Abstract

Over the past three decades, there has been a continuous demand for an increase in the transmission data rate in the aeronautical electronics and communication technologies. This has resulted in the update or creation of new defense and aerospace standards. MIL-STD-1553B is one such bus standard, extensively used in the aerospace and aircraft industry which currently falls short of the modern-day payload requirements as its links support a bandwidth capacity of 1-Mb/s. Present day solutions resort to using an aggregated use of numerous serial buses along with multiple MIL-STD-1553B buses to accommodate high bandwidth inter-aircraft communication requirement. This often leads to less deterministic and complicated systems which require a high cost to develop, integrate, maintain and test. This thesis focuses on improving the bandwidth performance of legacy MIL-STD-1553 data bus by retrofitting redesigned 1553 controllers and analog transceiver upgrades to drive the existing buses at higher data-rates. Therefore, this work explores the enhancement at both digital and analog levels in order to minimize wiring complexity, cable weight and integration cost/time while retaining the valuable features of legacy 1553 standard. Firstly, this research proposes a hardware realization of a 100-Mb/s enhanced data rate MIL-STD-1553B digital controller chip: MIL-STD-1553+, for this purpose. This controller features an integrated remote terminal, bus monitor and bus controller, thus offering a complete 1553 solution at this increased throughput. This work first carried out a comparative study of the reported power and critical path timing aspects of possible architectural techniques with which the existing 1-Mb/s controller design can be improved. Based on this study, a finalization of a synchronous back-end and host processor interface to a true dual port memory to facilitate faster read-and-write memory accesses during controller run-time. A suitable technique of employing high speed majority-based free-running decoders has been chosen at the protocol controller unit‟s front-end interface to receive 100-Mb/s 1553 data. These dual-redundant decoders incorporate both inverted and non-inverted clocks in order to bring the overall clocking rate down to 800-MHz, which conventionally would need a clock of at least 1.6-GHz, thus reducing the power dissipation of the suggested controller. This thesis also suggested partitioning the existing protocol control architecture into two concurrently running state machines which enable the scaled-up scheduling, storage and retrieval of 1553 messages, at 100-Mb/s bit-rate over channel. Finally, the throughput and area-power performance analysis done for a gate-level synthesis and post-layout simulation on a 65-nm, 1.2 V CMOS technology node respectively. This enhanced integrated controller is also implemented on FPGA and checked for block and system level functionality using software logic analyzer IP‟s. Secondly, low power hardware realization of a legacy 1553 analog transceiver upgrade in a 1.8 V, 0:18 m CMOS technology has been made to be able to receive upto 100-Mb/s. Moreover, this transceiver upgrade will support existing legacy 1553 hardware and devices not dedicated to this boosted data rate as well. For this design, we propose a power-saving technique which equalizes the channel inter-symbol interference (ISI) at 100-Mb/s, based on a reconstructed discretized eye diagram accumu-lated after averaging out a large set of comparator decisions, clocked at only 1-MHz. This work presents the working principle, analog design components and digital histogram processing algorithm associated to this transceiver upgrade in detail. This thesis post-layout simulated each design component in this upgrade to verify functionality and followed this with a throughput performance analysis of this system in a communication environment, to observe an optimum error-rate performance.

