Abstract

Social media is now a deep-rooted part of our daily lives. Whether for entertainment or information acquisition, it serves as a communication hub for billions of users. In this thesis, we dive into the realm of text classification by taking a two-fold approach, namely, contrasting text in professional and humorous fields. First, we understand the nuances of human communication via a previously unexplored social media platform, Blind. Next, we identify how the nuances of human communication are exploited by looking at humor. Our aim is to conduct a thorough analysis of these contrasting worlds to demonstrate that they work on the same underlying structures and goals. This provides a comprehensive analysis of the landscape within social media. In the non-humorous domain, Blind has emerged as an anonymous platform with the unique goal of satisfying the growing need for taboo workplace discourse. Employees come on the platform to discuss issues ranging from layoffs, compensation, interview advice, career progression and more. In our work, for the first time, we explore the platform in detail by scraping and analyzing two datasets: 767,224 Blind Posts and 63,477 Blind Company Reviews containing seven years of industry data. Using the Blind Posts dataset, we dissect the popular discussion topics of employees, find mappings of global events like work-from-home, return-to-office, and layoffs, and aggregate the sentiments of the platform for a comprehensive temporal analysis. We then propose our novel content classification pipeline. We first filter relevant content with an accuracy of 99.25% and then further annotate relevant textual context into ten categories with an accuracy of 78.41% based on the Blind Posts. Using the Blind Company Reviews, we conduct content and metrical analyses on the data for a complete view of the platform and complete our novel content classification pipeline, by adding the ability to mine opinions of employees, with an accuracy of 98.29%. For the humor domain, we utilize the Short Jokes dataset which has data from r/jokes and r/cleanjokes subreddits on Reddit, totaling 231,657 text jokes. After getting the humorous data, we use linguistically motivated features inspired by the Incongruity theory of humor and the General Theory of Verbal Humor (GTVH). These features allow us to consider humor instruments from the phonetic level to the pragmatic level, considering things like alliteration chain lengths, text polarity, slangs, etc. We train multiple machine learning and transformer models and achieve an accuracy of 63% and 98.90%, respectively. To understand the rift in the results better, we analyze the style and the semantics of the text in detail. Finally, we formalize the results across tasks and explain the consistently superior results of transformers. We finally gain valuable insights into the common underlying structure of text classification tasks.

Abstract

Time perception plays a crucial role in understanding conscious processing. Over cognitive history, various time perception methodologies have been used to measure conscious processes, with the Libet Clock method gaining attention for implicitly measuring the sense of agency through the intentional binding effect. During intentional binding, the perceived temporal distance between an action and its sensory consequence is shorter when the action is voluntary. This phenomenon is considered a proxy to measure intentionality and the sense of agency. One prominent theory explaining this effect is sensory recalibration, suggesting that the brain adjusts the interval between a voluntary action and its sensory consequence. However, a critical question can arise: does the recalibration cause the entire outcome to shift in time, or does it expand in time, compensating for the recalibration? Research indicates that the intended outcome expands in time but with an intriguing caveat�the expansion effect is highest with shorter action-outcome delays and disappears with longer delays. This observation contrasts with what the literature on the binding effect seems to suggest. Since the methodology employed to measure this effect is an inferential way to assess the intentional binding effect, it is expected that expansion should have been observed for longer and shorter delays, if not exclusively. The literature suggests a dissociation of the sensitivity of the binding effect based on action-outcome delay ranges, with predictive methods focusing on shorter delays and inferential methods on longer delays. The thesis explores the difference between objective measurements and literature suggestions, proposing that the discrepancy may arise from how components of the intentional binding effect are operationalised. The study argues that the dependence on the action-outcome delay range is not specific to the methodology but rather how voluntary actions and intended outcomes are processed within the intentional binding paradigm. In summary, this thesis delves into the assessment of conscious processing through time perception, contributing to a better understanding of the temporal dynamics of intended outcomes in the intentional binding effect. It highlights the importance of methodological factors like cues and delays in influencing binding development, extending insights into the sense of agency, intentionality, and causality.

