The impact of user- and system-initiated personalization on the user experience at large sports events

This article describes an experimental study investigating the impact on user experience of two approaches of personalization of content provided on a mobile device, for spectators at large sports events. A lab-based experiment showed that a system-driven approach to personalization was generally preferable, but that there were advantages to retaining some user control over the process. Usability implications for a hybrid approach, and design implications are discussed, with general support for countermeasures designed to overcome recognised limitations of adaptive systems

Multinomial logistic regression probability ratio-based feature vectors for Malay vowel recognition

Vowel Recognition is a part of automatic speech recognition (ASR) systems that classifies speech signals into groups of vowels. The performance of Malay vowel recognition (MVR) like any multiclass classification problem depends largely on Feature Vectors (FVs). FVs such as Mel-frequency Cepstral Coefficients (MFCC) have produced high error rates due to poor phoneme information. Classifier transformed probabilistic features have proved a better alternative in conveying phoneme information. However, the high dimensionality of the probabilistic features introduces additional complexity that deteriorates ASR performance. This study aims to improve MVR performance by proposing an algorithm that transforms MFCC FVs into a new set of features using Multinomial Logistic Regression (MLR) to reduce the dimensionality of the probabilistic features. This study was carried out in four phases which are pre-processing and feature extraction, best regression coefficients generation, feature transformation, and performance evaluation. The speech corpus consists of 1953 samples of five Malay vowels of /a/, /e/, /i/, /o/ and /u/ recorded from students of two public universities in Malaysia. Two sets of algorithms were developed which are DBRCs and FELT. DBRCs algorithm determines the best regression coefficients (DBRCs) to obtain the best set of regression coefficients (RCs) from the extracted 39-MFCC FVs through resampling and data swapping approach. FELT algorithm transforms 39-MFCC FVs using logistic transformation method into FELT FVs. Vowel recognition rates of FELT and 39-MFCC FVs were compared using four different classification techniques of Artificial Neural Network, MLR, Linear Discriminant Analysis, and k-Nearest Neighbour. Classification results showed that FELT FVs surpass the performance of 39-MFCC FVs in MVR. Depending on the classifiers used, the improved performance of 1.48% - 11.70% was attained by FELT over MFCC. Furthermore, FELT significantly improved the recognition accuracy of vowels /o/ and /u/ by 5.13% and 8.04% respectively. This study contributes two algorithms for determining the best set of RCs and generating FELT FVs from MFCC. The FELT FVs eliminate the need for dimensionality reduction with comparable performances. Furthermore, FELT FVs improved MVR for all the five vowels especially /o/ and /u/. The improved MVR performance will spur the development of Malay speech-based systems, especially for the Malaysian community

AN EXAMINED LIFE OF A LANGUAGE TEACHER OF CHINESE: AN AUTOETHNOGRAPHIC INVESTIGATION INTO AGENCY

There is a paucity of research about and done by L2 Chinese educators regarding the theoretical construct of agency. It is also noted that the qualitative inquiry is marginalized in L2 Chinese research field, let alone the narrative study of the agency of experienced by L2 Chinese-teachers. In this dissertation research, I aim at filling in the gap by conducting a longitudinal autoethnography which captures over a decade (1997-2017) of my personal and professional development with an agency perspective. The highly personalized autoethnographic accounts open up my personal and professional life as an experienced, college-level, transnational, early 40’s female native Chinese teacher from mainland China. Using socio-cultural sensibilities and ecological approach of agency to scrutinize the paradigm shifts and behavioral changes over extended periods of time, I strive to make visible my active sense-making of affordances and constraints of diverse societal and educational surroundings in Indonesia, the US, China and the US again. I hope to exam the personal world and intellectual, professional trajectory over a long time to extend readers’ sociological understanding (Sparkes, 2000) of the rich and complex life of a language teacher. The critical reflexive analysis, deep reflection, and writing as analysis inquiry of my own transformations are demonstrated in multiple shifting identities over time and across different milieu, from English as a foreign language teacher, to Chinese as a second language teacher within China and to Chinese as a foreign language teacher outside Chinese-speaking context, from a teacher-researcher, teacher mentor, teacher educator, to a lifelong teacher-learner. The manifestations of various forms of agency-as-achievements and the evolvement of agency-as-capacities have also been examined. One of the main impetuses of this autoethnographic project is creating an alternative narrative of a nonconformist so as to challenge the existing stereotypical narratives of Chineseness in work-abroad native-speaking China-born teachers as well as traditional development trajectory of language teachers. My concrete experiences as a transcultural, bilingual, and bicultural (L1 Chinese, L2 English) language educator together with intellectual biography exhibit a unique personal, scholarly and professional growth in a postmodern, globalized, multicultural era through various social identities and evolving agency development. Using the power of autoethnography, I make explicit the multiplicity of self-representation and critically self-reflective learning about agency. This work hopes to inspire reflective and reflexive practices in other L2 educators, especially experienced in-service language teachers, to destabilize their ideologies and beliefs regarding L2 education and reflective practice, to educate their attention to social aspects of language learning and teaching, and to humanized language education. Ultimately, readers are encouraged to move into action to explore the notion of agency and use the power of autoethnography in language education and on language education

