Datasets:

nellaivijay
/

ml-research-daily

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gdelt1	News about machine learning OR ML OR deep learning	gdelt	https://example.com/news/1	News Reporter	2026-04-19	News article covering machine learning OR ML OR deep learning...	0.694247	News Agency	US	2,026	4	19	16	2	7	["deep-learning"]	1	["machine learning", "deep learning"]	2	{"abstract_length_score": 0.064, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.21280000000000002}	news	false	false	0	0.4	neutral	News article covering machine learning OR ML OR deep learning	61	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 7, "shared_subfields": ["deep-learning"], "shared_keywords": ["deep learning", "machine learning"], "shared_tags": []}, {"id": "github_CodeBonker_Agri-World", "title": "Agri-World", "similarity_score"...	3
github_JigyasaRana_PYTHON_PBL	PYTHON_PBL	github	https://github.com/JigyasaRana/PYTHON_PBL	JigyasaRana	2026-04-26	None # PYTHON_PBL Network Intrusion Detection for Cyber Security. Project Overview: This project focuses on detecting normal and abnormal network behavior using Machine Learning techniques. The system analyzes network data features to identify possible intrusions and improve cyber security monitoring. Objective: 1.An...	0.528571	null	null	2,026	4	26	17	2	0	["anomaly-detection"]	1	["machine learning"]	1	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	0.05	0.583333	neutral	None # PYTHON_PBL Network Intrusion Detection for Cyber Security. Project Overview: This project focuses on detecting normal and abnormal network behavior using Machine Learning techniques. The system analyzes network data features to identify possible intrusions and improve cyber security...	294	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Jupyter Notebook	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["anomaly-detection"], "shared_keywords": ["machine learning"], "shared_tags": []}, {"id": "github_mohamedyoussef-cloud_malicious-web-request-detection", "title": "malicious-web...	4
arxiv_2604.21931v1	Seeing Fast and Slow: Learning the Flow of Time in Videos	arxiv	https://arxiv.org/abs/2604.21931v1	Yen-Siang Wu, Rundong Luo, Jingsen Zhu, Tao Tu, Ali Farhadi, Matthew Wallingford, Yu-Chiang Frank Wang, Steve Marschner, Wei-Chiu Ma	2026-04-23	How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual conc...	0.5	null	null	2,026	4	23	17	2	3	["deep-learning", "auto-ml", "transfer-learning", "graph-learning", "federated-learning", "nlp", "recommendation", "time-series", "interpretability", "computer-vision", "optimization", "generative-ai", "anomaly-detection", "reinforcement-learning"]	7	["fine-tuning", "classification", "self-attention", "transformer", "llm", "embedding", "machine learning", "supervised", "interpretability", "computer vision", "generative", "reinforcement learning", "hyperparameter", "anomaly detection", "deep learning", "convolutional", "clustering", "neural network", "optimization",...	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.002685	0.344815	neutral	How can we tell whether a video has been sped up or slowed down. How can we generate videos at different speeds. Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time	263	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	[{"id": "github_airdmhund1_predictive-maintenance-vibration", "title": "predictive-maintenance-vibration", "similarity_score": 21, "shared_subfields": ["federated-learning", "nlp", "computer-vision", "anomaly-detection", "reinforcement-learning"], "shared_keywords": ["machine learning", "anomaly detection", "llm"], "sh...	5
arxiv_2604.21930v1	Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability	arxiv	https://arxiv.org/abs/2604.21930v1	Nicolae Filat, Ahmed Hussain, Konstantinos Kalogiannis, Elena Burceanu	2026-04-23	Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the same stream can induce d...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "time-series"]	2	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	-0.039583	0.503125	neutral	Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the...	298	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21927v1	Fine-Tuning Regimes Define Distinct Continual Learning Problems	arxiv	https://arxiv.org/abs/2604.21927v1	Paul-Tiberiu Iordache, Elena Burceanu	2026-04-23	Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime, defined by the trainable ...	0.5	null	null	2,026	4	23	17	2	3	["graph-learning", "recommendation", "optimization", "transfer-learning"]	4	["optimization", "fine-tuning"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.126667	0.406667	neutral	Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benchmarking CL methods, comparative evaluations typically keep the fine-tuning regime fixed. In this paper, we argue that the fine-tuning regime,...	297	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21923v1	The Sample Complexity of Multicalibration	arxiv	https://arxiv.org/abs/2604.21923v1	Natalie Collina, Jiuyao Lu, Georgy Noarov, Aaron Roth	2026-04-23	We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i.i.d. samples from an unknown distribution and must output a (possibly randomized) predictor whose population multicalibration error, measured by Expected Calibration Error (ECE), is at most $\varepsilon$ with respe...	0.5	null	null	2,026	4	23	17	2	3	[]	0	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.044444	0.577778	neutral	We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i. i	108	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21914v1	VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis	arxiv	https://arxiv.org/abs/2604.21914v1	Songen Gu, Yuhang Zheng, Weize Li, Yupeng Zheng, Yating Feng, Xiang Li, Yilun Chen, Pengfei Li, Wenchao Ding	2026-04-23	Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to camera viewpoint changes when training with a fixed camera. In this paper, we propose VistaBot, a novel framework that integrates feed-forw...