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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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MAGIC: detecting advanced persistent threats via masked graph representation learning 24

MAGIC: detecting advanced persistent threats via masked grap...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Zian Jia Yun Xiong Yuhong Nan Yao Zhang Jinjing Zhao Mi Wen Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Software Engineering Sun Yat-sen University China National Key Laboratory of Science and Technology on Information System Security China Shanghai University of Electric Power China

ISBN: (纸本)9781939133441

Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming increasing common and pose great threat to various enterprises and institutions. Data provenance analysis on provenance graphs has emerged as a common approach in APT detection. However, previous works have exhibited several shortcomings: (1) requiring attack-containing data and a priori knowledge of APTs, (2) failing in extracting the rich contextual information buried within provenance graphs and (3) becoming impracticable due to their prohibitive computation overhead and memory *** this paper, we introduce MAGIC, a novel and flexible self-supervised APT detection approach capable of performing multi-granularity detection under different level of supervision. MAGIC leverages masked graph representation learning to model benign system entities and behaviors, performing efficient deep feature extraction and structure abstraction on provenance graphs. By ferreting out anomalous system behaviors via outlier detection methods, MAGIC is able to perform both system entity level and batched log level APT detection. MAGIC is specially designed to handle concept drift with a model adaption mechanism and successfully applies to universal conditions and detection scenarios. We evaluate MAGIC on three widely-used datasets, including both real-world and simulated attacks. Evaluation results indicate that MAGIC achieves promising detection results in all scenarios and shows enormous advantage over state-of-the-art APT detection approaches in performance overhead.

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Beam shaping based on a silicon optical phased array chip

Beam shaping based on a silicon optical phased array chip

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IEEE International Conference on Group IV Photonics

作者： Wenbo Ren Haipeng Liu Wei Jiang Xincheng Xia Zejun Yan Jijun Feng Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System (Ministry of Education) School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

In this study, we present a method for free-space beam shaping and steering based on a silicon optical phased array, which addresses the theoretical limitation of traditional bulk optics. We theoretically analyze the beam propagation properties with changes in the applied phase. Different beam profiles can be shaped by varying the phase combination, while a high-order quasi-Bessel beam can be generated with a cubic change to the phase modulation. The simulated results are validated further experimentally, and they match one another well. The presented method is expected to have broad application prospects for optical communications, free-space optical interconnects, and light detection and ranging.

关键词： Phased arrays Optical interconnections Free-space optical communication Phase modulation Optical propagation Silicon photonics Optics

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Surpass Teacher: Enlightenment Structured Knowledge Distillation of Transformer

Surpass Teacher: Enlightenment Structured Knowledge Distilla...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ran Yang Chunhua Deng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

It is difficult to train a trustworthy transformer model on a small image classification dataset. This research proposes a sophisticated structured knowledge distillation algorithm that uses CNNs as Transformer's sophisticated teachers, significantly lowering the number of training datasets needed. To better to develop the potential for CNN tutors, this research configures a public data set for CNN teaching as an enlightenment textbook to guide Transformer's training and avoid falling into local optimization prematurely. The distillation process then employs a “learn-digest-self-distillation” learning strategy to enable the Transformer to assimilate CNN knowledge in a structured manner. Sufficient experiments show that the proposed method is significantly better than the direct training Transformer under the condition of limited data sets. Moreover, in order to show the practical application value, this research contributed a practical data set for the classification of smoking and calling. The corresponding code and dataset will be released at https://***/wustdch/surpass-teacher if this paper is accepted.

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Situation Awareness on Blockchain Based on Markov Game Model 4

Situation Awareness on Blockchain Based on Markov Game Model

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4th International Symposium on Smart and Healthy Cities, ISHC 2022

作者： Wang, Peng Qian, Baojian Yan, Yaoxi Sun, Jinyu Chen, Fubing Huaiyin Normal University Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis Jiangsu Huaian223300 China School of Computer Science and Technology Huaiyin Normal University Jiangsu Huaian223300 China

ISBN: (纸本)9781665491808

Aiming at the network security problem of the blockchain system, the game model based on Markov is proposed. The model perceives the network security situation of the blockchain system, accurately and comprehensively evaluates the current security status of the system through clustering screening and attack game, dynamically evaluates the security situation of the system, and establishes an attack transfer network and the best reinforcement scheme. The experiment shows that the Markov game model based on 51% double spend attack can evaluate and predict the current and future security status of the blockchain system, and can reduce the asset loss caused by blockchain attacks to a certain extent. © 2022 IEEE.

