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Feasibility study and design of an underground entry/access structure at an underground gold mine

- Carlisles, B., Koroznikova, Larissa, Javidan, Fatemeh, Khandelwal, Manoj

  • Authors: Carlisles, B. , Koroznikova, Larissa , Javidan, Fatemeh , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 56th U.S. Rock Mechanics/Geomechanics Symposium, Santa Fe, USA, 26-29 June 2022, 56th U.S. Rock Mechanics/Geomechanics Symposium
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  • Description: This paper investigates the viability of an increase in waste rock storage by backfilling the Falcon pit and extending the portal of the Fosterville gold mine, located in Bendigo, Australia. The structure will maintain the current access to the tunnel whilst developing a fillable void. Once completed the project will allow for a total increase of 900, 000 cubic metres of storage. Furthermore, a finite element study has been conducted to investigate the structural performance of a proposed design using corrugated steel sheets. Stresses and displacements are studied taking into account various design factors such as steel properties and geometry. Results demonstrate the location of critical stress values according to the proposed design. The selection of optimum steel geometry is also investigated with regards to the factor of safety. © 2022 ARMA, American Rock Mechanics Association.

Fuzzy-based operational resilience modelling

- Ur-Rehman, Attiq, Kamruzzuman, Joarder, Gondal, Iqbal, Jolfaei, Alireza

Geometric design of the limaçon rotary compressor using bayesian optimization

- Lu, Kui, Sultan, Ibrahim, Phung, Truong

  • Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 3rd International Conference on Energy and Power, ICEP 2021, Chiang Mai, Thailand, 18-20 November 2021, AIP Conference Proceedings 2681 Vol. 2681
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  • Description: In the design of positive displacement compressors, the mathematical relationship between design parameters and design objectives is usually impractical and costly to be extracted, making the optimization process becomes a 'black-box' problem. In the available literature, the Bayesian optimization method, compared to other optimization techniques, has been proven as an accurate and efficient method to solve the 'black-box' problem. However, the application of such a method in the design of the rotary compressor has not been discussed in published papers. Hence, the current study is intended to employ Bayesian optimization to geometric design a class of positive displacement compressors, which is known as the limaçon compressor. In this paper, the geometric characteristics of the limaçon compressor are presented, and a function, which incorporates volumetric and geometric aspects, is employed to evaluate the optimization process and to reveal the optimum design scenario as per design requirements. A case study is offered to prove the validity of the presented approach. © 2022 American Institute of Physics Inc.. All rights reserved.

Graph augmentation learning

- Yu, Shuo, Huang, Huafei, Dao, Minh, Xia, Feng

  • Authors: Yu, Shuo , Huang, Huafei , Dao, Minh , Xia, Feng
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 31st ACM Web Conference, WWW 2022, Virtual, online, 25 April 2022, WWW 2022 - Companion Proceedings of the Web Conference 2022 p. 1063-1072
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  • Description: Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization. © 2022 ACM.

High step-up common grounded switched Quasi Z-source dc-dc converter using coupled inductor

- Samadian, Ataollah, Marangalu, Milad, Hosseini, Seyed, Sabahi, Mehran, Islam, Md Rabiul, Shah, Rakibuzzaman

Human pose based video compression via forward-referencing using deep learning

- Rajin, S.M. Ataul Karim, Murshed, Manzur, Paul, Manoranjan, Teng, Shyh, Ma, Jiangang

  • Authors: Rajin, S.M. Ataul Karim , Murshed, Manzur , Paul, Manoranjan , Teng, Shyh , Ma, Jiangang
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022, Suzhou, China,13-16 December 2022, 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
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  • Description: To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored 'big' surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding. © 2022 IEEE.

