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Using log analytics and process mining to enable self-healing in the Internet of Things

Environment Systems and Decisions 2022年第2期42卷 234-250页

作者： Singh, Prasannjeet Saman Azari, Mehdi Vitale, Francesco Flammini, Francesco Mazzocca, Nicola Caporuscio, Mauro Thornadtsson, Johan Department of Computer Science and Media Technology Linnaeus University Växjö Sweden Department of Electrical Engineering and Information Technologies University of Naples Federico II Naples Italy School of Innovation Design and Engineering Mälardalen University Västerås Sweden Information Sigma Technology Gothenburg Sweden

The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing and industrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system, detecting and fixing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known as Process Mining, which—in combination with log-file analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physical systems. Issues addressed in this paper include generation of consistent Event Logs and definition of a roadmap toward effective Process Discovery and Conformance Checking to support Self-Healing in IoT. © 2022, The Author(s).

关键词： Anomaly detection Cyber-physical systems Data driven Resilience Self-diagnostics Self-repair

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Mcrood: Multi-Class Radar Out-Of-Distribution Detection

Mcrood: Multi-Class Radar Out-Of-Distribution Detection

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sabri Mustafa Kahya Muhammet Sami Yavuz Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Technical University of Munich School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Infineon Technologies AG

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing.

关键词： Legged locomotion Pipelines Neural networks Radar detection Detectors Signal processing Radar imaging

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SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust Scene Understanding

SRI-Graph: A Novel Scene-Robot Interaction Graph for Robust ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dong Yang Xiao Xu Mengchen Xiong Edwin Babaians Eckehard Steinbach Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology (LMT) and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Germany

We propose a novel scene-robot interaction graph (SRI-Graph) that exploits the known position of a mobile manipulator for robust and accurate scene understanding. Compared to the state-of-the-art scene graph approaches, the proposed SRI-Graph captures not only the relationships between the objects, but also the relationships between the robot manipulator and objects with which it interacts. To improve the detection accuracy of spatial relationships, we leverage the 3D position of the mobile manipulator in addition to RGB images. The manipulator's ego information is crucial for a successful scene understanding when the relationships are visually uncertain. The proposed model is validated for a real-world 3D robot-assisted feeding task. We release a new dataset named 3DRF-Pos for training and validation. We also develop a tool, named LabelImg-Rel, as an extension of the open-sourced image annotation tool LabelImg for a convenient annotation in robot-environment interaction scenarios * . Our experimental results using the Movo platform show that SRI-Graph outperforms the state-of-the-art approach and improves detection accuracy by up to 9.83%.

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Unbiased Estimating Equation on Inverse Divergence and its Conditions

Unbiased Estimating Equation on Inverse Divergence and its C...

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IEEE International Symposium on information Theory

作者： Masahiro Kobayashi Kazuho Watanabe Information and Media Center Toyohashi University of Technology Toyohashi Aichi Japan Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi Aichi Japan

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper focuses on the Bregman divergence defined by the reciprocal function, called the inverse divergence. For the loss function defined by the monotonically increasing function $f$ and inverse divergence, the conditions for the statistical model and function $f$ under which the estimating equation is unbiased are clarified. Specifically, we characterize two types of statistical models, an inverse Gaussian type and a mixture of generalized inverse Gaussian type distributions, to show that the conditions for the function $f$ are different for each model. We also define Bregman divergence as a linear sum over the dimensions of the inverse divergence and extend the results to the multi-dimensional case.

关键词： Estimation Probability density function Mathematical models information theory

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Unbiased Estimating Equation on Inverse Divergence and Its Conditions

arXiv

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arXiv 2024年

作者： Kobayashi, Masahiro Watanabe, Kazuho Information and Media Center Toyohashi University of Technology Aichi Toyohashi Japan Department of Computer Science and Engineering Toyohashi University of Technology Aichi Toyohashi Japan

This paper focuses on the Bregman divergence defined by the reciprocal function, called the inverse divergence. For the loss function defined by the monotonically increasing function f and inverse divergence, the conditions for the statistical model and function f under which the estimating equation is unbiased are clarified. Specifically, we characterize two types of statistical models, an inverse Gaussian type and a mixture of generalized inverse Gaussian type distributions, to show that the conditions for the function f are different for each model. We also define Bregman divergence as a linear sum over the dimensions of the inverse divergence and extend the results to the multi-dimensional case. Copyright © 2024, The Authors. All rights reserved.

