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RankFIRST: Visual Analysis for Factor Investment By Ranking Stock Timeseries

IEEE Transactions on Visualization and computer Graphics 2022年 PP卷 1-10页

作者： Guo, Huijie Liu, Meijun Yang, Bowen Sun, Ye Qu, Huamin Shi, Lei SKLSDE and Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University China Hong Kong University of Science and Technology Hong Kong

In the era of quantitative investment, factor-based investing models are widely adopted in the construction of stock portfolios. These models explain the performance of individual stocks by a set of financial factors, e.g., market beta and company size. In industry, open investment platforms allow the online building of factor-based models, yet set a high bar on the engineering expertise of end-users. State-of-the-art visualization systems integrate the whole factor investing pipeline, but do not directly address domain users' core requests on ranking factors and stocks for portfolio construction. The current model lacks explainability, which downgrades its credibility with stock investors. To fill the gap in modeling, ranking, and visualizing stock time series for factor investment, we designed and implemented a visual analytics system, namely RankFIRST. The system offers built-in support for an established factor collection and a cross-sectional regression model viable for human interpretation. A hierarchical slope graph design is introduced according to the desired characteristics of good factors for stock investment. A novel firework chart is also invented extending the well-known candlestick chart for stock time series. We evaluated the system on the full-scale Chinese stock market data in the recent 30 years. Case studies and controlled user evaluation demonstrate the superiority of our system on factor investing, in comparison to both passive investing on stock indices and existing stock market visual analytics tools. IEEE

关键词： Investments

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Controllable Wireless Sensing via Target-Mounted Intelligent Reflecting Surface

Controllable Wireless Sensing via Target-Mounted Intelligent...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiaodan Shao Rui Zhang Institute for Digital Communications Friedrich-Alexander University of Erlangen-Nürnberg Germany School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

Intelligent reflecting surface (IRS) is regarded as a promising technique to control wireless channels for improving the communication and/or sensing performance of wireless systems. In this paper, we propose a new target-mounted IRS approach for wireless sensing, where IRS is mounted on the outer/echo surface of a target for either enhancing the IRS/target reflected signal towards the legitimate radar station (LRS) to facilitate its detection of the target, or suppressing that towards the unauthorized radar station (URS) to prevent its detection of the target, by flexibly tuning the IRS reflection. To this end, we assume that sensors are installed along with the reflecting elements at IRS to detect the angles of arrival (AoA) of radar signals from the LRS/URS, based on which IRS reflection is designed to achieve signal enhancement/suppression in the angle of departure (AoD) towards the LRS/URS, respectively. Simulation results are presented to verify the performance of the proposed controllable wireless sensing approach with target-mounted IRS, as compared to various benchmark schemes.

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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Joint Location and Power Optimization for Secure Communications in Multi-UAVs Enabled Cellular Networks

Joint Location and Power Optimization for Secure Communicati...

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International Conference on Networking and Network Applications (NaNA)

作者： Fei Deng Kaiguo Qian Shikai Shen Sunyan Hong Bin Yang Xuanxuan Li Jun He College of Information Engineering Kunming University Kunming China Key Laboratory of Data Governance and Intelligent Decision in Universities of Yunnan Kunming China School of Computer and Information Engineering Chuzhou University Chuzhou China Office of Science and Technology Kunming University Kunming China

This paper investigates joint location and power optimization for secure communication in a multi-unmanned acrial vehicles (U A V s) enabled cellular network consisting of interference link, eavesdropping link, cellular link and D2D link. To this end, we first derive the expression of secure rate. Then, we formulate the maximization of secure rate as a constrained optimization problem. Finally, we solve it using the method of exhaustion.

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for data Sharing in Wireless Sensor Networks

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IEEE Transactions on Services Computing 2024年第1期18卷 226-238页

作者： Haijiang Wang Jianting Ning Wei Wu Chao Lin Kai Zhang School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Data Science City University of Macau Macau China College of Education Sciences Hong Kong University of Science and Technology Guangzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “ Key Forge Attack ” and “ Trapdoor Forge Attack ” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA $^{2}$ SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA $^{2}$ SE and prove its security. Finally, empirical evaluations demonstrate that KA $^{2}$ SE is computationally efficient in comparison with existing schemes.

