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4th IEEE International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Padmapriya, G. Rajaram, Gnanajeyaraman Raj, T Saju Ponnaian, Geetha Majid, Hind Hussein, Abdul Redha Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chengalpattu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India National University of Science and Technology Collage of Dentist Dhi qar64001 Iraq Al-kunooze University College Sabr Computer Engineer Department Basra Iraq

ISBN: (纸本)9798350360165

Using internally integrated sensors, this work tackles the major difficulty of establishing robust and accurate proprioception in soft robots working under loose packing limitations. Instead of assuming constant curvature, we use a modified off-the-shelf resistive sensor to detect local deformations at several places. Across the soft body, we improve the resolution of local deformations by forming several electrical connections. With an average relative error of around 4%, our method incorporates these sensor measurements into a finite element method (FEM) model to estimate the soft body form and magnitude of external force. Especially, our technique uses fuzzy logic which allows for internal fluidic actuation. Promising for soft manipulation platforms, the suggested integrated sensing and modelling approach shows a 13% relative inaccuracy in external force computation within the 0 to 5 N range. © 2024 IEEE.

关键词： fuzzy logic healthcare applications Soft Robots Soft Sensors and Actuators

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Logical Foundations of Negotiation: Strategies and Preferences 9

Logical Foundations of Negotiation: Strategies and Preferenc...

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9th International Conference on Principles of Knowledge Representation and Reasoning, KR 2004

作者： Meyer, Thomas Foo, Norman Kwok, Rex Zhang, Dongmo National ICT Australia Sydney Node School of Computer Science and Engineering University of New South Wales Sydney Australia Knowledge Systems Group School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computing and Information Technology University of Western Sydney Sydney Australia

ISBN: (纸本)1577351991

This paper is a contribution towards the body of literature which views negotiation in a qualitative light. It builds on an existing logical framework for negotiation between rational, cooperative, truthful agents proposed in (Meyer, Kwok, & Zhang 2003). We show that agents equipped with negotiation strategies corresponding to basic AGM belief revision operations are capable of reaching exactly the permissible deals defined and discussed in (Meyer, Kwok, & Zhang 2003). Each agent has to present a set of weakened demands to the other party who, in return, is obliged to accept all weakened demands. The acceptance of demands is modelled by basic AGM belief revision. We extend the logical framework of (Meyer, Kwok, & Zhang 2003) by considering scenarios in which the initial demand sets of agents may vary. We show that it forces agents to limit their negotiation strategies to AGM belief revision satisfying the supplementary AGM postulates. This leads to the redefinition of a negotiation strategy as a preference relation on demands. This extended framework provides a description of the deals that an agent ought to consider as reasonable, but provides no information on how it should go about choosing a particular deal. We conclude with suggestions on how negotiation strategies can be used to do so. Copyright © 2004, American Association for Artificial Intelligence (***). All rights reserved.

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A Hybrid Electric Vehicle Energy Supply System via Direct and Asynchronous V2V Charging Modes

A Hybrid Electric Vehicle Energy Supply System via Direct an...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Cui, Jixing Liu, Shuohan Cao, Yue Zhang, Xu Zhou, Huan Ren, Xuefeng Wuhan University School of Cyber Science and Engineering China Lancaster University School of Computing and Communications United Kingdom Suzhou Research Institute of Wuhan University 215123 China Xi'an University of Technology Department of Computer Science and Engineering China China Three Gorges University College of Computer and Information Technology China Huali ISmartWays Technology Inc China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In recent years, great attention has been paid on Electric Vehicles (EVs) in terms of environmental pollution. Here, EVs can greatly reduce the environmental pollution, compared with traditional Internal Combustion Vehicles (ICVs). However, since EVs cannot be replenished fast like ICVs, the rigid deployment of charging infrastructure and its limited charging capability, leads to service congestion particularly due to a large number of EVs being parked with charging demand. Compared to CSs with rigid extension in location and charging facilitates, the Vehicle-to-Vehicle (V2V) charging service provides a spatial and temporal advance in flexibility, with potential to supplement with G2V charging mode, which can supplement or even replace the G2V Charging Mode. In this paper, we propose a hybrid V2V charging scheme, consisting of direct and asynchronous V2V charging modes, to achieve a great charging flexibility and alleviate the burden for grid load. Here, we estimate the Minimum Waiting Time (MWT) under each mode, as guidance to switch between modes and optimize charging service under each mode. Results show that our proposed hybrid V2V charging scheme outperforms literature works, in terms of average waiting time and number of full charged EVs. © 2022 IEEE.

