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DeCrypt: a 3DES inspired optimised cryptographic algorithm

Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4745-4755页

作者： Chowdhury, Deepraj Dey, Ajoy Garai, Ritam Adhikary, Subhrangshu Dwivedi, Ashutosh Dhar Ghosh, Uttam Alnumay, Waleed S. Department of Electronics and Communication Engineering IIIT Naya Raipur India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Dr. B.C. Roy Engineering College Durgapur India Centre for Business Data Analytics Department of Digitalization Copenhagen Business School Frederiksberg2000 Denmark Meharry Medical College 1005 Dr DB Todd Jr Blvd NashvilleTN37208 United States Riyadh Community College Computer Science Department King Saud University Riyadh Saudi Arabia

Triple data Encryption Standard (also known as 3DES) is a symmetric encryption algorithm. European Traffic Management System popularly uses 3DES for authentication and encryption. However, as per a draft published by NIST in 2018, 3DES is officially being retired and not suggested to use for new applications. Several attacks were imposed on 3DES, and the biggest threat to 3DES is meet in the middle attack. Therefore for long term security, it is essential to enhance the security of such algorithms. This paper proposed a new cipher DeCrypt inspired by 3DES, an improved version of the 3DES algorithm, and secured against meet in the middle attack. As per the experiment performed, DeCrypt cipher is 61 per cent faster than 3DES, providing long-term and better security against sweet-32 attacks than other symmetric algorithms. The proposed algorithm is also faster than 3DES due to reduced encryption and decryption time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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An evaluation and query algorithm for the influence of spatial location based on RkNN

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Frontiers of computer science 2021年第2期15卷 81-89页

作者： Jingke XU Yidan ZHAO Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang110819China School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang110168China

This paper is devoted to the investigation of the evaluation and query algorithm problem for the influence of spatial location based on RkNN(reverse k nearest neighbor).On the one hand,an object can make contribution to multiple ***,for the existing measures for evaluating the influence of spatial location,an object only makes contribution to one location,and its influence is usually measured by the number of spatial objects in the *** this case,a new measure for evaluating the influence of spatial location based on the RkNN is *** the weight of the contribution is determined by the distance between the object and the location,the influence weight definition is given,which meets the actual *** the other hand,a query algorithm for the influence of spatial location is introduced based on the proposed ***,an algorithm named INCH(INtersection’s Convex Hull)is applied to get candidate regions,where all objects are ***,kNN and Range-k are used to refine ***,according to the proposed measure,the weights of objects in RkNN results are computed,and the influence of the location is *** experimental results on the real data show that the optimized algorithms outperform the basic algorithm on *** addition,in order to provide the best customer service in the location problem and make the best use of all infrastructures,a location algorithm with the query is presented based on *** influence of each facility is calculated in the location program and the equilibrium coefficient is used to evaluate the reasonability of the location in the *** smaller the equilibrium coefficient is,the more reasonability the program *** actual application shows that the location based on influence makes the location algorithm more reasonable and available.

关键词： spatial data reverse k nearest neighbor influence of spatial location location algorithm

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Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation

arXiv

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arXiv 2023年

作者： Sun, Jiamou Xing, Zhenchang Lu, Qinghua Xu, Xiwei Zhu, Liming Hoang, Thong Zhao, Dehai Data61 Eveleigh CSIRO Sydney Australia Research School of Computer Science CECS Australian National University Canberra Australia School of Computer Science University of New South Wales Sydney Australia

Due to convenience, open-source software is widely used. For beneficial reasons, open-source maintainers often fix the vulnerabilities silently, exposing their users unaware of the updates to threats. Previous works all focus on black-box binary detection of the silent dependency alerts that suffer from high false-positive rates. Open-source software users need to analyze and explain AI prediction themselves. Explainable AI becomes remarkable as a complementary of black-box AI models, providing details in various forms to explain AI decisions. Noticing there is still no technique that can discover silent dependency alert on time, in this work, we propose a framework using an encoder-decoder model with a binary detector to provide explainable silent dependency alert prediction. Our model generates 4 types of vulnerability key aspects including vulnerability type, root cause, attack vector, and impact to enhance the trustworthiness and users’ acceptance to alert prediction. By experiments with several models and inputs, we confirm CodeBERT with both commit messages and code changes achieves the best results. Our user study shows that explainable alert predictions can help users find silent dependency alert more easily than black-box predictions. To the best of our knowledge, this is the first research work on the application of Explainable AI in silent dependency alert prediction, which opens the door of the related domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Multi-Scale Noise-Guided Progressive Network for Image Splicing Detection and Localization

SSRN

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SSRN 2024年

作者： Zhang, Dengyong Jiang, Ningjing Li, Feng Chen, Jiaxin Liao, Xin Yang, Gaobo Ding, Xiangling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer and Communication Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

Image splicing can easily be used in illegal activities, which may have negative impact on society. Therefore, detecting splicing images and precisely localizing tampered regions are challenging tasks. In this work, we propose a dual-branch Multi-Scale Noise-Guided Progressive Network (MSNP-Net). The multi-resolution branch extracts deep semantic features of images while suppressing redundant noise. A multi-scale noise-guided branch is designed to capture more subtle tampering artifacts and guide the network to strengthen the learning of spatial structure features. The two branches complement and restrict each other. The features of different scales are fused in a gradual mechanism, and the feature expression is aggregated through the spatial channel feature aggregation module (SCAM). A large number of experiments show that the MSNP-Net proposed in this paper has better completeness and lower false alarm rate for the detection and localization of spliced images. It can achieve more refined detection results, and has good stability and robustness. Significantly outperforms other state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Semantics

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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?

