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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif E. A. Khan, Tahir Abbas Farooq, Umer Zulfiqar, Sidra Khan, Shan Al-Kassem, Amer Hani Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan LGU Department of Computer Science Lahore Pakistan College of Business Management Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

cellular computing is promising and exciting domain. As the field continues to evolve, several future facets are expected to emerge. One potential facet is the development of more sophisticated and efficient cellular-based computing systems that can perform complex calculations and data processing tasks. This could lead to the creation of new tools and technologies in various fields, including medicine, biotechnology, and environmental monitoring. Moreover, technology has changed the way of communication, sharing and exchanging data. Mobile computing has grown to be very vital as the number of cellular devices have elevated fairly in the preceding years. In this paper, we have mentioned cellular computing, aspects of cell computing, working of cell computing. Also, we have discussed that what challenges are most common in the development of cellular computing. In last we discussed merits, demerits, application, and its future. © 2023 IEEE.

关键词： Mobile computing

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Enhance Cancer Text Classification Using Multi Word Embedding and Ensemble Learning 17

Enhance Cancer Text Classification Using Multi Word Embeddin...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Mahdi, Mohammed Fadhil Abd, Dhafar Hamed Abdalkafor, Ahmed Subhi Hussain, Abir Jaafar Al-Mansour University College Baghdad Iraq College of Computer Science and Information Technology University Of Anbar Ramadi Iraq Department of Computer Sciences College of Computer and Information Technology University Of Anbar Anbar Iraq Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

The explosion of medical literature over the past decade has resulted in efficient and accurate techniques for text categorization to handle huge amount of data. This work combines ensemble learning methods with coupled multi-word embedding techniques to improve cancer text classification. The intricate semantic links present in medical tests are frequently outside the scope of traditional word embedding models, resulting in not ideal categorization results. To address this problem, we employ e continuous bag-of-words and Skip-gram approaches that yield more complete word representations capturing multiple linguistic nuances. Subsequently, such embeddings are passed through LGBM, CatBoost, and NGBoost ensemble learning classifiers to enhance classification accuracy. With 99.868% accuracy rate, LGBM and CatBoost were the most successful ensemble approaches examined. These approaches provide solid foundation for future work on the use of ensemble methods with complex word representations and for advancing the field of medical text classification. © 2024 IEEE.

关键词： Embeddings

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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How Far are App Secrets from Being Stolen? A Case Study on Android

arXiv

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arXiv 2025年

作者： Wei, Lili Huang, Heqing Cheung, Shing-Chi Li, Kevin Department of Electrical and Computer Engineering McGill University Canada Department of Computer Science City University of Hong Kong China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Android apps can hold secret strings of themselves such as cloud service credentials or encryption keys. Leakage of such secret strings can induce unprecedented consequences like monetary losses or leakage of user private information. In practice, various security issues were reported because many apps failed to protect their secrets. However, little is known about the types, usages, exploitability, and consequences of app secret leakage issues. While a large body of literature has been devoted to studying user private information leakage, there is no systematic study characterizing app secret leakage issues. How far are Android app secrets from being stolen? To bridge this gap, we conducted the first systematic study to characterize app secret leakage issues in Android apps based on 575 potential app secrets sampled from 14,665 popular Android apps on Google Play. We summarized the common categories of leaked app secrets, assessed their security impacts and disclosed app bad practices in storing app secrets. We devised a text mining strategy using regular expressions and demonstrated that numerous app secrets can be easily stolen, even from the highly popular Android apps on Google. In a follow-up study, we harvested 3,711 distinct exploitable app © 2025, CC BY-NC-ND.

关键词： Information leakage

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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IoT Security Based On Lightewight Cryptographic (LWC) Algorithms: A survey 25

IoT Security Based On Lightewight Cryptographic (LWC) Algori...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Suleiman Al-Zou'bi, Samah Askar, Sami Alqawasmi, Khaled Al-Sherideh, Ala'a Saeb Samara, Ghassan Almatarneh, Rami Khouj, Mohammed Odeh, Mahmoud Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The Internet of Things is millions of devices that communicate with each other and transmit sensitive information between them. The information is collected through the sensors on these devices, and is transmitted between the devices via the Internet. However, ensuring security in terms of authenticity, confidentiality, integrity, authentication, and data integrity is considered one of the challenges facing the Internet of Things, because ensuring these matters is through encryption, and traditional encryption algorithms are not suitable and cannot be applied in the Internet of Things environment, which is characterized by limited resources, so it was necessary From searching for algorithms suitable for the limited Internet of Things environment. In this paper, a comparison will be made between the literature studies conducted in this field in terms of challenges and possible solutions, in an attempt to find the gaps in them. © 2024 IEEE.

