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9th International Conference on Big data and Information Analytics, BigDIA 2023

作者： Feng, Yanxia Rao, Yanghui Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9798350330076

Word embedding learning is a powerful technique to represent words' rich semantics as low-dimensional vectors, but it may encode harmful social biases. Such biases can leave negative impacts on downstream applications that rely on word embeddings, e.g., text classification and sentiment analysis. In this work, we design a novel approach to debias word embeddings using a causal graph that separates gender and semantic information within words. Our method is theoretically interpretable and can effectively alleviate the issue of gender bias from words in real-world corpora without destructing their semantic meanings. We perform a comprehensive comparison for our proposed approach with existing state-of-the-art techniques using a range of evaluation metrics and tasks, and demonstrate that our method can not only mitigate bias mixed in word embeddings, but also improve their performance on downstream tasks. © 2023 IEEE.

关键词： Embeddings

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Non-English Natural Language Interface to databases: A Systematic Review 13

Non-English Natural Language Interface to Databases: A Syste...

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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Karimi, Sadullah Rasel, Annajiat Alim Abdullah, Matin Saad Department of Computer Science and Engineering School of Data and Sciences Brac University Dhaka Bangladesh

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

A natural language interface (NLI) facilitates communication between a user and a system using a natural language used by a human as a medium. Instead of a complex user interface with numerous controls, the user enters input via natural language in the form of text, speech, signs, etc. The system responds via natural language in the form of text, speech, signs, etc. The use of a natural language interface has been increasing. To interact with a dataset or a database, such a system or interface is not always available. To access data, users need the knowledge of Structured Query Language (SQL) or need to learn an alternative graphical tool or database browser with varying limitations. For non-English speakers, it may create further issues in accessing data. English users can access the database using the natural language interface to database (NLIDB) which converts natural language to English-like Structured Query Language (SQL). For non-English users, the query in non-English language needs to be converted to SQL directly or via the English language as an intermediary. Researchers have proposed NLIDB systems for native speakers of many languages. In this research, we present results from a systematic review of non-English NLIDB with their various approaches. This research aims to assist future NLIDB researchers in understanding the current state and to identify the research gaps. © 2022 IEEE.

关键词： User interfaces

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Capturing the Essence: Exploring Cutting-Edge Feature Extraction Methods in Digital Image Processing 3

Capturing the Essence: Exploring Cutting-Edge Feature Extrac...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Prabu, S. Natarajan, B. George, Roshan Sujitha, S. Bharathi, P Divya Rajeswari, S. School of Computer Science and Engineering VIT University Chennai India Amrita Vishwa Vidyapeetham Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India

ISBN: (纸本)9798350317022

Feature Extraction (FE) plays a vital role in the fields of image processing, machine learning and pattern recognition. Binarization, resizing, thresholding and normalization are the initial steps applied before extracting features from an image. FE methods are applied to images to extract the most essential features that help in detection, recognition and classification tasks. FE techniques are used in numerous applications, such as object recognition, character recognition and pattern recognition. This research study presents a comprehensive research study of different image FE techniques, including SURF, BRISK, PCA and SIFT, which have been employed for various applications in recent times. This research study explains each method's fundamental concepts and pros and cons and demonstrates in what scenario which feature extraction technique will be better. This research work also presents a summary table of FE techniques used in various applications with their accuracy percentage. © 2023 IEEE.

关键词： Edge Detection Feature Extraction Feature Selection Image Processing Image Representation

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SPHASE: Multi-Modal and Multi-Branch Surgical Phase Segmentation Framework based on Temporal Convolutional Network

