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IEEE International Symposium on Requirements engineering

作者： A.J. van Schouwen D.L. Parnas J. Madey Bell Northern Research Limited Ottawa ONT Canada Communications Research Laboratory Department of Electrical and Computer Engineering McMaster University Hamilton ONT Canada Institute of Informatics University of Warmia and Mazury Warsaw Poland

A functional approach to specifying the requirements of a computer system is discussed. The method allows system requirements to be documented, whether the system is implemented using a computer and peripheral devices, or, for example, relays and analog components. The method is a refinement of the method used in the A-7 aircraft software requirements model document. The approach is illustrated with examples derived from a software requirements document for a water level monitoring system.< >

关键词： Documentation Software design computer peripherals Buildings Software systems Analog computers Relays computerized monitoring Aerospace engineering Design engineering

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On the scalability of ad hoc routing protocols

On the scalability of ad hoc routing protocols

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： C.A. Santivanez B. McDonald I. Stavrakakis R. Ramanathan Internet. Research Department BBN Technologies GTE Cambridge MA USA Electrical and Computer Engineering Department Northeastern University Boston MA USA Department of Informatics University of Athens (NKUA) Athens Greece

A novel framework is presented for the study of scalability in ad hoc networks. Using this framework, the first asymptotic analysis is provided with respect to network size, mobility, and traffic for each fundamental class of ad hoc routing algorithms. Protocols studied include the following: plain flooding (PF), standard link state (SLS), dynamic source routing (DSR), hierarchical link state (HierLS), zone routing protocol (ZRP), and hazy sighted link state (HSLS). It is shown that PF and ZRP scale better with mobility, SIJS and ZRP scale better with respect to traffic, and HSLS scales better with respect to network size. The analysis provides deeper understanding of the limits and trade-offs inherent in mobile ad hoc network routing. Our analysis is complemented with a simulation experiment comparing HSLS and HierLS. An important contribution of this paper is that HSLS is an scalable, easy-to-implement, alternative to hierarchical approaches for large ad hoc networks.

关键词： Scalability Routing protocols Ad hoc networks Telecommunication traffic Algorithm design and analysis Floods Laser sintering Traffic control Mobile ad hoc networks Analytical models

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Cypher by Example: A Visual Query Language for Graph Databases

SSRN

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SSRN 2023年

作者： Rabuzin, Kornelije Šestak, Martina Faculty of Organization and Informatics University of Zagreb Pavlinska ulica 2 Varaždin42000 Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

Considering the constantly increasing connectivity in terms of data exchanged between applications, devices etc., modern IT systems are in need of a data storage solution able to capture and handle data about connections and patterns formed between system participants (entities). As a potential solution for this challenge, graph database emerged in the previous decade. Since the standardization of a graph database query language is still an undergoing effort, any interested parties can interact with a graph database and explore graph data by using query languages such as Cypher or Gremlin, which are supported by modern Graph Database Management Systems (GDBMSs). Even though the syntax of these languages is well developed and documented, potential end-users with little knowledge of graph databases and the graph data model can still face a steep learning curve before being able to write queries on their own data stored in a form of a property graph. Consequently, this brings to a limited interest among the users to implement graph databases in their domains, as there is obviously a missing tool for maintaining the graph database in a visual manner. To address this gap, this paper introduces Cypher by Example, a visual graph query language consisting of a visual interface and a set of operations for interacting with the underlying graph database. The paper presents the basic elements of the proposed query language and demonstrates its effectiveness and completeness on a series of Cypher queries executed for a sample education domain. © 2023, The Authors. All rights reserved.

关键词： Query languages

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The effects of visual and control latency on piloting a quadcopter using a head-mounted display

arXiv

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arXiv 2018年

作者： Zhao, Jingbo Allison, Robert S. Vinnikov, Margarita Jennings, Sion Department of Electrical Engineering and Computer Science York University Toronto Canada Department of Informatics New Jersey Institute of Technology Newark United States Flight Research Laboratory National Research Council Ottawa Canada

Recent research has proposed teleoperation of robotic and aerial vehicles using head motion tracked by a head-mounted display (HMD). First-person views of the vehicles are usually captured by onboard cameras and presented to users through the display panels of HMDs. This provides users with a direct, immersive and intuitive interface for viewing and control. However, a typically overlooked factor in such designs is the latency introduced by the vehicle dynamics. As head motion is coupled with visual updates in such applications, visual and control latency always exists between the issue of control commands by head movements and the visual feedback received at the completion of the attitude adjustment. This causes a discrepancy between the intended motion, the vestibular cue and the visual cue and may potentially result in simulator sickness. No research has been conducted on how various levels of visual and control latency introduced by dynamics in robots or aerial vehicles affect users' performance and the degree of simulator sickness elicited. Thus, it is uncertain how much performance is degraded by latency and whether such designs are comfortable from the perspective of users. To address these issues, we studied a prototyped scenario of a head motion controlled quadcopter using an HMD. We present a virtual reality (VR) paradigm to systematically assess the effects of visual and control latency in simulated drone control scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Helmet mounted displays

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Challenges of deep learning methods for COVID-19 detection using public datasets

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informatics in Medicine Unlocked 2022年 30卷 100945页

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Dahal, Lavsen Roy, Shidhartho Wahid, Sifat Redwan Elahi, Md. Toufick E. Martí, Robert Khanal, Bishesh Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Nepal Applied Mathematics and Informatics Institute for Research (NAAMII) Nepal Computer Vision and Robotics Institute University of Girona Spain

Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas

关键词： Chest computed tomography and X-ray Convolutional neural networks COVID-19 disease Ensemble classifier

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Effect of CLAHE-based Enhancement on Bean Leaf Disease Classification through Explainable AI

Effect of CLAHE-based Enhancement on Bean Leaf Disease Class...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Teuku Rizky Noviandy Aga Maulana Faris Khowarizmi Kahlil Muchtar Department of Informatics Master of Artificial Intelligence Universitas Syiah Kuala Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Telematics Research Center (TRC) Universitas Syiah Kuala Aceh Indonesia

Global agriculture faces a major threat from plant diseases, particularly those affecting bean leaves, resulting in significant crop yield losses and impacting farmers' livelihoods and food production. To overcome the limitations of manual visual inspection methods, researchers have adopted computer vision and deep learning techniques for automated disease detection. This study improves the accuracy of classifying bean leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the EfficientNetB0 architecture. Explainable AI technique, specifically Grad-CAM, is employed to analyze predicted images for transparency and interpretability. The proposed approach outperforms models trained on original images, achieving higher accuracy, precision, recall, and F1 score. CLAHE proves to be effective in optimizing CNN-based models for plant disease detection. Grad-CAM analysis reveals the model's focus on disease-related features, enhancing understanding of its behavior. This research contributes to the development of robust automated systems for plant disease detection, benefiting farmers and agricultural practitioners in decision-making.

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RoboFiSense: Attention-Based Robotic Arm Activity Recognition with WiFi Sensing

arXiv

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arXiv 2023年

作者： Zandi, Rojin Behzad, Kian Motamedi, Elaheh Salehinejad, Hojjat Siami, Milad Department of Electrical & Computer Engineering Northeastern University BostonMA United States Kern Center for the Science of Health Care Delivery Department of Artificial Intelligence and Informatics Mayo Clinic RochesterMN United States

Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms’ activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access and may raise privacy concerns, for example, in nursing facilities. This research pioneers an innovative approach harnessing channel state information (CSI) measured from WiFi signals, subtly influenced by the activity of robotic arms. We developed an attention-based network to classify eight distinct activities performed by a Franka Emika robotic arm in different situations. Our proposed bidirectional vision transformer-concatenated (BiVTC) methodology aspires to predict robotic arm activities accurately, even when trained on activities with different velocities, all without dependency on external or internal sensors or visual aids. Considering the high dependency of CSI data on the environment motivated us to study the problem of sniffer location selection, by systematically changing the sniffer’s location and collecting different sets of data. Finally, this paper also marks the first publication of the CSI data of eight distinct robotic arm activities, collectively referred to as RoboFiSense. This initiative aims to provide a benchmark dataset and baselines to the research community, fostering advancements in the field of robotics sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotic arms

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A Comparative Empirical Analysis of 21 Machine Learning Algorithms for Real-World Applications in Diverse Domains

Research Square

引用

Research Square 2021年

作者： Nti, Isaac Kofi Aning, Justice Kwame Ayawli, Beklisi Ben Frimpong, Kyeremeh Appiah, Albert Yaw Nyarko-Boateng, Owusu Sunyani Technical University Ghana Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani Ghana Department of Computer Science Sunyani Technical University Sunyani Ghana Department of Electrical & Electronic Engineering Sunyani Technical University Sunyani Ghana

The application of computational methods like Machine Learning (ML) and Artificial Intelligence (AI) in several fields has recently received increased attention from academicians and field professionals. However, the availability of several of these computational techniques requires expertise to make the right choice. In making things easy for non-experts and inexperienced practitioners to make a suitable choice, some studies sought to compare the performance of different ML algorithms (MLAs) in solving problems in unique fields. However, most of the previous studies were domain centered on a handful of MLAs, which does not give a fairground for all-inclusive comparative analysis. Also, many of these studies compared MLAs for classification tasks to the best of our knowledge without considering the same MLAs performance for regression problems. This paper examines the performance of twenty-one (21) MLAs for classification and regression tasks based on six datasets from different domains. Empirically we compare their prediction results based on accuracy, balanced accuracy, F-score, Area Under the Curve (AUC), root mean square error, r-squared and adjusted r-squared. The random forest algorithm gave a consistent performance across all the six datasets in classification and regression tasks. However, on average, the XGBoost outperformed all MLAs applied in this study. The dummy algorithm and the linear regression were more moderate than the rest of the applied MLAs in computational complexity. Nevertheless, the overall study outcome shows that MLAs algorithms efficiently solve everyday challenges in elections outcome, financial fraud, network intrusion detection, meteorological forecast and heart diseases discovery;but their performance varies across domains and dataset dimensions. © 2021, CC BY.

关键词： Classification (of information)

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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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Full-Duplex Wireless for 6G: Progress Brings New Opportunities and Challenges

arXiv

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arXiv 2023年

作者： Smida, Besma Sabharwal, Ashutosh Fodor, Gabor Alexandropoulos, George C. Suraweera, Himal A. Chae, Chan-Byoung The Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL77005 United States The Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Ericsson Research KTH Royal Institute of Technology Stockholm Sweden The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Electronic Engineering University of Peradeniya Peradeniya20400 Sri Lanka The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks. © 2023, CC BY-NC-ND.

关键词： Wireless networks

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