Image quality assessment metric fusing traditional and dempster-shafer theory

- Kaur, Roopdeep, Karmakar, Gour

  • Authors: Kaur, Roopdeep , Karmakar, Gour
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 26th International Conference on Pattern Recognition, ICPR 2022 Montréal, 21-25 August 2022, Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, Montreal, QC, Canada, August 21–25, 2022, Proceedings, Part II Vol. 13644 LNCS, p. 482-497
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  • Description: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, from the outdoor environment are impacted by various parameters such as lens blur, dirty lens and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as Oriented FAST and Rotated BRIEF and Structural Content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as Mean Square Error, Peak signal-to-noise ratio, Structural Content, Oriented FAST and Rotated BRIEF and Structural Similarity Index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of dataset having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we present two new image quality assessment metrics. For our combined metric, results show that the maximum values of impact level created by barrel distortion, blurriness and dirtiness are 66.6%, 87.9% and 94.4%, respectively. These results demonstrate the effectiveness of our metric to assess the impact level more accurately. The second approach fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster-Shafer (DS) theory. Our metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all above-mentioned camera impacts, our metric DS exhibits 100 % assessment reliability, which includes an enormous improvement over other metrics. © 2023, Springer Nature Switzerland AG.

Implementation and efficiency calculation of fuel-cell vehicles using a bidirectional DC/DC converter with ZVS

- Rajabi, Alireza, Marangalu, Milad, Rajaei, Amirhossein, Shahir, Farzad, Islam, Md Rabiul, Shah, Rakibuzzaman

Informing app design to reduce self-management challenges identified for chronic disease

- Firmin, Sally, Khurram, Sadaf

  • Authors: Firmin, Sally , Khurram, Sadaf
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 246-249
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  • Description: Self-managing chronic diseases is challenging for patients and involves handling a range of long-term treatments. Patients have many responsibilities to manage. Feeling overwhelmed by this information can lead to a sense of hopelessness and depression. Mobile apps can be beneficial support for chronically ill patients to selfmanage their condition. Many apps are available for various purposes, such as fitness and daily wellbeing, but none are customised for chronically ill patients who need to manage their disease daily. The purpose of this paper is to propose an approach to identify the critical challenges faced by patients when self-managing their chronic illness. These challenges will inform the design of a customised mobile app that is user friendly and will assist patients in managing their disease efficiently and effectively. A qualitative interpretive approach analysed through the theoretical lens of the theory of planned behaviour (TPB) is the proposed methodology for this project. Interpretivism is about understanding people in their natural world. The social world of people with chronic illnesses is lonely and isolating. The TPB connects a persons attitudes and behaviour. Understanding chronically ill patients feelings of isolation and helplessness through data gathered by semi-structured open-ended interviews will enable a thematic analysis of the critical challenges chronically ill patients face. This paper provides a unique approach for identifying and analysing critical issues that the chronically ill face from a patient viewpoint. These issues will be used to customise the design of an app for self-management purposes. © 2022 Association for Computing Machinery. All rights reserved.

Instructors’ perceptions of the development of work-readiness through simulations

- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew

  • Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
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  • Description: The global ERP software market is expected to reach $117.09 billion by 2030 (Biel, July 12, 2022). To boost graduate work-readiness, Australian institutions are adopting new pedagogical strategies by familiarising Information systems (IS) students with this highly sought-after software. One of these techniques is simulation games that provide students with a risk-free, real-world simulation of popular software to develop soft and hard skills needed by the IS industry. This exploratory study employed the Grounded Theory approach to evaluate instructors' perceptions of the influence of simulation games on the work-readiness of information systems students. We conducted semi-structured interviews with (Enterprise Resource Planning Simulation) ERPsim game laboratory instructors. The authors utilised Work Readiness Integrated Competency Model to map the three learning outcomes from the interviews’ analysis: abilities, knowledge, and attitudes. The mapping demonstrated that simulation games could support the development of specific skills and attitudes needed by the information systems sector. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.

Integrating steady-state and dynamic gene expression data for improving genetic network modelling

- Gill, Jaskaran, Chetty, Madhu, Shatte, Adrian, Hallinan, Jennifer

  • Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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  • Description: Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling. © 2022 IEEE.

