Dataset Name: Linked In Job Postings (2023 - 2024)
Basic Description: LinkedIn Job Postings (2023 - 2024)
📖 FULL DATASET DESCRIPTION:
Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper
Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. .
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings
📊 Additional information:
File count not found
Views: 126,000
Downloads: 53,100
📚 RELATED NOTEBOOKS:
1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84
URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration
2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58
URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis
@Machine_learn
Basic Description: LinkedIn Job Postings (2023 - 2024)
📖 FULL DATASET DESCRIPTION:
Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper
Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. .
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/arshkon/linkedin-job-postings
📊 Additional information:
File count not found
Views: 126,000
Downloads: 53,100
📚 RELATED NOTEBOOKS:
1. "Decoding the Job Market: An In-depth Exploration | Upvotes: 84
URL: https://www.kaggle.com/code/pratul007/decoding-the-job-market-an-in-depth-exploration
2. LinkedIn Job Postings 2023 Data Analysis | Upvotes: 58
URL: https://www.kaggle.com/code/enricofindley/linkedin-job-postings-2023-data-analysis
@Machine_learn
❤6
Dataset Name: Online Payments Fraud Detection Dataset
Basic Description: Online payment fraud big dataset for testing and practice purpose
📖 FULL DATASET DESCRIPTION:
The below column reference:
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (186 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset
@Machine_learn
Basic Description: Online payment fraud big dataset for testing and practice purpose
📖 FULL DATASET DESCRIPTION:
The below column reference:
📥 DATASET DOWNLOAD INFORMATION
🔴 Dataset Size: Download dataset as zip (186 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rupakroy/online-payments-fraud-detection-dataset
@Machine_learn
❤4
🔹 Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
🔹 Publication Date: Published on Aug 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15213
• PDF: https://arxiv.org/pdf/2508.15213
@Machine_learn
🔹 Publication Date: Published on Aug 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.15213
• PDF: https://arxiv.org/pdf/2508.15213
@Machine_learn
❤2🔥1
🚀 AI Agents for Android Apps
📌 GitHub: https://github.com/actionstatelabs/android-action-kernel
@Machine_learn
📌 GitHub: https://github.com/actionstatelabs/android-action-kernel
@Machine_learn
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🔹 Title: Self-Rewarding Vision-Language Model via Reasoning Decomposition
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19652
• PDF: https://arxiv.org/pdf/2508.19652
@Machine_learn
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19652
• PDF: https://arxiv.org/pdf/2508.19652
@Machine_learn
❤2
🔹 Title: Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19493
• PDF: https://arxiv.org/pdf/2508.19493
• Project Page: https://zhixin-l.github.io/SAPA-Bench
• Github: https://github.com/Zhixin-L/SAPA-Bench
@Machine_learn
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19493
• PDF: https://arxiv.org/pdf/2508.19493
• Project Page: https://zhixin-l.github.io/SAPA-Bench
• Github: https://github.com/Zhixin-L/SAPA-Bench
@Machine_learn
❤1
Dataset Name: Gallstone Dataset (UCI)
Basic Description: Gallstone Dataset (UCI Machine Learning Repository)
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (81 kB)
🔰 Direct dataset download link:
URL not found
📊 Additional information:
==================================
File count not found
Views: 1,128
Downloads: 246
📚 RELATED NOTEBOOKS:
==================================
1. Heart Attack Risk Prediction Dataset | Upvotes: 274
URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset
@Machine_learn
Basic Description: Gallstone Dataset (UCI Machine Learning Repository)
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (81 kB)
🔰 Direct dataset download link:
URL not found
📊 Additional information:
==================================
File count not found
Views: 1,128
Downloads: 246
📚 RELATED NOTEBOOKS:
==================================
1. Heart Attack Risk Prediction Dataset | Upvotes: 274
URL: https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset
@Machine_learn
❤3👍1
🔹 Title: CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20096
• PDF: https://arxiv.org/pdf/2508.20096
• Project Page: https://github.com/OpenIXCLab/CODA
• Github: https://github.com/OpenIXCLab/CODA
@Machine_learn
🔹 Publication Date: Published on Aug 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.20096
• PDF: https://arxiv.org/pdf/2508.20096
• Project Page: https://github.com/OpenIXCLab/CODA
• Github: https://github.com/OpenIXCLab/CODA
@Machine_learn
❤1
🔹 Title: Predicting the Order of Upcoming Tokens Improves Language Modeling
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19228
• PDF: https://arxiv.org/pdf/2508.19228
• Github: https://github.com/zaydzuhri/token-order-prediction
@Machine_learn
🔹 Publication Date: Published on Aug 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.19228
• PDF: https://arxiv.org/pdf/2508.19228
• Github: https://github.com/zaydzuhri/token-order-prediction
@Machine_learn
❤1
هر ثانیه توقف، یعنی از دست رفتن زمان، هزینه و فرصت…
پایداری دیگر یک انتخاب نیست؛ یک ضرورت است.
