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International Journal of Digital Content Technology and its Applications 2012年第13期6卷 542-548页

作者： Jaswar Maimun, Adi Selamat, Ali Priyanto, A. Zamani Pauzi Djuansjah, J.R.P. Naval Architecture and Offshore Engineering Department Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Software Engineering Department Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Johor Bahru Malaysia Material Engineering Department Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

A good systematic input and output management system is more essential for ship maneuvering simulation to eliminate unnecessary error due to many considered factors such as hull shapes, shallow water, narrow channel, trim in loading and ballast condition, and propulsion system. In this paper, we proposed an Integrated Ship Maneuverability Simulation (SMS) tool to be used in investigating turning circle characteristics of a very large crude oil carrier (VLCC) at 35 degree of rudder angle. The input and output datasets from the surrounding environments of VLCC have been captured using special sensors and converted as input file to the Integrated Ship Maneuverability Simulation tool. The forces and moments acting on the hull which have been induced by propeller and rudder during maneuvering are calculated independently. Then, these forces and moments are integrated to the SMS tool in order to get a total dynamic effect on ship maneuvering performance. The simulation results using SMS tool have been compared with the experimental datasets for validation. From the comparison, the proposed SMS tool has been able to simulate ship maneuvering performance good agreement with the experimental dataset.

关键词： Rudders

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Using Sparse Matrices to Prevent information Leakage in Cloud Computing

Using Sparse Matrices to Prevent Information Leakage in Clou...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Khaled Khan Mahboob Shaheen Yongge Wang Computer Science and Engineering KINDI Computing Lab Qatar University Qatar Department of Math and Physics North South University Dhaka Bangladesh Software and Information Systems University of North Carolina Charlotte USA

ISBN: (纸本)9781538675045

Cloud computing represents the promise of outsourcing of scientific computing such as matrix multiplication. However, this can introduce new vulnerabilities such as information leakage. Cloud server intentionally or unintentionally may reveal sensitive input matrices of the client as well as multiplication results to unauthorised entities. In this paper, we propose two protocols that use sparse matrices to prevent information leakage in outsourcing matrix multiplications to cloud computing without encryption. The protocols are considered lightweight compared to other comparable approaches. We also provide a running example to demonstrate how the protocols ensure no information leakage of client data.

关键词： Sparse matrices Cloud computing Servers Protocols Encryption software

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Online Intelligent Resource Management for Power-Delay Tradeoff in Backhaul-Limited Mobile Edge Computing systems

Online Intelligent Resource Management for Power-Delay Trade...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yuan Sun Gang Yang Xing-She Zhou Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computer Science and Engineering Northwestern Polytechnical University Xi???an China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the quality of experience for mobile device users, as it can offload computation intensive tasks from mobile devices to edge clouds. Nevertheless, task offloading also incurs additional overhead in both power consumption and execution delay. In essence, energy consumption and task delay cannot be optimized simultaneously as these two objectives contradict each other. Although many studies have been conducted to make tradeoff between them, the backhaul limitation of MEC-enabled wireless networks has not been tackled. In this paper, we study the power-delay tradeoff for MEC in backhaul-limited wireless networks. An average weighted sum power consumption minimization problem with task buffer stability constraints is formulated to investigate the problem, and a Lyapunouv Optimization based online intelligent computation and communication resource management algorithm is also proposed to solve it. In particular, at each time slot, optimal CPU-cycle frequencies, transmit power and backhaul capacity allocation are efficiently determined. Performance analysis indicates that under the proposed algorithm an [O(1/V), O(V)] tradeoff exists between the power consumption and execution delay, where V is a control parameter. Finally, simulations are conducted to validate the analytic results and demonstrate the changes of the system performance with various algorithm parameters.

关键词： Task analysis Mobile handsets Power demand Resource management Delays Optimization Wireless communication

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On mitigating hop-to-hop congestion problem in IoT enabled Intra-Vehicular communication

On mitigating hop-to-hop congestion problem in IoT enabled I...

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International Conference on software engineering and computer systems (ICSECS)

作者： Md. Arafatur Rahman Muhammad Nomani Kabir Saiful Azad Jahan Ali IBM Center of Excellence University Malaysia Pahang Malaysia Faculty of Computer Systems and Software Engineering University Malaysia Pahang Malaysia Faculty of Information and Communication Technology University Tunku Abdul Rahman Malaysia

Internet of Things enabled Intra-Vehicular Network (IVN) refers to the network where large number of sensors are connected with each other for sharing the vehicle's status information in order to develop a smart vehicular system. The number of sensor nodes in the vehicle has increased significantly due to the increasing vehicular applications. The phenomenon of congestion poses a problem in the IVN where the traffic load and number of sensors are increased. This problem can be resolved by mitigating the limitation of the existing Media Access Control (MAC) protocols. In this paper, we address this issue and proposed a MAC strategy for solving this problem in this network. Furthermore, we discuss the design of IVN scenario and the performance is evaluated in terms of end-to-end delay. The simulation results reveal the effectiveness of our proposal.

关键词： Sensors Media Access Protocol Wireless sensor networks Vehicles Delays Niobium

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Real-time Prediction of Fast and Slow Delivery of Mental Commands in a Motor Imagery BCI: An Entropy-based Approach

Real-time Prediction of Fast and Slow Delivery of Mental Com...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Sareh Saeedi Ricardo Chavarriaga Jose del R. Millan Michael C. Gastpar Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuroprosthetics School of Engineering EPFL Laboratory for Information in Networked Systems School of Computer and Communication Sciences EPFL

ISBN: (纸本)9781467317139

Providing adaptive shared control for Brain-computer Interfaces (BCIs) can result in better performance while reducing the user's mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as "fast" trials and to those longer than the median as "slow" trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2-3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time.

