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Active model selection for positive unlabeled time series classification

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 36th IEEE International Conference on Data Engineering, ICDE 2020 Vol. 2020-April, p. 361-372
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  • Description: Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle. © 2020 IEEE.
  • Description: E1

An approach to map geography mark-up language data to resource description framework schema

- Faqir, Ammara, Mahmood, Aqsa, Qazi, Kiran, Malik, Saleem

  • Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
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  • Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
  • Description: E1

DEFINE: friendship detection based on node enhancement

- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng

  • Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
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  • Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
  • Description: E1

Discovery of small group interactions and performance from project emails

- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally

  • Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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  • Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

Discovery of small group interactions and performance from project emails

- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally

  • Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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  • Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

Enhancing linear time complexity time series classification with hybrid bag-of-patterns

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 Vol. 12112 LNCS, p. 717-735
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  • Description: In time series classification, one of the most popular models is Bag-Of-Patterns (BOP). Most BOP methods run in super-linear time. A recent work proposed a linear time BOP model, yet it has limited accuracy. In this work, we present Hybrid Bag-Of-Patterns (HBOP), which can greatly enhance accuracy while maintaining linear complexity. Concretely, we first propose a novel time series discretization method called SLA, which can retain more information than the classic SAX. We use a hybrid of SLA and SAX to expressively and compactly represent subsequences, which is our most important design feature. Moreover, we develop an efficient time series transformation method that is key to achieving linear complexity. We also propose a novel X-means clustering subroutine to handle subclasses. Extensive experiments on over 100 datasets demonstrate the effectiveness and efficiency of our method. © 2020, Springer Nature Switzerland AG.

Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations

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

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
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  • Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.

Intensive care nurses’ knowledge of evidence-based guidelines for prevention of ventilator-associated events : an evaluation study

- Madhuvu, Auxillia, Morphet, Julia, Plummer, Virginia, Endacott, Ruth

Motivational factors of Australian mobile gamers

- Greenwood, Jordan, Achterbosch, Leigh, Meredith, Grant, Vamplew, Peter

  • Authors: Greenwood, Jordan , Achterbosch, Leigh , Meredith, Grant , Vamplew, Peter
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 4th-6th February 2020 p. 6
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  • Description: Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.

Partial undersampling of imbalanced data for cyber threats detection

- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal

  • Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
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Pre-trained language models with limited data for intent classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Karmakar, Gour, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Karmakar, Gour , Walls, Darren
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 International Joint Conference on Neural Networks, IJCNN 2020
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  • Description: Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-bystep methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements. © 2020 IEEE.

Restoring Murray River floodplain wetlands : does the sediment record inform on watering regime?

- Gell, Peter

  • Authors: Gell, Peter
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: River Research and Applications Vol. 36, p. 620-629
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  • Description: The floodplain wetlands of the southern Murray Darling Basin (MDB) have been subject to the impacts of catchment and water resource development for more than a century. Their current degraded state is attributed to the regulation of the rivers and abstraction of water volume for irrigation. The MDB Plan is to return at least 2,750 Gl of mean annual flow to the system to restore the condition of waterways. Considerable recent investment in infrastructure enables water to be released into the basin's floodplain wetlands. The proposed watering regime is underpinned by modelling that suggests that, before regulation, overbank flows would have occurred regularly as discharge peaked in winter and spring. Sediment cores have been extracted from over 50 floodplain wetlands of the southern Murray Basin. Those from several, large meander wavelength billabongs extend for 1,000–5,000 years suggesting that these sites were permanently inundated over that time. Others extend to ~200 years and are presumed not to have accumulated sediment until more recently. The records of most wetlands, however, only extend to the onset of river regulation in the 1920s, suggesting that before then they were not inundated for sufficient duration for net accumulation to occur. Preserved diatoms suggest that the shallow, plant-dominated wetlands of the past have transitioned to deep, turbid water systems today. As rivers are identified as a source of sediment to wetlands, less regular inundation, rather than more, is a viable option in restoring the ecological function of these floodplain wetlands and in slowing sediment infill. © 2019 John Wiley & Sons, Ltd.
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Towards smart online dispute resolution for medical disputes