关键词： Blockchain

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Vflh: A Following-the-Leader-History Based Algorithm for Adaptive Online Convex Optimization with Stochastic Constraints

SSRN

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SSRN 2022年

作者： Yang, Yifan Chen, Lin Zhou, Pan Ding, Xiaofeng Department of Computer Science University of California Santa BarbaraCA93106 United States National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430072 China

This paper considers online convex optimization (OCO) with generated i.i.d. stochastic constraints, where the distribution of environment is changing and the performance is measured by \textit{adaptive regret}. The stochastic constraints are disclosed at each round to the learner after the decision is made. Different from the previous non-adaptive constrained OCO algorithm which is generalized based on the traditional online gradient descent algorithm, we propose a novel \textit{Virtual queue based Following-the-Leader-History} (VFLH) strategy to make the constrained OCO algorithm adaptive and design the VFLH algorithm. In this setting, the algorithm firstly generalizes all the experts in the specified time interval, then decides the optimal decision with each expert and gives out the final choice by combining the predictions of active experts. In terms of the algorithm performance, there are two performance metrics to be evaluated, i.e., the bounds of constraint violation and regret. For the constraint violation, we bring in the multi-objective drift analysis approach to deal with the constraints via a virtual queue which provides a new method to bound the constraints violations. For the regret, we give out its upper bound and then achieve a corresponding lower bound that proves the upper bound is sufficiently tight. Finally, we provide some numerical experiments to validate the feasibility of our algorithm. © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Prediction of Drug-Target Interactions Using Molecular Graph and GDNet-DTI Model

Prediction of Drug-Target Interactions Using Molecular Graph...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Shuai Lin, Xiaoli Yu, Haiping Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Inf Ormation Processing and Realtime Industrial System School of Computer Science Huanggang Normal University Wuhan China

ISBN: (纸本)9781665401265

The prediction of drug-target interactions (DTIs) is of great significance to the fields of drug design and drug development. However, traditional biological experiments are time-consuming and cost-effective, which has prompted more people to turn their attention to the use of computers to assist in predicting DTIs. This paper proposes an improved prediction model based on multiple graph representation methods, which is GDNet-DTI that combined GCN and DeepWalk. First, a molecular map with atoms as nodes and chemical bonds as edges is generated using the SMILE sequence of drugs, and then GIN is used to extract the features of molecular map for better obtaining the complex interactions between atoms. For target proteins, the protein sequence is first represented by a word vector, and then the one-dimensional convolution is used to extract features for extracting the different levels of features. Then, based on obtained drug features and target features, a DTI-graph is generated, in which drugs and targets are represented as nodes and interactions are represented as edges. Finally, GDNet-DTI are used to obtain node neighborhood information and graph topology information of the DTI-graph. Compared with other advanced models, the results show that GDNet-DTI combined with multiple graph features can predict DTIs more accurately and effectively with DrugBank and four benchmark datasets. In addition, a case study with COVID-19 data is presented, which shows that the proposed method has the potential to predict the actual DTIs and can contribute to the development of drug discovery. © 2021 IEEE.

关键词： Forecasting

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Lightdnn: A Lightweight Dual Neural Network Framework for Rapid Multiphysics Reconstruction in Hot Die Forging

SSRN

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SSRN 2025年

作者： Wang, Jue Xu, Xinyi Ye, Changxin Zhao, Fan Huangfu, Wei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation Institute for Advanced Materials and Technology University of Science and Technology Beijing University of Science and Technology Beijing Beijing100083 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China

Rapid and accurate reconstruction of multiphysics during hot die forging processes is essential for manufacturing optimization, quality assurance, and intelligent process control. While conventional finite element-based acceleration methods fail to meet sub-second requirements, existing sequence-based data-driven approaches suffer from two fundamental limitations: physically inconsistent predictions due to coordinate misalignment, and unsustainable computational costs from sequence-length-dependent parameter scaling. To address these challenges, we propose LightDNN, a lightweight dual-neural network framework that employs a coordinate-oriented paradigm to ensure physical consistency while maintaining computational efficiency for multiphysics reconstruction of hot die *** proposed framework operates through two components: a boundary nodes tracking network that describes the geometric deformation by predicting nodal coordinates' evolution, and a physical fields reconstruction network that reconstructs multiphysics distributions through coordinate-based physical quantities *** validation on 1,960 samples with different forging strategies demonstrates superior performance over existing sequential models in both geometric deformation prediction and 9 physical quantities distributions reconstruction, while simultaneously achieving a 96-98% reduction in number of parameters compared to conventional sequential models and millisecond-scale inference capability (62.68±3.79 ms on CPU and 2.91±0.26 ms on GPU).The proposed framework demonstrates strong potential for industrial deployment while establishing a novel paradigm for efficient multiphysics modeling in hot die forging processes. © 2025, The Authors. All rights reserved.

关键词： Forgings

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How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?

How to Train Your Neural Network for Molecular Structure Gen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai Zhao Yanmin Liu Longyang Dian Shiwei Sun Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China State Key Laboratory of Microbial Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS 2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist.

关键词： Training Solid modeling Accuracy Biological system modeling Fingerprint recognition Predictive models Mass spectroscopy Data models Noise measurement Overfitting

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