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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Novamizanti, Ledya Suksmono, Andriyan Bayu Danudirdjo, Donny Budiman, Gelar Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Institut Teknologi Bandung Sch. of Elec. Engineering and Informatics Research Collaboration Center for Quantum Technology 2.0 Bandung Indonesia Telkom University School of Electrical Engineering Research Collaboration Center for Quantum Technology 2.0 Bandung Indonesia

ISBN: (纸本)9798350315516

Robust reversible watermarking (RRW) schemes play a critical role in vulnerable applications, such as medical imaging, military and remote sensing, which require high fidelity and robustness. Furthermore, an RRW is challenging because it can solve watermark embedding and extraction in lossless and lossy environments. A two-stage RRW approach based on independent embedding domain (IED) has been presented by Wang et al. This paper proposes an RRW scheme through IED with a reversible watermarking method based on pixel value ordering (PVO). In contrast to Wang et al.'s scheme, which relies on threshold-based histogram shifting in reversible phase, our proposed method designs and modifies only the highest and lowest values in a block of pixels. Reversibility is guaranteed as it keeps the PVO of each block unchanged during embedding, thereby reducing distortion in the embedded content. The RRW approach based on IED and PVO results improved imperceptibility over the current state-of-The-Art technique. © 2023 IEEE.

关键词： Embeddings

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Gamification of cyber ranges in cybersecurity education 20

Gamification of cyber ranges in cybersecurity education

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jelo, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

There is a lack of cybersecurity experts, and this problem is getting worse with the increasing number and importance of IT systems together with rising number and sophistication of attacks. Rapid development in cybersecurity necessitates constant training and practice to keep up with the latest threats. Studying IT, and especially cybersecurity is considered difficult and there is an urgent need to encourage interest of students in this field. One promising avenue to improving the situation is gamification. Gamification has been used in education to improve enthusiasm of students, increasing their numbers, information uptake and retention. Leveraging social connections and human competitiveness can further motivate students to take part and engage in the learning activities they would otherwise not consider. Virtualized environments offer means to scale up the practical training as well as share latest teaching methods. Cyber ranges used by educational institutions often are such open platforms that provide such practical cybersecurity training. Moreover, with some thoughtful planning they can prove to be popular serious games. In this paper, we explore opportunities for hands-on cybersecurity education by using gamified scenarios in cyber range virtualized environment. We focus on cyber testing environments, what their types are, and what it includes. We place emphasis on creating basic game scenario close to real-world situations. © 2022 IEEE.

关键词： Students

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation 11

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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11th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Identification of Conflict Opinion in Aspect-Based Sentiment Analysis using BERT-based Method 9

Identification of Conflict Opinion in Aspect-Based Sentiment...

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9th International Conference on Computer, Control, informatics and Its Applications: Digital Transformation Towards Sustainable Society for Post Covid-19 Recovery, IC3INA 2022

作者： Nuryani, Nuryani Purwarianti, Ayu Widyantoro, Dwi Hendratmo Indonesia School of Electrical and Informatics Engineering Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781450397919

Aspect-based sentiment analysis (ABSA) is an NLP task for predicting sentiment polarities of specific aspects in a given opinion sentence. Recent research shows that deep learning and language modeling like BERT has become state-of-The-Art in NLP tasks, including ABSA. However, most methods still ignore conflict opinion or methods that reached high performance in 2-class (positive and negative), and 3-class (positive, negative, and neutral) classification will be degraded when applied in a 4-class classification where conflict opinion is included. In this paper, we propose a BERT-based method that can identify and handle aspects containing conflict opinions in three steps: (i) designing input representation for BERT-based sentence-pair classification task, (ii) processing two-label sentiment classification for each aspect, and lastly (iii) translating the second step result to 4-class sentiment classification. Experimental results on the SemEval-2014 restaurant domain dataset demonstrate that our proposed method has effectively identified conflict opinion and achieved better results on 3-class and 4-class classification tasks. © 2022 ACM.

关键词： Sentiment analysis

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Deep learning application in diagnosing breast cancer recurrence

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Multimedia Tools and Applications 2025年第13期84卷 12265-12297页

作者： Jam, Zeinab Albadvi, Amir Atashi, Alireza Department of Information Technology Engineering-Industrial and Systems Engineering Faculty Tarbiat Modares University Tehran Iran Medical Informatics Department Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Digital Health School of Medicine Tehran University of Medical Sciences Tehran Iran

Patients' lives can always be saved when diseases, especially special diseases, are detected early. The chances of a patient surviving can be increased by early detection. Breast cancer is one of the deadliest and common cancers. After recovering from breast cancer, patients are always worried about recurrence and return. The use of modern technology, however, can help predict disease recurrence at an early stage, allowing patients to receive treatment sooner. Significant strides have been achieved in deep learning, demonstrating strong performance in handling unstructured data challenges. However, when it comes to predicting tabular data, deep learning hasn't quite matched its success with unstructured data. Presently, ensemble models relying on gradient-boosted decision trees (GBDT) are frequently favored for tabular data prediction tasks. Typically, these GBDT-based models outshine deep learning approaches. Many novel deep learning techniques are emerging for handling tabular data. TabNet, for instance, mirrors decision tree feature selection within a neural network framework. AutoInt addresses high dimensionality by condensing data through embedding layers. Tab Transformer adapts the transformer model, generating text representations for categorical attributes. Despite their innovation, these methods remain less recognized compared to those for image and text data processing. In this study, 158 different characteristics of 5142 breast cancer patients from 1997 to 2019 were examined. We aim to evaluate deep learning techniques effectiveness in detecting breast cancer recurrence. Through examination of evaluation metrics, it becomes evident that deep learning approaches applied to tabular data surpass traditional machine learning algorithms, even when dealing with imbalanced datasets. Ultimately, the results derived from each algorithm analyzed and concluded with a review and comparison of the findings. © The Author(s), under exclusive licence to Springer Science+

