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Mining Repetitive Negative Sequential Patterns with Gap Constraints

ACM Transactions on Knowledge Discovery from Data 2025年第4期19卷

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hebei Key Laboratory of Big Data Computing Tianjin China Hefei University of Technology Hefei China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this article explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： sequential pattern mining gap constraint negative sequential pattern key-value pair

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Knowledge transfer for Object Detection with Evolution architecture search 4

Knowledge transfer for Object Detection with Evolution archi...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Li, Jiaquan Hong, Haokai Shi, Minghui Lin, Qiuzhen Zhou, Fenfen Tan, Kay Chen Jiang, Min Xiamen University School of Informatics Fujian China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longjing Environment Protection Co. Ltd Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep learning has been proved to achieve excellent results in various fields, and appropriate network architecture and sufficient data play an important role. Due to the high cost of annotation for the task of object detection, domain adaptation methods have been introduced in this field. But these methods are based on the rigid network architecture and bound to the input dimension of the adaptive module, which is not only difficult to better balance accuracy and speed of detection model, but also can not use the multi-scale training method, resulting in the reduction of the application scenario of the model. Inspired by this problem, we propose a new object detection method based on multi-scale adversarial domain adaptation and network architecture search. An evolutionary algorithm is adopted to help search the network architecture to balance accuracy and speed. The ability of domain adaptation can also be effectively improved by the searched architecture. The experimental results have demonstrated the significant improvement that benefited from the framework in terms of its performance and computational efficiency in solving unlabeled object detection. © 2022 IEEE.

关键词： Network architecture

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yi Ding Lu Zhao Liming Yuan Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China School of Computer and Information Engineering Tianjin Chengjian University Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability.

关键词： Technological innovation Adaptive systems Aggregates Neural networks Force Feature extraction Transformers

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Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-View Representation Learning for Multi-Instance Learni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lu Zhao Liming Yuan Zhenliang Li Xianbin Wen School of Computer and Information Engineering Tianjin Chengjian University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using $1 \times1$ convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance; ii) Compared to building multi-view representations directly from scratch, extracting them automatically using $1 \times1$ convolutions is more economical, and may be more effective since $1 \times1$ convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability.

关键词： Training Representation learning Retinopathy Supervised learning Buildings Diabetes Bioinformatics

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AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

arXiv

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arXiv 2023年

作者： Zhou, Jingchun He, Zongxin Lam, Kin-Man Wang, Yudong Zhang, Weishi Guo, Chunle Li, Chongyi School of Information Science and Technology Dalian Maritime University China School of Computer Science and Engineering Huizhou University China Department of Electrical and Electronic Engineering Hong Kong Polytechnic University Hong Kong School of Electrical and Information Engineering Tianjin University China VCIP CS Nankai University China

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on Non-Maximum Suppression (NMS) with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at: https://***/zhoujingchun03/AMSP-UOD. Copyright © 2023, The Authors. All rights reserved.

关键词： Vortex flow

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Fine-Grained Vectorized Merge Sorting on RISC-V: From Register to Cache

arXiv

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arXiv 2024年

作者： Zhang, Jin Zhou, Jincheng Zhang, Xiang Ma, Di Gong, Chunye School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China

Merge sort as a divide-sort-merge paradigm has been widely applied in computer science fields. As modern reduced instruction set computing architectures like the fifth generation (RISC-V) regard multiple registers as a vector register group for wide instruction parallelism, optimizing merge sort with this vectorized property is becoming increasingly common. In this paper, we overhaul the divide-sort-merge paradigm, from its register-level sort to the cache-aware merge, to develop a fine-grained RISC-V vectorized merge sort (RVMS). From the register-level view, the inline vectorized transpose instruction is missed in RISC-V, so implementing it efficiently is non-trivial. Besides, the vectorized comparisons do not always work well in the merging networks. Both issues primarily stem from the expensive data shuffle instruction. To bypass it, RVMS strides to take register data as the proxy of data shuffle to accelerate the transpose operation, and meanwhile replaces vectorized comparisons with scalar cousin for more light real value swap. On the other hand, as cache-aware merge makes larger data merge in the cache, most merge schemes have two drawbacks: the in-cache merge usually has low cache utilization, while the out-of-cache merging network remains an ineffectively symmetric structure. To this end, we propose the half-merge scheme to employ the auxiliary space of in-place merge to halve the footprint of naïve merge sort, and meanwhile copy one sequence to this space to avoid the former data exchange. Furthermore, an asymmetric merging network is developed to adapt to two different input sizes. Experiments on the RISC-V processor SG2042 show that four fine-grained optimization schemes including register strided transpose, hybrid merging network, half-merge strategy, and asymmetric merging network, improve performance by 4.05%, 19.88%, 12.23%, and 11.04% respectively. Importantly, the overall performance is 1.34x faster than the parallel sorting in the Boost C++ librar

