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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Albladi, Aish Seals, Cheryl D. Department of Computer Science and Software Engineering Auburn University AuburnAL United States

The growing use of digital media or social media has given a platform to the people to deliver their ideas and viewpoints openly. It is facilitating the rapid spread of contrasting opinions openly. Ultimately, this has developed echo chambers or polarized platforms. These platforms either encourage conspiracy theories or critical narratives. So, nowadays, it has become crucial for people to be aware of being part of such narratives. Moreover, it is also necessary for the organisations to identify the conspiracy creators or the ones who do not possess a conspiracy mentality. This can be made possible by the use of natural language processing techniques to do this task automatically. This study presents the solutions to the two most pressing challenges in today's era. The subtask one of this study provides a solution for the identification of conspiracy narratives and critical narratives using the BERT model. Our model achieved an MCC score of 0.80. Secondly, the other subtask helps to recognize the span of the topic relevant to the important constituents of both categories of oppositional narratives, i.e., agent, facilitator, victim, campaigner, objective, negative effect. The overall accuracy of the model is 95%. These models can be used for the development of an algorithm that can automatically classify the conspiracy theories and critical narratives. © 2024 Copyright for this paper by its authors.

关键词： Social networking (online)

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A Network Model Based on ISIS and EBGP Routing Protocol

A Network Model Based on ISIS and EBGP Routing Protocol

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2023 International Conference on Information Network and computer Communications, INCC 2023

作者： Tao, Jun Yu, Wanfeng He, Hao Wang, Chengdong Anhui Institute of Information Engineering School of Computer and Software Engineering WuHu China

ISBN: (纸本)9798350336139

ISIS and BGP are wildly used routing protocols, after the basic concepts of them are expounded, the drawbacks of network systems based on traditional IGP protocols were explained subsequently. To overcome the above drawbacks, a network model based on EBGP and ISIS was proposed innovatively. This new network model has flexible structure, strong performance and superior redundancy, what's more, it supports rich communication applications, so this model is suitable for the giant networks. Simulation experiment indicates this model based is more optimal than the model based on IBGP and OSPF protocol. Finally, a real network system was constructed based on this model, which runs stably and all network parameters are normal. © 2023 IEEE.

关键词： Routing protocols

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Predicting Stock Prices Using Hybrid LSTM and ARIMA Model

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 424-432页

作者： Ma, Chi Wu, Jie Hu, Hui Chen, YueNai Li, JingYan School of Computer Science and Engineering Huizhou University Guangdong Huizhou516007 China the School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Mathematics and Statistics Huizhou University Guangdong Huizhou516007 China School of Computer Science and Engineering Huizhou University Guangdong Huizhou516007 China

With the continuous development of China's financial market and the gradual improvement of the financial system, investors are increasingly interested in participating in investments. At the same time, there is a strong demand for accurate and effective financial information services. In recent years, many researchers have begun to pay attention to the financial field and try to use a variety of artificial intelligence technologies to solve the problem of financial data prediction in this field. Aiming at the problem of stock trend prediction, we present a hybrid model based on Long Short-Term Memory (LSTM) and Auto Regressive Integrated Moving Average (ARIMA) approach to predict stock prices. Through sample training and experiments in multiple data sets and different market stages, it is shown that the proposed Mixture model has higher prediction accuracy and stability than the single deep learning model, ARIMA model after wavelet transform and the models in the references, and has a stronger market adaptability. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Long short-term memory

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Evaluation of the Influence of Priorities in Multichannel Data Transmission Systems

Evaluation of the Influence of Priorities in Multichannel Da...

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2023 International Russian Smart Industry Conference, SmartIndustryCon 2023

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint-Petersburg Russia

ISBN: (纸本)9781665464291

Subject of research. Evaluation of the impact of priority data transmission in multi-channel systems of high load, presented as a queuing system with prioritization of applications. Method. The impact of priorities on the efficiency of data transmission in multichannel systems was assessed using simulation methods and mathematical statistics. Main results. A numerical method for calculating time indicators based on a simulation model of a multichannel data transmission system with a non-uniform load is proposed. Simulation experiments have been carried out to evaluate the impact of priority data transmission at various coefficients of variation in the intensity of requests on time indicators in a multichannel system. The areas of efficiency of using priorities in highly loaded multichannel data transmission systems are shown. Practical significance. The presented results can be used in the design of high-load multichannel computer data transmission systems. © 2023 IEEE.

