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arXiv 2025年

作者： Abdullah, Abdulhady Abas Badawi, Soran Abdullah, Dana A. Hamad, Dana Rasul Taher, Hanan Abdulrahman Muhamad, Sabat Salih Ahmed, Aram Mahmood Hassan, Bryar A. Aula, Sirwan Abdolwahed Rashid, Tarik A. Artificial Intelligence and Innovation Centre University of Kurdistan Hewlér Kurdistan Region Erbil Iraq Language Center Charmo University Kurdistan Region Chamchamal Iraq Department of Computer Science College of Science Knowledge University Kurdistan Region Erbil44001 Iraq Department of Information System ICTC Directorate Ministry of Higher Education and Scientific Research Kurdistan Region Iraq Computer Science Department Faculty of Science Soran University Soran Kurdistan Region Erbil Iraq Pedagogical Training Course for Professional Teacher Development Centre Duhok Polytechnic University Kurdistan Region Duhok Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani Kurdistan Region46023 Iraq

The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance. © 2025, CC BY.

关键词： Semantics

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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Refining BERT embeddings for document hashing via mutual information maximization

arXiv

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arXiv 2021年

作者： Ou, Zijing Su, Qinliang Yu, Jianxing Zhao, Ruihui Zheng, Yefeng Liu, Bang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Artificial Intelligence Sun Yat-sen University Guangdong China Tencent Jarvis Lab Rali & Mila Université de Montréal Guangdong Key Lab. of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Existing unsupervised document hashing methods are mostly established on generative models. Due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly, but instead to model the features extracted from them (e.g. bag-of-words (BOW), TFIDF). In this paper, we propose to learn hash codes from BERT embeddings after observing their tremendous successes on downstream tasks. As a first try, we modify existing generative hashing models to accommodate the BERT embeddings. However, little improvement is observed over the codes learned from the old BOW or TFIDF features. We attribute this to the reconstruction requirement in the generative hashing, which will enforce irrelevant information that is abundant in the BERT embeddings also compressed into the codes. To remedy this issue, a new unsupervised hashing paradigm is further proposed based on the mutual information (MI) maximization principle. Specifically, the method first constructs appropriate global and local codes from the documents and then seeks to maximize their mutual information. Experimental results on three benchmark datasets demonstrate that the proposed method is able to generate hash codes that outperform existing ones learned from BOW features by a substantial margin. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Manoharan, Poongodi Kumar, Lilhore Umesh Seth, Bijeta alsekait, Deema Mohammed Simaiya, Sarita Hamdi, Mounir Raahemifar, Kaamran Department of Computer Science and Engineering Amity University Haryana Gurugram India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing University of Louisiana Lafayette USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering B. M. Institute of Engineering & Technology Sonipat India Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia College of Information Sciences and Technology Data Science and Artificial Intelligence Program State College USA School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Faculty of Engineering University of Waterloo Waterloo Canada

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big data Cost function Minimization

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Unsupervised embedding learning from uncertainty momentum modeling

arXiv

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arXiv 2021年

作者： Zhou, Jiahuan Tang, Yansong Su, Bing Wu, Ying Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Engineering Science University of Oxford OX1 3PJ United Kingdom Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Existing popular unsupervised embedding learning methods focus on enhancing the instance-level local discrimination of the given unlabeled images by exploring various negative data. However, the existed sample outliers which exhibit large intra-class divergences or small inter-class variations severely limit their learning performance. We justify that the performance limitation is caused by the gradient vanishing on these sample outliers. Moreover, the shortage of positive data and disregard for global discrimination consideration also pose critical issues for unsupervised learning but are always ignored by existing methods. To handle these issues, we propose a novel solution to explicitly model and directly explore the uncertainty of the given unlabeled learning samples. Instead of learning a deterministic feature point for each sample in the embedding space, we propose to represent a sample by a stochastic Gaussian with the mean vector depicting its space localization and covariance vector representing the sample uncertainty. We leverage such uncertainty modeling as momentum to the learning which is helpful to tackle the outliers. Furthermore, abundant positive candidates can be readily drawn from the learned instance-specific distributions which are further adopted to mitigate the aforementioned issues. Thorough rationale analyses and extensive experiments are presented to verify our superiority. © 2021, CC BY.

关键词： Embeddings

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TransLSTM: A hybrid LSTM-Transformer model for fine-grained suggestion mining

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 8卷

作者： Samad Riaz Amna Saghir Muhammad Junaid Khan Hassan Khan Hamid Saeed Khan M. Jaleed Khan National Centre for Artificial Intelligence University of Engineering and Technology Peshawar Pakistan School of Electrical Engineering and Computer Sciences National University of Sciences and Technology Islamabad Pakistan Military College of Signals National University of Sciences and Technology Islamabad Pakistan Data Science Institute University of Galway Galway Ireland SiParadigm Diagnostics NJ United States Medical Sciences Division University of Oxford Oxford United Kingdom

Digital platforms on the internet are invaluable for collecting user feedback, suggestions, and opinions about various topics, such as company products and services. This data is instrumental in shaping business strategies, enhancing product development, and refining service delivery. Suggestion mining is a key task in natural language processing, which focuses on extracting and analysing suggestions from these digital sources. Initially, suggestion mining utilized manually crafted features, but recent advancements have highlighted the efficacy of deep learning models, which automatically learn features. Models like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) have been employed in this field. However, considering the relatively small datasets and the faster training time of LSTM compared to BERT, we introduce TransLSTM, a novel LSTM-Transformer hybrid model for suggestion mining. This model aims to automatically pinpoint and extract suggestions by harnessing both local and global text dependencies. It combines the sequential dependency handling of LSTM with the contextual interaction capabilities of the Transformer, thus effectively identifying and extracting suggestions. We evaluated our method against state-of-the-art approaches using the SemEval Task-9 dataset, a benchmark for suggestion mining. Our model shows promising performance, surpassing existing deep learning methods by 6.76% with an F1 score of 0.834 for SubTask A and 0.881 for SubTask B. Additionally, our paper presents an exhaustive literature review on suggestion mining from digital platforms, covering both traditional and state-of-the-art text classification techniques.

