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Using EEG Emotion Models in Viewer Experience Design: An Exploratory Study 18

Using EEG Emotion Models in Viewer Experience Design: An Exp...

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4th Annual international conference on HCI and UX (CHIuXiD)

作者： Deja, Jordan Aiko Cabredo, Rafael De La Salle Univ Ctr Complex & Emerging Technol Manila Philippines

ISBN: (纸本)9781450364294

TV Commercials are often considered to be noise that viewers see in between their favorite shows. these have been used for mostly commercial and advertising purposes. Such practice has been an established one for decades. Most advertising researchers have always relied on traditional means and techniques in measuring viewer experiences and under-standing conversion rates from these TV Commercials. In this work-in-progress, we present our preliminary findings from an exploratory study on understanding consumer behaviors and emotions especially when watching TV Commercials. Tar-get consumers for a specific commercial product underwent one-on-one semi-structured interviews and from viewing sessions where their EEG data were collected using the 5-channel EMOTIV Insight headset. To triangulate the emotion modeling phase, external observers referred to as coders annotated their emotions with the aid of emotion recognition via facial recognition. Initial Analysis of the interviews along with the early version of the model revealed several interesting points on visual and audio elements that triggered viewer valence. the collection of EEG data was also able to uncover a few insights that aided in the design of a better viewing experience for the target consumers. We intend to expand the findings in the future work to build a model from a bigger data and user base along with a wider array of emotions to choose from. Also, we intend to employ more advanced experiment designs to determine if we can directly correlate viewer experiences into actual product and commercial conversion.

关键词： EEG Valence User Modeling Experience Design machine learning

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Proceedings of the international conference on Advanced Intelligent Systems and Informatics 2018

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2018年

作者： Aboul Ella Hassanien Mohamed Fahmy Tolba Khaled F. Shaalan Ahmad Taher Azar

ISBN: (纸本)9783319990095

this book presents the proceedings of the 4th international conference on Advanced Intelligent Systems and Informatics 2018 (AISI2018), which took place in Cairo, Egypt from September 1 to 3, 2018. this international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). the book is divided into several main sections: Intelligent Systems; Robot Modeling and Control Systems; Intelligent Robotics Systems; machine learning Methodology and Applications; Sentiment Analysis and Arabic Text Mining; Swarm Optimizations and Applications; Deep learning and Cloud Computing; Information Security, Hiding, and Biometric recognition; and data Mining, Visualization and E-learning.

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MicroDeep: In-network Deep learning by Micro-sensor Coordination for Pervasive Computing 4

MicroDeep: In-network Deep Learning by Micro-sensor Coordina...

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4th IEEE international conference on Smart Computing (SMARTCOMP)

作者： Fukushima, Yuta Miura, Daiki Hamatani, Takashi Yamaguchi, Hirozumi Higashino, Teruo Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan

ISBN: (纸本)9781538647059

the conventional wireless sensor networks are designed to gather information from sensors, and pervasive computing systems involve such wireless sensor networks that consist of a number of tiny-/micro-sensors to sense the real-world objects and phenomena. In addition, they also need to conduct training tasks for highly-intelligent processing like pattern recognition and anomaly detection, but doing such tasks in cloud servers may often be impossible or expensive due to lack of communication facility, data transfer overhead, privacy concerns and so on. In this paper, we propose a unique approach to carry out deep learning (both training/testing) among wireless sensors nodes for in-situ, edge-heavy processing. the idea is to appropriately assign neurons of CNN (Convolutional Neural Network) to wireless nodes, each of which has limited processing capability but can have some power when they are united. For this purpose, we have designed a distributed, restricted version of CNNs. We conducted two experiments using real data;one is for anomaly detection of temperature in an over-1,400m(2) lounge space using 50 temperature sensors to confirm the learning capability as well as communication overhead, and another is for activity recognition using a 6x6 array of thin-, energy-efficient film-type infra-red sensors with micro-processors to demonstrate our concept.

