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Reverse-Engineering Optimization Techniques of High-Level Synthesis: Practical Insights into Accelerating Applications with AMD-Xilinx Vitis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第7期44卷 2558-2570页

作者： Koronis, Jorge Garnica, Oscar Hidalgo, J. Ignacio Davila, Juan Lanchares Spain Department of Computer Architecture and Automation Faculty of Computer Science Universidad Complutense de Madrid Spain

Modern AI applications contain computationally expensive sections. Accelerator cards and tools like AMD Vitis HLS leverage high-level synthesis and hardware (HW) optimizations to create custom HW designs to accelerate ***, the learning curve is steep, even for those with previous knowledge of HW design, due to the complexity of the optimization techniques and limited information on their interactions and HW effects. This paper quantitatively analyzes the interactions of optimization techniques after reverse engineering Vitis' optimization directives, both in isolation and in *** 150 experiments were conducted to investigate three distinct goals: assessing pragma behavior and the rules governing pragma application and optimizations, modeling Vitis HLS latency estimates, and evaluating the impact of optimizations on design space exploration, specifically area and latency. These experiments involve different combinations and placements of optimizations in the loop and function hierarchy of the test bench. Our findings offer guidance on using Vitis pragmas and identify promising configurations for optimizing latency and area. © 1982-2012 IEEE.

关键词： Reverse engineering

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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery:A Systematic Review

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Big Data Mining and Analytics 2025年第2期8卷 407-429页

作者： Antonia Ivanda LjiljanaŠerić Maja Braović Department of Electronics and Computer Science Faculty of Electrical EngineeringMechanical Engineering and Naval ArchitectureUniversity of SplitSplit 21000Croatia

Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of modern machine learning techniques,such as Convolutional Neural Networks(CNN)on MultiSpectral Images(MSI).This systematic review examines the application of 1D-,2D-,3D-,and 4D-CNNs to MSI,following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)*** review addresses three Research Questions(RQ):RQ1:“In which application domains different CNN models have been successfully applied for processing MSI data?”,RQ2:“What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”,and RQ3:“How does the degree of CNN complexity impact the performance of classification,regression or segmentation tasks for multispectral satellite imagery?”.Publications are selected from three databases,Web of science,IEEE Xplore,and *** on the obtained results,the main conclusions are:(1)The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data;(2)Publications implementing 1D-,2D-,and 3D-CNNs mostly utilize *** 4D-CNN,there are limited number of studies,and all of them use segmentation;(3)This study shows that 2D-CNNs prevail in all application domains,but 3D-CNNs prove to be better for spatio-temporal pattern recognition,more specifically in agricultural and environmental monitoring applications.1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs,but they show good performance in spectral analysis tasks.4D-CNNs are more complex and still underutilized,but they have potential for complex data *** details about metrics according to each CNN are provided in the text and supplementary files,offering a comprehensive overview of the evaluation metrics for each type of machine learning technique

关键词： remote sensing MultiSpectral Images(MSI) satellite images Convolutional Neural Networks(CNN) machine learning classification regression segmentation

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Application of machine learning techniques to predict patient’s satisfaction of indoor environmental quality in Jordanian hospitals

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13673-13681页

作者： Ali, Hikmat H. Abdullah, Malak Wedyan, Musab Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

The indoor environment is an integral part of the hospital design since it impacts patients’ health, well-being, and healing process. Although the machine learning approach has been widely adopted in many fields, limited studies applied the machine learning approach to indoor environmental quality (IEQ) in hospitals and the impact of the room type on patients’ satisfaction with IEQ. Accordingly, the current study aims to bridge this gap using the machine learning approach. The research used mixed design methods to assess indoor environmental quality. The data was gathered using self-reported data and field monitoring of environmental parameters inside patients’ rooms. King Abdullah University Hospital (KAUH) was used to represent hospitals in Jordan. Machine learning proceeded in several stages, starting from prepossessing, training the algorithms, and testing the results. The experiments were conducted with the same dataset for training and testing and evaluated using the same classification metrics using Python programming language. Besides, the most important features were conducted using Random Forest regardless of room type, in each room type, and each category of IEQ between room types. The present findings confirmed a variation in the essential features. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Hospitals

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Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

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computers, Materials & Continua 2023年第3期74卷 5705-5721页

作者： Zulqar Nain B.Shahana Shehzad Ashraf Chaudhry P.Viswanathan M.S.Mekala Sung Won Kim Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siKorea Department of Computer Science and Engineering KLEFIndia Department of Computer Engineering Faculty of Engineering and ArchitectureIstanbul Gelisim UniversityIstanbul34310Turkey School of Computer Science and Engineering VIT UniversityVelloreIndia