关键词： Pollution

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Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 801-816页

作者： J.Jean Justus M.Thirunavukkarasan K.Dhayalini G.Visalaxi Adel Khelifi Mohamed Elhoseny Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India School of Computing Science and Engineering Galgotias UniversityUttar Pradesh203201India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science&Engineering Bharath Institute of Higher Education and ResearchChennai600073India Department of Computer Science and Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates College of Computer Information Technology American University in the EmiratesDubaiUnited Arab Emirates Faculty of Computers and Information Mansoura UniversityEgypt

Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

关键词： Clustering data aggregation energy consumption cluster head selection wireless sensor networks

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A customized CNN architecture for detecting double and multiple JPEG compression in small blocks

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Engineering Analysis with Boundary Elements 2025年 178卷

作者： Israr Hussain Naeem Hussain Shunquan Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China College of Electronic and Information Engineering Shenzhen 518060 China School of Information and Communications Technology Shenzhen City Polytechnic Shenzhen 518116 China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen 518116 China

Detecting double JPEG compression is crucial for verifying the authenticity and integrity of JPEG images. However, this poses significant challenges, particularly when the quality factor (QF) of the first compression is substantially higher than of the second compression, and when working with small-sized blocks. To overcome these challenges, we propose a customized convolutional neural network (CNN) architecture specifically designed for detecting double and multiple JPEG compression. The main contribution of our work is the innovative use of raw DCT coefficients as input to the CNN, enabling end-to-end feature extraction and enhancing the detection of subtle compression artifacts that might be missed by traditional techniques. The framework incorporates an ABS layer after the first convolution to eliminate the symmetry information that are not useful for JPEG compression detection, applies batch normalization (BN) to stabilize training, and reduces model complexity by employing 1 × 1 convolutions in deeper layers. Experimental results show that the proposed customized architecture significantly improves detection accuracy compared to other state-of-the-art methods, particularly in challenging small-block scenarios, and demonstrates effectiveness in detecting both double and multiple JPEG compression.

关键词： Convolutional neural networks

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Factors influencing SaaS adoption by MSMEs 7

Factors influencing SaaS adoption by MSMEs

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7th International Conference on Software Engineering and computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Ayuningtyas, Ayuningtyas Azmi, Zafril Rizal M Abdullah Arshah, Ruzaini Ahmad, Muhammad Aliif Khubrani, Mousa Faculty of Technology and Informatics Universitas Dinamika Surabaya East Java Indonesia Faculty of Computing Universiti Malaysia Pahang Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Malaysia Faculty of Computer Science Information Technology Jazan University Saudi Arabia

ISBN: (纸本)9781665414074

Cloud computing has been around for quite long, yet some organizations have never used it, especially MSMEs in Indonesia. Several factors hinder its implementation by MSMEs, including unequal infrastructure for using the internet in Indonesia. This paper assesses the literature on cloud computing, SaaS, e-Commerce, and e-wallet to determine the initial factors that affect technology implementation. The results showed that the frameworks widely used to obtain and assess variables include the DOI, TAM, and TOE. Furthermore, compatibility and the pressure from competitors to adopt technology are the highly considered variables. Among the factors, perceived usefulness and ease of use is also deemed by MSMEs in implementing e-commerce and e-wallet. © 2021 IEEE.

关键词： Software as a service (SaaS)

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The SWENET project: Bridging the gap from bodies of knowledge to curriculum development 33

The SWENET project: Bridging the gap from bodies of knowledg...