Abstract

Egocentric cameras are wearable cameras mounted on a person’s head or shoulder. With their ability to capture what the wearer is seeing, such cameras are spawning new set of exciting applications in computer vision. Recognising activity of the wearer from an egocentric video is an important but challenging problem. This problem is more challenging than third person activity recognition due to unavailability of wearer’s pose. Unstructured movement of the camera due to natural head motion of the wearer causes sharp changes in the visual field of the egocentric camera making problem even more challenging. This causes many standard third person action recognition techniques to perform poorly on such videos. On the other hand, objects present in the scene and hand gestures of the wearer are the most important cues for first person action recognition. However, such cues are difficult to segment and recognize in an egocentric video. Carefully crafted features based on hands and objects cues for the problem have been shown to be successful for limited targeted datasets. In the first part of our work, we propose a novel representation of the first person actions derived from feature trajectories. The features are simple to compute using standard feature tracking and does not assume segmentation of hand/objects or recognizing object or hand pose unlike in many previous approaches. We train a bag of words classifier with the proposed features and report a significant performance improvement on publicly available datasets. In the second part of the thesis, we propose convolutional neural networks ( CNN s) for end to end learning and classification of wearers actions. The proposed network makes use of egocentric cues by capturing hand pose, head motion and saliency map. The proposed network model is compact and therefore can be trained from relatively small number of labeled egocentric videos that are available in egocentric settings. We show that the proposed network can generalize and give state of the art performance on various egocentric action datasets widely different from each other visually as well as dynamically.

Abstract

Software engineering activities such as code reviews, change management, knowledge management, issue tracking etc. tend to be heavily process oriented and are repetitive in nature. Serious games, simulations, augmented reality, gamification are seen as potential options to improve human motivation towards performing process oriented repetitive tasks. However, gamification differs from other techniques as the original intent of the activity is not overly impacted. The idea of making a user lose or win is optional while gamifying an application. All these factors add on building a healthy competitive environment which is suitable for software product development industry as well as academic community. Prior research has shown gamification helped in building interesting e-learning systems which impact the academic community by improving the learning experience of students. Gamification of such activities is done by composing the core activities with game design elements like extit{badges, points} etc. Gamification can increase developers' interest in performing such activities. While there are various frameworks/applications that assist in gamification, extending and configuring the frameworks to add any/all the desired game design elements has not been adequately addressed. Implying configurable rules within a rule engine instead of implementing business logic to award game design elements is still unaddressed. Addressing the aforementioned issues in this thesis, an extensible architectural framework for gamification of software engineering activities is designed and developed. The framework supports building new game design elements and gamifying software engineering processes. Game design elements are implemented as rest-full services. Gamifying an application involves adding features without modifying the original look and feel of the application. The framework consists of three major components : 1) A base framework consisting of software packages required to support an SE activity. 2) Game design elements which are implemented as micro services using python's Flask micro framework and 3) Basic front end components namely table, matrix and list which support the UI of game design elements. %These components are developed as Ember.js UI components. The validation of this work is done in three ways. 1) A Gamified instance of code review activity is created using the framework developed. 2) To evaluate the ease of use and extensibility of the framework, developers are asked to extend the framework and instantiate gamified applications from the framework and our results are encouraging - developers are able to quickly develop new services as well as use the existing framework to instantiate new gamified instances for software engineering activities. 3) The impact of tool developed is quantitatively analyzed by calculating the usefulness of code review comments. The number of comments given using the gamified code review tool are significantly similar to the number of comments given when code review is performed using existing tools. Thus indicating that the tool developed has the required elements of a code review tool. %We performed a study consisting of nearly 180+ participants to measure the usefulness of code review comments given by them during code review process in a gamified versus non-gamified environment using machine learning techniques. Further, we assess the impact of gamification based on Code smells and bugs identified in a gamified and non-gamified environment. % and has five game design elements implemented as services, and exposed using restful APIs. %We also built and used our own production ready framework to instantiate gamified applications for SE activities. To validate our claims of extensibility and productivity improvement in developer while gamified application development, we considered a group of developers consisting of undergraduate students, industry experts working as teams to build gamified applications. We present out results which demonstrate the adaptability of our framework by developers. %we instantiate a gamified version of the code review process from our extensible architectural framework that supports gamification of various software engineering activities. %In industry, inspections, reviews, and refactoring are considered as necessary software engineering activities for enhancing quality of code. In academia, such activities are rarely taught and practiced at Undergraduate level due to various reasons like limited skill set, limited knowledge of the available tools, time constraints, project setting, project client availability, flexibility with Syllabus, etc. However, we argue that such activities are an essential part of introductory software engineering courses and can result in improvement of coding skills, knowledge of coding standard and compliance to the same, and peer communication within teams. We have studied the use of such a

