Smile detection in the wild based on transfer learning

Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling. Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper. Unlike previous works which use either hand-engineered features or train deep convolutional networks from scratch, a well-trained deep face recognition model is explored and fine-tuned for smile detection in the wild. Three different models are built as a result of fine-tuning the face recognition model with different inputs, including aligned, unaligned and grayscale images generated from the GENKI-4K dataset. Experiments show that the proposed approach achieves improved state-of-the-art performance. Robustness of the model to noise and blur artifacts is also evaluated in this paper

Understanding Technology Mediated Learning in Higher Education: A Repertory Grid Approach

Given the considerable opportunities that Web 2.0 technologies are seen to present for the enhancement of learning and teaching, understanding what motivates today’s students to use this technology in their learning is crucial. Drawing from technology mediated learning (TML) and Uses and Gratifications (U&G) perspectives, this study investigates university students’ motivations for using Web 2.0 technologies in learning. The Repertory Grid Interview technique (RGT) is used to interview 16 participants and capture their technology use motivations. A grounded approach was used to resolve eleven categories of motivations: Access and Content Control, Accessibility, Communication Efficiency, Communication Mode, Communication Quality, Course Management, Information Seeking, Interaction, Learning Capability, Managing Contents, and Self-Disclosure. The findings suggest that today’s students have different motivations for using technologies when it comes to learning

Synthesis and evaluation of N⁶-substituted apioadenosines as potential adenosine A₃ receptor modulators

Adenosine receptors (ARs) trigger signal transduction pathways inside the cell when activated by extracellular adenosine. Selective modulation of the A(3)AR subtype may be beneficial in controlling diseases such as colorectal cancer and rheumatoid arthritis. Here, we report the synthesis and evaluation of beta-D-apio-D-furano- and alpha-D-apio-L-furanoadenosines and derivatives thereof. Introduction of a 2-methoxy-5-chlorobenzyl group at N-6 of beta-D-apio-D-furanoadenosine afforded an A(3)AR antagonist (10c, K = 0.98 mu M), while a similar modification of an alpha-D-apio-L-furanoadenosine gave rise to a partial agonist (11c, K-i = 3.07 mu M). The structural basis for this difference was examined by docking to an A(3)AR model; the antagonist lacked a crucial interaction with Thr94

Analyzing Students’ Technology Use Motivations: An Interpretive Structural Modeling Approach

Despite being more meaningful and accurate to consider student technology use motivations as a set of interactive needs and expectations, the possible underlying hierarchical relationships among motivations receive little attention. Drawn from Uses and Gratifications (U&G) approach and from Means-End Chain (MEC) theory, this study investigates how student technology use motivations can be represented as a set of interrelated and hierarchically organized elements. A set of relevant data concerning students’ technology use motivations was collected by the Repertory Grid Interview Technique (RGT) and analyzed qualitatively using content analysis. Eleven identified student technology use motivations were structured by adopting interpretive structure modeling (ISM) technique. By using Multiplication Applied to Classification (MICMAC) technique, eleven identified factors were further classified into three different types of variables: means, consequences, and ends. The findings of this study have significant theoretical and practical implications to both researchers and managers

Motivations for Using CMC and Non-CMC Media in Learning Contexts: A Uses and Gratifications Approach

As the use of computer-mediated communication (CMC) by students in the university learning contexts increases, there is a need to better understand students’ motivations for using CMC and non-CMC media in their learning. By employing the uses and gratifications (U&G) perspective, this paper identified 7 motivation dimensions including information seeking, convenience, connectivity, problem solving, content management, social presence, and social context cues. Furthermore, this study found that overall CMC media were not functional alternatives to non- CMC media. However, this study revealed some specific similarities and differences between CMC and non-CMC media in terms of each specific motivation dimension. Finally, the paper concluded with a discussion of the implications for both IS researchers, higher education and organizations

A \u27uses and gratifications\u27 approach to understanding the role of wiki technology in enhancing teaching and learning outcomes

The use of the Wikis in both post-graduate and undergraduate teaching is rapidly increasing in popularity. Much of the research into the use of this technology has focused on the practical aspects of how the technology can be used and is yet to address why it is used, or in what way it enhances teaching and learning outcomes. A comparison of the key characteristics of the constructivist learning approach and Wikis suggests that Wikis could provide considerable support of this approach, however research into the motivations for using the technology is required so that good teaching practices may be applied to the use of Wikis when utilized in the higher education context. This study articulates a research design grounded in the Technology Mediated Learning (TML) paradigm that could be used to explore teachers and students’ motivations for using Wiki technology to enhance teaching and learning outcomes. Using the ‘Uses and Gratification’ approach, a popular technique used for understanding user motivation in technology adoption, a two-stage research design is set out. Finally, the paper concludes with a discussion of the implications for both information systems researchers and higher education

A Typology and Hierarchical Framework of Technology Use in Digital Natives’ Learning

The technological capability of digital natives is thought to have considerable implications on the way they communicate, socialize, think and learn. Some researchers have even suggested that fundamental changes to the educational system are required to cater for the needs of this new cohort of learner, although such claims have little empirical support. In this study, we adopt a structural approach to the investigation of the digital natives’ motivations for using technologies in learning. Based on in-depth interviews with 16 digital natives, a cluster analysis was used to segment respondents into two distinct groups: independent learners and traditional learners. Interpretive Structural Modelling (ISM) was used to develop a hierarchical structural model of technology use motivations for each group. The results show that these two groups are driven to achieve the same learning goals by different paths. Implications are drawn for both educators and managers from both research and practical perspectives

Iterative Translation Refinement with Large Language Models

Large language models have shown surprising performances in understanding instructions and performing natural language tasks. In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing. We show that by simply involving a large language model in an iterative process, the output quality improves beyond mere translation. Extensive test scenarios with GPT-3.5 reveal that although iterations reduce string-based metric scores, neural metrics indicate comparable if not improved translation quality. Further, human evaluations demonstrate that our method effectively reduces translationese compared to initial GPT translations and even human references, especially for into-English directions. Ablation studies underscore the importance of anchoring the refinement process to the source input and a reasonable initial translation