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39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

作者： Bo Zhang College of Computing and Data Science Nanyang Technological University Singapore Singapore

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Injective coloring of edge corona product of graphs 8

Injective coloring of edge corona product of graphs

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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Zhao, Wenying Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The injective chromatic number χi(G) of a graph G is the minimum number of colors needed to color the vertices of G such that different vertices with the same neighbor are colored differently. In this here, we get some exact values of the injective chromatic number of the edge corona product of some graphs. © Published under licence by IOP Publishing Ltd.

关键词： Graph theory

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Integrating data and Rules: A Hybrid Approach for Robust Lane Change Intention Prediction Under Distribution Shift

Integrating Data and Rules: A Hybrid Approach for Robust Lan...

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International Conference on Intelligent Transportation

作者： Yao Long Teng Htet Naing Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Lane change intention prediction is critical in improving road safety. In general, this task is achieved via two distinct approaches: The data-based approach and the rule-based approach. Generally, the former outperforms the latter under in-distribution scenarios. However, under out-distribution scenarios, data-based methods tend to perform extremely poorly. Rule-based methods are robust to such problems since they are formulated based on theory. Instead of relying exclusively on each approach, it is more advantageous to combine them to exploit both of their strengths. Thus, in this paper a physics guided hybrid model is proposed for lane change intention prediction to improve model robustness under distribution shift. The effectiveness of the proposed model is demonstrated in the experimental results where the proposed model outperforms rule-based models under in-distribution scenarios by 4%, and outperforms both rule-based models and data-based models in out-distribution scenarios by 3% and 60% respectively in the highD dataset.

关键词： Training Transforms Predictive models Prediction algorithms Robustness Road safety data models Calibration Physics Intelligent transportation systems

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The optimal scaling factor based on Secant Method for image watermarking using the hybrid DTCWT-DCT domain.

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer science and Mathematics 2024年第1期5卷 237-262页

作者： Lebcir, Mohamed Awang, Suryanti Benziane, Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Center of Excellence for Artificial Intelligence & Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Kuantan26300 Malaysia Faculty of Science and Technology University of Djelfa Algeria

This paper proposes an implementation of the secant optimization method on the watermark embedding in DTCWT domain. An objective function combining PSNR and NC (The Normalized Correlation) values calculated for a set of common attacks is used in the optimization process. The aim is to enhance the tradeoff between robustness and imperceptibility against the various image processing attacks via an automatic and careful selection of the scaling factor using optimization. The watermark is inserted in the high energy bands of DCT-transformed sub-vectors, which were generated from the DTCWT coefficient decomposition of the host image. Experimental results performed on a set of 512×512 greyscale images, show better results than the existing schemes for most common image attacks such as compression, low-pass filtering and noise addition. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Image watermarking

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A Framework for Identifying the Learners' Engagement in E-learning Environments Using a Hybrid CNN Architecture 6

A Framework for Identifying the Learners' Engagement in E-le...

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6th International Conference on computing, Electronics and Communications Engineering, iCCECE 2023

作者： Anny, Tasfia Nuzhat Chowdhury, Jafrin Iqbal Ahsan, Tanveer Zahur, Istiaque Patwary, Muhammed J. A. Miraz, Mahdi H. International Islamic University Computer Science and Engineering Chittagong Bangladesh Sonargaon University Mechanical Engineering Bangladesh Xiamen University Malaysia School of Computing and Data Science Malaysia

ISBN: (纸本)9798350340921

In the realm of online education, precise identification of the participants and their engagement is critical for optimal learning results. With the aim of precisely monitoring attendance and engagement in an online learning environment, this study suggests an end-To-end framework based on a hybrid convolutional neural network (CNN) architecture. The complete evaluation employs two separate CNN models: CNN-I for identifying learners' faces in individual images and CNN-II for measuring engagement through facial expressions, eye gaze and head movement. Two custom datasets have been developed and annotated to allow for an in-depth assessment of the proposed approach. dataset-I contains 4000 individual face images, whereas dataset-II has 6720 images depicting various interactions of the learners, such as engaged, bored, frustrated, confused, drowsy and looking away. However, on dataset-I and the VGG-face dataset, the CNN-I model surpasses a deep CNN (DCNN) with superior accuracy rates of 95.25% and 88.60%, respectively. On dataset-II and the FER2013 dataset, the CNN-II model obtains similarly outstanding accuracy rates of 93.45% and 83.74%, respectively. The specialised CNN-I and CNN-II models outperform the hybrid CNN model in their particular tasks, albeit having an accuracy rate of 88%. The research highlights the enormous potential of straightforward architecture integrated with specific datasets, emphasising their computational effectiveness and efficiency in the domains of engagement and facial recognition for online learning. © 2023 IEEE.

关键词： Face recognition

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Topological Clustering of Agents in Information Contagions: Application to Financial Markets

arXiv

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arXiv 2024年

作者： Goel, Anubha Hansen, Henri Kanniainen, Juho Computing Science/Financial Computing and Data Analytics Group Tampere University Tampere33720 Finland

Building on topological data analysis and expert knowledge, this study introduces a Mapper-based approach to cluster agents based on their tendency to be influenced by information spread. The context of our paper is financial markets with an aim to identify agents trading opportunistically on insider information while minimizing false positives, a critical challenge in financial market surveillance. We verify and demonstrate our methods using both synthetic and empirical data on insider networks and investor-level transactions in a stock market. Recognizing the sensitive nature of insider trading cases, we design a conservative approach to minimize false positives, ensuring that innocent agents are not wrongfully implicated. We find that the mapper-based method systematically outperforms other methods on synthetic data with ground truth. We also apply the method to empirical data and verify the results using a statistical validation method based on persistence homology. Our findings indicate that the proposed Mapper-based technique effectively identifies a subset of agents who tend to take advantage of inside information they have received. This method is highly adaptable to various applications involving the spread of information or diseases, where agents exhibit only indirect evidence of their carrier status (symptoms) through their behavior. © 2024, CC BY-SA.

关键词： Financial markets

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy 9

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Chinnasamy, A. Selvakumari, P. SRMIST School of Computing Department of Data Science and Business Systems Chennai India Anna University Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd's clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found. © 2023 IEEE.

关键词： Mean square error

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services computing 2025年第3期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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A New Carry Look-Ahead Adder Architecture Enabling Improved Speed and Energy Efficiency

A New Carry Look-Ahead Adder Architecture Enabling Improved ...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Padmanabhan Balasubramanian Douglas Maskell College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

We present a new carry look-ahead adder (NCLA) architecture that makes use of non-uniform-size CLA modules in contrast to the existing CLA architecture which typically makes use of uniform-size CLA modules. This helps to achieve improved speed and energy efficiency for the NCLA architecture compared to the existing CLA architecture without incurring notable area and power penalties. To demonstrate the advantage of NCLA, several adders were implemented ranging from the slow ripple carry adder to the generally considered fastest parallel-prefix adder viz. the Kogge-Stone adder (KSA), and their performance metrics were compared. The 32-bit addition was considered an example, and the different adders were implemented in a semi-custom design method using a 28-nm CMOS standard digital cell library. Based on the synthesis results, the NCLA was found to facilitate significant optimizations in design metrics compared to its high-speed counterparts. To give some examples, the NCLA achieved: (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to a high-speed CLA while occupying slightly more area, (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder while occupying 8% more area, (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to a speed-optimized carry select adder while requiring 37% less area, and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the KSA.

关键词： Microprocessors Computer architecture Signal processing Energy efficiency Libraries Distance measurement Delays Adders Standards Optimization

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