Abstract

Computational modeling helps us study the mechanisms underlying different cognitive and neural processes and relate them to the corresponding experimental data. Besides relating to the experimental data, a computational model should be able to propose a testable hypothesis. This thesis attempts to develop a computational model of a group of sub-cortical nuclei in the brain, called the Basal Ganglia (BG), to understand their role in reward based action selection and decision making. One of the exist-ing biologically plausible models of the BG is simulated and the model is able to learn a probabilistic two-armed bandit task using reinforcement learning. This model is able to choose the best option and reach optimal performances after only a few trials. Further, an extended decision making framework involving the BG is developed, accommodating a variety of stimulus representations and realistic scenarios. The framework is used to study the influ- ence of exogenous factors such as stimulus salience and delay in stimulus presentation. Analysis of the results suggests that a reward based action selection model, which performs optimally otherwise, performs sub-optimally when the visual characteristics of the stimuli such as salience are changed and when delays are introduced in the successive presentation of stimuli. The influence of salience changes and delays of presentation of stimuli on decision making form novel testable predictions from the com-putational experiments reported here. The developed framework is quite simplistic with as few as 72 neurons representing all the brain structures involved in decision making. It has been successfully expanded to a large scale both in terms of neuron populations and extending to a variety of tasks that the BG are known to be involved in. Scaling up involved exploring various large scale neural modeling frameworks like DANA 1 and NEF 2 (Nengo). The model is scaled using DANA to as many as 66,000 neurons. With the large scale model, the functional properties and neural activity patterns have been found to be consistent with the simplistic model. Scaling up using the large scale neural modeling platform, Nengo, also yielded the same dynamics of the simplistic model but now the model is expanded to a massive scale of 300K neurons. The power of converting existing models to efficiently weighed neural networks in Nengo is exploited in this work. Challenges involving scaling simple models, the dynamics while connecting largely and differently populated structures and pros and cons involved in using various modeling frameworks are discussed. Crucial aspects in a computational model, like parameter tuning and detailed neural implementations, while moving from a simplistic to large-scale model, are also studied. While the focus of the thesis has been to match the qualitative behavior at small and large scales, such large scale models have intrinsic advantages such as the ability to implement sophisticated stimulus representation, graceful degradation of performance in the face of injury / insult to the neural substrate and implementation of a biologically plausible architecture that matches details of the anatomy.

Abstract

Membrane proteins account for more than half of present day drug targets, but an understanding of structure-function relationships in these proteins continues to be a challenge, owing to the difficulties associated with experimental investigations of these proteins. The work in this thesis makes use of all-atom molecular dynamics simulations to address important problems pertaining to membrane proteins. A major problem in drug design targeting membrane proteins is limited availability of experimental structures. Toward this end, an approach has been proposed here for modeling the structures of α-helical membrane proteins. While the approach is able to provide structural models for a number of channel proteins, possible improvements, that would make the approach more suitable for other membrane proteins, are proposed. The other aspect of membrane proteins that has been difficult to investigate experimentally is an atomistic level understanding of the conduction and selectivity mechanism in channels and transporters. This thesis provides insights into the mechanism of transport in a number of channels and transporters, including two viral ion channels, a mammalian aquaporin, and a bacterial urea transporter. In the first part of the thesis, it is shown that the protein Vpu from human immunodeficiency virus type 1 (HIV-1) exists in a pentameric state, and a structural model for the oligomeric form of the protein is proposed. Free energy calculations performed on the structural model are able to provide a thermodynamic basis for the weak ion channel activity of the protein. In the next part of the thesis, a structure modeling approach is described that attempts to predict the structures of α-helical membrane proteins by minimizing unfavorable contacts. The approach works well for α-helical channels, and can be extended to all α-helical membrane proteins. This is followed by an investigation of ion conduction and selectivity in the p7 channel from hepatitis C virus (HCV). It is shown that a hydrophobic stretch in the channel preferentially allows the singly charged K+ rather than the doubly charged Ca2+ to pass through, and that interionic repulsion is crucial for ion conduction through the pore. Water transport through the human aquaporin AQP2 is then examined, revealing an almost barrierless permeation profile for water transport. The implications of the restriction of water orientation in the middle region of the pore are discussed. Finally, the mechanism of urea transport through the bacterial urea transporter dvUT is studied, and it is shown that permeating urea molecules are able to overcome a hydrophobic barrier arising from the existence of pore-facing phenylalanine residues by forming stacking interactions with these residues. While the work on structure modeling reveals some “rules” that membrane proteins follow at the time of oligomerization, the studies on transport across membrane proteins suggest that transport proteins have their unique mechanisms for determining selectivity, depending on what chemical species they conduct.

