Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. Various research communities are contributing methods for ITL and a large subset of this research is robot-centric with a focus on developing algorithms that can learn online, quickly. This paper studies the ITL problem from a human-centered perspective to provide guidance for robot design so that human teachers can interact with ITL robots naturally. In this paper, we present 1) a cognitive task analysis of an interactive teaching study (N=10) that extracts and classify various actions intended and executed by human teachers when teaching a robot; 2) in-depth discussion of the teaching approach employed by two participants to understand the need for personal adaptation to individual styles; and 3) requirements for ITL robot design based on our analyses informed by plan-based theories of dialogue, specifically SharedPlans.
Spam is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. There are various types of spam such as email spam, forum spam, online classified ads spam, attachment spam, social networking spam etc. For the purpose of this paper, we would like to concentrate more on social networking spam (SNS). SNS is when unwanted messages or posts are sent to people in bulk, or when a single click of a seemingly harmless link reposts the link on other profiles, thus spreading the spam like a virus. We plan to use an adaptive neuro fuzzy inference system (ANFIS) that incorporates the advantages of both the neural networking concepts and fuzzy logic to identify the spam messages on such websites.
Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot’s knowledge, capabilities, shortcomings, goals, and intentions. The research question that I investigate is “How can the robot help the human build a better mental model of the robot?”
Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial-related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with different commercial implementations, it nevertheless faces problems in certain real-world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video compression artefacts, and so on. To solve the problems described above, a face detection technique called Convolution Neural Network with Constant Error Carousel dependent Long Short Term Memory (CNN-CEC-LSTM) is proposed in this paper. This research implemented a novel network structure and designed a special feature extraction that employs a self-channel attention (SCA) block and a self-spatial attention (SSA) block that adaptively aggregates the feature maps in both channel and spatial domains to learn the inter-channel and inter-spatial connection matrices; additionally, matrix multiplications are conducted for a This approach first smoothed the initial image with a Gaussian filter before measuring the gradient image. The Canny-Kirsch Method edge detection algorithm was then used to identify human face edges. The proposed method is evaluated against two recent difficult face detection databases, including the IIT Kanpur Dataset. The experimental findings indicate that the proposed approach outperforms the most current cutting-edge face recognition approaches.
Face recognition has become more sophisticated in biometric based authentication systems, which uses various facial features. The authentication mechanism is still required with more features to be used and has to be done in a short time. Existing face recognition algorithms are more scalable in time and memory used, also produces high frequency of false positive results. To overcome the problem of false positive results, we propose MEN-Mouth, Eye and Nose features based multi attribute feature selection method for face recognition. The proposed method extracts the facial features like Nose, mouth and eye, from extracted features we compute eccentric measures for each of the feature. The eccentric measure is computed between four axis co-ordinates of facial features. Computed features are converted into single feature, and computes feature weight based on computed feature set. The computed feature weight is used to recognize the person.
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