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information Systems and Computing technology (ISCTech), International Conference on

作者： Yingli Liu Guangtao Zhang Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table’s structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table’s structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology.

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Zhihao Ping Wei Dai Xiaodong Fu Li Liu Lijun Liu Ning Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computing Sciences The College at Brockport State University of New York Brockport NY

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM

关键词： Biological system modeling Biological tissues Transcriptomics Contrastive learning Feature extraction Brain modeling Spatial databases Gene expression Resource management Image reconstruction

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images

An Integrated Image Classification Approach to Detect the Fl...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Mohammed Siddique Tasneem Ahmed Mohd Shahid Husain Department of Computer Application Advanced Computing Research Lab Integral University Lucknow India Faculty of Information Technology Majan University College Muscat Oman Department of Information Technology CAS-Ibri University of Technology and Applied Sciences Muscat Oman

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures.

关键词： Radio frequency Satellites Time series analysis Urban areas Vegetation mapping Radar polarimetry Rivers

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A big data smart agricultural system: recommending optimum fertilisers for crops

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International Journal of information technology (Singapore) 2023年第1期15卷 249-265页

作者： Ngo, Vuong M. Duong, Thuy-Van T. Nguyen, Tat-Bao-Thien Dang, Cach N. Conlan, Owen School of Computer Science Technological University Dublin Dublin Ireland Ho Chi Minh City Open University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Science and Technology Application for Sustainable Development Research Group Ho Chi Minh City University of Transport Ho Chi Minh City Viet Nam School of Computer Science Trinity College Dublin Dublin Ireland

Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen (N), phosphorus (P) and potassium (K) for the top ten most popular crops in EU. © 2022, The Author(s).

关键词： Crop yield Data warehouse Electronic agricultural record Nutrient

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TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules

arXiv

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arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： information filtering

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Heng Qian Chuan Li Chengli Li Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Opo-electronic Information Technology School of Physics and Electronic Information Yunnan Normal University Kunming China

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad 2 /Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method.

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Collaborative Learning Assessment via information and Communication technology

Collaborative Learning Assessment via Information and Commun...

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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Khan, Asif Hasana, Mohammad Kamrul Ghazal, Taher M. Islam, Shayla Alzoubi, Haitham M. Mokhtar, Umi Asma' Alam, Rizwan Ahmad, Munir Integral University Department of Computer Application Lucknow India Faculty of Information Science and Technology Selangor UKM Bangi43600 Malaysia Skyline University Collage United Arab Emirates Institute of Computer Science and Digital Innovation Ucsi University Malaysia Csscive Bhopal India National Collage of Business Administration and Economics

ISBN: (纸本)9781665461665

In technological revolution, information technology has changed the face on how Education is experienced and perceived. Learning is quickly becoming an imperative. ICT have great potential to spread learning however, the benefits of these technologies have to be made available during the daily learning hours of the students. This paper concentrates on specifying the potentials of using e-learning systems in teaching and learning and exploring how Machine Learning can be incorporated to analyses daily learning activities of students and specially content preparation and presentation tools application of e-Learning as a teaching aid for teachers. We also analyzed the e-learning facilities available in the present era that can be used by teachers to make a collaborative and participative environment of learning for the students. Results proved the productivity of the proposed model which comes from the choice of more fitting highlights chosen by the Learning algorithm. Furthermore, proposed system is used to accurately and effectively predict to evaluate collaborative learning of students performance in its early stage. © 2022 IEEE.

关键词： Students

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A New Fourth-Order Sparse Array Generator Based on Sum-Difference Co-Array Analysis

A New Fourth-Order Sparse Array Generator Based on Sum-Diffe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodong Guo Hua Chen Hongguang Lin Wei Liu Qing Shen Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Information and Electronics Beijing Institute of Technology China

In this paper, based on sum-difference co-array analysis, a new fourth-order sparse array called sum-difference-FODC (SD-FODC) is proposed, allowing the construction of a fourth-order DCA with long consecutive lags using the continuous segments in the second-order DCA and SCA of the original array. It has a closed-form expression for sensor positions that can be generated by two arbitrary nonuniform linear arrays (NLAs) called generators. If the second-order SCA and DCA of the generators have long consecutive segments, the designed fourth-order sparse array can achieve a large number of uDOFs. Numerical results are provided to demonstrate the superior performance of the proposed design.

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Adaptive Ensemble Model for Ultra-short-term Wind Power Forecasting 11

Adaptive Ensemble Model for Ultra-short-term Wind Power Fore...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Yunlong Jin, Huaiping Jin, Huaikang Cao, Yundong Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming650000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Stable and reliable wind power forecasting is of great significance for grid dispatching. However, wind power has strong nonlinear characteristics. Its amount of historical data is very large, and traditional machine learning methods cannot fit the nonlinear relationship well. Furthermore, owing to the seasonality and periodicity of the wind power, the performance of the offline model inevitably deteriorates. To address these problems, this study proposes an adaptive ensemble model for ultra-short-term wind power forecasting. First, a long short-term memory (LSTM) network is established with historical wind power data for an entire year to ensure a strong generalization performance. Second, local weighted partial least squares (LWPLS) were used to obtain LSTM network prediction errors in real time by adaptive modeling and further improving its prediction accuracy. In addition, just-in-time learning (JITL) is used to ensure the adaptability of forecasting using LWPLS. Finally, to fully integrate the prediction advantages of different prediction models, a random forest (RF) was used to transform the determination of the ensemble weight into a three-classification problem. The adaptive fusion of the three different forecasting mechanisms is realized by weighting through the posterior probability of classification. Finally, the effectiveness and superiority of the proposed method are verified in a practical case. © 2022 IEEE.

关键词： Error correction

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