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Digital Image Recovery Based on Lifting Wavelet Transform

Journal of Physics: Conference Series 2021年第1期1962卷

作者： Taha Basheer Taha Dujan B. Taha Ruzelita Ngadiran Phaklen Ehkhan IT Department Faculty of Sciences TISHK International University Erbil Iraq. Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Centre of Excellence Advanced Computing (AdvComp) Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.

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Building Explainable Predictive Analytics for Location-Dependent Time-Series Data

Building Explainable Predictive Analytics for Location-Depen...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Yao-Yi Chiang Yijun Lin Meredith Franklin Sandrah P. Eckel José Luis Ambite Wei-Shinn Ku Spatial Sciences Institute University of Southern California Los Angeles CA USA Department of Computer Science Department of Preventive Medicine Department of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (纸本)9781728167381

There are increasing numbers of online sources of real-time and historical location-dependent time-series data describing various types of environmental phenomena, e.g., traffic conditions and air quality levels. When coupled with the information that characterizes the natural and built environments, these location-dependent time-series data can help better understand interactions between and within human social systems and the ecosystem. Nevertheless, these data are still limited by their spatial and temporal resolution for downstream use (e.g., generating residential-level environmental exposures for human health studies). In this paper, we present a vision of a general machine learning framework for explainable predictive analytics for location-dependent time-series data. The framework will effectively deal with data-and model-related challenges for general scientific predictive analytics on spatiotemporal environmental phenomena. The challenges include how to identify the main features driving the phenomena, how to handle complex spatiotemporal variations in the phenomena, and how to utilize sparse ground truth measurements for training and validation. The resulting framework will enable fine spatial and temporal scale environmental exposure assessment and allow researchers to carry out unprecedented inquiries, such as understanding relationships between health outcomes and long-term air pollution exposures.

关键词： Spatiotemporal phenomena Predictive models Machine learning Atmospheric modeling Data models Air pollution

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Orthogonal Boubaker-Turki polynomials algorithm for problems arising in engineering

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AIP Conference Proceedings 2022年第1期2386卷

作者： Osama alabdali Suha Shihab Mohammed Rasheed Taha Rashid 1College of Education for Pure Sciences University Of Anbar Al-Anbar Iraq 2Applied Science Department University of Technology Baghdad Iraq 3Computer and Microelectronics System Faculity of Engineering University Technology Malaysia (UTM) Skudai 81310 Johor Bahru Malaysia

The approximate solutions of optimal control problems are investigated in this article with Duffing oscillator model. Duffing oscillator models have lots of interesting engineering application. The orthogonal Boubaker-Turki polynomials first are presented with some new interesting properties. Then, an algorithm based on iterative technique is considered with orthogonal Boubaker-Turki polynomials as a basis functions. By utilizing OBTPs, the proposed technique produces a simple procedure to get an optimal approximate solution that can use to problems that are more complex. Convergence of the algorithm is discussed and numerical examples are solved. The results obtained illustrate and emphasize the efficiency of the suggested algorithm when comparing the results with results obtained in previous works.

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. all rights reserved.

关键词： Image segmentation

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10 quick tips for making your software outlive your job

arXiv

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arXiv 2025年

作者： Littauer, Richard Wilson, Greg Ainali, Jan alOmar, Eman Abdullah Arabas, Sylwester Saibene, Yanina Bellini Bubendorfer, Kris Champion, Kaylea Dillon, Clare Helske, Jouni Huybrechts, Pieter Katz, Daniel S. Liao, Chang Lippert, David Liu, Fang Marshall, Pierre McCloy, Daniel R. McInerney, Ian Mkaouer, Mohamed Wiem Ojha, Priyanka Treude, Christoph White, Ethan P. CURIOSS SustainOSS GNOME Foundation Te Herenga Waka Victoria University of Wellington Te Whanganui-a-Tara Aotearoa Wellington New Zealand Third Bit TorontoON Canada Open By Default Italy Department of Systems and Enterprises Stevens Institute of Technology HobokenNJ United States AGH University of Krakow Poland rOpenSci LP Santa Rosa Argentina Te Herenga Waka Victoria University of Wellington Wellington New Zealand Computing and Software Systems University of Washington Bothell BothellWA United States CURIOSS Lero University of Galway Ireland INVEST Research Flagship Centre University of Turku Finland Brussels Belgium University of Illinois Urbana-ChampaignIL United States Pacific Northwest National Laboratory RichlandWA United States Open Source Program Office George Washington University WashingtonDC United States Georgia Institute of Technology GA United States Oxford University United Kingdom Institute for Learning & Brain Sciences University of Washington SeattleWA United States Department of Mechanical Engineering Imperial College London London United Kingdom Department of Computer Science University of Michigan-Flint FlintMI United States Singapore Management University Singapore Department of Wildlife Ecology and Conservation University of Florida GainesvilleFL United States

Loss of key personnel has always been a risk for research software projects. Key members of the team may have to step away due to illness or burnout, to care for a family member, from a loss of financial support, or because their career is going in a new direction. Today, though, political and financial changes are putting large numbers of researchers out of work simultaneously, potentially leaving large amounts of research software abandoned. This article presents ten tips to help researchers ensure that the software they have built will continue to be usable after they have left their present job—whether in the course of voluntary career moves or researcher mobility, but particularly in cases of involuntary departure due to political or institutional changes. © 2025, CC BY-SA.

