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Privacy-Preserving Multi-Keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective 38

Stabilize the Latent Space for Image Autoregressive Modeling...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhu, Yongxin Li, Bocheng Zhang, Hang Li, Xin Xu, Linli Bing, Lidong School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Zhejiang University China

Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encode pixels into a more compact latent space and learn the data distribution in the latent space instead of directly from pixels. However, this practice raises a pertinent question: Is it truly the optimal choice? In response, we begin with an intriguing observation: despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. This finding contrasts sharply with the field of NLP, where the autoregressive model GPT has established a commanding presence. To address this discrepancy, we introduce a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling by applying K-Means on the latent features of self-supervised learning models. Experimental results show that image autoregressive modeling with our tokenizer (DiGIT) benefits both image understanding and image generation with the next token prediction principle, which is inherently straightforward for GPT models but challenging for other generative models. Remarkably, for the first time, a GPT-style autoregressive model for images outperforms LDMs, which also exhibits substantial improvement akin to GPT when scaling up model size. Our findings underscore the potential of an optimized latent space and the integration of discrete tokenization in advancing the capabilities of image generative models. The code is available at https://***/DAMO-NLP-SG/DiGIT. © 2024 Neural information processing systems foundation. All rights reserved.

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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A Machine Learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

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ILRM: Imitation Learning-Based Resource Management for Integrated CPU–GPU Edge Systems With Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This letter focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability constraints. We propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: 1) offline Oracle generation and 2) online IL. In the offline phase, we design a fast-converging heuristic to generate near-optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize IL using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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An Ontology Learning Framework for unstructured Arabic Text 7

An Ontology Learning Framework for unstructured Arabic Text

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Taye, Mohammad Mustafa Abulail, Rawan Al-Oudat, Mohammad Philadelphia University Data Science and Artificial Intelligence Amman Jordan Philadelphia University Computer Science Amman Jordan

ISBN: (纸本)9798350383065

Ontologies are widely regarded as valuable sources of semantics and interoperability in all artificially intelligent systems. Due to the rapid growth of unstructured data on the web, studying how to automatically get ontology from unstructured text is important. Therefore, ontology learning (OL) is an important process in the business world. It involves finding and extracting concepts from the text so that these concepts can be used for things such as information retrieval. Unfortunately, learning ontology is not easy for some reasons, and there has not been much research on how to automatically learn a domain-specific ontology from *** Studying Arabic text is not as developed as learning Latin text. There is almost no automated support for using Arabic literary knowledge in semantically enabled systems. Machine learning (ML) has proven beneficial in numerous fields, including text mining. By employing neural language models such as AraBERT, it is possible to obtain word embeddings as distributed word representations from textual input using machine learning. However, the application of machine learning to aid the development of Arabic ontology is largely unexplored. This research examines the performance of AraBERT for ontology learning tasks in Arabic. Early performance results as an application of Arabic ontology learning are promising. In this research, we provide a method for populating an existing ontology with instance information extracted from the input natural language text. This prototype has achieved an information extraction accuracy of 91%. © 2023 IEEE.

关键词： Ontology

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Machine Learning Approaches for the Classification of Knee Osteoarthritis

Machine Learning Approaches for the Classification of Knee O...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tariq, Tayyaba Suhail, Zobia Nawaz, Zubair University of the Punjab Department of Computer Science Lahore Pakistan University of the Punjab Department of Data Science Lahore Pakistan

ISBN: (纸本)9798350322972

Machine learning systems have gained tremendous attention in solving complex tasks. For instance, image classification using machine learning has multiple applications in the medical field, where different imaging modalities are used for diagnosing, detecting, and classifying diseases. This study aims to analyze different machine-learning approaches for detecting knee osteoarthritis using X-rays. We used different machine learning algorithms to classify X-ray images of the knee to various levels of osteoarthritis severity. We have solved the problem as a binary classification task, multi-class classification, and ordinal regression task. The Histogram of Oriented Gradients and Haralick Features are used for feature extraction in the binary classification task. For Binary Classification, six experiments are performed using different machine learning classifiers. Logistic regression has a maximum accuracy of 84.50%, F measure of 81%, AUC of 0.84, Mathew's Correlation Coefficient of 0.69, and Cohen's Kappa 0.66. 10-Fold cross-validation also reveals that the models' performance is consistent across all folds. Furthermore, texture and shape features are extracted for ordinal classification using the histogram of oriented gradients and dense DAISY descriptors. Finally, separate experiments are performed, and results for both descriptors are compared. Our implemented approaches have shown state-of-the-art results. © 2023 IEEE.

关键词： Classification (of information)

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SkinMate: Mobile-Based Application for Detecting Multi-Class Skin Diseases Classification Using Pre-Trained MobileNetV2 on CNN Architecture 9

SkinMate: Mobile-Based Application for Detecting Multi-Class...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Fahrudin, Tresna Maulana Illah, Ibnu Zahy'Atha Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Data Science Surabaya Indonesia

ISBN: (纸本)9798350306835

The skin has a very significant role in humans. But unfortunately, the skin is one of the organs vulnerable to disease. Skin diseases can have an impact on the annoying itching, pain, emotional, and social feelings of the sufferer. The causes of skin diseases include bacteria, fungi, viruses, dust, and allergies. Delay in treatment can exacerbate the condition of the affected skin. Therefore, the research proposed a skin disease detection mobile application for early diagnosis. The total of skin disease images is 2.747 data, which is divided into five classes and separated into training and validation data. The category of the dataset includes Acne Vulgaris, Actinic Keratosis, Nail Fungus, Psoriasis, and Seborrheic Keratoses. The development of this application uses several augmentation techniques so that the model gets input training data from various image forms, and then uses pre-trained MobileNetV2, which has low latency and low power as needed on mobile devices. The development of this application also uses the TensorFlow Lite model for inference with ML Kit on Android. The model performance gave the best accuracy of 90.62%, precision of 89%, recall of 90%, and F1- score of 89%. These results indicate that the classifying of skin diseases using the pre-trained MobileNetV2 on CNN architecture is feasible. © 2023 IEEE.

关键词： Convolutional neural networks

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