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2022 International Research Conference on Smart Computing and Systems engineering, SCSE 2022

作者： Kiridana, Y.M.W.H.M.R.P.J.R.B. Weerarathna, P.L.M. Wijesingha, W.P.D.Y. Aashiq, M.N.M. Kumara, W.G.C.W. Haleem, M.A.L.A. South Eastern University of Sri Lanka Dept. of Electrical and Telecommunication Engineering Sri Lanka South Eastern University of Sri Lanka Dept. of Computer Science and Engineering Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

The development of autonomous vehicle driving systems and Intelligent Transportation System (ITS) have drawn massive attention since the 1980s. For the development of ITS, road sign detection and identification are considered to be very important due to the vital information provided by road signs. Generally, real-time video-based methods are used as the source of images for the operation of ITS. But they are inefficient and costly due to certain limitations like weather conditions, lighting conditions, and limited range in obtaining quality images. To overcome the limitations of the video-based approach, this research aims to develop techniques for detecting and identifying road signs by using Google Street View (GSV) as the image source, OpenCV for image processing and CNN for road sign identification. EdleNet, LeNet-5, and DenseNet were identified as accurate CNN models. Using images from GSV, generating a database of road signs with the relevant coordinates was possible, which is currently unavailable in Sri Lanka. In addition, this process leads to the generation of a valuable image dataset of Sri Lankan road sign images, and a web interface with mapped road signs. Consequently, this research would yield useful findings that may be applied to future research and provide the means to develop ITS, accident-avoidance systems, and driver assistance systems. © 2022 IEEE.

关键词： Machine learning

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Pioneering Research on a Neurodiverse ADHD Workforce in the Future Construction Industry

Pioneering Research on a Neurodiverse ADHD Workforce in the ...

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Construction Research Congress 2024, CRC 2024

作者： Chang, Woei-Chyi Ismael Becerra, Joshua Karalunas, Sarah L. Esmaeili, Behzad Yu, Lap-Fai Hasanzadeh, Sogand Lyles School of Civil Engineering Purdue Univ. West LafayetteIN United States Dept. of Psychological Sciences Purdue Univ. West LafayetteIN United States School of Industrial Engineering and Civil Engineering Purdue Univ. West LafayetteIN United States Dept. of Computer Science George Mason Univ. FairfaxVA United States

ISBN: (数字)9780784485293

ISBN: (纸本)9780784485293

While incorporating novel technologies aims to facilitate the inclusion of the future construction industry, the empirical investigation of diverse workers' performance is necessary to better understand the strategies to broaden their participation. The literature has rarely focused on inclusivity in terms of involving workers with various disabilities in the construction sector. Thus, this study aims to pioneer the discussion on inclusivity in construction by focusing on workers with attention-deficit/hyperactivity disorder (ADHD). A future bricklaying worker-AI collaborative task was developed to examine workers' attentional allocation, situational awareness (SA), and productivity in future works. The results indicated that ADHD workers exhibited a lower SA of dynamic objects by focusing on the primary task with no increase in productivity, leading to lower overall performance. This study provided insights into (1) understanding ADHD workers' performance on human-AI teaming of future jobsites, and (2) proposing strategies to facilitate inclusivity in the future construction industry. © CRC 2024. All rights reserved.

关键词： Construction industry

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Study of Feature Extraction Techniques for BCI Processing

Study of Feature Extraction Techniques for BCI Processing

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2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Sudheer, Devulapalli Jothiaruna, N. Potti, Anupama Gangappa, M. Ramasubbareddy, Somula Computer Science and Engineering Vnr Vjiet Hyderabad India Vellore. Institute of Technology Schoolf Computer Science and Enginerring Tamilnadu Vellore India Sree Dattha Institute of Engineering and Science Computer Science and Engineering Hyderabad India Vnr Vjiet Dept. Information Technology Hyderabad India

ISBN: (纸本)9781665454995

Brain computer interface (BCI) is used to identify electrical activity in human brain using the electroencephalog-raphy (EEG). EEG records the electrical activity by placing the electrodes on the scalp. By using the recorded information it will able to classify the different types of abnormalities happening in brain. For extracting the information from signal without losing any information, some feature extraction methods have been used using deep learning concepts. The methods are Time Frequency distribution, Fast Fourier Transform, Eigen Vector, Wavelet Transform, Auto Regressive, Independent Component Analysis, Principal Component Analysis, Empirical Method Decomposition, Hilbert Huang Transform and Local Discriminant bases. Comparison have been done between the methods to show how effectively methods without losing an information. The classification accuracies of the feature methods are compared with each other. The study concluded the hybrid methods such as domain specific and automated features togeather shown better performance. © 2022 IEEE.

