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arXiv 2024年

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model’s performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model’s initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Translation (languages)

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater Management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Thanigaivelu, P.S. Sairam, A. Vijayan, P. Kumar, A. Suresh Mohankumar, N. Ganeshbabu, T.R. Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur Tamil Nadu Chennai India Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Saveetha School Of Engineering Tamil Nadu Chennai India Saveetha Engineering College Department Of Artificial Intelligence And Data Science Tamil Nadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department Of Management Studies Tamil Nadu Chennai India Maharashtra Nagpur India Department Of Electronics And Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350371567

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage. © 2024 IEEE.

关键词： Internet of things

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms

A Music Recommender System to Identify the Emotional State o...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Muruganantham A C. Malarvizhi Divya Vahini. S D. Saranya A. Uthiramoorthy Department of Computer Science (PG) Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science PSG Institute of Advanced Studies Coimbatore India Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Information Technology Sri Eshwar College of Engineering Coimbatore Department of Computer Science Rathinam College of Arts and Science

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods--with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be--make tailored entertainment paths in the digital era.

关键词： Support vector machines Emotion recognition Recurrent neural networks Machine learning algorithms Accuracy Mood Face recognition Computational modeling Convolutional neural networks Recommender systems

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Beyond Deterrence: A Systematic Review of the Role of Autonomous Motivation in Organizational Security Behavior studies 25

Beyond Deterrence: A Systematic Review of the Role of Autono...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Chen, Xiaowei Schöni, Lorin Distler, Verena Zimmermann, Verena Institute for Advanced Studies University of Luxembourg Esch-sur-Alzette Luxembourg Security Privacy & Society ETH Zurich Zurich Switzerland Department of Computer Science Aalto University Espoo Finland Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland

ISBN: (纸本)9798400713941

What drives employees to ensure security when handling information assets in organizations? There is growing interest from the security behavior community in how autonomous motivators shape employees' security-related behaviors. To reconcile the scattered viewpoints on autonomous motivation and synthesize findings from studies utilizing various theoretical frameworks, we systematically reviewed relevant publications. We present a preregistered literature review that investigated (a) what forms of autonomous motivation have been examined in organizational security contexts, (b) which behaviors/behavioral intentions are related to autonomous motivators, and (c) how autonomous motivation affects employees' security behaviors. Based on an initial set of 432 papers, filtered down to 45 studies, we identified 17 unique autonomous motivators and three types of related security behaviors. This review not only develops a refined taxonomy of autonomous motivation related to security behaviors but also charts a path forward for future research on autonomous motivation in human-centered security. © 2025 Copyright held by the owner/author(s).

关键词： Motivation

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Deep Learning-Based Image Analysis to Track Vehicles

Deep Learning-Based Image Analysis to Track Vehicles

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Disruptive Technologies (ICDT), International Conference on

作者： P Prabakaran R. Jayasudha Mukta Sandhu Sandeep Gupta Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Palwal Haryana India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun India

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT

arXiv

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arXiv 2024年

作者： Hashemi, Seyed Mohammad Hossein Safari, Leila Taromi, Amirhossein Dadashzadeh Department of Computer Engineering Faculty of Engineering University of Zanjan Zanjan45195-313 Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences Zanjan45137-66731 Iran

In the field of medical science, the reliable detection and classification of brain tumors from images remain a formidable challenge due to the rarity of tumors within the patient population. The ability to detect tumor cases in anomaly scenarios is paramount for ensuring timely interventions and improved patient outcomes. This study addresses this challenge by leveraging deep learning (DL) techniques to detect and classify brain tumors in challenging situations. This study employs deep learning (DL) techniques to address this challenge, utilizing a dataset from the National Brain Mapping Lab (NBML) comprising around 30,000 slices belonging to 81 patients, including 30 tumor cases and 51 normal cases. Our approach consists of two phases: detection and classification. In the first phase, extensive data pre-processing simulated real-world conditions, adjusting the dataset to reflect an anomaly distribution of 9 normal cases for every 1 tumor case. Next, a YOLOv8n model was fine-tuned to detect tumor regions. To assess the model's performance at the patient level, we introduced the Patient-to-Patient (PTP) metric, which evaluates the ability to identify tumor cases across an entire population rather than measuring the performance for individual slices. This approach provides a more clinically relevant evaluation of model reliability. The model achieved F1-score of 0.98 and PTP-F1-Score of 1.0, correctly classifying all the patients within the testing population. In the classification phase, the Data Efficient Image Transformer (DeiT) was used to distill a Vision Transformer (ViT) model from a ResNet152 teacher model. DeiT was selected for its ability to train effectively on small datasets. The distilled classifier achieved F1-score of 0.92 after 20 epochs, while the ResNet152 model achieved 0.97, albeit with higher computational costs. This study demonstrates notable advancements in the reliable detection and classification of brain tumors in challenging situations, of

关键词： Brain mapping

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Anomaly Detection in Evolutionary Social Networks leveraging Community Structure 15

Anomaly Detection in Evolutionary Social Networks leveraging...