Abstract

Syntactic parsing in NLP is the task of working out the grammatical structure of sentences. Some of the purely formal approaches to parsing such as phrase structure grammar, dependency grammar have been successfully employed for a variety of languages. While phrase structure based constituent analysis is possible for fixed order languages such as English, dependency analysis between the grammatical units have been suitable for many free word order languages such as Indian languages. All these parsing approaches rely on identifying the linguistic units based on their formal syntactic properties and establishing the relationships between such units in the form of a tree. Dravidian languages which are spoken in Southern India are morphologically-rich, agglutinative languages whose characterization on purely structural terms such as adjectives, adverbs, conjunctions, postpositions as well as traditional interpretations of tense and finiteness pose problems in their syntactic analysis which are well-discussed in literature. We propose that the morpho-syntactic structures of Dravidian languages are better analysed from the theoretical perspectives of “Cognitive Grammar” or “Construction Grammar” where every grammatical structure is treated as a symbol that directly maps to meaningful conceptualizations. In other words, natural language is not treated as a formal system but as a functional system that is entirely symbolic or semiotic right from lexicon to grammar. Through linguistic evidences we point out that morpho-syntactic structures in Dravidian languages have their basis in meaningful discourse conceptualizations. Subsequently we hierarchically arrange all these conceptualizations into construction schemas that exhibit multiple-inheritance relationships and we explain all concrete morpho-syntactic structures as instances of these schemas. Based on this fresh theoretical grounding, we model parsing as automatic identification of meaningful dependency relations between such meaningful construction units. We formulated an annotation scheme for labelling the construction units and dependency relations that can exist between these units. Our approach to full parser annotation shows an average MALT LAS of 82.21% on Tamil gold annotated corpus of 935 sentences in a five-fold validation experiment. We conducted experiments by varying training data size, annotation scheme, length of a sentence in terms of number of chunks, granularity of tags and report the parser results of these scenarios. Finally, we build a pipeline with splitter, construction labeller, grouper as intermediate layers before MALT parser input and release the working full parser module.