关键词： Feature extraction

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Examining Amazon Customer Reviews using PySpark and AWS: A Data Lake Approach

Examining Amazon Customer Reviews using PySpark and AWS: A D...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Remya S Abburi Sai Karthik Maddala H S M Krishna Karthik Marasani Jayasurya Samudrala Yashwanth Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham

In the contemporary world, big data analysis with PySpark and AWS has become quite handy. Large multinational companies, including Walmart, trivago, and countless others are using big data. And with the assistance of AWS, which offers a variety of services like glue, Athena, S3, etc., becoming extremely beneficial for cloud storage purposes and also for creating data migration pipelines. Any RDBMS database server might send tens of gigabytes of data to an Amazon S3 bucket using the ETL architecture. Our primary goal in this project is to use PySpark to construct an ETL pipeline for data migration. Amazon wireless devices review analysis, Amazon watches review analysis, Amazon books review analysis, Amazon shoes review analysis, and Amazon musical instruments review analysis are the five separate datasets on which we conducted our analysis. The datasets considered for the analysis are Amazon watch reviews, book reviews, shoe reviews, wireless device reviews and musical instrument reviews . The goal of the project is to obtain the ratio of the number of trustworthy, real reviews to untrusted reviews and a comparison in done in the form of graphs.

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Grace Hanna Nathan Boyar Nathan Garay Mina Maleki Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation.

关键词： Deep learning Accuracy Conferences Artificial neural networks Big Data Natural language processing Real-time systems Pattern recognition Software development management

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Minimize Web Applications vulnerabilities through the early Detection of CRLF Injection

arXiv

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arXiv 2023年

作者： Hasan, M.D. Asibul Rahman, Md. Mijanur Dept. of Computer Science and Engineering Southeast University Bangladesh

Carriage return (CR) and line feed (LF), also known as CRLF injection is a type of vulnerability that allows a hacker to enter special characters into a web application, altering its operation or confusing the administrator. Log poisoning and HTTP response splitting are two prominent harmful uses of this technique. Additionally, CRLF injection can be used by an attacker to exploit other vulnerabilities, such as cross-site scripting (XSS). Email injection, also known as email header injection, is another way that can be used to modify the behavior of emails. The Open Web Application Security Project (OWASP) is an organization that studies vulnerabilities and ranks them based on their level of risk. According to OWASP, CRLF vulnerabilities are among the top 10 vulnerabilities and are a type of injection attack. However, CRLF vulnerabilities can also lead to the discovery of other high-risk vulnerabilities, and it fosters a better approach to mitigate CRLF vulnerabilities in the early stage and help secure applications against known vulnerabilities. Although there has been a significant amount of research on other types of injection attacks, such as Structure Query Language Injection (SQL Injection). There has been less research on CRLF vulnerabilities and how to detect them with automated testing. There is room for further research to be done on this subject matter in order to develop creative solutions to problems. It will also help to reduce false positive alerts by checking the header response of each request. Automated alerts from security systems can provide a quicker and more accurate understanding of potential vulnerabilities and can help to reduce false positive alerts. Despite the extensive research on various types of vulnerabilities in web applications, CRLF vulnerabilities have only recently been included in the research. Utilizing automated testing as a recurring task can assist companies in receiving consistent updates about their systems and enhance the

关键词： Cybersecurity

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Efficient Host Intrusion Detection using Hyperdimensional Computing

Efficient Host Intrusion Detection using Hyperdimensional Co...

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IEEE International Conference on Big Data

作者： Yujin Nam Rachel King Quinn Burke Minxuan Zhou Patrick McDaniel Tajana Rosing Dept. of Computer Science and Engineering UC San Diego USA Dept. of Computer Sciences University of Wisconsin-Madison Madison USA Computer Science Department Illinois Institute of Technology Chicago USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Modern host-based intrusion detection systems (HIDS) rely on querying provenance graphs—graph representations of activity history on a system—to detect and respond to security threats present on a system. However, as the complexity and number of applications running on a system increase, the size of provenance graphs also increase, and thus the latency to query them. State-of-the-art designs deliver query latencies that are impractical for modern threat detection. In this paper, we introduce a hyper-dimensional computing (HDC) approach to querying provenance graphs for HIDS. By encoding provenance graphs and attack patterns/signatures into hyper-dimensional vectors, we can implement a query engine using simple vector operations. Our approach is hardware accelerator compatible, providing further speedups under resource-constrained environments. Our evaluation on a real-world dataset shows that our approach achieves > 90% detection accuracy and up to 4, 242× speedups over the state-of-the-art. This shows that HDC-based approaches can effectively deal with scaling issues in modern HIDS.

