This work is part of a larger project exploring how affective computing can support the design of player-adaptive video games. We investigate how controlling some of the game mechanics using biofeedback affects physio...
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(纸本)9798400703218
This work is part of a larger project exploring how affective computing can support the design of player-adaptive video games. We investigate how controlling some of the game mechanics using biofeedback affects physiological reactions, performance, and the experience of the player. More specifically, we assess how different game speeds affect player physiological responses and game performance. We developed a game prototype with Unity1 which includes a biofeedback loop system based on the level of physiological activation through skin resistance (SKR) measured with a smart wristband. In two conditions, the player moving speed was driven by SKR, to increase (respectively decrease) speed when the player is less activated (SKR decreases). A control condition was also used where player speed is not affected by SKR. We collected and synchronized biosignals (heart rate [HR], skin temperature [SKT] and SKR), and game information, such as the total time to complete a level, the number of ennemy collisions, and their timestamps. Additionally, emotional profiling (TIPI, I-Panas-SF), measured using a Likert scale in a post-task questionnaire, and semi-open questions about the game experience were used. The results show that SKR was significantly higher in the speed down condition, and game performance improved in the speed up condition. Study collected data involved 13 participants (10 males, 3 females) aged from 18 to 50 (M = 24.30, SD = 9.00). Most of the participants felt engaged with the game (M = 6.46, SD = 0.96) and their level of immersion was not affected by wearing the prototype smartband. Thematic analysis (TA) revealed that the game speed impacted the participants stress levels such as high speed was more stressful than hypothesized;many participants described game level-specific effects in which they felt that their speed of movement reflected their level of stress or relaxation. Slowing down the participants indeed increased the participant stress levels, but coun
1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods ...
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1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods also exist,such as peer-to-peer networks and distributed hash *** technology,which is decentralized,anonymous,non-tamperable and anti-attack,can be applied to covert communication to solve the problems of traditional methods and improve the quality of communication.
With the growing demand for global trade transportation, the shipping container market has gained an increasingly important position. As a key issue of the market, container pricing is regarded as an important indicat...
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Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of different structures. Current methods o...
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Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data...
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Electricity is essential for keeping power networks balanced between supply and demand,especially since it costs a lot to *** article talks about different deep learning methods that are used to guess how much green e...
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Electricity is essential for keeping power networks balanced between supply and demand,especially since it costs a lot to *** article talks about different deep learning methods that are used to guess how much green energy different Asian countries will *** main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising *** is a new deep learning model called the Green-electrical Production Ensemble(GP-Ensemble).It combines three types of neural networks:convolutional neural networks(CNNs),gated recurrent units(GRUs),and feedforward neural networks(FNNs).The model promises to improve prediction *** 1965–2023 dataset covers green energy generation statistics from ten Asian *** to the rising energy supply-demand mismatch,the primary goal is to develop the best model for predicting future power *** GP-Ensemble deep learning model outperforms individual models(GRU,FNN,and CNN)and alternative approaches such as fully convolutional networks(FCN)and other ensemble models in mean squared error(MSE),mean absolute error(MAE)and root mean squared error(RMSE)*** study enhances our ability to predict green electricity production over time,with MSE of 0.0631,MAE of 0.1754,and RMSE of *** may influence laws and enhance energy management.
The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture image...
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The sudden advent of COVID-19 pandemic left educational institutions in a difficult situation for the semester evaluation of students;especially where the online participation was difficult for the students. Such a si...
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The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolu...
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The interpretability of deep learning models has emerged as a compelling area in artificial intelligence *** safety criteria for medical imaging are highly stringent,and models are required for an ***,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and ***,the interpretability of CNNs has come into the *** medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning ***,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these *** this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac *** enhanced GPU training system implemented interpretable global average pooling for graphics using deep *** deep learning tasks were *** included data management,neural network architecture,and *** system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment *** results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning *** model is lightweight and more convenient to deploy on mobile devices than transfer learning models.
Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essen...
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Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.
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