Thank you for reading learning to teach in higher education. As you may know, people have look numerous times for their favorite books like this learning to teach in higher education, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious bugs inside their laptop. learning to teach in higher education is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the learning to teach in higher education is universally compatible with any devices to read.

Learning To Teach In Higher Education

With the widespread application of mobile edge computing (MEC), MEC is serving as a bridge to narrow the gaps between medical staff and patients. Relatedly, MEC is also moving toward supervising in ...

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.

/pdf/overview-of-the-hasoc-track-at-fire-2019-hate-speech-and-27y6nbsevs.pdf

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.

/pdf/semeval-2020-task-12-multilingual-offensive-language-59c04s046m.pdf

SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)

Different levels of an intelligent video surveillance system (IVVS) are studied in this review.Existing approaches for abnormal behavior recognition relative to each level of an IVVS are extensively reviewed.Challenging datasets for IVVS evaluation are presented.Limitations of the abnormal behavior recognition area are discussed. With the increasing number of surveillance cameras in both indoor and outdoor locations, there is a grown demand for an intelligent system that detects abnormal events. Although human action recognition is a highly reached topic in computer vision, abnormal behavior detection is lately attracting more research attention. Indeed, several systems are proposed in order to ensure human safety. In this paper, we are interested in the study of the two main steps composing a video surveillance system which are the behavior representation and the behavior modeling. Techniques related to feature extraction and description for behavior representation are reviewed. Classification methods and frameworks for behavior modeling are also provided. Moreover, available datasets and metrics for performance evaluation are presented. Finally, examples of existing video surveillance systems used in real world are described.

Abnormal behavior recognition for intelligent video surveillance systems

Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer's comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called "Neuromarketing". This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of "likes" and "dislikes" by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.

Analysis of EEG signals and its application to neuromarketing

Recent advances in EEG technology makes brain-computer-interface (BCI) an exciting field of research BCI is primarily used to adopt with the paralyzed human body parts However, BCI in envisioned speech recognition using electroencephalogram (EEG) signals has not been studied in details Therefore, developing robust speech recognition system using EEG signals was proposed In this paper, we propose a coarse-to-fine-level envisioned speech recognition framework with the help of EEG signals that can be thought of as a serious contribution in this field of research Coarse-level classification is used to differentiate/categorize text and non-text classes using random forest (RF) classifier Next, a finer-level imagined speech recognition of each class has been carried out EEG data of 30 text and not-text classes including characters, digits, and object images have been imagined by 23 participants in this study A recognition accuracy of 8520 and 6703% has been recorded at coarse- and fine-level classifications, respectively The proposed framework outperforms the existing research work in terms of accuracy We also show the robustness in envisioned speech recognition

Envisioned speech recognition using EEG sensors

Sign language is the only means of communication for speech and hearing impaired people. Using machine translation, Sign Language Recognition (SLR) systems provide medium of communication between speech and hearing impaired and others who have difficulty in understanding such languages. However, most of the SLR systems require the signer to sign in front of the capturing device/sensor. Such systems fail to recognize some gestures when the relative position of the signer is changed or when the body occlusion occurs due to position variations. In this paper, we present a robust position invariant SLR framework. A depth-sensor device (Kinect) has been used to obtain the signer’s skeleton information. The framework is capable of recognizing occluded sign gestures and has been tested on a dataset of 2700 gestures. The recognition process has been performed using Hidden Markov Model (HMM) and the results show the efficiency of the proposed framework with an accuracy of 83.77% on occluded gestures.

A position and rotation invariant framework for sign language recognition (SLR) using Kinect

Evolutionary decision fusion has applications in biometric authentication and verification Gray wolf optimizer (GWO) is one such evolutionary decision fusion approach that can be used to tune the fusion parameters in a multimodal data acquisition system Human gait is a proven biometric trait with applications in security and authentication However, acquiring human-gait data can be erroneous due to various factors and multimodal fusion of such erroneous gait data can be challenging In this paper, we propose a new decision fusion-based approach to solve the above problem Gait data is recorded simultaneously using motion sensors and visible-light camera The signals of the motion sensors are modeled using a long short-term memory neural network and corresponding video recordings are processed using a three-dimensional convolutional neural network GWO has been used to optimize the parameters during fusion It has been chosen based on the underlying hunting strategy that leads to better approximation of the solution Interestingly, in our case it converges quicker than other optimization techniques such as genetic algorithm or particle swarm optimization To test the model, a dataset involving 23 males and females has been recorded while they perform four different types of walks, including, normal walk, fast walk, walking while listening to music , and walking while watching multimedia content on a mobile  An overall accuracy of 913% has been recorded across all test scenarios Results reveal that the proposed study can further be explored to design robust gait biometric systems

Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm

Recognition of 3-D texts drawn by fingers using Leap motion sensor can be challenging for existing text recognition frameworks. The texts sensed by Leap motion device are different from traditional offline and on-line writing systems. This is because of frequent jitters and non-uniform character sizes while writing using Leap motion interface. Moreover, because of air writing, characters, words, and lines are usually connected by continuous stroke that makes it difficult to recognize. In this paper, we present a study of segmentation and recognition of text recorded using Leap motion sensor. The segmentation task of continuous text into words is performed using a heuristic analysis of stroke length between two successive words. Next, the recognition of each segmented word is performed using sequential classifiers. In this paper, we have performed 3-D text recognition using hidden Markov model (HMM) and bidirectional long short-term memory neural networks (BLSTM-NNs). We have created a data set consisting of 560 Latin sentences drawn by ten participants using Leap motion sensor for experiments. An accuracy of 78.2% has been obtained in word segmentation, whereas 86.88% and 81.25% accuracies have been recorded in word recognition using BLSTM-NN and HMM classifiers, respectively.

Rajkumar Saini

Papers

Analysis of EEG signals and its application to neuromarketing

Envisioned speech recognition using EEG sensors

A position and rotation invariant framework for sign language recognition (SLR) using Kinect

Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm

Study of Text Segmentation and Recognition Using Leap Motion Sensor