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International Conference on Electrical engineering and Informatics, ICEEI

作者： Richard Medyanto Nicholas Sanjaya Suryadiputra Liawatimena Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.

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A Comparative Evaluation of the SIR and SEIZ Epidemiological Models to Describe the Diffusion Characteristics of COVID-19 Polarizing Viewpoints on Online Social Networks 58

A Comparative Evaluation of the SIR and SEIZ Epidemiological...

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58th Hawaii International Conference on System sciences, HICSS 2025

作者： Maleki, Maryam Agarwal, Nitin Systems Engineering Program California State University Dominguez Hills United States COSMOS Research Center UA - Little Rock AR United States International Computer Science Institute University of California Berkeley United States

ISBN: (纸本)9780998133188

To understand and characterize the diffusion trends of opposing viewpoints on Twitter (X), we applied two epidemiological models to six datasets related to COVID-19. We compared the results of the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models. We collected six datasets indicative of polarizing viewpoints related to contentious subjects surrounding the COVID-19 pandemic. Three of the datasets fall into an anti-subject hashtag group, and three of the datasets fall into a pro-subject hashtag group. The timeframe of each dataset is from January 1, 2020, to the end of 2021. Our findings demonstrate that while both the SIR and the SEIZ models are able to evaluate the propagation trends of the polarizing viewpoints, the SEIZ model is more accurate with relatively less error compared to the SIR model. This work sets the stage for ultimately leading to the ability to develop methods to prevent the propagation of ideas that lack scientific evidence while promoting the spread of scientifically backed ideas. © 2025 IEEE computer Society. All rights reserved.

关键词： Tweets

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Multimodal Cross-Lingual Features and Weight Fusion for Cross-Cultural Humor Detection 4

Multimodal Cross-Lingual Features and Weight Fusion for Cros...

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4th Multimodal Sentiment Analysis Challenge and Workshop, MuSe 2023, In conjunction with ACM Multimedia 2023

作者： Xie, Heng Cui, Jizhou Cao, Yuhang Chen, Junjie Tao, Jianhua Fan, Cunhang Liu, Xuefei Wen, Zhengqi Lu, Heng Yang, Yuguang Lv, Zhao Li, Yongwei Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Information Science and Technology ShanghaiTech University Shanghai China Ximalaya Inc. Shanghai China College of Computer and Information Engineering Tianjin Normal University Tianjin China Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400702709

Sentiment analysis plays a crucial role in interpreting human interactions across different modalities. This paper addresses the Cross-Cultural Multilingual Humour Detection Sub-Challenge in the MuSe 2023 Multi-modal Sentiment Analysis Challenge, which aims to identify humor instances in cross-cultural situations using multimodal data. In this paper, we explore the unique window length processing of audio data to more accurately and fully capture humor information. Furthermore, we employ a data augmentation technique for text to bridge the gap between the train set and the test set. This augmentation involves converting German and English samples in the database to each other, as well as translating both languages into Chinese. These techniques aim to enhance the model's generalization across different languages and alleviate the scarcity of humor samples. For multimodal fusion, we use the late fusion technique applied to different Gated Recurrent Unit (GRU) models with modality-specific weights obtained through gradient descent. Experimental results demonstrate the effectiveness of our approach, achieving an Area Under the Curve (AUC) score of 0.872 on the MuSe-Humor test set. This performance ranked second place in the sub-challenge. © 2023 ACM.

关键词： Sentiment analysis

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Unveiling Vulnerability of Self-Attention 30

Unveiling Vulnerability of Self-Attention

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liong, Khai Jiet Wu, Hongqiu Zhao, Hai Xiaoshan, Li Minjie, Bian Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Pre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipulating word inputs, they are limited by their means of generating adversarial samples, lacking generalization to versatile real-world attack. This paper studies the basic structure of transformer-based PLMs, the self-attention (SA) mechanism. (1) We propose a powerful perturbation technique HackAttend, which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. We show that state-of-the-art PLMs fall into heavy vulnerability that minor attention perturbations (1%) can produce a very high attack success rate (98%). Our paper expands the conventional text attack of word perturbations to more general structural perturbations. (2) We introduce S-Attend, a novel smoothing technique that effectively makes SA robust via structural perturbations. We empirically demonstrate that this simple yet effective technique achieves robust performance on par with adversarial training when facing various text attackers. Code is publicly available at ***/liongkj/HackAttend. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Salt Hash Password Using MD5 Combination for Dictionary Attack Protection 6

Salt Hash Password Using MD5 Combination for Dictionary Atta...

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6th International Conference on computer and Informatics engineering, IC2IE 2023

作者： Nugroho, Agung Mantoro, Teddy The Republic of Indonesia Defense University Cyber Defense Engineering Study Program Sains and Defense Technology Faculty Bogor Indonesia Sampoerna University Media-Tech Lab Departement of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350345162

Using MD5 hashing of passwords in web logins is highly vulnerable to dictionary attacks. One online application for cracking hashed passwords using a dictionary attack that is free and easy to use is ***. Even so, the use of hashing passwords using MD5 which has a low level of security is still commonly found to secure web login passwords. This research presents an increase in security through a combination of salt hash passwords when using MD5. The methodology of this study uses a quantitative experimental method to apply hash salt passwords using the MD5 combination. The password cracking test was carried out using the *** online hash cracking application on weak passwords such as 'admin' with the addition of a password hash salt using the MD5 combination. The results showed that Salt Password using the MD5 combination could not be cracked by *** using a dictionary attack. © 2023 IEEE.

