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Picture Coding Symposium, PCS

作者： Hongyue Huang Xilong Zhou Hongbo Ning Haopeng Lu Qi Zhang Yanpeng Liang Wanjun Lyu Chuanmin Jia Xinfeng Zhang Liuxin Zhang Siwei Ma School of Computer Science Peking University Peking Lenovo Beijinet Co. Ltd. School of Electronic and Computer Engineering Peking University Shenzhen Graduate School School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Wangxuan Institute of Computer Technology Peking University School of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (数字)9798350358483

ISBN: (纸本)9798350358490

Recent advancements in telecommunication technologies have exceeded the requirements of numerous video-based Real-Time Communication (RTC) applications. Mean-while, in light of the growing demand for immersive 3D visual experience, researchers are currently focusing on developing next-generation telepresence systems. This paper presents a novel immersive conferencing system that offers a smooth, high-fidelity, and life-size autostereoscopic display of remote user portraits. To generate the binocular stereo vision in real time, an adaptive low-complexity view synthesis method based on an accelerated Real-time Intermediate Flow Estimation (RIFE) model is employed, which performs direct cross-view generation based on decoded multi-view videos and tracked eye positions of the watching user. Thanks to eliminating the complex 3D modeling procedure that relies on depth images, the proposed system requires significantly fewer computational resources and lower video transmission bandwidth compared to existing immersive conferencing systems. Therefore, the proposed system is low-cost and flexible when accommodating diverse conferencing environments.

关键词： Visualization Three-dimensional displays Telepresence Computational modeling Focusing Estimation Real-time systems

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Semantic information Reasoning and Multi-Step Cross-Modal Interaction Network for Image-Text Retrieval

Semantic Information Reasoning and Multi-Step Cross-Modal In...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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Testability-Driven Development: An Improvement to the Tdd Efficiency

SSRN

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SSRN 2023年

作者： Parsa, Saeed Zakeri-Nasrabadi, Morteza Turhan, Burak School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Information Technology and Electrical Engineering Oulu Finland

Test-first development (TFD) is a software development approach involving automated tests before writing the actual code. TFD offers many benefits, such as improving code quality, reducing debugging time, and enabling easier refactoring. However, TFD also poses challenges and limitations, requiring more effort and time to write and maintain test cases, especially for large and complex projects. Refactoring for testability is improving the internal structure of source code to make it easier to test. Refactoring for testability can reduce the cost and complexity of software testing and speed up the test-first life cycle. However, measuring testability is a vital step before refactoring for testability, as it provides a baseline for evaluating the current state of the software and identifying the areas that need improvement. This paper proposes a mathematical model for calculating class testability based on test effectiveness and effort and a machine-learning regression model that predicts testability using source code metrics. It also introduces a testability-driven development (TsDD) method that conducts the TFD process toward developing testable code. TsDD focuses on improving testability and reducing testing costs by measuring testability frequently and refactoring to increase testability without running the program. Our testability prediction model has a mean squared error of 0.0311 and an R2 score of 0.6285. We illustrate the usefulness of TsDD by applying it to 50 Java classes from three open-source projects. TsDD achieves an average of 77.81% improvement in the testability of these classes. Experts’ manual evaluation confirms the potential of TsDD in accelerating the TDD process. © 2023, The Authors. All rights reserved.

关键词： Software testing

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Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder

Cross-modal retrieval based on Attention Embedded Variationa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Semantic Relation-Based Cross Attention Network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention Network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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NFT Appraisal Using Machine Learning 23

NFT Appraisal Using Machine Learning

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5th Asia Pacific information technology Conference, APIT 2023

作者： Dawod, Ahmed Dawod Mohammed Munkhdalai, Lkhagvadorj Park, Kwang Ho Ryu, Keun Ho Pham, Van Huy Database and Bioinformatics Laboratory College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399500

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917. © 2023 ACM.

关键词： Blockchain

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Solving Time-Independent Linear Matrix-Vector Equation (TILMVE) Simplified from Time-Dependent Situation 42

Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Sol...

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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Yunong Lu, Ji Mao, Mingzhi Li, Shuai School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Software Engineering Sun Yat-sen University Zhuhai519082 China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu90570 Finland

ISBN: (纸本)9789887581543

We present a continuous reciprocal-kind Zhang dynamics (RKZD) model for solving the time-dependent linear matrixvector equation. On the basis of the model, we deduce its simplified form for solving the time-independent linear matrix-vector equation (TILMVE). Subsequently, for more efficient computation and easier implementation in digital hardware, we utilize Euler forward difference formula (EFDF) to discretize the continuous RKZD model, resulting in a discrete RKZD algorithm. Finally, numerical experimental results attest to the feasibility and high effectiveness of the discrete RKZD algorithm for solving TILMVE. Comparisons with the discrete gradient neural network (or termed discrete gradient dynamics), Jacobi iteration, as well as Gauss-Seidel iteration highlight the superior convergence properties of the discrete RKZD algorithm. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Computational efficiency

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