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Modification of polyp size and shape from two endoscope images using RBF neural network 4th

Modification of polyp size and shape from two endoscope imag...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Iwahori, Yuji Tsuda, Seiya Woodham, Robert J. Bhuyan, M.K. Kasugai, Kunio Chubu University Matsumoto-cho 1200 Kasugai Aichi487-8501 Japan Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Department of Gastroenterology Aichi Medical University Nagakute-cho Aichi-gun Aichi480-1195 Japan

ISBN: (纸本)9783319276762

In the medical imaging applications, endoscope image is used to observe the human body. the VBW (Vogel-Breus-Weickert) model is proposed as a method to recover 3-D shape under point light source illumination and perspective projection. However, the VBW model recovers relative, not absolute, shape. Here, shape modification is introduced to recover the exact shape. Modification is applied to the output of the VBW model. First, a local brightest point is used to estimate the reflectance parameter from two images obtained with movement of the endoscope camera in depth. A Lambertian sphere image is generated using the estimated reflectance parameter and VBW model is applied for a sphere. then Radial Basis Function Neural Network (RBF-NN) learning is applied. the NN implements the shape modification. NN input is the gradient parameters produced by the VBW model for the generated sphere. NN output is the true gradient parameters for the true values of the generated sphere. Here, regression analysis is introduced in comparison with the performance by NN. Depth can then be recovered using the modified gradient parameters. Performance of shape modification by NN and regression analysis was evaluated via computer simulation and real experiment. the result suggests that NN gives better performance than the regression analysis to improve the absolute size and shape of polyp. © Springer international Publishing Switzerland 2015.

关键词： Regression analysis

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Blockchain-Based Multirole Authentication and Authorization in Smart Contracts with a Hierarchical Factory pattern

Blockchain-Based Multirole Authentication and Authorization ...

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Blockchain Computing and Applications (BCCA), international conference on

作者： Muhammad Bin Saif Sara Migliorini Fausto Spoto Department of Computer Science University of Verona Verona Italy

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

the recent spread of smart contracts in several application domains presents challenges in scalability, efficiency, management, and security. In this regard, some design patterns have been proposed to factorize common parts, prevent replicated deployment by promoting reusability, and apply some role-based access control (RBAC) techniques during smart contract execution. In particular, the factory pattern has been widely recognized as a common practice to solve the scalability issue, reducing also the increased gas costs related to both smart contract deployment and its subsequent use. However, in real-world applications, the factory pattern can be considered too limited since it only allows the instantiation of a single type or family of contracts. On the other side, no solutions have been proposed that tightly integrate a factory pattern with RBAC functionalities. In this paper, we propose an extension of the factory pattern, called the hierarchical factory pattern, which integrates a multirole authentication and authorization mechanism tailored explicitly to this pattern since it allows a secure, efficient, and role-specific interaction between the various layers of the hierarchy. the paper ends with some considerations about performance and scalability.

关键词： Authorization Costs Scalability Smart contracts Authentication Production facilities pattern recognition Blockchains Optimization

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A feature selection-based framework for human activity recognition using wearable multimodal sensors 11

A feature selection-based framework for human activity recog...

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Proceedings of the 6th international conference on Body Area Networks

作者： Mi Zhang Alexander A. Sawchuk University of Southern California Los Angeles CA

ISBN: (纸本)9781936968299

Human activity recognition is important for many applications. this paper describes a human activity recognition framework based on feature selection techniques. the objective is to identify the most important features to recognize human activities. We first design a set of new features (called physical features) based on the physical parameters of human motion to augment the commonly used statistical features. To systematically analyze the impact of the physical features on the performance of the recognition system, a single-layer feature selection framework is developed. Experimental results indicate that physical features are always among the top features selected by different feature selection methods and the recognition accuracy is generally improved to 90%, or 8% better than when only statistical features are used. Moreover, we show that the performance is further improved by 3.8% by extending the single-layer framework to a multi-layer framework which takes advantage of the inherent structure of human activities and performs feature selection and classification in a hierarchical manner.

关键词： human activity recognition wearable multimodal sensors feature selection feature design pattern recognition

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle

Enhancing Interoperability of Digital Twin in the Maintenanc...

