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نتیجه جستجو - Affective computing

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Memory level neural network: A time-varying neural network for memory input processing
شبکه عصبی سطح حافظه: یک شبکه عصبی با زمان متفاوت برای پردازش ورودی حافظه-2020
Affective computing is an important foundation for implementing brain-like computing and advanced machine intelligence. However, the instantaneous and memory fusion input characteristic makes current neural networks not suitable for affective computing. In this paper, we propose an affective computing oriented memory level neural network. A ‘‘switch” has been added to the memory level neurons, which will achieve a transition from the instantaneous input to the memory input when the temporal integration of inputs above a certain threshold. Then, the ‘‘switch” is continualized by an adjustable sigmoid function whose parameters are tuned to adjust the speed of the transition and the mixing ratio of the two inputs. Multiple memory level neurons form a deep time-varying neural network capable of handling fusional inputs. We demonstrate on both process datasets and static datasets that the memory level neural network successfully converges on both datasets and meets the error accuracy requirements.
Keywords: Memory level neuron | Affective computing | Memory level transition | Time-related memory input | Time-varying output | MLNN
مقاله انگلیسی
2 Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals
تجزیه و تحلیل عاطفی داده های بزرگ چند متغیره: یک مرور جامع با استفاده از سیگنال های متنی ، صوتی ، تصویری و فیزیولوژیکی-2020
Affective computing is an emerging multidisciplinary research field that is increasingly drawing the attention of researchers and practitioners in various fields, including artificial intelligence, natural language processing, cognitive and social sciences. Research in affective computing includes areas such as sentiment, emotion, and opinion modelling. The internet is an excellent source of data required for sentiment analysis, such as customer reviews of products, social media, forums, blogs, etc. Most of these data, called big data, are unstructured and unorganized. Hence there is a strong demand for developing suitable data processing techniques to process these rich and valuable data to produce useful information. Early surveys on sentiment and emotion recognition in the literature have been limited to discussions using text, audio, and visual modalities. So far, to the authors knowledge, a comprehensive survey combining physiological modalities with these other modalities for affective computing has yet to be reported. The objective of this paper is to fill the gap in this surveyed area. The usage of physiological modalities for affective computing brings several benefits in that the signals can be used in different environmental conditions, more robust systems can be constructed in combination with other modalities, and it has increased anti-spoofing characteristics. The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.
Keywords: Affective computing | Multimodal fusion | Sentiment databases | Sentiment analysis | Affective applications
مقاله انگلیسی
3 A mobile application to report and detect 3D body emotional poses
یک برنامه کاربردی تلفن همراه برای گزارش و کشف نکات سه بعدی عاطفی بدن-2019
Most research into automatic emotion recognition is focused on facial expressions or physiological signals, while the exploitation of body postures has scarcely been explored, although they can be useful for emo- tion detection. This paper first explores a mechanism for self-reporting body postures with a novel easy- to-use mobile application called EmoPose. The app detects emotional states from self-reported poses, classifying them into the six basic emotions proposed by Ekman and a neutral state. The poses identi- fied by Schindler et al. have been used as a reference and the nearest neighbor algorithm used for the classification of poses. Finally, the accuracy in detecting emotions has been assessed by means of poses reported by a sample of users.
Keywords: Affective com puting | App | Emotion detection | Mobile application | Pose detection | Expert system
مقاله انگلیسی
4 Cognitive-affective regulation process for micro-expressions based on Gaussian cloud distribution
فرایند تنظیم عاطفی شناختی برای بیان میکرو براساس توزیع ابری گاوسی-2017
In this paper, we explore the process of emotional state transition. And the process is impacted by emotional state of interaction objects. First of all, the cognitive reasoning process and the micro-expressions recognition is the basis of affective computing adjustment process. Secondly, the threshold function and attenuation function are proposed to quantify the emotional changes. In the actual environment, the emotional state of the robot and external stimulus are also quantified as the transferring probability. Finally, the Gaussian cloud distribution is introduced to the Gross model to calculate the emotional transitional probabilities. The experimental results show that the model in humanecomputer interaction can effectively regulate the emotional states, and can significantly improve the humanoid and intelligent ability of the robot. This model is consistent with experimental and emulational significance of the psychology, and allows the robot to get rid of the mechanical emotional transfer process.Copyright © 2016, Chongqing University of Technology. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Micro-expression | Cognitive-affective regulation | Gaussian cloud distribution | Transferring probability | Emotional intensity
مقاله انگلیسی
5 TensiStrength: Stress and relaxation magnitude detection for social media texts
TensiStrength: تشخیص اندازه استرس و ارامش برای متون رسانه های اجتماعی-2017
Computer systems need to be able to react to stress in order to perform optimally on some tasks. This article describes TensiStrength, a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical ap proach and a set of rules to detect direct and indirect expressions of stress or relaxation, particularly in the context of transportation. It is slightly more effective than a compara ble sentiment analysis program, although their similar performances occur despite differ ences on almost half of the tweets gathered. The effectiveness of TensiStrength depends on the nature of the tweets classified, with tweets that are rich in stress-related terms being particularly problematic. Although generic machine learning methods can give better per formance than TensiStrength overall, they exploit topic-related terms in a way that may be undesirable in practical applications and that may not work as well in more focused contexts. In conclusion, TensiStrength and generic machine learning approaches work well enough to be practical choices for intelligent applications that need to take advantage of stress information, and the decision about which to use depends on the nature of the texts analysed and the purpose of the task.