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "deep-learning", "generative-ai", "time-series"]	4	["attention"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.053994	0.38224	neutral	Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to camera viewpoint changes when training with a fixed camera. In this paper, we propose VistaBot, a novel framework that...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21911v1	When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs	arxiv	https://arxiv.org/abs/2604.21911v1	Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor, Alasdair Newson, Matthieu Cord	2026-04-23	Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the visual input. Prior work has attributed hallucinations in LVLMs to factors such as limitations of the vision backbone or the dominance of the...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "graph-learning", "optimization", "transfer-learning"]	6	["optimization", "fine-tuning"]	2	{"abstract_length_score": 1.0, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.55}	preprint	true	false	0.119898	0.461224	neutral	Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i. e. , outputs that are not grounded in the visual input	193	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21909v1	Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision	arxiv	https://arxiv.org/abs/2604.21909v1	Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin	2026-04-23	Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom, and in which direction. We show that these directional confusions reveal distinct inductive biases that are invisibl...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "reinforcement-learning", "generative-ai"]	3	["classification"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	-0.022321	0.352679	neutral	Humans and modern vision models can reach similar classification accuracy while making systematically different kinds of mistakes - differing not in how often they err, but in who gets mistaken for whom, and in which direction. We show that these directional confusions reveal distinct inductive...	298	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21905v1	Low-Rank Adaptation Redux for Large Models	arxiv	https://arxiv.org/abs/2604.21905v1	Bingcong Li, Yilang Zhang, Georgios B. Giannakis	2026-04-23	Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive whi...	0.5	null	null	2,026	4	23	17	2	3	["deep-learning", "optimization", "federated-learning", "transfer-learning"]	4	["deep learning", "optimization", "fine-tuning"]	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.090476	0.180952	neutral	Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants,...	300	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21903v1	A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models	arxiv	https://arxiv.org/abs/2604.21903v1	Max Defez, Filippo Quarenghi, Mathieu Vrac, Stephan Mandt, Tom Beucler	2026-04-23	Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio between the low-resolution...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "deep-learning", "generative-ai", "time-series", "auto-ml"]	6	["attention", "hyperparameter"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	-0.018824	0.331473	neutral	Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21896v1	Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models	arxiv	https://arxiv.org/abs/2604.21896v1	Chee Wei Tan, Yuchen Wang, Shangxin Guo	2026-04-23	This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create, customize, and deploy L...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "generative-ai", "transfer-learning"]	5	["llm", "reinforcement learning", "fine-tuning"]	3	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.042267	0.396517	neutral	This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create,...	299	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21893v1	Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors	arxiv	https://arxiv.org/abs/2604.21893v1	Sherly Alfonso-Sánchez, Cristián Bravo, Kristina G. Stankova	2026-04-23	Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative data sources can be in...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "deep-learning", "graph-learning", "optimization"]	5	["neural network", "transformer", "convolutional", "embedding"]	4	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.021978	0.241229	neutral	Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constraining how this information can be incorporated and evaluated in claim-frequency models. This study examines how geographic information from alternative...	300	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21891v1	A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment	arxiv	https://arxiv.org/abs/2604.21891v1	Muhy Eddin Za'ter, Anna Van Boven, Bri-Mathias Hodge, Kyri Baker	2026-04-23	Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly and heavily governed b...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "deep-learning"]	2	["deep learning", "transformer", "attention", "self-attention"]	4	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.033971	0.509414	neutral	Maintaining instantaneous balance between electricity supply and demand is critical for reliability and grid instability. System operators achieve this through solving the task of Unit Commitment (UC),ca high dimensional large-scale Mixed-integer Linear Programming (MILP) problem that is strictly...	300	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21890v1	EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents	arxiv	https://arxiv.org/abs/2604.21890v1	Praval Sharma, Ashok Samal, Leen-Kiat Soh, Deepti Joshi	2026-04-23	Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as informed decision-making in emergencies. Therefore, it is necessary to develop automated event extraction approaches. However, existing datasets for algorithm development...