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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nagababu, Kancharla Ananthi, S. Sunheriya, Neeraj Venkata Siva Prasad, Ch. Jayanthi, J. Fawareh, Hamed Aditya University Department of Computer Applications Surampalem India St. Joseph's College of Engineering Department of Cse Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Sona College of Technology Department of Cse Salem India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies. © 2024 IEEE.

关键词： Anonymous Identical Users FRE Identity Resolution SLIS Social Media Network User Identification

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Exploiting Secure Multi-Hop Transmissions with NOMA Networks: Performance Analysis

Exploiting Secure Multi-Hop Transmissions with NOMA Networks...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we analyze the secrecy performance of secure multi-hop transmission for the NOMA system. More specifically, the multi-hop transmission can extend the coverage of wireless transmission. However, according to the principle of the wireless medium, an eavesdropper can overhear the legitimate users' transmission, which leads to serious security issues. In order to analyze the relationship between network parameters and secrecy outage probability, we derive exact closed-form expressions for the secrecy outage probability (SOP) of cell center and cell edge users, respectively. The numerical results show that the simulation and analysis results are tightly matched. Additionally, we investigate the impact of the number of hops and the distance on the secrecy performance.

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Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tran, Thong-Nhat Interdonato, Giovanni Da Costa, Daniel Benevides An, Beongku Hongik University Dept. of Electronics and Computer Engineering Graduate School Korea Republic of Italy Digital Science Research Center Technology Innovation Institute Abu Dhabi Masdar City United Arab Emirates Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350370218

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks. © 2023 IEEE.

关键词： Cell-free massive MIMO max-min fairness power control reconfigurable intelligent surface spectral efficiency

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker Dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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A Review on Data Balancing Techniques and Machine Learning Methods 5

A Review on Data Balancing Techniques and Machine Learning M...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Parmar, Gaurav Gupta, Rimi Bhatt, Tejas Sahani, G.J. Panchal, Brijeshkumar Y. Patel, Hiren S.V.I.T Dept. of Computer Engineering Gujarat Vasad India Software Developer Tüv Süd South Asia Pvt. Ltd India

ISBN: (纸本)9781665474672

Researchers in the discipline of Data Mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. Therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. This research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher's work learned to determine the most effective method for disseminating the data. There will be a discussion of ways to enhance categorization abilities in the conclusion. The future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations. © 2023 IEEE.

关键词： Data mining

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Noise Aware Utility Optimization of NISQ Devices

Noise Aware Utility Optimization of NISQ Devices

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Jean-Baptiste Waring Christophe Pere Sébastien Le Beux Dept. of Electrical & Computer Engineering Concordia University Montréal Québec Dept. of Computer Science & Software Engineering INTRIQ Laval University Québec QC Canada

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

In order to enter the era of utility, noisy intermediate-scale quantum (NISQ) devices need to enable long-range entanglement of large qubit chains. However, due to the limited connectivity of superconducting NISQ devices, long-range entangling gates are realized in linear depth. Furthermore, a time-dependent degradation of the average CNOT gate fidelity is observed. Likely due to aging, this phenomenon further degrades entanglement capabilities. Our aim is to help in the current efforts to achieve utility and provide an opportunity to extend the utility lifespan of current devices -albeit by selecting fewer, high quality resources. To achieve this, we provide a method to transform user-provided CNOT and readout error requirements into a compliant partition onto which circuits can be executed. We demonstrate an improvement of up to 52 % in fidelity for a random CNOT chain of length 50 qubits and consistent improvements between 11.8% and 47.7% for chains between 10 and 40 in varying in increments of 10, respectively.

关键词： Superconducting devices Performance evaluation Program processors Runtime Quantum entanglement Qubit Transforms

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Noise Aware Utility Optimization of NISQ Devices

arXiv

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arXiv 2024年

作者： Waring, Jean-Baptiste Pere, Christophe Le Beux, Sébastien Dept. of Electrical & Computer Engineering Concordia University MontréalQC Canada Dept. of Computer Science & Software Engineering INTRIQ Laval University QuébecQC Canada

In order to enter the era of utility, noisy intermediate-scale quantum (NISQ) devices need to enable long-range entanglement of large qubit chains. However, due to the limited connectivity of superconducting NISQ devices, long-range entangling gates are realized in linear depth. Furthermore, a time-dependent degradation of the average CNOT gate fidelity is observed. Likely due to aging, this phenomenon further degrades entanglement capabilities. Our aim is to help in the current efforts to achieve utility and provide an opportunity to extend the utility lifespan of current devices —albeit by selecting fewer, high quality resources. To achieve this, we provide a method to transform user-provided CNOT and readout error requirements into a compliant partition onto which circuits can be executed. We demonstrate an improvement of up to 52% in fidelity for a random CNOT chain of length 50 qubits and consistent improvements between 11.8% and 47.7% for chains between 10 and 40 in varying in increments of 10, *** Codes 81P68 © 2024, CC BY-NC-SA.

关键词： Quantum entanglement

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Kernel Fashion Context Recommender System (KFCR): A Kernel Mapping Fashion Recommender System Algorithm Using Contextual Information

Kernel Fashion Context Recommender System (KFCR): A Kernel M...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Waseem Abbas Uzair Rasheed Sadia Khalid Mustansar Ghazanfar Ali Hassan Dept of Computer and Software Engineering College of EME NUST Islamabad Pakistan Department of Engineering and Computing University of East London London United Kingdom

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

In Fashion, Recommender System represents a growing trend. They enable to offer the customer online fully personalized shopping experience. Many known names on the Fashion market such as Asos (***) or Zalando (***), have already bet on this technology to retain customers, boosting profits. Multiple filtering approaches exist, developed to cope with all the inherent challenges driven by the lack of information and the ephemeral nature of fashion items. However, even if the methods adopted are very varied, the main motive remains the same: reducing the margin of error at all costs to produce the nearest reality prediction. Accuracy, scalability, flexibility, and performance have become the keywords when creating a fully skilled Recommender System. To achieve these objectives, it appears that including in the model user's context information, such as time, location, mood, occasion, weather, or people's influence can be the answer. Not including contextual information is eluding a fundamental element in the decision-making process for the purchase of a particular piece of clothing. In this paper, we decided to apply to the Fashion domain issues the scalable context-aware algorithm to target the customer's tastes better, producing predictions as close to his preferences as possible. This new algorithm named KFCR uses the kernel mapping framework developed by (KMR) to complement context information. We evaluated and compared this new system to the Basic KMR, as well as to other widely used context-aware approaches like preand post-filtering techniques. Once evaluated, the KFCR was more accurate than both non-context-aware and context-aware approaches.

关键词： System kernels Accuracy Mood Filtering Scalability Decision making Prediction algorithms Market research Recommender systems Meteorology

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia Nguyen, Duy H. N. An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798331507022

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance. © 2025 IEEE.

关键词： Convex optimization

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