Abstract

Two critical challenges arise in the interconnected realm of online platforms: the need to ensure equitable representation for entities and the emphasis on identifying deceptive practices. This work aims to address both by introducing CAFIN to decrease the disparity in the representations of GNNs, and by detecting and studying anomalies on the Google Play Store reviewer network for fraud prevention, moving in the direction of reshaping digital ecosystems to be both fair and trustworthy. Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks - Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18% to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness. Google Play Store�s policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of the reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. We also discuss how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play

Abstract

Machine Learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of Machine Learning-Enabled Systems (MLS). However, numerous software engineering challenges persist in propelling these MLS into production, largely due to various run-time uncertainties that impact the overall Quality of Service (QoS). These uncertainties emanate from ML models, software components, and environmental factors. Self-adaptation techniques present potential in managing runtime uncertainties, but their application in MLS remains largely unexplored. As a solution, this thesis proposes Machine Learning Model Balancer, a novel concept focusing on managing uncertainties related to ML models by using multiple models in runtime. Subsequently, the thesis introduces AdaMLS, an novel approach that leverages the Machine Learning Model Balancer concept for continuous adaptation. AdaMLS extends the traditional MAPE-K loop, employing lightweight unsupervised learning for dynamic model switching, thereby ensuring consistent QoS in dynamic environments. The effectiveness of AdaMLS is demonstrated through an object detection use case, showcasing its ability to effectively mitigate run-time uncertainties and surpass both naive approaches and standalone models in terms of QoS. We further developed SWITCH, an exemplar to demonstrate the practical application of our research in self-adaptation of MLS. The discussion on SWITCH highlights its role as a tool designed to enhance self-adaptive capabilities in MLS through dynamic model switching in runtime. SWITCH is developed as to cater to a broad range of ML scenarios. It features advanced input handling, real-time data processing, and logging for adaptation metrics, supplemented with an interactive real-time dashboard for system observability. Through its architecture and user-friendly interface, SWITCH not only demonstrates adaptability and performance but also serves as a valuable platform for researchers, practitioners, and students to explore self-adaptation in MLS. Beyond the primary focus on ensuring optimal QoS, we also explore the application of the Machine Learning Model Balancer concept in two main areas. Firstly, the EcoMLS approach leverages this concept to enhance the sustainability of MLS. By optimally balancing energy consumption with model confidence through runtime ML model switching, EcoMLS marks a significant step towards sustainable, energyefficient ML solutions. Secondly, RelMLS approach to systems, where machine learning is deployed in streaming mode. Through experiments and implementation, this approach has shown promising results in object detection use case, adopting a software architecture-based solution to dynamically switch between models based on contextual reliability. This thesis encapsulates a comprehensive exploration of the Machine Learning Model Balancer concept, from its theoretical introduction to practical applications in diverse MLS scenarios. Through AdaMLS, SWITCH, EcoMLS, and RelMLS implementations, we demonstrate the potential of our approaches in enhancing both the QoS and sustainability of MLS. Our findings suggest that the judicious application of model switching and self-adaptation techniques can significantly mitigate run-time uncertainties, paving the way for more resilient, efficient, and adaptable MLS.

Abstract

Sexual objectification is a form of objectification where a person is reduced to his/her sexual functionalities. In general, women and minority people are primary victims of sexual objectification, although sexualization of men has been on the rise. Initial research believed sexual objectification to be a behavioral phenomenon. That is, a person is sexually objectified through thoughts or actions of the objectifier. For example, studies have found a strong correlation between the body-biased gaze (greater attention to sexual body parts), also termed the objectifying gaze, and the sexual objectification of women. Recent studies indicate that the manifestation of sexual objectification may extend to sub-conscious cognitive processes. There are fundamental differences in brain activity when processing human stimuli versus object stimuli. A recent study indicates that when objectified, sexualized female images but not sexualized male images are visually processed more locally, similar to objects. For decades, there have been concerns regarding the growing hyper-sexualization of women in popular media like music videos, video games, etc., leading to content analysis studies on the music videos in various countries across the globe. And the results show that female characters� bodies are overly emphasized, primarily through revealing clothes and close-up shots of their bodies or sexual body parts. Frequent exposure to these objectifying media can lead to the manifestation of the objectifying gaze in the viewer. For example, a recent study reported that after watching sexualized pop music videos, viewers gaze objectified female images more. That is, they spent more time fixating on the sexual body parts of the female characters. In this thesis, we explore different forms of gaze objectification of women and the priming effect of sexually objectifying music videos on the viewers� gaze when looking at female images in a South Asian context. The thesis comprises of two studies. In the first study, we explore gaze objectification of nonsexualized female images. We have used the eye-tracking mechanism to capture the gaze pattern and the local/global paradigm to understand the underlying visual process when looking at non-sexualized female images. In the subsequent study, we investigate the priming effect of camera-induced objectifying gaze, operationalized via sexualized music video (MV), when looking at target female images in traditional (saree, salwar) and western (shirt-pant, short-dress) attire. Our results show both genders� gaze-objectified female sexual body parts. Participants spent more time looking at female sexual body parts. And male (vs. female) images are processed more locally, similar to objects. The findings suggest that women are gaze-objectified even in non-sexualized attire. In the second study, we found that, when primed with sexualized music videos (vs. non-sexualized), female images are more gaze-objectified. Our studies provide concrete evidence supporting existing research, which emphasizes that women experience elevated levels of gaze objectification. Additionally, exposure to sexualized media is identified as a factor contributing to the heightened objectification of women. This highlights the connection between media exposure and a cultural context where women are more commonly objectified. Recognizing and addressing these dynamics becomes crucial in understanding and mitigating the impact of sexual objectification.

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.