关键词： Edge Computing Sensor Network Deep learning Decentralization

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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4th EAI international conference on Smart Objects and Technologies for Social Good, GOODTECHS 2018

ISBN: (纸本)9781450365819

the proceedings contain 57 papers. the topics discussed include: IoT and data visualization to enhance hyperlocal data in a smart campus context;extracting learning outcomes using machine learning and white space analysis;automated extraction of symptoms related to rare diseases from scientific publications;software and hardware setup for emotion recognition during video game fruition;adaptive deployment of software in smart-city;design and development of a linked open data-based web portal for sharing IoT health and fitness datasets;designing eco-feedback systems for a university campus;on assessing the accuracy of air pollution models exploiting a strategic sensors deployment;using game technology to automatize neuropsychological tests and research in active aging;back to the basics: security of software downloads for smart objects;and technology and aging: users' preferences in wearable sensor networks.

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Utilize smart insole to recognize basketball motions 4

Utilize smart insole to recognize basketball motions

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4th IEEE international conference on Computer and Communications, ICCC 2018

作者： Shi, Shaodong Zhou, Qing F. Peng, M. Cheng, Xusheng School of Computer and Information Hefei University of Technology Hefei China School of Electrical Engineering Intelligentization Dongguan University of Technology Dongguan China

ISBN: (纸本)9781538683392

Statistics on motions (e.g. turning around) in basketball games are important reference information for training after the games. In this paper, we propose a system that uses smart insole to recognize basketball motions. Relying on the built-in motion sensor in the insole, three-axis acceleration data and three-axis angular velocity data about three different basketball motions can be collected. these three basketball motions are dribble, jump, and turning around. the K-means clustering algorithm is utilized to complete the recognition task, and a new method is used to initialize the center points, which solves the problem of large difference between the results of each experiment. In the case of training data and test data from different players, the average accuracy can reach 98.9%. © 2018 IEEE.

关键词： machine learning

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the *** Project: Deep learning for Particle Tracking 18

The *** Project: Deep Learning for Particle Tracking

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18th international Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT)

作者： Tsaris, Aristeidis Anderson, Dustin Bendavid, Josh Calafiura, Paolo Cerati, Giuseppe Esseiva, Julien Farrell, Steven Gray, Lindsey Kapoor, Keshav Kowalkowski, Jim Mudigonda, Mayur Prabhat Spentzouris, Panagiotis Spiropoulou, Maria Vlimant, Jean-Roch Zheng, Stephan Zurawski, Daniel Fermilab Natl Accelerator Lab POB 500 Batavia IL 60510 USA Lawrence Berkeley Natl Lab Berkeley CA USA CALTECH Pasadena CA 91125 USA Haute Ecole Specialis Suisse Occidentale Fribourg Switzerland

Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experiments for years. However, these state-of-the-art techniques are inherently sequential and scale quadratically or worse with increased detector occupancy. the *** project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as FPGAs or GPUs. In this paper we present the evolution and performance of our recurrent (LSTM) and convolutional neural networks moving from basic 2D models to more complex models and the challenges of scaling up to realistic dimensionality/sparsity.

关键词： Charged particles

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4th international conference on IoT Technologies for HealthCare, HealthyIoT 2017

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4th international conference on Internet of things (IoT) Technologies for HealthCare, HealthyIoT 2017

