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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Michael Conrad Meyer Yu Wang Department of Computer Science and Electrical Engineering Eastern Washington University Spokane WA USA Software Research and Development Division TriOrb Inc Kitakyushu Japan

Major disasters can often destroy communication networks. To address this issue, companies such as NTT have developed movable base stations (MBSs), which can be easily deployed to construct an emergency communication network. A sample of how MBSs can be connected to form a network can be seen in Fig. 1. However, these networks have performance drawbacks. In order to improve their performance, we propose an algorithm that determines the best way for the MBSs to be connected. The proposed model and algorithm resulted in a reduction in the overall delay and average delay of the system compared to the greedy algorithm. For the realistic constraints, the genetic algorithm (GA) was so successful that its worst-case delay almost matched the average delay of the greedy algorithm. The simulations did show that for extremely high communication rates or extremely slow processing rates, the impact of the topology on the system delay was minimal, and thus both algorithms had similar results. This is because the system bottleneck in these cases is the processing times. However, for the realistic network constraints, the GA outperforms the greedy algorithm by approximately 40% in the fog system and 90% in the cloud system.

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Evaluating the Impact of Pair Documentation on Requirements Quality and Team Productivity

arXiv

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arXiv 2023年

作者： Qamar, Nosheen Sabahat, Nosheen Mashmool, Amir Mosavi, Amir Department of Software Engineering University of Management and Technology Lahore54000 Pakistan Department of Computer Science Forman Christian College University Lahore54000 Pakistan Department of Computer Science Bioengineering Robotics and System Engineering University of Genova Genova16126 Italy Obuda University Budapest1034 Hungary

The most important deliverable of the requirements engineering process is the software/system requirements specification (SRS) document. Requirements documentation is important during the complete software development lifecycle to share the vision and effective communication between major stakeholders. The Standish Group reported that the top factors behind project failures are related to requirements. By giving the right level of attention to key requirements good quality software can be produced. Therefore, more research is needed in this area and this study is trying to fill this gap. This empirical study aims to examine the importance of pair documentation. Unconventional documentation refers to the approach when two persons work on the same document’s requirements collaboratively just like pair programming on the requirements quality and team productivity. Twenty pairs of documentation writers worked into two groups – one group using pair documentation, i.e., the experimental group, and the other one using conventional documentation i.e., the control group. The resultant requirements’ documents for the same project, produced by both groups were then compared. It is observed that there is a significant improvement in the quality and productivity of the experimental group using pair documentation. The findings of this study may assist requirement engineers in forming efficient teams that can create high-quality SRS documents. Copyright © 2023, The Authors. All rights reserved.

关键词： Requirements engineering

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Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects

arXiv

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arXiv 2023年

作者： Khatoonabadi, Sayedhassan Costa, Diego Elias Mujahid, Suhaib Shihab, Emad Lab Department of Computer Science & Software Engineering Concordia University MontrealQC Canada MontrealQC Canada Mozilla Corporation MontrealQC Canada

Pull Requests (PRs) that are neither progressed nor resolved clutter the list of PRs, making it difficult for the maintainers to manage and prioritize unresolved PRs. To automatically track, follow up, and close such inactive PRs, Stale bot was introduced by GitHub. Despite its increasing adoption, there are ongoing debates on whether using Stale bot alleviates or exacerbates the problem of inactive PRs. To better understand if and how Stale bot helps projects in their pull-based development workflow, we perform an empirical study of 20 large and popular open-source projects. We find that Stale bot can help deal with a backlog of unresolved PRs as the projects closed more PRs within the first few months of adoption. Moreover, Stale bot can help improve the efficiency of the PR review process as the projects reviewed PRs that ended up merged and resolved PRs that ended up closed faster after the adoption. However, Stale bot can also negatively affect the contributors as the projects experienced a considerable decrease in their number of active contributors after the adoption. Therefore, relying solely on Stale bot to deal with inactive PRs may lead to decreased community engagement and an increased probability of contributor abandonment. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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Clinical Significance of Measles and Its Prediction Using Data Mining Techniques: A Systematic Review

Clinical Significance of Measles and Its Prediction Using Da...

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International Conference on Artificial Intelligence and Data engineering, AIDE 2019

作者： Rao, Abhishek S. D’Mello, Demian Antony Anand, R. Nayak, Sneha Department of Information Science and Engineering NMAM Institute of Technology Nitte UdupiKarnataka574110 India Department of Computer Science and Engineering Canara Engineering College Bantwal India Department of Master of Computer Application NMAM Institute of Technology Nitte UdupiKarnataka574110 India Department of Biotechnology Engineering NMAM Institute of Technology Nitte UdupiKarnataka574110 India

