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Chinese Intelligent Systems Conference, CISC 2020

作者： Zhao, Yixin Xing, Bin Huang, Wei Luo, Shiyu Li, Peng Xie, Wenjing The School of Computer and Information Science Southwest University Chongqing400715 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. National Engineering Laboratory for Industrial Big-Data Application Technology Chongqing400065 China

ISBN: (纸本)9789811584572

This paper studies the full-state rendezvous control problem of multiple nonholonomic unicycle systems. A distributed state feedback time-varying control law is derived by exploiting model transformation, input-output feedback linearization technology, linear cooperation control theory and graph theory. Based on the carefully constructed output-like variable, the input-output feedback linearization approach overcomes the underactuated control challenge, makes the choice of controller parameters independent of initial states of vehicles, and allows the linear control theory applicable, rather than using the Lyapunov method and hence requiring the information flow undirected. Stability analysis proves that all the unicycle vehicles globally exponentially converge to a common fixed location and orientation, provided that the communication topology is directed and having a spanning tree. A numerical simulation is implemented for five nonholonomic unicycle vehicles, demonstrating the effectiveness of the proposed control scheme. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

arXiv

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arXiv 2023年

作者： Li, Xiao Zhu, Zhihui Li, Qiuwei Liu, Kai School of Data Science The Chinese University of Hong Kong Shenzhen China China The Department of Electrical and Computer Engineering The University of Denver United States The Decision Intelligence Lab Damo Academy Alibaba Group US United States Computer Science Division Clemson University United States

The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms. © 2023, CC BY.

关键词： Matrix algebra

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ML) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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Deep Learning Classification of Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma with Four-Phase Ct Images

SSRN

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SSRN 2024年

作者： Zhu, Wentao Wu, Yuanfeng Feng, Zhan Cui, Heng Rao, Fan Ni, Yangfan Chen, Geng Xia, Yong Zhejiang Lab Hangzhou China Department of Radiology College of Medicine The First Affiliated Hospital Zhejiang University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

Liver cancer is one of the leading causes of cancer-related death globally. The differentiation of hepatic tumors is critical for designing an accurate treatment plan and improving the five-year survival rate. Recently, multiphase CT images have become the first-line examination approach for clinical diagnosis. Existing methods have been proposed to distinguish hepatic tumors based on deep learning using multiphase CT. However, CNN-based models fail to explicitly establish temporal relationships among different phases, and RNN-based methods rely on expensive calculations due to the recurrent mechanism. In this paper, we proposed a transformer-based deep learning method for identifying intrahepatic cholangiocarcinoma and hepatocellular carcinoma using four-phase CT images as input. Specifically, a difference attention module was developed to improve the feature representation by using the phase difference as interphase temporal information. Moreover, a transformer-based structure was used for classification to explore the long-term context and capture the temporal relationship among phases using a multihead self-attention module. Experiments with clinical datasets are implemented to evaluate the performance of the proposed method and mainstream methods. In the experiments, the proposed method demonstrated superior performance in classifying ICC and HCC on the test set, with an accuracy of 83.72%, a sensitivity of 0.7511, a specificity of 0.9200 and an AUC of 0.90. The results indicate that the proposed method outperforms the traditional deep learning based methods. The architecture can be utilized in other researches using multiple images as input. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

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computers, Materials & Continua 2020年第2期62卷 917-927页

作者： Haiwen Chen Guang Yu Fang Liu Zhiping Cai Anfeng Liu Shuhui Chen Hongbin Huang Chak Fong Cheang College of Computer Science National University of Defense TechnologyChangshaChina School of Data Science and Computer Science Sun Yat-sen UniversityGuangzhouChina College of Information and Engineering Central South UniversityChangshaChina Faculty of Information Technology Macao University of Science and TechnologyMacao

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every *** to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled *** generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are *** is a time-consuming and laborious work to label anomaly for company *** an unsupervised model to detect unlabeled data is an urgent need at *** this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem.