Intelligent techniques for prediction of drilling rate for percussive drills in topically weathered limestone

- Bhatawdekar, Ramesh, Roy, Bishwajit, Changtham, Saksarid, Khandelwal, Manoj, Armaghani, Danial, Mohamad, Edy, Pathak, Pranjal, Mondal, Subhrojit, Kumar, Radhikesh, Azlan, Mohd

  • Authors: Bhatawdekar, Ramesh , Roy, Bishwajit , Changtham, Saksarid , Khandelwal, Manoj , Armaghani, Danial , Mohamad, Edy , Pathak, Pranjal , Mondal, Subhrojit , Kumar, Radhikesh , Azlan, Mohd
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 457-471
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  • Description: Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weathering. Weathering of limestone takes place very rapidly in tropical (wet) climatic regions. Previous researchers have correlated different individual rock mass properties with rate of drilling. However, single property of limestone is not adequate to correlate with the drilling rate. In this study, sensitivity analysis of different properties of weathered limestone was carried out with respect to drilling rate. Rock density, rock quality designation (RQD), geological strength index (GSI), point load index (PLI) and Schmidt hammer rebound number (SHRN) were identified as crucial input parameters. 113 data sets were collected with the foregoing five input parameters and the output parameter as drilling rate of percussive drills. Data was analysed with multi variable regression analysis (MVRA) which showed R2 value as 0.54. Artificial neural network (ANN) has been widely used for solving various engineering problems. On the other hand, optimization problems are solved by the Biogeography Based Optimization (BBO) model. Further this data was analysed with a hybrid intelligent model namely BBO- ANN. The R2 values for training data set and testing data set 0.638 and 0.761 respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

International workshop on data-driven science of science

- Bu, Yi, Liu, Meijun, Zhai, Yujia, Ding, Ying, Xia, Feng, Acuña, Daniel, Zhang, Yi

  • Authors: Bu, Yi , Liu, Meijun , Zhai, Yujia , Ding, Ying , Xia, Feng , Acuña, Daniel , Zhang, Yi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022, Washington, USA, 14-18 August 2022, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining p. 4856-4857
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  • Description: Citation data, along with other bibliographic datasets, have long been adopted by the knowledge and data discovery community as an important direction for presenting the validity and effectiveness of proposed algorithms and strategies. Many top computer scientists are also excellent researchers in the science of science. The purpose of this workshop is to bridge the two communities (i.e., the knowledge discovery community and the science of science community) together as the scholarly activities become salient web and social activities that start to generate a ripple effect on broader knowledge discovery communities. This workshop will showcase the current data-driven science of science research by highlighting several studies and constructing a community of researchers to explore questions critical to the future of data-driven science of science, especially a community of data-driven science of science in Data Science so as to facilitate collaboration and inspire innovation. Through discussion on emerging and critical topics in the science of science, this workshop aims to help generate effective solutions for addressing environmental, societal, and technological problems in the scientific community. © 2022 Owner/Author.

Issues and challenges with information systems for asset management, maintenance and reliability

- Chattopadhyay, Gopinath, Menta Venkata, Hari, Oseni, Taiwo, Reddy, Venkat, Singh, Amar, Kandra, Harpreet

  • Authors: Chattopadhyay, Gopinath , Menta Venkata, Hari , Oseni, Taiwo , Reddy, Venkat , Singh, Amar , Kandra, Harpreet
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
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  • Description: Technological changes have been happening in production facilities and infrastructure industries in an ever-increasing rate for asset management. maintenance and reliability. The use of an Asset Management System (AMS) in line with ISO55000 as a documented system and supported by information system is a long- term journey and development efforts will continue to be an essential part of its utilisation for retaining value of and realising value from assets. It is essential to regularly update information, systems and technologies to meet operational, legal, and regulatory requirements (Hastings, 2021). Data integrity is important factor in AMS. While changes are inevitable, the use of AMS must incorporate both the need to make changes, and the ability to exercise control over the validity of changes (Hastings, 2021). A management team that meticulously cares about its assets and incorporates a proactive culture by demonstrating leadership and providing necessary support for excellence to asset management supported by data driven risk informed decisions through effective utilisation of information systems is key to success of any organisation. Good practices in data collection, recording and analysis using user-friendly information system, capability and motivation are essential for risk informed and value driven decision making through systems, tools and technologies. This helps in moving to higher levels of asset management maturity and utilisation of information system for reducing costs, risks and enhancing performance. The scope of this article is limited to some sectors, their issues and challenges and related needs in this area from user perspective. It covers literature review along with reflections from industries on the current gaps, lessons learned and opportunities for improvements. © 2022 IEEE.

