Recent proliferation of online platforms for Games of skill have made—an excellent source of recr... more Recent proliferation of online platforms for Games of skill have made—an excellent source of recreation and relaxation, and the safest avenues for realising personal worth, respect, and recognition— readily accessible on the tip of a finger. A primary driver towards this enjoyment is the skill of a person in responding to complex game dynamics and states, which eventually impacts the game outcomes. Hence, from both player and platform perspectives, improving the player skills—or conversely, reducing player mistakes—is paramount to improve player experience and engagement. In this talk, we focus on unique personalized and near-real-time up-skilling framework depending on specific mistake contexts identified for the players. This framework leverages a suite of models to determine the correct action in a given game state. For a specific case-study of Rummy—a popular skill game in India—these are CNN-driven deep learning models. However, our framework can be plugged with any other suite, action set, and game state representations (depending on the game) within a broad construct of getting the best reference action set in a game state. Players’ actions are then benchmarked w.r.t. the reference actions as adherence measures. A lower adherence essentially indicates deviations from correct actions, i.e., the mistakes. An explainable set of rules are accordingly derived from game features, which sets the contexts for these mistakes, leading to opportunities for targeted up-skilling.
Body area networks (BANs) are networks of wireless sensors and medical devices embedded in clothi... more Body area networks (BANs) are networks of wireless sensors and medical devices embedded in clothing, worn on or implanted in the body, and have the potential to revolutionize healthcare by enabling pervasive healthcare. However, due to their critical applications affecting human health, challenges arise when designing them to ensure they are safe for the user, sustainable without requiring frequent battery replacements and secure from interference and malicious attacks. This book lays the foundations of how BANs can be redesigned from a cyber-physical systems perspective (CPS) to overcome these issues. Introducing cutting-edge theoretical and practical techniques and taking into account the unique environment-coupled characteristics of BANs, the book examines how we can re-imagine the design of safe, secure and sustainable BANs. It features real-world case studies, suggestions for further investigation and project ideas, making it invaluable for anyone involved in pervasive and mobile healthcare, telemedicine, medical apps and other cyber-physical systems.
Docker containers are becoming an attractive implementation choice for next-generation microservi... more Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.
2018 IEEE World Congress on Services (SERVICES), 2018
Making design choices for big data systems is not trivial. If not planned out efficiently, keepin... more Making design choices for big data systems is not trivial. If not planned out efficiently, keeping in mind the practical requirements, there's a possibility that the deployed system can lack important features to match up the application or it may contain over-sophisticated methods that incurs a large cost, but little increase in the efficiency, output. To equip the end user towards wise design choices, we have proposed a decision support framework for big data systems that can evaluate the suitability over numerous design combinations and outputs the one most efficient for the end-user requirement.
The First International Conference on AI-ML-Systems, 2021
Recent proliferation of online platforms for Games of skill have made—an excellent source of recr... more Recent proliferation of online platforms for Games of skill have made—an excellent source of recreation and relaxation, and the safest avenues for realising personal worth, respect, and recognition— readily accessible on the tip of a finger. A primary driver towards this enjoyment is the skill of a person in responding to complex game dynamics and states, which eventually impacts the game outcomes. Hence, from both player and platform perspectives, improving the player skills—or conversely, reducing player mistakes—is paramount to improve player experience and engagement. In this talk, we focus on unique personalized and near-real-time up-skilling framework depending on specific mistake contexts identified for the players. This framework leverages a suite of models to determine the correct action in a given game state. For a specific case-study of Rummy—a popular skill game in India—these are CNN-driven deep learning models. However, our framework can be plugged with any other suite, action set, and game state representations (depending on the game) within a broad construct of getting the best reference action set in a game state. Players’ actions are then benchmarked w.r.t. the reference actions as adherence measures. A lower adherence essentially indicates deviations from correct actions, i.e., the mistakes. An explainable set of rules are accordingly derived from game features, which sets the contexts for these mistakes, leading to opportunities for targeted up-skilling.