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "graph-learning", "recommendation", "transfer-learning"]	5	["pre-trained"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.106122	0.480867	neutral	Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as informed decision-making in emergencies. Therefore, it is necessary to develop automated event extraction approaches	266	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21889v1	TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale	arxiv	https://arxiv.org/abs/2604.21889v1	Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di, Rui Wang	2026-04-23	Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionabl...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning", "graph-learning", "anomaly-detection"]	5	["llm", "clustering"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.09369	0.536964	neutral	Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring,...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	rust	Unknown	false	cold	null	null
arxiv_2604.21886v1	The Dyson Minds 2025 Workshop: SETI around Black Holes	arxiv	https://arxiv.org/abs/2604.21886v1	Olivia Curtis, Van Hunter Adams, Daniel Angerhausen, Joseph Bates, Anamaria Berea, Steven J. Dick, Martin Elvis, Sunil P. Khatri, Richard Linares, Manushaqe Muco et al.	2026-04-23	The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds" -- large-scale post-...	0.5	null	null	2,026	4	23	17	2	3	["reinforcement-learning", "generative-ai", "time-series", "recommendation", "interpretability", "federated-learning", "anomaly-detection"]	7	[]	0	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 1.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.55}	preprint	false	true	0.081772	0.500313	neutral	The Dyson Minds 2025 Workshop, held at the Center for Brains, Minds & Machines at MIT and organized by Penn State, MIT, and The Ultraintelligence Foundation, brought together researchers in astrophysics, engineering, artificial intelligence, computer science, and philosophy to examine "Dyson Minds"...	302	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21885v1	A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents	arxiv	https://arxiv.org/abs/2604.21885v1	Praval Sharma	2026-04-23	Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and thus rarely generaliz...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "deep-learning", "graph-learning"]	4	["attention", "llm"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.119505	0.671703	neutral	Event extraction is essential for event understanding and analysis. It supports tasks such as document summarization and decision-making in emergency scenarios. However, existing event extraction approaches have limitations: (1) closed-domain algorithms are restricted to predefined event types and...	301	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	swift	Unknown	false	cold	null	null
arxiv_2604.21879v1	Addressing Image Authenticity When Cameras Use Generative AI	arxiv	https://arxiv.org/abs/2604.21879v1	Umar Masud, Abhijith Punnappurath, Luxi Zhao, David B. Lindell, Michael S. Brown	2026-04-23	The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by our cameras are considered authentic and faithful. However, with the increasing integration of deep-learning modules into...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "graph-learning", "generative-ai"]	4	["generative"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.083135	0.572619	neutral	The ability of generative AI (GenAI) methods to photorealistically alter camera images has raised awareness about the authenticity of images shared online. Interestingly, images captured directly by our cameras are considered authentic and faithful. However, with the increasing integration of...	296	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21871v1	Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions	arxiv	https://arxiv.org/abs/2604.21871v1	Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti, Wenchao Dong, Jaehong Kim, Meeyoung Cha	2026-04-23	Human moral judgment is context-dependent and modulated by interpersonal relationships. As large language models (LLMs) increasingly function as decision-support systems, determining whether they encode these social nuances is critical. We characterize machine behavior using the Whistleblower's Dilemma by varying two e...	0.5	null	null	2,026	4	23	17	2	3	["computer-vision", "nlp", "reinforcement-learning"]	3	["llm"]	1	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.071998	0.398706	neutral	Human moral judgment is context-dependent and modulated by interpersonal relationships. As large language models (LLMs) increasingly function as decision-support systems, determining whether they encode these social nuances is critical. We characterize machine behavior using the Whistleblower's...	298	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
arxiv_2604.21870v1	Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning	arxiv	https://arxiv.org/abs/2604.21870v1	Kaitlin Gili, Mainak Nistala, Kristen Wendell, Michael C. Hughes	2026-04-23	STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segments with a high concentration of such reasoning. We offer a solution in the form of an interpretable ...	0.5	null	null	2,026	4	23	17	2	3	["nlp", "reinforcement-learning", "recommendation", "interpretability"]	4	["machine learning", "interpretability"]	2	{"abstract_length_score": 1.0, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4}	preprint	false	false	0.204286	0.489762	neutral	STEM education researchers are often interested in identifying moments of students' mechanistic reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segments with a high concentration of such reasoning. We offer a solution in the form...	302	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Unknown Author	Unknown	Unknown	false	cold	null	null
