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SSRN 2024年

作者： Chen, Xianyi Hui, Mi Xu, Huiguang He, Junjie Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology No.219 Ningliu Road Pukou District Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology No.219 Ningliu Road Pukou District Nanjing210044 China School of Software Nanjing University of Information Science and Technology No.219 Ningliu Road Pukou District Nanjing210044 China NARI Group Corporation State Grid Electric Power Research Institute Nanjing211106 China

A well-trained deep learning model usually consumes a lot of time and computational resources. However, it is vulnerable to infringement as a digital achievement once published. Recently, many impressive works have emerged for deep model copyright protection. They primarily embed watermarks by modifying model parameters or training samples, which can easily lead to model performance degradation. To this end, we propose a new structure-based DNN orthogonal watermarking method, in which the output features of the orthogonal layer are restricted to be parallel or orthogonal to each other by imposing forward constraint rules. It improves the correlation between the watermark and the model parameters, which improves the feature diversity and enables the model's watermark to effectively resist parameter-based attacks, thus improving the overall performance of the model. Extensive experiments have demonstrated that, the proposed scheme has great fidelity and robustness against various watermark removal attacks, such as parameter fine-tuning and model pruning. © 2024, The Authors. All rights reserved.

关键词： Watermarking

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Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Vasantharaj, A. Ahamed, M. Irshad Prema, K. Mudigonda, Aditya Sapaev, J.B. Thandapani, Kavitha Kalaignar Karunanidhi Institute of Technology Information Electronics and Communication Engineering India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineerin Tamilnadu Nagapattinam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Vgy Technology Avadi Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350336474

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%. © 2023 IEEE.

关键词： Forecasting

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Full-Coverage Mapping of Daily High-Resolution XCO2 across China from 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Yi Yan, Jining Zhong, Liheng Bao, De Sun, Leigang Li, Guangyu China University of Geosciences School of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Ant Group World Financial Center Beijing100020 China Hebei Academy of Sciences Institute of Geographical Sciences Shijiazhuang050011 China Hebei Technology Innovation Center for Geographic Information Application Shijiazhuang050011 China Environmental Development Center of the Ministry of Ecology and Environment China

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1° spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1° resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R2 of 0.86 and an RMSE of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. © 1980-2012 IEEE.

关键词： Atmospheric movements

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A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

A comparison of unsupervised and supervised machine learning...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Zamri, Nurnadiah Pairan, Mohammad Ammar Azman, Wan Nur Amira Wan Abas, Siti Sabariah Abdullah, Lazim Naim, Syibrah Tarmudi, Zamali Gao, Miaomiao Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut Malaysia Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia Management Science Research Group Terengganu Kuala Nerus Malaysia Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Universiti Technology MARA Johor Branch Segamat Johor Malaysia Faculty of Biology Medicine and Health School of Medical Siences University of Manchester Manchester United Kingdom

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario. © 2022 Elsevier B.V.. All rights reserved.

关键词： Rivers

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STRUCTURE-ORIENTED PROGRESSIVE LOW-RANK IMAGE RESTORATION FOR DEFENDING ADVERSARIAL ATTACKS

STRUCTURE-ORIENTED PROGRESSIVE LOW-RANK IMAGE RESTORATION FO...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Zhao, Zhiqun Wang, Hengyou Sun, Hao Cao, Wenming He, Zhihai Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO65211 United States School of Science Beijing University of Civil Engineering and Architecture Beijing100044 China Guangdong Multimedia Information Service Engineering Technology Research Center Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665438643

Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. On the other hand, human eyes recognize objects based on their global structures and semantic cues, instead of local image textures. In this work, we propose to develop a structure-oriented progressive low-rank image completion method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums. Our experimental results demonstrate the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures. © 2021 IEEE computer Society. All rights reserved.