Anthropomorphized chatbots in mental health applications

The number of people suffering from mental health disorders is steadily rising as a result of growing social and economic inequality, ongoing political conflict, and, not least, the COVID 19 pandemic. The rapid progress of artificial intelligence, and within it chatbots, presents an opportunity to address these deficiencies by reducing treatment barriers and providing economic benefits to service providers and consumers. To assure the effectiveness of chatbots in psychological health applications, they have to be accepted by users. A chatbot’s acceptance in mental health interventions is influenced by the benefits of intelligent machines, their expectation of nonjudgmental and unbiased support, and the effect of stigma on trust and belief in healthcare. Based on these insights, the experimental study examines whether users of psychological health apps more readily accept chatbots as opposed to physical health apps. Furthermore, the humanization of chatbots is a proven tool to enhance the quality of interaction with users. Thus, this dissertation additionally aims to investigate if a humanized chatbot entity affects their acceptance in the context of mental health apps. The results suggest that chatbots are more widely accepted in mental health applications compared to physical health applications. Moreover, the findings lead to the recommendation to implement humanized entities in chatbots within mental health applications. The results provide a rationale for conducting additional research to investigate the subject in greater depth. Due to the continuous development of AI, the utilization of chatbots in mental health care should be investigated continuously.O número de pessoas que sofrem de perturbações de saúde mental está a aumentar constantemente devido à desigualdade social e económica, conflitos políticos e da pandemia de COVID-19. O rápido progresso da inteligência artificial representa uma oportunidade para resolver estas perturbações, reduzindo os obstáculos ao tratamento e proporcionando benefícios económicos aos prestadores de serviços e aos pacientes. Para garantir a eficácia dos chatbots nas aplicações de saúde mental, estes têm de ser aceites pelos utilizadores. Esta aceitação nas intervenções de saúde mental é influenciada pelos benefícios das máquinas inteligentes, pela sua expectativa de apoio imparcial e sem juízos de valor e pelo efeito do estigma na confiança e na crença nos cuidados de saúde. Com base nestes conhecimentos, o estudo experimental examina se os chatbots são mais facilmente aceites pelos utilizadores de aplicações de saúde psicológica do que aplicações de saúde física. Além disso, a humanização dos chatbots é uma ferramenta comprovada para melhorar a qualidade da interacção com os utilizadores. Assim, esta dissertação tem como objetivo investigar se uma entidade chatbot humanizada afeta a sua aceitação no contexto de aplicações de saúde mental. Os resultados sugerem que os chatbots são melhor aceites em aplicações de saúde mental do que em aplicações de saúde física. Além disso, os resultados levam à recomendação da implementação de entidades humanizadas em chatbots dentro de aplicações de saúde mental. Devido ao desenvolvimento contínuo da IA, a utilização de chatbots nos cuidados de saúde mental deve ser investigada numa base contínua

Goal-Driven Process Navigation for Individualized Learning Activities in Ubiquitous Networking and IoT Environments

Abstract: In the study, we propose an integrated adaptive framework to support and facilitate individualized learning through sharing the successful process of learning activities based on similar learning patterns in the ubiquitous learning environments empowered by Internet of Things (IoT). This framework is based on a dynamic Bayesian network that gradually adapts to a target student's needs and information access behaviours. By analysing the log data of learning activities and extracting students' learning patterns, our analysis results show that most of students often use their preferred learning patterns in their learning activities, and the learning achievement is affected by the learning process. Based on these findings, we try to optimise the process of learning activities using the extracted learning patterns, infer the learning goal of target students, and provide a goal-driven navigation of individualized learning process according to the similarity of the extracted learning patterns

Perceptual borderline for balancing multi-class spontaneous emotional data

Speech is a behavioural biometric signal that can provide important information to understand the human intends as well as their emotional status. The paper is centered on the speech-based identification of the seniors’s emotional status during their interaction with a virtual agent playing the role of a health professional coach. Under real conditions, we can just identify a small set of task-dependent spontaneous emotions. The number of identified samples is largely different for each emotion, which results in an imbalanced dataset problem. This research proposes the dimensional model of emotions as a perceptual representation space alternative to the generally used acoustic one. The main contribution of the paper is the definition of a perceptual borderline for the oversampling of minority emotion classes in this space. This limit, based on arousal and valence criteria, leads to two methods of balancing the data: the Perceptual Borderline oversampling and the Perceptual Borderline SMOTE (Synthetic Minority Oversampling Technique). Both methods are implemented and compared to state-of-the-art approaches of Random oversampling and SMOTE. The experimental evaluation was carried out on three imbalanced datasets of spontaneous emotions acquired in human-machine scenarios in three different cultures: Spain, France and Norway. The emotion recognition results obtained by neural networks classifiers show that the proposed perceptual oversampling methods led to significant improvements when compared with the state-of-the art, for all scenarios and languages.The research presented in this paper is conducted as partof the project EMPATHIC and of the MENHIR MSCAaction that have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 769872 an No 823907 respective