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SSRN 2024年

作者： Kalaria, Rudri Kayes, A.S.M. Rahayu, Wenny Pardede, Eric Salehi, Ahmad S. La Trobe University VIC3086 Australia Data Engineering and Knowledge Management Group Australia

Securing Internet of Things (IoT) devices is paramount to mitigate unauthorised access and potential cyber threats, safeguarding the integrity of transmitted and processed data within interconnected devices. Identifying malicious IoT devices necessitates vigilant monitoring of network traffic, behaviour analysis, and implementing security measures, including Anomaly Detection Systems (ADSs), Intrusion Detection Systems (IDSs), and regular firmware updates. Traditional security approaches need to be revised for safeguarding IoT systems due to their inherent limitations in accommodating the resource-constrained nature of these *** introduce IoTPredictor, an advanced security approach designed to predict and detect malicious activities in IoT devices. Leveraging Hidden Markov Models (HMMs), IoTPredictor integrates an ADS to proactively detect and thwart attacks within the complex IoT-fog computing landscape. Our conceptual approach begins with categorising IoT devices into genuine, compromised, and counterfeit. We propose an HMM-based state transition model that captures potential transitions between states, such as normal, compromised, or counterfeit operations. We introduce an algorithm for estimating probabilities associated with next-state predictions to facilitate predictive ***, we present a formal approach for analysing communications between different states, enhancing the precision of the security framework. To validate the effectiveness of IoTPredictor, we conduct a series of experiments and provide a comprehensive evaluation. The results demonstrate the robustness and efficiency of our proposed security framework in predicting and preventing malicious activities, thereby contributing to the overall security enhancement of IoT devices within the complex IoT-fog computing network. © 2024, The Authors. All rights reserved.

关键词： Hidden Markov models

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electrocardiography Signals Using Convolutional Recurrent Neural Networks

Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electro...

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2021 Computing in Cardiology, CinC 2021

作者： Osnabrugge, Niels Keresztesi, Kata Rustemeyer, Felix Kaparakis, Christos Battipaglia, Francesca Bonizzi, Pietro Karel, Joel Maastricht University Department of Data Science and Knowledge Engineering Netherlands

ISBN: (纸本)9781665479165

Automatic identification of cardiac abnormalities through the ECG with a reduced lead system (less than the standard 12-lead) can provide a valuable easy to use and lower cost diagnostic alternative to ordinary 12-lead ECG devices. This study investigates the use of Convolutional Recurrent Neural Networks (CRNNs) to identify cardiac abnormalities in 12, 6, 4, 3 and 2 lead ECG data. Multi-label classification with CRNNs relies on effective data pre-processing, model architecture and hyperparameter tuning. ECG signals were first pre-processed and then zero-padded or clipped to have an equal duration of 10 seconds). Additionally, a wavelet-based ECG segmentation algorithm was used to extract the characteristics and locations of the PQRST complexes (features), and both PQRST fiducial points and extracted features were used as inputs to two Convolutional Recurrent Neural Networks (CRNN), respectively, each one consisting of eight layers. The two CRNNs were subsequently concatenated. Final challenge results of the proposed method achieved an official score of -0.35 for the all-lead combination and a rank of 36 (team name: heartMAASters). In the discussion we provide some theoretical considerations on why we would expect the enhanced model to show a better performance. © 2021 Creative Commons.

关键词： Electrocardiography

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RGB-D SLAM Method Based on Feature Association in Dynamic Environment 6

RGB-D SLAM Method Based on Feature Association in Dynamic En...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Fang, Baofu Wang, Hao Wang, Hao Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665453110

Simultaneous localization and mapping (SLAM) is one of the current research hotspots. However, in visual SLAM for dynamic environments, inaccurate detection of object motion states and incomplete dynamic region culling will lead to large localization errors. To address these issues, this paper proposes an RGB-D SLAM method based on feature association. The method has strongly correlated features in time and space according to the input image sequence. Using the moving probability of the feature points in the previous frame, the movement of the feature points in the current frame is calculated in combination with the dynamic corner points screened in the current frame. Then, the motion state of the object is determined according to the proportion of different feature points. Then combined with semantic information and object depth information, the fast search method is used to obtain accurate dynamic regions. Finally, the selected effective feature points are used to estimate the camera pose and establish a static map of the environment. This paper evaluates the robustness and accuracy of our method on the TUM dataset and real environment, and the results show that our method can significantly improve the system tracking effect and reduce the system tracking error compared with other SLAM methods in dynamic environments. © 2022 IEEE.

