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14th International Conference on Web-Based Learning, ICWL 2015

作者： Wahl, Harald Galler, Rudolf Winiwarter, Werner Department of Information Engineering and Security University of Applied Sciences Technikum Wien Vienna Austria Research Group Data Analytics and Computing University of Vienna Vienna Austria

ISBN: (纸本)9783319255149

Intelligent Integrated Computer-Assisted Language Learning (iiCALL) describes various approaches in the field of second language learning. The term "Integrated" refers to approaches which operate inside established workspaces such as Web-browsers and e-mail clients, whereas the term "Intelligent" relates to appropriate methods from the field of computer linguistics. The iiCALL environments are composed of several single systems, which are developed by specialists from different domains with specific background-knowledge (e.g. didactic experts, linguists, or software developers). High-quality solutions of such learning environments are characterized by solid technical delivery and proper language content. Both demand semantic interoperability between these systems as well as flexibility and scalability of the entire environment. Similar challenges exist in healthcare. Hence, the idea of the Health Level Seven V3 standard is adapted to meet the requirements for iiCALL. This paper addresses this challenge and presents a development framework for the conception of iiCALL, as well as a software framework for generic and transparent information exchange within those environments. © Springer International Publishing Switzerland 2015.

关键词： Natural language processing systems

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Partition of unity networks: Deep hp-approximation

Partition of unity networks: Deep hp-approximation

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AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, AAAI-MLPS 2021

作者： Lee, Kookjin Trask, Nathaniel A. Patel, Ravi G. Gulian, Mamikon A. Cyr, Eric C. Extreme Scale Data Science and Analytics Department Sandia National Laboratories Center for Computing Research Sandia National Laboratories AlbuquerqueNM87123 United States

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networks (POUnets) which incorporate these elements directly into the architecture. Classification architectures of the type used to learn probability measures are used to build a mesh-free partition of space, while polynomial spaces with learnable coefficients are associated to each partition. The resulting hpelement-like approximation allows use of a fast least-squares optimizer, and the resulting architecture size need not scale exponentially with spatial dimension, breaking the curse of dimensionality. An abstract approximation result establishes desirable properties to guide network design. Numerical results for two choices of architecture demonstrate that POUnets yield hp-convergence for smooth functions and consistently outperform MLPs for piecewise polynomial functions with large numbers of discontinuities. Copyright © 2021for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Deep neural networks

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Advanced Techniques in Network Traffic Analysis: Utilizing Wireshark For In-Depth Live data Packet Inspection And Information Capture 2

Advanced Techniques in Network Traffic Analysis: Utilizing W...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Chowhan, Santhosh Saxena, Abhilash Kumar University Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

The escalating frequency and sophistication of cyber-attacks have placed a spotlight on the importance of Quality of Service (QoS) and robust network security mechanisms. Effective traffic analysis and distribution are critical for maintaining the integrity of network applications and safeguarding data. Our research focuses on the development and assessment of a network intrusion detection system (NIDS) that utilizes the advanced packet analysis capabilities of Wireshark, a renowned network protocol analyzer. The system is designed to enhance network security by enabling more efficient and accurate data collection through network monitoring tools. This serves as the foundation for detecting and thwarting a wide range of malicious activities, including malware and spyware incursions, within network traffic. The innovation of our approach lies in its integration of packet sniffing technology with AI-driven analysis techniques. By applying Machine Learning algorithms to the data captured by Wireshark, our NIDS is capable of identifying patterns indicative of intrusive behavior, which might elude traditional detection methods. We present a comprehensive evaluation of our system, showcasing its efficacy in real-time intrusion detection and its potential for integration into existing network infrastructures. This study contributes to the ongoing discourse in cybersecurity, offering a sophisticated tool in the battle against digital threats and reinforcing the role of intelligent systems in protecting network environments © 2023 IEEE.

关键词： data Packet Analysis Techniques Network Security and Forensics Network Traffic Analysis packets Real-Time Network Monitoring Wireshark for data Packet Inspection

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A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption

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Journal of Systems and Information Technology 2019年第4期21卷 419-438页

作者： Talukder, Shamim Chiong, Raymond Dhakal, Sandeep Sorwar, Golam Bao, Yukun Department of Management North South University Dhaka Bangladesh School of Electrical Engineering and Computing The University of Newcastle Newcastle Australia School of Business and Tourism Southern Cross University Bilinga Australia Centre for Big Data Analytics Jiangxi University of Engineering Xinyu China

Purpose: Despite the widespread use of mobile government (m-government) services in developed countries, the adoption and acceptance of m-government services among citizens in developing countries is relatively low. The purpose of this study is to explore the most critical determinants of acceptance and use of m-government services in a developing country context. Design/methodology/approach: The unified theory of acceptance and use of technology (UTAUT) extended with perceived mobility and mobile communication services (MCS) was used as the theoretical framework. data was collected from 216 m-government users across Bangladesh and analyzed in two stages. First, structural equation modeling (SEM) was used to identify significant determinants affecting users' acceptance of m-government services. In the second stage, a neural network model was used to validate SEM results and determine the relative importance of the determinants of acceptance of m-government services. Findings: The results show that facilitating conditions and performance expectancy are the two important precedents of behavioral intention to use m-government services, and performance expectancy mediates the relationship between MCS, mobility and the intention to use m-government services. Research limitations/implications: Academically, this study extended and validated the underlying concept of UTAUT to capture the adoption behavior of individuals in a different cultural context. In particular, MCS might be the most critical antecedent towards mobile application studies. From a practical perspective, this study may provide valuable guidelines to government policymakers and system developers towards the development and effective implementation of m-government systems. Originality/value: This study has contributed to the existing, but limited, literature on m-government service adoption in the context of a developing country. The predictive modeling approach is an innovative approach in the field of te

