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24th International Conference on Advanced Communication Technology, ICACT 2022

作者： DInh, Truong Duy Vishnevsky, Vladimir Pham, Van Dai Le, Duc Tran Kirichek, Ruslan Koucheryavy, Andrey Bonch-Bruevich Saint-Petersburg State University of Telecommunications Prospekt Bolshevikov Department of Software Engineering and Computer Technology St. Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow Russia The University of Danang-University of Science and Technology Information Technology Faculty 54 Nguyen Luong Bang Danang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia

ISBN: (纸本)9791188428090

In the last decade, unmanned aerial systems have emerged in different fields, such as entertainment, military, Internet of Things, emergencies. In these systems, unmanned aerial vehicles (UAVs) communicate with each other based on a flying network to provide services to customers such as live streaming, high-speed access point, etc. The mobility of UAVs can bring numerous advantages in emergency cases, for example, rescuing and searching victims in areas where the network infrastructure is not available. However, the crucial task needed to be done first is to locate the victims' coordinates. In this paper, we propose a method for detecting the coordinates of subscribers with the Wi-Fi signals generated from victims' phones in the absence of network infrastructure of communication operators using a flying network for emergencies based on UAV-swarms. We develop a new protocol for communication between UAVs and UAV-swarms. A structure of UAV-swarms is considered to optimize the searching time. The results of the proposed method are achieved by simulation. © 2022 Global IT Research Institute-GiRI.

关键词： Wi-Fi

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Knowledge Graphs for the Life sciences: Recent Developments, Challenges and Opportunities

arXiv

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arXiv 2023年

作者： Chen, Jiaoyan Dong, Hang Hastings, Janna Jiménez-Ruiz, Ernesto López, Vanessa Monnin, Pierre Pesquita, Catia Škoda, Petr Tamma, Valentina Department of Computer Science University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Institute for Implementation Science in Health Care University of Zurich Switzerland School of Medicine University of St. Gallen Switzerland City University of London United Kingdom SIRIUS University of Oslo Norway IBM Research Europe Dublin Ireland Université Côte d’Azur Inria CNRS I3S France LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Department of Computer Science University of Liverpool United Kingdom

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research. © 2023, CC BY.

关键词： Knowledge graph

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BERT based classification system for detecting rumours on Twitter

arXiv

引用

arXiv 2021年

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Informatics Department of Mathematics and Natural Sciences Faculty Sebelas Maret University Surakarta Indonesia School of Computer Science and Software Engineering The University of Western Australia PerthWA Australia

The role of social media in opinion formation has far reaching implications in all spheres of society. Though social media provide platforms for expressing news and views, it is hard to control the quality of posts due to the sheer volumes of posts on platforms like Twitter and Facebook. Misinformation and rumours have lasting effects on society, as they tend to influence people's opinions and also may motivate people to act irrationally. It is therefore very important to detect and remove rumours from these platforms. The only way to prevent the spread of rumors is through automatic detection and classification of social media posts. Our focus in this paper is the Twitter social medium, as it is relatively easy to collect data from Twitter. The majority of previous studies used supervised learning approaches to classify rumours on Twitter. These approaches rely on feature extraction to obtain both content and context features from the text of tweets to distinguish rumours and non-rumours. Some researchers reported that tweets contain a linguistic pattern that can be used as a crucial features in identifying rumours on Twitter. Manually extracting features however is time consuming considering the volume of tweets. We propose a novel approach to deal with this problem by utilising sentence embedding using BERT to identify rumours on Twitter, rather than the usual feature extraction techniques. BERT is a novel transformer that can capture the contextual meaning of words. We use sentence embedding using BERT to represent each tweet's sentences into a vector according to the contextual meaning of the tweet. We classify those vectors into rumours or non-rumours by using various supervised learning techniques. Our BERT based models improved the accuracy by approximately 10% as compared to previous methods. Our best model uses a multi-layer perceptron and achieves 0.869 accuracy, 0.855 precision, 0.848 recall and 0.852 F1 score. Copyright © 2021, The Authors. All rights r

关键词： Social networking (online)

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Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

引用

6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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An Activity Recognition Framework for Overlapping Activities using Transfer Learning

An Activity Recognition Framework for Overlapping Activities...

