Tsinghua Issue- Generative AI, Learning And New Literacies

Launched in November 2022, OpenAI\u27s ChatGPT garnered over 100 million users within two months, sparking a surge in research and concern over potential risks of extensive AI experiments. The article, originating from a conference presentation by Tsinghua University and NTHU, Taiwan, provides a nuanced overview of Generative AI. It explores the classifications, applications, governance challenges, societal implications, and development trajectory of Generative AI, emphasizing its transformative role in employment and education. The piece highlights ChatGPT\u27s significant impact and the strategic adaptations required in various sectors, including medical education, engineering, information management, and distance education. Furthermore, it explores the opportunities and challenges associated with incorporating ChatGPT in educational settings, emphasizing its support in facilitating personalized learning, developing 21st-century competencies, fostering self-directed learning, and enhancing information accessibility. It also illustrates the integration of ChatGPT and text-to-image models in high school language courses through the lens of new literacies. The text uniquely integrates three layers of discourse: introductions to Generative AI by experts, scholarly debates on its merits and drawbacks, and practical classroom applications, offering a reflective snapshot of the current and potential states of Generative AI applications while emphasizing the interconnected discussions across various layers of discourse

Children\u27s Literature for Cultural Understanding between Students in Taiwan and Mainland China

Based on a portion of the results of a research project that explores the possibilities of using Mandarin children\u27s literature for cultural understanding between the students of Taiwan and Mainland China, this paper discusses in depth what existing U. S. research may or may not help in constructing Mandarin reading lists and thus provides insights for applications in other settings. It also comes up with a new strategy for cultural understanding with twin texts from two culture

An Examination of the Effects of Communication Media on Geographically Separated mentors and Protégés: Does Distance Matter?

The purpose of this study was to explore and evaluate the extent to which a formal mentoring relationship could transition into an informal mentoring relationship after geographically separating a formal mentor-protégé dyad. This study also explored the moderating effects of individual effectiveness. Data were collected from 283 military graduate students attending an 18-month graduate program. The results of this research revealed protégé perceptions of mentoring effectiveness increased with the length of the relationship. Furthermore, this study found formal mentoring relationships were capable of transitioning into informal mentoring relationships

An Enhanced Hybrid MobileNet

Complicated and deep neural network models can achieve high accuracy for image recognition. However, they require a huge amount of computations and model parameters, which are not suitable for mobile and embedded devices. Therefore, MobileNet was proposed, which can reduce the number of parameters and computational cost dramatically. The main idea of MobileNet is to use a depthwise separable convolution. Two hyper-parameters, a width multiplier and a resolution multiplier are used to the trade-off between the accuracy and the latency. In this paper, we propose a new architecture to improve the MobileNet. Instead of using the resolution multiplier, we use a depth multiplier and combine with either Fractional Max Pooling or the max pooling. Experimental results on CIFAR database show that the proposed architecture can reduce the amount of computational cost and increase the accuracy simultaneously

New Constructions of Zero-Correlation Zone Sequences

In this paper, we propose three classes of systematic approaches for constructing zero correlation zone (ZCZ) sequence families. In most cases, these approaches are capable of generating sequence families that achieve the upper bounds on the family size ($K$) and the ZCZ width ($T$) for a given sequence period ($N$). Our approaches can produce various binary and polyphase ZCZ families with desired parameters $(N,K,T)$ and alphabet size. They also provide additional tradeoffs amongst the above four system parameters and are less constrained by the alphabet size. Furthermore, the constructed families have nested-like property that can be either decomposed or combined to constitute smaller or larger ZCZ sequence sets. We make detailed comparisons with related works and present some extended properties. For each approach, we provide examples to numerically illustrate the proposed construction procedure.Comment: 37 pages, submitted to IEEE Transactions on Information Theor

Inclusions properties at 1673 K and room temperature with Ce addition in SS400 steel

Inclusion species formed in SS400 steel with Ce-addition was predicted by thermodynamic calculation. The analysis of the inclusion morphology and size distribution was carried out by applying Scanning Electron Microscopy (SEM) and Transmission Electron Microscope (TEM). Nano-Fe3O4 particles were also found in cerium-deoxidized and -desulfurized steel and their shapes were nearly spherical. The complex Ce2O3 inclusions covering a layer of 218 nm composed by several MnS particles with similar diffraction pattern. Most importantly, the complex Ce2O3 characterized by using TEM diffraction is amorphous in the steel, indicating that Ce2O3 formed in the liquid iron and then MnS segregated cling to it

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study

This paper proposes a using Cellular-Based Vehicle Probe (CVP) at road-section (RS) method to detect and setup a model for traffic flow information (info) collection and monitor. There are multiple traffic collection devices including CVP, ETC-Based Vehicle Probe (EVP), Vehicle Detector (VD), and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem, monitor and control. The main project has been applied at Tai # 2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018. This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory (LTSM) from recurrent neural network (RNN) model. We also provide a model verification and validation methodology with RNN for cross verification of system performance