关键词： Reduced instruction set computing

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Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning

arXiv

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arXiv 2024年

作者： Yang, Juncheng Li, Zuchao Xie, Shuai Yu, Wei Li, Shijun Du, Bo School of Computer Science Wuhan University Hubei Wuhan China National Engineering Research Center for Multimedia Software Hubei Wuhan China School of Electronic Information Engineering Henan Polytechnic Institute Henan Nanyang China JD Explore Academy Beijing China

The chain-of-thought technique has been received well in multi-modal tasks. It is a step-by-step linear reasoning process that adjusts the length of the chain to improve the performance of generated prompts. However, human thought processes are predominantly non-linear, as they encompass multiple aspects simultaneously and employ dynamic adjustment and updating mechanisms. Therefore, we propose a novel Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning in multi-modal representation learning. The proposed AGoT models the human thought process not only as a chain but also models each step as a reasoning aggregation graph to cope with the overlooked multiple aspects of thinking in single-step reasoning. This turns the entire reasoning process into prompt aggregation and prompt flow operations. Experiments show that our multi-modal model enhanced with AGoT soft-prompting achieves good results in several tasks such as text-image retrieval, visual question answering, and image recognition. In addition, we demonstrate that it has good domain generalization performance due to better reasoning. © 2024, CC BY.

关键词： Image enhancement

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Convergence of 5G with Internet of Things for Enhanced Privacy

Convergence of 5G with Internet of Things for Enhanced Priva...

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Future of Information and Communication Conference, FICC 2021

作者： Batool, Amreen Sun, Baoshan Saleem, Ali Ali, Jawad School of Computer Science and Software Engineering Tiangong University Tianjin China Department of Computer Science COMSATS University Islamabad Lahore Pakistan Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9783030730994

In this paper, we address the issue of privacy in 5th generation (5G) driven Internet of Things (IoT) and related technologies while presenting a comparison with previous technologies for communication and unaddressed issues in 5G. Initially, an overview of 5G driven IoT is presented with details about both technologies and eventually leading to problems that 5th generation will face. Details about 5G are also presented while comparing them with previous technologies. The architecture of 5G is presented hence explaining layers of 5G and technologies like SDN, NFV and cloud computing that compose these layers. The architecture for 5g based IoT is also presented for providing visual understanding as well as explained based on how this addresses the issues present in 4G. Privacy is highlighted in 5G driven IoT while providing details about how SDN, NFV and cloud computing helps in elimination of this issue. The issues presented will be compared with 4G based IoT and solutions are provided about mitigation of these issues particularly bandwidth and security. Moreover, techniques used by 4G and 5G technologies for handling the issues of privacy in IoT are presented in a nutshell as a table. Paper also presents a detailed overview of technologies making 5G possible meanwhile giving an explanation about how these technologies resolve privacy issues in 5G. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Proportional Feature Pyramid Network for Lane Detection

Proportional Feature Pyramid Network for Lane Detection

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiapeng Hui Guoyun Lian Jiansheng Wu Shuting Ge Jinfeng Yang Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen China School of Computer Science and Software Engineering University of Science and Technology Anshan China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This paper proposes a Proportional Feature Pyramid Network(P-FPN) through fusing the weights into the FPN for lane detection. In our method, the high-level features are explored to predict lanes coarsely while local- detailed features are leveraged to improve localization accuracy. Extensive experiments on the widely used lane detection dataset, CULane dataset, demonstrate that the proposed method achieves the competitive results compared with several state-of-the-art approaches.

关键词： Location awareness Lane detection Network topology Computational modeling computer architecture Predictive models Feature extraction

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Robust Secure Resource Optimization for RIS-Aided Backscatter Communication with HWIs and Channel Uncertainties

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IEEE Transactions on Vehicular Technology 2025年

作者： Gao, Jingjing Xu, Yongjun Huang, Chongwen Li, Xingwang Zhou, Jihua Yuan, Yiming Jia, Huibin Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications Advanced Science Research Institute Chongqing400065 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Southwest University School of Software Southwest University College of Computer and Information Science Chongqing400715 China North China Electric Power University Department of Electronic and Communication Engineering Baoding071000 China

Reconfigurable intelligent surface (RIS)-aided backscatter communication (BackCom) has recently attracted a lot of attention due to its potential to enhance energy efficiency (EE) and transmission robustness by reconstructing radio channel environments in a low-power-consumption way. However, most of the existing resource optimization approaches focused on perfect channel state information and ideal hardware conditions, so that the received signals may be distorted by channel uncertainties (e.g., channel estimation errors) and hardware impairments (HWIs) (e.g., distortion noises). To this end, we proposed a more realistic robust secure structure for a RIS-aided BackCom system with the consideration of channel uncertainties, HWIs, and one eavesdropper. Then, we formulate an EE-based maximization resource allocation problem by optimizing the transmit power of base station, the artificial noise power, the RIS's phase shift matrix, and the time slots. To tackle this non-convex problem, an iteration-based robust resource allocation algorithm is proposed by employing the worst-case approach, the variable substitution approach, and the alternating optimization approach. Simulation results demonstrate that the proposed algorithm has 14.84% increase in the EE and 8.78% reduction in outage probability compared to the other algorithms. © 2025 IEEE.

关键词： Channel estimation

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