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international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Dedy Ariansyah Bens Pardamean Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. this paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. the results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management.

关键词： Ontologies Software systems Data models Enterprise resource planning Digital twins Prognostics and health management Interoperability

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Effectiveness of marker-based augmented reality game on computational thinking skills and cognitive load for middle school students

Effectiveness of marker-based augmented reality game on comp...

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international conference on Advanced Learning Technologies (ICALT)

作者： Toluchuri Shalini Shankar Rao Ajay Shankar Tiwari Utanko Mitra Kaushal Kumar Bhagat Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal India

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

Computational thinking (CT) gained significant importance in education, leading to a surge in the development of dedicated instructional platforms. the cultivation of students’ CT skills posed a challenge, often involving the introduction of abstract and challenging-to-visualize programming concepts. this study aimed to bridge this gap by introducing an augmented reality-based coding game—an interactive learning platform designed to facilitate students in acquiring programming skills and enhancing their grasp of computational thinking concepts. the augmented reality (AR) digital game is specifically directed towards fundamental CT skills, such as pattern recognition, decomposition, abstraction, and developing algorithms. To evaluate the effectiveness of the AR game in enhancing CT skills, a study was conducted with 124 middle school students, entailing in grades 6th and 8th. the experimental group (N = 63) utilized the AR game, while the control group (N = 61) relied on conventional text materials. the findings of the experiment strongly supported the integration of AR technology along with game-based learning as a means to enhance CT knowledge and skills while simultaneously reducing cognitive load.

关键词： Codes Education Games Writing Cognitive load Encoding pattern recognition

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Mining closed sequential patterns - A novel approach

Mining closed sequential patterns - A novel approach

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international conference on New Trends in Information and Service Science, NISS

作者： Sophia Banu Rahaman M. Shashi Department of Information Technology Manipal University Dubai UAE Department of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (纸本)9781467308762

Generation of data with an inherent sequential nature is the order of today's digital society. this kind of data is composed of discrete events that have either a temporal or spatial ordering and is generally obtained by sectors like telecommunication networks, E-Commerce, Internet servers and gene databases, medical domain to name a few. the ability to explore and exploit the sequential nature of the data for prediction leverages strategic decision making and problem solving. Symbolic sequence data consists of long sequence of ordered events with possible relationships among them. Symbolic sequence mining techniques aim at extracting frequent sequential patterns from huge collections of event sequences based on the user defined minimum support threshold. For a given set of symbols / events due to the possible repetition of events an infinite large number of sequences is possible and hence the task of extracting frequent sequences is complex. Whereas in closed sequential patterns, the set of sequential patterns automatically eliminates a lot of redundancy from the set of all frequent sequences and provides a concise set of patterns maintaining completeness. A novel approach is proposed for extracting closed sequential patterns which can be applied to fields that prefer complete and concise number patterns for analysis to aid the process of effective decision making.

关键词： Sequential pattern Mining Closed Sequential patterns Aggregate Tree Condensed AggregateTtree Sequential Sequences Number patterns gene banks decision making spatial ordering SEQUENTIAL pattern recognition

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Revisiting the Benefits of Knowledge Distillation against Adversarial Examples

Revisiting the Benefits of Knowledge Distillation against Ad...

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Data Science and Information Technology (DSIT), international conference on

作者： Jiawen Li Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Deep neural networks (DNNs) have always been a popular base model in many image classification tasks. However, some recent works suggest that there are some man made images will easily lead DNNs give wrong predictions. these images are generated by adding some invisible perturbations into clean images, which are the so-called adversarial examples. Since they are almost similar to original data but can fool DNNs, adversarial examples can bring the insecurity to many life-critical applications. Adversarial knowledge distillation is a positive defense method to attach improved robustness to the target DNNs against adversarial examples. In distillation, student models are trained by soft labels coming from robust pre-trained teacher networks. However, it is hard for researchers to ensure that supervised information comes from a better teacher model. In this paper, We focus on four potential factors of the teacher that may have an effect to the distillation performance of student model, and investigate whether and when they can boost the model robustness against adversarial attacks. Multiple quantitative experiment illustrate that these factors have impact on the distillation process and these results can be considered to choose a better teacher model. Further studies can focus on the combination of adversarial knowledge distillation and other defense schemes. Empirical results in this paper may also help select proper teacher in new complex distillation tasks.