关键词： Encryption algorithms

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Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions 31

Cultural Alignment in Large Language Models: An Explanatory ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Masoud, Reem I. Liu, Ziquan Ferianc, Martin Treleaven, Philip Rodrigues, Miguel Department of Electronic and Electrical Engineering University College London United Kingdom Department of Computer Science University College London United Kingdom Department of Electrical Engineering King Abdulaziz University Saudi Arabia AI Centre University College London United Kingdom

ISBN: (纸本)9798891761964

The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals and societies with diverse cultural backgrounds. While the discourse has focused mainly on political and social biases, our research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify cultural alignment using Hofstede's cultural dimension framework, which offers an explanatory cross-cultural comparison through the latent variable analysis. We apply our approach to quantitatively evaluate LLMs-namely Llama 2, GPT-3.5, and GPT-4-against the cultural dimensions of regions like the United States, China, and Arab countries, using different prompting styles and exploring the effects of language-specific fine-tuning on the models' behavioural tendencies and cultural values. Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions. Our study demonstrates that while all LLMs struggle to grasp cultural values, GPT-4 shows a unique capability to adapt to cultural nuances, particularly in Chinese settings. However, it faces challenges with American and Arab cultures. The research also highlights that fine-tuning LLama 2 models with different languages changes their responses to cultural questions, emphasizing the need for culturally diverse development in AI for worldwide acceptance and ethical use. For more details or to contribute to this research, visit our GitHub page https://***/reemim/Hofstedes_CAT. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Consistent diffusion meets tweedie: training exact ambient diffusion models with noisy data 24

Consistent diffusion meets tweedie: training exact ambient d...

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Proceedings of the 41st International Conference on Machine Learning

作者： Giannis Daras Alexandros G. Dimakis Constantinos Daskalakis Department of Computer Science University of Texas at Austin and Archimedes AI Department of Electrical and Computer Engineering University of Texas at Austin Department of Electrical Engineering and Computer Science MIT and Archimedes AI

Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in Ambient diffusion. Our key technical contribution is a method that uses a double application of Tweedie's formula and a consistency loss function that allows us to extend sampling at noise levels below the observed data noise. We also provide further evidence that diffusion models memorize from their training sets by identifying extremely corrupted images that are almost perfectly reconstructed, raising copyright and privacy concerns. Our method for training using corrupted samples can be used to mitigate this problem. We demonstrate this by fine-tuning Stable Diffusion XL to generate samples from a distribution using only noisy samples. Our framework reduces the amount of memorization of the fine-tuning dataset, while maintaining competitive performance.

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A Robust Evolutionary Gravity Neocognitron Neural Network Model for Construction Cash Flow Prediction in Complex Project Management

A Robust Evolutionary Gravity Neocognitron Neural Network Mo...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Joseph, X.Feix Selvaraj, G. Babu, M. Arumuga Banu, A. Shajitha Bhuvanesh, A. Keerthanadevi, R. Loyola Institute of Technology and Science Department of Electrical and Electronics Engineering Kanyakumari India Ramco Institute of Technology Department of Mathematics Rajapalayam India Christ the King Engineering College Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to keep expenses in check while building projects are being carried out, accurate cash flow forecasting is crucial. The project's unique features and risk considerations make it challenging, despite extensive study, to develop a reliable model for predicting future cash flows. Furthermore, both sequence and non-sequence elements have a significant influence on the building projects' cash flow. As a result, the authors of this research suggested a new inference model based on artificial intelligence (AI) called Evolutionary Gravity Neocognitron Neural Network (EG-NN). This model takes project complexity into account and attempts to handle non-sequence elements in time series issues. By attaining the highest values of 2.35%, MAPE of 5.65%, MAE of 2.03%, and coefficient of determination of 0.99, the experimental findings on thirteen construction projects have validated the EG-NN as the top model for cash flow forecasting. When compared to other AI-based inference models, the statistical result shows that cash flow forecasting can be improved by at least 14.1 percent in terms of root-mean-square error (RMSE) and 15.2 percent in terms of mean absolute error (MAE). Managers may benefit from the EG-NN network model's ability to predict and manage construction projects' cash flow. © 2024 IEEE.

关键词： Project management

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