2017 IEEE International Conference on Cloud Engineering (IC2E), 2017
Docker containers are becoming an attractive implementation choice for next-generation microservi... more Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
Games of skill are an excellent source of recreation, entertainment and relaxation. However when ... more Games of skill are an excellent source of recreation, entertainment and relaxation. However when these games are played with real money, the important facet of responsible game play must be addressed rigorously and efficiently by game providers. Ensuring game prudence, whereby users play real-money skill games only for entertainment purposes, and do so well within their resourceful means, necessary for the wellness of players, and also for the sustained engagement and retention of players in the system. To this end, in this work we present a deep learning model that helps identify players who are on the verge of displaying irresponsible or addictive game play in a fully unsupervised manner. We use a combination of long short-term memory and adversarial auto-encoder networks to analyze game play along three tell-tale dimensions of immoderation, namely, money, time and despair. Our model provides a state of the art solution for identifying a precise set of problem gamers in skill-based cash games well ahead of time, effectively addressing the challenges of (i) extreme class imbalance, (ii) sparse and incomplete ground truth, (iii) overlapping behavioral patterns between risky and non-risky but highly engaged players.
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
The prevalence of mobile devices and applications strongly motivate mobile crowdsourcing for faci... more The prevalence of mobile devices and applications strongly motivate mobile crowdsourcing for facilitating location-dependent services. We propose LoRUS, a Location-based Relevant User determination System for efficiently retrieving the top-k relevant mobile users in a given spatial window.
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
A key challenge in participatory sensing systems has been the design of incentive mechanisms that... more A key challenge in participatory sensing systems has been the design of incentive mechanisms that motivate individuals to contribute data to consuming applications. Emerging trends in urban development and smart city planning indicate the use of citizen reports to gather insights and identify areas for transformation. Consumers of these reports (e.g. city agencies) typically associate non-uniform utility (or values) to different reports based on the spatio-temporal context of the reports. For example, a report indicating traffic congestion near an airport, in early morning hours, would tend to have much higher utility than a similar report from a sparse residential area. In such cases, the design of an incentive mechanism must motivate participants, via appropriate rewards (or payments), to provide higher utility reports when compared to less valued ones. The main challenge in designing such an incentive scheme is two-fold: (i) lack of prior knowledge of participants in terms of the...
5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), 2022
Recommendation in outcome based platforms (eg. skill gaming, financial trading) where users get r... more Recommendation in outcome based platforms (eg. skill gaming, financial trading) where users get rewards based on their choices and the subsequent non-deterministic outcomes from such choices presents a unique set of challenges. Unlike other online services (e.g., e-commerce, social media), these platforms often see: (a) distinctive longitudinal behavior patterns in users’ transactions, (b) enormous content generated by the user interactions, (c) a dynamic interplay between the users’ transactional history and outcomes towards their future behavior, (d) ordinal nature of transaction elements. Motivated by these observations, we propose ComParE (Competing Parallel Networks with Expert Network), a novel personalized recommendation framework that: (i) exploits the distinct behavioral trends in the data, by training competing parallel networks as local experts, (ii) trains a global expert network to get the overall picture for the final prediction, and (iii) introduces custom loss functions to learn inherent ordering and interpretations of the classes being predicted. With the example of personalizing entry fee choices for the game of Rummy on the RummyCircle platform we show: (i) distinct and robust user Personas found based on historical entry fee selections; (ii) significant boosts in the entry fee predictions through ComParE with both neural networks and classical ML algorithms for local and global experts; (iii) substantial lifts over platform default as shown through offline analysis. ComParE significantly outperforms other baselines including well known deep learning models, as shown through offline experiments.
Proceedings of the 2nd International Workshop on Network Data Analytics, 2017
Cities today are typically plagued by multiple issues such as âĂŞ traffic jams, garbage, transit ... more Cities today are typically plagued by multiple issues such as âĂŞ traffic jams, garbage, transit overload, public safety, drainage etc. Citizens today tend to discuss these issues in public forums, social media, web blogs, in a widespread manner. Given that issues related to public transportation are most actively reported across web-based sources, we present a holistic framework for collection, categorization, aggregation and visualization of urban public transportation issues. The primary challenges in deriving useful insights from web-based sources, stem from: (a) the number of reports; (b) incomplete or implicit spatio-temporal context; and the (c) unstructured nature of text in these reports. This paper provides the text categorization techniques that can be adopted to address specifically these challenges. The work initiates with the formal complaint data from the largest public transportation agency in Bangalore, complemented by complaint reports from web-based and social med...
