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2018 International computers, Signals and Systems Conference, ICOMSSC 2018

作者： Xu, Wenge Liang, Haining Yue, Yong Xi'an Jiaotong Liverpool University Dept. of Computer Science and Software Engineering Suzhou China

ISBN: (纸本)9781538667514

This research, conducts an experiment to investigate whether there is a difference in user performance and preference for two types of head-mounted displays (HMDs) when users need to perform directional motion movements such as moving one foot forward and backward (or leftward or rightward). The two types of HMDs we have considered are virtual reality (VR) and augmented reality (AR), which represent the two most commonly marketed HMDs. The AR device chosen for this research is the Meta 2 while the chosen VR device is the Oculus RIFT CV1. The results of our experiment show that there is a close significant difference on task completion time between AR and VR condition when users perform motion direction tasks. Also, no significant effect has been found on the accuracy of making these motion movements between the AR and VR conditions. In terms of user preference, the results show that there is no significant effect on workload, motion sickness, immersion, and user experience. These results suggest that both AR and VR HMDs are suitable for interfaces that can rely on body motions like tapping on the floor using one foot in any of the directions around the user. © 2018 IEEE.

关键词： Augmented reality

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Goal-Aware RSS for Complex Scenarios via Program Logic

arXiv

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arXiv 2022年

作者： Hasuo, Ichiro Eberhart, Clovis Haydon, James Dubut, Jérémy Bohrer, Rose Kobayashi, Tsutomu Pruekprasert, Sasinee Zhang, Xiao-Yi Pallas, Erik André Yamada, Akihisa Suenaga, Kohei Ishikawa, Fuyuki Kamijo, Kenji Shinya, Yoshiyuki Suetomi, Takamasa Tokyo101-8430 Japan Dept. Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609-2280 United States Inst. Software & Systems Engineering University of Augsburg Universitätstraße 6a AugsburgD-86135 Germany Cyber Physical Security Research Center AIST Aomi 2-4-7 Tokyo135-0064 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Mazda Motor Corporation Fuchu730-8670 Japan SOKENDAI The Graduate University for Advanced Studies Japan Tokyo Japan

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules;we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement. © 2022, CC BY-NC-ND.

关键词： computer circuits

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Android malware detection using feature selections and random forest 4

Android malware detection using feature selections and rando...

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4th International Conference on software Security and Assurance, ICSSA 2018

作者： Eom, Taehoon Kim, Heesu An, SeongMo Park, Jong Sou Kim, Dong Seong Dept. of Computer Engineering Korea Aerospace University Seoul Korea Republic of Dept. of Computer Science and Software Engineering University of Queensland Australia

ISBN: (纸本)9781538692103

Malicious software (Malware) applications in Android ecosystem is one of the critical issues. Manual detection of malware is not cost-effective and cannot keep up with the fast evolution of malware development in Android. A machine learning based malware detection has attempted to automate the detection of malware in Android. In this paper, we present new Android malware detection methods. The main idea of our proposed approach is to use three different feature selection methods before malware detection model using a machine learning algorithm is constructed. We used both Malware Genome Project dataset and our own crawled dataset to show the effectiveness of the proposed methods. © 2018 IEEE.

关键词： Feature Selection

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A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study

A Novel AI-enabled Framework to Diagnose Coronavirus COVID-1...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Halgurd S. Maghded Kayhan Zrar Ghafoor Ali Safaa Sadiq Kevin Curran Danda B. Rawat Khaled Rabie Department of Software Eng. Faculty of Engineering Koya University Kurdistan Region-F.R. Iraq Department of Software Eng. Salahaddin University-Erbil Kurdistan Region-F.R. Iraq School of Math and Computer Science University of Wolverhampton Wolverhampton UK School of Computing Ulster University Londonderry UK Dept. of Electrical Eng. and Computer Science Howard University Washington DC USA Dept. of Electrical and Electronic Eng. Manchester Metropolitan University UK

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today's smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors' signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease.

关键词： Computed tomography Diseases Lung Temperature sensors Viruses (medical) Temperature measurement

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Vanadium Redox & Lithium Ion Based Multi-Battery Hybrid Energy Storage System for Microgrid

Vanadium Redox & Lithium Ion Based Multi-Battery Hybrid Ener...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Oluwatobiloba Adeleke Abhisek Ukil Xibeng Zhang School of Electronics and Computer Science University of Southampton United Kingdom Dept. of Electrical Computer and Software Engineering University of Auckland New Zealand

Energy Storage System plays a vital role in assisting Microgrids to control fluctuating load demand with intermittent power supply. As well as enabling power quality to monitored and controlled, this paper introduces a power storage device, Vanadium oxide flow battery which would facilitate the expansion and allow flexibility with the Microgrid network.

