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21st Annual International Conference on Mobile systems, Applications and Services, MobiSys 2023

作者： Li, Dong Zhang, Kaiyan Cao, Dongping Shen, Jing Kang, Run Mao, Qiankang Chen, Jiawei Douyin Co. Ltd. China

ISBN: (纸本)9798400701108

As client applications evolve with new businesses and features, new performance overheads may be introduced. For example, in the application startup scenario, the initialization of newly connected SDKs and the addition of disk access operations on the main thread can increase the startup time, leading to a decline in user experience. Traditional performance testing detects application indicators through automated testing, video frame splitting, or log tracking, but due to fluctuations in the offline environment, test results often fluctuate, and confidence is not high. To optimize performance in a more refined way, it is necessary to not only evaluate performance through indicators but also to pinpoint the cause of the problem accurately. Domino is a client-side performance testing platform based on Trace analysis. In this demonstration, we will provide two versions of AndroidDemo applications, simulate several performance issues, and use the Domino platform to more precisely locate performance problems and provide development repair suggestions. © 2023 Owner/Author(s).

关键词： performance monitoring client applications accuracy measurement

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A Bibliometric analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, computing and Engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The acm International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards Post-disaster Damage Assessment using Deep Transfer Learning and GAN-based Data Augmentation 23

Towards Post-disaster Damage Assessment using Deep Transfer ...

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24th International Conference on Distributed computing and Networking (ICDCN)

作者： Banerjee, Sourasekhar Patel, Yashwant Singh Kumar, Pushkar Bhuyan, Monowar Umea Univ Umea Sweden Thapar Inst Engn & Technol Patiala Punjab India Indian Inst Informat Technol Ranchi Ranchi Bihar India

ISBN: (纸本)9781450397964

Cyber-physical disaster systems (CPDS) are a new cyber-physical application that collects physical realm measurements from IoT devices and sends them to the edge for damage severity analysis of impacted sites in the aftermath of a large-scale disaster. However, the lack of effective machine learning paradigms and the data and device heterogeneity of edge devices pose significant challenges in disaster damage assessment (DDA). To address these issues, we propose a generative adversarial network (GAN) and a lightweight, deep transfer learning-enabled, fine-tuned machine learning pipeline to reduce overall sensing error and improve the model's performance. In this paper, we applied several combinations of GANs (i.e., DCGAN, DiscoGAN, ProGAN, and Cycle-GAN) to generate fake images of the disaster. After that, three pre-trained models: VGG19, ResNet18, and DenseNet121, with deep transfer learning, are applied to classify the images of the disaster. We observed that the ResNet18 is the most pertinent model to achieve a test accuracy of 88.81%. With the experiments on real-world DDA applications, we have visualized the damage severity of disaster-impacted sites using different types of Class Activation Mapping (CAM) techniques, namely Grad-CAM++, Guided Grad-Cam, & Score-CAM. Finally, using k-means clustering, we have obtained the scatter plots to measure the damage severity into no damage, mild damage, and severe damage categories in the generated heat maps.

关键词： Cyber-Physical systems Generative Adversarial Networks Deep Learning Damage Assessment Class Activation Mapping Clustering

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The social embeddedness of peer production: A comparative qualitative analysis of three Indian language Wikipedia editions 22

The social embeddedness of peer production: A comparative qu...

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CHI Conference on Human Factors in computing systems (CHI)

作者： Khatri, Sejal Shaw, Aaron Dasgupta, Sayamindu Hill, Benjamin Mako Univ Washington Seattle WA 98195 USA Northwestern Univ Evanston IL USA Univ North Carolina Chapel Hill Chapel Hill NC USA

ISBN: (纸本)9781450391573

Why do some peer production projects do a better job at engaging potential contributors than others? We address this question by comparing three Indian language Wikipedias, namely, -Malayalam, Marathi, and Kannada. We found that although the three projects share goals, technological infrastructure, and a similar set of challenges, Malayalam Wikipedia's community engages language speakers in contributing at a much higher rate than the others. Drawing from a grounded theory analysis of interviews with 18 community participants from the three projects, we found that experience with participatory governance and free/open-source software in the Malayalam community supported high engagement of contributors. Counterintuitively, we found that fnancial resources intended to increase participation in theMarathi and Kannada communities hindered the growth of these communities. Our fndings underscore the importance of social and cultural context in the trajectories of peer production communities.

关键词： online communities peer production community engagement Wikipedia Indian Wikipedias social computing knowledge equity underrepresented languages social embeddednessonline communities peer production community engagement Wikipedia Indian Wikipedias social computing knowledge equity underrepresented languages social embeddedness

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2nd International Conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

2nd International Conference on Advanced Network Technologie...

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2nd International Conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

ISBN: (纸本)9783031281822

The proceedings contain 79 papers. The special focus in this conference is on Advanced Network Technologies and Intelligent computing. The topics include: Effective Link Prediction in Complex Networks Using Differential Evolution Based Extreme Gradient Boosting Algorithm;Hybrid Partitioning for Embedded and Distributed CNNs Inference on Edge Devices;Application of Abelian Mechanism on UFS-acm for Risk analysis;support systems and Technological Capabilities in Digital Transformation According to the Nature of the Company;an Improved Approach of Image Steganography Based on Least Significant Bit Technique for Secure Communication in Cloud;enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing systems;design of Energy Efficient IoT-Based Smart Street Light System;an Optimized Low-Cost 4 × 4 Multiplier Circuit Design Using Reversible Logic Gates;an Improved Resource Allocation Process Using Modified Max-Min Algorithm for Dynamic Cloud;design of an Energy Aware Cluster-Based Routing Scheme to Minimize Energy Consumption in Wireless Sensor Networks;a Load Threshold Allocation Approach: Tackling Blackouts Through Brownout;Predictive analysis of Ambient Environment for Urban N Cultivation Using IOT Based Hydroponic System;blockchain-Aided Keyword Search over Encrypted Data in Cloud;Impact of Selfish Nodes on the Performance of AODV and DSR Routing Protocols;a Deadlock-Free and Adaptive Prime Perspective Turn Model for 3D-Mesh Based Network-on-Chips;Effect of Channel Slot Time on Performance of IEEE 802.15.6-Based Medical Body Area Network;coviBand - Wearable Social Distancing and Contact Tracing Device Using Signal Strength Indicator Metrics;Average Time Based PRoPHET Routing Protocol for Opportunistic Networks;analysis and Implementation of Microservices Using Docker;automated Energy Modeling Framework for Microcontroller-Based Edge computing Nodes.

