4,094 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    A Spark Of Emotion: The Impact of Electrical Facial Muscle Activation on Emotional State and Affective Processing

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    Facial feedback, which involves the brain receiving information about the activation of facial muscles, has the potential to influence our emotional states and judgments. The extent to which this applies is still a matter of debate, particularly considering a failed replication of a seminal study. One factor contributing to the lack of replication in facial feedback effects may be the imprecise manipulation of facial muscle activity in terms of both degree and timing. To overcome these limitations, this thesis proposes a non-invasive method for inducing precise facial muscle contractions, called facial neuromuscular electrical stimulation (fNMES). I begin by presenting a systematic literature review that lays the groundwork for standardising the use of fNMES in psychological research, by evaluating its application in existing studies. This review highlights two issues, the lack of use of fNMES in psychology research and the lack of parameter reporting. I provide practical recommendations for researchers interested in implementing fNMES. Subsequently, I conducted an online experiment to investigate participants' willingness to participate in fNMES research. This experiment revealed that concerns over potential burns and involuntary muscle movements are significant deterrents to participation. Understanding these anxieties is critical for participant management and expectation setting. Subsequently, two laboratory studies are presented that investigated the facial FFH using fNMES. The first study showed that feelings of happiness and sadness, and changes in peripheral physiology, can be induced by stimulating corresponding facial muscles with 5ā€“seconds of fNMES. The second experiment showed that fNMES-induced smiling alters the perception of ambiguous facial emotions, creating a bias towards happiness, and alters neural correlates of face processing, as measured with event-related potentials (ERPs). In summary, the thesis presents promising results for testing the facial feedback hypothesis with fNMES and provides practical guidelines and recommendations for researchers interested in using fNMES for psychological research

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (ā€˜AIā€™) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics ā€“ and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĆ³lica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Enabling Deep Neural Network Inferences on Resource-constraint Devices

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    Department of Computer Science and EngineeringWhile deep neural networks (DNN) are widely used on various devices, including resource-constraint devices such as IoT, AR/VR, and mobile devices, running DNN from resource-constrained devices remains challenging. There exist three approaches for DNN inferences on resource-constraint devices: 1) lightweight DNN for on-device computing, 2) offloading DNN inferences to a cloud server, and 3) split computing to utilize computation and network resources efficiently. Designing a lightweight DNN without compromising the accuracy of DNN is challenging due to a trade-off between latency and accuracy, that more computation is required to achieve higher accuracy. One solution to overcome this challenge is pre-processing to extract and transfer helpful information to achieve high accuracy of DNN. We design the pre-processing, which consists of three processes. The first process of pre-processing is finding out the best input source. The second process is the input-processing which extracts and contains important information for DNN inferences among the whole information gained from the input source. The last process is choosing or designing a suitable lightweight DNN for processed input. As an instance of how to apply the pre-processing, in Sec 2, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For the recognition of five different transportation modes, we design a deep learning-based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples. Offloading DNN inferences to a server is a solution for DNN inferences on resource-constraint devices, but there is one concern about latency caused by data transmission. To reduce transmission latency, recent studies have tried to make this offloading process more efficient by compressing data to be offloaded. However, conventional compression techniques are designed for human beings, so they compress data to be possible to restore data, which looks like the original from the perspective of human eyes. As a result, the compressed data through the compression technique contains redundancy beyond the necessary information for DNN inference. In other words, the most fundamental question on extracting and offloading the minimal amount of necessary information that does not degrade the inference accuracy has remained unanswered. To answer the question, in Sec 3, we call such an ideal offloading semantic offloading and propose N-epitomizer, a new offloading framework that enables semantic offloading, thus achieving more reliable and timely inferences in highly-fluctuated or even low-bandwidth wireless networks. To realize N-epitomizer, we design an autoencoder-based scalable encoder trained to extract the most informative data and scale its output size to meet the latency and accuracy requirements of inferences over a network. Even though our proposed lightweight DNN and offloading framework with the essential information extractor achieve low latency while preserving DNN performance, they alone cannot realize latency-guaranteed DNN inferences. To realize latency-guaranteed DNN inferences, the computational complexity of the lightweight DNN and the compression performance of the encoder for offloading should be adaptively selected according to current computation resources and network conditions by utilizing the DNN's trade-off between computational complexity and DNN performance and the encoder's trade-off between compression performance and DNN performance. To this end, we propose a new framework for latency-guaranteed DNN inferences called LG-DI, which predicts DNN performance degradation given a latency budget in advance and utilizes the better method between the lightweight DNN and offloading with compression. As a result, our proposed framework for DNN inferences can guarantee latency regardless of changes in computation and network resources while maintaining DNN performance as much as possible.ope

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Exploring the Predictors of Indonesian Reading Literacy based on PISA Data

