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Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

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publications

CuVPP: Filter-based Longest Prefix Matching in Software Data Planes

Published in IEEE International Conference on Cluster Computing (CLUSTER), 2015

Programmability in the data plane has become increasingly important as virtualization is introduced into networking and software-defined networking becomes more prevalent. Yet, the performance of programmable data planes on commodity hardware is a major concern, in light of ever-increasing network speed and routing table size. This paper focuses on IP lookup, specifically the longest prefix matching for IPv6 addresses, which is a major performance bottleneck in programmable switches. As a solution, the paper presents CuVPP, a programmable switch that uses packet batch processing and cache locality for both instructions and data by leveraging Vector Packet Processing (VPP). We thoroughly evaluate CuVPP with both real network traffic and file-based lookup on a commodity hardware server connected via 80 Gbps network links and compare its performance with the other popular approaches. Our evaluation shows that CuVPP can achieve up to 4.5 million lookups per second with real traffic, higher than the other trie- or filter-based lookup approaches, and scales well even when the routing table size grows to 2 million prefixes.

Recommended citation: Kwon, Minseok, Neupane, Krishna, et.al. International Conference on Cluster Computing (CLUSTER) 2020 https://ieeexplore.ieee.org/abstract/document/9229596

EmoD: An end-to-end approach for investigating emotion dynamics in software development

Published in IEEE International Conference on Software Maintenance and Evolution (ICSME), 2019

Emotions are an integral part of human nature. Emotion awareness is critical to any form of interpersonal communication and collaboration, including these in the software development process. Recently, the SE community starts having growing interests in emotion awareness in software development. While researchers have accomplished many valuable results, most extant research ignores the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach to capture and model emotion dynamics rather than focuses on extracting isolated emotion states. In this paper, we proposed such an approach–EMOD. EMOD is able to automatically collect project teams’ communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We developed a prototype tool that instantiates the EMOD approach by assembling state-of-the-art NLP, SE, and time series techniques. We demonstrate the utility of the tool using the IPython’s project data on GitHub and a visualization solution built on EmoD. Thus, we demonstrate that EMOD can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation.

Recommended citation: Neupane, Krishna, et.al. International Conference on Software Maintenance and Evolution (ICSME) 2019 https://par.nsf.gov/servlets/purl/10127273

An approach for investigating emotion dynamics in software development

Published in International Conference on Automated Software Engineering (ASE) , 2019

Emotion awareness is critical to interpersonal communication, including that in software development. The SE community has studied emotion in software development using isolated emotion states but it has not considered the dynamic nature of emotion.To investigate the emotion dynamics, SE community needs an effective approach. In this paper, we propose such an approach which can automatically collect project teams communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We demonstrate that this approach can provide end to end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation using the IPythons project data on GitHub.

Recommended citation: Neupane, Krishna, International Conference on Automated Software Engineering (ASE) 2019

MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations

Published in 2021 IEEE International Conference on Data Mining (ICDM), 2021

Recommender systems have been widely used to predict users’ interests and filter information from a large number of candidate items. However, accurately capturing the interests of users having limited interactions with a system remains a long-lasting challenge. Furthermore, existing recommender systems primarily focus on predicting user preferences without quantifying the prediction uncertainty. Uncertainty can help to quantify the model confidence when making a recommendation where low model confidence could serve as a more accurate indicator of a user’s cold-start level than simply using the number of interactions. We present a novel recommendation model that seamlessly integrates a meta-learning module with an evidential learning approach. The former module generalizes meta knowledge to tackle cold-start recommendations by exploiting fast adaptation. The latter quantifies both aleatoric and epistemic uncertainty without performing expensive posterior inference. Evidential learning achieves this by placing evidential priors and treating the output of the meta-learning module as evidencebased pseudo counts and learns a function to directly predict the evidence of a target interaction. Experiments on four benchmark datasets justify that our proposed model captures the uncertainty of users and demonstrates its superior performance over the state-of-the-art recommendation models.

Recommended citation: Neupane, Krishna, et.al. IEEE International Conference on Data Mining (ICDM) 2021 https://arxiv.org/pdf/2204.00970.pdf

A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations

Published in AAAI Conference on Artificial Intelligence (AAAI-22), 2022

We present a novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently. Making effective recommendations to these time-sensitive cold-start users is critical to maintain the user base of a recommender system. Due to the sparse recent interactions, it is challenging to capture these users’ current preferences precisely. Solely relying on their historical interactions may also lead to outdated recommendations misaligned with their recent interests. The proposed model leverages historical and current user-item interactions and dynamically factorizes a user’s (latent) preference into time-specific and time-evolving representations that jointly affect user behaviors. These latent factors further interact with an optimized item embedding to achieve accurate and timely recommendations. Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive coldstart recommendation model.

Recommended citation: Neupane, Krishna, et.al. AAAI Conference on Artificial Intelligence (AAAI) 2022 https://arxiv.org/pdf/2204.00970.pdf