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Modeling Acceleration Properties for Flexible INTRA HEVC Complexity Control

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第11期31卷 4454-4469页

作者： Huang, Yan Song, Li Xie, Rong Izquierdo, Ebroul Zhang, Wenjun Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Queen Mary Univ London Multimedia & Vis Grp London E1 4NS England

It is a very well-known fact, that the high complexity of the High Efficiency Video Coding standard (HEVC) is the main hurdle for its wide deployment and use. To tackle this problem, a number of recent research outcomes exploit heuristic algorithms and machine learning, including deep learning, to reduce the coding complexity. However, in most cases, each encoder module, i.e., encoding process, is first accelerated individually, and then different acceleration algorithms are manually combined. Without a holistic strategy, the acceleration potential of multi-module combination is not exploited and the Rate-Distortion (RD) loss is generally not well controlled. To tackle these shortcomings, this paper exploits the acceleration properties of different modules, i.e., the numerical representation of potential time saving and possible RD loss, from which a heuristic model is explored. Then a Heuristic Model Oriented Framework (HMOF) is proposed which adapts the properties of modules to underlying acceleration algorithms. In the framework, two advanced acceleration algorithms, including Border Considered CNN (BC-CNN)-based Coding Unit (CU) partition and Naive Bayes-based Prediction Unit (PU) partition, are proposed for the CU and PU modules, respectively. Further, by leveraging the heuristic model as the guidance to combine the proposed acceleration algorithms, HMOF is globally optimized, where different time saving budgets are wisely allocated to different modules and a theoretically minimal RD loss is achieved. According to the experimental results, through fusing a suitable deep learning technique and a Bayes-Based prediction, the proposed acceleration framework HMOF enable multiple acceleration choices. Here the proposed joint optimization strategy help to make a choice leading to the best cost-performance. Furthermore, within the proposed framework, intra coding time can be precisely controlled with negligible Bjontegaard delta bit-rate (BDBR) loss. In this context, a

关键词： Acceleration Complexity theory Encoding Heuristic algorithms Prediction algorithms Partitioning algorithms machine learning algorithms Acceleration complexity control high efficiency video coding intra prediction convolutional neural network Naive Bayes

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Development and validation of a hybrid model for prediction of viable cell density, titer and cumulative glucose consumption in a mammalian cell culture system

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COMPUTERS & CHEMICAL ENGINEERING 2024年 184卷

作者： Yatipanthalawa, Bhagya S. Fitzsimons, Shauna E. Wallace Horning, Tizita Lee, Yih Yean Gras, Sally L. Univ Melbourne Dept Chem Engn Parkville Vic 3010 Australia Univ Melbourne Bio21 Mol Sci & Biotechnol Inst Parkville Vic 3010 Australia CSL Innovat 655 Elizabeth St Melbourne Vic 3000 Australia

Among the many approaches to build predictive models for bioprocesses, hybrid models have gained increased attention due to their ability to combine data driven approaches with process knowledge. This study develops and compares hybrid models that predict the performance of a mammalian CHO cell culture producing a therapeutic product. Three machine learning algorithms, MLP, Random Forest and XGBoost regressors are compared to understand the effect of algorithm choice on the rates predicted and overall performance of the hybrid model. When combined in series with mechanistic equations, all three algorithms could predict next day Viable Cell Density, titer and the cumulative amount of glucose consumed with low error, although decision treebased algorithm required less computational time for training. The models performed well using a dataset for a new product, without further model training. Retraining the model on a subset of data from the new product improved the prediction accuracy of VCD and titer. The potential for application and further model developments are also outlined.

关键词： Serial hybrid model machine learning algorithms Predictive modelling Bioprocess modelling

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Intrusion Detection System for the MIL-STD-1553 Communication Bus

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第4期56卷 3010-3027页

作者： Stan, Orly Cohen, Adi Elovici, Yuval Shabtai, Asaf Ben Gurion Univ Negev IL-84105 Beer Sheva Israel