github_Shivangrmittal_Movie-recommendation-system	Movie-recommendation-system	github	https://github.com/Shivangrmittal/Movie-recommendation-system	Shivangrmittal	2026-04-26	Machine Learning basic project which recommend five similar movies for a selected movie # Movie-recommendation-system Machine Learning basic project which recommend five similar movies for a selected movie ...	0.471429	null	null	2,026	4	26	17	2	0	["recommendation"]	1	["machine learning"]	1	{"abstract_length_score": 0.21, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.24200000000000002}	repository	false	false	0	0.2625	neutral	Machine Learning basic project which recommend five similar movies for a selected movie # Movie-recommendation-system Machine Learning basic project which recommend five similar movies for a selected movie	206	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["recommendation"], "shared_keywords": ["machine learning"], "shared_tags": []}]	1
github_Aalam-data-science_Apple_Stock_Price_Prediction	Apple_Stock_Price_Prediction	github	https://github.com/Aalam-data-science/Apple_Stock_Price_Prediction	Aalam-data-science	2026-04-26	"A Machine Learning project that predicts Apple stock prices using Linear Regression and historical data from Yahoo Finance." # Stock Price Prediction using Machine Learning 📈 This project predicts the future stock prices of Apple Inc. (AAPL) using historical data and Linear Regression. ## 🚀 Overview - **Data Sour...	0.471429	null	null	2,026	4	26	17	2	0	["computer-vision"]	1	["machine learning", "regression"]	2	{"abstract_length_score": 0.63, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.326}	repository	false	false	-0.05	0.075	neutral	"A Machine Learning project that predicts Apple stock prices using Linear Regression and historical data from Yahoo Finance. " # Stock Price Prediction using Machine Learning 📈 This project predicts the future stock prices of Apple Inc. (AAPL) using historical data and Linear Regression	289	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Jupyter Notebook	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["computer-vision"], "shared_keywords": ["machine learning"], "shared_tags": []}, {"id": "github_LeeviKamarainen_LeeviKamarainen", "title": "LeeviKamarainen", "similarity_score"...	5
github_mohamedyoussef-cloud_malicious-web-request-detection	malicious-web-request-detection	github	https://github.com/mohamedyoussef-cloud/malicious-web-request-detection	mohamedyoussef-cloud	2026-04-26	Machine learning notebook for malicious web request detection with leakage-aware evaluation. # Malicious Web Request Detection This repository contains an academic machine learning project for detecting malicious web requests. The project includes: - A leakage-aware training notebook - A Streamlit dashboard - A liv...	0.471429	null	null	2,026	4	26	17	2	0	["anomaly-detection"]	1	["machine learning"]	1	{"abstract_length_score": 0.597, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.4694}	repository	true	false	0.116234	0.278571	neutral	Machine learning notebook for malicious web request detection with leakage-aware evaluation. # Malicious Web Request Detection This repository contains an academic machine learning project for detecting malicious web requests. The project includes: - A leakage-aware training notebook - A...	293	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Jupyter Notebook	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["anomaly-detection"], "shared_keywords": ["machine learning"], "shared_tags": []}, {"id": "github_JigyasaRana_PYTHON_PBL", "title": "PYTHON_PBL", "similarity_score": 5, "shared...	4
github_Abdelhamid27_Ai-Projects-	Ai-Projects-	github	https://github.com/Abdelhamid27/Ai-Projects-	Abdelhamid27	2026-04-26	None Bank Customer Churn Prediction 🚀📌 Project OverviewThis project aims to predict Customer Churn for a banking institution. By analyzing customer demographics and financial behavior, we developed a machine learning system that identifies customers who are likely to leave the bank. This allows the bank to take proa...	0.471429	null	null	2,026	4	26	17	2	0	["nlp", "graph-learning"]	2	["machine learning"]	1	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	0.033333	0.433333	neutral	None Bank Customer Churn Prediction 🚀📌 Project OverviewThis project aims to predict Customer Churn for a banking institution. By analyzing customer demographics and financial behavior, we developed a machine learning system that identifies customers who are likely to leave the bank. This allows...	299	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 8, "shared_subfields": ["nlp", "graph-learning"], "shared_keywords": ["machine learning"], "shared_tags": []}, {"id": "github_CodeBonker_Agri-World", "title": "Agri-World", "similarity_score": 5, "sha...	5
github_Sus306_PresentacionVC	PresentacionVC	github	https://github.com/Sus306/PresentacionVC	Sus306	2026-04-26	None # Memoria del Proyecto: Visión por Computador en la Industria ## De las técnicas deterministas clásicas al Deep Learning ### Datos del Documento - Asignatura: Visión por Computador - Universidad: Universitat Rovira i Virgili (URV) - Autores: Marina Oteiza Álvarez, Susana Triviño Nortes, Angelina Ru...	0.471429	null	null	2,026	4	26	17	2	0	["deep-learning"]	1	["deep learning"]	1	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.45180000000000003}	repository	true	false	0	0.4	neutral	None # Memoria del Proyecto: Visión por Computador en la Industria ## De las técnicas deterministas clásicas al Deep Learning ### Datos del Documento - Asignatura: Visión por Computador - Universidad: Universitat Rovira i Virgili (URV) - Autores: Marina Oteiza Álvarez, Susana...	295	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "gdelt1", "title": "News about machine learning OR ML OR deep learning", "similarity_score": 5, "shared_subfields": ["deep-learning"], "shared_keywords": ["deep learning"], "shared_tags": []}, {"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": ...	3
