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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning

DeepDect: A Facial Deepfake Video Detection Application usin...

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IEEE Conference on systems, Process & Control (ICSPC)

作者： Wan Xin Tay Hui Na Chua Muhammed Basheer Jasser Bayan Issa Richard T.K. Wong Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of ${8 4. 0 6 \%}$, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes.

关键词： Uniform resource locators Deepfakes Adaptation models Accuracy Computational modeling Feature extraction Data models Robustness Ensemble learning Overfitting

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing Computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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Ontology-based Document Recommendation System using Topic Modeling

Ontology-based Document Recommendation System using Topic Mo...

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International Conference on Semantic computing

作者： Yijian Hu Shih-Yu Chang Kai-Rui Hsu Jaydeep Chakraborty Srividya K. Bansal School of Computing Informatics and Decision Systems Engineering (CIDSE) Arizona State University Tempe Arizona USA

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

For now, most search engines have limitations on finding the most suitable results from documents at a semantic level. This paper aims to provide users with more accurate document search results not only at a syntactic level but also on a semantic level. For example, when a user searches “coffee” on Amazon, does the user only want coffee? Coffee is a kind of functional drink, the user may also want to know other functional drinks such as tea or Redbull. Coffee helps people stay awake, the user may just want something to help him/her stay awake or focused. In this project, document data from a question-and-answer website called Stack Exchange is analyzed and compared by using the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) topic modeling algorithm. After completing topic modeling, using an ontology built with Protégé, data is further processed at a semantic level. We utilize the ontology rules and instances to optimize the search results.

关键词： Computational modeling Semantics Ontologies Syntactics Search engines Data models Resource management

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Distributed asynchronous policy iteration for sequential zero-sum games and minimax control

arXiv

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arXiv 2021年

作者： Bertsekas, Dimitri School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

We introduce a contractive abstract dynamic programming framework and related policy iteration algorithms, specifically designed for sequential zero-sum games and minimax problems with a general structure. Aside from greater generality, the advantage of our algorithms over alternatives is that they resolve some long-standing convergence difficulties of the "natural" policy iteration algorithm, which have been known since the Pollatschek and Avi-Itzhak method [PoA69] for finite-state Markov games. Mathematically, this "natural" algorithm is a form of Newton's method for solving Bellman's equation, but Newton's method, contrary to the case of single-player DP problems, is not globally convergent in the case of a minimax problem, because the Bellman operator may have components that are neither convex nor concave. Our algorithms address this difficulty by introducing alternating player choices, and by using a policy-dependent mapping with a uniform sup-norm contraction property, similar to earlier works by Bertsekas and Yu [BeY10], [BeY12], [YuB13]. Moreover, our algorithms allow a convergent and highly parallelizable implementation, which is based on state space partitioning, and distributed asynchronous policy evaluation and policy improvement operations within each set of the partition. Our framework is also suitable for the use of reinforcement learning methods based on aggregation, which may be useful for large-scale problem instances. © 2021, CC BY-NC-ND.

关键词： Dynamic programming

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Template-based Question Answering analysis on the LC-QuAD2.0 Dataset

Template-based Question Answering analysis on the LC-QuAD2.0...

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International Conference on Semantic computing

作者： Akshay Kumar Dileep Anurag Mishra Ria Mehta Siddharth Uppal Jaydeep Chakraborty Srividya K. Bansal School of Computing Informatics Decision Systems Engineering (CIDSE) Arizona State University Mesa Arizona USA

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

In recent years, template-based question answer has picked up steam as a solution for evaluating RDF triples. Once we delve into the domain of template-based question answering, two important questions arise which are, the size of the dataset used as the knowledge base and the process of training used on that knowledge base. Previous studies attempted this problem with the LC-QuAD dataset and recursive neural network for training. This paper studies the same problem with a larger and newer benchmark dataset called LC-QuAD 2.0 and training using different machine learning models. The objective of this paper is to provide a comparative study using the newer LC-QuAD 2.0 dataset that has an updated schema and 30,000 question-answer pairs. Our study will focus on using and comparing two Machine Learning models and 3 different pre-processing techniques to generate results and identify the best model for this problem.

