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7th Workshop on Narrative Extraction From Texts, Text2Story 2024

作者： Matus, Mauricio Urrutia, Diego Meneses, Claudio Keith, Brian School of Journalism Universidad Católica del Norte Antofagasta Chile Department of Computing & Systems Engineering Universidad Católica del Norte Antofagasta Chile

This article presents the ongoing work on developing a methodology for the systematic analysis and narrative of heritage image archives, focusing on the photo archive by Robert Gerstmann of the Sacambaya Expedition in 1928. This work combines state-of-the-art artificial intelligence techniques, such as advanced algorithms used in computer visión like convolutional neural networks, with Large Language Models (LLM) for generation purposes. The intent is to establish a practical and accessible framework in this area for institutions and individuals. The proposed method incorporates human-generated image labels with LLMs to produce narratives that aid researchers and users in their sense-making process while they explore a large archive of images. Through this iterative process, we aim to contribute not only to the understanding of this specific historical photo collection but also to the broader development of scalable solutions for the exploration and interpretation of heritage image archives. We seek to achieve a deeper understanding of the contents and meanings of the analyzed files, suggesting and highlighting new clustering of these materials and thematic/narrative connections that may not have been considered by a human observer. © 2024 Copyright for this paper by its authors.

关键词： Iterative methods

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Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation Testbed for SDN-Enabled IoT Devices 13

Machine Learning Based Distributed Denial-of-Services Attack...

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13th International Conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Sritharan, Kiruthikan Elagumeeharan, Rojeemeeharan Nakkeeran, Senthuran Mohamed, Agaar Ganegoda, Binura Yapa, Kanishka Department of Computer Systems Engineering Faculty of Computing SLIIT Colombo Sri Lanka

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

One of the most important innovations in contemporary technology since the 1980s is the Internet of Things [1]. One of the most popular current technologies today is the Internet of Things (IoT), which enables various devices to communicate with one another, exchange data, and be controlled by other devices that have been granted authentication. As these sophisticated technologies were created and developed, cyber threats against them also grew significantly. Since devices may be controlled if a network or a specific device is penetrated by an attacker, there are particularly significant hazards to the IoT environment. The downside is that because IoT devices are connected to local networks, if an attacker gained access to a weak device, the entire local network will be compromised, exposing important data and disrupting operations. One of the treatments for DDoS attacks on IoT devices is the topic of our research. To be more precise, our research suggests an Automated IoT Vulnerability Testbed for DDoS Attacks in an SDN environment, constructed using the Python programming language and numerous machine learning techniques. The network traffic that is coming to the IoT devices will be analyzed as part of our suggested solution, which will help to thwart successful DDoS attacks. © 2022 IEEE.

关键词： Internet of things

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Evaluation of Work Stealing Algorithms 15th

Evaluation of Work Stealing Algorithms

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15th Colombian Congress on Advances in computing, CCC 2021

作者： Numpaque, Juan Sebatían Cardozo, Nicolás Systems and Computing Engineering Department Universidad de los Andes Bogotá Colombia

ISBN: (纸本)9783031199509

Work stealing is a common model in parallel computing used to schedule the execution of tasks. In this model, tasks are assigned dynamically to different processors as they are generated by a computation. However, to improve execution time, idle processors can steal tasks from other processors. Different underlying techniques have been proposed for work stealing, with FIFO and LIFO among the most used techniques. In this work we propose an alternative to these techniques, based on task priority, as a means to avoid fairness problems in the way that tasks are stolen across processors. To evaluate our technique we use a benchmark of different computation topologies variating the amount of tasks, the dependance between tasks, and the number of processors used. Our results show different performances for the three evaluated techniques. In cases managing smaller computations with fewer processors, both FIFO and LIFO perform better. When we increase the size of the computations and the number of processors used, our proposed priority-based technique performs better. With respect to the fairness of the algorithms, they are all unbalanced and no significant conclusion can be reached. © 2022, Springer Nature Switzerland AG.

关键词： Parallel processing systems

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Text Messages with Emoticons Analysis on Sentiment Tools 2022

Text Messages with Emoticons Analysis on Sentiment Tools

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6th International Conference on Compute and Data Analysis, ICCDA 2022

作者： Teh, Phoey Lee Lim, Wei Yek Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia

ISBN: (纸本)9781450395472

Emoticons have been widely used in virtual communications to represent facial expressions of emotion. It often contains a hidden value when it is used along with text messages. However, not all sentiment tools can measure the sentiment of emojis. This paper investigates how emojis play their roles in text messaging by evaluating the available set of sentiment tools online. Prototype that can analyse the sentiment on text messaging containing emojis is created. The comparison is made on text messaging with emojis with other sentiment analysis tools available online. This study presented the comparison of the results and feedback from responses about the accuracy, consistency, usefulness, satisfaction of the mechanism. © 2022 ACM.

