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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

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Learning from noisy labels via dynamic loss thresholding

arXiv

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arXiv 2021年

作者： Yang, Hao Jin, Youzhi Li, Ziyin Wang, Deng-Bao Miao, Lei Geng, Xin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China AI Technology Application Department Huawei Technologies Collaborative Innovation Center of Wireless Communications Technology China

Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method named Dynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages supervised learning techniques. Experiments on CIFAR-10/100 and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems for the first time. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Direction based charging in rechargeable wireless sensor network 1

引用

4th International Conference on IoT as a Service, IoTaaS 2018

作者： Batool, Sadia Tong, Fei Li, Songyuan He, Shibo Zhejiang University Hangzhou China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (数字)9783030146573

ISBN: (纸本)9783030146566

. One of the main issues in rechargeable wireless sensor networks (RWSNs) is the stainability of network operation. Recently, wireless power transmission technology has been applied in RWSNs to transmit wireless power from chargers to sensor nodes. In this paper, we focus on how a charger changes its direction to cover different nodes for energy provisioning and, how to sense maximum data from the area of network. We intend to tackle the challenge that how to cover different nodes from each direction so that the charger can provide maximum power to maintain network performance. In this regard, a direction scheduling algorithm is proposed to fill the charging demand. Furthermore, a proposed Linear Programming based solution is proposed to determine maximum data sensing rate. The simulation results show that the proposed method performs better compared with existing methods. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Linear programming

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Detecting Cryptojacking Traffic Based on network Behavior Features

Detecting Cryptojacking Traffic Based on Network Behavior Fe...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaoyan Hu Zhuozhuo Shu Xiaoyi Song Guang Cheng Jian Gong School of Cyber Science & Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China Research Base of International Cyberspace Governance (Southeast University) Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China

ISBN: (纸本)9781728181059

Bitcoin and other digital cryptocurrencies have de-veloped rapidly in recent years. To reduce hardware and power costs, many criminals use the botnet to infect other hosts to mine cryptocurrency for themselves, which has led to the proliferation of mining botnets and is referred to as cryptojacking. At present, the mechanisms specific to cryptojacking detection include host-based, Deep Packet Inspection (DPI) based, and dynamic network characteristics based. Host-based detection requires detection installation and running at each host, and the other two are heavyweight. Besides, DPI-based detection is a breach of privacy and loses efficacy if encountering encrypted traffic. This paper de-signs a lightweight cryptojacking traffic detection method based on network behavior features for an ISP, without referring to the payload of network traffic. We set up an environment to collect cryptojacking traffic and conduct a cryptojacking traffic study to obtain its discriminative network traffic features extracted from only the first four packets in a flow. Our experimental study suggests that the machine learning classifier, random forest, based on the extracted discriminative network traffic features can accurately and efficiently detect cryptojacking traffic.

关键词： Privacy Botnet Telecommunication traffic Inspection Feature extraction Hardware Cryptography

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Semi-supervised partial label learning via confidence-rated margin maximization 20

Semi-supervised partial label learning via confidence-rated ...

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Proceedings of the 34th International Conference on Neural information Processing Systems

作者： Wei Wang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

ISBN: (纸本)9781713829546

Partial label learning assumes inaccurate supervision where each training example is associated with a set of candidate labels, among which only one is valid. In many real-world scenarios, however, it is costly and time-consuming to assign candidate label sets to all the training examples. To circumvent this difficulty, the problem of semi-supervised partial label learning is investigated in this paper, where unlabeled data is utilized to facilitate model induction along with partial label training examples. Specifically, label propagation is adopted to instantiate the labeling confidence of partial label examples. After that, maximum margin formulation is introduced to jointly enable the induction of predictive model and the estimation of labeling confidence over unlabeled data. The derived formulation enforces confidence-rated margin maximization and confidence manifold preservation over partial label examples and unlabeled data. We show that the predictive model and labeling confidence can be solved via alternating optimization which admits QP solutions in either alternating step. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of the proposed semi-supervised partial label learning approach.

