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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Gunawan, Alexander A. S. Kusnadi, Maximilianus Raymond Joseph, James Tristan Josan, William Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382914

This study examines the significant influence of emotional states on individuals, recognizing that people are often confronted with numerous challenges and problems in their lives. Such challenges can give rise to feelings of despair and tension, especially in those who struggle to manage them effectively. Depression, which is not just limited to adults, can also have a significant impact on children who may not fully grasp their socio-economic conditions. One of the serious consequences of unresolved emotional distress, particularly depression, is an elevated risk of suicide. Within this framework, facial recognition technology presents itself as a powerful instrument for identifying human emotions. The purpose of this study is to assess the efficacy of facial recognition systems in detecting indicators of depression. Using the YOLOv5 algorithm and comparing it with its precursors, YOLOv4 and YOLOv7, the research conducts a simulation experiment to examine the ability of the system to precisely interpret a range of emotional states, including happiness, sadness, and anger. The research findings suggest that the YOLO algorithm, particularly in its most recent versions, displays exceptional precision in identifying and classifying intricate facial expressions, thus showcasing its potential as a beneficial device in mental health evaluations. © 2023 IEEE.

关键词： Face recognition

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Improving Universal Rendering Performance on NuxtJS-based Web Application 11

Improving Universal Rendering Performance on NuxtJS-based We...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Angkasa, Harta Farell, Darren Wijaya, Erik Hendrawan Putra Achmad, Said Fitrianah, Devi Bina Nusantara University School of Computer Science Cyber Security Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350305968

Universal rendering is a way to render a website by combining server and client-side rendering. Universal rendering promises to boost the performance and SEO score of the website. However, it can be computationally exhausting for the server. This prompts researchers to address this problem in multiple different environments. Two parameters that could be used to measure the aforementioned metrics are Time to First Byte (Or Latency) and Time to Interactive. This paper addressed the aforementioned problem of universal rendering in a NuxtJS-based web application. JMeter and Lighthouse was used to test each proposed solution's performance and SEO score. Initially, there are three proposed solutions: lazy loading, SSR caching, and asset compression. This research proves that the aforementioned solutions effectively improve universal rendering performance in NuxtJS version 3.0.0. Each method has its pros and cons. For example, lazy loading and SSR caching are effective for improving the latency score, while asset compression is effective for enhancing the TTI score. © 2023 IEEE.

关键词： Websites

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Cyber Security in Smart Cities and Its Challenges: A Systematic Literature Review 3

Cyber Security in Smart Cities and Its Challenges: A Systema...

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Ruatta, Benedicto Theodore Piekarsa, Gabriel Jonatan Christian, Jeremia Edbert, Ivan Sebastian Gunawan, Alexander Agung Santoso School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

At this time, the development of information and communication technology has mostly been used maximally to improve the quality of life of citizens around the world. The benefits that are obtained make some cities evolve into smart cities in improving living standards and provide services in the health sector because it can control information technology and communication and cybersecurity challenges that are always evolving in every era. However, Behind the developments and benefits of it all, there are risks that cannot be avoided. Risk and these threats can be overcome through the national spectrum by implementing cybersecurity in the virtual world in developing the city into a smart city. Therefore, to respond to cybersecurity in smart cities, cybersecurity must evolve in the same direction, so that the strategy that has been formed will become the basis and reference in implementing cyber security in support of smart city development and the challenges that exist. This paper will outline the relationship between the two factors, the significance of cyber security for smart cities, how such cities can be attacked, and how to defend against such attacks. And after conducting the study, the authors reach the conclusion that in order for any smart cities to develop, flourish, and ensure that everyone living there is safe from any threat, cyber security is essential. © 2023 IEEE.

