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Quantum 2.0, QUANTUM 2022

作者： Wang, Imbert Ramesh, Anirudh Kramnik, Danielius Fargas Cabanillas, Josep M. Stojanović, Vladimir Kumar, Prem Popović, Miloš A. Department of Electrical and Computer Engineering Photonics Center Boston University BostonMA02215 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA94720 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We present progress towards realizing electronic-photonic quantum systems on-chip;particularly, entangled photon-pair sources, placing them in the context of previous work, and outlining our vision for mass-producible... 详细信息

ISBN: (纸本)9781957171111

关键词： Quantum entanglement

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis

Clustering the Various Categorical Data: An Exploration of A...

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Emerging Technology (INCET), International Conference for

作者： Raman Kumar Peeta Basa Pati K. Deepa Suresh Yanan Department of Computer Science and Engineering Amrita School of Computing Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Engineering University of Technology and Applied Sciences-Al Musannah Sultanate of Oman

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm.

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Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges

arXiv

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arXiv 2024年

作者： Cao, Yuchen Dai, Jianglai Wang, Zhongyan Zhang, Yeyubei Shen, Xiaorui Liu, Yunchong Tian, Yexin Khoury College of Computer Science Northeastern University United States Department of EECS University of California Berkeley United States Center for Data Science New York University United States School of Engineering and Applied Science University of Pennsylvania United States Georgia Institute of Technology College of Computing United States

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It highlights biases and methodological challenges encountered throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. The review reveals significant biases affecting model reliability and generalizability. A predominant reliance on Twitter (63.8%) and English-language content (over 90%) limits diversity, with most studies focused on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally. © 2024, CC BY.

关键词： Adversarial machine learning

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Cloud-Based Smart Plant Watering System using IOT-A Review

Cloud-Based Smart Plant Watering System using IOT-A Review

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Yammanuru Chaitanya Reddy Vuppu Surya Prakash Gajjala Sri Vardhan Reddy Anjana Snehith Santhosh Ruchitha Reddy Jagdish Chandra Patni Sudhir Kumar Chaturvedi Nilesh Bahadure Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Aerospace Engineering Alliance School of Applied Engineering Alliance University Bengaluru India Department of computer science and engineering school of technology GSFC University Vadodara Gujarat India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

Water management in agriculture needs to be done efficiently because of the increasing scarcity of water in the world. Through the design and implementation of a Smart Plant Watering System with Cloud Analysis, this study offers a novel solution to this problem. The main goal of this paper is to use cloud-based analytics and Internet of Things (IoT) technology to optimize water usage while maintaining plant health. Our study suggests an effective method from the existing methods, that incorporates sensors to track important environmental variables, particularly the nearby plant's temperature and moisture content. The processes use the sensor readings in real time and depending on pre-established thresholds, activates an electric motor to release water when it is required.

关键词： Temperature sensors Cloud computing Irrigation Wireless sensor networks Plants (biology) Water conservation Internet of Things Stakeholders Intelligent sensors Stress

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A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases

arXiv

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arXiv 2024年

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania United States Khoury college of computer science Northeastern University United States Center for Data Science New York University United States Georgia Institute of Technology College of Computing United States Department of EECS University of California Berkeley United States

Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evaluates 36 studies that apply machine learning (ML) and deep learning (DL) models to detect fake news, spam, and fake accounts on social media. Using the Prediction model Risk Of Bias ASsessment Tool (PROBAST), the review identified key biases across the ML lifecycle: selection bias due to non-representative sampling, inadequate handling of class imbalance, insufficient linguistic preprocessing (e.g., negations), and inconsistent hyperparameter tuning. Although models such as Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) networks showed strong potential, over-reliance on accuracy as an evaluation metric in imbalanced data settings was a common flaw. The review highlights the need for improved data preprocessing (e.g., resampling techniques), consistent hyperparameter tuning, and the use of appropriate metrics like precision, recall, F1 score, and AUROC. Addressing these limitations can lead to more reliable and generalizable ML/DL models for detecting deceptive content, ultimately contributing to the reduction of misinformation on social media. © 2024, CC BY.

关键词： Adversarial machine learning

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HPC-Coder: Modeling Parallel Programs using Large Language Models

arXiv

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arXiv 2023年

作者： Nichols, Daniel Marathe, Aniruddha Menon, Harshitha Gamblin, Todd Bhatele, Abhinav Department of Computer Science University of Maryland College ParkMD United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Livermore Computing Lawrence Livermore National Laboratory LivermoreCA United States

Parallel programs in high performance computing (HPC) continue to grow in complexity and scale in the exascale era. The diversity in hardware and parallel programming models make developing, optimizing, and maintaining parallel software even more burdensome for developers. One way to alleviate some of these burdens is with automated development and analysis tools. Such tools can perform complex and/or remedial tasks for developers that increase their productivity and decrease the chance for error. Until recently, such tools for code development and performance analysis have been limited in the complexity of tasks they can perform, especially for parallel programs. However, with recent advancements in language modeling, and the availability of large amounts of open-source code related data, these tools have started to utilize predictive language models to automate more complex tasks. In this paper, we show how large language models (LLMs) can be applied to tasks specific to high performance and scientific codes. We introduce a new dataset of HPC and scientific codes and use it to fine-tune several pre-trained models. We compare several pre-trained LLMs on HPC-related tasks and introduce a new model, HPC-Coder, fine-tuned on parallel codes. In our experiments, we show that this model can auto-complete HPC functions where generic models cannot, decorate for loops with OpenMP pragmas, and model performance changes in scientific application repositories as well as programming competition solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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Nonlinear Impulsive Control Design for Biologically Grounded NSM Tumor Growth Model Using Exact Linearized Mapping