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Development of Marker-Based Web Augmented Reality Educational Board Games for Learning Process Support in Computer Science

Development of Marker-Based Web Augmented Reality Educationa...

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Experiment@ international conference (exp.at)

作者： Oleksandr Blazhko Natalia Shtefan Department of Information Systems Odessa Polytechnic National University Odesa Ukraine Department of Information Technology Odessa State Environmental University Odesa Ukraine

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

this article demonstrates the results of the implementation of the strategy while students are studying the topics of the discipline “Operating systems” based on the development of educational board games with augmented reality technology. Relationships between the development of education augmented reality board games and soft skills in computer science are shown. Recommendations on the use of play-components of game mechanics (random, select, manage, move, shoot, write) for board game elements (dice, coin, card, token) with paper markers are given.

关键词： Computer science Operating systems Education Games Teamwork pattern recognition Problem-solving

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pattern recognition approach to Predict Renewable Energy Consumption

Pattern Recognition approach to Predict Renewable Energy Con...

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international Multi-Topic ICT conference (IMTIC)

作者： Asif Gulraiz Syed Sajjad Haider Zaidi Abdul Samad National University of Science and Technology Islamabad Pakistan Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781665482950

the microgrid has been presented in recent years to meet energy requirements as a complementary option. the micro grid comprises of renewable generation, energy storage units and demand management via the low-voltage distribution network, which are part of the intelligent grid implementation. Renewable energy resources, including solar and wind energy, are now used worldwide because of the quick technological progress and environmental benefits. However, it is important to integrate renewable production in the micro grid in advance, for this reason, future power generation from renewable sources must now be predicted. Predicting future energy generation will be helpful in finding requirement for grid integration. Forecasting is the ability to determine periods of stable generation from renewable sources. In this paper a renewable energy generation is predicted which is comprised of solar and wind energy to know the requirements for energy in future. With the help of these results Grid will be configured well in advance to fulfill electricity generation requirements from renewable energy resources. ANN (Artificial Neural Network) and ARMA (Auto-Regressive Moving Average) models are used for prediction of different energy resources. Raw data is first processed using feature extraction technique and then it is used in ANN and ARMA modelling.

关键词： Renewable energy sources Wind energy Artificial neural networks Production Microgrids Predictive models Smart grids

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Identification and Sorting of Waste using Artificial Intelligence Based on Convolutional Neural Network

Identification and Sorting of Waste using Artificial Intelli...

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international conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

作者： Fahmi Fahmi Baharsyah Pratama Lubis Department of Electrical Engineering Universitas Sumatera Utara Medan INDONESIA

ISBN: (纸本)9798350320725

Indonesia is one of the biggest producers of waste in the world. Every day there is rubbish that is not thrown in the trash, those that have been thrown in its place but are still wrong in distinguishing the type of garbage. In general, one of the types of waste is based on its nature, some are easy to decompose (organic), and some are difficult to decompose (inorganic). this research introduces five organic and inorganic waste objects by seeing whether the system can read objects at every position in the background and the number of objects in 1 picture frame. this system uses Tensorflow with a sample of 100 images for each object. Each sample object is given an identifier according to the object’s name and type. the sample is given an identifier for the system to learn to recognize the patterns and shapes of objects from the data objects we take. the results showed that the average percentage of system accuracy in detecting objects reached 90%, with the highest data accuracy reaching 99% tested on the banana peel, leaves, grass, cardboard, styrofoam, broken glass, bottles, cans, and nails. Furthermore, the authors try to put two or more different objects in the same frame and produce an average percentage value of 90%, as well as testing two or more similar objects in one frame simultaneously and producing an average value of 90%. From the test results, grass and bottle objects are samples that do not have read errors in the system design. For testing two or more objects in one frame, the author puts two similar objects and objects of different types. the result is that the system can recognize objects in the frame.

关键词： Shape Neural networks Nails Glass products pattern recognition Object recognition Convolutional neural networks

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