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AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th international workshop on Artificial Intelligence Technologies for Legal Documents and the 1st international workshop on Knowledge Graph Summarization, co-located with the 21st international Semantic Web Conference, ISWC 2022

AI4LEGAL-KGSUM 2022 - Joint Proceedings of the 3th Internati...

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3rd international workshop on Artificial Intelligence Technologies for Legal Documents and the 1st international workshop on Knowledge Graph Summarization, AI4LEGAL-KGSUM 2022

The proceedings contain 14 papers. The topics discussed include: an anonymization tool for open data publication of legal documents;building and analyzing the Brazilian legal knowledge graph;introduction of artificial intelligence in Belgian court: failures, challenges and opportunities;LawSampo portal and data service for publishing and using legislation and case law as linked open data on the semantic web;finding case law: leveraging machine learning research to enhance public access to UK judgments;evaluation of data augmentation for named entity recognition in the German legal domain;towards building a legal virtual assistant based on knowledge graphs;an Indian court decision annotated corpus and knowledge graph;and summaries of knowledge graph entities: first steps to measure the relevance of symptoms to infer diseases.

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On Using Pretext Tasks to Learn Representations from Network Logs 1

On Using Pretext Tasks to Learn Representations from Network...

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1st ACM international workshop on Native Network Intelligence (NativeNI) Part of 18th international Conference on Emerging Networking Experiments and Technologies (CoNEXT)

作者： Boffa, Matteo Vassio, Luca Mellia, Marco Drago, Idilio Milan, Giulia Ben Houidi, Zied Rossi, Dario Politecn Torino Italy Turin Italy Univ Torino Turin Italy Huawei Technol Co Ltd Paris France

ISBN: (纸本)9781450398879

learning meaningful representations from network data is critical to ease the adoption of AI as a cornerstone to process network logs. Since a large portion of such data is textual, Natural Language Processing (NLP) appears as an obvious candidate to learn their representations. Indeed, the literature proposes impressive applications of NLP applied to textual network data. However, in the absence of labels, objectively evaluating the goodness of the learned representations is still an open problem. We call for a systematic adoption of domain-specific pretext tasks to select the best representation from network data. Relying on such tasks enables us to evaluate different representations on side machine learning problems and, ultimately, unveiling the best candidate representations for the more interesting downstream tasks for which labels are scarce or unavailable. We apply pretext tasks in the analysis of logs collected from SSH honeypots. Here, a cumbersome downstream task is to cluster events that exhibit a similar attack pattern. We propose the following pipeline: first, we represent the input data using a classic NLP-based approach. Then, we design pretext tasks to objectively evaluate the representation goodness and to select the best one. Finally, we use the best representation to solve the unsupervised task, which uncovers interesting behaviours and attack patterns. All in all, our proposal can be generalized to other text-based network logs beyond honeypots.

关键词： Representation learning Cybersecurity NLP Honeypots

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Lateral Aerodynamic Parameters Estimation using Neuro Artificial Bee Colony Fusion Algorithm (NABC) 1

Lateral Aerodynamic Parameters Estimation using Neuro Artifi...

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1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Kumar, Prashant Sonkar, Sarvesh Ghosh, Ajoy Kanti Philip, Deepu Aerospace Engineering Indian Institute of Technology Kanpur India Design Programme Indian Institute of Technology Kanpur India Industrialand Management Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Aerodynamic parameter estimation entails modelling force and moment coefficients as well as computing stability and control derivatives from flight data. This topic has been thoroughly researched utilizing traditional procedures such as output, filter, and equation error methods. machine learning, such as artificial neural networks, provides an alternate way to these model-based methodologies. This paper proposes a novel estimation technique for aerodynamic parameters of a real aircraft in the presence of system and measurement uncertainty. A fusion between biologically inspired optimization i.e., Artificial Bee Colony (ABC) optimization and widely used Artificial Neural Network (ANN), which mimics the functional unit of the brain, the neuron, has been demonstrated to be novel and a promising method to the challenges of system identification and parameter estimation (sensor noise). The obtained results were compared to Least Square, and Maximum Likelihood Method (MLE), benchmark estimation techniques. © 2022 IEEE.