ISBN: (纸本)9789811535130

Data science techniques used in the past era for data extraction are now being replaced by data mining methods due to a lot of contemporary trends and challenges. Data mining includes implicit data extraction, which is previously unknown but potentially useful information, with the help of databases to generate new information. Medical data mining is an area of challenges since the data involves in it is imprecise, inconsistent and massive. Medical diagnostics systems are evaluated by employing large information databases, but it endures many failures to extract data from the databases. There exists no tool to discover the major relationships concerning the data. In such a case, the core knowledge of healthcare data is extracted by applying the data mining methods, thus helping in turning raw data into useful information. Measles is an immunizable disease most likely seen in infants and young children. Measles (rubeola), is an RNA virus, belongs to the Morbillivirus genus, and Paramyxoviridae family. It is one among the six major killer diseases, with accounts to 90% secondary infection rate with susceptible contacts. In this paper, the authors present a systematic review of measles cases and its mortality with the help of the taxonomical tree structure. The published research which utilizes the application of data mining techniques for early prediction of measles in terms of methods used, algorithms utilized and results obtained are evaluated. The paper provides major consolidation of data with respect to motive, study and methods used in the literature. The authors present the summary of the research review findings and research gaps for further study and possible application. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Machine Learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis

Machine Learning Algorithms for Intrusion Detection Performa...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： B. Md. Irfan V. Poornima S Mohana Kumar Upendra Singh Aswal N. Krishnamoorthy Ramya Maranan Department of Information Technology Nalsar University of Law Hyderabad Telengana India Department of Computer Science VISTASPallavaram Chennai Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamilnadu Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems.

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The good, the bad, and why: unveiling emotions in generative AI 24

The good, the bad, and why: unveiling emotions in generative...

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Proceedings of the 41st International Conference on Machine Learning

作者： Cheng Li Jindong Wang Yixuan Zhang Kaijie Zhu Xinyi Wang Wenxin Hou Jianxun Lian Fang Luo Qiang Yang Xing Xie Microsoft Research Beijing China and CAS Institute of Software Beijing China Microsoft Research Beijing China Department of Computer Science William & Mary Williamsburg Virginia America School of Psychology Beijing Normal University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Emotion significantly affects our daily behaviors and interactions. Although recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance the performance of the AI model, 2) EmotionAttack to impair the performance of the AI model, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. More importantly, EmotionDecode reveals that AI models can comprehend emotional stimuli similar to the dopamine mechanism in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models, thus boosting the research and development of human-AI collaboration and mitigating potential risks.

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The effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks

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Journal of Optical Communications 2024年第s1期45卷 s253-s259页

作者： Eid, Mahmoud M. A. Zaki Rashed, Ahmed Nabih Ahammad, Mohd. Sultan Paul, Bikash Kumar Ahmed, Kawsar Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Sukrabad Dhaka1207 Bangladesh

This study presents the effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks. The received signal power and max Q factor are measured in the presence of vertical-cavity surface-emitting lasers (VCSELs) bias current and modulation current at maximum propagation distance for the wireless network. The signal is enhanced with increasing of bias and modulation peak currents of the VCSEL device. The optimum received power and max. Q factor is also examined at Tx./Rx. the pointing error of 0.1 mrad and propagation reach of 5 km at available bit rates transmission of 10 Gb/s. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Surface emitting lasers

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks

Optimizing Drug Dosage and Medication Management for Geriatr...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Beemkumar N Daljeet Pal Singh Zahid Ahmed Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bagalore Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources.

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On the Robustness of Latent Diffusion Models

arXiv

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arXiv 2023年

作者： Zhang, Jianping Xu, Zhuoer Cui, Shiwen Meng, Changhua Wu, Weibin Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Tiansuan Lab Antgroup China School of Software Engineering Sun Yat-sen University China

Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusion models is not well studied. Previous works only focus on the adversarial attacks against the encoder or the output image under white-box settings, regardless of the denoising process. Therefore, in this paper, we aim to analyze the robustness of latent diffusion models more thoroughly. We first study the influence of the components inside latent diffusion models on their white-box robustness. In addition to white-box scenarios, we evaluate the black-box robustness of latent diffusion models via transfer attacks, where we consider both prompt-transfer and model-transfer settings and possible defense mechanisms. However, all these explorations need a comprehensive benchmark dataset, which is missing in the literature. Therefore, to facilitate the research of the robustness of latent diffusion models, we propose two automatic dataset construction pipelines for two kinds of image editing models and release the whole dataset. Our code and dataset are available at https://***/jpzhang1810/LDM-Robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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An Advanced Full Stack Decentralized Approach for Secure Election Voting Process

An Advanced Full Stack Decentralized Approach for Secure Ele...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Neelam Singh Prashant Rohila Kashif Anwar Tanweer Alam Vandana Rawat Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Science Islamic University of Madinah Saudi Arabia

ISBN: (纸本)9781665474177

Designing a safe electronic voting system offering secrecy with impartial approach while being transparent and flexible is a challenging task. Full Stack decentralized applications are smart contract decentralized application that runs on a Decentralized Blockhain Network. Problems constructing a vote casting software at the Central Web Server are: the votes at the database will be modified and will be counted extra or eliminated entirely, also the supply code at the net server can also be modified at any time. Rather than having a separate central server database we can deploy Blockchain. Blockchain is a network and database which handles all the transaction. Blocks on the network are added if the transaction is valid and the Blocks also ensure that the data distributed across the Blockchain network are valid. In this paper, we will elaborate the design and development of a Decentralized Approach for Election Voting System using Solidity language to make our smart contract. This Decentralized Approach will have the option for the voter to cast a vote in an election by their Ethereum account address which has public and private key address. This approach aims to provide a secure and trusted voting transaction.

关键词： Costs Voting Smart contracts Full stack Decentralized applications Real-time systems Web servers

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