantum Semiconductors and Photon-based BioNanotechnology Group Department of Electrical and Computer Engineering Université de Sherbrooke 3000 boul. de l’Université SherbrookeQCJ1K 0A5 Canada Biotechnology Laboratory Environmental Health Science and Research Bureau Healthy Environments and Consumer Safety Branch Health Canada OttawaONK1A 0K9 Canada

The reusability of biosensors is crucial for advancing laboratory operations and environmental monitoring. We have investigated digital photocorrosion (DIP) of GaAs/AlGaAs nanoheterostructures for detection of electrically charged biomolecules in water. This study presents the concept of a regenerable aptasensor based on DIP of a GaAs/AlGaAs biochip composed of GaAs (12 nm)-AlGaAs (10 nm) nanolayers. Specific immobilization of Bacillus thuringiensis kurstaki spores was achieved using thiolated aptamers that upon interaction with the spores were immobilized on the biochip surface. After completing a biosensing run based on DIP of the first pair of GaAs-AlGaAs nanolayers, the biochip was regenerated by the exposure to 3X PBS, which released the spores and prepared the same biochip for a consecutive biosensing run with the second pair of nanolayers. We argue that a chip with stacks of numerous pairs of GaAs/AlGaAs nanolayers, engineering of which is readily available with the current semiconductor manufacturing technology, can deliver a large amount of biosensing data before getting swapped with a new unit. The biochip should be of high interest to the development of biosensing workstations for quasi-continuous operation in out-of-laboratory settings attractive for environmental monitoring. © 2024, The Authors. All rights reserved.

关键词： Biochips

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External-Wrench Estimation for Aerial Robots Exploiting a Learned Model

arXiv

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arXiv 2025年

作者： Alharbat, Ayham Ruscelli, Gabriele Diversi, Roberto Mersha, Abeje Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This paper presents an external wrench estimator that uses a hybrid dynamics model consisting of a first-principles model and a neural network. This framework addresses one of the limitations of the state-of-the-art model-based wrench observers: the wrench estimation of these observers comprises the external wrench (e.g. collision, physical interaction, wind);in addition to residual wrench (e.g. model parameters uncertainty or unmodeled dynamics). This is a problem if these wrench estimations are to be used as wrench feedback to a force controller, for example. In the proposed framework, a neural network is combined with a first-principles model to estimate the residual dynamics arising from unmodeled dynamics and parameters uncertainties, then, the hybrid trained model is used to estimate the external wrench, leading to a wrench estimation that has smaller contributions from the residual dynamics, and affected more by the external wrench. This method is validated with numerical simulations of an aerial robot in different flying scenarios and different types of residual dynamics, and the statistical analysis of the results shows that the wrench estimation error has improved significantly compared to a model-based wrench observer using only a first-principles model. © 2025, CC BY-NC-ND.

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Vision-based price suggestion for online second-hand items

arXiv

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arXiv 2020年

作者： Han, Liang Yin, Zhaozheng Xia, Zhurong Guo, Li Tang, Mingqian Jin, Rong Stony Brook University Department of Computer Science United States Alibaba Group China

Different from shopping in physical stores, where people have the opportunity to closely check a product (e.g., touching the surface of a T-shirt or smelling the scent of perfume) before making a purchase decision, online shoppers rely greatly on the uploaded product images to make any purchase decision. The decision-making is challenging when selling or purchasing second-hand items online since estimating the items’ prices is not trivial. In this work, we present a vision-based price suggestion system for the online second-hand item shopping platform. The goal of vision-based price suggestion is to help sellers set effective prices for their second-hand listings with the images uploaded to the online platforms. First, we propose to better extract representative visual features from the images with the aid of some other image-based item information (e.g., category, brand). Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model. According to two demands from the platform, two different objective functions are proposed to jointly optimize the classification model and the regression model. For better model training, we also propose a warmup training strategy for the joint optimization. Extensive experiments on a large real-world dataset demonstrate the effectiveness of our vision-based price prediction system. Copyright © 2020, The Authors. All rights reserved.

关键词： Sales

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Unique Least Common Ancestors and Clusters in Directed Acyclic Graphs

arXiv

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arXiv 2023年

作者： Shanavas, Ameera Vaheeda Changat, Manoj Hellmuth, Marc Stadler, Peter F. Department of Futures Studies University of Kerala Trivandrum695 581 India Dept. Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstrasse 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria Faculty of Ciencias Universidad Nacional de Colombia Bogotá Colombia

We investigate the connections between clusters and least common ancestors (LCAs) in directed acyclic graphs (DAGs). We focus on the class of DAGs having unique least common ancestors for certain subsets of their minimal elements since these are of interest, particularly as models of phylogenetic networks. Here, we use the close connection between the canonical k-ary transit function and the closure function on a set system to show that pre-k-ary clustering systems are exactly those that derive from a class of DAGs with unique LCAs. Moreover, we show that k-ary T -systems and k-weak hierarchies are associated with DAGs that satisfy stronger conditions on the existence of unique LCAs for sets of size at most k. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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Investigation of traffic radar coverage efficiency under different placement strategies

Investigation of traffic radar coverage efficiency under dif...

引用

International Conference on Internet of Things 2024 (ICIoT 2024)

作者： Shun Yao Jin Zhou David Chieng Chiew Foong Kwong Boon Giin Lee Jianqiang Li Yusen Chen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo People's Republic of China School of Computer Science University of Nottingham Ningbo People's Republic of China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen People's Republic of China Zhejiang Communication Investment Group Co. Ltd. Zhejiang People's Republic of China

ISBN: (数字)9781837242177

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang Communications Investment group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring.

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