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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1869-1881页

作者： Guo, Wenzhe Yang, Kuilian Stratigopoulos, Haralampos-G. Aboushady, Hassan Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia Sorbonne Université Cnrs LIP6 Paris75005 France

Rapid advancements in 5G communication and the Internet-of-things have prompted the development of cognitive radio sensing for spectrum monitoring and malicious attack detection. An end-to-end radio classification system is essential to realize efficient real-time monitoring at the edge. This work presents an end-to-end neuromorphic system enabled by an efficient spiking neural network (SNN) for radio classification. A novel hardware-efficient spiking encoding method is proposed leveraging the Sigma-Delta modulation mechanism in analog-to-digital converters. It requires no additional hardware components, simplifies the system design, and helps reduce conversion latency. Following a designed hardware-emulating conversion process, the classification performance is verified on two benchmark radio modulation datasets. A comparable accuracy to an artificial neural network baseline with a difference of 0.30% is achieved on the dataset RADIOML 2018 with more realistic conditions. Further analysis reveals that the proposed method requires less power-intensive computational operations, leading to 22× lower computational energy consumption. In addition, this method exhibits more than 99% accuracy on the dataset when the signal-to-noise ratio is above 0 dB. The SNN-based classification module is realized on field programmable gate array (FPGA) with a heterogeneous streaming architecture, achieving high throughput and low resource utilization. Therefore, this work demonstrates a promising solution for constructing an efficient high-performance end-to-end radio classification system. © 2020 IEEE.

关键词： Wireless sensor networks

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Enhancing Image Security with Rossler Attractor-Based Encryption 2

Enhancing Image Security with Rossler Attractor-Based Encryp...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

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Board 262: Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning

Board 262: Enhancing Deep Knowledge Tracing via Diffusion Mo...

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2024 ASEE Annual Conference and Exposition

作者： Dong, Xishuang Fu, Yujian Kuo, Ming-Mu Sarker, Shouvon Qian, Lijun Li, Xiangfang Electrical and Computer Engineering Department Roy G. Perry College of Engineering Prairie View A&M University United States Electrical Engineering and Computer Science Department Alabama A&M University United States

In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be made to meet individual needs. Recent advancements in deep learning have successfully enhanced knowledge tracking through Deep Knowledge Tracing (DKT). This paper introduces generative AI models to further enhance DKT. Generative AI models, rooted in deep learning, are trained to generate synthetic data, addressing data scarcity challenges in various applications across fields such as natural language processing (NLP) and computer vision (CV). This study aims to tackle data shortage issues in student learning records to enhance DKT performance for PAL. Specifically, it employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The proposed method's effectiveness is validated through extensive experiments on ASSISTments datasets. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance, particularly in scenarios with small data for training and large data for testing. © American Society for engineering Education, 2024.

关键词： Contrastive Learning

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On the long-term average rates of deterministic communication sessions’ flows

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4365-4373页

作者： Eyinagho, Monday Ofori Falaki, Samuel Oluwole Department of Electrical and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Computer Science Federal University of Technology Akure Nigeria

The operation of the present Internet in terms of service provisioning is majorly Best-Effort, in which traffic is processed as quickly as possible, without any guarantee as to timeliness or actual delivery. However, the rapid transformation into a commercial infrastructure which the Internet is currently undergoing has brought to the fore, the necessity for service quality levels’ quantification. The guaranteed and controlled-load services are two types of services that are designed to meet this necessity. The operation of these services is based on the Internet engineering Task Force’s (IETF’s) admission control principle that entails setting up a flow which must conform to IETF’s T-SPEC (Traffic Specification). A flow’s sustainable rate in bits/sec., which is the minimum rate required to serve the flow, is one of the four parameters of the T-SPEC. Also, the providers of Transport Ethernet (or Carrier Ethernet) services define a bandwidth profile, which enables the Service Providers bill for bandwidth usage, and also, engineer their networks’ resources in order to provide performance assurances for conformant or in-profile PDUs (Protocol Data Units). One of the parameters of this profile is the CIR (Committed Information Rate) in bits/sec. Therefore, an issue that must be dealt with by both Service Providers and Subscribers in this context is, coming into agreement on a value for this parameter. But, to the best of our knowledge, no generally known method(s) and/or formula(s) for determining this parameter presently exist in the literature. In this paper, the derivation of a formula that can be used to compute this parameter is described. The utility of the formula is demonstrated via an illustrative practical example, and the long-term average rates/sustainable rates/CIRs that were computed by utilizing the formula are quite revealing when viewed in the context of literature available rates. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's I

关键词： Average rate of a communication session Carrier Ethernet Committed information rate Deterministic communication Guaranteed service Traffic specification

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job Information 2

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordianian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics - predicted run time, waiting time, and resource requirement - to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario. © 2024 IEEE.

