Browsing by Author "Murali, S."
Now showing 1 - 5 of 5
- Results Per Page
- Sort Options
Item Crowdsourcing for disaster relief: A multi-platform model(2016) Murali, S.; Krishnapriya, V.; Thomas, A.In this paper, we propose a model to deal with and manage disasters, taking into consideration the limitations of technology which hamper effective crisis management while handling the needs of victims, volunteers and government agencies. Our model aims at providing both an online and offline platform for data aggregation, dissemination and analysis. We propose a three level model which attempts to reduce the impact of the disaster on the victims by providing a platform that helps coordination between all parties involved, and ensures availability of resources and information. We discuss the existing solutions like Ushahidi and try to incorporate successful features into our model. Further, we use various techniques ranging from Natural Language Processing (NLP) to crowd sourcing, and ensure a robust, scalable solution which can be used by all the parties involved. � 2016 IEEE.Item Crowdsourcing for disaster relief: A multi-platform model(Institute of Electrical and Electronics Engineers Inc., 2016) Murali, S.; Krishnapriya, V.; Thomas, A.In this paper, we propose a model to deal with and manage disasters, taking into consideration the limitations of technology which hamper effective crisis management while handling the needs of victims, volunteers and government agencies. Our model aims at providing both an online and offline platform for data aggregation, dissemination and analysis. We propose a three level model which attempts to reduce the impact of the disaster on the victims by providing a platform that helps coordination between all parties involved, and ensures availability of resources and information. We discuss the existing solutions like Ushahidi and try to incorporate successful features into our model. Further, we use various techniques ranging from Natural Language Processing (NLP) to crowd sourcing, and ensure a robust, scalable solution which can be used by all the parties involved. © 2016 IEEE.Item Implementation and evaluation of Proportional Integral Controller Enhanced (PIE) algorithm in ns-3(2016) Shravya, K.S.; Murali, S.; Tahiliani, M.P.This paper proposes a new ns-3 model and presents the evaluation results for Proportional Integral controller Enhanced (PIE), a recently designed Active Queue Management (AQM) mechanism to address the problem of bufferbloat. The problem of bufferbloat arises due to the presence of large unmanaged buffers in routers. This leads to high queuing latency and significantly degrades the performance of time-sensitive and interactive traffic. AQM mechanisms that aim to address the problem of bufferbloat try to achieve an optimal trade-off between high link utilization and low mean queue length. PIE is a lightweight AQM mechanism that tries to achieve the same. To our knowledge, ns-3 network simulator does not have a model for simulating PIE. Hence, in this paper, we implement a ns-3 model for PIE, and show that the results obtained from it are in line with those obtained from the ns-2 model of PIE, implemented by its authors. � 2016 ACM.Item Implementation and evaluation of Proportional Integral Controller Enhanced (PIE) algorithm in ns-3(Association for Computing Machinery acmhelp@acm.org, 2016) Shravya, K.S.; Murali, S.; Tahiliani, M.P.This paper proposes a new ns-3 model and presents the evaluation results for Proportional Integral controller Enhanced (PIE), a recently designed Active Queue Management (AQM) mechanism to address the problem of bufferbloat. The problem of bufferbloat arises due to the presence of large unmanaged buffers in routers. This leads to high queuing latency and significantly degrades the performance of time-sensitive and interactive traffic. AQM mechanisms that aim to address the problem of bufferbloat try to achieve an optimal trade-off between high link utilization and low mean queue length. PIE is a lightweight AQM mechanism that tries to achieve the same. To our knowledge, ns-3 network simulator does not have a model for simulating PIE. Hence, in this paper, we implement a ns-3 model for PIE, and show that the results obtained from it are in line with those obtained from the ns-2 model of PIE, implemented by its authors. © 2016 ACM.Item SELF-PERCEPT: Introspection Improves Large Language Models’ Detection of Multi-Person Mental Manipulation in Conversations(Association for Computational Linguistics (ACL), 2025) Khanna, D.; Seth, P.; Murali, S.; Guru, A.; Shukla, S.; Tyagi, T.; Chaurasia, S.; Ghosh, K.Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation’s nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept. ©2025 Association for Computational Linguistics.