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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： Chenqi Bao Alexander Flueck Jianmin Bao Xuan Shen Computer of Engineering Illinois Institute of Technology (College of IOTNanjing University of Posts and Telecommunications)Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology (College of IOT Nanjing University of Posts and Telecommunications)Ministry of Education

Visualization is critical in power systems engineering since the gigantic amount of data generated by power systems is difficult,and at times impossible,to be analyzed numerically. Therefore,the need for powerful visualization software has been increasingly acute due to the growth in size of power system models. To improve PPV’s performance in order to accompany larger systems,this paper focuses on the development of PPV (Perfect Power Visualization),a 2-D power system visualization software program that processes and conver ts numerical data to graphic symbols based on their types,positions and states. The goal of PPV is to let operators visualize and interact with large-scale power systems in a consumer lever personal computer in real time. Also,it was optimized again with smarter data management systems,refinement of overall structure,and a newer platform that enables GPU acceleration,all of which further increased the performance of PPV by a factor of ten times that of its initial capabilities.

关键词： PPV,Power System Visualization,Java FX,GPU acceleration,Performance Optimization

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MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

arXiv

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arXiv 2023年

作者： Wang, Zhuoyuan Mi, Jiacong Lu, Shan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd. Jiangsu Nanjing211161 China

The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in their singular reliance on one modality of molecular information, such as molecule image or SMILES representations, thus neglecting the potential complementarity of various molecular modalities. In response to this limitation, we propose MolIG, a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. MolIG model innovatively leverages the coherence and correlation between molecule graph and molecule image to execute self-supervised tasks, effectively amalgamating the strengths of both molecular representation forms. This holistic approach allows for the capture of pivotal molecular structural characteristics and high-level semantic information. Upon completion of pre-training, Graph Neural network (GNN) Encoder is used for the prediction of downstream tasks. In comparison to advanced baseline models, MolIG exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups such as MoleculeNet Benchmark Group and ADMET Benchmark Group. © 2023, CC BY.

关键词： Molecules

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AN AFFINE INVARIANT EULIDEAN DISTANCE BETWEEN IMAGES

AN AFFINE INVARIANT EULIDEAN DISTANCE BETWEEN IMAGES

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： MELODY Z.W.LIAO JUN-YAN WANG WU-FAN CHEN YUAN Y.TANG School of Applied Math University of Electronic Science and Technology of China Key Lab of Medical School of Communication & Information Engineering University of Electronic Science and Technology o Key Lab of Medical Imaging School of Biomedical Engineering Southern Medical University Guangzhou School of Computer Science Chongqing University Department of Computer Science Hong Kong Baptist U

In this paper, we propose a novel affine invariant Eulidean Distance of images, named annulus Eulidean Distance (AED). Unlike the traditional Eulidean distance, the AED takes into account the spatial correlation between ***, it is invariant to the scale, rotation and shift transformation for images. The method is motivated that the annulus is intuitively rotation invariant, then combined with the centralization and normalization of the images, the AED is variant to affine transformation (AT). The key advantage of this distance is that it is a simple and tractable way to measure the difference between images with some deformations. Some examples in Optical Character Recognition (OCR) and Texture Retrieval are presented to show the power of the *** results demonstrate that the AED is a simple,efficient and powerful way to measure the difference between images.

关键词： Annulus Eulidean distance Eulidean distance (ED) optical character recognition texture retrieval image metric

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Homomorphic Signature for Identity Authentication in Cloud Computing

Homomorphic Signature for Identity Authentication in Cloud C...

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中国密码学会2012年会

作者： Zhiwei Wang Wei Zhang Danwei Chen College of Computer Nanjing University of Posts and TelecommunicationsNanjingJiangsu 210003China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks NanjingJiangsu 210003 Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Post

In this paper,we introduce a new kind of homomorphic signature,which is suitable for identity authentication in cloud computing.A user firstly computes the full signature on its all identity attributes,and sends the full signature to the intermediate channel server (the intermediate channel server should check and verify it).During the valid period of his full signature,if the user wants to require a cloud service on a special identity (only involves part of identity attributes),he only needs to securely send a {0,1}n vector to the intermediate channel *** intermediate channel server who doesn't know the secret key can compute the partial signature on the special identity,and then sends it to the cloud *** give a formal secure definition of this homomorphic signature,and construct a scheme from GHR *** prove that our scheme is secure under GHR signature.

