Innovation of Touchless Touchscreen Technology in Automotive User Interface

Inside a car environment music system plays a major role for entertaining people. In this paper, a music system with touchless and vision based GUI is granted. This menu-driven UI is offered by controlling actions of the fist. Both these algorithms can be brought by the deep learning technique. Convolutional Neural Network (CNN) is used in the hand posture recognition technique to make the user interface interactive to perform the actions initiated by the user. Long-Term Recurrent Convolutional Neural Network (LRCNN) algorithm is used to ignite the touchless interface by the gestures. When a fist movement is carried out, a sequence is captured in the form of multiple image frames. So this can be accomplished using the deep learning technique. Sampled images are taken from the video sequence that is captured during the gesture recognition. Key frame extraction technique is adopted to obtain finer images from the video sequence using sparse learning. Sparse dictionary learning is used as it is individually optimized for the video sequence but, is expensive computationally

Alignment to the Actions of a Robot

Alignment is a phenomenon observed in human conversation: Dialog partners’ behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human–computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors’ beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version

Interrogating autism from a multidimensional perspective: an integrative framework.

Autism Spectrum Disorder (ASD) is a condition characterized by social and behavioral impairments, affecting approximately 1 in every 44 children in the United States. Common symptoms include difficulties in communication, interpersonal interactions, and behavior. While symptoms can manifest as early as infancy, obtaining an accurate diagnosis may require multiple visits to a pediatric specialist due to the subjective nature of the assessment, which may yield varying scores from different specialists. Despite growing evidence of the role of differences in brain development and/or environmental and/or genetic factors in autism development, the exact pathology of this disorder has yet to be fully elucidated by scientists. At present, the diagnosis of ASD typically involves a set of gold-standard diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS), the Autism Diagnostic Interview-Revised (ADI-R), and the more cost-effective Social Responsive Scale (SRS). Administering these diagnostic tests, which involve assessing communication and behavioral patterns, along with obtaining a clinical history, requires the expertise of a team of qualified clinicians. This process is time-consuming, effortful, and involves a degree of subjectivity due to the reliance on clinical judgment. Aside from conventional observational assessments, recent developments in neuroimaging and machine learning offer a fast and objective alternative for diagnosing ASD using brain imaging. This comprehensive work explores the use of different imaging modalvities, namely structural MRI (sMRI) and resting-state functional MRI (rs-fMRI), to investigate their potential for autism diagnosis. The proposed study aims to offer a new approach and perspective in comprehending ASD as a multidimensional problem, within a behavioral space that is defined by one of the available ASD diagnostic tools. This dissertation introduces a thorough investigation of the utilization of feature engineering tools to extract distinctive insights from various brain imaging modalities, including the application of novel feature representations. Additionally, the use of a machine learning framework to aid in the precise classification of individuals with autism is also explored in detail. This extensive research, which draws upon large publicly available datasets, sheds light on the influence of various decisions made throughout the pipeline on diagnostic accuracy. Furthermore, it identifies brain regions that may be impacted and contribute to an autism diagnosis. The attainment of high global state-of-the-art cross-validated, and hold-out set accuracy validates the advantages of feature representation and engineering in extracting valuable information, as well as the potential benefits of employing neuroimaging for autism diagnosis. Furthermore, a suggested diagnostic report has been put forth to assist physicians in mapping diagnoses to underlying neuroimaging markers. This approach could enable an earlier, automated, and more objective personalized diagnosis

Robust and Deployable Gesture Recognition for Smartwatches

Funding Information: This work was supported by the Department of Communications and Networking – Aalto University, Finnish Center for Artificial Intelligence (FCAI) and the Academy of Finland projects Human Automata (Project ID: 328813), BAD (Project ID: 318559), Huawei Technologies, and the Horizon 2020 FET program of the European Union (grant CHIST-ERA-20-BCI-001). Publisher Copyright: © 2022 ACM. Open Access fee has been paid, but the PDF version does not contain information on OA licence.Gesture recognition on smartwatches is challenging not only due to resource constraints but also due to the dynamically changing conditions of users. It is currently an open problem how to engineer gesture recognisers that are robust and yet deployable on smartwatches. Recent research has found that common everyday events, such as a user removing and wearing their smartwatch again, can deteriorate recognition accuracy significantly. In this paper, we suggest that prior understanding of causes behind everyday variability and false positives should be exploited in the development of recognisers. To this end, first, we present a data collection method that aims at diversifying gesture data in a representative way, in which users are taken through experimental conditions that resemble known causes of variability (e.g., walking while gesturing) and are asked to produce deliberately varied, but realistic gestures. Secondly, we review known approaches in machine learning for recogniser design on constrained hardware. We propose convolution-based network variations for classifying raw sensor data, achieving greater than 98% accuracy reliably under both individual and situational variations where previous approaches have reported significant performance deterioration. This performance is achieved with a model that is two orders of magnitude less complex than previous state-of-the-art models. Our work suggests that deployable and robust recognition is feasible but requires systematic efforts in data collection and network design to address known causes of gesture variability.Peer reviewe

A Conversational and Compositional Grid for Freshman University Students

The purpose of this paper is to put together a tool for Freshman University Students with an ESL level, which will assist them to avoid errors in syntax precision and sentence generation. Both these aspects are problematic for students with a SOV language as mother-tongue who then have to produce with a SVO challenge. When their own language is a post-positional language as opposed to English as a prepositional language, that situation may complicate matters for these students even more. The grid is designed in such a way to allow the student to start from the left and work his way to the right selecting one item from the list constructing a meaningful communication as he/she goes along. The overall intention is towards greater precision and correctness, raising the level of accuracy in syntax and other grammatical aspects. The grammar selected for this purpose is the traditional grammar chosen for its simplicity, stability, and continuity functional in millennia of grammar didactics. The role of transformational-generative grammars are not overlooked but none of the recent grammar approaches in sentence grammar, discourse grammar, HPSG (Head-Driven Phrase Structure Grammar), universal grammar or syntax grammar could serve the purpose of designing this tool except sequencers or DM (discourse markers) discussed by Heine (2013). The limitation to this study is that the Conversational Grid tool has not been tested yet and that task calls for another future article describing the results of experimentation utilizing this tool