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arXiv 2025年

作者： Manas, Kumar Schlauch, Christian Paschke, Adrian Wirth, Christian Klein, Nadja Department of Mathematics and Computer Science Freie Universität Berlin Germany Continental Automotive Technologies GmbH AI Lab Berlin Germany Karlsruhe Institute of Technology Scientific Computing Center Germany Fraunhofer Institute for Open Communication Systems Berlin Germany

Deep learning-based trajectory prediction models have demonstrated promising capabilities in capturing complex interactions. However, their out-of-distribution generalization remains a significant challenge, particularly due to unbalanced data and a lack of enough data and diversity to ensure robustness and calibration. To address this, we propose SHIFT (Spectral Heteroscedastic Informed Forecasting for Trajectories), a novel framework that uniquely combines well-calibrated uncertainty modeling with informative priors derived through automated rule extraction. SHIFT reformulates trajectory prediction as a classification task and employs heteroscedastic spectral-normalized Gaussian processes to effectively disentangle epistemic and aleatoric uncertainties. We learn informative priors from training labels, which are automatically generated from natural language driving rules, such as stop rules and drivability constraints, using a retrieval-augmented generation framework powered by a large language model. Extensive evaluations over the nuScenes dataset, including challenging low-data and cross-location scenarios, demonstrate that SHIFT outperforms state-of-the-art methods, achieving substantial gains in uncertainty calibration and displacement metrics. In particular, our model excels in complex scenarios, such as intersections, where uncertainty is inherently higher. Project page: https://***/SHIFT/. © 2025, CC BY.

关键词： Deep learning

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive communications and Networking 2025年

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Design and utilization of an FPGA cluster to implement a digital wireless channel emulator

Design and utilization of an FPGA cluster to implement a dig...

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22nd International Conference on Field Programmable Logic and Applications, FPL 2012

作者： Buscemi, Scott Sass, Ron Advanced Communication Technologies Lab. SPAWAR Systems Center Atlantic Charleston SC United States Reconfigurable Computing Systems Lab. University of North Carolina at Charlotte Charlotte NC United States

ISBN: (纸本)9781467322560

A Digital Wireless Channel Emulator (DWCE) is a system that is capable of emulating the RF environment for a group of radios. A major issue with current designs is that they do not scale to a large enough number of nodes to emulate meaningful network due to the amount of computations required for such a system. This paper proposes a novel DWCE design based on a cluster of FPGA devices. By decentralizing the signal processing it is possible to increase the available computational resources to the point where they are no longer the limiting factor to scalability. It demonstrates feasibility by analyzing the computational and network requirements of the application and showing how the proposed design meets those needs. The paper also describes the physical design of a new 64 node FPGA cluster that is being manufactured. An implementation on prototype nodes of this cluster demonstrates the machine will support a high fidelity emulation for 32 radios or a lower-fidelity emulation for 128 radios. © 2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Ethical encounters in HCI: Research in sensitive settings 33

Ethical encounters in HCI: Research in sensitive settings

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33rd Annual CHI Conference on Human Factors in computing systems, CHI EA 2015

作者： Waycott, Jenny Davis, Hilary Thieme, Anja Branham, Stacy Vines, John Munteanu, Cosmin Computing and Information Systems University of Melbourne Parkville Australia Culture Lab School of Computing Science Newcastle University United Kingdom Information Systems UMBC BaltimoreMD21250 United States Institute of Communication Culture Information and Technology University of Toronto Mississauga Canada

ISBN: (纸本)9781450331463

With HCI researchers conducting studies in increasingly sensitive and difficult settings, ethics is emerging as a key concern for the HCI community. New technologies are now being designed and evaluated in settings that involve vulnerable or marginalized participants and that can be emotionally challenging for researchers. Research in these settings can produce complex ethical dilemmas that are often emergent, diverse, and highly contextualized. In addition, there may be discrepancies between the realities of HCI fieldwork and the formal and often rigid processes of obtaining ethics approval in research institutions. Given these issues, it is important for researchers to communally reflect on ethical encounters in HCI research. This workshop will provide a forum for researchers to share experiences about ethical challenges they have faced. These discussions will be used to develop a handbook of practical lessons representing the breadth and depth of ethical issues emerging in HCI research in sensitive settings.