Abstract

Complex tasks are often handled through software implementation in combination with high per- formance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map size and geometric constraints, FPGA based mas- sively parallel architectures are designed for super fast path planning via Rapidly Exploring Random Trees(RRT) algorithm. A Rapidly Exploring Random Tree (RRT) is an algorithm designed to efficiently search non-convex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally using samples drawn randomly from the search space. RRTs can be viewed as a technique to gener- ate open-loop trajectories for nonlinear systems with state constraints. But the algorithm is weighed down by extravagant waiting times in computation to completion, owing to its probabilistically random searching of the environment. Hence, the RRT algorithm can consume precious time cycles in generat- ing a solution to the problem of exploring a given environment. Efforts to ameliorate the unsatisfactory exploration time have focused on two areas : a.) Algorithmic design improvements b.) Parallelizing the algorithm. While Algorithmic design changes such as biased exploration, controlled exploration, faster nearest neighbour search have minimised the exploration times, the most breakthrough improve- ments have been achieved by attempts at parallelizing RRT. Here, parallelizing RRT refers to the design philosophy of integrating multiple RRT modules into an architecture that allows each of the RRT mod- ule to work in parallel while also enabling mechanisms for all of the RRT modules to collaborate and cooperate to achieve faster exploration of the environment. In the field of robotics, an FPGA is capable of delivering tightly packed, energy efficient infrastruc- tures adept in fast real time performance. An FPGA allows gate level control of system architecture. This allows the designer to tap the potential of hardware design, allowing control over minute details of arithmetic design, real time parallelization, pipe-lining of sequential processes. The hardware level flexibility afforded by an FPGA results in designs that are not only fast, but also small and power ef- ficient. FPGA boards generally consume small physical area, making it an ideal solution for robotic systems with constrained dimensions. FPGA implementation of Image processing descriptors, Random Decision Tree Body Part Recognition Using FPGA and FPGA based collision avoidance further validate this belief. This work presents two FPGA based massively parallel architectures : 1.) Hierarchical and 2.) Com- binatorial. These architectures are also analyzed against hardware implementations of other scalable RRT methods for motion planning, mainly Distributed RRT and K-Distributed RRT. Notable further- ance of acceleration capabilities was observed with the proposed architectures delivering a sizable gain over the other implementations while maintaining uniformity in exploration. To further quantify the rea- soning behind using FPGAs as the choice of parallel platform, quantitative analysis of the performance metrics of the RRT algorithm across numerous embedded hardware solutions was performed. It was ob- served that FPGA enables hardware level optimizations that offer real time performance and economical power consumption levels. Further, to validate the empirical functioning of the architectures in a real world scenario, a Zynq-SoC based differential drive robotic system was developed as a test platform to run the architectures in real time in a complex environment filled with obstacles. In summary, the designed architectures, proven adept at running in real-time on a robot, delivered a sizable gain over other state of the art algorithms, while maintaining critical parameters of the exploration like uniformity and density and also maintaining critical mobile robot requirements of small size and ambivalent power consumption levels.

Abstract

The Web 2.0, through its different platforms such as blogs, social networks, microblogs, or forums allows users to freely write content on the Internet, with the purpose to provide, share and use information. It is known that these type of platforms are among the top visited websites1, and their interest is growing more and more. Given the important role that social media usage is increasingly playing in daily life, a growing body of literature has emerged in the research community that aims to mine social media content, or to evaluate the linguistic aspects of that content in order to better understand its dynamics (Golder et al., 2011) [22]. This user-generated content has a huge drawback, which is the informal nature of the language used. Non-standard abbreviations, contracted spelling variations, casual grammatical structure are just some of the aspects of social media language. Over the past few decades, sociolinguists have been interested in a phenomena called “code mixing”, which has been observed in social media data. Code-Mixing refers to the embedding of linguistic units such as phrases, words or morphemes of one language into an utterance of another language. It is frequently seen in user generated content on social media like Facebook and Twitter, especially by multilingual users. Apart from the inherent linguistic complexity, the analysis of code-mixed content poses complex challenges owing to the presence of spelling variations, transliteration and non-adherence to a formal grammar. Due to the presence of such data all across social media, there is also a need to understand it. For any downstream Natural Language Processing task, tools that are able to process and analyse codemixed data are required. The first steps to understanding this data are language identification and word normalisation systems, so that we obtain the standard form of a crude sentence from social media. In this thesis, we have developed a system for language identification and word normalisation for Hindi-English code-mixed social media text (CMST). We have provided annotation guidelines for our system, after analysing the complex nature of the dataset used. Using this system, we have released a dataset of 1446 code-mixed Hindi-English sentences along with the associated language and normalisation labels. To the best of our knowledge, our work is the first attempt at the creation of an annotated linguistic resource for this language pair, which is also made public. We have also performed experiments with shallow parsing, in an attempt to build a complete pipeline from raw data to shallow parsed data. Our pipeline consists of 4 modules - Language Identification, Normalisation, POS Tagging, and Shallow Parsing. As far as we understand, we are the first to attempt shallow parsing on code-mixed social media text. This system has been released online.