关键词： machine learning

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Towards Automatic Parsing of structured Visual Content through the Use of Synthetic data 26

Towards Automatic Parsing of Structured Visual Content throu...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Schoelch, Lukas steinhaeuser, Jonas Beichter, Maximilian Seibold, Constantin Yang, Kailun Knaeble, Merlin Schwarz, Thorsten Maedche, Alexander stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.

关键词： Training Measurement Visualization Annotations Supervised learning static VAr compensators Solids

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data mining and machine learning Techniques for Credit Card Fraud Detection 1

Data Mining and Machine Learning Techniques for Credit Card ...

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1st international Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kalhotra, Satish Kumar Dongare, Shivprasad Vaijnathrao Kasthuri, A. Kaur, Daljeet Department of Education Rajiv Gandhi University Rono Hills Doimukh Arunachal Pradesh India Department of Commerce Mahatma Basweshwar Mahavidyalaya Maharashtra Latur India Tamil Nadu Salem India Department of Computer Science and Engineering Desh Bhagat University Panjab India

ISBN: (纸本)9781607685395

In the recent era, everybody is dealing with the digital data. In such scenario individual one heavily depends on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the biggest challenge for the IT Sector to identify and solve such critical problems. This critical issue can be tackled with the help of machine learning. This paper mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results. © The Electrochemical Society

关键词： Classification Credit Card data mining Fraud Detection machine learning

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IEEE international Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings

IEEE International Conference on Advances in Electronics, Co...

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1st IEEE international Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

ISBN: (纸本)9798350348057

The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machine learning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machine learning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.

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Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and L...

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1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Marusani, Jagadeesh Sudha, B.G. Darapaneni, Narayana Pes University Data Science and Machine Learning Bengaluru India Data Science and Machine Learning Great Learning Bengaluru India Artificial Intelligence and Machine Learning Great Learning Bengaluru India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection. © 2022 IEEE.

关键词： Object detection

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international Conference on pattern recognition / 8th international workshop on Image mining - Theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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A learning procedure for detection of process anomalies in the production of metal long products and a new industrial case study 1

A learning procedure for detection of process anomalies in t...

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1st IFAC workshop on Control of Complex Systems (COSY)

作者： Weber, Andre Denker, Joachim Jelali, Mohieddine Mannstaedt GmbH Mendener Str 51 D-53840 Troisdorf Germany ASINCO GmbH Adv Solut Ind Control Kiekenbusch 11a D-47269 Duisburg Germany TH Koln Cologne Lab Artificial Intelligence & Smart Autom Betzdorfer Str 2 D-50679 Cologne Germany

The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern data mining methods can be used for a highly efficient analysis of process data, to detect process anomalies in the process data, e.g. from rolling mills by statistical pattern recognition. This paper proposes a data-based strategy for detecting process anomalies within a hot rolling mill for long products. Suitable data are identified and selected from existing sensors and processed within a new database. This central database is used to train classification algorithms. The reliability of two prominent classifiers based on Principal Component Analysis (PCA) and One-Class Support Vector machines (OC-SVM) has been evaluated. From the comparison in this respective use case, it has been concluded that satisfying results can be obtained, but PCA is highly dependent on the data distribution. The OC-SVM has also been implemented and tested and offers advantages when the data sets have a more complex distribution. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： industry4.0 data mining machine learning anomaly detection steel production PCA SVM OC-SVM

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Knowledge Graphs for Cybersecurity: A Framework for Honeypot data Analysis 1

Knowledge Graphs for Cybersecurity: A Framework for Honeypot...

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1st IEEE international Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Andrew, Yevonnael Lim, Charles Budiarto, Eka Swiss German University Information Technology Department Banten Tangerang Indonesia

ISBN: (纸本)9798350339437

In the field of cybersecurity, the complexity and diversity of data present significant challenges for effective analysis. This paper explores the use of knowledge graphs as a tool to enhance the analysis of honeypot data. We detail the entire process, from data collection and pre-processing to the creation of the knowledge graph and its subsequent analysis. Our approach enables complex query analysis and provides insights into the sequence and patterns of attacker commands in a specific session, as well as a summary of activities originating from a specific IP address. However, the adaptability of the transformation process may vary depending on the characteristics of the source documents. Our paper underscores the importance of knowledge graphs in enabling more effective threat detection and response mechanisms through a more comprehensive and deeper analysis of honeypot data. Future research could explore real-time graph updating, pattern recognition with machine learning, threat prediction, and attack attribution. © 2023 IEEE.

关键词： Knowledge graph

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