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    Reading achievement in Indonesia has remained low since 2000 when it first participated in PISA. In Indonesia, reading is not a specific subject, but rather an essential skill integrated with other subjects like Bahasa Indonesia, English, Social Sciences, Natural Sciences, and Mathematics, and as such, it is assessed in the PISA test. Apart from the cognitive tests, PISA also collects additional information related to schoolsā€™, teachersā€™, parentsā€™, and studentsā€™ characteristics and perceptions that are related to studentsā€™ cognitive ability. Thus, the main research topics in this field are reading literacy and the factors associated with reading ability. The research study examines student and school factors and their relationship impact o student reading literacy in Indonesia, considering paper-based (PISA 2000, 2009, and follow up 2020) and computer-based reading performance (PISA 2018). A quantitative research design is used based on the research problems addressed in general that fell within the factors of reading achievement based on PISA data. This approach is used to confirm the validity and reliability of the constructs included in this study and to examine the relationships that exist among those constructs. Data collection consists of primary and secondary data collection. The study uses secondary data from PISA, as well as primary data collected in 2020 concerning the reading questionnaire and cognitive test. Secondary data from PISA 2000, 2009, and 2018 student and school questionnaires are used to examine how schools and students interrelate, which affects student achievement. The study also uses primary data collected in 2020 in a follow-up study with questionnaires adopted from PISA 2018 as the latest test with additional variables from parents and teachers. In addition to taking account of school and student factors, the results of 2020 are compared with those taken in 2000, 2009, and 2018. Thus, the longitudinal study of reading literacy based on PISA data is attempted. All constructs except the demographic items are validated using the confirmatory factor analysis (CFA) and Rasch Analysis. An analysis of all constructs that have already been anchored to the weighted likelihood estimates is conducted using structural equation modelling (SEM) and hierarchical linear modelling (HLM). To examine the factors that significantly influence studentsā€™ reading literacy in Indonesia over the four cycles, the structural equation model (with single and path analysis) and hierarchical linear model are applied. The study hypothesises that school-level factors affect the reading literacy of students. The structural equation model is used to impose a theoretical model on student variables and school variables measured by observed variables. With this model, the study explains the interrelationships between construct and observed variables. Meanwhile, a hierarchical linear model is used since the data had students who are nested in schools or students who were nested in classrooms, and classrooms are nested in schools. With this model, the study examines the effects of group variables (school- and teacher-level) and individual variables (student-level) and seeks the interaction across levels. In the analysis of the hierarchical approach, it is determined that there are consistency and nonconsistency factors towards reading literacy throughout the four cycles of analysis. There is evidence of consistent predictors at the student level in the factors of gender, reading engagement, and time spent reading. At school-level, the significant factors are: school sector in the 2000, 2018 and 2020 cycles; school location in the 2018 and 2020 cycles; ICT in the 2020 cycle; resources and technology in the 2018 cycle; assessment in the 2000 and 2018 cycles; leadership in the 2018 cycle; and school climate in the 2000 cycle. It is surprising to find that no factor was significant at the teacher-level in the 2020 cycle but a direct effect is found between teacher professional and teacher lesson activities. At student-level, the significant factors are: gender in the 2000, 2009, and 2018 cycles; the number of books in the 2000 cycle; home and educational resources in the 2018 cycle; reading engagement in the 2000, 2009, and 2018 cycles; reading diversity in the 2000, 2009, and 2018 cycles; reading online in the 2018 cycle; reading strategies in the 2009 cycle, reading confidence in the 2018 cycle, and reading time in the 2000, 2009, and 2018 cycles. The predictors are consistently available in the factor of gender, reading engagement, and reading time. In addition, the results indicate that computer-based tests (2018 cycle) provided more predictors than text-based tests (2000, 2009, and 2020 cycles). This research is particularly valuable in terms of its contributions to the theoretical, practical, and methodological aspects of reading literacy in Indonesia. This study suggests that, in general, private schools and schools located in rural or village areas require more attention regarding ICT, technology, assessment, leadership, and school climate. This likewise suggest that males should receive greater attention to reading activities, such as reading engagement and reading diversity, as well as reading states, such as reading strategies, reading confidence, and reading time. Meanwhile, females should receive more attention when it comes to online reading. Teacher professional activities plays an important role in supporting the delivery of better lessons in the classroom. In addition, it is important not to underestimate parental support in terms of the income and education of the parents. It would be beneficial for the Indonesian government in the future to maintain a curriculum based on autonomy to increase student reading achievement. Likewise, the government should include teacher and parent survey in future PISA Tests so that a more comprehensive analysis of the factors influencing reading ability can be conducted.Thesis (Ph.D.) -- University of Adelaide, School of Education, 202
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