关键词： Semantics

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Selecting Features by their Resilience to the Curse of Dimensionality

arXiv

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arXiv 2023年

作者： Stubbemann, Maximilian Hille, Tobias Hanika, Tom Knowledge & Data Engineering Group University of Kassel Hesse Kassel Germany

Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity feature selection aims to identify features that are crucial to learn from said data. While measures of relevance and pairwise similarities are commonly used, the curse of dimensionality is rarely incorporated into the process of selecting features. Here we step in with a novel method that identifies the features that allow to discriminate data subsets of different sizes. By adapting recent work on computing intrinsic dimensionalities, our method is able to select the features that can discriminate data and thus weaken the curse of dimensionality. Our experiments show that our method is competitive and commonly outperforms established feature selection methods. Furthermore, we propose an approximation that allows our method to scale to datasets consisting of millions of data points. Our findings suggest that features that discriminate data and are connected to a low intrinsic dimensionality are meaningful for learning procedures. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Semantic SLAM Based on Compensated Segmentation and Geometric Constraints in Dynamic Environments 6

Semantic SLAM Based on Compensated Segmentation and Geometri...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Fang, Baofu Zhou, Shuai Wang, Hao Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665453110

Most of the existing slam algorithms are designed based on the assumption of a static environment, this strong assumption limits the practical application of most slam systems. The main reason is that moving objects will cause feature mismatch in the pose estimation process, which in turn affects the accuracy of localization and mapping. In this paper, we propose a SLAM algorithm in a dynamic environment. First, we use the BlendMask network to detect potential moving objects to generate masks for dynamic objects. The geometrically constrained joint optical flow method is used to detect dynamic feature points. Secondly, aiming at the failure of semantic segmentation network segmentation, a missed detection compensation algorithm based on the invariance of adjacent frame speed is proposed. Finally, a keyframe selection strategy is proposed to construct a semantic octree graph containing only static objects. We evaluate our algorithm on TUM RGB-D and real scene datasets. The experimental results show that the algorithm has high accuracy and real-time performance. © 2022 IEEE.

关键词： Semantics

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Enhancing brain decoding using attention augmented deep neural networks 29

Enhancing brain decoding using attention augmented deep neur...

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Abdellaoui, Ismail Alaoui Fernández, Jesús García Sahinli, Caner Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9782875870827

Neuroimaging techniques have shown to be valuable when studying brain activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), and different deep learning models to perform brain decoding. Specifically, we investigate to which extent one can infer the task performed by a subject based on its MEG data. In order to capture the most relevant features of the signals, self and global attention are incorporated into our models. The obtained results show that the inclusion of attention improves the performance and generalization of the models across subjects. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Magnetoencephalography

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Maastricht University's Large-Scale Multilingual Machine Translation System for WMT 2021 6

Maastricht University's Large-Scale Multilingual Machine Tra...

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6th Conference on Machine Translation, WMT 2021

作者： Liu, Danni Niehues, Jan Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9781954085947

We present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021. Starting form the provided baseline system, we investigated several techniques to improve the translation quality on the target subset of languages. We were able to significantly improve the translation quality by adapting the system towards the target subset of languages and by generating synthetic data using the initial model. Techniques successfully applied in zero-shot multilingual machine translation (e.g. similarity regularizer) only had a minor effect on the final translation performance. © 2021 Association for Computational Linguistics

关键词： Computational linguistics

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A Semantic Tableau Method for Argument Construction 1

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32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020

作者： Roos, Nico Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (数字)9783030766405

ISBN: (纸本)9783030766399

A semantic tableau method, called an argumentation tableau, that enables the derivation of arguments, is proposed. First, the derivation of arguments for standard propositional and predicate logic is addressed. Next, an extension that enables reasoning with defeasible rules is presented. Finally, reasoning by cases using an argumentation tableau is discussed. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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