关键词： Brain-computer Interface (BCI) Shared Control EEG Entropy Fasting experimentation Trials commands Imagery (Psychotherapy) images Median Motor trial Classifiers Obstetric Delivery Electroencephalography

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Acquisition of direct sequence signals in the presence of data

Acquisition of direct sequence signals in the presence of da...

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IEEE International Conference on Communications (ICC)

作者： T. Yaguchi L.B. Milstein Information Systems Laboratory Canon Inc. Ohta Tokyo Japan Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

The performance of a parallel acquisition scheme for a direct-sequence spread-spectrum receiver operating in the presence of data is presented. The effects of the data modulation on key quantities such as probability of false alarm, probability of correct detection, and mean time to acquisition are analyzed in both the search mode and the lock mode, and a search-lock strategy aimed at minimizing the acquisition time is described.< >

关键词： Convolvers Laser mode locking Spread spectrum communication Surface acoustic waves Switches information systems Laboratories Signal generators Correlators Error correction

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Exploring Social Media Data for MOOC Recommendation

Exploring Social Media Data for MOOC Recommendation

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International Conference on systems of Collaboration (SysCo)

作者： Sara Assami Najima Daoudi Rachida Ajhoun Department of Computer Science Smart Systems Laboratory Mohammed Vth University Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School (ESI) Rabat Morocco

ISBN: (数字)9781728142166

ISBN: (纸本)9781728142173

MOOCs (Massive Open Online Courses) are a key tool for open education stakeholders and high-level universities to face the problem of limited resources and make education accessible worldwide. Our study relies on the hypothesis of the influence of learning adaptation on the learners' lack of motivation in MOOCs. In this sense, a recommendation approach could stimulate the learner's interest in specific MOOCs. In previous research, we have identified the recommendation criteria to recommend MOOCs adapted to the learner's needs and motivations. Then, we modeled the ontology of the learning actors' profiles for the matching of the characteristics of learners with adapted MOOCs. Afterward, we designed the functional architecture and use cases of a semantic recommendation system to stimulate learners' interest in MOOCs. However, the recommendation process is still hindered by the learner profile on these platforms that doesn't represent the learner's interests and motivations. Yet, social media profiles extraction can be an external data source for learners' profile enrichment. So, the purpose of this research is to use social media mining for the acquisition of personal and professional data about learners. Therefore, we apply the first phases of data mining on a social media dataset to explore the potentially meaningful data for MOOC recommendation.

关键词： Social networking (online) Data mining Educational courses Recommender systems computer aided instruction

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Universal deep network for steganalysis of color image based on channel representation

arXiv

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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A NSGA-II with alternating direction method of multipliers mutation for solving multiobjective robust principal component analysis problem

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3722-3733页

作者： Yuan, Weitao Liang, Xiaodan Chen, Hanning Na, Lin Zou, Tao School of Computer Science and Software Engineering Tianjin Polytechnic University Tanjin300387 China Beijing Shenzhou Aerospace Software Technology Company Limited Beijing110000 China Laboratory of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Robust Principal Component Analysis (RPCA), which is a popular parsimony model, is becoming increasingly important for researchers to do data analysis and prediction. The RPCA formulation is made of two components: sparse penalty and low rank penalty. These two competing terms are balanced with one parameter, which is essential for the effectiveness of RPCA. However, in real-world applications, the lack of data adaptive methods for choosing the right parameter hinders the popularization of RPCA. In this work, RPCA is generalized to a multiobjective optimization problem without any balancing parameter. The new model is named as Multiobjective Robust Principal Component Analysis (MRPCA). We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve RPCA problem, which is a high dimensional convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. To handle this dimensionality problem, we introduce a novel mutation, termed as Alternating Direction Method of Multipliers mutation (ADMM mutation), that works well in high dimensional decision space. Numerical experiments show that this modified NSGA-II, which converges much faster than the standard one, can deal with the curse of dimensionality well. Furthermore, numerical image reconstruction test confirms that the reconstruction performance of our modified NSGA-II is better than the traditional proximal algorithm, which is usually used to solve RPCA problem. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Multiobjective optimization

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Evolving Classification Rules by Unconstrained Gene Expression Programming

Evolving Classification Rules by Unconstrained Gene Expressi...

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International Workshop on Intelligent systems and Applications, ISA

作者： Jianwei Zhang Zhijian Wu Jinglei Guo Min Peng Yingjiang Zhang Chunzhi Wang State Key Laboratory of Software Engineering Wuhan University of China Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping & Remote Sensing Wuhan University of China Wuhan China School of Computer Hubei University of Technology Wuhan China

Unconstrained Gene Expression Programming (UGEP), a new unconstrained linear encoded Gene Expression Programming (GEP), is introduced and applied to solve classification problems in this paper. Different from GEP, both amount and length of the genes are dynamically adjusted in the UGEP chromosome during the evolution process. Experiment results indicate that UGEP perform better than GEP in classification problems.

关键词： Gene expression Biological cells Genetic programming Linear programming Data mining Classification tree analysis Evolutionary computation Laboratories Testing Decision trees

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