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When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars. The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis. To enhance accuracy and comprehension in complex situations, DFN makes use of a combination of deep learning and fusion techniques. With a minimum gain of 6.4% in accuracy for the SUN-RGB-D dataset and 3.6% for the NYU-Dv2 dataset. Findings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized.

Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

The effects of traction load, plate thickness, and pin radius on stress and deformation distribution were investigated by simulation symmetrical pin traction experiment and analytical calculation methods. At the beginning of traction, both contact angle and maximum stress increase with the traction load. As the traction continues, maximum stress is steady and only the contact angle increases with the traction load. The stress concentration increases with the decrease of pin radius and does not change with other parameters under the same pin radius. The increase of specimen thickness can only reduce the contact angle but does not lessen the stress concentration, and thicker specimens can bear more traction load when reaching the same contact angle. Strengthening during deformation is caused not only by the increase of dislocation density in martensite matrix but also by retained austenite (RA) strain-induced martensite transformation (SIMT), which is conducive to the transfer of plastic deformation.

To enhance the efficacy of traditional English enlightenment education for children, this research delves into a multi-modal teaching approach and incorporates a hidden Markov model to refine the precision of speech recognition. Within the input recognition module, voice input is combined with operational input, resulting in a multi-modal fusion perception module designed to amalgamate students’ learning and operational intent. Concurrently, a multi-modal natural interaction module for intention understanding is formulated to augment the quality of interaction during the teaching. The research findings revealed that the accuracy of the speech input recognition model surpassed that of the conventional model, increasing from 57.14 to 71.05%. The success rate of interactive perception across the seven learning dimensions within the fusion perception module surpassed 95%. Additionally, the success rate of the intention-based multi-modal interaction module exceeded 98%. The English teaching model developed in the study demonstrates superior teaching performance, effectively enhancing the efficacy of early childhood English education.

Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

It has been suggested that immersive virtual reality (VR) technology allows knowledge-building experiences and in this way provides an alternative educational process. Important key features of constructivist educational computer-based environments for science teaching and learning, include interaction, size, transduction and reification. Indeed, multi-sensory VR technology suits very well the needs of sciences that require a higher level of visualization and interaction. Haptics that refers to physical interactions with virtual environments (VEs) may be coupled with other sensory modalities such as vision and audition but are hardly ever associated with other feedback channels, such as olfactory feedback. A survey of theory and existing VEs including haptic or olfactory feedback, especially in the field of education is provided. Our multi-modal human-scale VE VIREPSE (virtual reality platform for simulation and experimentation) that provides haptic interaction using a string-based interface called SPIDAR (space interface device for artificial reality), olfactory and auditory feedbacks is described. An application that allows students experiencing the abstract concept of the Bohr atomic model and the quantization of the energy levels has been developed. Different configurations that support interaction, size and reification through the use of immersive and multi-modal (visual, haptic, auditory and olfactory) feedback are proposed for further evaluation. Haptic interaction is achieved using different techniques ranging from desktop pseudo-haptic feedback to human-scale haptic interaction. Olfactory information is provided using different fan-based olfactory displays (ODs). Significance of developing such multi-modal VEs for education is discussed. [PUBLICATION ABSTRACT]

Collaborative learning in high-fidelity simulators is an important part of how master mariner students are preparing for their future career at sea by becoming part of a ship’s bridge team. This study aims to inform the design of multimodal learning analytics to be used for providing automated feedback to master mariner students engaged in collaborative learning activities in high-fidelity navigation simulators. Through a design ethnographic approach, we analyze video records of everyday training practices at a simulator center in Scandinavia, exploring (a) how feedback is delivered to students during collaborative activities in full-mission simulators and (b) which sensors are needed and why they are needed for capturing the multimodal nature of professional performance, communication, and collaboration in simulation-based collaborative learning. Our detailed analysis of two episodes from the data corpus shows how the delivery of feedback during simulations consists of recurring, multidimensional, and multimodal feedback cycles, comprising instructors’ close monitoring of student’s actions to continuously assess the fit between the learning objectives and the ongoing task. Through these embedded assessments, feedback that draws on the rich semiotic resources of the simulated environment, while considering aspects of realism and authenticity, is provided. Considering the multidimensional and multimodal nature of feedback in professional learning contexts, we identify technologies and sensors needed for capturing professional performance in simulated environments.

PurposeIn the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces.Design/methodology/approachAn experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data.FindingsThis study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments.Originality/valueThis study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.

Soft robots have tremendous potential for applications in various fields, owing to their safety and flexibility embedded at the material level. Soft robots, especially bio-inspired soft legged robots, have become one of the most active fields of current research in robotics thanks to their superior mobility and ability to face complex terrains. However, it is arduous to establish a dynamic simulation model for soft robots, owing to their hyper-redundant degrees of freedom, hyper-elasticity, and nonlinearity of their soft structures. In this study, we designed, simulated, and fabricated a hexapod robot that achieves walking, crawling, pronking, and rolling with wheeled legs plus a soft body capable of shape change. A robot prototype was fabricated using 3D printing technology and soft silicone pneumatic networks. Actuators, battery power, and control boards were integrated into the body of the robot for untethered locomotion. We have explored the capabilities of the robot in different conditions, especially in scenarios that simulate lunar and Martian environments, demonstrating the motion performance of the robot. The results have shown promising potentials of the developed robot for future applications in planetary lava tube exploration. Our experimental and simulation results also show good agreements that indicate the potential predictive roles of simulation tools for soft robot design, planning, and control.

Juggling is a complex motor skill that requires multiple sub-skills and cannot be mastered without extensive practice. Although prior studies have quantified performance differences between novice and expert jugglers, none have attempted to quantitatively decompose these components or model their contribution to juggling performance. This longitudinal study presents a multimodal evaluation system that integrates computer vision, motion capture, and biosensing to quantify three key elements of juggling ability: Sequencing, Prediction, and Accuracy. Twenty beginners completed a 10-day, three-ball juggling experiment combining visuo-haptic virtual reality (VR) and real-world practice, with half training in reduced gravity, previously shown to enhance early-stage motor learning. The fitted Gamma-Log generalized linear model (GLM) indicated that Sequencing is the dominant factor of early skill acquisition, followed by Prediction and Accuracy. This study provides the first computational decomposition of juggling, demonstrates how multiple elements jointly contribute to performance, and results in a principled approach to characterizing motor learning in complex real-world tasks.