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information Technology (ACIT)

作者： Mahmoud Obaid Allam Abumwais Rami Hodrob Suhail Odeh Murad Obaid Computer Systems Engineering Arab American University Jenin Palestine Software Engineering Bethlehem University Bethlehem Palestine Management Information Systems Cyprus International University Northern Cyprus Turkey

Blockchain technology has the potential to disrupt the banking and financial sector, even if existing institutions are unable to benefit from it. Most banks are looking to use blockchain technology for smart contracts, payments, and trading platforms in order to reduce fraud, secure and expedite transactions, cut costs, increase data quality, and enable Know Your Customer (KYC). In recent years, blockchain has gained a lot of attention as one of the most popular ways to secure data transfer through decentralized peer-to-peer systems. Blockchain is an immutable ledger that enables secure, decentralized transaction execution. This complex yet secure system has a stellar reputation and is attracting a growing number of clients. This paper proposes a new conceptual framework for using mobile payment blockchain technology to address the needs of customers (both consumers and business owners) for faster, safer, more affordable, real-time, and secure payments that also eliminate the need for intermediary parties to approve and reconcile transactions. The framework reduces operational risk by ensuring the transparency and immutability of all transactions.

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Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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Optoelectronics Letters 2021年第5期17卷 271-275页

作者： WU Xu-jie SONG Bin-bin WU Ji-xuan HUANG Wei The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information EngineeringTiangong UniversityTianjin 300387China

A refractive index(RI)sensor based on elliptical core photonic crystal fiber(EC-PCF)has been *** asymmetric elliptical core introduces the polarization-dependent characteristics of the fiber core *** performances of intermodal interference between the intrinsic polarization fiber core modes are investigated by contrast in two interferometers based on the Mach-Zehnder(M-Z)and Sagnac interference *** addition,the RI sensing characteristics of the two interferometers are studied by successively filling the three layers air holes closest to the elliptical core in the *** results show that the M-Z interference between LP_(01)and LP_(11)mode in the same polarized direction is featured with the incremental RI sensing sensitivity as the decreasing interference length,and the infilled scope around the elliptical core has a weak correlation with the RI sensing *** to the high birefringence of LP11 mode,the Sagnac interferometer has better RI sensing performance,the maximum RI sensing sensitivity of 12000 nm/RIU is achieved under the innermost one layer air holes infilled with RI matching liquid of RI=1.39 at the pre-setting EC-PCF length of 12 cm,which is two orders of magnitude higher than the M-Z interferometer with the same fiber *** series of theoretical optimized analysis would provide guidance for the applications in the field of biochemical sensing.

关键词： Sagnac PCF Intermodal interference based refractive index sensor employing elliptical core photonic crystal fiber

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UW-Adapter: Adapting Monocular Depth Estimation Model in Underwater Scenes

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IEEE Transactions on Multimedia 2025年

作者： Ye, Xinchen Chang, Yue Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Liaoning China Shandong University of Science and Technology College of Computer Science and Engineering China

Estimating depth maps from monocular underwater images poses one of the most challenging problems in underwater applications. Due to the lack of large-scale paired underwater color-depth datasets for effective training, existing style transfer-based and self-supervision-based approaches can improve the performance of depth estimation to some extent, but they remain unsatisfactory. Leveraging the power of massive training datasets, foundation models designed for terrestrial monocular depth estimation have demonstrated superior performance across various scenes. These models provide rich prior knowledge of 3D perception, which can be valuable for underwater depth estimation. Upon this, we introduce tunable adapters (UW-Adapter) that tailor a pre-trained foundation model specifically for underwater depth estimation, customizing it to the unique characteristics of underwater imagery. Our approach involves freezing the parameters of the pre-trained model and updating only the adapters through self-supervision. To address the complex degradation of underwater images, we propose two adapters: the transmission adapter and the high-frequency adapter. These adapters incorporate depth clues and high-frequency information as prior knowledge, thereby enhancing the performance of pre-trained model in underwater depth estimation. Experimental results demonstrate that by integrating lightweight adapters into off-the-shelf depth estimation foundation models, our method achieves superior performance across multiple datasets. © 2025 IEEE.

关键词： Underwater photography

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on software engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： computer languages Codes Supervised learning Natural languages Semantics Task analysis software engineering

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Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 288-298页

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kandiy, Sergey Smirnov, Oleksii Ulianovska, Yuliia Kobylianska, Olena Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security Faculty of Comupter Science V. N. Karazin Kharkiv National University 4 Svobody Sq Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Cybersecurity & Software Academic Department Central Ukrainian National Technical University 8 University Avenue Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Vernadskogo Street 2/4 Dnipro49000 Ukraine

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research. © The Author(s).

