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arXiv 2024年

作者： Li, Hanzhe Wang, Xiangxiang Feng, Yuan Qi, Yaqian Tian, Jingxiao Computer Engineering New York University NY United States Computer Science University of Texas at Arlington ArlingtonTX United States Interdisciplinary Data Science Duke University NC United States Quantitative Methods and Modeling Baruch Collegue CUNY 55 Lexington Ave NY United States Electrical and Computer Engineering San Diego State University SD United States

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge computing

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Economic analysis of one type of criminal activity 14

Economic analysis of one type of criminal activity

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Troyanovskiy, V.M. Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9781665412308

This paper provides a formal analysis of the regularities of functioning and development of economic crimes. It was expedient to find out the conditions in which theft would not be worthwhile, i.e., supplement legal research with the economic analysis. The analysis results will help to find ways of rational crime counteraction and minimize the damage from it. © 2021 IEEE.

关键词： Economic analysis

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A High-Capacity Image Steganography Based on LSB Inversion and Summing of Bit Pairs

A High-Capacity Image Steganography Based on LSB Inversion a...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Loay Mamdouh Saddam Bekhet Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Department of Quantitative Methods Faculty of Commerce South Valley University Qena Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Nowadays, computer users face a challenge in maintaining the security of their information. There are many techniques for transferring information safely over networks. Image steganography, one of these techniques, is an effective way to conceal important information. The Least Significant Bit (LSB) technique is a famous method for data hiding. This technique exchanges the LSBs of the cover image pixel with the mystery message bits. However, it was always the challenging problem of increasing the number of bits utilized for embedding that led to a decreased quality of the stego image. Hence, this research targets optimizing the stego image quality by inverting the LSBs of image pixels to reduce the changes that occur in pixels. This paper proposes a new method based on summing the pairs and inverting the LSBs of the cover image pixels. The secret data are arranged in pairs, and then the sum of every two successive pairs is computed. After that, the secret bits are embedded by inverting the pixel bits depending on that sum. Finally, for more enhancement of the results, some of the inverted bits will be inverted again. Rigorous experiments using benchmark datasets confirm that the proposed scheme yields superior results compared to the substitution LSB scheme. Also, shows the effectiveness of the proposed approach in achieving a high payload with acceptable visual quality for stego image that cannot be noticed by human eyes when compared with state-of-the-art steganography techniques.

关键词： Image quality Visualization Steganography Gray-scale Benchmark testing Distortion computer security Standards Faces Payloads

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A framework for inclusive AI learning design for diverse learners

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Song, Yukyeong Weisberg, Lauren R. Zhang, Shan Tian, Xiaoyi Boyer, Kristy Elizabeth Israel, Maya School of Teaching & Learning College of Education University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States

As artificial intelligence (AI) becomes more prominent in children's lives, an increasing number of researchers and practitioners underscored the importance of integrating AI as learning content in K-12. Despite the recent efforts in developing AI curricula and guiding frameworks in AI education, the educational opportunities often do not provide equally engaging and inclusive learning experiences for all learners. To promote equality and equity in society and increase competitiveness in the AI workforce, it is essential to broaden participation in AI education. However, the framework that guides teachers and learning designers into inclusive learning design tailored for AI education is lacking. Universal Design for Learning (UDL) provides guidelines for making learning more inclusive across disciplines. Based on the principles of UDL, this paper proposes a framework to guide the design of inclusive AI learning. We conducted a systematic literature review to identify AI learning design-related articles and synthesized them into our proposed framework. Our new framework includes the core component of AI learning content (i.e., five big ideas), anchored by the three UDL principles (the "why," "what," and "how" of learning), and six praxes with pedagogical examples of AI education. Alongside this, we present an illustrative example of the application of our proposed framework in the context of a middle school AI summer camp. We hope this paper will guide researchers and practitioners in designing more inclusive AI learning experiences. © 2024 The Author(s)