Abstract

The evolution of RPS/RPSSL can be studied from multiple perspectives. Once the rate reactions pertaining to birth, death, predation, and other activities including migration and mutation are drawn for the system, the simulation of the system can be done through multiple stochastic techniques like the stochastic lattice simulation or the Gillespie simulation. In this thesis, we study system evolution using two techniques. In a small-scale system with mutation, we study the extinction of a system under the lens of First-Passage formulation. We formulate a First-Passage Problem to derive the exact analytical solutions for extinction state and extinction time. We then verify the agreement between our First-passage solutions, Gillespie solutions, and mean-field theory. The first-passage solutions provide us a deeper look towards species extinction compared to stochastic simulations. Our findings suggest first-extinction time and state distribution in a system with mutation follows intriguing behaviour which promotes coexistence. There also exists a depression in the state space post which mutation extends the first-extinction time. Moreover, a system devoid of mutation exhibits a discernible inclination towards probabilities that lean in the direction of an endangered state space. However, the formulation of a first-passage problem is computationally very expensive and stochastic simulations become redundant for multiple initial conditions with same system parameters. Hence, we move towards a more complex problem of understanding and forecasting a system�s evolution through Machine Learning. To provide the system with tractable information, we move to lattice-based simulation where we draw actions during each timestep using the Gillespie method. We also tackle a more intricate problem of migration, through which the spatio-temporal visualizations create spiral patterns in a system. Using Machine Learning we expedite the tasks of extinction prediction and system evolution by generating lattices using machine learning - which were computationally expensive using simulations and first-passage problems

Abstract

In the rapidly evolving landscape of Natural Language Processing (NLP) applications, a critical need arises for a versatile data creation pipeline capable of addressing the diverse requirements of various tasks. This thesis introduces a Data Creation Pipeline that significantly enhances the efficiency of data creation for a spectrum of NLP applications, including Abstractive Summarization, Question Answering, Paraphrasing, Legal Named Entity Recognition, Headline Classification, Semantic Relatedness, and Machine Translation correction. This pipeline offers a unified and adaptable solution, streamlining the entire data creation pipeline. The motivation for this pipeline stems from the availability and limitations of existing task-specific tools for open-source usage. While these tools excel in their designated areas, they lack the flexibility to accommodate a wide range of NLP applications. Our pipeline bridges this gap by offering a solution that ensures quality data collection. Key contributions of this work include the development of a systematic and extensible data creation pipeline that begins with the scraping and extraction of pertinent information from news articles. This encompasses not only the article text but also metadata such as publish date, author, category, summary, highlights, headline, sub-headline, tags, images, external links, and miscellaneous details. A noteworthy feature is the pipeline�s capability to derive pre-annotations from instruction-based models. This unique approach transforms the annotation task into a correction task, expediting the annotation process while contributing to the iterative improvement of instruction-based models. The impact of this pipeline on modern data collection methods for NLP applications is profound. By offering a versatile tool that accommodates a myriad of tasks, it streamlines the entire data creation process. The iterative model training based on human instructions not only ensures the development of state-of-the-art models for specific tasks but also signifies a paradigm shift in the way instruction-based models are refined over time. Also, language diversity is a critical aspect of NLP, and our pipeline acknowledges this by supporting a wide range of languages. This inclusivity ensures that the pipeline can be applied globally, fostering linguistic diversity in NLP research and development. The uniqueness of the Data Creation pipeline lies in its adaptability to various NLP tasks, serving as a comprehensive solution for data creation. The iterative improvement guided by human instructions sets it apart from existing pipelines, offering a dynamic and efficient approach to developing high-performance NLP models.

