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Decision Aid sciences and Application (DASA), International Conference on

作者： Aghiad Khedr Suleiman Darin Shafek Mahmoud Mahfuri Mohammad Khishe Ali Saeed Sameh Ghwanmeh Cybersecurity and Cloud Computing Technologies Engineering Dept. Alma'moon University College Baghdad iraq Computer Engineering Techniques Dept. Alma'moon University College Baghdad iraq Computer and information Technology Department International University for Science and Technology Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq Computer Science Dept College of Computer Information Technology American University in the Emirates

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: “CASIA Iris 1.0”. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system.

关键词： Deep learning Analytical models Adaptation models Accuracy Transfer learning Feature extraction Cameras Mathematical models Pupils Iris recognition

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How to Detect AI-Generated Texts?

How to Detect AI-Generated Texts?

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trung T. Nguyen Amartya Hatua Andrew H. Sung Department of Computer Science Winona State University Winona MN USA AI Center of Excellence Fidelity Investments Boston MA USA School of Computing Sciences and Computer Engineering The University of Southern Mississippi Hattiesburg MS USA

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research.

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Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning

arXiv

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arXiv 2024年

作者： Chang, Tyler Gillette, Andrew Maulik, Romit Mathematics and Computer Science Division Argonne National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Information Sciences and Technology Department The Pennsylvania State University United States

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently;(2) comparative performance among distinct interpolants can aid in validation goals;(3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository. © 2024, CC BY-NC-SA.

关键词： Interpolation

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GPS-based geolocation of consumer IP addresses

arXiv

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arXiv 2021年

作者： Saxon, James Feamster, Nick Department of Computer Science Center for Data and Computing University of Chicago

This paper uses two commercial datasets of IP addresses from smartphones, geolocated through the Global Positioning System (GPS), to characterize the geography of IP address subnets from mobile and broadband ISPs. Datasets that ge olocate IP addresses based on GPS offer superlative accuracy and precision for IP geolocation and thus provide an unprecedented opportunity to understand both the accuracy of existing geolocation databases as well as other properties of IP addresses, such as mobility and churn. We focus our analysis on large cities in the United States. After evaluating the accuracy of existing geolocation databases, we analyze the circumstances under which IP geolocation databases may be more or less accurate. We find that geolocation databases are more accurate on fixed-line than mobile networks, that IP addresses on university networks can be more accurately located than those from consumer or business networks, and that often the paid versions of these databases are not significantly more accurate than the free versions. We then characterize how quickly subnets associated with fixed-line networks change geographic locations, and how long residential broadband ISP subscribers retain individual IP addresses. We find, generally, that most IP address assignments are stable over two months, although stability does vary across ISPs. Finally, we evaluate the suitability of existing IP geolocation databases for understanding Internet access and performance in human populations within specific geographies and demographics. Although the median accuracy of IP geolocation is better than 3 km in some contexts, we conclude that relying on IP geolocation databases to understand Internet access in densely populated regions such as cities is premature. © 2021, CC BY-NC-ND.

关键词： Global positioning system

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COMPOSITING WITH 2D VECTOR FIELDS BY USING SHAPE MAPS THAT CAN REPRESENT INCONSISTENT, IMPOSSIBLE, AND INCOHERENT SHAPES

arXiv

引用

arXiv 2024年

作者： Akleman, Ergun Wang, Youyou Gonen, Ozgur Visual Computing & Computational Media Texas A&M University College StationTX77831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77831 United States Department of Architecture Texas A&M University College StationTX77831 United States

In this paper, we present a new compositing approach to obtain stylized reflections and refractions with a simple control. Our approach does not require any mask or separate 3D rendering. Moreover, only one additional image is sufficient to obtain a composited image with convincing qualitative reflection and refraction effects. We have also developed linearized methods that are easy to compute. Although these methods do not directly correspond to the underlying physical phenomena of reflection and refraction, they can provide results that are visually similar to realistic 3D rendering. The main advantage of this approach is the ability to treat images as "mock-3D" shapes that can be inserted into any digital paint system without any significant structural change. The core of our approach is the shape map, which encodes 2D shape and thickness information for all visible points of an image of a shape. This information does not have to be complete or consistent to obtain interesting composites. In particular, the shape maps allow us to represent impossible and incoherent shapes with 2D non-conservative vector fields. © 2024, CC BY.