关键词： Human computer interaction

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Implementation of VANET routing using computational intelligence

Implementation of VANET routing using computational intellig...

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2016 International Conference on communication and Signal Processing, ICCSP 2016

作者： Hegde, Nayana Manvi, Sunilkumar S. Electronics and Communication Engineering Department SKIT College of Engineering Bengaluru India Wireless Information Systems Lab School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9781509003969

Vehicular Ad hoc Networks (VANETs) is an emerging technology that allows vehicles on roads to form a self-organized network without the aid of a permanent infrastructure. VANETs have received increasing research attention in recent years because of its vast applications. They play a significant role in enhancing safety, comfort and entertainment. Vehicle to vehicle (V2V) and Vehicle to Infrastructure are the two types of communications supported by VANETs. VANETs can use a wide range of routing algorithms as per requirement of need or situation for transmission of data. This paper is an attempt to document the routing challenges of VANETs. Performance evaluation of routing algorithms under realistic models is done by simulation. The simulator used is NS2. To evaluate the performance different node density is used. Different scenarios are tested with realistic mobility models. Ant Colony algorithm implementation is done for solving routing problems of VANET. © 2016 IEEE.

关键词： Vehicular ad hoc networks

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Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

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Computers, Materials & Continua 2022年第10期73卷 953-963页

作者： Muhammad Umar Nasir Muhammad Adnan Khan Muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120GyeonggidoKorea Faculty of Computing Riphah International UniversityIslamabad45000Pakistan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia Canadian University Dubai00000UAE Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic ***,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic *** is a challenging process since it must be completed effectively and *** research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical *** the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic *** predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be *** predict genetic disease,deep and machine learning approaches are *** most productive tool model provides more precise *** simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation *** approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

关键词： Genetic disorder machine learning deep learning single gene inheritance gene disorder mitochondrial gene inheritance disorder

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QoS-driven service selection for multi-tenant SaaS

QoS-driven service selection for multi-tenant SaaS

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2012 IEEE 5th International Conference on Cloud computing, CLOUD 2012

作者： He, Qiang Han, Jun Yang, Yun Grundy, John Jin, Hai Faulty of Information and Communication Technologies Swinburne University of Technology Melbourne VIC Australia Services Computing Technology and Systems Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769547558

Cloud-based software applications (Software as a Service - SaaS) for multi-tenant provisioning have become a major development paradigm in Web engineering. Instead of serving a single end-user, a multi-tenant SaaS provides multiple end-users with the same functionality but with potentially different quality-of-service (QoS) values. The service selection for such a SaaS is a complex decision-making process which involves a number of stakeholders with different QoS requirements. SaaS developers need to compose services with different QoS values to meet end-users' different multidimensional QoS constraints for the SaaS. Furthermore, they also need to satisfy SaaS providers' optimisation goals for the SaaS, such as least resource cost and best system performance. Existing QoS-aware service selection approaches are oriented at a single tenant. They do not consider the characteristics of multi-tenant SaaS and hence are ineffective and inefficient when applied to compose multi-tenant SaaS. In this paper, we introduce a novel QoS-driven approach for helping SaaS developers select the services for composing multi-tenant SaaS, which achieves SaaS providers' optimisation goals while fulfilling the end-users' different levels of QoS constraints. The proposed approach is evaluated using an example SaaS synthetically generated based on a dataset of real-world Web services. Experimental results show that our approach significantly outperforms existing approaches in terms of both effectiveness and performance. © 2012 IEEE.

