Design and Study of Emotions in Virtual Humans for Assistive Technologies

This thesis presents the design and study of emotionally aligned prompts given by virtual humans for persons with cognitive disabilities such as Alzheimer’s disease and related dementias (ADRD). Our goal is to understand how emotions in virtual humans are interpreted by people. Persons with ADRD often need assistance from a care partner to complete activities of daily living such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations by giving automated prompts or cues. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption. As a step in this direction, we designed and conducted user study with three different virtual humans, who expressively communicate prompts for a simple handwashing task. The user study was conducted in two phases. The phase I study had all age group people as participants and involved a female virtual human character with facial expressions and body gestures. The phase II study had elderly people as participants and involved both male and female virtual human characters with a focus on their facial expressions. Prompts were evaluated with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results of the phase I study showed that, people generally agree on the evaluation dimension, whereas in phase II, we had more consensus on evaluation and potency dimensions and were close to consensus on activity. This thesis gives an overview of the hand washing system, and then details the design of the virtual human character and prompts and the results and analysis of the user study for both phases

Affectively Aligned Cognitive Assistance Using Bayesian Affect Control Theory

Abstract. This paper describes a novel emotionally intelligent cognitive assistant to engage and help older adults with Alzheimer’s disease (AD) to complete activities of daily living (ADL) more independently. Our new system combines two research streams. First, the development of cognitive assistants with artificially intelligent controllers using partially observable Markov decision processes (POMDPs). Second, a model of the dynamics of emotion and identity called Affect Control Theory that arises from the sociological literature on culturally shared sentiments. We present background material on both of these research streams, and then demonstrate a prototype assistive technology that combines the two. We discuss the affective reasoning, the probabilistic and decision-theoretic reasoning, the computer-vision based activity monitoring, the embodied prompting, and we show results in proof-of-concept tests.

Endoplasmic Reticulum Stress-Induced JNK Activation Is a Critical Event Leading to Mitochondria-Mediated Cell Death Caused by β-Lapachone Treatment

β-lapachone (β-lap) is a bioreductive agent that is activated by the two-electron reductase NAD(P)H quinone oxidoreductase 1 (NQO1). Although β-lap has been reported to induce apoptosis in various cancer types in an NQO1-dependent manner, the signaling pathways by which β-lap causes apoptosis are poorly understood.β-lap-induced apoptosis and related molecular signaling pathways in NQO1-negative and NQO1-overexpressing MDA-MB-231 cells were investigated. Pharmacological inhibitors or siRNAs against factors involved in β-lap-induced apoptosis were used to clarify the roles played by such factors in β-lap-activated apoptotic signaling pathways. β-lap leads to clonogenic cell death and apoptosis in an NQO1- dependent manner. Treatment of NQO1-overexpressing MDA-MB-231 cells with β-lap causes rapid disruption of mitochondrial membrane potential, nuclear translocation of AIF and Endo G from mitochondria, and subsequent caspase-independent apoptotic cell death. siRNAs targeting AIF and Endo G effectively attenuate β-lap-induced clonogenic and apoptotic cell death. Moreover, β-lap induces cleavage of Bax, which accumulates in mitochondria, coinciding with the observed changes in mitochondria membrane potential. Pretreatment with Salubrinal (Sal), an endoplasmic reticulum (ER) stress inhibitor, efficiently attenuates JNK activation caused by β-lap, and subsequent mitochondria-mediated cell death. In addition, β-lap-induced generation and mitochondrial translocation of cleaved Bax are efficiently blocked by JNK inhibition.Our results indicate that β-lap triggers induction of endoplasmic reticulum (ER) stress, thereby leading to JNK activation and mitochondria-mediated apoptosis. The signaling pathways that we revealed in this study may significantly contribute to an improvement of NQO1-directed tumor therapies

Mechanism-Based Screen for G1/S Checkpoint Activators Identifies a Selective Activator of EIF2AK3/PERK Signalling

Human cancers often contain genetic alterations that disable G1/S checkpoint control and loss of this checkpoint is thought to critically contribute to cancer generation by permitting inappropriate proliferation and distorting fate-driven cell cycle exit. The identification of cell permeable small molecules that activate the G1/S checkpoint may therefore represent a broadly applicable and clinically effective strategy for the treatment of cancer. Here we describe the identification of several novel small molecules that trigger G1/S checkpoint activation and characterise the mechanism of action for one, CCT020312, in detail. Transcriptional profiling by cDNA microarray combined with reverse genetics revealed phosphorylation of the eukaryotic initiation factor 2-alpha (EIF2A) through the eukaryotic translation initiation factor 2-alpha kinase 3 (EIF2AK3/PERK) as the mechanism of action of this compound. While EIF2AK3/PERK activation classically follows endoplasmic reticulum (ER) stress signalling that sets off a range of different cellular responses, CCT020312 does not trigger these other cellular responses but instead selectively elicits EIF2AK3/PERK signalling. Phosphorylation of EIF2A by EIF2A kinases is a known means to block protein translation and hence restriction point transit in G1, but further supports apoptosis in specific contexts. Significantly, EIF2AK3/PERK signalling has previously been linked to the resistance of cancer cells to multiple anticancer chemotherapeutic agents, including drugs that target the ubiquitin/proteasome pathway and taxanes. Consistent with such findings CCT020312 sensitizes cancer cells with defective taxane-induced EIF2A phosphorylation to paclitaxel treatment. Our work therefore identifies CCT020312 as a novel small molecule chemical tool for the selective activation of EIF2A-mediated translation control with utility for proof-of-concept applications in EIF2A-centered therapeutic approaches, and as a chemical starting point for pathway selective agent development. We demonstrate that consistent with its mode of action CCT020312 is capable of delivering potent, and EIF2AK3 selective, proliferation control and can act as a sensitizer to chemotherapy-associated stresses as elicited by taxanes