Layered malicious nodes detection with graph attention network in human-cyber-physical networks

- Lin, Yuhang, Huang, Yanze, Hsieh, Sun-Yuan, Lin, Limei, Xia, Feng

Learning, Un-Learning, and Relearning in 4IR in Rural Environments

- Kishore, N, Pretorius, Jan, Chattopadhyay, Gopi

  • Authors: Kishore, N , Pretorius, Jan , Chattopadhyay, Gopi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022, Anand, India, 12-15 December 2022, 2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022
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  • Description: The Fourth Industrial Revolution (4IR) requires constant learning by people and machines to belong. Every person and all devices have become a node (i.e., attached to a network) [1] to "exist."The relevance of possessing the most important literacies and skills has put pressure on people from all ages and walks of life. For humans to obtain knowledge (technology, data, and human literacy) in 4IR, there will be a new generation of knowledge acquisition in the different stages of the human cycle due to radical industrial change. The need for 21st-century children to incorporate 4IR in their learning is to improve STEM (Science, Technology, Engineering, and Mathematics) performance and critical thinking ability. The learning phase for children will create more logical and analytical adults that can co-exist with robots, cobots (collaborative robots), and AI (Artificial Intelligence). This form of learning is achieved through teaching children coding and robotics at schools from early childhood development (ECD) up until adulthood [2]. However, the critical condition of acquiring the set of skills is having access to them frequently. In rural areas, the basics for humanity to exist is the first battle that is fought daily before the children can even think of education. Hence, there are two battles kids in South Africa (especially the rural area) must overcome. However, once the children have access to education, learning 4IR will be the only type of knowledge they know. The same first-time learning of 4IR cannot be applied to adults with skills acquired previously through formal qualifications and trade experience, as one needs to thrive in the digital economy and new trends [3]. One must become a lifelong learner. Hence, the significant challenges people who live in Rural areas must overcome are their daily challenges while competing to stay ahead of the latest industrial revolution. This gives birth to the new norm of unlearning the previous survival techniques and re-learning how to co-exist in the digital machine world. The article shows the impact of 4IR and asset management interventions in the health sector in rural environments, enabling professionals to learn, unlearn, and relearn. The introduction of a new Health System using 4IR shares the benefits to healthcare workers and patients. © 2022 IEEE.

Machine learning-based agoraphilic navigation algorithm

- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, 17-20 October 2022, Proceedings IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Vol. 2022-October
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  • Description: This paper presents a novel machine learning-based Agoraphilic (free space attraction) navigation algorithm. The proposed algorithm is capable of undertaking local path planning for mobile robots in unknown dynamic environments with a moving goal. The inability to track and reach a moving goal is one of the common weaknesses of most existing navigation algorithms operating in dynamic environments. High uncertainty involved in dynamic environments is also another major challenge. The novel machine learning-based approach helps the proposed algorithm to successfully overcome these challenges. This paper also introduces the integrated modular-based architecture for free-space attraction-based algorithms. This allows the algorithm to incorporate ten different modules with miscellaneous algorithms to perform sub-tasks such as tracking, prediction, map generation, machine learning-based free space attraction force generation and robot motion command generation. The new modular-based architecture integrates those sub-modules to create the robot's driving force. This driving force is the single attractive force to pull the robot towards the moving goal via current free space leading to future free space passages. The proposed algorithm was experimentally tested under a dynamic environment. The experiment was focused on testing the behaviour of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that the Agoraphilic algorithm is successful in reaching a moving goal in an unknown dynamically cluttered environment. © 2022 IEEE.