关键词： Grid computing

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QUBO Models for the FIFO Stack-Up Problem and Experimental Evaluation on a Quantum Annealer

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SN computer science 2024年第7期5卷 818页

作者： Gebler, Colin Rethmann, Jochen Ueberholz, Peer Department of Electrical Engineering and Computer Science Niederrhein University of Applied Sciences Reinarzstr. 49 Krefeld 47805 Germany

Quantum annealing has been applied to combinatorial optimization problems in recent years. In this paper we study the possibility to use quantum annealing for solving the combinatorial FIFO Stack-Up problem, where bins have to be stacked-up from a conveyor belt onto pallets. The problem is NP-hard and can be solved using linear programming approaches. We developed two QUBO (quadratic unconstrained binary optimization) objective functions based on a bin stack-up solution and a pallet stack-up solution for this problem suitable for a quantum annealer. The number of variables was minimized to increase the performance and their dependence on the number of bins and pallets was discussed. The performances of both methods were studied for various small problem sizes on a D-Wave quantum annealer. We found that only tiny instances could be solved and looked at the terms of the QUBO-formulations, which cause the quantum annealer to fail for larger problem sizes. Furthermore we compare the results to the performance of a classic computer using the same QUBO-formulations. © The Author(s) 2024.

关键词： FIFO Stack-Up problem NP-hard Quantum annealer QUBO formulation

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Classification and Source Location Indication of Jamming Attacks Targeting UAVs via Multi-output Multiclass Machine Learning Modeling

Classification and Source Location Indication of Jamming Att...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Alkhatib, M. McCormick, M. Williams, L. Leon, A. Camerano, L. Shamaileh, K. Al Devabhaktuni, V. Kaabouch, N. Purdue University Northwest Electrical and Computer Engineering Department HammondIN46323 United States Illinois State University Electrical and Computer Engineering Department NormalIL61761 United States University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND58202 United States

ISBN: (纸本)9798350324136

This paper introduces machine learning (ML) as a solution for the detection and range localization of jamming attacks targeting the global positioning system (GPS) technology, with applications to unmanned aerial vehicles (UAVs). Different multi-output multiclass ML models are trained with GPS-specific sample datasets obtained from exhaustive feature extraction and data collection routines that followed a set of realistic experimentations of attack scenarios. The resulting models enable the classification of four attack types (i.e., barrage, single-Tone, successive-pulse, protocol-Aware), the jamming direction, and the distance from the jamming source by yielding a detection rate (DR), misdetection rate (MDR), false alarm rate (FAR), and F-score (FS) of 98.9%, 1.39%, 0.28%, and 0.989, respectively. © 2024 IEEE.

关键词： Global positioning system (GPS) jamming classification jamming localization machine learning (ML) unmanned aerial vehicles (UAVs).

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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Characterizing In-Kernel Observability of Latency-Sensitive Request-Level Metrics with eBPF

Characterizing In-Kernel Observability of Latency-Sensitive ...

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2024 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2024

作者： Rezvani, Mohammadreza Jahanshahi, Ali Wong, Daniel University of California Riverside Department of Computer Science and Engineering RiversideCA United States University of California Riverside Department of Electrical and Computer Engineering RiversideCA United States

ISBN: (纸本)9798350376388

This paper explores a novel server observability approach using eBPF (extended Berkeley Packet Filter) for detailed request-level performance metrics of data center latency-sensitive applications. Utilizing eBPF system call tracing, we evaluate if syscall activity can reconstruct high-level application behaviors and bypass the need for direct userspace reporting of performance metrics. Through careful selection of eBPF events, we demonstrate that certain syscall statistics can provide robust insight into request-level metrics. In addition, we demonstrate that these metrics can also be robust to networking effects, such as packet loss. By demonstrating the ability for eBPF to provide request-level observability, we can potentially enable many non-intrusive, low-overhead use cases for feedback in system management runtime frameworks, such as resource allocation, scheduling, and power management. © 2024 IEEE.

关键词： Observability

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