关键词： Accuracy Costs Intrusion detection Vectors Encoding Threat assessment Security History Hardware acceleration Engines

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Multi-Day Intermodal Travel Planning for Urban Cities Using Ising Machines

Multi-Day Intermodal Travel Planning for Urban Cities Using ...

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International Conference on Intelligent Transportation

作者： Siya Bao Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University

The multi-day intermodal travel planning problem (MITPP) is an optimization problem (OP) and it generates the optimal sequences of point-of-interests (POIs) and hotels while searching for the most suitable transport modes between POIs and hotels. Conventional methods and solvers using von Neumann computers provide good approximate solutions to the OPs, but the computation time grows exponentially dealing with large-scale or complex OPs. Meanwhile, Ising machines or quantum annealing machines are non-von Neumann computers that are designed to solve complex OPs. In this paper, we focus on solving the MITPP by a two-phase Ising-based method. The first POI clustering phase aims at generating POIs clusters for sightseeing days and the second POI routing phase generates travel routes for each day with the optimal transport modes. Practical factors such as POI satisfaction, POI duration, hotel fee, and transportation fee are included in the MITPP. We map these elements onto quadratic unconstrained binary optimization (QUBO) models. For evaluation, we use a real-world dataset in Sapporo, Japan. Empirical results confirm that the proposed method can effectively solve the MITPP both in terms of solution quality and execution time and outperforms a conventional solver, a conventional method, and the latest Ising-based method.

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Onboard Near-Real-Time Detection of Harmful Algal Blooms Using a Convolutional Neural Network and Multispectral Imagery from a Satellite in Low Earth Orbit

Onboard Near-Real-Time Detection of Harmful Algal Blooms Usi...

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OCEANS

作者： Jonathan D. Parsons Mae L. Seto Computer Science Dept. Dalhousie University Halifax Canada Mechanical Engineering Dept. Dalhousie University Halifax Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

Freshwater harmful algal blooms (HABs) pose significant ecological and public health risks worldwide. Detecting HABs soon after they form is critical to managing the damage they cause. While in-situ measurements are more accurate at detecting and measuring their toxicity levels, satellite imagery is more adept.at capturing the spatial and temporal dynamics of these blooms over large geographic regions. Satellites can also more persistently monitor for HABs. In the past, empirical methods and machine learning methods have used multispectral satellite imagery to estimate HAB biomass. To build upon the current body of research, this paper investigates an approach to expedite HAB detection by utilizing a convolutional neural network (CNN) deployed onboard a CubeSat in low Earth orbit to detect HABs in near-real-time. The CNN is trained with multispectral imagery from the Sentinel-2 satellite constellation aggregated with in-situ cyanobacteria cell counts from the Seabass CAML dataset. The results successfully demonstrated the capability of a CNN to detect cyanobacterial blooms using multispectral imagery. After classifying HAB predictions into 5 severity classes, the best performing model achieved a RMSE of 1.33 between HAB severity levels. Training the CNN on 30m GSD imagery with RGB and red edge (B05) bands achieved a RMSE of 1.83 between HAB severity levels, which was inadequate for detecting HABs in small inland water bodies. Improved performance was observed with 10m ground sample distance (GSD) band combinations. The best performing networks utilized all of Sentinel-2's 10m and 20m spectral bands.

关键词： Satellites Image edge detection Low earth orbit satellites Cyanobacteria Predictive models Extraterrestrial measurements Data models Satellite images Convolutional neural networks Spatial resolution

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High Throughput and Energy Efficient SHA-2 ASIC Design for Continuous Integrity Checking Applications

High Throughput and Energy Efficient SHA-2 ASIC Design for C...

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IEEE European Test Symposium (ETS)

作者： Asimina Koutra Vasileios Tenentes Dept. of Computer Science and Engineering University of Ioannina Greece

High throughput and energy efficient integrated cryptographic hash primitives are important for the continuous integrity checking and tampering detection in secure access management mechanisms of on-chip instrumentation, such as the IJTAG. However, previous SHA-256 cores focus only on throughput. In this paper, we synthesize with a 32 nm CMOS Technology SHA-256 cores that can be integrated in ASICs, and we present insights on their achieved throughput and energy efficiency. Moreover, we present a novel clock-gated design for reducing dynamic power dissipation of SHA-256 cores; and a novel Multi-Vt design for reducing static power dissipation of SHA-256 cores. The proposed designs can achieve upto 25.9% improvement of the energy efficiency of existing SHA-256 designs, without impacting their performed throughput. To the best of our knowledge, this is the first work that applies low power design techniques on SHA-256 cores.

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