github_airdmhund1_predictive-maintenance-vibration	predictive-maintenance-vibration	github	https://github.com/airdmhund1/predictive-maintenance-vibration	airdmhund1	2026-04-25	AI agent-orchestrated predictive maintenance via vibration analysis # Predictive Maintenance via Vibration Analysis A full-stack predictive maintenance system that detects electrical equipment faults through vibration analysis. An ESP32 with an ADXL355 accelerometer samples at 1000 Hz and publishes data over MQTT to ...	0.470607	null	null	2,026	4	25	17	2	1	["computer-vision", "nlp", "reinforcement-learning", "federated-learning", "anomaly-detection"]	5	["machine learning", "llm", "anomaly detection"]	3	{"abstract_length_score": 0.572, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.4638520547945205}	repository	true	false	0	0	neutral	AI agent-orchestrated predictive maintenance via vibration analysis # Predictive Maintenance via Vibration Analysis A full-stack predictive maintenance system that detects electrical equipment faults through vibration analysis. An ESP32 with an ADXL355 accelerometer samples at 1000 Hz and...	294	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 21, "shared_subfields": ["federated-learning", "nlp", "computer-vision", "anomaly-detection", "reinforcement-learning"], "shared_keywords": ["machine learning", "anomaly detection", "llm"], "shared_ta...	5
github_aditdhall_MARS-ANS	MARS-ANS	github	https://github.com/aditdhall/MARS-ANS	aditdhall	2026-04-22	None # ANS: AI Autonomous Navigation System for Mars Rovers AI 710 — Principles of Machine Learning Rochester Institute of Technology — Spring 2026 Team: Adit Dhall · Matthew Landon · Thejas Nagesh Gowda --- ## Overview ANS is an end-to-end autonomous navigation system for Mars rovers during communicat...	0.468141	null	null	2,026	4	22	17	2	4	["reinforcement-learning"]	1	["machine learning", "reinforcement learning"]	2	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.989041095890411, "overall_quality_score": 0.2996082191780822}	repository	false	false	0.182143	0.653571	neutral	None # ANS: AI Autonomous Navigation System for Mars Rovers AI 710 — Principles of Machine Learning Rochester Institute of Technology — Spring 2026 Team: Adit Dhall · Matthew Landon · Thejas Nagesh Gowda --- ## Overview ANS is an end-to-end autonomous navigation system for Mars...	298	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Jupyter Notebook	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 7, "shared_subfields": ["reinforcement-learning"], "shared_keywords": ["machine learning", "reinforcement learning"], "shared_tags": []}, {"id": "github_CodeBonker_Agri-World", "title": "Agri-World", ...	5
github_Philopateer-Nabil_featherstore	featherstore	github	https://github.com/Philopateer-Nabil/featherstore	Philopateer-Nabil	2026-04-26	None # Feature Store A lightweight, production-quality machine-learning feature store that runs entirely on your laptop. It demonstrates the production ML systems thinking that goes into real feature platforms (Feast, Tecton, SageMaker FS) — point-in-time correctness, lineage, versioning, online/offline serving, ...	0.357143	null	null	2,026	4	26	17	2	0	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.45180000000000003}	repository	true	false	-0.045982	0.384821	neutral	None # Feature Store A lightweight, production-quality machine-learning feature store that runs entirely on your laptop. It demonstrates the production ML systems thinking that goes into real feature platforms (Feast, Tecton, SageMaker FS) — point-in-time correctness, lineage, versioning,...	298	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	MIT License	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_MinasKaragiorgi_private-markets-validator", "title": "private-markets-validator", "similarity_s...	5
github_CodeBonker_Agri-World	Agri-World	github	https://github.com/CodeBonker/Agri-World	CodeBonker	2026-04-26	None # CropSeek LLM 🌾 ### AI-Powered Agriculture Decision Support System CropSeek LLM is a production-grade backend API that helps farmers make data-driven decisions using a combination of Machine Learning, Deep Learning, Large Language Models (LLMs), and Live Weather Intelligence. It is not a simp...	0.357143	null	null	2,026	4	26	17	2	0	["nlp", "reinforcement-learning", "deep-learning"]	3	["machine learning", "deep learning", "llm"]	3	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3018}	repository	false	false	0.250108	0.522619	neutral	None # CropSeek LLM 🌾 ### AI-Powered Agriculture Decision Support System CropSeek LLM is a production-grade backend API that helps farmers make data-driven decisions using a combination of Machine Learning, Deep Learning, Large Language Models (LLMs), and **Live Weather...	288	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 15, "shared_subfields": ["nlp", "deep-learning", "reinforcement-learning"], "shared_keywords": ["deep learning", "machine learning", "llm"], "shared_tags": []}, {"id": "github_LeeviKamarainen_LeeviKam...	5
github_LeeviKamarainen_LeeviKamarainen	LeeviKamarainen	github	https://github.com/LeeviKamarainen/LeeviKamarainen	LeeviKamarainen	2026-04-26	None # Leevi Kämäräinen AI Engineer · M.Sc. Computational Engineering · LUT University · Finland --- ### AI / ML `LLM Agents` `Multi-Agent Systems` `RAG` `Context Engineering` `Prompt Engineering` `Azure OpenAI` `Computer Vision` `Machine Learning` ### Languages & Frameworks \| \| \| \| \| \| \| \| \|---\|---\|---\|---\|--...	0.357143	null	null	2,026	4	26	17	2	0	["computer-vision", "nlp", "reinforcement-learning"]	3	["machine learning", "llm", "computer vision"]	3	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.45180000000000003}	repository	true	false	0	0	neutral	None # Leevi Kämäräinen AI Engineer · M. Sc. Computational Engineering · LUT University · Finland --- ### AI / ML `LLM Agents` `Multi-Agent Systems` `RAG` `Context Engineering` `Prompt Engineering` `Azure OpenAI` `Computer Vision` `Machine Learning` ### Languages & Frameworks \| \| \| \| \| \| \|...	302	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 15, "shared_subfields": ["nlp", "computer-vision", "reinforcement-learning"], "shared_keywords": ["machine learning", "llm", "computer vision"], "shared_tags": []}, {"id": "github_airdmhund1_predictiv...	5