关键词： Human engineering

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Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

arXiv

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arXiv 2021年

作者： Qiu, Jianing Lo, Frank P.-W. Gu, Xiao Jobarteh, Modou L. Jia, Wenyan Baranowski, Tom Steiner-Asiedu, Matilda Anderson, alex K. McCrory, Megan A. Sazonov, Edward Sun, Mingui Frost, Gary Lo, Benny Department of Computing Hamlyn Centre Imperial College London LondonSW7 2AZ United Kingdom Department of Surgery and Cancer Hamlyn Centre Imperial College London LondonSW7 2AZ United Kingdom Section for Nutrition Research Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Neurological Surgery University of Pittsburgh PA United States USDA/ARS Children’s Nutrition Research Center Department of Pediatrics Baylor College Of Medicine HoustonTX United States Department of Nutrition and Food Science University of Ghana Legon-Accra Ghana Department of Foods and Nutrition University of Georgia AthensGA United States Department of Health Sciences Boston University BostonMA United States Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL United States

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviours of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this paper, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real life settings. © 2021, CC BY-NC-ND.

关键词： Cameras

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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

lMFF: Efficient and Scalable layered Materials Force Field o...

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Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

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Security Analysis of Wireless Train Control Systems

Security Analysis of Wireless Train Control Systems

引用

IEEE Globecom Workshops

作者： Dongfeng Fang Shengjie Xu Hamid Sharif Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

In order to enhance the performance of safety, capacity and timely schedules in railroad operations, information communication technologies (ICT) are introduced into railroad systems. Different entities are connected with information exchange, such as base stations, wayside devices, and locomotive systems. Based on ICT, there are many critical messages are transmitted on the air to improve the railroad operations. In this paper, we present security analysis of a general wireless train control systems (WTC). We first present a general WTC system model. Based on the general WTC system model, we study a trust model, attack models, and security objectives. A case study for identity-based security schemes is presented over the general WTC system model for securing the communication between the back office and the locomotive system. An identity (ID) - based signcryption scheme and an ID-based signature scheme are proposed to achieve confidentiality, integrity, authentication and end-to-end security. Security analysis and performance analysis of the proposed schemes are studied to show that our proposed scheme can achieve required security services with low computational and communication overhead.

关键词： Base stations Wireless communication Authentication Communication system security Control systems Jamming

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Efficient Network Anomaly Detection for Edge Gateway Defense in 5G

Efficient Network Anomaly Detection for Edge Gateway Defense...

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IEEE Globecom Workshops

作者： Shengjie Xu Dongfeng Fang Hamid Sharif The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo CA Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Various cyber intrusions are constantly invading the edge access networks, disturbing the assured services, and further destroying critical cyberinfrastructure. Efficient and effective edge gateway defense against malicious threats for a multitude of connected physical end devices will remain as a significant problem in the next-generations wireless communications. Edge gateway equipped with network anomaly detection will serve as a first line to protect the online systems, while the robust anomaly detection under label scarcity scenarios is still challenging. In this paper, we propose an efficient network anomaly detection scheme for edge gateway defense in 5G. We first explore the system model of 5G edge access networks. We discuss the motivations of deploying an edge network intelligence for defending against cyber intrusions, and then propose an efficient network anomaly detection scheme. The proposed scheme performs well under label scarcity scenarios, and it supports the requirement with computational efficient real-time processing. Finally, we conduct a case study, and we demonstrate that a proposed scheme achieves solid performance under several evaluation metrics, and it retains efficiency in terms of computational and communication overhead.

关键词： Image edge detection Anomaly detection Logic gates Loading 5G mobile communication Base stations Computational modeling

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A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

TechRxiv

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TechRxiv 2021年

作者： Baghdadi, Asma Fourati, Rahma Aribi, Yassine Daoud, Sawsen Dammak, Mariem Mhiri, Chokri Siarry, Patrick alimi, Adel Chabchoub, Habib Esprit School of Business Computer Science and Applied Mathematics Department Tunis Tunisia University of Sfax BP 1173 Sfax3038 Tunisia Laboratoire Images Signaux et Systèmes Intelligents Université Paris Est Créteil 120 rue Paul Armangot Vitry-sur-Seine94400 France University College of Ranyah Taif University Sciences and Technology department Taif Saudi Arabia Department of neurology Habib Bourguiba University Hospital Sfax Tunisia Neuroscience Laboratory Sfax University SfaxLR-12-SP-19 Tunisia College of Business Al Ain University United Arab Emirates LiSSi Université de Paris 12 Créteil94010 France Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a crucial patient-dependent step for the treatment selection process. The selection of the proper treatment relies on the correct identification of the seizure type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model. © 2021, CC0.

关键词： Patient treatment

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