Mechanism-based screen for G1/S checkpoint activators identifies a selective activator of EIF2AK3/PERK signalling.

Human cancers often contain genetic alterations that disable G1/S checkpoint control and loss of this checkpoint is thought to critically contribute to cancer generation by permitting inappropriate proliferation and distorting fate-driven cell cycle exit. The identification of cell permeable small molecules that activate the G1/S checkpoint may therefore represent a broadly applicable and clinically effective strategy for the treatment of cancer. Here we describe the identification of several novel small molecules that trigger G1/S checkpoint activation and characterise the mechanism of action for one, CCT020312, in detail. Transcriptional profiling by cDNA microarray combined with reverse genetics revealed phosphorylation of the eukaryotic initiation factor 2-alpha (EIF2A) through the eukaryotic translation initiation factor 2-alpha kinase 3 (EIF2AK3/PERK) as the mechanism of action of this compound. While EIF2AK3/PERK activation classically follows endoplasmic reticulum (ER) stress signalling that sets off a range of different cellular responses, CCT020312 does not trigger these other cellular responses but instead selectively elicits EIF2AK3/PERK signalling. Phosphorylation of EIF2A by EIF2A kinases is a known means to block protein translation and hence restriction point transit in G1, but further supports apoptosis in specific contexts. Significantly, EIF2AK3/PERK signalling has previously been linked to the resistance of cancer cells to multiple anticancer chemotherapeutic agents, including drugs that target the ubiquitin/proteasome pathway and taxanes. Consistent with such findings CCT020312 sensitizes cancer cells with defective taxane-induced EIF2A phosphorylation to paclitaxel treatment. Our work therefore identifies CCT020312 as a novel small molecule chemical tool for the selective activation of EIF2A-mediated translation control with utility for proof-of-concept applications in EIF2A-centered therapeutic approaches, and as a chemical starting point for pathway selective agent development. We demonstrate that consistent with its mode of action CCT020312 is capable of delivering potent, and EIF2AK3 selective, proliferation control and can act as a sensitizer to chemotherapy-associated stresses as elicited by taxanes

Socially Intelligent Affective AI

Artificial Intelligence has aimed to give the systems or agents, the ability to learn, perceive, recognize, plan, reason and act. Affective Computing has brought into focus the importance of giving AI systems, the capability to perceive, detect, utilize and generate emotion, affect, sentiment or feelings. To have a meaningful human-computer interaction, we need to design and develop a more socially intelligent and affective AI. My doctoral research goal is to delve deeper into some of these aspects, firstly by surveying computational models implemented in AI that uses emotion in decision-making or behaviour; secondly, by creating new model to predict social event context and affect in group videos; thirdly, to predict the social identities in visual scenes; and lastly to combine information about context, identities, behaviour and emotion in a social interaction scene to predict social incoherence and to recommend appropriate behaviour

A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing

A cloud computing environment offers a simplified, centralized platform or resources for use when needed at a low cost. One of the key functionalities of this type of computing is to allocate the resources on an individual demand. However, with the expanding requirements of cloud user, the need of efficient resource allocation is also emerging. The main role of service provider is to effectively distribute and share the resources which otherwise would result into resource wastage. In addition to the user getting the appropriate service according to request, the cost of respective resource is also optimized. In order to surmount the mentioned shortcomings and perform optimized resource allocation, this research proposes a new Agent based Automated Service Composition (A2SC) algorithm comprising of request processing and automated service composition phases and is not only responsible for searching comprehensive services but also considers reducing the cost of virtual machines which are consumed by on-demand services only

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes

In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary objective while respecting some constraints with respect to secondary objectives. Such problems can be naturally modeled as constrained partially observable Markov decision processes (CPOMDPs) when the environment is partially observable. In this work, we describe a technique based on approximate linear programming to optimize policies in CPOMDPs. The optimization is performed offline and produces a finite state controller with desirable performance guarantees. The approach outperforms a constrained version of point-based value iteration on a suite of benchmark problems