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2nd International Conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

2nd International Conference on Advanced Network Technologie...

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2nd International Conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

ISBN: (纸本)9783031281792

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proceedings of FTXS 2022: Workshop on Fault Tolerance for HPC at eXtreme Scale, Held in conjunction with SC 2022: The International Conference for High Performance computing, Networking, Storage and analysis

Proceedings of FTXS 2022: Workshop on Fault Tolerance for HP...

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12th IEEE/acm Workshop on Fault Tolerance for HPC at eXtreme Scale, FTXS 2022

ISBN: (纸本)9781665488471

The proceedings contain 5 papers. The topics discussed include: ClusterLog: clustering logs for effective log-based anomaly detection;recovery of distributed iterative solvers for linear systems using non-volatile ram;ReStore: in-memory replicated storage for rapid recovery in fault-tolerant algorithms;implicit actions and non-blocking failure recovery with MPI;and towards precision-aware fault tolerance approaches for mixed-precision applications.

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Rice Quality analysis Based on Physical Attributes Using Image Processing and Machine Learning 1

Rice Quality Analysis Based on Physical Attributes Using Ima...

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1st IEEE International Conference on Advances in Electronics, Communication, computing and Intelligent Information systems, ICAECIS 2023

作者： Yashaswini, S. Dubey, Pushpalatha Rajesh Kumar, S. Cambridge Institute of Technology Department of Cse Bangalore India

ISBN: (纸本)9798350348057

This paper proposes a methodology to automate the quality control of rice grain, as its been manually analysed by veteran rice inspector which are inaccurate, collection of data of various rice types to analyse quality based on their physical attributes such as Area of the grain, Perimeter, Width and Height, Aspect Ratio, Major and Minor Axis. We have used techniques such as Digital Image Processing, Computer Vision - which involves pre-processing, morphological operation, edge detection, object detection and finally object measurement. Training of the system involving manual and Machine Learning which involves feeding of all data results from image processing into CSV file and formulating hypothesis for manual and SVM for machine based training, and then test to classify the quality into higher or lower grades based on the two methods and concluding the best methodology from observation. © 2023 IEEE.

关键词： Support vector machines

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To Trust or to Stockpile: Modeling Human-Simulation Interaction in Supply Chain Shortages 22

To Trust or to Stockpile: Modeling Human-Simulation Interact...

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CHI Conference on Human Factors in computing systems (CHI)

作者： Mohaddesi, Omid Grifn, Jacqueline Ergun, Ozlem Kaeli, David Marsella, Stacy Harteveld, Casper Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450391573

Understanding decision-making in dynamic and complex settings is a challenge yet essential for preventing, mitigating, and responding to adverse events (e.g., disasters, financial crises). Simulation games have shown promise to advance our understanding of decision-making in such settings. However, an open question remains on how we extract useful information from these games. We contribute an approach to model human-simulation interaction by leveraging existing methods to characterize: (1) system states of dynamic simulation environments (with Principal Component analysis), (2) behavioral responses from human interaction with simulation (with Hidden Markov Models), and (3) behavioral responses across system states (with Sequence analysis). We demonstrate this approach with our game simulating drug shortages in a supply chain context. Results from our experimental study with 135 participants show different player types (hoarders, reactors, followers), how behavior changes in different system states, and how sharing information impacts behavior. We discuss how our findings challenge existing literature.

关键词： human-simulation interaction hidden markov models principal component analysis sequence analysis gamette supply chain

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Information Aggregation for Constrained Online Control

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proceedings of the acm on measurement and analysis of computing systems 2021年第2期5卷 1–35页

作者： Li, Tongxin Chen, Yue Sun, Bo Wierman, Adam Low, Steven H. CALTECH Pasadena CA 91125 USA Tsinghua Univ Beijing Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

This paper considers an online control problem involving two controllers. A central controller chooses an action from a feasible set that is determined by time-varying and coupling constraints, which depend on all past actions and states. The central controller's goal is to minimize the cumulative cost;however, the controller has access to neither the feasible set nor the dynamics directly, which are determined by a remote local controller. Instead, the central controller receives only an aggregate summary of the feasibility information from the local controller, which does not know the system costs. We show that it is possible for an online algorithm using feasibility information to nearly match the dynamic regret of an online algorithm using perfect information whenever the feasible sets satisfy a causal invariance criterion and there is a sufficiently large prediction window size. To do so, we use a form of feasibility aggregation based on entropic maximization in combination with a novel online algorithm, named Penalized Predictive Control (PPC) and demonstrate that aggregated information can be efficiently learned using reinforcement learning algorithms. The effectiveness of our approach for closed-loop coordination between central and local controllers is validated via an electric vehicle charging application in power systems.

关键词： online control closed-loop control model predictive control regret analysis electric vehicle charging

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