github_kechengzhang28_kechengzhang28	kechengzhang28	github	https://github.com/kechengzhang28/kechengzhang28	kechengzhang28	2026-04-25	None ## 👋 Hi there, I'm [Kecheng Zhang](https://github.com/kechengzhang28) ### About Me My focus is on Artificial Intelligence and Machine Learning. As a continuous explorer of technology, I am passionate about tracking cutting-edge concepts and dedicated to translating innovation into interesting and beneficial pra...	0.356321	null	null	2,026	4	25	17	2	1	["graph-learning"]	1	["machine learning"]	1	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.45180000000000003}	repository	true	false	-0.0625	0.6125	neutral	None ## 👋 Hi there, I'm [Kecheng Zhang](https://github. com/kechengzhang28) ### About Me My focus is on Artificial Intelligence and Machine Learning. As a continuous explorer of technology, I am passionate about tracking cutting-edge concepts and dedicated to translating innovation into...	292	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 5, "shared_subfields": ["graph-learning"], "shared_keywords": ["machine learning"], "shared_tags": []}, {"id": "github_Abdelhamid27_Ai-Projects-", "title": "Ai-Projects-", "similarity_score": 5, "shar...	2
github_MinasKaragiorgi_private-markets-validator	private-markets-validator	github	https://github.com/MinasKaragiorgi/private-markets-validator	MinasKaragiorgi	2026-04-26	Data extraction and validation tool for Private Markets funds # Private Markets Fund Data Validator 📊 > A Python tool for extracting, validating, and managing fund data from documents - supporting Private Markets Quantitative Due Diligence workflows. [![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)](...	0.3	null	null	2,026	4	26	17	2	0	["reinforcement-learning", "federated-learning"]	2	[]	0	{"abstract_length_score": 0.566, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.31320000000000003}	repository	false	false	0.010938	0.35	neutral	Data extraction and validation tool for Private Markets funds # Private Markets Fund Data Validator 📊 > A Python tool for extracting, validating, and managing fund data from documents - supporting Private Markets Quantitative Due Diligence workflows. [. [Python](https://img	276	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	MIT License	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 6, "shared_subfields": ["federated-learning", "reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_fabioc-aloha_fabioc-aloha", "title": "fabioc-aloha", "similarity_scor...	5
github_pritam-09-ops_me	me	github	https://github.com/pritam-09-ops/me	pritam-09-ops	2026-04-26	just me <!-- Header Section --> <div align="center"> <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&customColorList=1,2,3&height=300&section=header&text=Hi%20👋,%20I'm%20Pritam&fontSize=70&animation=fadeIn&fontAlignY=38&desc=Energy%20Science%20Student%20@%20IIT%20Bombay%20%7C%20ML%20%26%2...	0.3	null	null	2,026	4	26	17	2	0	["nlp", "reinforcement-learning", "optimization"]	3	[]	0	{"abstract_length_score": 0.512, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.3024}	repository	false	false	-0.1	0.1	neutral	just me <. -- Header Section --> <div align="center"> <img src="https://capsule-render. vercel	97	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 9, "shared_subfields": ["optimization", "nlp", "reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_CodeBonker_Agri-World", "title": "Agri-World", "similarity_score": 6...	5
github_fabioc-aloha_fabioc-aloha	fabioc-aloha	github	https://github.com/fabioc-aloha/fabioc-aloha	fabioc-aloha	2026-04-25	Self-updating GitHub profile portfolio and the public proof that the Alex cognitive architecture works end to end without a human in the loop. A multi-model pipeline classifies, clusters, narrates, and stitches a hundred-plus repositories into a single composed canvas every morning, with an independent Responsible-AI r...	0.299178	null	null	2,026	4	25	17	2	1	["computer-vision", "reinforcement-learning", "generative-ai", "federated-learning"]	4	[]	0	{"abstract_length_score": 0.85, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.52}	repository	true	false	0.032653	0.172279	neutral	Self-updating GitHub profile portfolio and the public proof that the Alex cognitive architecture works end to end without a human in the loop. A multi-model pipeline classifies, clusters, narrates, and stitches a hundred-plus repositories into a single composed canvas every morning, with an...	294	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Unknown	MIT License	true	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 12, "shared_subfields": ["federated-learning", "generative-ai", "computer-vision", "reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_airdmhund1_predictive-maintenanc...	5
github_Maari7_IoT-Device-Intrusion-Detection	IoT-Device-Intrusion-Detection	github	https://github.com/Maari7/IoT-Device-Intrusion-Detection	Maari7	2026-04-25	None # HX-IDL Proj Hybrid Explainable Intrusion Detection for IoT Botnet Traffic ## 1) Executive Summary This project implements an end-to-end, research-oriented and production-aware Intrusion Detection System (IDS) pipeline for IoT botnet detection using the N-BaIoT style Doorbell device traffic (benign + Gafgyt +...	0.299178	null	null	2,026	4	25	17	2	1	["interpretability", "anomaly-detection"]	2	["supervised"]	1	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30125205479452055}	repository	false	false	0	0	neutral	None # HX-IDL Proj Hybrid Explainable Intrusion Detection for IoT Botnet Traffic ## 1) Executive Summary This project implements an end-to-end, research-oriented and production-aware Intrusion Detection System (IDS) pipeline for IoT botnet detection using the N-BaIoT style Doorbell device...	296	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 8, "shared_subfields": ["interpretability", "anomaly-detection"], "shared_keywords": ["supervised"], "shared_tags": []}, {"id": "github_mohamedyoussef-cloud_malicious-web-request-detection", "title": ...	4