关键词： Deep neural networks

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River quality classification using different distances in k-nearest neighbors algorithm

River quality classification using different distances in k-...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Zamri, Nurnadiah Pairan, Mohammad Ammar Azman, Wan Nur Amira Wan Abas, Siti Sabariah Abdullah, Lazim Naim, Syibrah Tarmudi, Zamali Gao, Miaomiao Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut Malaysia Management Science Research Group Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia Management Science Research Group Terengganu Kuala Nerus Malaysia Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Universiti Technology MARA Johor Branch Johor Segamat Malaysia Faculty of Biology Medicine and Health School of Medical Siences University of Manchester Manchester United Kingdom

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality. © 2022 Elsevier B.V.. All rights reserved.

关键词： Rivers

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

关键词： Competition

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A Review on the need of Clustering Techniques Used for Wireless Sensor Networks

A Review on the need of Clustering Techniques Used for Wirel...

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Salil Bharany Ateeq Ur Rehman M. Tariq Sadiq Muhammad Farrukh Meshal Alharbi Amjad Hussain Ghassan F. Issa CET and Technology Department Amritsar India Department of Electrical Engineering Government College University Lahore Pakistan School of Architecture & Engineering University of Brighton UK Department of Computer Science Minhaj University Lahore Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia NCBA&E School of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan School of Information Technology Skyline University College Sharjah UAE

Wireless sensor networks have been implemented in various software to help gather and analyze data from the physical world. Wireless sensor networks are networks of multiple tiny sensors powered by low-energy batteries (WSNs). WSN lifetime is a critical factor. This is because, after WSNs have been deployed in a given application, the inaccessibility of the sensing nodes makes it impossible to recharge or replace the power source that is limited in energy. Until recently, WSNs were thought to be the same. All the network nodes have the same power, processing speed, and operational capacity. However, scientists have developed heterogeneous WSNs, in which the properties of individual nodes may vary, to extend the lifespan of networks. In order to extend a network's helpful life, energy-efficient protocols must be designed. Even though WSN has its own problems, it has brought a fresh perspective to studying real-time intelligent systems. WSN (Wireless Sensor Network) has gained significant popularity in the recent years, owing to its vast applications in real-time scenarios, such as disaster management, pollution monitoring, temperature monitoring, traffic monitoring, transport monitoring, healthcare monitoring, battlefield monitoring, and border security surveillance, to name a few. Numerous sensor nodes are employed in these applications. They are typically placed in the sensing field, where they automatically transfer data to the base station (BS) through energy transmission (i.e., battery in the sensor). In WSN, clustering is one method for optimizing power consumption at the sensor node. The sensor nodes are grouped and given a leader when they undergo clustering. This study overviews various WSN clustering algorithms, each categorized according to various clustering properties.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Traffic control Probabilistic logic Real-time systems Sensors

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A Hybrid Approach Using Survey and Ism Analysis for Security Coding Risks and Best Practices for Gsd Enterprises

SSRN

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SSRN 2023年

作者： Khan, Rafiq Ahmad Akbar, Muhammad Azeem Alzahrani, Abdulrahman Habibullah, Safa Alwageed, Hathal Salamah Alzahrani, Musaad School of Software Northwestern Polytechnical University Shaanxi Xian710129 China Software Engineering Research Group Department of Computer Science & IT University of Malakand Pakistan Software engineering LUT University Finland Information Systems and Technology Department College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Department of Computer Science Al-Baha University Al-Baha65799 Saudi Arabia

Context: Global software development (GSD) offers quality results, cost-effectiveness, and uninterrupted project delivery. However, integrating security into GSD remains a ***: This study aims to enhance security in GSD projects by developing a hybrid approach using an empirical survey and Interpretive Structural Model (ISM).Methods: Initially, we conduct a systematic literature review and questionnaire survey with 50 GSD security experts. Moreover, 13 experts were invited to analyze the interrelationships among the practices using ***: Initially, we identified 13 major security-coding risks and 82 practices to mitigate these risks. The ISM analysis revealed that out of the identified security-coding practices, "never submit security measures to illegitimate authority", "avoiding buffer overflow and format string vulnerabilities", "control the brute force attack", and "identify a middleman attack" were considered fully dependent. While "avoid revealing information to achieve a secure design" is entirely independent within the GSD security coding ***: The study aids GSD professionals in assessing readiness in establishing contractual trust, understanding the current process pros and cons, and addressing urgent issues in secure software development processes. © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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