关键词： Training Computational modeling Knowledge based systems Semantics Machine learning Knowledge discovery Resource description framework

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Generating active explicable plans in human-robot teaming

arXiv

引用

arXiv 2021年

作者： Hanni, Akkamahadevi Zhang, Yu School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ United States

Intelligent robots are redefining a multitude of critical domains but are still far from being fully capable of assisting human peers in day-to-day tasks. An important requirement of collaboration is for each teammate to maintain and respect an understanding of the others’ expectations of itself. Lack of which may lead to serious issues such as loose coordination between teammates, reduced situation awareness, and ultimately teaming failures. Hence, it is important for robots to behave explicably by meeting the human’s expectations. One of the challenges here is that the expectations of the human are often hidden and can change dynamically as the human interacts with the robot. However, existing approaches to generating explicable plans often assume that the human’s expectations are known and static. In this paper, we propose the idea of active explicable planning to relax this assumption. We apply a Bayesian approach to model and predict dynamic human belief and expectations to make explicable planning more anticipatory. We hypothesize that active explicable plans can be more efficient and explicable at the same time, when compared to explicable plans generated by the existing methods. In our experimental evaluation, we verify that our approach generates more efficient explicable plans while successfully capturing the dynamic belief change of the human teammate. Copyright © 2021, The Authors. All rights reserved.

关键词： Intelligent robots

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN Computer Science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Treed-Gaussian Processes with Support Vector Machines as Nodes for Nonstationary Bayesian Optimization

Treed-Gaussian Processes with Support Vector Machines as Nod...

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2021 Winter Simulation Conference, WSC 2021

作者： Candelieri, Antonio Pedrielli, Giulia University of Milano-Bicocca Management and Statistics Department of Economics Piazza Ateneo Nuovo 1 Milan Italy School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave Tempe United States

ISBN: (纸本)9781665433112

A large family of black box methods rely on surrogates of the unknown, possibly non linear non convex reward function. While it is common to assume stationarity of the reward, many real-world problems satisfy this assumption only locally, hindering the spread application of such methods. This paper proposes a novel nonstationary regression model combining decision Trees and Support Vector Machine (SVM) classification for a hierarchical non-axis-aligned partition of the input space. Gaussian Process (GP) regression is performed within each identified subregion. The resulting nonstationary regression model is the Treed Gaussian process with Support Vector Machine (SVMTGP), and we investigate the sampling efficiency from using our a model within a Bayesian optimization (BO) context. Empirically, we show how the resulting algorithm, SVMTGP-BO never underperforms BO when this is applied to an homogeneous Gaussian process, while it shows always better performance compared to the homogeneous model with nonlinear functions with complex landscapes. © 2021 IEEE.

关键词： Support vector machines

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Epidemic Spreading in Group-Structured Populations

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Physical Review X 2023年第4期13卷 041054-041054页

作者： Siddharth Patwardhan Varun K. Rao Santo Fortunato Filippo Radicchi Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA School of Public Health Indiana University Bloomington Indiana 47408 USA

Individuals involved in common group activities or settings, e.g., college students that are enrolled in the same class and/or live in the same dorm, are exposed to recurrent contacts of physical proximity. These contacts are known to mediate the spread of an infectious disease; however, it is not obvious how the properties of the spreading process are determined by the structure of and the interrelation among the group settings that are at the root of those recurrent interactions. Here, we show that reshaping the organization of groups within a population can be used as an effective strategy to decrease the severity of an epidemic. Specifically, we show that when group structures are sufficiently correlated, e.g., the likelihood for two students living in the same dorm to attend the same class is sufficiently high, outbreaks are longer but milder than for uncorrelated group structures. Also, we show that the effectiveness of interventions for disease containment increases as the correlation among group structures increases. We demonstrate the practical relevance of our findings by taking advantage of data about housing and attendance of students at the Indiana University campus in Bloomington. By appropriately optimizing the assignment of students to dorms based on their enrollment, we are able to observe a twofold to fivefold reduction in the severity of simulated epidemic processes.

关键词： Community structure Complex systems Critical phenomena Epidemic Complex networks Epidemic spreading SIR model

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