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Correction to: Edge artificial intelligence-based affinity task offloading under resource adjustment in a 5G network

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Applied Intelligence 2022年第3期53卷 3638-3638页

作者： Wang, Jin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China

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Student Loss: Towards the Probability Assumption in Inaccurate Supervision

TechRxiv

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TechRxiv 2023年

作者： Zhang, Shuo Li, Jianqing Fujita, Hamido Li, Yuwen Wang, Dengbao Zhu, Tingting Zhang, Minling Liu, Chengyu The School of Instrument Science and Engineering The State Key Laboratory of Bioelectronics The School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The State Key Laboratory of Bioelectronics School of Instrument Science and Engineering Southeast University Nanjing210096 China HUTECH University Ho Chi Minh City Viet Nam University of Granada Granada Spain Regional Research Center Iwate Prefectural University Iwate Japan The School of Computer Science and Engineering Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under certain conditions. An implementation of the main codes is available at https://***/Zhangshuojackpot/Student-Loss. © 2023, CC BY.

关键词： Students

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A maximum margin clustering algorithm based on indefinite kernels

引用

Frontiers of computer Science 2019年第4期13卷 813-827页

作者： Hui XUE Sen LI Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast University Nanjing 210096 Chin Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China College of Science Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046 China

Indefinite kernels have attracted more and more attentions in machine learning due to its wider application scope than usual positive definite kernels. However, the research about indefinite kernel clustering is relatively scarce. Furthermore, existing clustering methods are mainly designed based on positive definite kernels which are incapable in indefinite kernel scenarios. In this paper, we propose a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art maximum margin clustering (MMC) model. IKMMC tries to find a proxy positive definite kernel to approximate the original indefinite one and thus embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels, which can be further optimized by an iterative approach. Concretely, at each iteration, given a set of initial class labels, IKMMC firstly transforms the clustering problem into a classification one solved by indefinite kernel support vector machine (IKSVM) with an extra class balance constraint and then the obtained prediction labels will be used as the new input class labels at next iteration until the error rate of prediction is smaller than a prespecified tolerance. Finally, IKMMC utilizes the prediction labels at the last iteration as the expected indices of ***, we further extend IKMMC from binary clustering problems to more complex multi-class scenarios. Experimental results have shown the superiority of our algorithms.

关键词： indefinite kernel maximum margin clustering support vector machine kernel method

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Tracing network-Wide Heavy Hitters in network Data Streams

Tracing Network-Wide Heavy Hitters in Network Data Streams

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International Conference on Communication Technology (ICCT)

作者： Aiping Zhou Qi Zhao Zhaoyi Wang Jin Qian School of Computer Science and Technology Taizhou University Taizhou China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Tracing network-wide heavy hitters in massive network traffic is important for applications such as traffic engineering, load balancing and anomaly detection. It is challenging to process packets at high speed and use the limited resource. Moreover, heavy hitters are distributed by nature due to packets spanning across the entire network. To this end, we propose a tracing algorithm of network-wide heavy hitters in network data streams, which mainly consists of preprocessing packet, updating data structure, constructing candidates of heavy hitters, estimating the size of heavy hitters. Our method constructs candidates of heavy hitters and estimates their size by only its summary data structure, such that it incurs small computation and memory access overhead, while achieving high identification accuracy. We present theoretical analysis on the space complexity, time complexity and accuracy. The experiments are conducted on the real network traffic and the results show that our method outperforms the related ones in terms of identification accuracy and estimation accuracy.

关键词： Monitoring IP networks Telecommunication traffic Arrays Size measurement Time complexity Protocols

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A Neural generative model for joint learning topics and topic-specific Word Embeddings

arXiv

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arXiv 2020年

作者： Zhu, Lixing He, Yulan Zhou, Deyu Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a word is generated by a hidden semantic vector encoding its contextual semantic meaning, and its context words are generated conditional on both the hidden semantic vector and global latent topics. Topics are trained jointly with the word embeddings. The trained model maps words to topic-dependent embeddings, which naturally addresses the issue of word polysemy. Experimental results show that the proposed model outperforms the word-level embedding methods in both word similarity evaluation and word sense disambiguation. Furthermore, the model also extracts more coherent topics compared with existing neural topic models or other models for joint learning of topics and word embeddings. Finally, the model can be easily integrated with existing deep contextualized word embedding learning methods to further improve the performance of downstream tasks such as sentiment classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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