(Solved): Create a class named "Paper". During the instantiation of this class, you must provide the path of ...

Create a class named "Paper". During the instantiation of this class, you must provide the path of the "paper list.txt" as a parameter. The job of the initialization ( init ) function is to read all the paper details from the provided file and store it in a suitable data structure. You can use any built-in dala structure of python except third party libraries such as Pandas. One possible data structure can be a list of dictionaries where every single dictionary will hold all the infermation (Title, Authors. Comments, and so on) for a single paper in a key: value pair and the list will hold all the 300 dictionaries This data siructure Must be saved as an instance variable, Let s call it "paper hist". Title Authors Comments Paper_id Subjects Conformal Prediction is Robust to Label Noise Bat-Sheva Einbinder, Stephen Bates, Anastasios \( N \). Angelopoulos, Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results Lukas Christ, Shahin Amiriparie Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets Deborah Pereg, Martin Villiger, Brett E A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks Chau Ph B2B Advertising: Joint Dynamic Scoring of Account and Users Atanu R. Sinha, Gautam Choudhary, Mansi Agarwal Score Modeling for Simulation-based Inference Tomas Geffner, George Papamakarios, Andriy Mnih N/A arXiv:2 Online Subset Selection using \$as-Core with no Augmented Regret Sourav Sahoo, Samrat Mukhopadhyay, Abhishek Sin Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data Yuqicheng Zhu, Class-Imbalanced Complementary-Label Learning via Weighted Loss Meng Wei, Yong Zhou, Zhongnian Li, Xinzheng Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure Shaohua Fan, Xiao Wang, Yanhu M Securing Federated Learning against Overwhelming Collusive Attackers Priyesh Ranjan, Ashish Gupta, Federico Momentum Gradient Descent Federated Learning with Local Differential Privacy Mengde Han, Tianqing Zhu, Wanleig Efficient block contrastive learning via parameter-free meta-node approximation Marco Anisetti, Claudio A. Ardag Toward Certification of Machine-Learning Systems for Low Criticality AIrborne Applications K. Dmitriev, SoftreeMax: Policy Gradient with Tree Search \( 6 a 1 \) Dala1, Assaf Hallak, Shie Mannor; Ga1 Chechik. Graph Soft-Contrastive Learning via Neighborhood Ranking _ Zhiyoan Ning, Pengfei Wang, Pengyang Wing, Ziyue