关键词： Cybersecurity

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VaeDiff-DocRE: End-to-end data Augmentation Framework for Document-level Relation Extraction 31

VaeDiff-DocRE: End-to-end Data Augmentation Framework for Do...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tran, Khai Phan Hua, Wen Li, Xue School of Electrical Engineering and Computer Science The University of Queensland Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761964

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE's latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE. Our code is released at: https://***/khaitran22/VaeDiff-DocRE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation 40

SSDRec: Self-Augmented Sequence Denoising for Sequential Rec...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Chi Han, Qilong Chen, Rui Zhao, Xiangyu Tang, Peng Song, Hongtao College of Computer Science and Technology Harbin Engineering University China School of Data Science City University of Hong Kong Hong Kong School of Cyber Science and Technology Shandong University China

ISBN: (纸本)9798350317152

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework-Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of SSDRec over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available online at https://***/zc-97/SSDRec. © 2024 IEEE.

关键词： User profile

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Development of Smart Lights to Reduce Electricity Usage by Using Fuzzy Logic 1

Development of Smart Lights to Reduce Electricity Usage by U...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Djayapranata, Daniel Thetrasakti, Renaldy Arnaldo Theodora, Regina Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350306156

In wealthy nations, the demand for electrical energy is steadily increasing, despite the scarcity of available resources. Considering that individuals spend approximately 80% of their lives indoors, it is imperative to implement energy-efficient lighting solutions to reduce electricity consumption. In this research, we have developed a prototype of an intelligent lamp that employs LUX sensors and fuzzy logic to automatically adjust power usage based on ambient light levels. The primary objective of this study is to minimize energy wastage through experimentation with the prototype's performance. Our findings indicate that, depending on the room's lighting conditions, this innovative smart lamp can achieve an average energy savings of 45.46%. © 2023 IEEE.

关键词： Fuzzy logic

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Security Implementation Againts a Server Using SonicWall 5

Security Implementation Againts a Server Using SonicWall

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Santoso Gunawan, Alexander Agung Wicaksana, Brahmantyo Adianto Ady Wijaya, Alexandre Christian Adi Poernama, Alexander Robi Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Robust security measures are required for server environments due to the complexity and sophistication of cyber-attacks, which are on the rise. This study investigates the use of SonicWall Network Security Manager (NSM) to create security against a server. For the purpose of defending servers against various cyberthreats, NSM provides centralized management, real-time monitoring, and advanced analytics. The purpose of this study is to offer a thorough examination of SonicWall NSM's security features and gauge how well it protects server architecture. The studies in this paper involve configuring a number of ports on the SonicWall network software in order to preserve and enhance the security of each port. The experiment's findings demonstrate how UDP port security on SonicWall network gear may both safeguard and reveal threats to SonicWall logs. This finding offers options to corporate network specialists so that they can safeguard themselves against data theft for the advantage of both parties. © 2023 IEEE.

关键词： Network security

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua science and Technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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Phishing Attack Awareness Among College Students 3

Phishing Attack Awareness Among College Students

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Kenneth, Alvin Hayashi, Bernard Bryan Lionardi, Jason Richie, Stevanus Achmad, Said Junior, Franz Adeta Nadia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Phishing is a form of cyber-attack that uses social engineering techniques to trick individuals into providing sensitive information or clicking on malicious links, often through email or text messages. Social engineering is the psychological manipulation of people into performing actions or disclosing sensitive information. In phishing, attackers use social engineering tactics such as creating a sense of urgency, posing as a trustworthy entity, and creating a sense of familiarity to manipulate the victim into taking the desired action. These attacks can have serious consequences, including financial loss and identity theft. In this research, we used an email phishing technique that asked respondents to change their password on an email account to find out awareness of phishing attacks on college students. The research results show that a small percentage of a certain amount of people are still unaware of phishing attacks and fell into the attacker's trap. Several respondents who are still deceived by social engineering using phishing emails indicate that it is necessary to conduct indoctrination and campaigns to raise the awareness of each student. © 2023 IEEE.

关键词： Electronic mail

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Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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