Keywords: Stress | Relaxation | Sentiment analysis | Opinion mining | Affective computing
مقاله انگلیسی
6 Affective experience modeling based on interactive synergetic dependence in big data
مدل سازی تجربه موثر بر اساس وابستگی توأم تعاملی در داده های های بزرگ-2015
Affective computing is important in human–computer interaction. Especially in interactive cloud computing within big data, affective modeling and analysis have extremely high complexity and uncertainty for emotional status as well as decreased computational accuracy. In this paper, an approach for affective experience evaluation in an interactive environment is presented to help enhance the significance of those findings. Based on a person-independent approach and the cooperative interaction as core factors, facial expression features and states as affective indicators are applied to do synergetic dependence evaluation and to construct a participant’s affective experience distribution map in interactive Big Data space. The resultant model from this methodology is potentially capable of analyzing the consistency between a participant’s inner emotional status and external facial expressions regardless of hidden emotions within interactive computing. Experiments are conducted to evaluate the rationality of the affective experience modeling approach outlined in this paper. The satisfactory results on real-time camera demonstrate an availability and validity comparable to the best results achieved through the facial expressions only from reality big data. It is suggested that the person-independent model with cooperative interaction and synergetic dependence evaluation has the characteristics to construct a participant’s affective experience distribution, and can accurately perform real-time analysis of affective experience consistency according to interactive big data. The affective experience distribution is considered as the most individual intelligent method for both an analysis model and affective computing, based on which we can further comprehend affective facial expression recognition and synthesis in interactive cloud computing. Keywords: Affective computing Affective experience distribution Synergetic dependence Interactive big data
مقاله انگلیسی
7 تحلیل داده های بزرگ، به عنوان یک سرویس برای ربات‌های خدمتکار انسان‌نمای عاطفی
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 19
این مقاله، به تشخیص و تجزیه و تحلیل نیازمندی‌های قابلیتی پیشرفته برای سرویس دهیِ ربات‌های خدمتکار انسان نما به کاربردهای هوشمند و پیچیده ای مانند آموزش و مراقبت از کودک در محیط‌های خانه‌ ای هوشمند آینده، می پردازد. به طور خاص، به همین منظور از یک مکانیسم یادگیری پیوسته و مشارکت توزیع شده (DCCL) به عنوان یک قابلیت کلیدی در ربات خدمتکار انسان‌نما استفاده گردیده است به طوری که این مکانیسم می‌تواند نقشی موفقیت‌آمیز در این نوع کاربردها داشته باشد. بر مبنای ابزارهایی که اخیراً در رابطه با تجزیه‌وتحلیل کلان داده‌ها ارائه گردیده است و همچنین بر مبنای فناوری‌های یادگیری ماشین توزیع شده که در قالب سرویس‌هایی ادغام گردیده‌اند، یک چهارچوب میان‌افزار DCCL جدیدی توسعه یافته است که به منظور تسهیلِ درک و فهم مکانیسم DCCL کاربرد دارد. همچنین در راستای معرفی یک مطالعه‌ی موردی از کاربرد چهارچوب و مکانیسم DCCL پیشنهادی، به معرفی یک کاربرد توصیه گر پرداخته‌ایم که بر مبنای گرایش و تمایل یک کودک، به توصیه و پیشهاد مناسب‌ترین اسباب بازی برای وی می¬پردازد.
کلمات کلیدی: محاسبات عاطفی | تحلیل داده های بزرگ | یادگیری مستمر | مشارکت توزیع شده | انسان‌نما | ربات‌های خدمتکار
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