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19th IEEE International Conference on Embedded and Ubiquitous computing, EUC 2021

作者： Sassani Sarrafpour, Bahman A. Alomirah, Abdulaziz Pang, Shaning Sarrafpour, Soshian School of Computing Engineering Applied Technology Unitec New Zealand Auckland New Zealand School of Computing Engineering Applied Technology Unitec New Zealand Auckland New Zealand Federation University School of Engineering Information Technology Physical Science Melbourne Australia Yale University School of Medicine Newhaven United States

ISBN: (纸本)9781665400367

Sybil attack is one of the most common and serious attacks in wireless sensor networks, in which a malicious node illegitimately forges several (fake) identities. These fake copies confuse and collapse the network. Sybil attack causes too many threats to the routing algorithm, data aggregation, fair resource allocation, voting system, and misbehavior detection. In this paper, we propose a new lightweight algorithm for detecting the Sybil attack in mobile wireless sensor networks using observer nodes. Observer nodes are normal, trustful nodes which have been initially programmed to observe the network and report malicious behaviors. An observer node counts the number of times a node has appeared as a common neighbor between itself and its neighbors. After collecting some information about its neighbors, each observer node considers the nodes whose counters are above a threshold as critical, and nodes having all critical nodes in their neighborhood are considered suspicious nodes. The results show that the true detection rate of the proposed algorithm is 98.1%, and its false detection rate is 0.5%, while similar algorithms could not achieve better than 95.4% and 1.2% for these metrics, respectively. In addition, the proposed algorithm outperforms other algorithms in terms of overhead. © 2021 IEEE.

关键词： Cybersecurity

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Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand

Effectiveness of Stock Index Forecasting using ARIMA model: ...

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Advancements in computing (ICAC), 2019 International Conference on

作者： Wajira Dassanayake Iman Ardekani Narmada Gamage Chandimal Jayawardena Hamid Sharifzadeh School of Applied Business Unitec Institute of Technology School of Computing Unitec Institute of Technology Sri Lanka Institute of Information Technology

ISBN: (纸本)9781665408639

Time series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.

关键词： Training Performance evaluation Analytical models Computational modeling Time series analysis Memory management Predictive models

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Coding Observer Nodes for Sybil Attacks Detection in Mobile Wireless Sensor Networks

Coding Observer Nodes for Sybil Attacks Detection in Mobile ...

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IEEE/IFIP International Conference on Embedded and Ubiquitous computing, EUC

作者： Bahman A. Sassani Sarrafpour Abdulaziz Alomirah Shaning Pang Soshian Sarrafpour School of Computing Engineering & Applied TechnologyUnitec New ZealandAucklandNew Zealand School of Engineering Information Technology & Physical Science Federation University Melbourne Australia School of Medicine Yale University Newhaven USA

ISBN: (纸本)9781665400374

关键词： Wireless communication Measurement Wireless sensor networks Observers Ubiquitous computing Routing Encoding

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Enhancing customers' knowledge and decision making using augmented reality 28

Enhancing customers' knowledge and decision making using aug...

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28th International Conference on Computers in Education, ICCE 2020

作者： Boonrat, Wisanoo Vaidya, Vimita Baghaei, Nilufar Sharifzadeh, Hamid Ahmed, Ali Casey, John Department of Information Technology Otago Polytechnic Auckland Campus New Zealand School of Natural and Computational Sciences Massey University Auckland New Zealand School of Computing Unitec Institute of Technology New Zealand School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9789869721455

Augmented reality has seen massive success in recent years as it provides an opportunity for a seamless and rich user interaction with the real world. Recent studies have shown augmented and virtual reality can play a significant role in the future of retail. Innovations can help customers make good choices and can improve their confidence and satisfaction in their purchasing decisions. It also reduces the physical interaction between customers and store especially during lockdowns (i.e. during pandemics such as COVID-19). This paper presents ARiel, an augmented reality application for mobile commerce (m-commerce), enabling customers to turn catalogued items into 3D animated models. It was developed using Google's ARCore platform. Customers can try on products such as jewellery or watches using their mobile phones without being physically present in the retail shop. Data about their purchasing decisions are logged in a database. We describe the process of designing and implementing ARiel and the outcome of an evaluation using Nielsen's ten usability heuristics. We also outline our plan for future work. © ICCE 2020 - 28th International Conference on Computers in Education, Proceedings. All rights reserved.

关键词： Augmented reality

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AgentPi: An IoT Enabled Motion CCTV Surveillance System

AgentPi: An IoT Enabled Motion CCTV Surveillance System

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Bahman A. Sassani Andrew David Xiaosong Li Farhad Mehdipour Unitec Institute of Technology Auckland New Zealand School of Computing and Information Technology Unitec Institute of Technology New Zealand Otago Polytechnic - Auckland International Campus Auckland New Zealand Otago Polytechnic - Auckland International Campus (OPAIC) Auckland New Zealand

ISBN: (纸本)9781728130255

In this paper, we introduce a prototype of surveillance system rigged with several vital modules and mobility as an optional functionality. The proposed cost-efficient system features motion and anomaly detection without physical sensors, instant trigger notices via SMS/text and e-mail, live streaming internally and remotely including recording upon anomaly detection. As a result, savings on resources such as storage space, personnel to monitor, time and costs are promoted. Mobility for this system is anticipated to be achieved through integration of the ever-evolving mobile broadband with the current vastly available 4G LTE and 3G UMTS technology. Most importantly, this proposed concept is affordable with the availability of open source resources and minimal hardware, so that everyone can proactively exercise security precaution for their premise or tangible property.