关键词： Curricula

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Managing Students in Hybrid Software Project Classes

Managing Students in Hybrid Software Project Classes

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Frontiers in Education (FIE) Conference

作者： Bruce R. Maxim Belen A. Garcia Department of Computer and Information Science University of Michigan-Dearborn Dearborn Michigan USA Hub for Teaching and Learning Resources University of Michigan-Dearborn Dearborn Michigan USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice paper describes the authors' experiences introducing active learning methodologies into a hybrid undergraduate software engineering project course. On our campus hybrid courses have both in-person and online students participating in a single course taught by the same instructor. The project team adapted the face-to-face class activities to accommodate the idiosyncrasies of an online course delivery environment. Using active learning and authentic assessment techniques, the authors sought to improve the levels of engagement exhibited by online students. The students in this course learn to use agile software engineering practices to deliver incremental software prototypes. The online and in-person students were surveyed at the conclusion of their course to measure their perceived levels of engagement with course activities. Data was collected from two recent offerings of this course. The results suggest that the neither the in-person or online students were not significantly different from one another on most survey responses or most performance measures.

关键词： Surveys Active learning Prototypes Agile software development Software Software engineering

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Optical image recognition strategy for keyword extraction and page ranking for slide recommendation system

Optical image recognition strategy for keyword extraction an...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Chukwuka Victor Obionwu S. M Laique Abbas Visakh Padmanabhan Taruna Tiwari David Broneske Gunter Saake Faculty of Computer Science Otto-von-Guericke University Magdeburg Germany Research Infrastructure and Methods German Centre for Higher Education Research and Science Studies Hannover Lower Saxony Germany

One of the main advantages of the traditional instructor/student interaction is the possibility of receiving immediate instructional feedback within the defined lecture periods. However, the transition into full online, and hybrid pedagogy has gradually replaced instructional feedback with peer feedback. While essential for knowledge transfer among learners, peer feedback largely depends on prior knowledge from learning engagements that may not fully be related to the particular course in question. What we have observed in our learning management system is lots of unnecessarily repeated errors which could easily be averted with the right instruction. To this end, we employed optical image recognition to extract structured query language-based keywords from lecture slides and exercise sheets. Using these keywords, we automatically rank the degree to which a lecture slide suitable matches an exercise task, and thus provide an automatic recommendation to students encountering errors in respective tasks during exercise task engagements. Overall, we achieved a precision of 0.707 for German lecture slides and 0.72 for English lecture slides, which validates our strategy of assisting students with automatic lecture slide recommendations during their online exercise engagements.

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Unveiling Developers' Mindset Barriers to Software Modeling Adoption

Unveiling Developers' Mindset Barriers to Software Modeling ...

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Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)

作者： Reyhaneh Kalantari Timothy C. Lethbridge Engineering Design and Teaching Innovation University of Ottawa Ottawa Canada Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

This paper presents a qualitative empirical study that seeks to delve into and illuminate the mindset barriers confronted by software developers that hinder the acceptance and use of software modeling techniques. Although software modeling provides acknowledged advantages, such as improving quality, facilitating superior comprehension of system behavior, and enhancing stakeholder communication, it remains significantly underutilized among developers. The primary objective of this research is to understand these barriers not as a result of just technological challenges, but rather as deeply-rooted mindset barriers entrenched within the developer community. Utilizing a grounded theory approach, we conducted extensive interviews with a broad range of developers from various backgrounds. The data were analyzed to identify common themes and patterns. The findings provide insights into the developers' perspectives, revealing key mindset barriers such as tooling and support concerns, challenges posed by modern work paradigms, behavioral resistance, lack of perceived value, knowledge deficiency and skill deficiency. The results of this study serve as a stepping stone towards developing strategies to overcome these mindset barriers, with the aim of fostering a wider acceptance of software modeling techniques. The study contributes to our understanding of the human factors at play in the broader software engineering field.

