Bystanders by Rowe, Mary, Giraldo-Kerr, Anna

“…If they take helpful action, they may be called “active” or “positive” bystanders, or “up-standers.” …”
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Internet based training for engineers in the aspect of six sigma by Chatta Kannappa Srinivas.

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Battery plug-in electric vehicle concept design : (motor-drive train and batterypack) by Liong, Farandy Angesti.

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Effective image synthesis for effective deep neural network training by Cui, Kaiwen

“…We propose a novel Generative Co-training (GenCo) network that adapts the co-training idea into data-limited generation for tackling its inherent over-fitting issue. …”
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Predicted Losses of Sulfur and Selenium in European Soils Using Machine Learning: A 2 Call for Prudent Model Interrogation and Selection by Jones, Gerrad D., Insinga, Logan, Droz, Boris, Feinberg, Aryeh, Stenke, Andrea, Smith, Jo, Smith, Pete, Winkel, Lenny H. E.

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OSPC: Multimodal Harmful Content Detection using Fine-tuned Language Models by Cai, Bill

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Effectiveness of a preventative multi-component training program for prospective memory performance among healthy older adults by Farzin, Azin

“…Cognitive training includes strategy- and process-based approaches. …”
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FSL-QuickBoost: Minimal-Cost Ensemble for Few-Shot Learning by Bai, Yunwei, Cai, Bill Yang, Tan, Ying Kiat, Zheng, Zangwei, Chen, Shiming, Chen, Tsuhan

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Self-Supervised Learning across the Spectrum by Shenoy, Jayanth, Zhang, Xingjian Davis, Tao, Bill, Mehrotra, Shlok, Yang, Rem, Zhao, Han, Vasisht, Deepak

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Scheduling and resource optimization in rail operations : models and algorithms by Birhade, Mahendra Pandharinath

“…In this essay, we develop a novel Integer Programming (IP) model that integrates the TTP and TPP into a single problem called the Integrated Train Timetabling and Platforming Problem (ITTPP). …”
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Object recognition using deep learning features by Zhu, Daiqing

“…This project proposed a more efficient algorithm called Data Driven Object Proposal which doesn’t need to train classifier. …”
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Virtual patient by Wong, Inge.

“…Standardized Patients have been widely used in hospitals and medical schools to train students. The term “Standardized Patients” refers to the specially trained actors who portray patients with particular health conditions or concerns. …”
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Object detection and tracking from surveillance videos by Yuan, Ziying

“…Wide use of video surveillance systems calls for powerful tools to extract information from video data. …”
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Design of machine learning based face recognition system by Yeo, Alvin Jin Kuang

“…In this report, a face recognition system called OpenFace which is an open source code based on a paper called FaceNet will be studied and implement. …”
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A survey of few-shot learning for biomedical time series by Li, C, Denison, T, Zhu, T

“…However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. …”

Exploring the Conceptual Definition of Islamic Financial Literacy from the Quran and Sunnah by Ab Rashid, Rosemaliza, Zainol, Zairani, Isa, Mohamad Yazid, Mohd Taib, Hasniza, Khairol Nizam, Aini Nur Hajjar

“…The recurring financial crisis calls for increasing attention on the crucial role of financial literacy. …”
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Adaptive generative adversarial network (GAN) for small datasets by Liu, Chang

“…This model provides a training model under a small data set, which can help us better train the neural network and improve training efficiency…”
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Development of an advanced nano-satellite VELOX-I – attitude control system by Ng, Yu Lian.

“…The VELOX series of satellite was started in Nanyang Technological University as an initiative to train its undergraduate students in space technology. …”
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Performance evaluation of SAFIS for benchmark problems by Sinara Vijayan.

“…The results indicate that the SAFIS algorithm exhibits very good performance with the regression benchmark problems in terms of training time, efficiency and number of hidden neurons to train the network. …”
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RanDumb: random representations outperform online continually learned representations by Prabhu, A, Sinha, S, Kumaraguru, P, Torr, PHS, Sener, O, Dokania, PK

“…Our primary contribution is empirically demonstrating that existing online continually trained deep networks produce inferior representations compared to a simple pre-defined random transforms. …”