关键词： Batteries State of charge Microgrids Vanadium Voltage control Lithium

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Mind Drone Chasing Using EEG-based Brain computer Interface

Mind Drone Chasing Using EEG-based Brain Computer Interface

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International Conference on Intelligent Environments (IE)

作者： Fatima Ali Al-Nuaimi Rauda Jasem Al-Nuaimi Sara Saaed Al-Dhaheri Sofia Ouhbi Abdelkader Nasreddine Belkacem Dept. Information Systems and Security CIT UAE University Al Ain UAE Dept. Comp. & Network Engineering CIT UAE University Al Ain UAE Dept. Computer Science & Software Eng. CIT United Arab Emirates University Al Ain UAE

ISBN: (数字)9781728161587

ISBN: (纸本)9781728161594

In this paper, we present a new way of controlling drone by using a P300-based brain-computer interface that supports the military field as assistive technology. The main idea is that the drone can be controlled by the soldier's brain activity using electroencephalogram (EEG) to chase other drones and discover hidden enemy areas. We assumed that we have two able-bodied users, the first role played by a user as a soldier aims to control the drone by using brain activity and the other role played by another user as an enemy aims to control manually the drone using Python program. This scenario allowed us to test the ability of chasing the enemy's drone. The results for this application was evaluated by the ability of the user to calibrate very well with the software and the ability of the program to receive and send commands using EEG signal to the drone for execution.

关键词： Drones Electroencephalography Portable computers Electrodes Visualization Brain software

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Quantifying the quality of indoor maps 4

Quantifying the quality of indoor maps

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4th ISPRS Geospatial Week 2019

作者： Assali, M. Pipelidis, G. Podolskiy, V. Iwaszczuk, D. Heinen, L. Gerndt, M. Dept. of Computer Architecture and Parallel Systems Dept. of Informatics Technical University of Munich Munich Germany Software and Systems Engineering Research Group Dept. of Informatics Technical University of Munich Munich Germany Remote Sensing and Image Analysis Group Dept. of Civil and Environmental Engineering Science Technische Universität Darmstadt Germany BMW Group IT Munich Germany

Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality. © Authors 2019.

关键词： Robots

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KerTSDroid: Detecting Android Malware at Scale through Kernel Task Structures

KerTSDroid: Detecting Android Malware at Scale through Kerne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xinning Wang Chong Li School of Engineering Ocean University of China Qingdao China Dept. of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (纸本)9781728125848

The explosive growth of data makes static and dynamic analysis in an individual machine difficult to detect Android malware. Hence many popular open-source frameworks for large-scale data processing have been used to deal with iterative machine learning tasks due to the efficient parallel computation and the in-memory abstraction. In this paper we first propose KerTSDroid, a novel Android malware detection framework, to predict the malicious applications in parallel through kernel task structures in Android kernel layer. Then a data provider is orchestrated and implemented to construct the real-world dataset of 112 kernel features from 12,750 Android applications. A parallel predictor is also provided to identify Android malicious applications. Our experiments demonstrate that a Decision Tree method of KerTSDroid can achieve the best precision rate (96%-99%) with a lower overhead on average, the other three methods (Naive Bayes, Logistic Regression, and Support Vector Machine) lead to lower precision rate while discriminating abnormal behaviors with the in-memory parallel-data.

关键词： Android system Kernel features Apache spark Malware detection Machine learning Methyltestosterone Androids Spyware Artificial Intelligence Machine Learning Scale formation Seeds abnormal behavior

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II)

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Haibin Zhu George Baciu Guiming Luo Fernando Rubio Jie Sui Runhe Huang Hironori Hiraishi Jun Peng Liang Chen FI2CICC Dept of ECE Univ. of Manitoba Winnipeg Canada FI2CICC Nipissing University ON Canada FI2CICC Dept. of Computing Hong Kong Polytechnic University Hong Kong FI2CICC Software Institute Tsinghua University Beijing China FI2CICC Dept. of Computer Systems &#x0026 Computation Complutense Univ. of Madrid Spain FI2CICC Aberdeen University Aberdeen UK FI2CICC Faculty of Computer and Information Sciences Hosei University Japan Faculty of Engineering Ashikaga University Tochigi Japan FI2CICC School of AI Chongqing Univ. of Science and Technology Chongqing China Dept. of Computer Science Univ. of Northern British Columbia BC Canada

ISBN: (纸本)9781665421201

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.

关键词： Knowledge engineering Intelligent vehicles Autonomous systems Conferences Cognitive informatics Cognitive robotics Cognitive systems

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Smart Parking System Based on Convolutional Neural Network Models

Smart Parking System Based on Convolutional Neural Network M...

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International Conference on Information science and Control engineering (ICISCE)

作者： Wenjin Zhang Jason Yan Cui Yu Dept. of Computer Science & Software Engineering Monmouth University NJ USA High Technology High School NJ USA

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

Most people have probably experienced the hassle of parking space scarceness. To alleviate this problem, the relatively traditional solution is making use of sensors, which has its drawbacks in terms of installation and maintenance cost especially if precise location is in consideration. In order to find a more affordable and functional solution, in this project, cameras are chosen for data input, and deep learning neural networks models are developed. Convolutional neural networks have achieved very good performance in the area of computer vision in recent years. We explore in this direction and propose VGG Extended Model and Mini Parking CNN Model. Testing is done to evaluate the time cost and recognition accuracy. Feasibility is prototyped, named as SmartParking. The Mini Parking CNN Model demonstrates excellent performance and this system built with it can help users to find empty parking space accurately and effectively.

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