Abstract

Designing embedded systems efficiently has always been of significant interest. This is primarily motivated by the fact that these embedded systems find application in a wide range of fields: industrial automation, commercial electronics, scientific and medical applications, and many more. With the inherent drive towards automation in today’s world, these applications are only ever-increasing. With the tremendous rise in the complexity of such systems and the need to satisfy multiple and mutually conflicting constraints, the design optimization problem has become extremely large and complex. Hence, there is an urgent necessity to develop new and efficient techniques for design of embedded systems. In this thesis, we apply and refine the design strategy of Hardware Software Partitioning (HW/SW Partitioning) to embedded systems. In the first part of the thesis, HW/SW Partitioning is introduced and then, a co-design methodology is utilized to enhance the widely used Fast Fourier Transform algorithm. A high performance and low-cost implementation is proposed which exploits the principles of Partitioning on a target system consisting of a Spartan 3E FPGA and an Atmega-16 micro-controller. A comparative analysis is then demonstrated, with respect to both, the hardware-only and software-only implementations, which depict the superior performance of the proposed implementation. It is then observed that as the problem size, i.e. complexity of target application/algorithm increased, making Partitioning decisions manually was not feasible. Hence, in order to utilize the true potential of HW/SW Partitioning (especially for large-scale applications), automation of the partitioning process was required. This formed the next focus of this thesis, which comprised of developing high speed and resource efficient algorithms for the problem of HW/SW Partitioning. The problem has been modeled as a multi-dimensional optimization problem with the aim of minimizing the area utilization, power dissipation, time of execution and system memory requirement of the implementation. The novelty of this work is in developing a fast paced, state-of-the-art algorithm which takes into consideration all the major design costs as mentioned above. The Greedy Metaheuristic Algorithm (GMA), with O(n) time complexity has been proposed, which significantly reduces the complexity of algorithms previously described in the literature. The proposed approach can also be efficiently implemented on resource constraint devices; such as low cost System On Chips (SoCs). The problem model being considered thus far was lacking one major factor, the communication costs incurred by the different hardware and software processing engines (PEs). With the contemporary age of Multi-Processor System on Chips (MPSoCs) and hybrid multi-core systems, this became a very important cost which could not be neglected. Thus, in this work, an extension to the previous model was introduced next. Subsequently, a two-phased algorithm, called Phased Greedy Metaheuristic Algorithm (PGMA) was proposed, which solved the problem of Partitioning, considering all the relevant communication costs. In order to evaluate the efficiency and performance of the proposed algorithms, a detailed empirical study was also performed. A comparison of the execution time and solution quality of the proposed algorithms with other major partitioning algorithms in the literature was depicted. The time of execution was found to be as low as 0.011 seconds, with deviation from optimal solution by less than 2% (solution error of under 2%), for partitioning an algorithm consisting of 2500 blocks. To the best of our knowledge, this is the fastest performance till date. Due to the heuristic nature of the algorithms, it was necessary to ascertain the solution quality (how optimal were the generated solutions) and compare them with other approaches. This analysis also confirmed the usefulness of the proposed algorithms. After an intensive theoretical design and performance evaluation, it became imminent to apply the proposed algorithms to real life embedded systems and determine their effectiveness. A comprehensive literature survey was conducted to find a suitable problem of relevance to HW/SW Partitioning. With the growing use and reach of digital photography, embedded systems have been increasingly used in the area of digital signal processing. Joint-Photographic Expert-Group (JPEG) Encoder was one such contemporary embedded system that is widely used in numerous applications. This has resulted in a demand for improved and more efficient constructions of the system. Any research carried out to improve the construction of JPEG encoder will have a significant impact in enhancing contemporary embedded system research. Thus, for this thesis, we chose to propose an efficient partitioning scheme for the JPEG encoder, using the proposed algorithm PGMA. A real-life case study was performed. Th