- Bellucci, Emilia, Stranieri, Andrew, Venkatraman, Sitalakshmi

  • Authors: Bellucci, Emilia , Stranieri, Andrew , Venkatraman, Sitalakshmi
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 3rd-7th February 2020. p. 1-5
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  • Description: With the advancements in technologies, digitization of health records in the healthcare industry is undertaking a rapid revolution. This is further fueled with the entrance of Internet of Things (IoT), where mobile health devices have resulted in an explosion of health data and increased accessibility via wireless communications and sensor networks. With the introduction of an Electronic Health Record (EHR) system as an important venture for the general health and wellbeing of a country's citizens, privacy issues and medical disputes are expected to rise. In addition to critical health information being documented and shared electronically, integrating data from diverse smart medical IoT devices are leading towards increasingly more complex disputes that require immense time and effort to resolve. Online dispute resolution (ODR) programs have been successfully applied to cost-effectively help disputants resolve commercial, insurance and other legal disputes, but as yet have not been applied to healthcare. This paper takes a modest step in this direction, firstly to identify the drivers of medical disputes that include patient empowerment and technology advancements and trends. Secondly, we explore dispute resolution models and identify the status and limitations of current ODR systems.
  • Description: This work was funded by the University of Ballarat Deakin University Collaborative Fund. 160134

Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location

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

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
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  • Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
  • Description: E1

An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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  • Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Assessing transformer oil quality using deep convolutional networks

- Alam, Mohammad, Karmakar, Gour, Islam, Syed, Kamruzzaman, Joarder, Chetty, Madhu, Lim, Suryani, Appuhamillage, Gayan, Chattopadhyay, Gopi, Wilcox, Steve, Verheyen, Vincent

  • Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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  • Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
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Bayesian analysis of random finite element method slip surfaces for slope stability

- Dyson, Ashley, Tolooiyan, Ali

  • Authors: Dyson, Ashley , Tolooiyan, Ali
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 5th ISRM Young Scholars' Symposium on Rock Mechanics and International Symposium on Rock Engineering for Innovative Future, YSRM 2019 p. 118-123
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  • Description: The Random Finite Element Method (RFEM) is a powerful technique for incorporating spatially variable shear strength parameters with slope stability numerical simulations. In this research, two-dimensional probabilistic analyses of a large open-cut brown coal mine are presented with particular consideration given to slope Factors of Safety (FoS), when faced with highly anisotropic cohesion and friction angle shear strength parameters. Bayesian methods are implemented to determine updated shear strength parameters based on Factors of Safety and Representative Slip Surfaces (RSS) categorizations. By this method, the impact of observed slip surface depths and safety factors is further investigated. Monte Carlo simulation is implemented in the Finite Element environment Abaqus, with an optimised Strength Reduction Method to determine Factors of Safety. Comparisons of conditional shear strength distributions are made for associated slope safety factors and shallow slip surfaces from a cross-section of the Yallourn open-cut brown coal mine, in Victoria, Australia. The updated shear strength distributions provide a greater understanding of the necessary conditions of particular slope failure mechanisms, contributing further understanding of the stability of Victorian brown coal mines. ©2019 Japanese Society for Rock Mechanics

Blockchain leveraged task migration in body area sensor networks

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
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  • Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
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Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control

- Farivar, Faezeh, Haghighi, Mohammad, Barchinezhad, Soheila, Jolfaei, Alireza

  • Authors: Farivar, Faezeh , Haghighi, Mohammad , Barchinezhad, Soheila , Jolfaei, Alireza
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1143-1148
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  • Description: Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.

Diabetic retinopathy detection : methods and challenges

- Patil, Shivani, Kulkarni, Pradnya

  • Authors: Patil, Shivani , Kulkarni, Pradnya
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2nd IEEE Pune Section International Conference, PuneCon 2019
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  • Description: Diabetic retinopathy (DR) mostly affects the people as a result of diabetes and it leads to blindness. Diabetic retinopathy (DR) affects the eyes as a result of increased blood glucose levels. Among people in the 70-year-old age group, 50% of deaths are associated with diabetes. Detection of diabetes at a early stage and taking a proper treatment can reduce vision loss among the patients. Once DR symptoms are recognized, the severity of the disease must be assessed in order to prescribe the correct medication. There are five stages of severity of diabetic retinopathy, Mild Non Proliferative Diabetic Retinopathy (NPDR),Moderate NPDR, Severe NPDR, Proliferative Diabetic Retinopathy(PDR) and No DR(Diabetic Retinopathy).This paper summarizes DR detection methods and problems. © 2019 IEEE.
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