github_dharmendra-developer_dharmendra-developer	dharmendra-developer	github	https://github.com/dharmendra-developer/dharmendra-developer	dharmendra-developer	2026-04-25	None <h1 align="center">Hi 👋, I'm Dharmendra Kumar Singh</h1> <img src="https://user-images.githubusercontent.com/74038190/212284100-561aa473-3905-4a80-b561-0d28506553ee.gif" width="100%"> <h3 align="center"> 🚀 Python Developer \| AI Enthusiast \| Future Software Engineer </h3> <p align="center"> <img src="https://...	0.299178	null	null	2,026	4	25	17	2	1	["computer-vision"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.4512520547945206}	repository	true	false	-0.075	0.10625	neutral	None <h1 align="center">Hi 👋, I'm Dharmendra Kumar Singh</h1> <img src="https://user-images. githubusercontent. com/74038190/212284100-561aa473-3905-4a80-b561-0d28506553ee	172	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["computer-vision"], "shared_keywords": [], "shared_tags": []}, {"id": "github_fabioc-aloha_fabioc-aloha", "title": "fabioc-aloha", "similarity_score": 3, "shared_subfields": ["...	5
github_nim-jpg_Focus-AI	Focus-AI	github	https://github.com/nim-jpg/Focus-AI	nim-jpg	2026-04-25	None # Focus3 Anti-procrastination & life prioritization app. Helps neurodivergent and overwhelmed users surface the three things that matter today across seven life themes (work, fitness, finance, diet, medication, development, household, personal), with Google Calendar integration and printable weekly planner s...	0.299178	null	null	2,026	4	25	17	2	1	["computer-vision", "reinforcement-learning"]	2	[]	0	{"abstract_length_score": 0.509, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.9972602739726028, "overall_quality_score": 0.30125205479452055}	repository	false	false	-0.051852	0.196296	neutral	None # Focus3 Anti-procrastination & life prioritization app. Helps neurodivergent and overwhelmed users surface the three things that matter today across seven life themes (work, fitness, finance, diet, medication, development, household, personal), with Google Calendar integration and...	296	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	TypeScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 6, "shared_subfields": ["computer-vision", "reinforcement-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_fabioc-aloha_fabioc-aloha", "title": "fabioc-aloha", "similarity_score":...	5
github_Poojasaravanan-16_urbanstressdetect	urbanstressdetect	github	https://github.com/Poojasaravanan-16/urbanstressdetect	Poojasaravanan-16	2026-04-22	None # 🏙️ Urban Micro-Stress Index Predictor\n\n[![CI/CD Pipeline](https://github.com/your-username/urban-stress-ml/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/your-username/urban-stress-ml/actions/workflows/ci-cd.yml)\n[![Security Scan](https://img.shields.io/badge/security-scanned-green.svg)](https:/...	0.296712	null	null	2,026	4	22	17	2	4	["federated-learning"]	1	[]	0	{"abstract_length_score": 0.509, "has_code_score": 1.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.989041095890411, "overall_quality_score": 0.44960821917808225}	repository	true	false	0	0	neutral	None # 🏙️ Urban Micro-Stress Index Predictor\n\n[. [CI/CD Pipeline](https://github. com/your-username/urban-stress-ml/actions/workflows/ci-cd	142	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	JavaScript	Unknown	false	cold	[{"id": "arxiv_2604.21931v1", "title": "Seeing Fast and Slow: Learning the Flow of Time in Videos", "similarity_score": 3, "shared_subfields": ["federated-learning"], "shared_keywords": [], "shared_tags": []}, {"id": "github_MinasKaragiorgi_private-markets-validator", "title": "private-markets-validator", "similarity_s...	5
medium_	DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling	medium	https://towardsdatascience.com/diy-ai-ml-solving-the-multi-armed-bandit-problem-with-thompson-sampling/	Jacob Ingle	2026-04-21	How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example The post DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling appeared first on Towards Data Science.	0.29589	null	null	2,026	4	21	17	2	5	[]	0	[]	0	{"abstract_length_score": 0.253, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 1.0, "overall_quality_score": 0.25060000000000004}	unknown	false	false	0.425	0.666667	positive	How you can build your own Thompson Sampling Algorithm object in Python and apply it to a hypothetical yet real-life example The post DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling appeared first on Towards Data Science	252	{"completeness_score": 92.5, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 96.25, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	Jacob Ingle	python	Unknown	false	cold	[]	0
github_yearzen1_SimpleMachineLearning	SimpleMachineLearning	github	https://github.com/yearzen1/SimpleMachineLearning	yearzen1	2026-04-21	基于 tsoding daily 的机器学习视频的 Python 版本 # 本项目是 tsoding daily的C语言实现机器学习的python 实现版本 ## chapter 1 - [【【中文字幕】ML in C - 第 1 集：机器学习入门 \| Tsoding Daily】](https://www.bilibili.com/video/BV1BmoFBzEht/?share_source=copy_web&vd_source=738fd7d41d7fd28ec6113ce945300796) - [实现一个y = 2x 模型](./chapter1/y=2x.py) - [实现一个or模型](./chapter1/...	0.29589	null	null	2,026	4	21	17	2	5	[]	0	[]	0	{"abstract_length_score": 0.54, "has_code_score": 0.0, "has_doi_score": 0.0, "engagement_score": 0.0, "recency_score": 0.989041095890411, "overall_quality_score": 0.3058082191780822}	repository	false	false	0	0	neutral	基于 tsoding daily 的机器学习视频的 Python 版本 # 本项目是 tsoding daily的C语言实现机器学习的python 实现版本 ## chapter 1 - [【【中文字幕】ML in C - 第 1 集：机器学习入门 \| Tsoding Daily】](https://www. bilibili. com/video/BV1BmoFBzEht/	193	{"completeness_score": 85.0, "consistency_score": 100, "validity_score": 100, "overall_quality_score": 92.5, "completeness_issues": 0, "consistency_issues": [], "validity_issues": []}	GitHub User	Python	MIT License	true	cold	[]	0