关键词： Encryption Cryptography Keyword search Wireless sensor networks Servers Privacy Cloud computing Security Probabilistic logic Authorization

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Proximal bundle methods for hybrid weakly convex composite optimization problems

arXiv

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arXiv 2023年

作者： Liang, Jiaming Monteiro, Renato D.C. Zhang, Honghao Goergen Institute for Data Science Department of Computer Science University of Rochester RochesterNY14620 United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332-0205 United States

This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update framework which includes various well-known bundle update schemes. In contrast to hard-to-check stationary conditions (e.g., the Moreau stationarity) used by other methods for solving HWC-CO, PBF uses a stationarity measure which is easily verifiable. © 2023, CC BY.

关键词： Convex optimization

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A Hybrid Method for Photonic Crystal Fiber Polarization Filter Based on Artificial Neural Network and Genetic Algorithms

SSRN

引用

SSRN 2023年

作者： Yang, Dan Qin, Huobin Li, Yijin Tang, Chang Xu, Bin Cheng, Tonglei College of Information Science & Engineering Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China School of Computer Science and Engineering Northeastern University Shenyang110169 China

In this paper, we proposed a design method for photonic crystal fiber polarization filters incorporating artificial neural network (ANN) and genetic algorithm (GA) to achieve high-performance polarization filters. The ANN is trained to accurately learn the relationship between the structure parameter and polarization properties of PCFs, and GA is utilized to select the structural parameters with optimal performance. Confinement loss, bandwidth, and resonance wavelength of the polarization filter serve as parameters in the multi-objective optimization process performed by the GA. Numerical results demonstrate that the designed PCF filter has high performance, with a confinement loss of 1995.75 dB/cm for the y polarization core mode at 1.55 μm, and crosstalk and bandwidth of 1720.15 dB and 932 nm, respectively. It indicates that the hybrid design method is a promising and viable approach for the development of optical applications. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

arXiv

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arXiv 2023年

作者： Xu, Lixiang Cui, Qingzhe Hong, Richang Xu, Wei Chen, Enhong Yuan, Xin Li, Chenglong Tang, Yuanyan The College of Artificial Intelligence and Big Data Hefei University Hefei230027 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia The School of Artificial Intelligence Anhui University Hefei230601 China The Zhuhai UM Science and Technology Research Institute FST University of Macau China

In recent years, the results of view-based 3D shape recognition methods have saturated, and models with excellent performance cannot be deployed on memory-limited devices due to their huge size of parameters. To address this problem, we introduce a compression method based on knowledge distillation for this field, which largely reduces the number of parameters while preserving model performance as much as possible. Specifically, to enhance the capabilities of smaller models, we design a high-performing large model called Group Multi-view Vision Transformer (GMViT). In GMViT, the view-level ViT first establishes relationships between view-level features. Additionally, to capture deeper features, we employ the grouping module to enhance view-level features into group-level features. Finally, the group-level ViT aggregates group-level features into complete, well-formed 3D shape descriptors. Notably, in both ViTs, we introduce spatial encoding of camera coordinates as innovative position embeddings. Furthermore, we propose two compressed versions based on GMViT, namely GMViT-simple and GMViT-mini. To enhance the training effectiveness of the small models, we introduce a knowledge distillation method throughout the GMViT process, where the key outputs of each GMViT component serve as distillation targets. Extensive experiments demonstrate the efficacy of the proposed method. The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB. The smaller models, GMViT-simple and GMViT-mini, reduce the parameter size by 8 and 17.6 times, respectively, and improve shape recognition speed by 1.5 times on average, while preserving at least 90% of the classification and retrieval performance. The code is available at https://***/bigdatagraph/*** Codes 68 Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

arXiv

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arXiv 2023年

作者： Zhang, Haoxuan Li, Haisheng Li, Nan Wang, Xiaochuan School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China

With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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