Ayush B Shah

This work applies Logically Constrained Reinforcement Learning (LCRL) frame-work for synthesizing policies for active monitoring and management of sensor modules in autonomous vehicles. LCRL allows synthesizing policies for unknown and continuous-state Markov Decision Processes (MDPs) such that a given linear time property is satisfied. We frame the problem of dynamic sensor module selection in automotive as an MDP and define the constraints as a Linear Temporal Logic (LTL) property. The LTL… Show more

This work applies Logically Constrained Reinforcement Learning (LCRL) frame-work for synthesizing policies for active monitoring and management of sensor modules in autonomous vehicles. LCRL allows synthesizing policies for unknown and continuous-state Markov Decision Processes (MDPs) such that a given linear time property is satisfied. We frame the problem of dynamic sensor module selection in automotive as an MDP and define the constraints as a Linear Temporal Logic (LTL) property. The LTL properties are used to guard and guide the MDP in finding an optimal policy. Defining a reward function over the state-action pairs of MDP based on the LTL property results in learning in a constrained environment. This approach leads to an improvement in performance and scalability for finding the right monitoring policies, as the safety properties enforces bounds on the search space. Show less

In this paper we present our experience in the design, modelling, implementation and evaluation of a conversational medical school tutor (MST), employing AI on the cloud. MST combines case-based tutoring with competency based curriculum review, using a natural language interface to enable an adaptive and rich learning experience. It is designed both to engage and tutor medical students through Digital Virtual Patient (DVP) interactions built around clinical reasoning activities and their… Show more

In this paper we present our experience in the design, modelling, implementation and evaluation of a conversational medical school tutor (MST), employing AI on the cloud. MST combines case-based tutoring with competency based curriculum review, using a natural language interface to enable an adaptive and rich learning experience. It is designed both to engage and tutor medical students through Digital Virtual Patient (DVP) interactions built around clinical reasoning activities and their application of foundational knowledge. DVPs in MST are realistic clinical cases authored by subject matter experts in natural language text. The context of each clinical case is modelled as a set of complex concepts with their associated attributes and synonyms using the UMLS ontology. The MST conversational engine understands the intent of the user’s natural language inputs by training Watson Assistant service and drives a meaningful dialogue relevant to the clinical case under investigation. The curriculum content is analysed using NLP techniques and represented as a related and cohesive graph with concepts as its nodes. The runtime application is modelled as a dynamic and adaptive flow between the case and student characteristics. We describe in detail the various challenges encountered in the design and implementation of this intelligent tutor and also present evaluation of the tutor through two field trials with third and fourth year students comprising of 90 medical students. Show less

There are only 0.3 psychiatrists and 0.047 psychologists per 100,000 people in India, compared to a country like the US, which has 29 psychologists per 100,000 people (according to WHO), thereby leading to a lack of counseling services and mental health-care. Fortunately, researchers in India have found mental health interventions delivered by lay counselors rather than specialists to be effective in treating and preventing mental health problems. However, choosing a lay counselor from a pool… Show more

There are only 0.3 psychiatrists and 0.047 psychologists per 100,000 people in India, compared to a country like the US, which has 29 psychologists per 100,000 people (according to WHO), thereby leading to a lack of counseling services and mental health-care. Fortunately, researchers in India have found mental health interventions delivered by lay counselors rather than specialists to be effective in treating and preventing mental health problems. However, choosing a lay counselor from a pool of candidates becomes a very important but time-consuming and tedious task because of our deficits in evaluating emotional capabilities, implicit biases, and facilitation skills in a resume and standard interview. In this paper, we present a highly scalable web application that can help in hiring emotionally intelligent lay-counselors. The backend framework measures several vital emotional intelligence features that are crucial in a prospective lay counselor. The framework uses multi-modal data and provides a ranking of potential counselors. The results and inferencing help establish the importance of each modality and give insights on features that are key to identify the emotional skills. We compare the predicted rankings to those given by the interviewers (a clinical psychologist and a psychiatrist) and recognize the benefits of automation of the process as well as a need for a deeper analysis of interview questions, discriminative features and importance of multi-modality assessments. Show less

Visual content has been proven to be effective for micro-learning compared to other media. In this paper, we discuss leveraging this observation in our efforts to build audio-visual content for young learners’ vocabulary learning. We attempt to tackle two major issues in the process of traditional visual curation tasks. Generic learning videos do not necessarily satisfy the unique context of a learner and/or an educator, and hence may not result in maximal learning outcomes. Also, manual video… Show more

Visual content has been proven to be effective for micro-learning compared to other media. In this paper, we discuss leveraging this observation in our efforts to build audio-visual content for young learners’ vocabulary learning. We attempt to tackle two major issues in the process of traditional visual curation tasks. Generic learning videos do not necessarily satisfy the unique context of a learner and/or an educator, and hence may not result in maximal learning outcomes. Also, manual video curation by educators is a highly labor-intensive process. To this end, we present a customizable micro-learning audio-visual content curation tool that is designed to reduce the human (educator) effort in creating just-in-time learning videos from a textual description (learning script). This provides educators with control of the content while preparing the learning scripts, and in turn can also be customized to capture the desired learning objectives and outcomes. As a use case, we automatically generate learning videos with British National Corpus’ (BNC) frequently spoken vocabulary words and evaluate them with experts. They positively recommended the generated learning videos with an average rating of 4.25 on a Likert scale of 5 points. The inter-annotator agreement between the experts for the video quality was substantial (Fleiss Kappa=0.62) with an overall agreement of 81%. Show less

