Socially Believable Robots

Long-term companionship, emotional attachment and realistic interaction with robots have always been the ultimate sign of technological advancement projected by sci-fi literature and entertainment industry. With the advent of artificial intelligence, we have indeed stepped into an era of socially believable robots or humanoids. Affective computing has enabled the deployment of emotional or social robots to a certain level in social settings like informatics, customer services and health care. Nevertheless, social believability of a robot is communicated through its physical embodiment and natural expressiveness. With each passing year, innovations in chemical and mechanical engineering have facilitated life-like embodiments of robotics; however, still much work is required for developing a “social intelligence” in a robot in order to maintain the illusion of dealing with a real human being. This chapter is a collection of research studies on the modeling of complex autonomous systems. It will further shed light on how different social settings require different levels of social intelligence and what are the implications of integrating a socially and emotionally believable machine in a society driven by behaviors and actions

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes

Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings

Drawing-based tests are cost-effective, noninvasive screening methods, popularly employed by psychologists for the early detection and diagnosis of various neuropsychological disorders. Computerized analysis of such drawings is a complex task due to the high degree of deformations present in the responses and reliance on extensive clinical manifestations for their inferences. Traditional rule-based approaches employed in visual analysis-based systems prove insufficient to model all possible clinical deformations. Meanwhile, procedural analysis-based techniques may contradict with the standard test conduction and evaluation protocols. Leveraging on the increasing popularity of convolutional neural networks (CNNs), we propose an effective technique for modeling and classifying dysfunction indicating deformations in drawings without modifying clinical standards. Contrary to conventional sketch recognition applications where CNNs are trained to diminish intra-shape class variations, we employ deformation-specific augmentation to enhance the presence of specific deviations that are defined by clinical practitioners. The performance of our proposed technique is evaluated using Lacks’ scoring of the Bender-Gestalt test, as a case study. The results of our experimentation substantiate that our proposed approach can represent domain knowledge sufficiently without extensive heuristics and can effectively identify drawing-based biomarkers for various neuropsychological disorders

Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs

Parkinson’s disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients’ fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset

An adaptive and efficient buffer management scheme for resource-constrained delay tolerant networks

Provisioning buffer management mechanism is especially crucial in resource-constrained delay tolerant networks (DTNs) as maximum data delivery ratio with minimum overhead is expected in highly congested environments. However, most DTN protocols do not consider resource limitations (e.g., buffer, bandwidth) and hence, results in performance degradation. To strangle and mitigate the impact of frequent buffer overflows, this paper presents an adaptive and efficient buffer management scheme called size-aware drop (SAD) that strives to improve buffer utilization and avoid unnecessary message drops. To improve data delivery ratio, SAD exactly determines the requirement based on differential of newly arrived message(s) and available space. To vacate inevitable space from a congested buffer, SAD strives to avoid redundant message drops and deliberate to pick and discard most appropriate message(s) to minimize overhead. The performance of SAD is validated through extensive simulations in realistic environments (i.e., resource-constrained and congested) with different mobility models (i.e., Random Waypoint and disaster). Simulation results demonstrate the performance supremacy of SAD in terms of delivery probability and overhead ratio besides other metrics when compared to contemporary schemes based on Epidemic (DOA and DLA) and PRoPHET (SHLI and MOFO). © 2015, Springer Science+Business Media New York

Segmentation and Recognition of Electronic Components in Hand-Drawn Circuit Diagrams

This paper presents an effective technique for segmentation and recognition of electronic components from hand-drawn circuit diagrams. Segmentation is carried out by using a series of morphological operations on the binarized images of circuits and discriminating between three categories of components (closed shape, components with connected lines, disconnected components). Each segmented component is characterized by computing the Histogram of Oriented Gradients (HOG) descriptor while classification is carried out using Support Vector Machine (SVM). The system is evaluated on 100 hand-drawn circuit diagrams with a total of 350 components. A segmentation accuracy of 87.7% while a classification rate of 92% is realized demonstrating the effectiveness of the proposed technique