引用

2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Bilal, Muhammad Maqsood, Muazzam Mehmood, Irfan Javaid, Mubashir Rho, Seungmin Comsats University Islamabad Department of Computer Science Pakistan University of Bradford Faculty of Engineering Informatics Department of Media Design and Technology United Kingdom Sejong University Department of Software Seoul Korea Republic of

ISBN: (纸本)9781728176246

Activity recognition is gaining popularity with the increase in digital content. In video data, there is a lot of information hidden that needs to be explored. Human Activity Recognition (HAR) in video streams applies to many areas, such as video surveillance, patient health monitoring, and behavior analytics. Variation in the environment, view-point changes, occlusion, and illumination are some main challenges in HAR. Among other challenges, there is also a similar activity or overlapping activity issue that has not been explored much in past. Resolving overlapping activities classes issues can be a major contribution to overall Human Activity Recognition. Hand-crafted methods and traditional Machine Learning methods were extensively explored in past. Recently, many Deep Learning-based methods are achieved high accuracy. Convolutional Neural Network (CNN) and 3D CNN methods outperform other methods. In this paper, we proposed a Transfer Learning-based Human Activity Recognition (TLHAR) for video data streams. We used VGG16 and InceptionV3, two pre-trained CNN models, and utilized their prior training knowledge for efficient activity recognition. The purposed system outperformed existing activity recognition methods and showed state-of-the-art accuracy and less computational cost requirements than other techniques by taking the benefits of Transfer Learning. © 2020 IEEE.

关键词： Convolutional neural networks

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An Efficient Liver Tumor Detection using Machine Learning

An Efficient Liver Tumor Detection using Machine Learning

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Kalsoom, Anum Moin, Anam Maqsood, Muazzam Mehmood, Irfan Rho, Seungmin Comsats University Islamabad Department of Computer Science Pakistan University of Bradford Faculty of Engineering Informatics Department of Media Design and Technology United Kingdom Sejong University Department of Software Seoul Korea Republic of

ISBN: (纸本)9781728176246

Liver Cancer is among the most commonly diagnosed diseases in today's modern era. Liver tumor segmentation is a fundamental task to perform early diagnosis and recommend a treatment. Manual segmentation is the traditional approach to achieve the required results but it has always been a time-consuming process. There are some anomalies like ambiguous gray level color ranges similar to other neighboring organs, irregular tumor shapes, and various uneven tumor sizes which are overlooked. Due to these reasons, some semi-automated and even fully automated techniques have been put forward. However, the advancement of machine learning has been very accommodating for addressing this issue. In this paper, we propose an unsupervised machine learning technique combined with a supervised mechanism that accurately performs liver tumor segmentation. We perform clustering on our collected dataset and extract LBP features as well as HOG features from these clusters. Furthermore, we perform classification which is based on these extracted features using KNN. Furthermore, we have compared our results with two classifiers namely SVM and Ensemble to achieve a better understanding. Our proposed technique outperformed existing techniques and showed encouraging results when compared to other methods. © 2020 IEEE.

关键词： Diseases

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Designing planar cubic B-spline curves with monotonic curvature for curve interpolation

引用

Computational Visual Media 2020年第3期6卷 349-354页

作者： Aizeng Wang Chuan He Fei Hou Zhanchuan Cai Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology&Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes,e.g.,in automobile and aircraft design[1].Used in conventional parametric CAD/CAM systems,general B-splines are not adequate for aesthetic *** for the straight line and circle,monotonic curvature distribution,associated with pleasing shape,is very difficult to achieve.