MIL-STD-1553 is a military standard that defines the specification of a serial communication bus that has been implemented in military and aerospace avionic platforms for over 40 years. MIL-STD-1553 was designed for a high level of fault tolerance while less attention was paid to cyber security issues. Thus, as indicated in recent studies, it is exposed to various threats. In this article, we suggest enhancing the security of MIL-STD-1553 communication buses by integrating a machine learning-based intrusion detection system (IDS);such an IDS will be capable of detecting cyber attacks in real time. The IDS consists of two modules: 1) a remote terminal (RT) authentication module that detects illegitimately connected components and data transfers and 2) a sequence-based anomaly detection module that detects anomalies in the operation of the system. The IDS showed high detection rates for both normal and abnormal behavior when evaluated in a testbed using real 1553 hardware, as well as a very fast and accurate training process using logs from a real system. The RT authentication module managed to authenticate RTs with +0.99 precision and +0.98 recall;and detect illegitimate component (or a legitimate component that impersonates other components) with +0.98 precision and +0.99 recall. The sequence-based anomaly detection module managed to perfectly detect both normal and abnormal behavior. Moreover, the sequence-based anomaly detection module managed to accurately (i.e., zero false positives) model the normal behavior of a real system in a short period of time (similar to 22 s).

关键词： Military standards Authentication machine learning algorithms Anomaly detection Timing Vegetation Intrusion detection Anomaly detection communication bus security intrusion detection MIL-STD-1553 machine learning Markov chain

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Diagnostic value of VEGF in peri-implantitis and its correlation with titanium particles: A controlled clinical study

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DENTAL MATERIALS 2024年第1期40卷 28-36页

作者： Rakic, Mia Canullo, Luigi Radovanovic, Sandro Tatic, Zoran Radunovic, Milena Souedain, Assem Weiss, Pierre Struillou, Xavier Vojvodic, Danilo Univ Complutense Madrid ETEP Etiol & Therapy Periodontal Dis Res Grp Madrid Spain Univ Genoa Dept Surg Sci DISC Genoa Italy Univ Bern Dept Periodontol Bern Switzerland Univ Belgrade Fac Org Sci Belgrade Serbia Mil Med Acad Dept Oral Implantol Belgrade Serbia Univ Belgrade Dept Oral Surg Fac Dent Med Belgrade Serbia Univ Nantes Fac Dent Surg Dept Periodontol InsermUMR 1229RMeSRegenerat Med & Skeleton Nantes France Univ Nantes RMeS Regenerat Med & Skeleton InsermUMR 1229 Nantes France Univ Nantes Fac Dent Surg Dept Periodontol Nantes France Mil Med Acad Inst Expt Med Belgrade Serbia Univ Complutense Madrid Fac Odontol Etiol & Therapy Periodontal Dis ETEP Res Grp Pza Ramon Y Cajal S-N Madrid Spain

Objectives: VEGF is prototypic marker of neovascularization, repeatedly proposed as intrinsic characteristic of peri-implantitis. This study aimed to assess pattern of VEGF in peri-implantitis, its correlation with titanium particles (TPs) and capacity as respective *** and methods: Pathological specificity of VEGF was assessed in peri-implant granulations using immunohistochemistry, periodontal granulations represented Ti-free positive controls. VEGF was correlated to TPs, identified using scanning electron microscopy coupled with dispersive x-ray spectrometry. Diagnostic accuracy, sensitivity and specificity of VEGF were estimated in PICF specimens from peri-implantitis, peri-implant mucositis (PIM) and healthy peri-implant tissues (HI) using machine learning ***: Peri-implantitis exhibited rich neovascular network with expressed density in contact zones toward neutrophil infiltrates without specific pattern variations around TPs, identified in all peri-implantitis specimens (mean particle size 8.9 +/- 24.8 mu m2;Ti-mass (%) 0.380 +/- 0.163). VEGF was significantly more expressed in peri-implantitis (47,065 +/- 24.2) compared to periodontitis (31,14 +/- 9.15), and positively correlated with its soluble concentrations in PICF (p = 0.01). VEGF was positively correlated to all clinical endpoints and significantly increased in peri-implantitis compared to both PIM and HI, but despite high specificity (96%), its overall diagnostic capacity was average. Two patient clusters were identified in peri-implantitis, one with 8-fold higher VEGF values compared to HI, and second with lower values comparable to ***: VEGF accurately reflects neovascularization in peri-implantitis that was expressed in contact zones toward implant surface without specific histopathological patter variation around TPs. VEGF answered requests for biomarker of peri-implantitis but further research is necessary to decrypt its exact underlying cause.