关键词： Rendering (computer graphics)

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Recovery of Distributed Iterative Solvers for Linear Systems Using Non-Volatile RAM

arXiv

引用

arXiv 2022年

作者： Fridman, Yehonatan Snir, Yaniv Levin, Harel Hendler, Danny Attiya, Hagit Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Atomic Energy Commission Google Mobileye Vision Technologies Scientific Computing Center Nuclear Research Center - Negev Department of Computer Science Technion - Israel Institute of Technology

HPC systems are a critical resource for scientific research and advanced industries. The increased demand for computational power and memory ushers in the exascale era, in which supercomputers are designed to provide enormous computing power to meet these needs. These complex supercomputers consist of numerous compute nodes and are consequently expected to experience frequent faults and crashes. Mathematical solvers, in particular, iterative linear solvers are key building block in numerous large-scale scientific applications. Consequently, supporting the recovery of distributed solvers is necessary for scaling scientific applications to exascale platforms. Previous recovery methods for iterative solvers are based on Checkpoint-Restart (CR), which incurs high fault tolerance overhead, or intrinsic fault tolerance, which require extra computation time to converge after failures. Exact state reconstruction (ESR) was proposed as an alternative mechanism to alleviate the impact of frequent failures on long-term computations. ESR has been shown to provide exact reconstruction of the computation state while avoiding the need for costly checkpointing. However, ESR currently relies on volatile memory for fault tolerance, and must therefore maintain redundancies in the RAM of multiple nodes. This not only incurs high memory overhead but also prevents ESR from being fully resilient, that is, resilient against a full system crash. Recent supercomputer designs feature emerging non-volatile RAM (NVRAM) technology, for example, the exascale Aurora that is planned to consist of Intel Optane™ DCPMM. This paper investigates how NVRAM can be utilized to devise an enhanced ESR-based recovery mechanism that is more efficient and provides full resilience. Our mechanism, called in-NVRAM ESR, provides full resiliency while significantly reducing both the memory footprint and the time overhead in comparison with the original ESR design (in-RAM ESR). In-NVRAM ESR is based on a novel MPI One

关键词： Random access storage

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Recovery of Distributed Iterative Solvers for Linear Systems Using Non-Volatile RAM

Recovery of Distributed Iterative Solvers for Linear Systems...

引用

Fault Tolerance for HPC at eXtreme Scale (FTXS)

作者： Yehonatan Fridman Yaniv Snir Harel Levin Danny Hendler Hagit Attiya Gal Oren Department of Computer Science Ben-Gurion University of the Negev Israel Atomic Energy Commission Google Mobileye Vision Technologies Scientific Computing Center Nuclear Research Center – Negev Department of Computer Science Technion – Israel Institute of Technology

ISBN: (纸本)9781665488488

HPC systems are a critical resource for scientific research and advanced industries. The increased demand for computational power and memory ushers in the exascale era, in which supercomputers are designed to provide enormous computing power to meet these needs. These complex supercomputers consist of numerous compute nodes and are consequently expected to experience frequent faults and *** solvers, in particular, iterative linear solvers are key building block in numerous large-scale scientific applications. Consequently, supporting the recovery of distributed solvers is necessary for scaling scientific applications to exascale platforms. Previous recovery methods for iterative solvers are based on Checkpoint-Restart (CR), which incurs high fault tolerance overhead, or intrinsic fault tolerance, which require extra computation time to converge after *** state reconstruction (ESR) was proposed as an alternative mechanism to alleviate the impact of frequent failures on long-term computations. ESR has been shown to provide exact reconstruction of the computation state while avoiding the need for costly checkpointing. However, ESR currently relies on volatile memory for fault tolerance, and must therefore maintain redundancies in the RAM of multiple nodes. This not only incurs high memory overhead but also prevents ESR from being fully resilient, that is, resilient against a full system *** supercomputer designs feature emerging non-volatile RAM (NVRAM) technology, for example, the exascale Aurora that is planned to consist of Intel Optane™ DCPMM. This paper investigates how NVRAM can be utilized to devise an enhanced ESR-based recovery mechanism that is more efficient and provides full resilience. Our mechanism, called in-NVRAM ESR, provides full resiliency while significantly reducing both the memory footprint and the time overhead in comparison with the original ESR design (in-RAM ESR). In-NVRAM ESR is based on a novel MPI One-Si

关键词： Nonvolatile memory Source coding Fault tolerant systems Redundancy Random access memory Supercomputers computer crashes

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

引用

arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation 39

Semi-Supervised Clustering Framework for Fine-grained Scene ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Jiarui Wang, Chuan Zhang, Jun Wu, Shuyi Jinjing, Zhao Liu, Zeming Yang, Liang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Institute of Information Engineering CAS China School of Computer Science and Technology Beijing JiaoTong University China Guangdong Provincial Key Lab of Intell. Info. Processing & Shenzhen Key Lab of Media Security Shenzhen University China Information Research Center of Military Science PLA Academy of Military Science China National Key Laboratory of Science and Technology on Information System Security China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Hebei University of Technology China

ISBN: (纸本)157735897X

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model’s robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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