关键词： Developing countries

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Early Detection of Activity in Untrimmed Videos Using Dense Optical Flow and Deep Neural Network 5

Early Detection of Activity in Untrimmed Videos Using Dense ...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Poonkodi, M. Vadivu, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Big Data Analytics School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665435246

Computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a 'seeing' machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities' initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes *** lead to the requirement of high power computing devices for recognition, which is considerably *** this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively. © 2021 IEEE.

关键词： Computer vision

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Role of Big data analytics and Edge computing in Modern IoT Applications: A Systematic Literature Review 2

Role of Big Data Analytics and Edge Computing in Modern IoT ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Saeed, Ali Khattak, Muazzam A. Khan Rashid, Sidra Quaid-i-Azam University Department of Computer Sciences Islamabad Pakistan Quaid-i-Azam University Icesco Chair for Big Data Analytics and Edge Computing Department of Computer Sciences Islamabad Pakistan

ISBN: (纸本)9781665498197

the internet of things has spread to every personal and commercial domain. The devices ranging from health monitoring band to heavy electrical and mechanical machinery monitoring nodes are generating data at volumes that is exploding at exponential rate. The data is not only increasing in volume but the velocity of the data generation has also increased. The high volume and velocity along with variety is becoming a bigger challenge every day. The industry and academia have come up with standards like IIoT 4.0 and new computational models like edge and fog are introduced. A systematic literature review is conducted to explore the current solutions and challenges in this domain. Different research questions are asked before the review to look for specific answers. The results highlights different challenges, future directions, suggestions and answers to the research questions initially asked. © 2022 IEEE.

关键词： data analytics

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Collective biobjective optimization algorithm for parallel test paper generation 24

Collective biobjective optimization algorithm for parallel t...

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24th International Joint Conference on Artificial Intelligence, IJCAI 2015

作者： Nguyen, Minh Luan Hui, Siu Cheung Fong, Alvis C.M. Data Analytics Department Institute for Infocomm Research Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore Singapore School of Computing Science University of Glasgow United Kingdom

ISBN: (纸本)9781577357384

Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple userspecified criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions. In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG. CBO is a multi-step greedy-based approximation algorithm, which exploits the submodular property for biobjective optimization of k-TPG. Experiment results have shown that CBO has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

关键词： Approximation algorithms

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Analysis of Complex Customer Networks: A Real-World Banking Example 45

Analysis of Complex Customer Networks: A Real-World Banking ...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Ljubicic, Karmela Mercep, Andro Kostanjcar, Zvonko Privredna Banka Zagreb Data Warehouse & Business Intelligence Department Zagreb10000 Croatia University of Zagreb Laboratory for Financial and Risk Analytics Faculty of Electrical Engineering and Computing Zagreb10000 Croatia

ISBN: (纸本)9789532331035

Complex customer networks are an important tool for better understanding of customer behaviour and customers' mutual interdependence. Hence, they have proven to be insightful in numerous customer-centric domains such as recommender systems and customer relationship management. Although previous research in this field is predominantly associated with telecommunication and e-commerce sectors, similar construction principles could be transferred to the financial sector, e.g. a customer network could be created based on transaction history or domain-specific relations such as loan debtor-guarantor or loan debtor-co-debtor. However, this research path is still underdeveloped. In this paper, we analyse real-world complex customer networks of a Croatian bank. Numerous graph metrics were calculated, and the obtained results prove that these networks are non-random. We also show that these networks are scale-free and exhibit the small-world effect based on graph properties like the node degree distribution and the average shortest path. © 2022 Croatian Society MIPRO.

关键词： Complex networks

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analytics-Based Decomposition of a Class of Bilevel Problems 6th

Analytics-Based Decomposition of a Class of Bilevel Problems

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6th World Congress on Global Optimization, WCGO 2019

作者： Fajemisin, Adejuyigbe Climent, Laura Prestwich, Steven D. School of Computing National College of Ireland Dublin Ireland Computer Science Department Cork Institute of Technology Cork Ireland Insight Centre for Data Analytics University College Cork Cork Ireland

ISBN: (纸本)9783030218027

This paper proposes a new class of multi-follower bilevel problems. In this class the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. The new class is formalised and compared with existing problems in the literature. We show that approaches currently in use for solving multi-follower problems are unsuitable for this class. Evolutionary algorithms can be used, but these are computationally intensive and do not scale up well. Instead we propose an analytics-based decomposition approach. Two example problems are solved using our approach and two evolutionary algorithms, and the decomposition approach produces much better and faster results as the problem size increases. © 2020, Springer Nature Switzerland AG.

关键词： Evolutionary algorithms

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