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IFAC-PapersOnLine 2023年第2期56卷 7228-7233页

作者： Zehor Belkhatir Soulaimane Berkane Larry Norton Allen Tannenbaum School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Department of Computer Science and Engineering University of Quebec in Outaouais Gatineau QC Canada Department of Medicine Memorial Sloan Kettering Cancer Center NY 10065 USA Department of Computer Science and Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY 11794 USA

This paper investigates the problem of personalized chemotherapy scheduling using a system-modeling approach based on a nonlinear impulsive control technique. In particular, this work deals with the so-called Norton-Simon-Massagué (NSM) model, a minimal biologically grounded model for controlled tumor growth, which has been accepted in clinical oncology for breast cancer treatment. In this paper, we continue this line of research, to advance the concept of closed-loop control a step further towards widespread clinical use. In the present work, the problem of chemotherapy drug scheduling is approached using an impulsive control strategy combined with a state-space observer. The technical challenges of the nonlinearity of the controlled system and the impulsive nature of control input are handled via the utilization of a novel mapping that transforms the control problem into a “simplified” linear discrete-time control problem. Theoretical guarantees regarding the sign and invertibility of the proposed transformation are demonstrated. Then, an observer-based state feedback controller is proposed for chemotherapy drug scheduling as a proof-of-concept control strategy. Simulation results showing the performance of the designed closed-loop controlled chemotherapy schedules in reducing and eradicating tumor volume are provided using model's parameters derived using real tumor data and Doxirubicin drug.

关键词： Controlled tumor growth Nonlinear impulsive control Norton-Simon-Massagué (NSM) model Exact linearized transformation observer-based state feedback

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An Empirical Analysis on Users' Acceptance and Usage of BYOD-Technology for Saudi Universities: A case study of Shaqra University

An Empirical Analysis on Users' Acceptance and Usage of BYOD...

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Khan, Nayyar Ahmed Khalid, Mohammad Nadeem Ahmad, Mohammad Siddiqi, Ahmed Masih Uddin Shukla, Vinod Kumar Shaqra University College of Computing and It Department of Computer Science Saudi Arabia Amity University Department of Mechanical Engineering United Arab Emirates Shaqra University College of Science and Humanities Dawadami Department of Computer Science Saudi Arabia Shaqra University Applied Medical Science College Health Rehabilitation Department Saudi Arabia Amity University Department of Information Technology United Arab Emirates

ISBN: (纸本)9781665420877

the environment of academics involves the usage of multiple levels of security when a student or an academician tries to enter the campus. 'Bring your own device', is now accepted across various industries and sectors. This paper is an illustration on the use of bring your own device for academic purposes where we propose a new system in order to use a smart phone with a Smart application, which acts as a key to access. As we all are aware that at various points and various checks many applications are being used and different type of servers are available for accessibility, but the use of bring your own device paradigm is actually providing a new taste and convenience of the user at the fingertips. The environment of academics, involves a large number of security mechanisms. Various governing servers regulate these mechanisms. The paper represents an empirical study for the readiness of the stakeholders, including students, staff members and administration to accept the BYOD technology. A simple model is proposed with the help of which, BYOD can be easily integrated in the system of Academia. This will felicitate students as well as staff members to use various services with the help of a single device and simple login procedure instead of various cumbersome server based systems. © 2021 IEEE.

关键词： Students

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Mobile commerce and its apps - Opportunities and threats in Malaysia 2

Mobile commerce and its apps - Opportunities and threats in ...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Aseh, Khairi Bamansoor, Samer Pande, Bishwajeet Abu-Ulbeh, Waheeb Yusoff, M. Hafiz Pathmanathan, P. Ravindran Jusoh, Julaily Aida Al Moaiad, Yazeed UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands MEDIU Faculty of Computer and Information Technology Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer Science and Informatics Cybersecurity Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia

ISBN: (纸本)9781665432221

The purpose of this study is to discuss on opportunities and threats of Foodpanda as an online E-commerce platform including their strength and weaknesses. Electronic Commerce commonly known as E - Commerce means buying and selling of goods and services through internet. E-commerce is a way of conducting business online. Though e-commerce is facing challenges in the market but it has scope to outsource the traditional markets. Nowadays, E-commerce is a very big and potential platform in business organization. There are so many platform of E-commerce and one of them is Foodpanda. Foodpanda is one of the leading online food delivery marketplaces and is spread out globally. Its main job is to enable users to place orders at nearby restaurants with the assistance of its website or via its mobile app. A study on Foodpanda will show on opportunities and threads of Foodpanda as one of the biggest online E-Commerce platform. © 2021 IEEE.

关键词： Mobile commerce

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