关键词： curvature monotonic curves

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Automated identification of security discussions in microservices systems: Industrial surveys and experiments

arXiv

引用

arXiv 2021年

作者： Rezaei-Nasab, Ali Shahin, Mojtaba Liang, Peng Basiri, Mohammad Ehsan Raviz, Seyed Ali Hoseyni Khalajzadeh, Hourieh Waseem, Muhammad Naseri, Amineh School of Computer Science Wuhan University Wuhan430072 China Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne3800 Australia Department of Computer Engineering Shahrekord University Shahrekord64165478 Iran Department of Computer Engineering Sirjan University of Technology Sirjan*** Iran

Lack of awareness and knowledge of microservices-specific security challenges and solutions often leads to ill-informed security decisions in microservices system development. We claim that identifying and leveraging security discussions scattered in existing microservices systems can partially close this gap. We define security discussion as "a paragraph from developer discussions that includes design decisions, challenges, or solutions relating to security". We first surveyed 67 practitioners and found that securing microservices systems is a unique challenge and that having access to security discussions is useful for making security decisions. The survey also confirms the usefulness of potential tools that can automatically identify such security discussions. We developed fifteen machine/deep learning models to automatically identify security discussions. We applied these models on a manually constructed dataset consisting of 4,813 security discussions and 12,464 non-security discussions. We found that all the models can effectively identify security discussions: an average precision of 84.86%, recall of 72.80%, F1-score of 77.89%, AUC of 83.75% and G-mean 82.77%. DeepM1, a deep learning model, performs the best, achieving above 84% in all metrics and significantly outperforms three baselines. Finally, the practitioners' feedback collected from a validation survey reveals that security discussions identified by DeepM1 have promising applications in practice. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

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A New Method for Detecting Altered Text in Document Images 1

引用

2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Lopresti, Daniel Seraogi, Bhagesh Chaudhuri, Bidyut B. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents can be altered to make an illegal deal, or the date on an airline ticket can be altered to gain entry to airport terminals by breaching security. To prevent such illicit activities, this paper presents a new method for detecting altered text in a document. The proposed method explores the relationship between positive and negative coefficients of a DCT to extract the effect of distortions caused by tampering operations. Here we divide DCT coefficients into positive and negative classes, then reconstructs images from the inverse DCT of the respective positive and negative coefficients. Next, we perform Laplacian filtering over reconstructed images for widening the gap between the values of text and other pixels. Then filtered images of positive and negative coefficients are fused by an average operation. For a fused image, we generate Canny and Sobel edge images in order to investigate the effect of distortion through quality measures, namely, MSE, PSNR and SSIM used as features. In addition, for the fused image, the proposed method extracts features based on histograms over the residual images. The features are then passed on to a deep Convolutional Neural Network for classification. The proposed method is tested on our own dataset as well as two standard datasets, namely IMEI and the ICPR 2018 Fraud Contest dataset. The results show that the proposed method is effective and outperforms existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Inverse problems

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A New DCT-FFT Fusion Based Method for Caption and Scene Text Classification in Action Video Images 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Manna, Suvojit Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malayasia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Ultimo Australia

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Achieving better recognition rate for text in video action images is challenging due to multi-type texts with unpredictable backgrounds. We propose a new method for the classification of captions (which is edited text) and scene texts (which is part of an image in video images of Yoga, Concert, Teleshopping, Craft, and Recipe classes). The proposed method introduces a new fusion criterion-based on DCT and Fourier coefficients to extract features that represent good clarity and visibility of captions to separate them from scene texts. The variances for coefficients of corresponding pixels of DCT and Fourier images are computed to derive the respective weights. The weights and coefficients are further used to generate a fused image. Furthermore, the proposed method estimates sparsity in Canny edge image of each fused image to derive rules for classifying caption and scene texts. Lastly, the proposed method is evaluated on images of five above-mentioned action image classes to validate the derived rules. Comparative studies with the state-of-the-art methods on the standard databases show that the proposed method outperforms the existing methods in terms of classification. The recognition experiments before and after classification show that the recognition performance rate improves significantly after classification. © 2020, Springer Nature Switzerland AG.

关键词： Classification (of information)

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