2018 IEEE World Congress on Services (SERVICES), 2018
Mining and profiling intent of players is an important facet in today's online games for the ... more Mining and profiling intent of players is an important facet in today's online games for the gaming companies to take targeted actions and provide personalized services. While analyzing and predicting player behavior have been studied in the online gaming domain, investigating intent behind those behaviors from the data has not been studied. We introduce the problem of player intent mining. With the example of online Rummy, a popular card game in India, we show how specific behavior aspects of player over time plays a key role in identifying their intent. Through both supervised and unsupervised techniques we achieve reasonable accuracy in identifying the intent.
Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019
To facilitate an environment of inclusive urban management, civic agencies need to listen to the ... more To facilitate an environment of inclusive urban management, civic agencies need to listen to the voices of citizens on web sources such as social media, online blogs, public forums and so on. Owing to the vastness and noisy nature of online data, it is challenging, yet important to mine actionable issues related to a city as faced by the citizens firsthand, so that timely measures can be taken by the administration to remedy them. In this work, we filter, analyze, and model web data on urban civic issues of a city, with respect to three modalities - semantics, spatial and temporal. We have come up with a novel approach that captures the contexts through dense distributed word embedding as well as identifies the latent issues through a generative model. Due to the scarcity of geo-tagged posts and delayed reporting, we rely primarily on the textual content of the data for location mining and temporal resolution. We present a first-of-a-kind unified system named CUrb that introduces a ...
Recent proliferation of online platforms for Games of skill have made—an excellent source of recr... more Recent proliferation of online platforms for Games of skill have made—an excellent source of recreation and relaxation, and the safest avenues for realising personal worth, respect, and recognition— readily accessible on the tip of a finger. A primary driver towards this enjoyment is the skill of a person in responding to complex game dynamics and states, which eventually impacts the game outcomes. Hence, from both player and platform perspectives, improving the player skills—or conversely, reducing player mistakes—is paramount to improve player experience and engagement. In this talk, we focus on unique personalized and near-real-time up-skilling framework depending on specific mistake contexts identified for the players. This framework leverages a suite of models to determine the correct action in a given game state. For a specific case-study of Rummy—a popular skill game in India—these are CNN-driven deep learning models. However, our framework can be plugged with any other suite, action set, and game state representations (depending on the game) within a broad construct of getting the best reference action set in a game state. Players’ actions are then benchmarked w.r.t. the reference actions as adherence measures. A lower adherence essentially indicates deviations from correct actions, i.e., the mistakes. An explainable set of rules are accordingly derived from game features, which sets the contexts for these mistakes, leading to opportunities for targeted up-skilling.
Body area networks (BANs) are networks of wireless sensors and medical devices embedded in clothi... more Body area networks (BANs) are networks of wireless sensors and medical devices embedded in clothing, worn on or implanted in the body, and have the potential to revolutionize healthcare by enabling pervasive healthcare. However, due to their critical applications affecting human health, challenges arise when designing them to ensure they are safe for the user, sustainable without requiring frequent battery replacements and secure from interference and malicious attacks. This book lays the foundations of how BANs can be redesigned from a cyber-physical systems perspective (CPS) to overcome these issues. Introducing cutting-edge theoretical and practical techniques and taking into account the unique environment-coupled characteristics of BANs, the book examines how we can re-imagine the design of safe, secure and sustainable BANs. It features real-world case studies, suggestions for further investigation and project ideas, making it invaluable for anyone involved in pervasive and mobile healthcare, telemedicine, medical apps and other cyber-physical systems.
Docker containers are becoming an attractive implementation choice for next-generation microservi... more Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.
2018 IEEE World Congress on Services (SERVICES), 2018
Making design choices for big data systems is not trivial. If not planned out efficiently, keepin... more Making design choices for big data systems is not trivial. If not planned out efficiently, keeping in mind the practical requirements, there's a possibility that the deployed system can lack important features to match up the application or it may contain over-sophisticated methods that incurs a large cost, but little increase in the efficiency, output. To equip the end user towards wise design choices, we have proposed a decision support framework for big data systems that can evaluate the suitability over numerous design combinations and outputs the one most efficient for the end-user requirement.