Research Collector Dataset

This dataset contains research results aggregated from multiple sources by the Research-Collector tool. Each item is enriched with comprehensive metadata, ML subfield classifications, quality scores, and temporal features.

Dataset Details

Topic: machine learning OR ML OR deep learning
Time Range: 2026-04-19T16:58:30.781464 to 2026-04-26T16:58:30.781471
Sources: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
Total Items: 41
Exported At: 2026-04-26T16:58:54.043350

Dataset Structure

Core Fields

id: Unique identifier
title: Title of the research item
source: Source platform (e.g., pubmed, arxiv, github, reddit, stackoverflow)
url: URL to original content
author: Author(s)
published_date: Publication date (ISO 8601 format)
citations: Number of citations (if available)
upvotes: Number of upvotes (if available)
downloads: Number of downloads (if available)
comments: Number of comments (if available)
content: Content/abstract/description
score: Relevance score

Enriched Metadata Fields

metadata_year: Publication year
metadata_month: Publication month
metadata_day: Publication day
metadata_week: Week of year
metadata_quarter: Quarter of year
metadata_days_since: Days since publication
metadata_ml_subfields: ML subfield classifications (JSON array)
metadata_subfield_count: Number of ML subfields
metadata_keywords: Extracted keywords (JSON array)
metadata_keyword_count: Number of keywords
metadata_quality_scores: Quality score metrics (JSON dict)
metadata_content_type: Content type (paper, preprint, repository, discussion, qa, news)
metadata_has_code: Whether item contains code
metadata_has_doi: Whether item has DOI
metadata_sentiment_polarity: Sentiment polarity score (-1 to 1)
metadata_sentiment_subjectivity: Sentiment subjectivity score (0 to 1)
metadata_sentiment_category: Sentiment category (positive, negative, neutral)
metadata_summary: Automatic summary of content (extractive)
metadata_summary_length: Length of summary in characters
metadata_data_quality: Data quality metrics (JSON dict)
- completeness_score: Field completeness percentage (0-100)
- consistency_score: Internal consistency score (0-100)
- validity_score: Data validity score (0-100)
- overall_quality_score: Overall data quality score (0-100)
metadata_trending_score: Engagement velocity score
metadata_trending_category: Trending category (hot, warm, cool, cold)
metadata_engagement_score: Raw engagement score
metadata_related_items: Related items with similarity scores (JSON array)
metadata_related_count: Number of related items

Source-Specific Metadata

PubMed: metadata_journal, metadata_doi, metadata_mesh_terms, metadata_publication_types, metadata_abstract_length
arXiv: metadata_arxiv_id, metadata_primary_category, metadata_categories, metadata_journal_ref
GitHub: metadata_stars, metadata_forks, metadata_language, metadata_license, metadata_topics, metadata_has_readme
Reddit: metadata_subreddit, metadata_link_flair_text, metadata_upvote_ratio, metadata_total_awards, metadata_is_gilded
Stack Overflow: metadata_tags, metadata_answer_count, metadata_has_accepted_answer, metadata_view_count, metadata_owner_reputation
Semantic Scholar: metadata_citation_count, metadata_influential_citation_count, metadata_fields_of_study, metadata_has_open_access
Medium: metadata_author, metadata_publication, metadata_read_time, metadata_claps
Kaggle: metadata_votes, metadata_usability_rating, metadata_file_count

Usage Examples

from datasets import load_dataset

# Load dataset
dataset = load_dataset("nellaivijay/ml-research-daily")
train_data = dataset["train"]

# Filter by source
pubmed_items = train_data.filter(lambda x: x["source"] == "pubmed")
github_items = train_data.filter(lambda x: x["source"] == "github")

# Filter by content type
papers = train_data.filter(lambda x: x.get("metadata_content_type") == "paper")
repositories = train_data.filter(lambda x: x.get("metadata_content_type") == "repository")

# Filter by ML subfield
cv_papers = train_data.filter(lambda x: "computer-vision" in x.get("metadata_ml_subfields", []))

# Filter by quality
high_quality = train_data.filter(lambda x: x.get("metadata_quality_scores", {}).get("overall_quality_score", 0) > 0.7)

# Sort by score
sorted_items = train_data.sort("score", reverse=True)

# Filter by date
recent_items = train_data.filter(lambda x: x.get("metadata_days_since", 999) < 30)

# Filter by trending category
trending_items = train_data.filter(lambda x: x.get("metadata_trending_category") == "hot")

# Filter by data quality
high_quality = train_data.filter(lambda x: x.get("metadata_data_quality", {}).get("overall_quality_score", 0) > 0.7)

# Filter by sentiment
positive_items = train_data.filter(lambda x: x.get("metadata_sentiment_category") == "positive")

# Get related items
item_with_related = train_data[0]
related_items = item_with_related.get("metadata_related_items", [])

Data Quality Features

Standardized Dates: All dates normalized to ISO 8601 format
ML Subfield Classification: Automatic classification into 15+ ML subfields
Quality Scoring: Multi-dimensional quality assessment (abstract length, code availability, DOI, engagement, recency)
Temporal Features: Year, month, week, quarter, days since publication
Keyword Extraction: Automatic extraction of technical keywords
Content Type Detection: Automatic classification of item type
Sentiment Analysis: Sentiment polarity, subjectivity, and category classification
Automatic Summarization: Extractive summaries for quick content overview
Data Quality Metrics: Completeness, consistency, and validity scores for each item
Trending Metrics: Engagement velocity analysis with trending categories
Cross-References: Related item detection based on shared subfields, keywords, and tags
Fuzzy Deduplication: Intelligent duplicate detection with metadata merging
Metadata Completeness: Fallback logic to infer missing metadata fields

Data Sources

This dataset aggregates research from:

Academic: PubMed, arXiv, Semantic Scholar, Crossref, Papers with Code
Professional: GitHub, Stack Overflow, Kaggle
Social: Reddit, Hacker News
News: GDELT
Blogs: Medium, Towards Data Science

Limitations

Data is limited to the specified time range
Some sources may have rate limits or API restrictions
Citation counts may vary between sources
ML subfield classification is based on keyword matching and may not be perfect

Source

Generated by Research-Collector, an educational multi-source research aggregation tool.

License

MIT License

Citation

If you use this dataset, please cite the repository URL: https://huggingface.co/datasets/nellaivijay/ml-research-daily

Downloads last month: 57

Number of rows:

Total file size:

81.5 kB