SPHASE: Multi-Modal and Multi-Branch Surgical Phase Segmenta...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Long, Jun Hong, Junkun Wang, Zidong Chen, Tingxuan Chen, Yunfei Yang, Liu Central South University Big Data Institution Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Surgical phase segmentation plays an important role in computer-assisted surgery systems, aiming to recognize what step or what action is operating in the video frame. Existing methods focus on improving the accuracy and precision of video segmentation, but ignore semantic consistency and temporal continuity of video frames in the intra-phase, which is necessary to apply in realistic computer-assisted equipment. Meanwhile, recent works almost extract long-term dependencies by Temporal Convolutional Network, but we heed high layers in TCN lose fine-grained information for detecting surgical steps and further affect phase segmentation task. To address these problems, we propose a Surgical Phase Segmentation Framework (SPHASE) which contains a multimodal feature fusion process and follows a multi-branch predictor. Moreover, we design a multimodal feature fusion mechanism when aggregate optical flow feature and I3D feature. The extensive experiments on AutoLaparo, Cholec80, and M2CAI2016 datasets demonstrate our method outperforms the state-of-the-art method by a large margin, especially in the JACC metric, which means SPHASE is more applicable in the surgical operating room. © 2023 IEEE.

关键词： attention mechanism multimodal feature fusion surgical phase segmentation temporal convolutional network

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Knowledge-Enhanced Difference-Aware Clinical Time Series Representation Learning for Diagnosis Prediction

Knowledge-Enhanced Difference-Aware Clinical Time Series Rep...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： An, Ying Shi, Yinghong Guo, Lin Sheng, Yu Chen, Xianlai Central South University Big Data Institute Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Predicting future health status based on historical patient visits is one of the essential tasks in healthcare. Many existing approaches attempt to enhance the representation learning capability of models by incorporating relevant medical knowledge, but their effectiveness is severely affected by the incompleteness and noise of the knowledge graphs. Moreover, due to the inability to capture temporal features at a fine-grained level, most existing methods also have limitations in learning the temporal development of patients' health status. To address these issues, we propose a Knowledge-Enhanced Difference-Aware clinical time series representation learning model (KEDA) for diagnosis prediction. In this model, we first combine the medical ontology graph and co-occurrence graph, and use hierarchical graph convolution and contrastive learning methods to enhance the semantic representation of medical entities. After that, a task-specific difference-aware temporal module is designed to improve the accuracy of patient representation, which adds two novel gated units in the original GRU to fuse multi-type clinical information based on the relationship between different types of data and prediction tasks and capture fine-grained temporal evolution of patient health status. We validate our model on two publicly available datasets, and the experimental results demonstrate that KEDA outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： diagnosis prediction Electronic Medical Record fine-grained temporal feature knowledge graph multi-type fusion

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Research on intelligent access control technology of face recognition model based on parameter sharing and dense connection

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International Journal of Computational Intelligence Studies 2023年第3-4期12卷 255-270页

作者： Xu, Yonghua School of Computer Engineering Jinling Institute of Technology Jiangsu Nanjing211169 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China

This study proposes a laboratory intelligent facial recognition system based on improved CNN, which significantly improves the accuracy of facial recognition by optimising the portrait recognition algorithm, improving CNN calculation and large parameter scale, and utilising perspective projection to improve portrait effect and sample utilisation. The experimental results show that the recognition rate has been improved by 10%, the CPU usage rate is less than 100%, and the model parameters have been reduced by more than 95%. This system can effectively and accurately recognise faces, making it suitable for embedded facial recognition devices. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Face recognition

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On the Security of Smart Home Systems:A Survey

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Journal of computer science & Technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Liu, Weisong Yang, Jianfeng Huang, Jiewen El-Latif, Ahmed A. Abd Zhou, Zhili School of Artificial Intelligence Guangzhou University Guangzhou China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Image steganography is a technology that embed secret information within a cover image to obtain a stego-image for covert communication. The transmission of undetectable stego-images via social media can facilitate secure and efficient covert communication. Recently, the generative image steganography has achieved promising performance with the rapid development of generative models. However, existing generative image steganography suffers from issues, i.e., the low quality of the generated stego-image and the limited hiding capacity. To address these issues, this paper proposes a generative image steganography scheme based on latent space vector coding and diffusion model. It consists of an optimized diffusion denoising network, the latent space vector coding mapping network, and the information extraction network. In this scheme, the secret information is encoded as a latent space vector, which is then transformed in the input diffusion model to generate the stego-image. Experimental results demonstrate that this scheme achieves superior performance compared with existing generative image steganography methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Vectors

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