🚀 ایرانGPU؛جایی که پروژهها متوقف نمیشوند.
🏛 تنها و اولین شرکت بورسی هوش مصنوعی ایران
🕒 بیش از ۵ سال سابقه فعالیت حرفهای
🌐 شبکهای از ۲۰+ دیتاسنتر غیرمتمرکز در سراسر کشور
🧠 مناسب تیمها، پژوهشگران و سازمانهای حرفهای AI
🛟 پشتیبانی ۲۴ ساعته، ۷ روز هفته
📈 تضمین SLA با دسترسپذیری 99.9٪ و ارائه سرور داخل ایران
📩 ثبت درخواست مشاوره | شروع مسیر هوشمندانه
https://b2n.ir/qk8423
پایداری دیگر یک انتخاب نیست؛ یک ضرورت است.
🚀 ایرانGPU؛جایی که پروژهها متوقف نمیشوند.
🏛 تنها و اولین شرکت بورسی هوش مصنوعی ایران
🕒 بیش از ۵ سال سابقه فعالیت حرفهای
🌐 شبکهای از ۲۰+ دیتاسنتر غیرمتمرکز در سراسر کشور
🧠 مناسب تیمها، پژوهشگران و سازمانهای حرفهای AI
🛟 پشتیبانی ۲۴ ساعته، ۷ روز هفته
📈 تضمین SLA با دسترسپذیری 99.9٪ و ارائه سرور داخل ایران
📩 ثبت درخواست مشاوره | شروع مسیر هوشمندانه
https://b2n.ir/qk8423
❤1
Forwarded from Papers
با عرض سلام مي خواهيم مقاله كنفرانسي با عنوان زير بنويسيم
Complex Sig: Complex deep model for signal classification
Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to
decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature.
This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR.
نتايج اوليه خوب بوده و قابل مقايسه با ساير مقالات تو اين حوزه ميباشد. نياز به ٢ يا سه نفر داريم كه مشاركت كنند.
Price:
2: 300$
3:200$
4:100$
@Raminmousa
@paper4money
@Machine_learn
Complex Sig: Complex deep model for signal classification
Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to
decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature.
This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR.
نتايج اوليه خوب بوده و قابل مقايسه با ساير مقالات تو اين حوزه ميباشد. نياز به ٢ يا سه نفر داريم كه مشاركت كنند.
Price:
2: 300$
3:200$
4:100$
@Raminmousa
@paper4money
@Machine_learn
❤2
Machine learning books and papers pinned «با عرض سلام مي خواهيم مقاله كنفرانسي با عنوان زير بنويسيم Complex Sig: Complex deep model for signal classification Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early…»
✨RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning
📝 Summary:
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da...
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18742
• PDF: https://arxiv.org/pdf/2602.18742
• Project Page: https://seungkukim.github.io/robocurate/
@Machine_learn
📝 Summary:
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da...
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18742
• PDF: https://arxiv.org/pdf/2602.18742
• Project Page: https://seungkukim.github.io/robocurate/
@Machine_learn
Papers
با عرض سلام مي خواهيم مقاله كنفرانسي با عنوان زير بنويسيم Complex Sig: Complex deep model for signal classification Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early…
فقط نفرات ۲ و ۳ از این مقاله باقی موندن...!
