An AI-Based Framework for Translating American Sign Language to English and Vice Versa

Abstract: In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a web-based tool to use in real-life application and we also present the tool as a proof of concept. With the evaluation, we found that the deep learning model converts the image signs into ASL words and sentences with high accuracy. The tool was also found to be very useful for people with hearing impairment and deaf symptoms. The main contribution of this work is the design of a system to convert ASL to English and vice versa

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset called DREAMER where the EEG signals were collected from multiple stimulus along with self-evaluation ratings. In our proposed method, we first calculate the Short-Time Fourier Transform (STFT) of the EEG signals and convert them into RGB images to obtain the spectrograms. Then we use a two dimensional Convolutional Neural Network (CNN) in order to train the model on the spectrogram images and retrieve the features from the trained layer of the CNN using a dense layer of the neural network. We apply Extreme Gradient Boosting (XGBoost) classifier on extracted CNN features to classify the signals into arousal, valence and dominance of human emotion. We compare our results with the feature fusion-based state-of-the-art approaches of emotion recognition. To do this, we applied various feature extraction techniques on the signals which include Fast Fourier Transformation, Discrete Cosine Transformation, Poincare, Power Spectral Density, Hjorth parameters and some statistical features. Additionally, we use Chi-square and Recursive Feature Elimination techniques to select the discriminative features. We form the feature vectors by applying feature level fusion, and apply Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers on the fused features to classify different emotion levels. The performance study shows that the proposed spectrogram image based CNN-XGBoost fusion method outperforms the feature fusion-based SVM and XGBoost methods. The proposed method obtained the accuracy of 99.712% for arousal, 99.770% for valence and 99.770% for dominance in human emotion detection.publishedVersio

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively

A Systemic Mapping Study of Business Intelligence Maturity Models for Higher Education Institutions

Higher education institutions (HEIs) are investing in business intelligence (BI) to meet the increasing demand for information stemming from their operations. Information technology (IT) managers in higher education may turn to BI maturity models to evaluate the current state of HEIs’ BI operation capabilities and evaluate the readiness for future improvements. However, generic BI maturity models do not have domain-specific attributes that ensure a high degree of compatibility with HEIs. This study’s objective is to survey maturity models that could be used in HEIs and identify those used for BI to perform an analysis of their qualities and identify future avenues for research into HEI-specific BI maturity models. A systemic mapping was undertaken via both a keyword and snowball search of five indexing services, 6037 articles were processed using inclusion and exclusion criteria resulting in the identification of forty-one academic works regarding maturity model uses which were mapped to ten categories. The mapping reveals an increasing number of publications featuring maturity models for HEI, particularly since 2018, focused on e-learning and ICT. A single instance of a BI maturity model for HEI emerged in 2022 within the European HEI context. The HE-BIA MM has more dimensions than most other models identified, yet only a single co-occurrence of dimensions was identified in name only. We conclude that BI maturity models for HEI are emerging as a field of research with future directions for research including exploring co-occurrence of dimensions with existing maturity models, performing case studies, and validation of HE-BIA MM outside the European HEI context

A Systemic Mapping Study of Business Intelligence Maturity Models for Higher Education Institutions

Higher education institutions (HEIs) are investing in business intelligence (BI) to meet the increasing demand for information stemming from their operations. Information technology (IT) managers in higher education may turn to BI maturity models to evaluate the current state of HEIs’ BI operation capabilities and evaluate the readiness for future improvements. However, generic BI maturity models do not have domain-specific attributes that ensure a high degree of compatibility with HEIs. This study’s objective is to survey maturity models that could be used in HEIs and identify those used for BI to perform an analysis of their qualities and identify future avenues for research into HEI-specific BI maturity models. A systemic mapping was undertaken via both a keyword and snowball search of five indexing services, 6037 articles were processed using inclusion and exclusion criteria resulting in the identification of forty-one academic works regarding maturity model uses which were mapped to ten categories. The mapping reveals an increasing number of publications featuring maturity models for HEI, particularly since 2018, focused on e-learning and ICT. A single instance of a BI maturity model for HEI emerged in 2022 within the European HEI context. The HE-BIA MM has more dimensions than most other models identified, yet only a single co-occurrence of dimensions was identified in name only. We conclude that BI maturity models for HEI are emerging as a field of research with future directions for research including exploring co-occurrence of dimensions with existing maturity models, performing case studies, and validation of HE-BIA MM outside the European HEI context

Engagement detection in online learning: a review

Abstract Online learners participate in various educational activities including reading, writing, watching video tutorials, online exams, and online meetings. During the participation in these educational activities, they show various engagement levels, such as boredom, frustration, delight, neutral, confusion, and learning gain. To provide personalized pedagogical support through interventions to online learners, it is important for online educators to detect their online learners’ engagement status precisely and efficiently. This paper presents a review of the state of the art in engagement detection in the context of online learning. We classify the existing methods into three main categories—automatic, semi-automatic and manual—considering the methods’ dependencies on learners’ participation. Methods in each category are then divided into subcategories based on the data types (e.g., audio, video, texts for learner log data etc.) they process for the engagement detection. In particular, the computer vision based methods in the automatic category that use facial expressions are examined in more details because they are found to be promising in the online learning environment. These methods are nonintrusive in nature, and the hardware and the software that these methods use to capture and analyze video data are cost-effective and easily achievable. Different techniques in the field of computer vision and machine learning are applied in these methods for the engagement detection. We then identify their challenges of engagement detection and explore available datasets and performance metrics for engagement detection, and provide recommendations for the future to advance the technology of engagement detection for online education

Segmentation of moving object for content based applications

This paper presents an edge segment based moving object segmentation algorithm independent of background model. The proposed method detects moving edges using three most recent frames where moving regions are extracted by employing a watershed based algorithm. Extracted moving segments help to ensure good visual quality even in limited bit rate multimedia communications by incorporating priority basedtransmission.</p