关键词： Sentiment analysis

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Base stations cooperation management algorithm based on the quality of experience and load balancing 9th

Base stations cooperation management algorithm based on the ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Lozano-Garzon, Carlos Montoya, Germán A. Donoso, Yezid Systems and Computing Engineering Department Universidad de Los Andes Bogota Colombia

Time is a crucial factor for decision making, especially in environments where it is necessary to provide a high quality of experience for the users and at the same time make efficient use of their resources;functions that in some cases may conflict. That is one of the challenges that mobile operators have now;the unceasing growth in terms of users and service requirements that generate not only technical challenges. In an initial approach to this problem, we proposed a multi-objective model for selecting the cooperation scheme between base stations and an evolutionary algorithm that provides a set of optimal solutions. However, to find the optimal set of solutions, the convergence time of the proposed algorithm varies between a few seconds to hours, which makes it feasible only for network planning exercises. In this paper, we present an algorithm that allows us to find an efficient solution to the multi-objective problem in a reasonable time for operational decision-making. © 2022 The Author(s).

关键词： Decision trees

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Profitable Investment in PV and BES Integrated with EV Charging Stations in Croatia - Myth or Reality? 7

Profitable Investment in PV and BES Integrated with EV Charg...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Grzanic, Mirna Hrga, Alen Capuder, Tomislav Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532901160

The increasing number of electric vehicles (EVs) poses a challenge to the power system operation and manage-ment. To deal with the growing consumption together with the uncertainty related to the time of arrival at the charging station and EV state of energy, the paper investigates the profitability of investment in different low-carbon (LC) technologies in the EV charging stations based on real-EV-data optimization. The model considers several real-life charging points in Croatia at different location (high-way, parking lot in the capital city and parking lot at the sea-side). Analyses show that the investment in PV and BES is profitable under current retail market options resulting in 3% to 70% cost reduction wit different LC technology options, while recently introduced EV charging reimbursement shortens the period of investment in LC technology for 8 years. © 2022 University of Split, FESB.

关键词： Solar panels

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Byte-Sized Reviewers: Exploring Review Generation through LNRG-S 3

Byte-Sized Reviewers: Exploring Review Generation through LN...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Li, Yiting Fu, Min Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Macquarie University Department of Computing Sydney Australia

ISBN: (纸本)9798350341546

The development of network technology has led to an explosive growth of user-generated comments. These contents are based on users' experiences after using services and products, reflecting users' evaluations. Automatically generating high-quality and controllable sentiment comments can help creators improve work quality, help service providers enhance service levels, and improve consumers' consumption experience. However, current methods have some limitations. Seq2seq models have difficulty maintaining semantic consistency and generating long texts, LSTM can maintain a memory state and generate coherent text, but is prone to overfitting, generating repetitive text, and having difficulty establishing local coherence. N-gram models text locally but cannot understand the global context. This paper proposes a hybrid architecture, called LNRG-S (LSTM N-gram Reviews Generator with Sentiments), which combines LSTM and N-gram to model both global and local dependencies, and trains separate LSTM generators for positive and negative samples to control sentiment polarity of generated comments. We computed the perplexity of comments generated by different models. LNRG-S has 43.93% and 18.89% lower perplexity than LSTM and n-gram respectively, demonstrating that LNRG-S generates more fluent and coherent text. © 2023 IEEE.

关键词： LSTM n-gram Perplexity Sentiment control User-generated reviews

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A Survey on Swarm Intelligence Algorithms Employed for Optimizing Machine Learning Techniques Used in Recommendation systems 11

A Survey on Swarm Intelligence Algorithms Employed for Optim...

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11th IEEE Conference on systems, Process and Control, ICSPC 2023

作者： Zet, Lim Cher Jasser, Muhammed Basheer Wong, Richard T.K. Chua, Hui Na Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350340860

With the exponential growth of digital data, recommendation systems or recommender systems are widely used in various domains assisting users in filtering and decision-making on massive information. Recommendation systems are capable of delivering personalized content to enhance user experience and satisfaction through users' preferences and behaviors. The machine learning algorithms employed in the recommendation systems facilitate the effectiveness of tasks achieved by those systems among which, for example, is providing accurate prediction that matches user preferences. Swarm Intelligence offers robust optimization mechanisms that have been successfully applied in various computational problems including recommendation systems for refining recommendation algorithms. To the best of our knowledge, there is no recent comprehensive survey on swarm intelligence algorithms used for optimizing machine learning techniques when used in recommendation systems. Therefore, this survey presents a survey of the swarm intelligence algorithms used in optimizing the machine learning techniques when employed in recommendation systems. We conducted a literature survey on recommendation systems and swarm intelligence, using relevant keywords and their variants, focusing on recent publications since 2019. Our findings highlight the use of swarm intelligence algorithms primarily in clustering, classification, and feature selection for recommendation systems. Swarm intelligence has significantly enhanced recommendation systems, especially in clustering and classification. However, the balance between computational complexity and processing speed remains a challenge. Future research could focus on refining these algorithms for better efficiency and effectiveness in recommendation systems. © 2023 IEEE.

关键词： Swarm intelligence

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