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SSRN 2023年

作者： Ahmed, Shahla Shaan Wang, Shaowei Tian, Yuan Chen, Tse-Hsun Zhang, Haoxiang Department of Computer Science University of Manitoba Canada School of Computing Queen’s University Canada Department of Computer Science and Software Engineering Concordia University Canada Huawei Canada Canada

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted ***: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition ***: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN and BERT models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models based on CNN architecture achieve precision ranging from 0.71 to 0.82. The trained model for automatic code content highlighting achieves a recall of 0.73 and an F1 score of 0.71, outperforming the trained models for other formatting styles. The BERT models have even lower recalls and F1 scores than the CNN models. Our analysis of failure cases indicates that the majority of the failure cases are missing identification (i.e., the model misses the content that is supposed to be highlighted) due to the models tend to learn the frequently highlighted words while struggling to learn less frequent ***: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow. © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Unsupervised clustering applied to the optimization of a Case-based Reasoning system for the selection of optimal image explanation methods 30

Unsupervised clustering applied to the optimization of a Cas...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Brito-Borges, Esteban E. Orozco-Del-Castillo, Mauricio G. Recio-García, Juan A. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

The goal of this paper is to develop a cluster-based retrieval process to select the optimal explanation method for a given image and its corresponding classification by a neural network model. We propose the use of a density clustering method to organize a case base consisting of images labeled according to their optimal explanation method. This approach presents a prediction accuracy similar to a standard nearest-neighbor method, but significantly reducing the required retrieval time. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Neural network models

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Energy-Efficient Dynamic Asynchronous Federated Learning in Mobile Edge computing Networks

Energy-Efficient Dynamic Asynchronous Federated Learning in ...

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IEEE International Conference on Communications (ICC)

作者： Guozeng Xu Xiuhua Li Hui Li Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

To break data silos and address the challenge of green communication, federated learning (FL) is widely used at network edges to train deep learning models in mobile edge computing (MEC) networks. However, many existing FL algorithms do not fully consider the dynamic environment, resulting in slower convergence of the model and larger training energy consumption. In this paper, we design a dynamic asynchronous federated learning (DAFL) model to improve the efficiency of FL in MEC networks. Specifically, we dynamically choose a certain number of mobile devices (MDs) by their arrival order to participate in the global aggregation at each epoch. Meanwhile, we analyze the energy consumption model of local update and upload update, and formulate the problem as a dynamic sequential decision problem to minimize the energy consumption, which is NP-hard. To address it, we propose an energy-efficient algorithm based on deep reinforcement learning named DDAFL, to intelligently determine the number of MDs participating in global aggregation according to the state of MEC networks at each epoch. Compared with baseline schemes, the proposed algorithm can significantly reduce energy consumption and accelerate model convergence.

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Applying explanation methods for the iterative refinement of an ANN-based depression screening tool 30

Applying explanation methods for the iterative refinement of...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Sosa-Espadas, Cristian E. Cetina-Aguilar, Manuel Soladrero, Jose A. Darias, Jesus M. Brito-Borges, Esteban E. Cuevas-Cuevas, Nora L. Orozco-Del-Castillo, Mauricio G. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Instituto de Tecnologías del Conocimiento Universidad Complutense de Madrid Spain

Depression, as one of many mental health disorders, is a serious health and economy problem, affecting over 300 million people of all ages. The diagnosis of depression is a very complex and time-consuming task for mental health professionals, who usually rely on self-report questionnaires as a screening process. However, the items used in these questionnaires are sometimes subjective, particularly to certain demographics, and could require much time and effort from the patient. In recent years, artificial intelligence techniques, such as artificial neural networks, have been commonly used to screen for depression, however, they operate as black-box models, i.e., they lack explainability and interpretability which are mandatory in health-related fields. In this work we propose not only an artificial neural network, but also a set of explainable artificial intelligence techniques to refine a large set of items from a psychological questionnaire into a more concise, explainable one. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Neural networks

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IREX: A reusable process for the iterative refinement and explanation of classification models 30

IREX: A reusable process for the iterative refinement and ex...

