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IEEE International Symposium on Multimedia

作者： Andreas Mallas Hara Papadatou Michalis Xenos Department of Computer Engineering and Informatics University of Patras Patras Greece

The text’s legibility can dramatically influence any device’s usability, and a wealth of research has examined the ideal font characteristics for various displays regarding legibility and readability. However, mobile devices have an additional factor that influences viewing conditions; their greatest strength, i.e., portability, is their biggest weakness in this regard. Furthermore, the freedom to use the device in diverse situations and positions (sitting, standing, in transit, or while waiting in line) can influence various facets related to legibility, such as the user’s holding position, operating style, and the environment’s light directed to the phone. The application developed aims to maintain user-preferred text font size -in all typical distances that users operate their phones- by automatically adjusting the font size according to the distance between the user and the device by using the device’s camera to calculate the distance in real-time. A two-part experiment (n = 34) revealed that the application scored an excellent usability score while the visual landmarks used showed a promising start at addressing an issue related to our approach.

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Eye-Tracking Based Automatic Summarization of Lecture Slides

Eye-Tracking Based Automatic Summarization of Lecture Slides

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International Conference on computer and Applications (ICCA)

作者： Stylianos Vazaios Andreas Mallas Michalis Xenos Computer Engineering and Informatics Department University of Patras Patras Greece

This paper presents a novel approach to automatically summarize lecture slides using eye-tracking data. The tool generates personalized summaries by analyzing learners’ visual attention patterns, with the aim of reducing information overload and enhancing comprehension. While existing text summarization methods often overlook individual preferences and attention patterns, our proposed approach considers all slide elements, including text and visuals. It dynamically selects areas of interest based on learners’ fixation points. The tool is developed as a web application, enabling users to access presentations, receive personalized summaries, and save them for optimized studying. It incorporates features such as administration and settings, slides uploading, and summary viewing. To evaluate the tool, we conducted an experiment involving 62 participants to assess usability and satisfaction with the automated summaries. The evaluation results indicated positive feedback in terms of ease of use and visual appeal. Participants expressed satisfaction with the generated summaries and were given the option to select their preferred summarization percentage. Our analysis revealed that summarization preferences are not related to prior knowledge. Overall, this tool demonstrates the potential of personalized summarization based on eye-tracking data, thereby enhancing learning experiences.

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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Tuning Quantum Computing Privacy through Quantum Error Correction

Tuning Quantum Computing Privacy through Quantum Error Corre...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhong, Hui Ju, Keyi Sistla, Manojna Zhang, Xinyue Li, Aohan Qin, Xiaoqi Fu, Xin Pan, Miao University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Kennesaw State University Department of Computer Science MariettaGA30060 United States The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo1828585 Japan

ISBN: (纸本)9798350351255

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing. © 2024 IEEE.

关键词： Quantum noise

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Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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Differentially private data publication with multi‐level data utility

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High-Confidence Computing 2022年第2期2卷 61-68页

作者： Honglu Jiang S.M.Sarwar Haotian Yu Sheikh Ariful Islam Department of Informatics and Engineering Systems The University of Texas Rio Grande ValleyBrownsvilleUSA Department of Computer science The University of Texas Rio Grande ValleyEdinburgUSA Department of Data Analytics The George Washington UniversityWashingtonUSA

Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive *** data publication schemes implicitly assume that all data analysts and users have the same data access privilege ***,it is not applicable for the scenario that data users often have different levels of access to the same data,or different requirements of data *** multi-level privacy requirements for different authorization levels pose new challenges for private data *** PPDP mechanisms only publish one perturbed and private data copy satisfying some privacy guarantee to provide relatively accurate analysis *** find a good tradeoffbetween privacy preservation level and data utility itself is a hard problem,let alone achieving multi-level data utility on this *** this paper,we address this challenge in proposing a novel framework of data publication with compressive sensing supporting multi-level utility-privacy tradeoffs,which provides differential ***,we resort to compressive sensing(CS)method to project a n-dimensional vector representation of users’data to a lower m-dimensional space,and then add deliberately designed noise to satisfy differential ***,we selectively obfuscate the measurement vector under compressive sensing by adding linearly encoded noise,and provide different data reconstruction algorithms for users with different authorization *** experimental results demonstrate that ML-DPCS yields multi-level of data utility for specific users at different authorization levels.

关键词： Differential privacy Compressive sensing Multi-level utility Privacy preservation

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Efficient Power Control for UAV Based on Trajectory and Game Theory

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computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Penetration of Distributed Generation in Microgrids: Characteristics and Challenges

Penetration of Distributed Generation in Microgrids: Charact...

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International Universities Power engineering Conference (UPEC)

作者： Kypros Tillyros Nicholas G. Christofides Michael Komodromos Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isolated topologies and attempts to review the application of microgrids within the two. In addition, it investigates methods to optimise power quality with the integration of multi-renewable generation to the system and discusses on the feasibility towards islanded operating microgrids. The proposed work is a result of a careful evaluation of the current literature on the topic. Consequently, the outcome of the given study is anticipated to facilitate future work on Microgrid implementation functioning in islanded mode.

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Financial Market Prediction Using Deep Neural Networks with Hardware Acceleration 12

Financial Market Prediction Using Deep Neural Networks with ...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Foroughi, Mohammad Hadi Bagherzadeh, Ali Rahmati, Dara Foroughi, Mehdi Gorgin, Saeid Shahid Beheshti University Computer Science and Engineering Department Tehran Iran University of Oslo Department of Informatics Oslo Norway Korea Republic of

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Financial market prediction has long been a hard-To-solve problem. Recently many machine learning and deep learning approaches have been taken into account to predict various properties of financial markets, namely volatility, price, and trend prediction. This paper proposes a novel multi-Timeframe 1-D convolutional neural network to predict market movements. The model is trained with actual foreign exchange data in an offline setting. The trained model is then deployed on a Xilinx Zedboard Zynq-7000 SoC field programmable gate array (FPGA) to accelerate the calculation and also reducing the power consumption compared to the known approaches. We evaluate the performance of our model on test-set data new to the model;the results show that the proposed model outperforms commonly used models such as long short term memory (LSTM), multi-layer perceptron (MLP), and single-Time-scale convolutional neural networks (CNN) with an accuracy of 76.60%. Although deploying the model on FPGA causes a slight accuracy reduction of 2.56%, the evaluations show that the accuracy of the hardware model is still higher than other models. Moreover, according to the evaluation results, performing the inference phase on hardware dramatically reduces latency which is a vital feature for this kind of applications with a factor of roughly 380. Power consumption is also compared and reduced when compared to an Intel server with Xeon ES-2658 processor. © 2022 IEEE.

关键词： Multilayer neural networks

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Influence Maximization in Dynamic Social Networks Under Partially Observable Environments

Influence Maximization in Dynamic Social Networks Under Part...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Gkolfo I. Smani Vasileios Megalooikonomou Department of Computer Engineering and Informatics University of Patras Patras Greece

Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among individuals. Although, most of the existing works on this task focus on static networks, in this paper we study the problem of influence maximization in dynamic social networks where the changes that occur over time can be observed by periodically probing nodes to update their connections. The goal of this work is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose three algorithms, MaxC, MaxDC and MaxT, to approximate the optimal solution with probing nodes, which achieve improvement on estimating the number of activated nodes over state-of-the-art-method by 2.88%, 4.95% and 5.39%, respectively.

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