CR

C.A. Raman

20 records found

Prediction-based Anomaly Detection in Multivariate Time-Series Data

Improving Wahoo Fitness Cycling Data Quality by Addressing Sensor Errors

Consumer-grade fitness trackers can produce unreliable physiological data due to sensor errors. The same holds for cycling data from Wahoo Fitness, where heart rate (HR) and power readings are essential for training and performance analysis. This thesis presents a prediction-base ...
Early detection of depression is crucial in mental healthcare. Augmenting depression diagnosing with AI seems to be promising in detecting depression from subtle non-verbal cues and early signs that can be missed from domain experts. For this to be achieved, AI procedures and dec ...
The emergence of Language Language Models (LLMs)-based agents represents a significant advancement in artificial intelligence (AI), offering new possibilities for complex problem-solving and interaction within a virtual environment. Our work is based on the Voyager paper [1], whi ...
This research paper aims to present how Theory of Mind (ToM) - the ability that allows humans to attribute mental states to others - can be used in the context of physically and virtually embodied computational agents. The focus is on using ToM for perspective-taking in environme ...
Continual learning (CL) enables intelligent systems to continually acquire, adapt, and apply knowledge, representing a dynamic paradigm in AI. For embodied agents—interacting with their environment physically and cognitively—CL enhances adaptability and reduces training costs sig ...
Active inference is a theory of the human brain characterising behaviour that minimises surprise. The free energy principle accounts for the adaptive behaviours of organisms through action, perception, and learning aimed at optimising reward or surprise. This study systematically ...
In the future, autonomous social robots are expected to seamlessly integrate into our society. To be perceived as interactive partners rather than mere tools, these robots must be embodied and capable of navigating complex, dynamic environments. This study explores the critical r ...
Virtual agents have demonstrated remarkable progress in both competitive and cooperative en- vironments. Embodied agents, which enhance AI interactions with the physical world, show great promise for a variety of use cases in both virtual and non-virtual settings. This literature ...
Recovering the appearance and physical parameters of elastic objects from multi-view video is essential for many applications that require simulation of the real world. Past methods for this task have provided accurate results in recovering physical properties; however, their re- ...
Human Theory of Mind (ToM), the ability to infer others’ mental states, is essential for effective social interaction. It allows us to predict behavior and make decisions accordingly.In Human Robot Interaction (HRI), however, this remains a significant challenge, especially in dy ...
Studies in Music Affect Content Analysis use varying emotion schemes to represent the states induced when listening to music. However, there are limited studies that explore the translation between these representation schemes. This paper explores the feasibility of using machine ...
Images possess the ability to convey a wide range of emotions, and extracting affective information from images is crucial for affect prediction systems. This process can be achieved through the application of machine learning algorithms. Categorical Emotion States (CES) and Dime ...
Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Vari ...
There is a correlation between music and affect which researchers try to define and use in technology to improve healthcare and users' experience in music-related technology. However, since affect is a complex term there is not a specified way on how to represent different affect ...
Affective Video Content Analysis aims to automatically analyze the intensity and type of affect (emotion or feeling) that are contained in a video and are expected to arise in users while watching that video. This study aims to provide a systematic overview of various affect repr ...
In human-human interactions, the majority of information is conveyed through body language, specifically facial expressions. Consequently, researchers have been interested in improving human-computer interactions through developing systems with automatic understanding of body lan ...
Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective ...
The objective of this report is to establish and present a machine learning model that effectively translates affect representation from emotional attributes such as arousal (passive versus active) and valence (negative versus positive) to dominance (weak versus strong). In the p ...
This research delves into the exploration of translation methods between affect representation schemes within the domain of text content analysis. We assess their performance on various affect analysis tasks while concurrently developing a robust evaluation framework. Furthermore ...
Living in a world where every single electronic device is online and interconnected, privacy is a growing concern. Finding the threshold where audio is unintelligible to transcription software is crucial when everything that we say can be recorded. Even if Automated Speech Recogn ...