关键词： dictionary attack md5 password hash salt

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Tracing Semantic Variation in Slang

arXiv

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arXiv 2022年

作者： Sun, Zhewei Xu, Yang Department of Computer Science University of Toronto Toronto Canada Cognitive Science Program University of Toronto Toronto Canada

The meaning of a slang term can vary in different communities. However, slang semantic variation is not well understood and under-explored in the natural language processing of slang. One existing view argues that slang semantic variation is driven by culture-dependent communicative needs. An alternative view focuses on slang’s social functions suggesting that the desire to foster semantic distinction may have led to the historical emergence of community-specific slang senses. We explore these theories using computational models and test them against historical slang dictionary entries, with a focus on characterizing regularity in the geographical variation of slang usages attested in the US and the UK over the past two centuries. We show that our models are able to predict the regional identity of emerging slang word meanings from historical slang records. We offer empirical evidence that both communicative need and semantic distinction play a role in the variation of slang meaning yet their relative importance fluctuates over the course of history. Our work offers an opportunity for incorporating historical cultural elements into the natural language processing of slang. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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CREAI 2022 - Preface to the First Workshop on Artificial Intelligence and Creativity 1

CREAI 2022 - Preface to the First Workshop on Artificial Int...

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1st Workshop on Artificial Intelligence and Creativity, CREAI 2022

作者： De Filippo, Allegra Milano, Michela Presutti, Valentina Saffiotti, Alessandro Department of Computer Science and Engineering University of Bologna Italy Department of Modern Languages Literature and Cultures University of Bologna Italy AASS Cognitive Robotic Systems Laboratory Örebro University Sweden

In recent years, Artificial Intelligence (AI) has gained increasing popularity in the area of art creation, by demonstrating its great potential. Research in this topic has developed AI systems able to generate creative outputs in fields such as music, painting, games, design and scientific discovery, either autonomously or in collaboration with humans. Therefore, AI also helped to analyze and study the mechanisms of creativity from a broader perspective: from the socio-anthropological to psychological, as well as cognitive impact of the autonomous creative processes of artificial intelligence. These advances are leading to new opportunities research perspectives, while also posing challenging questions related to authorship, integrity, bias and evaluation of AI artistic outputs. CREAI, the workshop on AI and creativity, tries to address these research lines and aims to provide a forum for the AI community to discuss problems, challenges and innovative approaches in the various sub-fields of AI and creativity. © 2021 Copyright for this paper by its authors.

关键词： Artificial intelligence

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Robust Frame-level Speaker Localization in Reverberant and Noisy Environments by Exploiting Phase Difference Losses

Robust Frame-level Speaker Localization in Reverberant and N...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shanmukha Srinivas Battula Hassan Taherian Ashutosh Pandey Daniel Wong Buye Xu DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Meta Reality Labs USA Center for Cognitive and Brain Sciences The Ohio State University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates robust speaker localization at the frame level on the basis of complex spectral mapping, which is capable of learning both the magnitude and phase of the target signal. Unlike prevailing deep learning methods for speaker localization, we perform MIMO (multi-input multi-output) based multi-channel speech enhancement first and then localize the enhanced speaker using weighted generalized cross correlation. In addition, we propose new multi-channel loss functions that incorporate phase differences in order to preserve inter-channel phase relations, which is key to accurate sound localization. Systematic evaluations using simulated and recorded room impulse responses demonstrate that the proposed model yields excellent frame-level speaker localization results in reverberant and noisy environments and outperforms related methods by a large margin, even surpassing their utterance-level results.

关键词： Location awareness Deep learning Accuracy Systematics Correlation Speech enhancement Signal processing Acoustics Noise measurement

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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A Hybrid Rolling and Flying Robot for Pipeline Fault Detection Using Deep Learning

A Hybrid Rolling and Flying Robot for Pipeline Fault Detecti...

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2023 Advances in science and engineering Technology International Conferences, ASET 2023

作者： Ghaly, Megan Mitra, Rohan Rayess, Abdullah Al Ahmed, Assem Varam, Dara Jaradat, Mohammad A. Pasquier, Michel Khalil, Ahmed American University of Sharjah Department of Mechanical Engineering Sharjah United Arab Emirates American University of Sharjah Department of Computer Science and Engineering Sharjah United Arab Emirates American University of Sharjah Mechatronics Engineering Gradute Program Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on manual procedures that introduce sources of error into the results. In this paper, the design for a hybrid rolling and flying unmanned aerial vehicle (UAV) for pipeline inspection is proposed. The UAV was developed with a rolling frame capable of landing on pipelines to externally inspect them for cracks, leaks, or faults by traversing along the pipeline's surface while maintaining its stability. The hybrid UAV uses a camera to classify cracks along the surface of the pipeline using deep learning models. The paper then tests several models and deploys the best model found during testing -MobileNet100 along with the proposed design for the UAV. Finally, the prototype that was presented in this paper has been tested within a controlled environment to verify the compliance of the flying, rolling and fault detection subsystems. © 2023 IEEE.

关键词： Fault detection

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