What Makes Good In-Context Demonstrations for Code Intellige...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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Mixed-cell-height Placement with Minimum-Implant-Area and Drain-to-Drain Abutment Constraints

Mixed-cell-height Placement with Minimum-Implant-Area and Dr...

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Design Automation Conference

作者： Guohao Chen Zheng Zeng Benchao Zhu Jiawei Li Kun Wang Jun Yu Jianli Chen School of Microelectronics Fudan University Shanghai China College of Computer and Data Science Fuzhou University Fuzhou China

In modern circuits, mixed-cell-height standard cells have been prevailing to meet various requirements and achieve better trade-offs among timing, power, and routability. Besides, the constraints of the minimum-implant-area (MIA) and drain-to-drain-abutment (DDA) arise as emerging challenges at advanced technology nodes. In this paper, we present an algorithm to address the mixed-cell-height placement problem with MIA and DDA constraints in three major stages: (1) post-global placement, (2) legalization, and (3) detailed placement. In the post-global stage, we first align mixed-cell-height standard cells to the desired rows by conjugate gradient method with dynamic step size and then reorder them by the shortest path algorithm to distribute the source nodes evenly. In the legalization stage, we propose a two-step combination algorithm to cluster cells and repack the clusters to minimize the wirelength, after which we presented a queue based method to address the inter-row MIA violations. In the detailed placement, an MIA-aware DDA reduction algorithm is adopted after multi-region partitioning to eliminate DDA violations without introducing MIA violations. Experimental results show that our algorithm can resolve all MIA and almost all DDA violations with a 13% reduction in displacement, a 4% reduction in HPWL, and 25% less runtime compared with the state-of-the-art work.

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks 4

A Novel Blockchain-Based Strategy for Energy Conservation in...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Sharma, Khemraj Goyal, Rohit Bhagat, Sukesh Kumar Agarwal, Sheenu Bisht, Girish Singh Hussien, Mohemmed Nanhua University Department of Business Administration Doctoral Program in Management Sciences Dalin62248 Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Uttaranchal University Department of Computer Science & Engineering Dehradun India Uttarakhand Technical University Department of Electronics and Communication Engineering Dehradun India Tula's Institute Dehradun India Department of Computer Science and Engineering Dehradun248001 India The Islamic University Najaf Iraq

ISBN: (纸本)9798331528324

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency. © 2024 IEEE.

关键词： Blockchain Network Network Communication security Wireless Sensor

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Asynchronous Parallel Stochastic Proximal Gradient Methods for Non-Convex Composite Optimization

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 638-662页

作者： He, Wen-Wu Gong, Xuan-Jin Cui, Zhuo-Xu School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China School of Mathematics and Statistics Wuhan University Wuhan430072 China

This paper considers the parallel stochastic composite optimization for general asynchronous cases where both the objective function and the regularizer are non-convex. In particular, based on the randomized stochastic proximal gradient protocol, an asynchronous parallel algorithm termed ARSPG is proposed which incorporates well the non-convex regularization to obtain the desired oracle property. Theoretical analysis shows that ARSPG has the convergence rate in the order of O(1/ϵ2). Further, a gradient-free version termed ARSPGF is developed to handle cases where only noisy objective function values are available. The analysis shows that ARSPGF has the same order of convergence rate as that of the normal version. Experiments over benchmark data sets validate well theoretical analyses and experimental results also show interesting practical aspects of the proposed algorithms. © 2025 ISSN 2414-8105 (Online).

关键词： Convex optimization

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Design of Airborne Intelligent Computing System Oriented to Intelligent Task Processing 26

Design of Airborne Intelligent Computing System Oriented to ...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Bai, Linting Wen, Pengcheng Xie, Jianchun Li, Yunxi Lin, Rongchao School of Computer Science Northwestern Polytechnical University Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missile-borne Computer Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aeronautics Data and Sensor Business Unit Avic Xi'an China

ISBN: (纸本)9798350345865

The rapid development of artificial intelligence technology promotes the gradual evolution of the 'three integration' of mechanization, informatization and intelligence. The mass data characteristics of airborne mission informatization put forward higher demands on the storage, transmission and management capabilities of airborne data. The characteristics of high complexity, strong confrontation, strong real-time, and uncertainty of airborne intelligent tasks pose more challenges to the intelligent processing of airborne data. This paper analyzes the data characteristics and mission characteristics in the process of airborne intelligence missions, and proposes an airborne intelligent computing architecture for airborne data processing of intelligent missions. The composition and function of each technical layer are expounded, and several problems of airborne intelligent computing technology are analyzed. Finally, the development trend of airborne intelligent technology development is prospected. © 2023 IEEE.

关键词： Digital storage

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