Add pages for volume v245

Gemfile

@@ -0,0 +1,15 @@
+source "https://rubygems.org"
+
+git_source(:github) {|repo_name| "https://github.com/#{repo_name}" }
+
+gem 'jekyll'
+
+group :jekyll_plugins do
+  gem 'github-pages'
+  gem 'jekyll-remote-theme'
+  gem 'jekyll-include-cache'
+  gem 'webrick'
+end
+
+# gem "rails"
+

README.md

@@ -0,0 +1,22 @@
+# PMLR 245
+
+To suggest fixes to this volume please make a pull request containing the changes requested and a justification for the changes.
+
+To edit the details of this conference work edit the [_config.yml](./_config.yml) file and submit a pull request.
+
+To make changes to the individual paper details, edit the associated paper file in the [./_posts](./_posts) subdirectory.
+
+For details of how to publish in PMLR please check https://proceedings.mlr.press/faq.html
+
+For details of what is required to submit a proceedings please check https://proceedings.mlr.press/spec.html
+
+
+
+Published as Volume 245 by the Proceedings of Machine Learning Research on 29 July 2024.
+
+Volume Edited by:
+  * Zeng Nianyin
+  * Ram Bilas Pachori
+
+Series Editors:
+  * Neil D. Lawrence

_config.yml

@@ -0,0 +1,80 @@
+---
+booktitle: Proceedings of 2024 International Conference on Machine Learning and Intelligent
+  Computing
+shortname: MLIC
+volume: '245'
+year: '2024'
+start: &1 2024-04-26
+end: 2024-04-28
+published: 2024-07-29
+layout: proceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: MLIC-2024
+month: 0
+cycles: false
+bibtex_editor: Nianyin, Zeng and Pachori, Ram Bilas
+editor:
+- given: Zeng
+  family: Nianyin
+- given: Ram Bilas
+  family: Pachori
+title: Proceedings of Machine Learning Research
+description: |
+  Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing
+    Held in Wuhan, China on 26-28 April 2024
+
+  Published as Volume 245 by the Proceedings of Machine Learning Research on 29 July 2024.
+
+  Volume Edited by:
+    Zeng Nianyin
+    Ram Bilas Pachori
+
+  Series Editors:
+    Neil D. Lawrence
+date_str: 26--28 Apr
+url: https://proceedings.mlr.press
+author:
+  name: PMLR
+baseurl: "/v245"
+twitter_username: MLResearchPress
+github_username: mlresearch
+markdown: kramdown
+exclude:
+- README.md
+- Gemfile
+- ".gitignore"
+plugins:
+- jekyll-feed
+- jekyll-seo-tag
+- jekyll-remote-theme
+remote_theme: mlresearch/jekyll-theme
+style: pmlr
+permalink: "/:title.html"
+ghub:
+  edit: true
+  repository: v245
+display:
+  copy_button:
+    bibtex: true
+    endnote: true
+    apa: true
+  comments: false
+volume_type: Volume
+volume_dir: v245
+email: ''
+conference:
+  name: Machine Learning and Intelligent Computing
+  url: https://www.icmlic.org
+  location: Wuhan, China
+  dates:
+  - *1
+  - 2024-04-27
+  - 2024-04-28
+analytics:
+  google:
+    tracking_id: UA-92432422-1
+orig_bibfile: "/Users/neil/mlresearch/v245/mlic24.bib"
+# Site settings
+# Original source:  /Users/neil/mlresearch/v245/mlic24.bib