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Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities

arXiv

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arXiv 2024年

作者： Roytburg, Dani Olorunisola, Deborah Soni, Sandeep Klein, Lauren Emory University Department of Quantitative Theory & Methods United States Emory University Department of Computer Science United States Yale University Department of Data Science and Statistics United States Emory University Department of English United States

In this project we describe a method of modeling semantic leadership across a set of communities associated with the #BlackLivesMatter movement, which has been informed by qualitative research on the structure of social media and Black Twitter in particular. We describe our bespoke approaches to time-binning, community clustering, and connecting communities over time, as well as our adaptation of state-of-the-art approaches to semantic change detection and semantic leadership induction. We find substantial evidence of the leadership role of BLM activists and progressives, as well as Black celebrities. We also find evidence of the sustained engagement of the conservative community with this discourse, suggesting an alternative explanation for how we arrived at the present moment, in which "anti-woke" and "anti-CRT" bills are being enacted nation-wide. © 2024, CC BY-NC-SA.

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TFSWA-UNet: Temporal-Frequency and Shifted Window Attention Based UNet for Music Source Separation

TFSWA-UNet: Temporal-Frequency and Shifted Window Attention ...

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2024 International Conference on Culture-Oriented science and Technology, CoST 2024

作者： Yao, Zhenyu Su, Yuping Yang, Honghong Wu, Xiaojun Zhang, Yumei Shaanxi Normal University Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an China

ISBN: (纸本)9798350380347

The performance of music source separation (MSS) has been greatly improved in recent years due to the rapid development of various neural network architectures. Spectrogram features are widely used in MSS tasks which consists of both time and frequency information. However, the time and frequency correlations and the local patterns of spectrogram have not been fully explored. In this paper, we propose a novel U-Net architecture named TFSWA-UNet in which a temporal-frequency and shifted window attention (TFSWA) based block is designed as bottleneck block. In the proposed TFSWA block, time sequence attention (TSA) block and frequency sequence attention (FSA) block are used to capture the global correlation of music spectrogram features within time and frequency sequences respectively. To further capture the local correlations of spectrogram features, shifted window attention based Swin transformer is also introduced into the TFSWA module, which computes self-attention within local non-overlapping windows and capture correlations from both temporal and frequency dimensions. Experiment results on MUSDB18 dataset indicate that the proposed TFSWA-UNet model outperforms the state-of-the-art MSS methods with SDR of 9.16dB on vocals. © 2024 IEEE.

关键词： Spectrographs

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Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda 14

Enhanced Labeling Technique for Reddit Text and Fine-Tuned L...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Kimera, Richard Rim, Daniela N. Kirabira, Joseph Udomah, Ubong Godwin Choi, Heeyoul Handong Global University Department of Advanced Convergence Pohang Korea Republic of Handong Global University School of Computer Science and Electrical Engineering Pohang Korea Republic of Department of Psychiatry Mbale Uganda University of Uyo Teaching Hospital Department of Psychiatry Akwa Ibom Uyo Nigeria

ISBN: (纸本)9798350313277

Depression is a global burden and one of the most challenging mental health conditions to control. Using the BDI questionnaire, experts can detect its severity early, administer appropriate medication to patients, and impede its progression. Owing to the fear of potential stigmatization, many patients turn to social media platforms such as Reddit for advice and assistance at various stages of their journey. This research extracts text from Reddit to facilitate the diagnostic process, employs a proposed labelling approach to categorize the text, and subsequently fine-tunes the Longformer model. The model's performance is compared against the baseline models, including Naive Bayes, Random Forest, Support Vector Machines, and Gradient Boosting. Our findings reveal that the Longformer model outperforms the baseline models in both English (48%) and Luganda (45%) languages on a custom-made dataset. © 2023 IEEE.

关键词： BART Depression Severity fine-tuning Longformer Luganda Reddit

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