Abstract

While designing computer applications to solve real world problems, engineers and Machine Learning researchers have the freedom to design models specific to the situation at hand. If the application is required to have high latency or low memory usage, the model is typically tweaked at the design stage itself to incorporate these properties, and such models cannot be used in cases where reliability or explainability takes precedence, even if the underlying task is the same. However, in the case of cognitive agents, the strategies and algorithms used to make decisions need to adapt to the situation in an online manner. A popular idea in cognitive science used to explain how animals make important tradeoffs is that of viewing Decision Making as an Evidence Accumulation process. By modelling Decision Making as sampling evidence until a threshold, it is possible for such models to showcase different behaviours as per the needs of the situation. In this thesis, we take the sequential sampling approach and combine it with Reinforcement Learning frameworks in an attempt to move towards a more comprehensive model of Decision Making. In the first portion of the thesis, we explore how Linear Ballistic Accumulators can be incorporated as an action selection mechanism into Q-Learning models, which we refer to as RLLBAs. One important advantage this brings about is the ability of RLLBAs to utilise reaction time data in addition to choice data. We compare the performance of RLLBAs and conventional models in a non-trivial Grid Navigation Task with three action choices. It was found that RLLBAs were able to predict the actions taken by the subjects as well as conventional RL models while at the same time providing good predictions of the reaction time data. In addition, it was also shown that RLLBAs show significant differences in the goodness-of-fit between various forms of arbitration between Model-Free and Model-Based RL, something which is typically harder to achieve with choice data alone. In the second portion of the thesis, we explore how evidence accumulation can be realized in RL neural networks. For this purpose, we take inspiration from existing literature on anytime neural networks and structured reservoirs. The central idea here is to structure the connections of the reservoir so that activity in the network propagates forward across time. As the activity propagates forward it undergoes more processing and becomes less noisy, meanwhile, as the output layer has access to earlier parts of the reservoir, the model can still respond quickly to sudden changes in the environment if relevant. On further experimenting with connectivity patterns found in the Basal Ganglia such as parallel pathways, we find that representing different inputs in different pathways based on the concept of stripes seen in working memory models offer superior accuracy in multi armed bandit tasks over conventional reservoirs.

Abstract

The building sector consumes a significant amount of energy. The three major building performance areas are comfort, energy efficiency, and demand response capabilities. In the recent past, widespread penetration of Distributed Energy Resources (DER) have increased interest in using end-user devices and equipment in buildings as flexible devices to balance grid supply and demand, i.e., �Grid-Responsive� buildings. DER refer small-scale power generation or storage technologies that can be deployed close to the point of energy consumption. These resources are often decentralized and include renewable energy sources such as solar panels, wind turbines, and energy storage systems like batteries. Integrating DER introduces many operational challenges and uncertainty into the grid. Demand Response (DR) strategies are applied to address these challenges. DR refers to the practice of actively adjusting electricity consumption in response to signals such as price or incentives from grid operators, utilities, or aggregators. It involves reducing or shifting electricity usage during peak periods or in response to grid constraints to balance supply and demand, enhance grid reliability, and avoid or mitigate grid emergencies. The approach that is used to manage the DR using price as the key operational parameter is called Transactive Controls (TC). TC is a market-based control paradigm that uses �price� as the key operational parameter. GridWise Architecture Council defined it as �a set of economic and control mechanisms that allow the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter�. Economists extensively dealt with TC in microeconomics. However, TC is a domain-free approach that integrates market-based coordination and value-based control for a group of resources to achieve global objectives. Furthermore, the Building Energy Management Systems (BEMS) manage TC at the building or zone level in a commercial building. For example, in case of load shedding, it switches off low priority zone air conditioning or raises the set-point for cooling irrespective of usage in the whole building; dims all the lights regardless of the occupants and their needs. The occupant does not get an opportunity at the time of the DR event to decide on which comfort parameters he is willing to forgo to meet the energy demand. Besides, building energy managers usually operate the buildings to maintain homogeneous indoor ambient conditions (like zone temperatures and lighting). However, occupants have individual thermal and visual comfort preferences. Maintaining a homogeneous indoor environment throughout the building/zones leads to unnecessary energy consumption as well as the inability to meet the comfort needs of the occupants. This has led the building science research community to pursue Personal Environment Control Systems (PECS), such as local thermal conditioning systems like heated computer keyboard, personal heaters, desk fans, and radiant cooling cubicles and task lighting systems such as desk lamps. PECS create favourable micro-ambient conditions around each occupant. Nevertheless, the literature study shows that there is a gap and a need for a system/framework to integrate PECS within the task environment and between task & ambient controls to address the challenges. Hence, as part of this thesis, a system, iSPACE - intelligent System for Personal Ambient Control and energy Efficiency, has been developed to address the challenges. iSPACE integrates transactive control methods to facilitate demand response strategies within personal environmental control systems. This thesis introduces an innovative approach to integrating Personal Environmental Control Systems (PECS) into building energy management, emphasising demand response through transactive controls. It addresses the limitations of traditional demand response mechanisms by empowering individual occupants to manage energy usage at a granular level. The proposed framework establishes a hierarchical distributed multi-agent system architecture, seamlessly integrating PECS devices like SmartHub, RadiantCubicle, and SmartStrip into building energy management systems. The focal point of this thesis is the implementation of transactive control within the building environment, facilitating interactions between energy-consuming devices and the building systems at a localized level. While traditional supply-demand balancing occurs at the grid level by utilities or balancing authorities, our research explores the efficacy of implementing transactive control within buildings to optimize energy usage and enables end-users to participate in managing DR events at the task level based on their priorities. Personalised demand response strategies are facilitated by enabling real-time interaction between occupants and energy-consuming devices. Key contributions include developing and implementing