ISBN: (纸本)9783319762128

the proceedings contain 17 papers. the special focus in this conference is on IoT Technologies for HealthCare. the topics include: Automatic Autism Spectrum Disorder Detection thanks to Eye-Tracking and Neural Network-Based Approach;Automatic Detector of Abnormal EEG for Preterm Infants;non-invasive Analytics Based Smart System for Diabetes Monitoring;cloud-Based data Analytics on Human Factor Measurement to Improve Safer Transport;run-Time Assurance for the E-care@home System;scalable Framework for Distributed Case-Based Reasoning for Big data Analytics;deep learning Based Person Identification Using Facial Images;vision-Based Remote Heart Rate Variability Monitoring Using Camera;how Accurate Are Smartphone Accelerometers to Identify Intermittent Claudication?;distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring;an Efficient Design of a machine learning-Based Elderly Fall Detector;characterization of Home-Acquired Blood Pressure Time Series Using Multiscale Entropy for Patients Treated Against Kidney Cancer;a Heterogeneous IoT-Based Architecture for Remote Monitoring of Physiological and Environmental Parameters;An RFID Based Activity of Daily Living for Elderly with Alzheimer’s;automated recognition and Difficulty Assessment of Boulder Routes.

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Bangla Sign Digits recognition Using HOG Feature Based Multi-Class Support Vector machine

Bangla Sign Digits Recognition Using HOG Feature Based Multi...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Md. Mahmudul Hasan Sk. Mohammad Masudul Ahsan Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9781728160405

ISBN: (纸本)9781728160412

Sign language or gesture language is used by the people who are unable to hear for communicating among themselves as well as with other persons. It is not as simple as the natural speaking language. It is a fully developed natural language having its grammar and lexicon. Gesture Languages are represented via the manual sign-stream or alphabet-stream together with non-manual components. As the spoken languages have various types of sounds, letters, digits, etc. Similarly, sign languages have their grammar, letter, digits, etc. In this paper, the main focus is sign digits as the sign numerical digits are a major part of a sign or gesture language which are not familiar to general people. So the goal is to make it understandable to the general people. For this purpose, an easily understandable model has been constructed with computer vision features and machine learning methods to recognize Bangla finger numerical digits. the histogram of oriented gradient features of images has been applied to train the classifier, here a multiple-class support vector machine has been employed to classify the images. In this paper, the proposed multiple-class Support Vector machine is trained with the HOG features of the images with the training images dataset to achieve the goal. the classifier is trained and tested for 900 training pictures and 100 test pictures of ten-digit classes respectively. After training and tests, the proposed classifier model has gained about 95 percent accuracy on the perception of Bangla sign numerical digits.

关键词： Assistive technology Support vector machines Gesture recognition Training Feature extraction Training data Testing

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Towards Open Domain Chatbots-A GRU Architecture for data Driven Conversations 4th

Towards Open Domain Chatbots-A GRU Architecture for Data Dri...

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4th international conference on Internet Science (INSCI)

作者： Kamphaug, Asmund Granmo, Ole-Christoffer Goodwin, Morten Zadorozhny, Vladimir I. Univ Agder Ctr Artificial Intelligence Res Kristiansand Norway Univ Pittsburgh Sch Comp & Informat Pittsburgh PA USA

ISBN: (纸本)9783319775470;9783319775463

Understanding of textual content, such as topic and intent recognition, is a critical part of chatbots, allowing the chatbot to provide relevant responses. Although successful in several narrow domains, the potential diversity of content in broader and more open domains renders traditional pattern recognition techniques inaccurate. In this paper, we propose a novel deep learning architecture for content recognition that consists of multiple levels of gated recurrent units (GRUs). the architecture is designed to capture complex sentence structure at multiple levels of abstraction, seeking content recognition for very wide domains, through a distributed scalable representation of content. To evaluate our architecture, we have compiled 10 years of questions and answers from a youth information service, 200 083 questions spanning a wide range of content, altogether 289 topics, involving law, health, and social issues. Despite the relatively open domain data set, our architecture is able to accurately categorize the 289 intents and topics. Indeed, it provides roughly an order of magnitude higher accuracy compared to more classical content recognition techniques, such as SVM, Naive Bayes, random forest, and K-nearest neighbor, which all seem to fail on this challenging open domain dataset.

关键词： Information services

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How Meta-heuristic Algorithms Contribute to Deep learning in the Hype of Big data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th international conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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