Mechanical performance of tire-derived aggregate permeable pavements under live traffic loads

- Raeesi, Ramin, Soltani, Amin, King, Russell, Disfani, Mahdi

  • Authors: Raeesi, Ramin , Soltani, Amin , King, Russell , Disfani, Mahdi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th International Conference on Transportation Geotechnics, ICTG 2021 Vol. 164, p. 515-528
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  • Description: Traditional pavements in urban areas are mainly rigid, impervious surfaces, resulting in augmented surface run off during rainfalls, thereby leading to flash-flooding and pollution of waterways. In comparison, permeable pavements permit percolation of water through surface layers, thus alleviating harmful environmental impacts. This study presents the authors’ recent experience in the development of an instrumented large-scale permeable pavement trial site—constructed using a combination of crushed rock (CR) and tire-derived aggregate (TDA), bonded by a polyurethane (PU) binder—located at a car park in South Australia. An area of approximately 400 m2 was paved using different TDA-based mix designs—different CR sizes, colors, and shapes, and different PU contents. Moreover, preliminary field performance monitoring results—including surface deflection measurements by the light weight deflectometer test, and strain measurements using optic fiber and strain gauge sensing techniques—are outlined and discussed in detail. The strength and stiffness of the TDA-based blend was found to be dependent on the TDA content, TDA-to-CR size ratio, CR shape, and PU content. The greater the angularity of the CR particles, the more effective the interlocking of the TDA and CR components, and thus the higher the developed strength and stiffness. The amount of strain generated in the permeable surface layer and at its interface with the lower screening layer were both found to decrease with a decrease in TDA content. Preliminary field observations indicate that the TDA-based system can indeed be a viable (and sustainable) solution to effectively mitigate flash-flooding while sustaining low–medium traffic loads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

MET-Meme : a multimodal meme dataset rich in metaphors

- Xu, Bo, Li, Tingtin, Zheng, Junzhe, Naseriparsa, Mehdi, Zhao, Zhehuan, Lin, Hongfei, Xia, Feng

Metric learning-based few-shot malicious node detection for IoT backhaul/fronthaul networks

- Zhou, Ke, Lin, Xi, Wu, Jun, Bashir, Ali, Li, Jianhua, Imran, Muhammad

  • Authors: Zhou, Ke , Lin, Xi , Wu, Jun , Bashir, Ali , Li, Jianhua , Imran, Muhammad
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 5777-5782
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  • Description: The development of backhaul/fronthaul networks can enable low latency and high reliability, but nodes in future networks like Internet of Things (IoT) can conduct malicious activities like flooding attack and DDoS attack, which can decrease QoS of smart backhaul/fronthaul network. Timely detection of malicious nodes in future networks is significant for low-latency backhaul/fronthaul networks. However, conventional supervised learning-based detection models require abundant malicious training samples, while capturing adequate malicious samples can not meet the requirement of timely detection. In this paper, we propose a novel few-shot malicious node detection system for improving QoS of IoT backhaul/fronthaul network, which can detect malicious nodes with unknown malicious activities through a limited number of network traffic samples. In our proposed system, we first design a fresh IoT traffic sample processing approach, which integrates normal activity samples and known malicious activity samples to generate training pairs. Then, we design a metric learning-based malicious node detection model training method, which employs a contrastive loss over distance metric to distinguish between similar and dissimilar pairs of samples. Besides, the trained model can detect nodes with unknown malicious activities by comparing real-time samples with few-shot samples of malicious nodes. Finally, the proposed system is evaluated on a real-world IoT network dataset named N-BaIoT. The exhaustive experiment results show that our model can achieve an average accuracy around 97.67 % when detecting malicious nodes with unknown malicious activities, which is comparable to state-of-the-art supervised learning models while our model only needs 5-shot samples of malicious node. © 2022 IEEE.