关键词： Cost functions

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Efficient and Secure Federated Knowledge Transfer Under Non-IID Settings in IoT

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IEEE Internet of Things Journal 2025年第13期12卷 24644-24655页

作者： Liu, Shuying Xie, Rongpeng Miao, Yinbin Peng, Jinye Leng, Tao Liu, Zhiquan Choo, Kim-Kwang Raymond Northwest University School of Information Science and Technology Xi’an710119 China Xian Yang Normal University Department of Computer Xian Yang712000 China Xidian University School of Cyber Engineering Xi’an710071 China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou646000 China Jinan University College of Cyber Security Guangzhou510632 China Technology Ltd Beijing100020 China The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

In the era of Internet of Things (IoT) and Federated Learning (FL), where distributed training models are essential, the FL paradigm has come into the spotlight for researchers. However, the inconsistency in the sources of client data and Non-Independent and Identically Distributed (Non-IID) heterogeneous characteristics lead to loss in model accuracy. Existing method which attempts to homogenize data distribution among clients based on Generative Adversarial Networks (GANs) incurs high computation overheads on clients in IoT. In this paper, we propose a lightweight Feature Prototype Knowledge Transfer (FPKT) mechanism. By capturing the essence of data categories, FPKT generates pseudo-features without requiring the original data features, thereby efficiently enhancing model accuracy. We formally prove that FPKT resists Chosen Plaintext Attack (CPA) and experiments demonstrate that our scheme achieves a hundredfold increase in computational efficiency and improves model accuracy by up to 40%. © 2014 IEEE.

关键词： Federated learning

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Towards a Smart Home : An Intelligent Approach to Environment Monitoring and Anti-Theft Alarming

Towards a Smart Home : An Intelligent Approach to Environmen...

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International Conference on Awareness Science and Technology (iCAST)

作者： Ravitha Wathsala Malith Silva Ravindu Kodikara Sadev Ekanayake Narmada Gamage Poojani Gunathilake Department of Computer Systems Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Recently Smart Home concept has been a popular choice as a solution for emerging security related problems. The primary objective of this research was to create a cyber-threat free fully functioning smart home monitoring and anti-theft alarming system with enhanced physical security mechanisms. The focus of this research was to create a holistic and secure smart home system, combining cutting-edge physical security measures. The study introduced novel Intruder Access Prevention methods rooted in human behavior and voice pattern recognition, while also incorporating blockchain and network traffic analysis to safeguard the homeowner's data. Furthermore, a pioneering voice-controlled monitoring mechanism, utilizing protective energy-saving plug technology, was devised to enhance safety within contemporary households. The human behavior recognition and voice recognition-based intruder access prevention system demonstrated over 80% accuracy in intruder prevention, while user data protection mechanism prevents the communication channel from cyber hackings. Further, the smart plug demonstrates reliable and accurate physical environment monitoring with minimum latency. These results underscore the system's significant contribution to home security, marking a noteworthy advancement in the Smart Home concept.

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Learning good state and action representations for Markov decision process via tensor decomposition

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 5157-5209页

作者： Chengzhuo Ni Yaqi Duan Munther Dahleh Mengdi Wang Anru R. Zhang Department of Electrical & Computer Engineering Princeton University Laboratory for Information & Decision Systems Massachusetts Institute of Technology Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Department of Biostatistics & Bioinformatics Duke University

The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning method to identify meaningful low-dimensional state and action representations from empirical trajectories. The method exploits the MDP's tensor structure by kernelization, importance sampling and low-Tucker-rank approximation. This method can be further used to cluster states and actions respectively and find the best discrete MDP abstraction. We provide sharp statistical error bounds for tensor concentration and the preservation of diffusion distance after embedding. We further prove that the learned state/action abstractions provide accurate approximations to latent block structures if they exist, enabling function approximation in downstream tasks such as policy evaluation.

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COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals

arXiv

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arXiv 2023年

作者： Shati, Asmaa Hassan, Ghulam Mubashar Datta, Amitava Department of Computer Science and Software Engineering University of Western Australia Australia Department of Information Systems King Khalid University Saudi Arabia

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Deep Learning-Based Audio Splicing Forgery Detection Using PVTv2 Transformer

Deep Learning-Based Audio Splicing Forgery Detection Using P...

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Innovations in Intelligent systems and Applications Conference

作者： Elif Kanca Tolgahan Gulsoy Arda Ustubioglu Beste Ustubioglu Selen Ayas Elif Baykal Kablan Guzin Ulutas Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Management Information Systems Trabzon University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Attacks on multimedia files by malicious users have become quite common, especially with the increase in the number of editing tools and their ease of use. Considering that such files can now be used both as evidence and for social visibility in all kinds of environments, it has become important to prove their authenticity. With the proposed method, the detection of merging forgeries in audio files has been carried out. For this purpose, the audio files received from the input are converted into cochleagram images. The PVTv2 based deep network architecture is trained with the generated cochleagram images. As a result of the training, the suspicious audio file given as input is labelled as original/fake. The proposed method gives 96.11% accuracy for 2s database, 94.63% accuracy for 3s database and 95.19% accuracy for 2s-3s database.

关键词： Training Technological innovation Accuracy Databases Splicing Network architecture Transformers Forgery Security Reliability

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