关键词： Diseases

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Unleashing the Full Potential: Increasing the Bit Configuration Options in Configurable Ring Oscillator PUF

Unleashing the Full Potential: Increasing the Bit Configurat...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Husam Kareem Oliver Krammer Dmitriy Dunaev Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Electronics Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Physically unclonable functions (PUFs) are essential primitives for enhancing hardware security, leveraging the variations in physical characteristics of integrated circuits (ICs) that happen during the manufacturing process. Among various PUF designs, the Ring Oscillator (RO) PUF offers design simplicity and high performance. However, RO-PUFs have been limited in their input-output combinations or what is referred to as challenge-response pairs (CRPs) within the scope of PUFs, classifying them as weak PUFs. Consequently, increasing the CRPs size of the RO-PUF will result in an enhancement of the PUF's overall security attributes. Increasing the CRPs size of a RO-PUF can be achieved through two main methods. Firstly, by increasing the number of ring oscillators in the design which leads to greater hardware demands. Secondly, configurability can be introduced into the individual ring oscillators of the RO-PUF without imposing a substantial demand on hardware resources. Therefore, this paper utilizes the configurability approach to maximize the size of the CRPs set. The evaluation process conducted in this study demonstrates that the proposed design provides excellent results across several performance metrics.

关键词： Ring oscillators Measurement Integrated circuits Electric potential Manufacturing processes Hardware security Artificial neural networks

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Simulation and Analysis Optimization Ku-Band Satellite Transponder 16

Simulation and Analysis Optimization Ku-Band Satellite Trans...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Rustanto Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia

ISBN: (纸本)9781665455947

In this paper the use of a 36Mbps Satellite Ku-Band transponder that will be used for internet services for VSAT terminals. The capacity of the Ku-Band transponder serves several VSATs that will be connected to broadband internet through the Hub station. Transponder Satellite is a resource that is limited in bandwidth and limited in power, of course, planning is needed in its application. Planning for the optimal use of satellite transponders is carried out using Network Simulator NS-2. The satellite transponder used for internet services provides bandwidth for both Hub stations and VSAT terminals. This satellite internet service uses the TCP/IP communication protocol which will access services from the VSAT terminal to the hub station. Planning the use of transponders with NS-2 will obtain QoS parameters including throughput, packet loss, end to end delay and packet received. This can be obtained from QoS information from VSAT that uses Ku-Band transponders for internet services. © 2022 IEEE.

关键词： Satellites

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Prediction Health Index Using Machine Learning and Its Correlation with Transformer Age on Historical Data 4

Prediction Health Index Using Machine Learning and Its Corre...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Sudrajad, Gemelfour Ardiatus Suwarno Subekti, Gemelthree Ardiatus Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Politeknik Negeri Madiun Department of Accountancy Madiun Indonesia

ISBN: (纸本)9798350318678

The transformer is a vital of equipment in the power transmission and distribution system. Optimum use of transformers can increase the economic value and reliability of electricity supply. Power apparatus is expected to operate close to or exceed the design life of the transformer. Health index (HI) is used to provide a general assessment of the condition of the transformer. In determining the health index, it can be done using conventional methods or non-conventional methods. According to the IEEE loading guide lines, the age of the transformer ranges from 25-30 years. The 'effective age' of the transformer can be determined by the probability of failure of insulation system or which can be based on HI. In this study, transformer oil testing was used to determine the health index and calculate the age of the transformer. The parameters used are BDV, Water, Acid, IFT, DGA, and 2-furaldhyde. In this study, 79 transformer oil sample data were used which had known health indexes from field assessments. The health index method uses calculation, ANN, and naïve Bayes methods. Parameters affecting the health index BDV (r = 0.57), water (r = -0.55), 2FAL (r = -0.58), and acetylene (r = - 0.25). Parameters that affect the life of the transformer are BDV (r = 0.45), water (r = -0.66), Acid (r = -0.53), IFT (r = - 0.49), 2FAL (r = - 0.58), and CO2 (r = -0.40). Parameters BDV, water, and 2 FAL have a significant correlation in determining the health index and predicting the life of the transformer. Parameters 2FAL and water are related to chemical reactions in the insulation system which result in aging of the transformer. With the deteriorating condition of the transformer (HI), it further increases the possibility of initial disturbance to failure of the transformer. © 2023 IEEE.

关键词： Power transformers

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Separated Artificial Neural Network Based Distribution System State Estimation

Separated Artificial Neural Network Based Distribution Syste...

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2023 IEEE PES Generation, Transmission and Distribution International Conference and Exposition, GTD 2023

作者： Pasic, Lejla Pasic, Azra Pasic, Alija Vokony, Istvan Biro, Jozsef Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics BudapestH-1111 Hungary Department of Telecommunications and Media Informatics Budapest Hungary Department of Electric Power Engineering Budapest Hungary ELKH-BME Information Systems Research Group Budapest Hungary

ISBN: (纸本)9781728170251

Distribution system state estimation is becoming an increasingly important feature in the modern power grid, but the lack of real measurements makes its implementation particularly difficult. A solution for this problem could be the implementation of Artificial Neural Networks for direct distribution system state estimation. In our work, we created an ANN-based distribution system state estimation which can function based on sparse data, the accuracy of the ANN-based state estimation can be further improved utilizing a separated distribution system state estimation (S-ANN), which estimates the state of the network separately according to measurement type based on the same input data. With this method, the root mean square error and standard deviation of the state estimation can be significantly decreased. © 2023 IEEE.

关键词： State estimation

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