关键词： Efficiency

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A Multimodal Method for Semi-Biometric Information Based User Identification in AR and VR Applications

A Multimodal Method for Semi-Biometric Information Based Use...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Li, Han Lyu, Ke Dossett, Owen Feng, Xianglong Miami University Department of Computer Science and Software Engineering Oxford45056 United Kingdom

ISBN: (纸本)9798350367942

Virtual Reality (VR) and Augmented Reality (AR) have witnessed a surge in popularity, revolutionizing various industries and enhancing user experiences. As these technologies continue to evolve, ensuring secure user identification becomes increasingly important. However, existing identification methods often come with vulnerabilities or require costly hardware implementations. To address these challenges, we propose a novel semi-biometric information-based user identification approach leveraging multimodal techniques. By analyzing the user's viewing patterns and gaze behavior in the runtime, we can accurately identify individuals. This approach offers a promising solution for seamless user identification in VR and AR applications, without compromising on user experience or requiring expensive hardware modifications. To verify the efficiency of our proposed algorithm, we test our algorithm using a public dataset, the result of which shows a high classification accuracy. Additionally, beyond traditional evaluation metrics, we perform a transferability test, demonstrating the adaptability of our solution to new scenarios with robust generality. © 2024 IEEE.

关键词： Biometrics

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions 6

Automated Hematological Analysis:Advances in Segmentation Ap...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Eram, Saleha Akram, Muhammad Usman Nayab, Maham National University of Sciences and Technology Dept. Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75-80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gan, Xiaoyun Lu, Peng Gan, Shanyu Xian, Youqian Peng, Kaichen Liu, Peng Li, Dongcheng School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9781665410205

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Neural Operator-Based Framework for Time Efficient Denoising of Displacement Fields in Ultrasound Elastography

Neural Operator-Based Framework for Time Efficient Denoising...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhu, Yihong Peng, Bo School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9781665410205

In ultrasound elastography, the noise present in the measured displacement fields has been a critical factor affecting the quality of the strain or elastic distribution reconstruction. Existing partial differential equation (PDE) based denoising algorithms can effectively remove noise to some extent but are limited by slow solving speeds. To address this challenge, we introduce a neural operator-based framework for denoising measured displacement fields in ultrasound elastography. By utilizing neural operators to learn a general solution operator for the denoising equation, the framework aims to achieve fast and accurate denoising of displacement fields. Experiments conducted on simulated data and tissue-mimicking phantom data demonstrate that this method performs comparably to traditional methods across various metrics. In particular, the proposed approach achieves processing speeds approximately 30 to 577 times faster than the finite difference method (FDM) in various data dimensions, making it a highly effective solution for rapid and scalable data processing in ultrasound elastography. © 2024 IEEE.

关键词： Ultrasonic applications

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms 4

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Bertrand, Simon Germain, Pascal Tawbi, Nadia Laval University Dept. of Computer Science & Software Engineering Québec Canada

ISBN: (纸本)9798350354775

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%. © 2024 IEEE.

关键词： Deep neural networks

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A Brief Review on Prediction Methods for Cloud Resource Management 9

A Brief Review on Prediction Methods for Cloud Resource Mana...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Kuang, Chenxing Qiu, Yunyun Cao, Weipeng Xiao, Zhijiao Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen China

ISBN: (纸本)9798350389500

The complexity of apps running on cloud platforms is evident in their nature. Every application has distinct needs for processing power and memory at various times. In order to effectively cater to tenants' varied computing requirements while reducing inefficiencies in resource usage, cloud service providers need to be able to predict workload variations with high accuracy and evaluate how cloud apps use resources. In the recent past, there has been a significant increase in research and engineering efforts in cloud computing. These efforts are focused on developing methods to address important issues such as predicting changes in workloads, assessing resource demands, perceiving quality of service (QoS), and detecting anomalies. By reviewing these algorithms in depth, this paper aims to provide academics a better grasp of the most recent developments in this area. © 2024 IEEE.

关键词： Quality of service

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