关键词： homomorphic signature identity authentication cloud computing GHR signature strong RSA assumption

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Floor plan construction via anonymous Wi-Fi signal fragment matching

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ICIC Express Letters, Part B: Applications 2014年第4期5卷 981-987页

作者： Zhou, Mu Tian, Zengshan Xu, Kunjie Tang, Xiaomou Wang, Zhuoran Chongqing Key Lab of Mobile Communications Technology School of Communication and Information Engineering Chongqing University of Posts and Telecommunications No. 2 Chongwen Road Nan'an Dist. Chongqing 400065 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Graduate Telecommunications and Networking Program University of Pittsburgh PA 15260 United States

Floor plan construction is the basis for fingerprint-based localization and people's activity learning, especially in the environments where the geometrical map is not available or does not exist. To this end, we propose a new received signal strength (RSS) fragment matching approach to identify the location points (LPs) in floor plan as the overlapped pieces between the fragments. Then, by means of the Smith-Waterman alignment, each LP can be recognized as a winning path in scoring space. Finally, we use the Allen's interval algebra to assemble the time-stamped LPs into a floor plan in target area. The experiments with the actual Wi-Fi RSS measurements recorded in HKUST campus demonstrate that our proposed approach performs better than the conventional K-means clustering and spectral clustering in floor plan construction. © 2014 ICIC International.

关键词： Algebra

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Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT networks

Deep Reinforcement Learning for Fresh Data Collection in UAV...

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IEEE Conference on computer communications Workshops, INFOCOM Wksps

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Bo Bai Science and Technology on Communication Network Laboratory Shijiazhuang Hebei China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China Theory Lab (FKA Future Network Theory Lab) 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

关键词： Sensors Trajectory Data collection Unmanned aerial vehicles Internet of Things Wireless sensor networks Energy consumption

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Characteristics analysis and optimization design of entities collaboration for cloud manufacturing

Characteristics analysis and optimization design of entities...

引用

作者： Li, Wenxiang Zhu, Chunsheng Wei, Xia Rodrigues, Joel J.P.C. Wang, Kun School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Instituto de Telecomunicações University of Beira Interior Covilhã Portugal Santa Rita do SapucaíMG Brazil University of Salamanca Salamanca Spain Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China University ITMO St. Petersburg Russia

By applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n-cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self-optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Dynamics

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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A high-resolution DOA estimation method with a family of nonconvex penalties

arXiv

引用

arXiv 2017年

作者： Wu, Xiaohuan Zhu, Wei-Ping Yan, Jun Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Department of Electrical and Computer Engineering Concordia University Montreal Canada School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

The low-rank matrix reconstruction (LRMR) approach is widely used in direction-of-arrival (DOA) estimation. As the rank norm penalty in an LRMR is NP-hard to compute, the nuclear norm (or the trace norm for a positive semidefinite (PSD) matrix) has been often employed as a convex relaxation of the rank norm. However, solving a nuclear norm convex problem may lead to a suboptimal solution of the original rank norm problem. In this paper, we propose to apply a family of nonconvex penalties on the singular values of the covariance matrix as the sparsity metrics to approximate the rank norm. In particular, we formulate a nonconvex minimization problem and solve it by using a locally convergent iterative reweighted strategy in order to enhance the sparsity and resolution. The problem in each iteration is convex and hence can be solved by using the optimization toolbox. Convergence analysis shows that the new method is able to obtain a suboptimal solution. The connection between the proposed method and the sparse signal reconstruction (SSR) is explored showing that our method can be regarded as a sparsity-based method with the number of sampling grids approaching infinity. Two feasible implementation algorithms that are based on solving a duality problem and deducing a closed-form solution of the simplified problem are also provided for the convex problem at each iteration to expedite the convergence. Extensive simulation studies are conducted to show the superiority of the proposed methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Covariance matrix

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Finformer: A Static-dynamic Spatiotemporal Framework for Stock Trend Prediction

Finformer: A Static-dynamic Spatiotemporal Framework for Sto...

引用

IEEE International Conference on Big Data

作者： Yi Zu Jiacong Mi Lingning Song Shan Lu Jieyue He School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Nanjing China School of Software Engineering Southeast University Nanjing China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Nanjing China

The core of quantitative investment lies in predicting future trends in stock prices. The future trend of a stock is closely related to the industry it belongs to and its relationship with other stocks. Although some research has focused on stock trend prediction in recent years, most studies have only considered the stock’s own time series feature, neglecting the spatial features between stocks. Some research has incorporated spatial information, but typically only considered predefined static relationships. At the same time, capturing dynamic spatial information in the market has been a long-standing challenge. Thus, we propose a spatio-temporal model, Finformer, in order to go beyond traditional time series models. We designed a sparse static-dynamic transformer to capture dynamic market spatial information as it changes over time and combined predefined relationships to extract highly correlated spatial features in the stock market. To effectively integrate spatial and temporal features, we introduced an adaptive spatio-temporal fusion module that dynamically fuses spatio-temporal features based on market conditions at different periods. Experiments on two real-world stock market datasets show that our proposed model outperforms the state-of-the-art baselines in the signal-based and portfolio-based metrics, which are widely concerned in the financial field. Ablation study and hyper-parameter study further reveal the effectiveness of each module in the model and the impact of hyper-parameters. The code will be made publicly available. 1

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