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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： An, Jiahao Dang, Huibo Department of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Department of Transportation Information Shaanxi College of Communications Technology Xi'an710018 China

Conversational recommendation system (CRS) aims to acquire user preferences of conversation and then make recommendations to users. Existing CRS enhance the characterization of items by introducing external information to improve the recommendation effect, but they ignore the most essential utterance semantic attributes in the dialogue, and do not fully con-sider the different emotion or act feedback of users. Based on this, this paper fully explores the utterance semantics, makes the construction of user characteristics fuller by extracting the act and emotion of the utterance, and predicts the act of the conversation process, and then enhances the response through the emotional vocabulary, to improve the interaction experience between the user and the system. A large number of experiments on public datasets show that the model proposed in this paper outperforms the most advanced methods. © 2024 J. An & H. Dang.

关键词： Conversational recommendation Personal recommendation Semantic fusion Utterance act

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TapFinger: Task Placement and Fine-Grained Resource Allocation for Edge Machine Learning 42

TapFinger: Task Placement and Fine-Grained Resource Allocati...

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42nd IEEE International Conference on computer communications, INFOCOM 2023

作者： Li, Yihong Zeng, Tianyu Zhang, Xiaoxi Duan, Jingpu Wu, Chuan Sun Yat-sen University School of Computer Science and Engineering China Peng Cheng Laboratory Department of Communications China The University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350334142

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources flexibly for ML task performance optimization. This paper proposes TapFinger, a distributed scheduler that minimizes the total completion time of ML tasks in a multi-cluster edge network, through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed online scheduling, we adopt multi-agent reinforcement learning (MARL), and propose several techniques to make it efficient for our ML-task resource allocation. First, TapFinger uses a heterogeneous graph attention network as the MARL backbone to abstract inter-related state features into more learnable environmental patterns. Second, the actor network is augmented through a tailored task selection phase, which decomposes the actions and encodes the optimization constraints. Third, to mitigate decision conflicts among agents, we novelly combine Bayes' theorem and masking schemes to facilitate our MARL model training. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 28.6% reduction in the average task completion time and improve resource efficiency as compared to state-of-the- art resource schedulers. © 2023 IEEE.

关键词： Resource allocation

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An Improved Convolutional Neural Network Model for DNA Classification

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computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Artificial Intelligence for Improved Maternal Healthcare: A Systematic Literature Review 16

Artificial Intelligence for Improved Maternal Healthcare: A ...

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16th IEEE AFRICON, AFRICON 2023

作者： Chemisto, Musa Gutu, Tar J.L. Kalinaki, Kassim Mwebesa Bosco, Darlius Egau, Percival Fred, Kirya Tim Oloya, Ivan Rashid, Kisitu Department of Electronics and Computer Engineering Soroti Uganda Department of Computer Science Mbale Uganda Isbat University Faculty of Information and Communications Technology Kampala Uganda Soroti Uganda Soroti Uganda

ISBN: (纸本)9798350336214

The integration of artificial intelligence (AI) in maternal health is a promising avenue for improving pregnancy, early childhood, and postnatal care. This systematic review analyzed 31 articles retrieved from Web of Science, PubMed, and Scopus, which were classified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and Mendeley referencing tool. Our interpretive study found that AI applications in maternal health can predict 48% of maternal complications using electronic medical records (EMR), 29% using medical images, 19% using genetic markers, and 4% using other medical features such as fetal heart rates and sensors. The accuracy of prematurity prediction using AI was 95.7%, while the XGBoost technique predicted neonatal mortality with 99.7% accuracy. The study underscores the potential benefits of AI in maternal healthcare and highlights the need for further research to improve maternal and child health outcomes, especially in resource-constrained sub-Saharan African regions where maternal mortality rates are significantly high. © 2023 IEEE.

关键词： Artificial intelligence

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ILRM: Imitation Learning Based Resource Management for Integrated CPU-GPU Edge Systems with Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This paper focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability *** propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: offline Oracle generation and online imitation learning. In the offline phase, we design a fast-converging heuristic to generate near optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize imitation learning using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Security Improvement for Deep Learning-Based Semantic Communication Systems

Security Improvement for Deep Learning-Based Semantic Commun...