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)*** devices enable limited computational capacity and energy availability that hamper end user *** designed a novel performance measurement index to gauge a device’s resource *** examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided *** this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float ***,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the *** approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation *** system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

关键词： Fog computing task allocation measurement models feasible node selection methods performance metrics

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Efficient network management scheme for device-to-device communication using channel indicators

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Telecommunication Systems 2025年第2期88卷 1-20页

作者： Rathod, Tejal Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Pau, Giovanni Alblehaİ, Fahad Alfarraj, Osama Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad382481 India Department of Computer Engineering - AI ML & DS Marwadi University Rajkot360003 India Faculty of Engineering and Architecture Kore University of Enna Enna94100 Italy Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia

Device-to-Device (D2D) communication maneuvers the proximity services for flexible communication between two nearby devices. It provides high data rates and enhances the spectral and energy efficiency of the D2D communication systems. The base station plays an important role in allocating cellular resources to D2D users. However, it suffers from challenges, such as interference, inefficient resource allocation, security, device discovery, and many more, which may deteriorate the performance of D2D communication. To overcome the aforementioned issues, in this paper, we present an analytics-based efficient network selection scheme that utilizes the reference channel indicators, i.e., receive strength signal indicator, received signal received power, and reference signal received quality to select the best micro base station (MBS) between the D2D user. We evaluated the proposed scheme in five different simulated scenarios wherein each scenario, there are a different number of MBS and D2D pairs. The MBS broadcasts the reference channel indicators at a specific interval of time, and the D2D pairs select the MBS that has the most promising indicator. Finally, all the proposed scenarios are compared and evaluated against the sum rate to analyze the performance of D2D communication with the reference channel indicators. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Resource allocation

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MadDroid: malicious adware detection in Android using deep learning

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Journal of Cyber Security Technology 2024年第3期8卷 163-190页

作者： Seraj, Saeed Pavlidis, Michalis Trovati, Marcello Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom Department of Computer Science Edge Hill University Omrskirk United Kingdom

The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Android (operating system)

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Improved Shark Smell Optimization Algorithm for Human Action Recognition

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computers, Materials & Continua 2023年第9期76卷 2667-2684页

作者： Inzamam Mashood Nasir Mudassar Raza Jamal Hussain Shah Muhammad Attique Khan Yun-Cheol Nam Yunyoung Nam Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Architecture Joongbu UniversityGoyang10279South Korea Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Human Action Recognition(HAR)in uncontrolled environments targets to recognition of different actions froma *** effective HAR model can be employed for an application like human-computer interaction,health care,person tracking,and video *** Learning(ML)approaches,specifically,Convolutional Neural Network(CNN)models had beenwidely used and achieved impressive results through feature *** accuracy and effectiveness of these models continue to be the biggest challenge in this *** this article,a novel feature optimization algorithm,called improved Shark Smell Optimization(iSSO)is proposed to reduce the redundancy of extracted *** proposed technique is inspired by the behavior ofwhite sharks,and howthey find the best prey in thewhole search *** proposed iSSOalgorithmdivides the FeatureVector(FV)into subparts,where a search is conducted to find optimal local features fromeach subpart of *** local optimal features are selected,a global search is conducted to further optimize these *** proposed iSSO algorithm is employed on nine(9)selected CNN *** CNN models are selected based on their top-1 and top-5 accuracy in ImageNet *** evaluate the model,two publicly available datasets UCF-Sports and Hollywood2 are selected.

关键词： Action recognition improved shark smell optimization convolutional neural networks machine learning

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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The Impact of Outcome-Based Education on College Students’ Self-Efficacy and Learning Engagement

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International Journal of Engineering Education 2025年第3期41卷 638-648页

作者： Li, Dekui Li, Yi Zhou, Fan Li, Chengyou Du, Shubo Hubei Water Resources Technical College Wuhan430070 China Liaocheng University Department of Computer Science Liaocheng252000 China Liaocheng University Department of Architecture Liaocheng252000 China

The impact of Outcome-Based Education (OBE) elements – learning objectives, assessment feedback, and teaching methods – on college students’ self-efficacy and learning engagement was investigated through a survey conducted among 416 second and third-year students at the School of computer science, Liaocheng University. Results demonstrate that clear learning objectives and high-quality assessment feedback significantly enhance self-efficacy, leading to increased cognitive, emotional, and behavioral engagement. In contrast, the influence of teaching methods on learning engagement is less pronounced, indicating a need for further refinement in their design and implementation. The mediating role of self-efficacy between OBE perception and learning engagement is highlighted, emphasizing the importance of effectively applying OBE principles in higher education. The findings contribute new empirical insights to educational psychology and provide practical guidance for future educational strategies. © 2025 TEMPUS Publications.

关键词： Students

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