关键词： Anomaly detection KPIs unsupervised learning algorithm

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Doubly Perturbed Task Free Continual Learning

arXiv

引用

arXiv 2023年

作者： Lee, Byung Hyun Oh, Min-Hwan Chun, Se Young Department of Electrical and Computer Engineering Seoul National University Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of INMC & IPAI Seoul National University Korea Republic of

Task Free online Continual Learning (TF-CL) is a challenging problem where the model incrementally learns tasks without explicit task information. Although training with entire data from the past, present as well as future is considered as the gold standard, naive approaches in TF-CL with the current samples may be conflicted with learning with samples in the future, leading to catastrophic forgetting and poor plasticity. Thus, a proactive consideration of an unseen future sample in TF-CL becomes imperative. Motivated by this intuition, we propose a novel TF-CL framework considering future samples and show that injecting adversarial perturbations on both input data and decision-making is effective. Then, we propose a novel method named Doubly Perturbed Continual Learning (DPCL) to efficiently implement these input and decision-making perturbations. Specifically, for input perturbation, we propose an approximate perturbation method that injects noise into the input data as well as the feature vector and then interpolates the two perturbed samples. For decision-making process perturbation, we devise multiple stochastic classifiers. We also investigate a memory management scheme and learning rate scheduling reflecting our proposed double perturbations. We demonstrate that our proposed method outperforms the state-of-the-art baseline methods by large margins on various TF-CL benchmarks. © 2023, CC BY-NC-SA.

关键词： Decision making

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Detection of Atrial Fibrillation based on Feature Fusion using Attention-based BiLSTM

Detection of Atrial Fibrillation based on Feature Fusion usi...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Weifang Xie Cang Chen Ruijie Zhao Yu Lu College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science and Engineering Central South University Changsha China College of Applied Sciences Shenzhen University Shenzhen China

Atrial fibrillation (AF) is a common cardiac arrhythmia, and its early detection is crucial for timely treatment. Conventional methods, such as Electrocardiogram (ECG), can be intrusive and require specialized equipment, whereas Photoplethysmography (PPG) offers a non-invasive alternative. In this study, we present a feature fusion approach for AF detection using attention-based Bidirectional Long Short-Term Memory (BiLSTM) and PPG signals. We extract frequency domain (FD) and time domain (TD) features from PPG signals, combine them with deep learning features generated from an attention-based BiLSTM network, and pass the fusion features through a softmax function. Our approach achieves high accuracy (96.5%) and favorable performance metrics (recall 93.20%, precision 94.50%, and F-score 93.09%), improving AF prediction and diagnosis, and providing support for clinicians in their diagnostic processes.

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Deep Hashing with Triplet Loss of Hash Centers and Dissimilar Pairs for Image Retrieval

Deep Hashing with Triplet Loss of Hash Centers and Dissimila...

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International Conference on computer Supported Cooperative Work in Design

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Artificial Intelligence Department Lizhi Inc. Beijing China Big Data Department Lizhi Inc. Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.

关键词： Training Representation learning Image coding Codes Federated learning Computational modeling Image retrieval

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An advanced algorithm for discrimination prevention in data mining

An advanced algorithm for discrimination prevention in data ...

引用

2022 IEEE Conference on Telecommunications, Optics and computer science, TOCS 2022

作者： Wang, Shuoyu Ren, Jie Fang, Hao Pan, Jun Hu, Xin Zhao, Tingyun Tibet University College of Information Science and Technology Lhasa China Lyuliang University Department of Computer Science and Technology Lvliang China Liaoning University of Traditional Chinese Medicine College of Foreign Languages Shenyang China Beihang University School of Electronic and Information Engineering Beijing China Yangtze Normal University School of Mathematics and Statistics Chongqing China Changzhou College of Information Technology School of Software and Big Data Changzhou China

ISBN: (纸本)9781665470537

data mining has been effectively utilized in huge expanded application areas. Associations overall go with significant business choices in light of mining of data. Discrimination in navigation and preservation of protection of data are a couple of significant difficulties in data mining. Discrimination is of two sorts, Direct Discrimination and Indirect Discrimination. Discrimination prevention includes two significant angles disclosure of direct and additionally roundabout discrimination and data transformation as alteration of separating rules without influencing data quality. Existing strategies for discrimination prevention utilize one of the three methodologies, in particular, the pre-process, in-process or post-process approach. Tests for the exhibition assessment of all calculations by fluctuating these info boundaries, each in turn to review and break down impact of these boundaries. © 2022 IEEE.

关键词： data mining

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Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

arXiv

引用

arXiv 2023年

作者： Bai, Xiaoyu Ma, Benteng Li, Changyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Poly-technical University Xi’an710072 China Sydney Polytechnic Institute NSW2000 Australia

Universal lesion detection (ULD) has great value for daily clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding substantial volumes of annotated data for effective training. Nevertheless, annotating medical images is costly and requires specialized knowledge. Additionally, the diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the data in the training set and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model’s performance varies at different iterations, leading to inconsistencies in the quality of the mined labels during training. This inconsistency limits their potential for performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training strategy to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as the basis, where the teacher model’s predictions are combined with incomplete annotations to train the student model. On top of that, we design a prediction bank to record all high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for subsequent retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two med

关键词： Forecasting

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