Abstract

In the literature several parsing systems for natural language processing exist which use different methods to parse sentences. Most of the previous work done in this domain was either done using hand written grammar or by using data-driven methods. Both these methods have pros as well as cons associated with them. As data-driven parsers rely on annotated treebanks they perform at high accuracies when they encounter familiar data that they have been trained on. The only downside to this method is that when they encounter unknown data in a sentence their performance drops drastically. On the other hand, when we come to parsers which use hand written grammars, they have a broad coverage on the language hence we could say that there is no such a thing as seen or unseen data. The only problem with this method is that it is very hard to write a grammar that has broad coverage in a language. In this thesis we develop a method where the con of one method is overcome by the pro of the other. In short we could say that we are building a parser which uses grammar rules extracted from a treebank (which would cover the aspect of broad coverage) and also has the data-driven approach included in it. With this thesis we try to achieve two goals: � Developing a novel approach to parsing text. � Being able to use the resulting parser using very minimal resources.

Abstract

This thesis focuses on the residential electricity demand in India, where the residential sector makes up 27% of the overall electricity usage. The study aims to provide a detailed understanding of the consumption patterns of household appliances. To achieve this, the electricity consumption of individual household appliances in 21 homes across different locations in Hyderabad was monitored. Device-level monitoring equipment, such as smart meters and sockets, was used to collect data for 61 days in summer (1 May 2023 to 30 June 2023) and 30 days in winter (1 Nov 2023 to 30 Nov 2023). The dwellers were chosen on a voluntary basis and had the option to opt out of the study at any point. The study focuses on two aspects of residential electricity consumption patterns: household and appliance levels. For the household-level study, five homes with a greater number of monitored appliances were selected and analyzed for load profiles of regularly used major appliances and daily electricity breakdown during the peak summer (15 May 2023 to 14 June 2023). Moreover, consumption patterns of ACs and geysers were studied individually in detail for the summer and winter. On the other hand, appliance-level analysis was conducted on the appliances, namely washing machine, refrigerator, television and water purifier for summer and winter. Load profiles of these appliances were created to understand seasonality in their usage patterns. The household-level analysis of five homes during peak summer found that six major appliances, namely AC, geyser (domestic water heater), refrigerator, washing machine, television and water purifier, consumed approximately 71% of the total daily electricity. Based on the analysis, it was found that the primary energy consumers in a typical home are the ACs and geysers. During periods of low solar availability, air conditioners accounted for the majority of electricity consumption, while geysers and washing machines were used more frequently during peak solar hours. By implementing a range of strategies, such as adjusting thermostat settings, utilizing fans alongside air conditioners, and opting for high-efficiency models of air conditioners, there is a potential to reduce daily electricity consumption by approximately 10% (437 Wh per home). Another potential measure to consider is the introduction of a district cooling system that incorporates thermal storage, which would shift the energy usage of air conditioners to daytime hours, thus further decreasing nighttime demand. Shifting the geyser usage can redirect 1.05 kWh of daily electrical energy consumption per household during peak solar hours. Additionally, replacing geysers with heat pumps can significantly reduce domestic hot water energy consumption to one-third of the current daily usage. By analyzing these consumption patterns, the study identifies peak consumption periods and explores potential strategies to shift electricity consumption to peak solar hours so that energy from solar PV can be utilized. Moreover, the study also focuses on appliance-level consumption patterns, highlighting the AC and geyser usage trends. It was observed that when the daily average temperatures are greater than 29�C, the energy consumption of ACs tends to be high. Conversely, geysers are utilized in both summer and winter seasons, with a higher energy consumption pattern evident during the winter months. The average load the ACs and geysers imparted during usage was 0.6 kW and 1.87 kW per appliance, respectively. The thesis concludes by providing recommendations on how households can reduce their energy usage and shift their usage patterns to peak solar hours. These include adjusting the setpoints of air conditioners to higher temperatures, using ceiling fans with air conditioners, and utilizing appliances with higher energy efficiency ratios. Ongoing research and adaptation will be crucial to maximize the benefits of these innovations for both consumers and the broader energy ecosystem. The findings of this study provide insights for policymakers, utility providers, and residents looking to reduce energy consumption and achieve a more sustainable energy system. Overall, this thesis report provides a detailed analysis of the electricity consumption patterns of households in Hyderabad and offers practical recommendations for shifting usage patterns to peak solar hours.