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33rd Annual Frontiers in Education, FIE 2003

作者： Lutz, Michael J. Hilburn, Thomas B. Hislop, Gregory W. McCracken, W. Michael Sebern, Mark J. Department of Software Engineering Rochester Institute of Technology United States Department of Computing Embry-Riddle Aeronautical University United States College of Information Science and Technology Drexel University United States College of Computing Georgia Institute of Technology United States Department of Electrical Engineering and Computer Science Milwaukee School of Engineering United States

ISBN: (纸本)0780379616

SWENET: The Network Community for Software Engineering Education is an NSF funded project to develop curriculum modules of value to faculty member's desiring to incorporate software engineering concepts in new or existing courses. By design, the modules are self-contained instructional units ranging from a single lecture to approximately one week of course material;in this way, instructors can adopt, adapt, and arrange modules as appropriate to their courses. The original goal was to provide appropriate coverage at the undergraduate level for the areas defined in the Software Engineering Body of Knowledge. Recently the focus has shifted to the more focused Software Engineering Education Body of Knowledge developed as part of the computing Curricula - Software Engineering effort. As such, SWENET is evolving to become a repository of course modules that can support a wide range of educational approaches within the general framework defined by these bodies of knowledge. © 2003 IEEE.

关键词： Curricula

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Exploring the Effectiveness of Various Deep Learning Techniques for Text Generation in Natural Language Processing

Exploring the Effectiveness of Various Deep Learning Techniq...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Singh, Baljap Kumar, Anubhav Kaur, Satjap Shekhar, Shashank Singh, Gurwinder Chandigarh University Apex Institute of Technology Department of Computer Science Mohali India Galgotias University School of Computing Science and Engineering Department of Cse Greater Noida India Sheridan College Missisauga Canada

ISBN: (纸本)9798350344387

Natural Language Processing (NLP) demands the generation of text that exhibits cohesion, fluidity, and semantic coherence. Text generation plays a pivotal role in achieving this objective. Over time, the evolution of Deep Learning (DL) techniques has led to the emergence of several methods for generating text, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers. This study undertakes a comprehensive examination of DL methods for text generation within the realm of *** providing a general overview of text generation and its inherent challenges, an extensive exploration of the various deep learning models and their adaptations is conducted. The strengths and limitations of these models are meticulously assessed, while their performance relative to more traditional approaches is also examined. To conclude, current trends are illuminated, and unanswered questions within this domain are posed. Beyond simply identifying areas ripe for further investigation, this review aims to equip both scholars and practitioners with a comprehensive understanding of the latest developments in DL-based text generation. © 2023 IEEE.

关键词： Algorithm Deep Learning NLP Optimizers Text Generation

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Integrating Degradation Learning into Image Restoration

Integrating Degradation Learning into Image Restoration

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Ma, Mingyu Ren, Dongwei Yang, Yajun College of Intelligence and Computing Tianjin University China School of Computer Science and Technology Harbin Institute of Technology China People's Daily Online State Key Laboratory of Communication Content Cognition China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Existing image restoration methods usually assume specific degradation model, e.g., linear combination of clean image and degradation map in image denoising and deraining. Benefiting from the power of deep learning, a restoration mapping can be learned from degraded image to latent clean image. In this paper, we propose to integrate degradation learning into image restoration (IDLIR), where degradation model can be learned from training samples. In particular, IDLIR is an iterative restoration framework, where latent clean image and degradation map can be extracted from current residual degraded image, and are then fused by a degradation network to reconstruct degraded image. Then the residual degradation image can be updated by computing the difference between input and reconstructed degraded images. By taking denoising and deraining as examples, IDLIR is compared with state-of-the-art methods on several benchmark datasets. IDLIR performs better than state-of-the-art methods quantitatively and qualitatively. © 2022 IEEE.

关键词： Image reconstruction

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BioModel Engineering

BioModel Engineering

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Formal Methods in Molecular Biology 2009

作者： Breitling, Rainer Gilbert, David Heiner, Monika Groningen Bioinformatics Centre University of Groningen Haren9751 Netherlands School of Information Science Computing and Mathematics Brunel University United Kingdom Department of Computer Science Brandenburg University of Technology Cottbus Germany

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