_posts/2024-07-29-chao24a.md

@@ -0,0 +1,52 @@
+---
+title: Research on Features Extraction and Classification for Images based on Transformer
+  Learning
+abstract: "Image processing and analysis have become an essential method in many areas
+  including medical impact, facial recognition, and social media analysis. With the
+  rapid development of big data and artificial intelligence technology, especially
+  the emergence of Transformer learning models, new methods have been brought to image
+  feature extraction and classification. However, the existing transformer model limits
+  the ability to handle variable-length sequences and understand complex sequence
+  relationships. In this work, we propose a novel transformer-based framework that
+  combines a self-attention mechanism and a multi-head attention technique to efficiently
+  extract features from complex image data. In addition, we introduce an improved
+  classifier that enables efficient image classification using extracted features.
+  Our method takes into account not only the local features of the image but also
+  the global relationships between different regions to achieve a more accurate representation
+  of the features. We simulate our model with existing convolutional neural networks
+  and other traditional machine learning methods in the public datasets including
+  CIFAR-10 and MNIST. From our experimental results, we can observe that our transformer-learning-based
+  framework shows significant performance improvement in image feature extraction
+  and classifica-tion tasks.\r "
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: chao24a
+month: 0
+tex_title: Research on Features Extraction and Classification for Images based on
+  Transformer Learning
+firstpage: 67
+lastpage: 75
+page: 67-75
+order: 67
+cycles: false
+bibtex_author: Chao, Wang
+author:
+- given: Wang
+  family: Chao
+date: 2024-07-29
+address:
+container-title: Proceedings of 2024 International Conference on Machine Learning
+  and Intelligent Computing
+volume: '245'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 7
+  - 29
+pdf: https://raw.githubusercontent.com/mlresearch/v245/main/assets/chao24a/chao24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-07-29-chunning24a.md

@@ -0,0 +1,49 @@
+---
+title: A Sound Source Location Method Based on Time Difference of Arrival with Improved
+  Dung Beetle Optimizer
+abstract: In microphone array sound source localization based on Time Difference of
+  Arrival (TDOA), traditional methods for solving the nonlinear equations of TDOA
+  lead to significant deviations and lower accuracy. To address this issue, this paper
+  proposes a TDOA-based sound source localization method using an Improved Dung Beetle
+  Optimizer (IDBO) algorithm. This method enhances the performance of the Dung Beetle
+  Optimizer (DBO) by employing strategies such as chaotic mapping, golden sine, and
+  adaptive tdistribution, and applies it to sound source localization. To evaluate
+  the performance of the IDBO, it is compared with DBO, Harris Hawk Optimizer (HHO),
+  Gray Wolf Optimizer (GWO), Bald Eagle Search (BES) algorithm, and Whale Optimization
+  Algorithm (WOA). The results showed that in solving benchmark functions and localization
+  models, it demonstrates faster convergence speed, higher localization accuracy,
+  and better stability.
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: chunning24a
+month: 0
+tex_title: A Sound Source Location Method Based on Time Difference of Arrival with
+  Improved Dung Beetle Optimizer
+firstpage: 165
+lastpage: 176
+page: 165-176
+order: 165
+cycles: false
+bibtex_author: Chunning, Song and Jindong, Zhang
+author:
+- given: Song
+  family: Chunning
+- given: Zhang
+  family: Jindong
+date: 2024-07-29
+address:
+container-title: Proceedings of 2024 International Conference on Machine Learning
+  and Intelligent Computing
+volume: '245'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 7
+  - 29
+pdf: https://raw.githubusercontent.com/mlresearch/v245/main/assets/chunning24a/chunning24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-07-29-cong24a.md

@@ -0,0 +1,52 @@
+---
+title: 'Research on Green Design Optimization of Ethnic Minority Architecture in Guangxi
+  Based on Machine Learning '
+abstract: Guangxi Zhuang Autonomous Region, as one of China’s ethnic minority areas,
+  possesses rich heritage resources in ethnic architecture, with dual objectives of
+  cultural preservation and ecological sustainability. This paper explores the design
+  principles of ethnic minority architecture in Guangxi, investigates the relationship
+  between architecture and climate adaptability, and integrates them with digital
+  fabrication technology. Utilizing parametric platforms and performance simulation
+  tools, the study examines the climate adaptability of ethnic minority architecture
+  in Guangxi. Through machine learning, models for lighting, thermal, and humidity
+  environments specific to Guangxi’s ethnic minority regions are developed. Optimization
+  parameters for architectural design are proposed, and the reliability and accuracy
+  of the models are demonstrated through training and testing, providing ecological
+  design optimization strategies and references for future research on green architecture
+  in ethnic minority areas.
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: cong24a
+month: 0
+tex_title: 'Research on Green Design Optimization of Ethnic Minority Architecture
+  in Guangxi Based on Machine Learning '
+firstpage: 366
+lastpage: 372
+page: 366-372
+order: 366
+cycles: false
+bibtex_author: Cong, Lu and Nenglang, Huang and Yang, Wu
+author:
+- given: Lu
+  family: Cong
+- given: Huang
+  family: Nenglang
+- given: Wu
+  family: Yang
+date: 2024-07-29
+address:
+container-title: Proceedings of 2024 International Conference on Machine Learning
+  and Intelligent Computing
+volume: '245'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 7
+  - 29
+pdf: https://raw.githubusercontent.com/mlresearch/v245/main/assets/cong24a/cong24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-07-29-fan24a.md

