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TechRxiv 2023年

作者： Ghazanfar, Mustansar Ali Anwar, Madiha Lee, Sin Wee Qazi, Nadeem Karimi, Amin Jhanjhi, N.Z. Javed, Ali School of Architecture Computing and Engineering University of East London United Kingdom Deputy Head of School of Computing Arden University United Kingdom School of Computer Science SCS Taylor’s University Malaysia Department of Software Engineering UET Taxila Pakistan

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results. © 2023, CC BY.

关键词： Sentiment analysis

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A Fair VNF assignment Algorithm for network functions virtualization

A Fair VNF assignment Algorithm for network functions virtua...

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2020 International Symposium on Networks, computers and Communications, ISNCC 2020

作者： Singh Ghai, Karanbir Yassine, Abdulsalam Choudhury, Salimur Lakehead University Department of Computer Science Thunderbay Canada Lakehead University Department of Software Engineering Thunderbay Canada

ISBN: (纸本)9781728156286

On-demand resource management is a challenging prospect, especially in communication networks where the users' requirements of network resources are volatile. This challenge is magnified by the current shift of hardware-dependent network technologies into virtual Network Functions (vNFs) in a cloud-based platform and the proliferation of the Internet of Things (IoT) networks. vNFs are an integral part of edge devices that aims to improve service providers' response time, eliminate redundancy, and end to end latency results in operators' lower operating costs significantly. The efficiency of such a virtualized system largely depends on the optimization of resource allocation between users and Virtual Machines (VM) or hosting devices. One approach to addressed this issue is to minimize the latency of the entire communication network, however, the concern is that this mechanism does not ensure fairness of resource allocation because some of the users may receive enhanced services than others. In the previous study [1], a Stable Matching Algorithm is proposed to minimize the latency between vNFs and VMs but the algorithm failed to ensure the fairness of the requested services and server elements. In this study, we further extend the previous model to ensure the fairness. Our proposed solution minimizes the maximum latency in the network. This problem is an NP-hard and hence we provide a local search based solution. The experimental result shows improved fairness from other approaches and the fairness index is close to the optimal, which in fact represents the elimination of imbalances to a great extent. © 2020 IEEE.

关键词： Resource allocation

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OAuth-Based Access Control Scheme Against Replay Attacks in IoT Environment 10th

OAuth-Based Access Control Scheme Against Replay Attacks in ...

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10th International Conference on computer science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Lee, Dae-Hwi Lee, Im-Yeong Department of Computer Science Engineering Soonchunhyang University Asan-si Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan-si Korea Republic of

ISBN: (纸本)9789811393402

The OAuth protocol is a standard that authorizes not only the IoT environment but also the client services connected to the Internet to access the resource of the user stored in the resource server without sharing the account information of the resource server. Each IoT service uses OAuth to reduce the exposure of user privacy and provides scalability and flexibility because it can take advantage of any standard-compliant service. However, in recent OAuth 2.0 protocols, security vulnerabilities such as replay attacks and CSRF are already announced. To prevent this, a variety of OAuth-based access control protocols are being researched. In this paper, we design an OAuth-based access control protocol that is safe for replay attacks in IoT environment, so that IoT service can securely access the user resource of external resource server. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authorization

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Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

Human Observation-Inspired Trajectory Prediction for Autonom...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Haicheng Liao Shangqian Liu Yongkang Li Zhenning Li Chengyue Wang Yunjian Li Shengbo Eben Li Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macau SAR China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Departments of Civil and Environmental Engineering and Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of Macau Macau SAR China Faculty of Innovation Engineering Macau University of Science and Technology Macau SAR China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel “adaptive visual sector” mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a “dynamic traffic graph” using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs.

关键词： Adaptation models Visualization Time series analysis Predictive models Cognition Trajectory Convolutional neural networks

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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The Multi-Task Deep Model-Based Pneumonia Detection 2

The Multi-Task Deep Model-Based Pneumonia Detection

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2021 2nd International Conference on computer Information and Big Data Applications, CIBDA 2021

作者： Ai, Xingfang Li, Zhongliang Cao, Min Li, Xuechen Information Engineering College Jiangmen Polytechnic Jiangmen Guangdong529000 China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518000 China Radiology Department Guangzhou Panyu Sanatorium Guangzhou Guangdong510000 China

As a common lung disease, pneumonia affects millions of people worldwide each year and is often detected during physical examination using chest X-ray images (CXRs), which are usually diagnosed by radiologists. This time-consuming task often leads to fatigue-based diagnostic error and cannot be performed in countries or areas lack of radiologists. In this work, we proposed a multi-task learning model: Mt-pnet. We learn from the experience of imaging doctors in the diagnosis of pneumonia, let the network pay attention to the patient's gender and age information in the diagnosis of pneumonia. The combined information is helpful for the network to learn robust image features for pneumonia diagnosis. At the same time, we also found that using multi-task model is helpful to improve the recall of patients with pneumonia, which has important clinical significance. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