关键词： Security Surveillance Streaming media Hardware Servers Cameras Wireless communication

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S-boxes Construction Based on Quantum Chaos and PWLCM Chaotic Mapping

S-boxes Construction Based on Quantum Chaos and PWLCM Chaoti...

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IEEE International Conference on Cognitive Informatics

作者： Jun Peng Shaoning Pang Du Zhang Shangzhu Jin Lixiao Feng Zuojin Li School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing China School of Computing and Information Technology Unitec Institute of Technology Auckland New Zealand Faculty of Information Technology Macau University of Science and Technology Macau S.A.R China Office of Academic Research Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis (decoding). In this paper, we incorporate quantum chaos and PWLCM chaotic mapping into a new method of S-box design. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, and sensitivity to the key, respectively. The results show that the cryptographic characteristics of proposed S-Box has met our design objectives and can be applied to data encryption, user authentication and system access control.

关键词： Chaos Boolean functions Electronic mail Encryption Design methodology information technology

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Multi-echelon Supply Chain Flexibility Enhancement Through Detecting Bottlenecks

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Global Journal of Flexible Systems Management 2016年第4期17卷 357-372页

作者： Kazemian, Iman Aref, Samin Department of Industrial Engineering College of Engineering University of Tehran North Kargar PO Box: 4563-11155 Tehran Iran Department of Computer Science University of Auckland Private Bag 92019 Auckland New Zealand Department of Computing and Information Technology Unitec Institute of Technology Private Bag 92025 Auckland New Zealand

This study suggests a supply chain design deploying a novel idea from production planning. The idea of capacity bottlenecks is used to improve flexibility in a multi-echelon multi-product supply chain. We suggest an optimization model that focuses on optimal capacity allocations to bottleneck points in order to enhance overall flexibility. The proposed mixed-integer linear programming model minimizes the total cost of facility establishment as well as their utilization and transportation cost. The performance of suggested model is investigated by several test problems with uncertainty in demand, cost, capacity, and product specifications. The results indicate the superiority of the suggested model to the previous flexibility formulation method. According to the numerical results, the proposed model decreases the total supply chain cost by up to 16 % on average. Another advantageous feature of the proposed model is its capability of solving previously insolvable test problems by optimizing flexibility levels. © 2016, Global Institute of Flexible Systems Management.

关键词： Bottleneck Flexibility Multi-echelon Network design Supply chain Uncertainty

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A process mining technique using pattern recognition 27

A process mining technique using pattern recognition

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CAiSE 2015 Forum at the 27th International Conference on Advanced information Systems Engineering, CAiSE 2015

作者： Liesaputra, Veronica Yongchareon, Sira Chaisiri, Sivadon Department of Computing and Information Technology Unitec Institute of Technology New Zealand School of Information Technology Shinawatra University Thailand

Several works have proposed process mining techniques to discover process models fromevent logs. With the existing works, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces in the logs. In this paper, we propose a novel technique for process mining based on using a pattern recognition technique called Maximal Pattern Mining (MPM). Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Furthermore, by using the MPM, the discovered models are generally much easier to understand.

关键词： Pattern recognition

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Efficient FxLMS algorithm with simplified secondary path models 40

Efficient FxLMS algorithm with simplified secondary path mod...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Ardekani, Iman T. Sharifzadeh, Hamid Rehman, Saeed Abdulla, Waleed H. Computing and Information Technology Department Unitec Institute of Technology Auckland New Zealand ECE Department University of Auckland Auckland New Zealand

ISBN: (纸本)9781467369978

This paper extends the existing work on the root locus analysis of FxLMS algorithm by considering secondary path modeling errors. Rules for sketching FxLMS root locus are set out. An analytic convergence condition is then derived from the root locus plot. A deliberately-misaligned secondary path model is proposed to be used as the data preparation filter in the FxLMS algorithm. The proposed filter increases the computational efficiency of the algorithm, without changing its convergence behavior. The theoretical results are verified in practice by busing an experimental system. © 2015 IEEE.

关键词： active noise control FxLMS algorithm root locus analysis secondary path modeling

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Keynote speech 1: Online machine learning for big data analytics by cognitive robots

Keynote speech 1: Online machine learning for big data analy...

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IEEE International Conference of Online Analysis and computing Science (ICOACS)

作者： Yingxu Wang Guoyin Wang Abdolhossein Sarrafzadeh Information Systems Lab Stanford University USA Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing 40065 China Department of Computing Faculty of Creative Industries and Business Unitec Institute of Technology New Zealand

It is recognized that both sources of and solutions to the big data challenges are human collective intelligence. Data are an abstract representation of the quantity of realistic entities and perceived objects. Big data play an indispensable role not only in a wide range of engineering applications, but also in the cognitive mechanisms of both humans and cognitive robots such as sensation, quantification, qualification, estimation, memory, and reasoning. This keynote lecture presents online big data analytic theories by machine learning as well as knowledge extraction by cognitive robotics. Data, information, knowledge, and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom up. It is discovered by the author that, although the cognitive unit of data is bit, that of knowledge is bir, i.e., a binary relation. A resent finding towards big data science is that big data systems in nature are a recursive n-dimensional typed hyperstructure (RNDTHS). This topological property of big data reveals that the mathematical foundation of big data science is underpinned by big data algebra (BDA), which is a denotational mathematical structure for efficiently dealing with the inherited complexities and unprecedented challenges in big data engineering. This leads to a coherent theory for big data modeling, analyses, mining, information elicitation, knowledge extraction, and intelligent generation for cognitive robots. Latest experiments demonstrate that cognitive robots may autonomously transform big data in vast linguistic databases (corpuses) into sophistic knowledge bases by cognitive machine learning.

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