Abstract

Text classification and generation are linchpins in the explosive growth of the internet, seamlessly organizing and generating vast volumes of textual information. These pivotal techniques not only enhance Natural Language Processing but contribute indispensably to the dynamic evolution of online communication and knowledge dissemination in the modern world. This is why we have selected Sexism Detection and automated Wikipedia article generation in Indian languages as our topics for researching techniques in text classification and text generation, respectively. Detecting sexism in text promotes inclusivity and gender equality online, whereas generating Wikipedia articles in Indian languages enhances the accessibility of knowledge to diverse communities. With the rapid growth of online communication on social media platforms, there has also been an increase in the amount of hate speech, especially in terms of sexist language online. The proliferation of such sexist language has an impact on the mental health and well-being of the users and, hence, underscores the need for automated systems to detect and flag such pieces of text online. In this thesis, we explore the effectiveness of both conventional machine-learning techniques and different modern techniques applied to BERT-based classifiers for detecting sexist text in the EDOS shared task. The results show that the performance of the baseline conventional classifiers and BERT-based classifiers is greatly improved when techniques like Data Resampling, Data Augmentation, Domain Adaptive Pre-training, and Learning Rate Scheduling are implemented. The surge of data on the internet has also seen the rise of a knowledge giant, Wikipedia. In today�s world, it is seen as one of the primary online sources for the distribution and consumption of free knowledge. Wikipedia users collaborate in a structured and organized manner to publish and update articles on numerous topics. English Wikipedia ranks at the top when it comes to the amount of information available for any language (> 6.7 million articles). However, the amount of information available in Indian languages is lagging far behind. The same article in Hindi may be vastly different from its English version and generally contains less information. This poses a problem for native Indian language speakers who are not proficient in English. Therefore, having the same amount of information in Indian languages will help promote knowledge among such speakers. Publishing the articles manually, which has been the status quo for decades, is a time-consuming process. To get the amount of information in native Indian languages up-to-speed with the amount of information in English, automating the whole article generation process is the best option. In this thesis, we present a stage-wise approach ranging from Data Collection to Summarization and Translation, and finally culminating with Template Creation. This approach ensures the efficient generation of a large amount of content in Hindi Wikipedia in less time. All in all, this work aims to develop automated systems to foster a safe digital space for users of social media platforms and propose a stage-wise approach for the efficient generation of Wiki articles, hence promoting the dissemination of knowledge for native Indian language speakers.

Abstract

Neural Radiance Fields (NeRFs) have emerged as a pivotal advancement in computer graphics and vision. They provide a framework for rendering highly detailed novel view images from sparse multi- view input data. NeRFs use a continuous function to represent scenes that can be estimated using neural networks. This approach enables the generation of photorealistic images for static scenes. Outside the domain of image synthesis, NeRFs have been widely adopted as a representation of several downstream including but not limited to scene understanding, augemented reality, scene nav- igation, segmentation, and 3D asset generation. In this thesis, we explore upon the segmentation and editing capabilities in radiance fields. We propose a fast style transfer method that leverages multi-view consistent generation of stylized priors to change the appearance vectors in a Tensorial Radiance Field. Our method promises a speed-up of several orders of magnitude in applying style transfer and adheres to the colorscheme from the style image better than previous works. Next, we tackle the task of segmentation in radiance fields. Our method uses a grid-based feature field which allows extremely fast feature querying and searching. Combined with our stroke-based seg- mentation, this allws the user to interactively segment objects in a captured radiance field. We improve the state-of-the-art in terms of segmentation quality by a huge margin and in terms of segmentation time by orders of magnitude. Our method enables basic editing capabilities like translation, appearance editing, removal, and composition for which we show preliminary results. We further explore the problem of composition of radiance fields. Composition of two radiance fields using ray marching requires twice the amount of memory and compute. We use distillation to fuse multiple radiance fields into one to circumvent this problem. Our distillation process is roughly thrice as fast as re-training and produces a unified representation for radiance fields.