Extending the Technology Acceptance Model to Consumer Perceptions of Fashion AI

This research intends to investigate consumers\u27 acceptance and purchase intention towards a fashion AI device so as to predict how consumers\u27 fashion sense will be affected by new technologies. The extended Technology Acceptance Model (TAM) was used as theoretical framework, along with performance risk and positive technology attitudes. Empirical data (with 313 valid responses) were collected from top 10 metropolitan areas in the US via Qualtrics Panel services. Structural equation modeling and multiple group analysis were used to estimate construct validity and test the proposed hypotheses and theoretical framework. Results indicated that consumers’ acceptance and purchase intention were predicted by favorable attitudes toward the fashion AI device and positive technology attitude. Usefulness, ease of use, enjoyment, and performance risk significantly influence customers’ attitudes. Consumers of different levels of fashion involvement have various purchase intention. Theoretical and practical implications were presented

Automated Semantic Segmentation for Autonomous Railway Vehicles

With the development of computer vision methods, the number of areas where autonomous systems are used has also increased. Among these areas is the transportation sector. Autonomous systems in the transportation sector are mostly developed for road vehicles, but highway rules and standards different between countries. In this study, models capable of semantic segmentation have been developed for autonomous railway vehicles with the help of the public dataset. Four different U-Net models were trained with 8500 images for four different scenarios. The model trained for binary semantic segmentation reached mean Intersection over Union (mIoU) value of 89.1%, while the models trained for multi-class semantic segmentation reached 83.2% mIoU, 79.7% mIoU and 29.6% mIoU. Information about the inclusion of high-resolution images in model training and performance metrics in semantic segmentation studies shared

New Trends in Second Language Learning and Teaching through the lens of ICT, Networked Learning, and Artificial Intelligence

In the last few decades, Information and Communications Technology (ICT) applications have been shaping the field of Computer Assisted Language Learning (CALL). Mobile Assisted Language Learning (MALL) paved the way for ubiquitous learning. The advent of new technologies in the early 21st century also added a social dimension to ICT that allowed for Networked Learning (NL). Given that language learning is fundamentally a socio-cultural experience, networked learning capabilities have provided the potential for language learning in community settings. This has revitalized the earlier frameworks provided by CALL. NL has empowered language learners today to connect globally, to access Open Educational Resources, and to self-regulate their learning processes beyond the scope of traditional curricula. In parallel, the rising pervasiveness of Artificial Intelligence (AI) applications and their relevance to language learning has led CALL to branch out into Intelligent CALL (ICALL). The first section of this article provides a brief historical overview of CALL, examines it through the lens of ICT, networked learning, and open access. The second section focuses on the implications of AI for creating new trends in second language education, the challenge for providing customization at scale, and raises important issues related to transparency and privacy for future research

Framing the effects of machine learning on science

Studies investigating the relationship between artificial intelligence (AI) and science tend to adopt a partial view. There is no broad and holistic view that synthesizes the channels through which this interaction occurs. Our goal is to systematically map the influence of the latest AI techniques (machine learning, ML and its sub-category, deep learning, DL) on science. We draw on the work of Nathan Rosenberg to develop a taxonomy of the effects of technology on science. The proposed framework comprises four categories of technology effects on science: intellectual, economic, experimental and instrumental. The application of the framework in the relationship between ML/DL and science allowed the identification of multiple triggers activated by the new techniques in the scientific field. Visualizing these different channels of influence allows us to identify two pressing, emerging issues. The first is the concentration of experimental effects in a few companies, which indicates a reinforcement effect between more data on the phenomenon (experimental effects) and more capacity to commercialize the technique (economic effects). The second is the diffusion of new techniques lacking in explanation (intellectual effect) throughout the fabric of science (instrumental effects). The value of this article is twofold. First, it provides a simple framework to assess the relations between technology and science. Second, it provides this broad and holistic view of the influence of new AI techniques on science. More specifically, the article details the channels through which this relationship occurs, the nature of these channels and the loci in which the potential effects on science unfolds

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

The prediction accuracy in many classical problems across multiple domains has seen a rise since computational tools such as multi-layer neural nets and complex machine learning algorithms have become widely accessible to the research community. In this research, we take a step back and examine the feature space in two problems from very different domains. We show that novel augmentation to the feature space yields higher performance. Emotion Recognition in Adults from a Control Group: The objective is to quantify the emotional state of an individual at any time using data collected by wearable sensors. We define emotional state as a mixture of amusement, anger, disgust, fear, sadness, anxiety and neutral and their respective levels at any time. The generated model predicts an individual’s dominant state and generates an emotional spectrum, 1x7 vector indicating levels of each emotional state and anxiety. We present an iterative learning framework that alters the feature space uniquely to an individual’s emotion perception, and predicts the emotional state using the individual specific feature space. Hybrid Feature Space for Image Classification: The objective is to improve the accuracy of existing image recognition by leveraging text features from the images. As humans, we perceive objects using colors, dimensions, geometry and any textual information we can gather. Current image recognition algorithms rely exclusively on the first 3 and do not use the textual information. This study develops and tests an approach that trains a classifier on a hybrid text based feature space that has comparable accuracy to the state of the art CNN’s while being significantly inexpensive computationally. Moreover, when combined with CNN’S the approach yields a statistically significant boost in accuracy. Both models are validated using cross validation and holdout validation, and are evaluated against the state of the art