We present our experimental results of simulating the school choice problem which deals with the assignment of students to schools based on each group’s complete preference list for the other group using two algorithms: Boston mechanism and student-proposing Gale-Shapley algorithm. We compare the effects of sophisticated students altering their preference lists with regards to these two algorithms. Our simulation results show that sophisticated students can benefit more in Boston mechanism compared to Gale-Shapley algorithm based on multiple evaluation metrics.
@misc{paudel2021sophisticated,bibtex_show={true},abbr={arXiv},title={Sophisticated Students in Boston Mechanism and Gale-Shapley Algorithm for School Choice Problem},author={Paudel, Abhishek},year={2021},eprint={2108.05951},archiveprefix={arXiv},primaryclass={cs.AI},url={https://arxiv.org/abs/2108.05951},pdf={https://arxiv.org/pdf/2108.05951.pdf}}
arXiv
Room Classification on Floor Plan Graphs using Graph Neural Networks
Paudel, Abhishek,
Dhakal, Roshan,
and Bhattarai, Sakshat
We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the map. We experiment with House-GAN dataset that consists of floor plan maps in vector format and train multilayer perceptron and graph neural networks. Our results show that graph neural networks, specifically GraphSAGE and Topology Adaptive GCN were able to achieve accuracy of 80% and 81% respectively outperforming baseline multilayer perceptron by more than 15% margin.
@misc{paudel2021room,bibtex_show={true},abbr={arXiv},title={Room Classification on Floor Plan Graphs using Graph Neural Networks},author={Paudel, Abhishek and Dhakal, Roshan and Bhattarai, Sakshat},year={2021},eprint={2108.05947},archiveprefix={arXiv},primaryclass={cs.LG},url={https://arxiv.org/abs/2108.05947},pdf={https://arxiv.org/pdf/2108.05947.pdf}}
2018
IEEE ICCCS
Using Personality Traits Information from Social Media for Music Recommendation
Paudel, Abhishek,
Bajracharya, Brihat Ratna,
Ghimire, Miran,
Bhattarai, Nabin,
and Baral, Daya Sagar
In 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)
2018
Music is an integral part of our life. People listen to music everyday as per their taste and mood. With the advancement and increase in volume of digital content, the choice for people to listen to diverse type of music has also increased significantly. Thus, the necessity of delivering the most suited music to the listeners has been an interesting field of research in computer science. One of the important measures to deliver the best music to listeners could be their personality traits. In order to determine the personality traits of a person, social media like Facebook can be a useful platform where people express their views on different matters, share their opinions and thoughts. This paper first describes the use of Naive Bayes classifier to determine the standard Big Five Personality Traits of a person based on their status updates on Facebook profile using basic natural language processing techniques, and then proceeds to present the use of thus obtained information about personality traits to enhance the widely implemented user-to-user collaborative filtering techniques for music recommendation.
@inproceedings{paudel2018using,bibtex_show={true},abbr={IEEE ICCCS},author={Paudel, Abhishek and Bajracharya, Brihat Ratna and Ghimire, Miran and Bhattarai, Nabin and Baral, Daya Sagar},booktitle={2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)},title={Using Personality Traits Information from Social Media for Music Recommendation},year={2018},pages={116-121},url={https://ieeexplore.ieee.org/document/8586831},doi={10.1109/CCCS.2018.8586831}}