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نتیجه جستجو - CLIPS

تعداد مقالات یافته شده: 14
ردیف عنوان نوع
1 Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery
بینایی عمیق مبتنی بر یادگیری برای تشخیص و طبقه بندی حرکات بخیه در جراحی با کمک روبات-2021
Background: Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision to automate the identification and classification of suturing gestures for needle driving attempts.
Methods: Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2- and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits.
Results: In this study, all models were able to reliably predict either the presence of a gesture (identification, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus convolutional long short-term memory unit) on performance.
Conclusion: Our results demonstrate computer vision’s ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment.
مقاله انگلیسی
2 Special interest tourism is not so special after all: Big data evidence from the 2017 Great American Solar Eclipse
جهانگردی با علاقه ویژه از همه مهم تر نیست: شواهد داده های بزرگ از خورشید گرفتگی بزرگ آمریکایی 2017-2020
This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism (SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns. Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is validated with the external data through hypothesis testing and compared with and contrasted against existing motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban destinations for developing SIT tourism are also discussed.
Keywords: Big data | Instagram photos | Social media | Spatial analysis | Special interest tourism | Astro-tourism
مقاله انگلیسی
3 The design of software development platform for CFETR plasma control system
طراحی بستر توسعه نرم افزار برای سیستم کنترل پلاسما CFETR-2020
The Plasma Control System (PCS) is a critical system of the tokamak device to guarantee the physical experiment operation. While the Chinese Fusion Engineering Testing Reactor (CFETR) PCS is in the preliminary development stage, the newly designed Plasma Control System Software Development Platform (PCS-SDP) will provide an effective, convenient, and visual development environment for PCS software developers. The PCS-SDP is developed based on the Eclipse framework as an extension and finally realized as an Eclipse plug-in. It is deployed in a thin-client C/S mode in which developers log in and work remotely and all the developments are carried on a development server. The PCS-SDP possesses an intuitive UI and contains modules of project management, algorithm management, visualization management, testing management, and version management. Because of these customized functions, the PCS-SDP makes the developers focus on the control logic design of the PCS algorithms without the need to pay attention to the PCS details; the work efficiency is improved significantly. In this paper, the requirements are analyzed, the system architecture and module design are presented, and some functions are demonstrated. The initial hardware environment deployment has been implemented and is also presented in this paper. Further efforts will be made to implement and demonstrate the functions of all modules on the EAST PCS, then serve CFETR PCS development and can be appropriate for most Plasma Control Systems
Keywords: Software platform | Plasma control system | Eclipse | Visualization | Algorithm management
مقاله انگلیسی
4 Autistic traits, personality, and evaluations of humanoid robots by young and older adults
ویژگی های اوتیستیک ، شخصیت و ارزیابی ربات های انسان دوستانه توسط افراد جوان و بزرگتر-2020
While research with individuals on the autistic spectrum has increased strongly, there is still a lack of research on autism/autistic traits in older adults. Children with autism have been proposed to benefit from interactions with social robots; for older adults, the potential role of robotics is currently being discussed. We combined these topics by assessing both young and older (Mean age ¼ 22 vs. 69 years) neurotypical adults’ evaluations of various humanoid robots presented in video clips, on multiple dimensions (likeability, companionship, dominance, threat, human-likeness). We additionally assessed autistic traits (Autism Spectrum Questionnaire – AQ) and Big- Five personality traits. Remarkably, older adults evaluated robots as more likeable. Compared to young adults, older adults also showed significantly higher levels of autistic traits (particularly in the AQ social interaction subscale), higher levels of conscientiousness, and lower levels of openness. We found strong positive correlations between ratings of likeability and human-likeness of robots across groups, and particularly in participants with high levels of autistic trait. Across robots, data also provided evidence for the uncanny valley phenomenon. Favourable evaluations of robots by older adults suggest potential for older adults on the autistic spectrum to benefit from social robots.
Keywords: Humanoid robots | Old adults | Autistic traits | Personality | Visual appearance
مقاله انگلیسی
5 Using an AI creativity system to explore how aesthetic experiences are processed along the brain’s perceptual neural pathways
استفاده از یک سیستم خلاقیت هوش مصنوعی برای بررسی نحوه پردازش تجارب زیبایی شناختی در مسیرهای عصبی ادراکی مغز-2020
With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be used to explore the nature of the dual-pathway neuro-architecture of the human visual system and how this relates to higher cognitive judgments such as aesthetic experiences that rely on these divergent information streams. We propose a theoretical framework for how the movement within percepts such as video clips, causes the engagement of reflexive attention and a subsequent focus on visual information that are primarily processed via the dorsal stream, thereby modulating aesthetic experiences that rely on information relayed via the ventral stream. We outline our recent study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences.
Keywords: Neuroscience | Brain simulation | Artificial intelligence | Deep learning | Visual pathways | Neural pathways | Neuro-architecture | Aesthetics
مقاله انگلیسی
6 Interestingness elements for explainable reinforcement learning: Understanding agents capabilities and limitations
عناصر جالب توجه برای یادگیری تقویتی قابل توضیح: درک توانایی ها و محدودیت های عوامل-2020
We propose an explainable reinforcement learning (XRL) framework that analyzes an agent’s history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent’s behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent’s strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.