关键词： Peri-implantitis VEGF Biomarker Diagnosis Titanium particles machine learning algorithms

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Federated Feature Selection for Cyber-Physical Systems of Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 9937-9950页

作者： Cassara, Pietro Gotta, Alberto Valerio, Lorenzo CNR Inst Informat Sci & Technol I-56124 Pisa Italy CNR Inst Informat & Telemat I-56124 Pisa Italy

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through machine learning algorithms, enable AI-based services at the Edge. In fact, only a subset of the collected data present informative attributes to be exploited at the Edge. Therefore, extracting such a subset is of utmost importance to limit computation and communication workloads. Doing that in a distributed manner imposes the AVs to cooperate in finding an agreement on which attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection (FFS) algorithm where the AVs collaborate to filter out, iteratively, the less relevant attributes in a distributed manner, without any exchange of raw data, thought two different components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. The FFS algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the AVs converge to a minimum achievable subset of features with both the datasets, i.e., 24 out of 2166 (99%) in MAV and 4 out of 8 (50%) in WESAD, respectively, preserving the informative content of data.

关键词： Feature extraction Monitoring machine learning algorithms Distance learning Computer aided instruction Training Distributed databases Artificial intelligence autonomous system feature selection federated learning human state monitoring Internet of things machine learning

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Nonlinear Adaptive Filtering With Kernel Set-Membership Approach

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1515-1528页

作者： Chen, Kewei Werner, Stefan Kuh, Anthony Huang, Yih-Fang Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA NTNU Norwegian Univ Sci & Technol Dept Elect Syst N-7491 Trondheim Norway Univ Hawaii Manoa Dept Elect Engn Honolulu HI 96882 USA

This paper develops nonlinear kernel adaptive filtering algorithms based on the set-membership filtering (SMF) framework. The set-membership-based filtering approach is distinct from the conventional adaptive filtering approaches in that it aims for the filtering error being bounded in magnitude, as opposed to seeking to minimize the time average or ensemble average of the squared errors. The proposed kernel SMF algorithms feature selective updates of their parameter estimates by making discerning use of the input data, and selective increase of the dimension in the kernel expansion. These result in less computational cost and faster tracking without compromising the mean-squared error performance. We show, through convergence analysis, that the sequences of parameter estimates of our proposed algorithms are convergent, and the filtering error is asymptotically upper bounded in magnitude. Simulations are performed which show clearly the advantages of the proposed algorithms in terms of lower computational complexity, reduced dictionary size, and steady-state mean-squared errors comparable to existing algorithms.

关键词： Kernel Signal processing algorithms Dictionaries Technological innovation Computational modeling machine learning algorithms Adaptation models Adaptive filters set-membership filtering kernel methods nonlinear systems

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A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第11期35卷 11039-11050页

作者： Wu, Jun He, Jingrui Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Illinois Sch Informat Sci Urbana IL 61801 USA

Multi-source domain adaptation studies the knowledge transferability from multiple labeled source domains to an unlabeled target domain under a distribution shift. However, little effort has been devoted to studying the adversarial vulnerability of multi-source domain adaptation approaches. Specifically, most existing techniques focus on learning the domain-invariant representation to mitigate the distribution shift across domains. In this paper, we theoretically show that the domain-invariant representation cannot guarantee the success of multi-source domain adaptation, when no labeled samples are available in the target domain. This result motivates us to propose a unified framework (AdaptAttack) for data poisoning adversarial attacks on multi-source domain adaptation. The key idea is to maliciously manipulate the label-informed data distributions of source domains by injecting perceptibly unnoticeable noise into the source data. In addition, it requires that the generated adversarial attacks are invisible to multi-source domain adaptation algorithms, i.e., the source classification errors and marginal discrepancies across domains are not negatively affected. Extensive experiments on public domain adaptation benchmarks confirm the effectiveness and computational efficiency of our proposed AdaptAttack framework in both white-box and black-box attack scenarios.