In this work, we take a step towards understanding and defending against spying browser extensions. These are extensions repurposed to capture online activities of a user and communicate the collected sensitive information to a third-party domain. We conduct an empirical study of such extensions on the Chrome Web Store. First, we present an in-depth analysis of the spying behavior of these extensions. We observe that these extensions steal a variety of sensitive user information, such as the… Show more

In this work, we take a step towards understanding and defending against spying browser extensions. These are extensions repurposed to capture online activities of a user and communicate the collected sensitive information to a third-party domain. We conduct an empirical study of such extensions on the Chrome Web Store. First, we present an in-depth analysis of the spying behavior of these extensions. We observe that these extensions steal a variety of sensitive user information, such as the complete browsing history (e.g., the sequence of web traversals), online social network (OSN) access tokens, IP address, and geolocation. Second, we investigate the potential for automatically detecting spying extensions by applying machine learning schemes. We show that using a Recurrent Neural Network (RNN), the sequence of browser API calls made by an extension can be a robust feature, outperforming hand-crafted features (used in prior work on malicious extensions) to detect spying extensions. Our RNN based detection scheme achieves a high precision (90.02%) and recall (93.31%) in detecting spying extensions. Show less

A fundamental component of networking infrastructure is the policy, used in routing tables and firewalls. Accordingly, there has been extensive study of policies. However, the theory of such policies indicates that the size of the decision tree for a policy is very large ( O((2n)d), where the policy has n rules and examines d features of packets). If this was indeed the case, the existing algorithms to detect anomalies, conflicts, and redundancies would not be tractable for practical policies… Show more

A fundamental component of networking infrastructure is the policy, used in routing tables and firewalls. Accordingly, there has been extensive study of policies. However, the theory of such policies indicates that the size of the decision tree for a policy is very large ( O((2n)d), where the policy has n rules and examines d features of packets). If this was indeed the case, the existing algorithms to detect anomalies, conflicts, and redundancies would not be tractable for practical policies (say, n = 1000 and d = 10). In this paper, we clear up this apparent paradox. Using the concept of 'rules in play', we calculate the actual upper bound on the size of the decision tree, and demonstrate how three other factors - narrow fields, singletons, and all-matches make the problem tractable in practice. We also show how this concept may be used to solve an open problem: pruning a policy to the minimum possible number of rules, without changing its meaning. Show less

When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a… Show more

When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machine accuracy is sub-optimal, and joint optimization of the combined system improves performance. Show less

The Internet of Things combines various earlier areas of research. As a result, research on the subject is still organized around these pre-existing areas: distributed computing with services and objects, networks (usually combining 6lowpan with Zigbee etc. for the last-hop), artificial intelligence and semantic web, and human-computer interaction. We are yet to create a unified model that covers all these perspectives - domain, device, service, agent, etc. In this paper, we propose the concept… Show more

The Internet of Things combines various earlier areas of research. As a result, research on the subject is still organized around these pre-existing areas: distributed computing with services and objects, networks (usually combining 6lowpan with Zigbee etc. for the last-hop), artificial intelligence and semantic web, and human-computer interaction. We are yet to create a unified model that covers all these perspectives - domain, device, service, agent, etc. In this paper, we propose the concept of cells as units of structure and context in the Internet of things. This allows us to have a unified vocabulary to refer to single entities (whether dumb motes, intelligent spimes, or virtual services), intranets of things, and finally the complete Internet of things. The question that naturally follows, is what criteria we choose to demarcate boundaries; we suggest various possible answers to this question. We also mention how this concept ties into the existing visions and protocols, and suggest how it may be used as the foundation of a formal model. Show less

A massive current research effort focuses on combining pre-existing 'Intranets' of Things into one Internet of Things. However, this unification is not a panacea; it will expose new attack surfaces and vectors, just as it enables new applications. We therefore urgently need a model of security in the Internet of Things. In this regard, we note that IoT descends directly from pre-existing research (in embedded Internet and pervasive intelligence), so there exist several bodies of related work:… Show more

A massive current research effort focuses on combining pre-existing 'Intranets' of Things into one Internet of Things. However, this unification is not a panacea; it will expose new attack surfaces and vectors, just as it enables new applications. We therefore urgently need a model of security in the Internet of Things. In this regard, we note that IoT descends directly from pre-existing research (in embedded Internet and pervasive intelligence), so there exist several bodies of related work: security in RFID, sensor networks, cyber-physical systems, and so on. In this paper, we survey the existing literature on RFID and WSN security, as a step to compiling all known attacks and defenses relevant to the Internet of Things. Show less