Keywords: Explainable AI | Reinforcement learning | Interestingness elements | Autonomy | Video highlights | Visual explanations
مقاله انگلیسی
7 Transfer learning of deep neural network representations for fMRI decoding
انتقال یادگیری بازنمایی های شبکه عصبی عمیق برای رمزگشایی fMRI-2019
Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view.
Keywords: Deep learning | Convolutional Neural Network | Transfer learning | Brain decoding | fMRI | MultiVoxel Pattern Analysis
مقاله انگلیسی
8 A fuzzy expert system for mitigation of risks and effective control of gas pressure reduction stations with a real application
یک سیستم خبره فازی برای کاهش خطرات و کنترل مؤثر ایستگاه های کاهش فشار گاز با کاربرد واقعی-2019
Environmental changes and increased uncertainty due to technical damage, explosions and large fires have caused the risk of an inevitable element in the gas industry. This study purposes developing a new hybrid fuzzy expert system as a decision support system to mitigate the risk associated with gas transmission stations. The designed knowledge-based system combines the procedural and descriptive rules based on experts’ judgments to analyze the complex relationships between the different components of a gas pressure reduction station. The developed fuzzy expert system is coded in C language integrated production system (CLIPS) and is linked with MATLAB software for calling fuzzy functions. A real case study of gas pressure reduction stations in Iranian gas industry is conducted to validate the proposed expert system model. The expert system provides more than one thousand rules based on expert knowledge to prevent the pressure drop and the quality loss of gas or shutting off gas flow which accordingly increases gas flow stability. The proposed expert system could minimize the risk of hazardous scenarios, such as leakage and corrosion, in the gas industry and provide an acceptable precision in the provision of periodic control strategies and appropriate response under an emergency condition.
Keywords: Expert systems | Gas city stations | Decision support | Fuzzy variables | Gas pressure
مقاله انگلیسی
9 Hybrid feature-based analysis of video’s affective content using protagonist detection
تجزیه و تحلیل مبتنی بر ویژگی ترکیبی از محتوای عاطفی فیلم با استفاده از تشخیص شخصیت-2019
Extracting an effective representation to analyze affection in videos is an inherent challenge. To address this problem, we present a novel emotion recognition system that can intelligently analyze and auto- matically recognize video emotions. We observe that most of the emotions in a video are closely related to the roles, especially those of the protagonist, which motivates us to explore how to utilize the pro- tagonist’s information to help recognize video emotions. By analyzing the traits of the protagonists, we suggest a new solution to detect the protagonists to adapt not only to the whole video but also to a video clip. Moreover, a new keyframe selection strategy based on the protagonist is designed to select a set of representative frames from video clips. Furthermore, the scale invariant feature transform (SIFT) features matrix, built from each keyframe, is fed into a convolutional neural network (CNN) to learn the discrimi- native representations, which makes the CNN and local features complement each other. Considering that emotions are usually continuous, we introduce temporal information into the CNN by using optical flow images. Additionally, we extract some handcrafted visual and audio features as a supplement. Finally, all the features, including the features learned from the CNN and the handcrafted features, are fused and input into a support vector machine (SVM) and a support vector regression (SVR) for video emotion recognition. The proposed system is validated on a public dataset (LIRIS-ACCEDE) and a new dataset (PM- SZU). The experimental results demonstrate the promising performance of the proposed system, which achieves better performance than the compared methods. Our designed system for recognizing video emotions could also facilitate the development of similar video expert systems, for applications such as video recommendations, video classification and video retrieval.
Keywords: Affective analysis | Video content analysis | Protagonist information | Convolutional neural network
مقاله انگلیسی
10 برآورد حجم ترافیک شهری با استفاده از داده های مداری
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 56
برآورد حجم ترافیک در مقیاس شهری، یک مسئله مهم مفید برای بسیاری از عملیات های حمل و نقلی و کاربردهای شهری است. این مقاله، یک چارچوب ترکیبی که هر دوی تکنیک های یادگیری ماشینی کیفی فنی و نظریه جریان ترافیکی کاملاً تایید شده را به منظور برآورد حجم ترافیک شهری ادغام می کند، پیشنهاد می کند. علاوه بر ویژگی های بافت شهری معمولی استخراج شده از منابع چندگانه، ما مجموعه ای از ویژگی های مربوط به مدارهای GPS را بر اساس استدلال های نظریه جریان ترافیکی استخراج می کنیم که اطلاعات بیشتری در مورد رابطه بین سرعت- جریان ارائه می دهد. با استفاده از اطلاعات مربوط به سرعت شبکه گسترده برآورد شده از مدل برآورد سرعت جابجایی، یک ویژگی سطح بالای وابسته به حجم برای اولین بار با استفاده از یک مدل گرافیکی نظارت نشده فهمیده شده است. سپس، یک مدل دوباره تفسیر شده ی حجمی جهت ترسیم ویژگی سطح بالای وابسته به حجم برای حجم پیش بینی شده با استفاده از یک مقدار اندک از داده های حقیقی میدانی برای تمرین معرفی شده است. چارچوب، با استفاده از یک مجموعه داده مداری GPS حاصل از 33،000 تاکسی پکن و داده حقیقی میدانی حجمی به دست آمده از 4980 کلیپ ویدیوئی ارزیابی شده است. نتایج، اثربخشی و پتانسیل چارچوب پیشنهادی را در برآورد حجم ترافیک شهری نشان می دهند.
واژگان شاخص: محاسبات شهری | برآورد حجم ترافیک | مدارها | نظریه جریان ترافیکی
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