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2024 International Conference on Advanced Technologies for communications, ATC 2024

作者： Tan, Hung Ngo Luu Nguyen, Van-Dinh Huynh-The, Thien Nguyen, Toan-Van Vo, Phuong Luu School of Computer Science and Engineering International University-VNU HCMC Viet Nam Vietnam National University Ho Chi Minh City Viet Nam College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi Viet Nam Hcmc University of Technology and Education Department of Computer and Communications Engineering Viet Nam

ISBN: (纸本)9798350353983

Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications. © 2024 IEEE.

关键词： Channel coding

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Exploiting PRNU and Linear Patterns in Forensic Camera Attribution under Complex Lens Distortion Correction 48

Exploiting PRNU and Linear Patterns in Forensic Camera Attri...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Montibeller, Andrea Perez-Gonzalez, Fernando University of Trento Department of Information Engineering and Computer Science Italy University of Vigo AtlanTTic Department of Signal Theory and Communications Spain

ISBN: (纸本)9781728163277

More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that the two main existing techniques, namely PRNU (Photo Response Non Uniformity)-based and linear-pattern-based, can be successfully combined to improve performance. Moreover, we introduce a novel method that is able to correctly invert adaptive distortion correction transformations by successively maximizing the peak-to-correlation energy (PCE) and the linear-pattern energy for much more reliable camera attribution. A novel validation procedure to quickly discard mismatched test images is also proposed. Finally, we show how great reductions in running time can be achieved by using a GPU for interpolation, resampling, and PCE computation. The code is available at https://***/AMontiB/PSLR. © 2023 IEEE.

关键词： Cameras

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Generalized Unique Reconstruction From Substrings

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IEEE Transactions on Information Theory 2023年第9期69卷 5648-5659页

作者： Yehezkeally, Yonatan Bar-Lev, Daniella Marcovich, Sagi Yaakobi, Eitan Institute for Communications Engineering School of ComputationInformation and Technology Technical University of Munich Munich80333 Germany Department of Computer Science Technion-Israel Institute of Technology Haifa3200003 Israel

This paper introduces a new family of reconstruction codes which is motivated by applications in DNA data storage and sequencing. In such applications, DNA strands are sequenced by reading some subset of their substrings. While previous works considered two extreme cases in which all substrings of pre-defined lengths are read or substrings are read with no overlap for the single string case, this work studies two extensions of this paradigm. The first extension considers the setup in which consecutive substrings are read with some given minimum overlap. First, an upper bound is provided on the attainable rates of codes that guarantee unique reconstruction. Then, efficient constructions of codes that asymptotically meet that upper bound are presented. In the second extension, we study the setup where multiple strings are reconstructed together. Given the number of strings and their length, we first derive a lower bound on the read substrings' length that is necessary for the existence of multi-strand reconstruction codes with non-vanishing rates. We then present two constructions of such codes and show that their rates approach 1 for values of that asymptotically behave like the lower bound. © 2023 IEEE.

关键词： Digital storage

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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection 1

Convolutional Neural Network Model based Deep Learning Appro...

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1st IEEE International Conference on Networking and communications, ICNWC 2023

作者： Dhanalakshmi, R. Thenmozhi, M. Saxena, Swati Mahalingam, Hemalatha Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India KCG College of Technology Department of Computer Science and Engineering Chennai India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350336009

Osteoporosis is a bone ailment which takes place because of minimum bone physique, damaging of micro-structure of bone, more over an excessive vulnerability to breakage. The main fitness difficulty throughout the globe is Osteoporosis, particularly in aged people. It may create spinal or hip breakages which can result in morbidity and burden. Hence the diagnosis of Osteoporosis at early stages and forecasting the existence of the fracture is highly essential. However automated analysis and diagnosis of osteoporosis from virtual radiographs could be very difficult as they have little variations. The proposed approach in this work uses high-dimensional textured function representations calculated from radiography pictures to distinguish healthy from osteoporotic issues. CNN helps to identify osteoporosis using structural MRI measurements of bone with high accuracy © 2023 IEEE.

关键词： Bone

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