关键词： Quality of service

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HY-Deepnet : A new Optimal Deep transfer learning empowered framework for an autonomous Alzheimer’s disease detection and diagnosis system

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Engineering Science and Technology, an International Journal 2025年 68卷

作者： S. Veluchamy R. Bhuvaneswari K. Ashwini Samah Alshathri Walid El-Shafai Department of Electronics and Communication Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Nagercoil India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Automated Systems and Computing Lab (ASCL) Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

A progressive neurological disorder, Alzheimer’s disease (AD) predominantly impacts memory and cognitive functions of diseased victims. The disease is usually identified by the buildup of abnormal protein deposits in the brain, resulting in the development of plaques and tangles that interfere with communication between nerve cells. Over time, those affected by Alzheimer’s undergo a diminishing mental capacity, affecting their daily activities and ultimately resulting in a loss of independence. While there is currently no remedy to reverse the advancement of Alzheimer’s disease, identifying the initiation of AD can prove highly beneficial within the medical field. This study presents an innovative framework for the detection and diagnosis of Alzheimer’s disease, employing deep transfer models and COATI optimization techniques. The proposed hybrid Deepnet (HY-Deepnet) framework consists of two main phases: detection and diagnosis. The detection phase aims at identifying the presence of disease with the aid of MRI images. This phase evaluates the performance of various deep learning models, including AlexNet, GoogLenet, SqueezeNet, VGGNet, ResNet and explores their combinations. Experimental results reveal that the combination of AlexNet, GoogLenet, and VGGNet outperforms other networks and their combinations, achieving an accuracy of 77.03% without optimization. The second phase focuses on the diagnosis of the detected disease into three different stages. The diagnosis phase is improved from COATI optimization techniques. The proposed HY-Deepnet thus attains an impressive overall accuracy of 97.6%, accompanied by precision, recall, and F1 scores of 0.978, 0.976, and 0.974, respectively. These results underscore the effectiveness of the framework in enhancing Alzheimer’s detection and diagnosis, particularly when leveraging the synergy of deep transfer models and COATI optimization techniques.

关键词： COATI optimization Pre-trained networks Alzheimer Deep learning Feature extraction

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Hybrid malware detection approach with feedback-directed machine learning

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Science China(Information Sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning

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A Range and Scaling Study of an FPGA-Based Digital Wireless Channel Emulator

A Range and Scaling Study of an FPGA-Based Digital Wireless ...

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Annual IEEE Symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Scott Buscemi Will Kritikos Ron Sass Advanced Communication Technologies Lab SPAWAR Systems Center Atlantic Charleston SC USA Reconfigurable Computing Systems Lab University of North Carolina Charlotte Charlotte NC USA

A Digital Wireless Channel Emulator (DWCE) is a system that is capable of emulating the RF environment for a group of wireless devices. A major issue with current designs is that they do not scale to a large enough number of nodes to emulate meaningful network. A reason for this lack of scalability is the large amount of computations and network capacity required for such a system. Previously documented DWCE systems implement a hub-and-spoke configuration that inhibits them from simply adding additional hardware to scale. This paper investigates the use of a FPGA cluster configured as a distributed system to provide the computational and network structure to scale a DWCE to support 1250 wireless devices. This scale is approximately two orders of magnitude larger than any other previously documented system. This paper presents multiple FPGA cluster configurations that use currently available hardware and describes the algorithms used to route the signals through the network and place the computational hardware on each FPGA. The low level VHDL Signal Path Component (SPC) is synthesized and mapped under different parameters to interpolate is resource utilization. One example FPGA build with enough SPCs to fill 80% of the FPGA resources is successfully run through the Xilinx tool-chain to determine the maximum FPGA system clock speed. Finally, the scaling results are presented that detail the maximum sample frequency of various sized DWCE systems which could be used to examine a variety of wireless devices.

关键词： Field programmable gate arrays Wireless communication Hardware Clocks Ad hoc networks Transceivers Equations

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