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Lung cancer

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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CFReID: Continual Few-shot Person Re-Identification

arXiv

引用

arXiv 2025年

作者： Ni, Hao Gao, Lianli Zeng, Pengpeng Shen, Heng Tao Song, Jingkuan Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science Tongji University China

Real-world surveillance systems are dynamically evolving, requiring a person Re-identification model to continuously handle newly incoming data from various domains. To cope with these dynamics, Lifelong ReID (LReID) has been proposed to learn and accumulate knowledge across multiple domains incrementally. However, LReID models need to be trained on large-scale labeled data for each unseen domain, which are typically inaccessible due to privacy and cost concerns. In this paper, we propose a new paradigm called Continual Few-shot ReID (CFReID), which requires models to be incrementally trained using few-shot data and tested on all seen domains. Under few-shot conditions, CFREID faces two core challenges: 1) learning knowledge from few-shot data of unseen domain, and 2) avoiding catastrophic forgetting of seen domains. To tackle these two challenges, we propose a Stable Distribution Alignment (SDA) framework from feature distribution perspective. Specifically, our SDA is composed of two modules, i.e., Meta Distribution Alignment (MDA) and Prototype-based Few-shot Adaptation (PFA). MDA aligns seen and unseen distributions through a prior distribution and employs meta-learning to simulate the training-testing process, enabling the model to perform domain adaptation with few-shot data in new domains. PFA incorporates a prototype anchor loss to enhance the learning signals, and utilizes prototype-guided feature learning to stabilize the source domain feature distribution while learning discriminative features, aiming to mitigate catastrophic forgetting. To support the study of CFReID, we establish an evaluation benchmark for CFReID on five publicly available ReID datasets. Extensive experiments demonstrate that our SDA can enhance the few-shot learning and anti-forgetting capabilities under few-shot conditions. Notably, our approach, using only 5% of the data, i.e., 32 IDs, significantly outperforms LReID’s state-of-the-art performance, which requires 700 to 1,000 IDs. So

关键词： Zero-shot learning

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A BiLSTM cardinality estimator in complex database systems based on attention mechanism

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CAAI Transactions on Intelligence technology 2022年第3期7卷 537-546页

作者： Qiang Zhou Guoping Yang Haiquan Song Jin Guo Yadong Zhang Shengjie Wei Lulu Qu Louis Alberto Gutierrez Shaojie Qiao School of Information Science and Technology Southwest Jiaotong UniversityChengduChina School of Software Engineering Chengdu University of Information TechnologyChengduChina School of Energy Power and Mechanical Engineering North China Electric Power UniversityBaodingChina Digital Media Art Key Laboratory of Sichuan ProvinceSichuan Conservatory of MusicChengduChina Department of Computer Science Rensselaer Polytechnic InstituteNew YorkNew YorkUSA

An excellent cardinality estimation can make the query optimiser produce a good execution *** there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as *** particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database *** dealing with complex queries,the existing cardinality estimators cannot achieve good *** this study,a novel cardinality estimator is *** uses the core techniques with the BiLSTM network structure and adds the attention ***,the columns involved in the query statements in the training set are sampled and compressed into ***,the Word2vec model is used to embed the word vectors about the query ***,the BiLSTM network and attention mechanism are employed to deal with word *** proposed model takes into consideration not only the correlation between tables but also the processing of complex *** experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are *** results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

关键词： attention BiLSTM cardinality estimation complex database systems query optimiser Word2vec

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Propagating Threat Scores with a TLS Ecosystem Graph Model Derived by Active Measurements

Propagating Threat Scores with a TLS Ecosystem Graph Model D...

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Network Traffic Measurement and Analysis Conference (TMA)

作者： Markus Sosnowski Patrick Sattler Johannes Zirngibl Tim Betzer Georg Carle Technical University of Munich Germany Department of Computer Engineering Chair of Network Architectures and Services TUM School of Computation Information and Technology

ISBN: (数字)9783903176645

ISBN: (纸本)9798350378887

The Internet is shaped by independent actors and heterogeneous deployments. With the wide adoption of Transport Layer Security (TLS), a whole ecosystem of intertwined entities emerged. Acquiring a comprehensive view allows searching for previously unknown malicious entities and providing valuable cyber-threat intelligence. Actively collected Internet-wide Domain Name System (DNS) and TLS meta-data can provide the basis for such large-scale analyses. However, in order to efficiently navigate the vast volumes of data, an effective methodology is required. This work proposes a graph model of the TLS ecosystem that utilizes the relationships between servers, domains, and certificates. A Probabilistic Threat Propagation (PTP) algorithm is then used to propagate a threat score from existing blocklists to related nodes. We conducted a one-year-long measurement study of 13 monthly active Internet-wide DNS and TLS measurements to evaluate the methodology. The latest measurement found four highly suspicious clusters among the nodes with high threat scores. External threat intelligence services were used to confirm a high rate of maliciousness in the rest of the newly found servers. With the help of optimized thresholds, we identified 557 domains and 11 IP addresses throughout the last year before they were known to be malicious. Up to 40% of the identified nodes appeared on average three months later on the input blocklist. This work proposes a versatile graph model to analyze the TLS ecosystem and a PTP analysis to help security researchers focus on suspicious subsets of the Internet when searching for unknown threats.

关键词： Analytical models Ecosystems Clustering algorithms Telecommunication traffic Data models Blocklists Internet

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