The First International Conference on AI-ML-Systems, 2021
Recent proliferation of online platforms for Games of skill have made—an excellent source of recr... more Recent proliferation of online platforms for Games of skill have made—an excellent source of recreation and relaxation, and the safest avenues for realising personal worth, respect, and recognition— readily accessible on the tip of a finger. A primary driver towards this enjoyment is the skill of a person in responding to complex game dynamics and states, which eventually impacts the game outcomes. Hence, from both player and platform perspectives, improving the player skills—or conversely, reducing player mistakes—is paramount to improve player experience and engagement. In this talk, we focus on unique personalized and near-real-time up-skilling framework depending on specific mistake contexts identified for the players. This framework leverages a suite of models to determine the correct action in a given game state. For a specific case-study of Rummy—a popular skill game in India—these are CNN-driven deep learning models. However, our framework can be plugged with any other suite, action set, and game state representations (depending on the game) within a broad construct of getting the best reference action set in a game state. Players’ actions are then benchmarked w.r.t. the reference actions as adherence measures. A lower adherence essentially indicates deviations from correct actions, i.e., the mistakes. An explainable set of rules are accordingly derived from game features, which sets the contexts for these mistakes, leading to opportunities for targeted up-skilling.
2017 IEEE International Conference on Cloud Engineering (IC2E), 2017
Docker containers are becoming an attractive implementation choice for next-generation microservi... more Docker containers are becoming an attractive implementation choice for next-generation microservices-based applications. When provisioning such an application, container (microservice) instances need to be created from individual container images. Starting a container on a node, where images are locally available, is fast but it may not guarantee the quality of service due to insufficient resources. When a collection of nodes are available, one can select a node with sufficient resources. However, if the selected node does not have the required image, downloading the image from a different registry increases the provisioning time. Motivated by these observations, in this paper, we present CoMICon, a system for co-operative management of Docker images among a set of nodes. The key features of CoMICon are: (1) it enables a co-operative registry among a set of nodes, (2) it can store or delete images partially in the form of layers, (3) it facilitates the transfer of image layers between registries, and (4) it enables distributed pull of an image while starting a container. Using these features, we describe—(i) high availability management of images and (ii) provisioning management of distributed microservices based applications. We extensively evaluate the performance of CoMICon using 142 real, publicly available images from Docker hub. In contrast to state-of-the-art full image based approach, CoMICon can increase the number of highly available images up to 3x while reducing the application provisioning time by 28% on average.
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020
Games of skill are an excellent source of recreation, entertainment and relaxation. However when ... more Games of skill are an excellent source of recreation, entertainment and relaxation. However when these games are played with real money, the important facet of responsible game play must be addressed rigorously and efficiently by game providers. Ensuring game prudence, whereby users play real-money skill games only for entertainment purposes, and do so well within their resourceful means, necessary for the wellness of players, and also for the sustained engagement and retention of players in the system. To this end, in this work we present a deep learning model that helps identify players who are on the verge of displaying irresponsible or addictive game play in a fully unsupervised manner. We use a combination of long short-term memory and adversarial auto-encoder networks to analyze game play along three tell-tale dimensions of immoderation, namely, money, time and despair. Our model provides a state of the art solution for identifying a precise set of problem gamers in skill-based cash games well ahead of time, effectively addressing the challenges of (i) extreme class imbalance, (ii) sparse and incomplete ground truth, (iii) overlapping behavioral patterns between risky and non-risky but highly engaged players.
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
The prevalence of mobile devices and applications strongly motivate mobile crowdsourcing for faci... more The prevalence of mobile devices and applications strongly motivate mobile crowdsourcing for facilitating location-dependent services. We propose LoRUS, a Location-based Relevant User determination System for efficiently retrieving the top-k relevant mobile users in a given spatial window.
