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A digital economy development index based on an improved hierarchical data envelopment analysis approach

European Journal of Operational Research 2024年第3期316卷 1146-1157页

作者： Guo, Chuanyin Song, Qiwei Yu, Ming-Miin Zhang, Jian School of Economics and Management Beijing Information Science & Technology University Beijing 100192 China Department of Transportation Science National Taiwan Ocean University Keelung Taiwan Beijing Key Lab of Green Development Decision Based on Big Data Beijing 100192 China

The digital economy is playing an increasingly important role in the global economy. National and international organizations commonly utilize a composite index composed of multi-dimensional indicators to monitor performance, analyse policies, and communicate in the digital economy. This study introduces a hierarchical framework for constructing a Digital Economy Development Index (DEDI). One of the key challenges is determining the attribute weights to be assigned by aggregating the sub-indicators with hierarchical dimensions for DEDI. The current study shows that an H-DEA model can be transformed into a parametric linear programming problem and develops an improved golden section algorithm to search for approximate optimal solutions. The newly developed method is highly robust and provides an alternative procedure to determine the optimal weights for building a DEDI. We established a hierarchical evaluation framework and utilized a new approach to measure the DEDI of 30 provinces in China from 2015 to 2020. The results show that inter-provincial digital economy development in China shows a sequential weakening from the east, centre, and northeast to the west. The East leads the country's digital economy overall, while there is a clear gradient gap between provinces. The central region needs to create a cluster for the development of digital industries. The Northeast should enhance the competitiveness of the industry across the board. The West must strengthen the construction of innovative talent and new infrastructure for the digital economy. These findings can serve as guidelines for designing China's ongoing digital economy development. © 2024 Elsevier B.V.

关键词： data envelopment analysis

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination 20

On-Line System of Garbage Image-Orientated Intelligent Class...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Tian, Jiayin Wang, Yaozhi Liu, Jiaxin Chen, Yan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350365856

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described. © 2024 IEEE.

关键词： Zero-shot learning

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic Engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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ImagineNet: Target Speaker Extraction with Intermittent Visual Cue Through Embedding Inpainting 48

ImagineNet: Target Speaker Extraction with Intermittent Visu...

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

作者： Pan, Zexu Wang, Wupeng Borsdorf, Marvin Li, Haizhou Institute of Data Science Singapore Singapore Nus Department of Electrical and Computer Engineering Singapore Singapore University of Bremen Machine Listening Lab Germany The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility. © 2023 IEEE.

关键词： Extraction

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Visual evaluation of graph representation learning based on the presentation of community structures

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Visual Informatics 2024年第3期8卷 29-31页

作者： Yong Zhang Lihong Cai Yuhua Liu Yize Li Songyue Li Yuming Ma Yuwei Meng Zhiguang Zhou Intelligent Big Data Visualization Lab Hangzhou Dianzi UniversityHangzhouChina Science&Informatization Department Zhejiang Provincial Energy Group CompanyHangzhouChina State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep ***,choosing the right method for different tasks can be *** within networks help reveal underlying structures and *** how different models preserve community properties is crucial for identifying the best graph representation for data *** paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community *** studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

关键词： Graph representation learning Community structure Visual perception

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RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation leveraging Hybrid CNN-Transformer Approach with Dense Skip Connection 8

RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation ...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Zou, Binfeng Chen, Yifei Chen, Zhanghao Sun, Yifei Huang, Yifan Qin, Feiwei Wang, Changmiao HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350397932

Orthopedic osteosarcoma is a prevalent malignant bone tumor. Preoperative planning, efficacy evaluation, and metastasis detection of osteosarcoma necessitate the use of magnetic resonance imaging (MRI). Due to the varying 10-cations, structures, sizes, and shapes of osteosarcomas among patients, as well as the high degree of tumor heterogeneity, segmenting osteosarcoma images manually presents a significant challenge for clinicians. In the meantime, the grayscale and texture features within the tumor in MRI images are not homogeneous, resulting in small grayscale differences between the tumor tissue and the surrounding normal tissue, which also presents a significant challenge for deep learning-based image segmentation methods. This paper proposes RTUNet++, a novel method for segmenting osteosarcoma MRI images. This method integrates the ResNet residual module, Transformer attention mechanism, and UNet++ Dense Skip Connection structure. We employ a CNN-Transformer hybrid architecture to prevent the loss of spatial feature data. Features with different semantic scales are aggregated at the decoder using multi-fusion dense jump connections to achieve flexible feature fusion. Consequently, in comparison experiments with various classical models, our proposed RTUNet++ achieves superior segmentation results on an osteosarcoma MRI image dataset. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

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Journal of Computer science & Technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

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