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15th IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2021

作者： Ghoshal, Arnab Kumar Das, Nabanita Asutosh College Department of Computer Science Kolkata India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781665467223

Social networks are temporally evolving by nature and in general, the evolution takes place gradually with time. But occasionally, the nodes may exhibit anomalous behavior that is to be detected and reported in real time. Usually, the social networks are represented as a temporal stream of weighted graphs, where edge weights stand for frequency or power of actions. Here, for identification of anomalies in evolutionary social networks, we follow an action based approach leveraging the community structure of the network, where it is shown that only the Community Boundary Nodes are enough to detect anomaly in a temporally evolving social network. We introduce a new definition of anomaly score for nodes based on its action history. To the best of our knowledge, this is the first work to exploit the community boundary nodes of an evolutionary social network to identify the graph snapshots containing anomalous events. Experimental studies on real-world as well as synthetic evolutionary networks show that the proposed technique achieves almost 13% improvement in F-Score compared to the state-of-the-art algorithms. Moreover, parallel implementation of the proposed technique results nearly 6 x speedup making it suitable for real time computation of anomaly in evolutionary social networks. © 2021 IEEE.

关键词： Anomaly detection

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Navigating the Edge: UAS Boundary Tracing for Efficient Volcanic Plume Monitoring

Navigating the Edge: UAS Boundary Tracing for Efficient Volc...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： John Ericksen Abir Islam Carter Frost Kevin Aubert G. Matthew Fricke Varsha Dani Rafael Fierro Tobias Fischer Scott Nowicki Jared Saia Melanie Moses Department of Computer Science University of New Mexico Department of Electrical Engineering University of New Mexico Center for Advanced Research Computing University of New Mexico Computer Science Department Rochester Institute of Technology Earth and Planetary Sciences University of New Mexico Santa Fe Institute Santa Fe

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

We present the implementation and validation of Sketch, an algorithm that uses two Unpiloted Aerial Systems (UASs) to trace the boundary of volcanic plumes. Sketchguarantees asymptotically optimal flight distance and turning by maintaining a sandwich invariant where one UAS stays inside the plume boundary (defined by a $\textbf{CO}_{2}$ concentration threshold) and the other UAS stays outside. The UASs adjust their flight paths based on real-time CO 2 measurements to maintain this invariant. This paper details the implementation of Sketchon a real-world UAS platform, the Dragonfly drone. We evaluate the efficacy of Sketchthrough extensive testing in physics-based simulations and real-world outdoor environments using virtual plumes. The algorithm is compared to a single-UAS baseline algorithm called Zigzag. Results show that Sketchmeets the expectations set by theory, and it is more efficient than Zigzag, achieving shorter flight paths, less turning, and faster mapping times. While Zigzagexhibits slightly higher accuracy in estimating plume area and boundary, Sketchoffers a more efficient real-time volcanic plume monitoring approach, especially in time-sensitive situations. These results demonstrate the feasibility and efficacy of Sketchfor real-time volcanic plume monitoring, paving the way for accurate $\textbf{CO}_2$ emission estimation in hazardous and challenging environments.

关键词： Accuracy Navigation Volcanic activity Estimation Turning Real-time systems Safety Security Monitoring Testing

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On-site Testing and Preventive Maintenance for a Fire Automation System Employing PSO, FA and ANN Algorithms

On-site Testing and Preventive Maintenance for a Fire Automa...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： B. Rubini G Kalaiarasi S. Vasuki S. Pradeep Kumar Suganiya Murugan C. Arul Stephen Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Advanced Computer Science and Engineering Vignan Foundation for Science Technology and Research Deemed to be University Guntur Andhrapradesh India Department of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Computing Technologies SRM Institute of Science and Technology Kaattankulathur Chennai India Department of Electronics and Communication Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

Engineering elements influencing in commercial and government buildings fire safety rendition buildings have been deliberated. For both new and existing buildings, all variables such as structure of building, damage due to fire from prevention compartments, Time of incidents evacuation routes should be control leverage on people’s evacuation routes, controlled, and additional standard fire protection equipment are imposed. This page outlines the classes of diesel fire pumps, batteries, and pumping systems, besides the elements prompting fire rendition. Onsite diesel generator pump must be connected to a hydrant and the water pumping pressure range must be customized to 8 bar using a pressure gauge. The substantive intent of this concert is to reinforce firefighter safety. Only experienced technicians and training professionals in fire guard are responsible for this part. To simulate the battery tightness position, an extra-critical condition has been considered. Owing to the position of the fire safety properties and battery reaction in buildings, the Firefly and Particle Swarm optimization algorithms have been exerted on the iterations with objective values used for analysis in the iterative process using Matlab 2021 software. Two well-known algorithms are compared to see which provides the best results. The ANN serves as a tool for analyzing and optimizing the pump’s rendition in maintaining an adequate water supply for fire emergencies.

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