关键词： Suggestion mining Long Short-Term Memory (LSTM) network Switch transformer Digital platforms

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Multiple Pseudo-Local Feedbacks for Controlling High-Order Oscillatory Objects 15

Multiple Pseudo-Local Feedbacks for Controlling High-Order O...

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument engineering, APEIE 2021

作者： Zhmud, V.A. Nosek, J. Mansurova, M.E. Semibalamut, V.M. Fomin, Yu. N. Stukach, O.V. Novosibirsk State Technical University Institute of Laser Physics Sb Ras Dept. of Automation Novosibirsk Russia Technical University of Liberec Inst. of Mechatronics and Computer Engineering Liberec Czech Republic Al-Farabi Kazakh National University Department of Artificial Intelligence and Big Data Almaty Kazakhstan Unified Geophysical Service of the Russian Academy of Sciences Siberian Branch of the Federal State Budgetary Institution of Science of the Federal Research Center Novosibirsk Russia Novosibirsk State Technical University Higher School of Economics Dept. of Information Defense Moscow Russia

ISBN: (纸本)9781665434089

Development of the Internet of things (IoT) technologies arises many additional problems as energy harvesting. In particular, a long-Time controlling object needs a device without an external power supply. The control of technical objects with a high order of mathematical model has a large difficulty if a feature of the object is a high tendency to oscillations due to the ratio of the coefficients in the model. In particular, the tendency to oscillations occurs if the object model has complex roots, or if the model has a transport delay. One such example is a model that consists of two oscillatory objects and a delay loop. Widespread controlling such an objects using a PID controller is very difficult since in the transient process in the resulting system there is a large overshoot and many oscillations. If such an object could be stabilized with additional local feedbacks, the control task would be greatly simplified, but the object does not have corresponding outputs and inputs, therefore local feedbacks cannot be used. The pseudo-local feedback approach assumes the use of virtual fragments of the object's mathematical model in the controller to form such connections that the system perceives as local ones. This paper proposed the use of several pseudo-local feedbacks to control an object with elements that increased its tendency to oscillate. The effectiveness of the proposed approach is confirmed by modeling. © 2021 IEEE.

关键词： Feedback

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Is GPT-4 fair? An empirical analysis in automatic short answer grading

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computers and Education: artificial intelligence 2025年 8卷

作者： Luiz Rodrigues Cleon Xavier Newarney Costa Dragan Gasevic Rafael Ferreira Mello Computer Engineering Coordination Federal Technological University of Paraná Apucarana Brazil Instituto Federal Goiano – IF Goiano Iporá Brazil CESAR School Centro de Estudos e Sistemas Avançados do Recife Recife Brazil Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Melbourne Australia

Short open-ended questions represent a central resource in formative and summative assessments both face-to-face and online settings, ranging from elementary to higher education. However, grading these questions remains challenging for instructors, raising attention to the field of Automatic Short Answer Grading (ASAG). While ASAG has yielded valuable contributions to learning analytics, it often faces generalizability issues. Accordingly, the rapid advancement in Large Language Models (LLMs) has motivated their adoption to empower ASAG systems. Despite that, previous research has not investigated whether LLMs are fair graders in the context of ASAG. Therefore, this paper presents an empirical analysis aimed to understand LLMs' fairness in ASAG by using human grades as a baseline, comparing them to GPT-4's answers, and investigating whether the LLM's grades are equivalent in grading answers from varied groups of humans. Our results demonstrated that, while GPT-4 tended to be more lenient in its grading, it maintained consistent evaluation standards when assessing responses from different groups of students. GPT-4 remained consistent for questions of different subjects and levels of Bloom's taxonomy and for people with different demographics. These findings suggest GPT-4 is a fair grader, supporting its potential to empower educators and developers in using and designing ASAG systems. Nevertheless, we recommend further research to investigate these findings and understand how to optimize GPT-4's grades.

关键词： Fairness GPT Natural language processing Automatic short answer grading

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Multi-photon imaging with rare-earth doped nanocrystals beyond the diffraction limit

Multi-photon imaging with rare-earth doped nanocrystals beyo...

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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser science APS/DLS

作者： Zhang, Qiming Liang, Liangliang Lamon, Simone Liu, Xiaogang Gu, Min Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Chemistry National University of Singapore Singapore117543 Singapore Laboratory of Artificial-Intelligence Nanophotonics School of Science RMIT University MelbourneVIC3001 Australia

Multi-photon imaging beyond the diffraction limit has been demonstrated with rare-earth doped nanocrystals. A resolution of 100 nm has been predicted with the excitation and depletion beam at a wavelength of 980 nm. &... 详细信息

ISBN: (纸本)9781943580675

关键词： Diffraction

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