@@ -0,0 +1,49 @@
+---
+title: Federated Learning Algorithm based on Gaussi-an Local Differential Noise
+abstract: In differential privacy-based federated learning, the data of different
+  clients are of-ten independently and identically distributed. During model training,
+  each client’s data will optimize and converge towards its own optimal direction,
+  causing a client drift phenomenon, resulting in a decrease in accuracy and making
+  it difficult to obtain the optimal global model. To address this issue, a federated
+  learning al-gorithm based on local differential privacy is proposed. Each client
+  is assigned its own control variable ci to control the model update direction, and
+  a global control variable c is set on the server side. The SCAFFOLD algorithm is
+  used to aggregate all client model parameters and control variables. During model
+  training, a correction term c-ci is added when updating parameters on the client
+  side, and the model training bias is adjusted according to the global control variable
+  obtained from the server side in the previous round, thereby controlling the model’s
+  iterative direction towards the global optimum. Experimental results on the CIFAR-10
+  datasets demonstrated the effectiveness of the new algorithm.
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: fan24a
+month: 0
+tex_title: Federated Learning Algorithm based on Gaussi-an Local Differential Noise
+firstpage: 325
+lastpage: 339
+page: 325-339
+order: 325
+cycles: false
+bibtex_author: Fan, Wu and Maoting, Gao
+author:
+- given: Wu
+  family: Fan
+- given: Gao
+  family: Maoting
+date: 2024-07-29
+address:
+container-title: Proceedings of 2024 International Conference on Machine Learning
+  and Intelligent Computing
+volume: '245'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 7
+  - 29
+pdf: https://raw.githubusercontent.com/mlresearch/v245/main/assets/fan24a/fan24a.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---

_posts/2024-07-29-fan24b.md

@@ -0,0 +1,57 @@
+---
+title: Decentralized Federated Learning Algorithm Based on Federated Groups and Secure
+  Multiparty Computation
+abstract: To solve the problem that the centralized federal learning based on privacy
+  protection relies on trusted central servers, has low resistance to malicious attacks,
+  and is prone to privacy leakage, this paper proposes a decentralized federated learning
+  algorithm based on federated groups and secure multiparty computation. By establishing
+  a federated group mechanism based on model relevance, each client has its own federated
+  group, and model parameters are only transmitted among fed- erated group members,
+  members outside the group are unable to access parameter information. Secret owners
+  utilize secret sharing algorithms to split their model parameters into several secret
+  shares, which are then transmitted to federated group members through secure channels.
+  Federated group members then aggregate all transmitted secret shares by weighted
+  averaging, and the secret owner receives the aggregated secret shares passed back
+  from all federated group members, and then uses the secret recovery algorithms to
+  recover secret, and obtains the updated parameter model. In the federated group,
+  while a member becomes a Byzantine node, it is removed from the federated group,
+  and another client is selected to join the group based on model relevance. So, each
+  client participating in federated learning serves as both a data node and a computing
+  node, federated learning eliminates reliance on servers and achieves decentralization.
+  The good privacy performance of the proposed algorithm model is theoretically analyzed,
+  and experiments on the FedML platform demonstrated that the algorithm has stronger
+  resistance to attacks.
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: fan24b
+month: 0
+tex_title: Decentralized Federated Learning Algorithm Based on Federated Groups and
+  Secure Multiparty Computation
+firstpage: 340
+lastpage: 348
+page: 340-348
+order: 340
+cycles: false
+bibtex_author: Fan, Wu and Maoting, Gao
+author:
+- given: Wu
+  family: Fan
+- given: Gao
+  family: Maoting
+date: 2024-07-29
+address:
+container-title: Proceedings of 2024 International Conference on Machine Learning
+  and Intelligent Computing
+volume: '245'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 7
+  - 29
+pdf: https://raw.githubusercontent.com/mlresearch/v245/main/assets/fan24b/fan24b.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---