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
A key challenge in participatory sensing systems has been the design of incentive mechanisms that... more A key challenge in participatory sensing systems has been the design of incentive mechanisms that motivate individuals to contribute data to consuming applications. Emerging trends in urban development and smart city planning indicate the use of citizen reports to gather insights and identify areas for transformation. Consumers of these reports (e.g. city agencies) typically associate non-uniform utility (or values) to different reports based on the spatio-temporal context of the reports. For example, a report indicating traffic congestion near an airport, in early morning hours, would tend to have much higher utility than a similar report from a sparse residential area. In such cases, the design of an incentive mechanism must motivate participants, via appropriate rewards (or payments), to provide higher utility reports when compared to less valued ones. The main challenge in designing such an incentive scheme is two-fold: (i) lack of prior knowledge of participants in terms of the...
5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), 2022
Recommendation in outcome based platforms (eg. skill gaming, financial trading) where users get r... more Recommendation in outcome based platforms (eg. skill gaming, financial trading) where users get rewards based on their choices and the subsequent non-deterministic outcomes from such choices presents a unique set of challenges. Unlike other online services (e.g., e-commerce, social media), these platforms often see: (a) distinctive longitudinal behavior patterns in users’ transactions, (b) enormous content generated by the user interactions, (c) a dynamic interplay between the users’ transactional history and outcomes towards their future behavior, (d) ordinal nature of transaction elements. Motivated by these observations, we propose ComParE (Competing Parallel Networks with Expert Network), a novel personalized recommendation framework that: (i) exploits the distinct behavioral trends in the data, by training competing parallel networks as local experts, (ii) trains a global expert network to get the overall picture for the final prediction, and (iii) introduces custom loss functions to learn inherent ordering and interpretations of the classes being predicted. With the example of personalizing entry fee choices for the game of Rummy on the RummyCircle platform we show: (i) distinct and robust user Personas found based on historical entry fee selections; (ii) significant boosts in the entry fee predictions through ComParE with both neural networks and classical ML algorithms for local and global experts; (iii) substantial lifts over platform default as shown through offline analysis. ComParE significantly outperforms other baselines including well known deep learning models, as shown through offline experiments.
Proceedings of the 2nd International Workshop on Network Data Analytics, 2017
Cities today are typically plagued by multiple issues such as âĂŞ traffic jams, garbage, transit ... more Cities today are typically plagued by multiple issues such as âĂŞ traffic jams, garbage, transit overload, public safety, drainage etc. Citizens today tend to discuss these issues in public forums, social media, web blogs, in a widespread manner. Given that issues related to public transportation are most actively reported across web-based sources, we present a holistic framework for collection, categorization, aggregation and visualization of urban public transportation issues. The primary challenges in deriving useful insights from web-based sources, stem from: (a) the number of reports; (b) incomplete or implicit spatio-temporal context; and the (c) unstructured nature of text in these reports. This paper provides the text categorization techniques that can be adopted to address specifically these challenges. The work initiates with the formal complaint data from the largest public transportation agency in Bangalore, complemented by complaint reports from web-based and social med...
2018 IEEE World Congress on Services (SERVICES), 2018
Mining and profiling intent of players is an important facet in today's online games for the ... more Mining and profiling intent of players is an important facet in today's online games for the gaming companies to take targeted actions and provide personalized services. While analyzing and predicting player behavior have been studied in the online gaming domain, investigating intent behind those behaviors from the data has not been studied. We introduce the problem of player intent mining. With the example of online Rummy, a popular card game in India, we show how specific behavior aspects of player over time plays a key role in identifying their intent. Through both supervised and unsupervised techniques we achieve reasonable accuracy in identifying the intent.
Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019
To facilitate an environment of inclusive urban management, civic agencies need to listen to the ... more To facilitate an environment of inclusive urban management, civic agencies need to listen to the voices of citizens on web sources such as social media, online blogs, public forums and so on. Owing to the vastness and noisy nature of online data, it is challenging, yet important to mine actionable issues related to a city as faced by the citizens firsthand, so that timely measures can be taken by the administration to remedy them. In this work, we filter, analyze, and model web data on urban civic issues of a city, with respect to three modalities - semantics, spatial and temporal. We have come up with a novel approach that captures the contexts through dense distributed word embedding as well as identifies the latent issues through a generative model. Due to the scarcity of geo-tagged posts and delayed reporting, we rely primarily on the textual content of the data for location mining and temporal resolution. We present a first-of-a-kind unified system named CUrb that introduces a ...
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Papers by Tridib Mukherjee