Abstract

In today’s era social media is our window into creating contextual systems of advanced technology. These systems span a breadth of industries and domains. Whether it is contextual phone assistants like Siri and Crotana or search based advertisements, all of them rely deeply on user data to teach themselves human-like responsiveness. A major chunk of the data available at our disposal is lexical data (primarily from social media platforms). Thus, psychology infered via lexical markers online is an indispensable part of the future of our technology. Lexical psychology and personality are paradigms that have been given immense attention for over a decade. Most state of the art techniques use pre-existing features from social media functions (for instance Number of followers , Likes on Facebook etc. to predict various personality models (such as Big Five , MBTI etc.) that have been around for over 70 years. Parts of our work attempt to tackle this approach of using pre-existing features (available for direct reference from social media). In doing so, we traced the conception of the Big Five model of personality to be concieved by means of extensive lexical analysis (both empirical and theoretical). Based on this knowledge, we illustrate the importance of additional features (informed by the fundamentals of psychology) in training robust personality pre- diction models. Training one such model ourselves, the results we achieved were a clear indicator of the effectiveness of lexical features versus stand-alone technological features to predict user personality. We then move on to tackle the drawbacks of the continued usage of the Big Five model to understand user personality since the last 70 years. At the time this model was proposed by Goldberg et al. the only mode for obtaining relevant user data was by means of psychometric tests. While the databases available to us have advanced significantly, the models used to capture lexical personality from these data reserves remain constant. They make available to us an overall persona of an individual. Contrary to these, abundant literature in the domain of psychology suggests that the persona of an individual is ever evolving. It is dynamic and changes temporally with the individual’s cognition, action and experiences. As opposed to this our technology today is still dependent on such overall personality models (static personality traits from Big Five and other’s of it’s likes). We have by means of this study attempted to discover, formulate and compute a dynamic model of representing the persona of an individual to overcome these limitations. In each of the domains described above an iterative model will elevate the contextual information by a manifold as opposed to the approximate static summarization of an individual’s persona. Thus, inspired by the Abhidhamma scholarship of Buddhism, we proposed PACMAN - the psycho computational model of an individual. The model theoretically represents a stochastic finite state ma- chine that encapsulates the moment by moment mental states of an individual. It is empowered by its genesis which lies in the foundations of Brentano’s western psychological theories, phenomenological thinking and strong parallels with the characteristic biomedical processes that define the psychology of an individual. Another feature which helps this model to carve it’s own niche is it’s close resemblance to a formal system. Much like a formal model is defined by a set of states and their inter state transitions, this model is also defined by it’s moments and transitional rules between these moments. It is adept in establishing a definite schema which can be ubiquitously populated by any given data reserve and helps in visualising the persona depicted. Thus, we first categorize the temporal (time to time) social media data into pre-defined mental states (concieved via Abhidhamma). These mental states are then popu- lated into the respective temporal moments of our automaton. Finally, they are used to draw various inferences about the lexical psychology and/or personalities of the respective social media users. We bring closure to our work by illustrating the functionality of our model in three different use cases. We first use our model to formalise the abstract idea of an individual in the concept of social machines. We also exemplify how our model tackles sparsity and non-uniformity of data in social media. The second use case is to model the psychological phenomenon of Anxiety, its conception, continuation etc. by means of lexical data available online. We finally introduce the lexico-psychological mental engagement factor to capture the maximum engagement of each individual with any given mental state. As opposed to the psychometric methods used to capture such a factor, our model uses lexical features to compute it. We discuss the importance of each