关键词： Training Adaptation models Prediction algorithms Robustness Knowledge transfer Minimization machine learning algorithms Adversarial attacks multi-source domain adaptation domain discrepancy

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Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

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IEEE ACCESS 2022年 10卷 1522-1532页

作者： Li, Jifang Li, Genxu Hai, Chen Guo, Mengbo North China Univ Water Resources & Elect Power Coll Elect Zhengzhou 450000 Peoples R China State Grid Henan Elect Power Co Luoyang Power Sup Luoyang 471009 Peoples R China

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO);through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

关键词： Support vector machines Oil insulation Classification algorithms Fault diagnosis Power transformer insulation Training machine learning algorithms Power transformers fault diagnosis support vector machine improved particle swarm optimization DGA feature multi-class AdaBoost algorithm

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Graph Convolutional Architectures via Arbitrary Order of Information Aggregation

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IEEE ACCESS 2020年 8卷 92802-92813页

作者： Zhou, Chunpeng Shi, Benyun Qiu, Hongjun Liu, Jiming Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Zhejiang Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing 211800 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

Graph representation learning (GRL) has recently drawn a lot of attention due to its advantage in solving various machine learning tasks on graphs/networks, ranging from drug design to recommendation systems. One typical GRL approach is graph embedding, the purpose of which is to learn a map that encodes or represents network elements as points in a low-dimensional vector space so that downstream machine learning methods can be easily implemented. Initially, most graph embedding algorithms learn such a map independently from subsequent machine learning tasks. Therefore, they have limitations in solving supervised machine learning tasks on networks. Later, a great deal of graph convolutional networks (GCNs) have been proposed to learn node representations in an end-to-end manner based on different information aggregation mechanisms. By treating network structure as a computational layer in a GCN, the associated information of nodes with higher-order proximity can be aggregated by increasing the number of layers (i.e., depth) of the GCN. As a consequence, the computational overhead will increase and the representations will be projected towards a steady state. To solve this problem, in this paper, we propose a multi-channel graph convolutional network (MCGCN) that allows higher-order information aggregation by enriching the number of input channels. Based on the notion of Katz index, our model can further achieve an arbitrary order of information aggregation without increasing the computational overhead. Comprehensive experiments on several benchmark networks demonstrate the effectiveness of the proposed architecture by comparing it with the-state-of-art GRL methods in terms of node classification and computational efficiency.

关键词： machine learning Task analysis Computer architecture Aggregates Neural networks machine learning algorithms Computational modeling Graph representation learning graph convolutional networks information aggregation node classification

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Exact and Approximate Squarers for Error-Tolerant Applications

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IEEE TRANSACTIONS ON COMPUTERS 2023年第7期72卷 2120-2126页

作者： Chen, Ke Xu, Chenyu Waris, Haroon Liu, Weiqiang Montuschi, Paolo Lombardi, Fabrizio Nanjing Univ Aeronaut & Astronaut Coll Elect Informat & Engn Nanjing 210016 Jiangsu Peoples R China Politecn Torino DAUIN I-10129 Turin Italy Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

Approximate computing is considered an innovative paradigm with wide applications to high performance and low power systems. These applications have relaxed requirements for accuracy, so they can tolerate errors in results and achieve high performance. In approximate computing, multipliers have been widely studied, but squarers (as similar schemes) have not received much attention. In this paper, an accurate squarer is designed based on a Radix-8 Booth-folding square algorithm to reduce the number of partial products and the depth of the partial product array. Several approximate squarers (R8AS1, R8AS2 and R8AS3) are proposed based on the exact squarer to reduce power and delay. Two approximate partial product generators are also designed to simplify the Radix-8 Booth square encoder in R8AS1 and R8AS2. In addition, approximate compressors with compensation are used in the partial product compression stage to reduce additional area and power consumption in R8AS3. Synthesis results for power, area, and delay at 28nm CMOS technology are presented. Compared with designs in the technical literature with the same accuracy, the proposed 16-bit designs reduce the PDP by 37%;in general, the PDP is decreased by up to 51%. Finally, the proposed approximate squarers are implemented in a square-law detector as a communication application and achieve an SNR close to 30dB. Also, the three proposed approximate squarers are applied to the k-means clustering algorithm for machine learning to accomplish high performance in classification.

关键词： Encoding Generators Compressors Hardware Approximation algorithms Clustering algorithms machine learning algorithms Radix-8 booth-folding square algorithm low power approximate squarers approximate compressors square-law detector k-means clustering

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