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30th International Conference on Case-Based Reasoning Workshop, ICCBR-WS 2022

作者： Sosa-Espadas, Cristian E. Cetina-Aguilar, Manuel Soladrero, Jose A. Darias, Jesus M. Brito-Borges, Esteban E. Cuevas-Cuevas, Nora L. Orozco-Del-Castillo, Mauricio G. Tecnológico Nacional de México IT de Mérida Department of Systems and Computing Merida Mexico AAAIMX Student Chapter at Yucatan Mexico Association for the Advancement of Artificial Intelligence Mexico Department of Software Engineering and Artificial Intelligence Universidad Complutense de Madrid Spain

This paper presents IREX: a reusable method for the Iterative Refinement and EXplanation of classification models. It has been designed for domain-expert users -without machine learning skills- that need to understand and improve classification models. This way, it only requires the expected classification outcomes given by a domain expert. IREX proposes a smart combination of XAI methods that identifies potential inconsistencies in the model, explaining the causes to the user. Following a cycle analogous to CBR, a set of candidate anomalous variables are identified (retrieved) and proposed for its revision by an expert user. Once revision is confirmed, the model is purged and retrained for further optimization. This is a novel process where explanation methods are not only applied to explore a black-box model, but also to detect which input variables led to misclassifications, proposing and explaining their negative impact to the domain-expert user. We propose an automatic evaluation approach based on computing the number of anomalous input variables that the expert was able to identify and its comparison to the evolution of the classification model's performance. Then we apply this evaluation method to demonstrate the performance of the proposal on the given dataset. Finally, we provide a reusable implementation that can be directly applied to other classification models and domains. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Iterative methods

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud computing

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Procedia Computer Science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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In-Depth Analysis of Automated Acne Disease Recognition and Classification

arXiv

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arXiv 2025年

作者： Jeny, Afsana Ahsan Junayed, Masum Shah Mia, Md Robel Islam, Md Baharul Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of CSE University of Connecticut StorrsCT06269 United States Department of CSE Daffodil International University Dhaka Bangladesh Dept. of Computing & Software Engineering Florida Gulf Coast University Fort MyersFL33965 United States

Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L ∗ a ∗ b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods. © 2025, CC BY.

关键词： Expert systems

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

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Advancements in computing (ICAC), International Conference on

作者： T.R Legrand K.M.R.A.I Bandara J.A.D Stefania Crishani L.W.P Uvindu Nelum Amarasena Dharshana Kasthurirathna Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.

关键词： Machine learning Aging Recommender systems Faces

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Inandirici Öǧeler ile Sanal Kültürel Müze Içerisinde Dijital Hikaye Aktarimi Digital Storytelling on a Virtual Heritage Museum with Believable Agents 15

Inandirici Öǧeler ile Sanal Kültürel Müze Içerisinde D...

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15th Turkish National software engineering Symposium, UYMS 2021

作者： Gulec, Ulas Yilmaz, Murat Kalan, Kaan Dikbayir, Hasan Saygin Merdivanli, Ogulcan Degirmenci, Utku Mert Clarke, Paul M. TED University Department of Software Engineering Ankara Turkey Gazi University Department of Computer Engineering Ankara Turkey Çankaya University Department of Computer Engineering Ankara Turkey Dublin City University School of Computing Computer Science Dublin Ireland

ISBN: (纸本)9781665410700

The preservation of cultural heritage plays a very important role in terms of the sustainability of societies since culture is one of the most important phenomena that hold individuals together. However, although the protection of cultural heritage is a very important issue for societies, there are serious problems in the accuracy of information or access to information due to the verbal or written communication of the subjects that need to be conveyed. In particular, there is a serious decrease in the sense of belonging in individuals due to the inability to transfer cultural heritage to the younger generations correctly. At this point, the aim of the study is to teach individuals the Turkish horror culture by experiencing them in a realistic environment with various different stories in a virtual reality-based gamified system instead of teaching them in writing or verbally. For this purpose, a 3D virtual museum was developed within the scope of the study, inspired by real characters and areas, and it was aimed for individuals to learn Turkish horror culture elements through tasks in different scenarios. The developed system was tested by 5 experts in the field as a preliminary study and the realism level of the created system was measured with the comments of the experts. According to the findings, it has been determined that the level of realism offered by the designed virtual environment is sufficient to explain the Turkish horror culture to individuals. © 2021 IEEE.

关键词： Virtual reality

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Deepwalk-aware graph convolutional networks

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Science China(Information Sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI Media Analytics and Computing Lab Department of Computer Science and Technology School of InformaticsXiamen University Media Analytics and Computing Lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu Lab School of Software Tsinghua University

Graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph *** fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

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