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Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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Evaluating BERT-based Pre-training Language Models for Detecting Misinformation

arXiv

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arXiv 2022年

作者： Anggrainingsih, Rini Hassan, Ghulam Mubashar Datta, Amitava Informatics Dept Faculty of Mathematics and Natural Sciences Sebelas Maret University Indonesia Department of Computer Science and Software Engineering University of Western Australia Australia

The rapid growth of internet technology and social media platforms has led to rapid changes in communication by providing many conveniences to users. Despite the comforts provided, it is challenging to control the quality of information due to the lack of supervision over all the information posted on online media. For instance, there are many problems in society because of the numerous rumours and the quick spread of fake news through online media. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection technique to limit the adverse effects of spreading misinformation. Previous studies mainly focused on finding and extracting the significant features of text data and used standard supervised learning techniques or deep learning approaches to identify misinformation. However, extracting features is timeconsuming and not a highly effective process due to the nonavailability of some features, affecting the effectiveness and performance of the detection models. Therefore, this study proposes the BERT-based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation. Furthermore, the performance of different language models (LM) containing different number of trainable parameters were compared, including RoBERTa, BERT, and DistillBERT. The proposed technique is tested on different twitter and fake news datasets to represent short and long text data respectively. The study also proposes a standardised distribution ratio for taining, validation and testing of the model. The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets. The results show that the proposed technique performs better than the state-of-the-art techniques. Moreover, the choice of LM either with significantly large or small number of parameters do

关键词： Social networking (online)

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Status Update on Phishing Emails Awareness: Jordanian Case 21

Status Update on Phishing Emails Awareness: Jordanian Case

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Qasaimeh, Malik Al-Manaseer, Hana Al-Manaseer, Haya Alghanim, Firas Software Engineering Department Computer Information Technology Jordan University of Science and Technology Irbid22110 Jordan King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman11941 Jordan

ISBN: (纸本)9781450390446

This study is a response to the rapid proliferation of high-risk phishing emails, representing one of the most dangerous cybercrimes and the primary medium for the deception of online users. This study aims to investigate the various characteristics of phishing email messages, and to measure Jordanian internet users' awareness and knowledge about these threats. To achieve these objectives, a quantitative approach was used based on an online survey that included a total of 1531 participants. The results showed that only a few participants had the ability to distinguish illegitimate from legitimate emails, as the majority of participants could not recognize illegitimate emails by answering with "legitimate email"or "I don't know". Similarly, only a few participants could distinguish phishing emails, with the majority being unable to recognize phishing emails by answering with "real email"or "I don't know". Therefore, more work to increase public awareness of phishing emails is recommended at the individual and institutional levels, so as to protect users from any possible phishing email threats. This study focuses on the aspects that are related to email phishing that include techniques used to generate such phishing emails on the basis of which we will identity preventive measures to protect Jordanian users and increase their awareness of phishing emails. © 2021 ACM.

关键词： Electronic mail

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Using HLPC for parallelization of autonomous tests of WEB applications from its GUI 34

Using HLPC for parallelization of autonomous tests of WEB ap...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Rossainz-López, Mario Islas-Fuentes, Jesús A. Pineda-Torres, Ivo Capel-Tuñón, Manuel Faculty of Computer Science Autonomous University of Puebla Av. San Claudio and 14 Sur Street San Manuel PueblaC.P. 72570 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

A proposal for parallelization of autonomous tests of Web applications from its graphical user interface (GUI) is presented to reduce its execution time since these increases exponentially due to the number of combinations that are generated with the different states of the fields, of the forms on the web application pages by creating a tree-like structure. It is proposed to use the model of high-level parallel compositions or HLPC as a suitable model of semi-automatic parallelization that defines a Treelike structure as a communication pattern between processes. The proposed HLPC, which we call HLPC-Tree, uses reinforcement learning that associates each node of the process tree as a slave object using the Q-Learning (QL) algorithm and achieves autonomous recognition of the fields of the forms and valid-invalid options to identify failures and display them with HTTP status codes. In addition, the Mechanize library is used to find the number of possible combinations through the states of the fields of the forms and to know how many nodes are generated at each level of the process tree in the HLPC-Tree when it grows in depth. Finally, the performance analysis of the proposed HLPC is shown with an analysis of the speedup and execution times in an 8-core machine to demonstrate good scalability in its accelerations compared to Amdahl's Law. © 2022 The Authors.

关键词： Reinforcement learning

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