Fri, 27 Feb 2026 (showing first 50 of 206 entries )
[1] arXiv:2602.23352 [pdf, html, other]
Stark localization of interacting particles
Wojciech De Roeck, Amirali Hannani, Alessio Lerose, Nathan Vandenbosch
Subjects: Mathematical Physics (math-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[2] arXiv:2602.23350 [pdf, html, other]
A strengthening of the dimensional Brunn-Minkowski conjecture implies the (B)-Conjecture
Sotiris Armeniakos, Jacopo Ulivelli
Comments: Comments are welcome!
Subjects: Functional Analysis (math.FA); Metric Geometry (math.MG)
[3] arXiv:2602.23343 [pdf, html, other]
Cyclic sieving for a class of rectangular domino tableaux
Laura Colmenarejo, Bridget Eileen Tenner, Camryn E. Thompson
Comments: 17 pages
Subjects: Combinatorics (math.CO)
[4] arXiv:2602.23340 [pdf, html, other]
Combinatorial Properties of the Raisonnier Filter
Spyridon Dialiatsis, Yurii Khomskii
Subjects: Logic (math.LO)
[5] arXiv:2602.23326 [pdf, html, other]
Spin Glass Concepts in Computer Science, Statistics, and Learning
Andrea Montanari
Comments: 33 pages; 2 pdf figures
Subjects: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[6] arXiv:2602.23325 [pdf, html, other]
Spanning tight components in 4-uniform hypergraphs
Francesco Di Braccio, Brian Hearn, Joanna Lada, Mihir Neve, Lu-Ming Zhang
Comments: 24 pages, 4 figures
Subjects: Combinatorics (math.CO)
[7] arXiv:2602.23323 [pdf, html, other]
Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control
Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark, Theodoros Tsatsanifos
Comments: arXiv admin note: substantial text overlap with arXiv:2108.02311. substantial text overlap with arXiv:2108.02311
Subjects: Optimization and Control (math.OC)
@Machine_learn
[1] arXiv:2602.23352 [pdf, html, other]
Stark localization of interacting particles
Wojciech De Roeck, Amirali Hannani, Alessio Lerose, Nathan Vandenbosch
Subjects: Mathematical Physics (math-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[2] arXiv:2602.23350 [pdf, html, other]
A strengthening of the dimensional Brunn-Minkowski conjecture implies the (B)-Conjecture
Sotiris Armeniakos, Jacopo Ulivelli
Comments: Comments are welcome!
Subjects: Functional Analysis (math.FA); Metric Geometry (math.MG)
[3] arXiv:2602.23343 [pdf, html, other]
Cyclic sieving for a class of rectangular domino tableaux
Laura Colmenarejo, Bridget Eileen Tenner, Camryn E. Thompson
Comments: 17 pages
Subjects: Combinatorics (math.CO)
[4] arXiv:2602.23340 [pdf, html, other]
Combinatorial Properties of the Raisonnier Filter
Spyridon Dialiatsis, Yurii Khomskii
Subjects: Logic (math.LO)
[5] arXiv:2602.23326 [pdf, html, other]
Spin Glass Concepts in Computer Science, Statistics, and Learning
Andrea Montanari
Comments: 33 pages; 2 pdf figures
Subjects: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[6] arXiv:2602.23325 [pdf, html, other]
Spanning tight components in 4-uniform hypergraphs
Francesco Di Braccio, Brian Hearn, Joanna Lada, Mihir Neve, Lu-Ming Zhang
Comments: 24 pages, 4 figures
Subjects: Combinatorics (math.CO)
[7] arXiv:2602.23323 [pdf, html, other]
Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control
Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark, Theodoros Tsatsanifos
Comments: arXiv admin note: substantial text overlap with arXiv:2108.02311. substantial text overlap with arXiv:2108.02311
Subjects: Optimization and Control (math.OC)
@Machine_learn
arXiv.org
Stark localization of interacting particles
We consider N interacting quantum particles on a one-dimensional lattice, and subjected to an external linear potential. For N = 1, the corresponding Hamiltonian is explicitly diagonalizable, with...
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