AN EXPLORATORY STUDY OF IT FIT MOTIVATION IN A CLOUD-COMPUTING CLASSROOM

In recent years, digital learning has received more attention from the field of education, and many schools in Taiwan have begun to introduce the cloud-computing classroom platform as another learning environment for students. However, as there remains a lack of research on fit and performance in the cloud-computing classroom, this study attempts to explore students’ views and effects when using the cloud-computing classroom. The research methods include case study and survey. Case study involved interviews with 18 students regarding their motivations and usage of the Ming Chuan University cloud-computing classroom. Based on the interview results, this study proposed three propositions, which were converted to three hypotheses. We collected data from a field survey and our results showed that (1) the user’s needs positively and affect the perceived fit; (2) the user’s usage of the cloud-computing classroom positively and affects the perceived fit; (3) the perceived fit in the usage of the cloud-computing classroom positively and affect user performance. Implications for academic researchers and practitioners are discussed

Product-based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201

Design of Pulse Forming Networks Triggered by High-Power Hydrogen Thyratron

Hydrogen thyratron is a switching device invented based on the phenomenon of gas discharge, and it is widely used in the field of high-power pulse technology. The design of Pulse Forming Network (PFN) triggered by hydrogen thyratron aims to control the switch of subsequent circuit, and shorten the gate-cathode voltage and conduction delay time by increasing the rise rate of the trigger voltage. However, in the currently adopted series resonance network design schemes, usually the value of inductance is very large, which can easily lead to the decline in the electromagnetic compatibility performance; moreover, the large distribution of network component parameters will greatly increase the fabrication difficulties. In view of the features of high-power hydrogen thyratron and the design requirements of PFN, this paper adopted the series resonance network design scheme to devise network series and parameters of the PFN and analyze the shortcomings of the series resonance network design scheme; then, it used the anti-resonance network to design a three-stage transform algorithm model, so as to achieve the purpose of reducing the inductance of the PFN and the difficulty of capacitance model selection in engineering practice. At last, simulation results verified the correctness and feasibility of the designed three-stage transform algorithm model, providing evidences for the pulse network projects of hydrogen thyratron and other high-power equipment in terms of implementation paths, methods, and algorithm models

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the category of query instance. However, current metric-based methods generally treat all instances equally and consequently often obtain biased class representation, considering not all instances are equally significant when summarizing the instance-level representations for the class-level representation. For example, some instances may contain unrepresentative information, such as too much background and information of unrelated concepts, which skew the results. To address the above issues, we propose a novel metric-based meta-learning framework termed instance-adaptive class representation learning network (ICRL-Net) for few-shot visual recognition. Specifically, we develop an adaptive instance revaluing network with the capability to address the biased representation issue when generating the class representation, by learning and assigning adaptive weights for different instances according to their relative significance in the support set of corresponding class. Additionally, we design an improved bilinear instance representation and incorporate two novel structural losses, i.e., intra-class instance clustering loss and inter-class representation distinguishing loss, to further regulate the instance revaluation process and refine the class representation. We conduct extensive experiments on four commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS, and FC100 datasets. The experimental results compared with the state-of-the-art approaches demonstrate the superiority of our ICRL-Net

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal$\_$Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance

Organocatalyzed Asymmetric Reaction Using α-Isothiocyanato Compounds

Organocatalyzed asymmetric reaction using α-isothiocyanato compounds has received much attention in the past 5 years, and significant progress has been made for three types of isothiocyanato compounds, including α-isothiocyanato amides, esters, and phosphonates. This chapter covers the recent advances of α-isothiocyanato compounds in the organocatalytic asymmetric reaction

Secure Software Development: Issues and Challenges

In recent years, technology has advanced considerably with the introduction of many systems including advanced robotics, big data analytics, cloud computing, machine learning and many more. The opportunities to exploit the yet to come security that comes with these systems are going toe to toe with new releases of security protocols to combat this exploitation to provide a secure system. The digitization of our lives proves to solve our human problems as well as improve quality of life but because it is digitalized, information and technology could be misused for other malicious gains. Hackers aim to steal the data of innocent people to use it for other causes such as identity fraud, scams and many more. This issue can be corrected during the software development life cycle, integrating security across the development phases, and testing of the software is done early to reduce the number of vulnerabilities that might or might not heavily impact an organisation depending on the range of the attack. The goal of a secured system software is to prevent such exploitations from ever happening by conducting a system life cycle where through planning and testing is done to maximise security while maintaining functionality of the system. In this paper, we are going to discuss the recent trends in security for system development as well as our predictions and suggestions to improve the current security practices in this industry.Comment: 20 Pages, 4 Figure