February 27 ~ 28, 2026, Vancouver, Canada
Grace Llego 1, Jim Alves-Foss21 Center for Secure and Dependable Systems, University of Idaho. Moscow, ID USA,
Since about 2010, there has been increased attention in gauging and improving digital citizenship across the world. One important aspect of digital citizenship is cybersecurity awareness. This paper reports on a review of 35 studies that have been conducted in this area, primarily consisting of surveys of students. The primary purpose of this review was to compare and evaluate the methods used in these studies to provide guidance towards future studies. The studies surveyed from 32 to over 3000 participants evaluated cybersecurity awareness and/or practice, with about half of them providing copies of the survey questions to allow for follow-on comparative studies.
Cyber security awareness, Surveys. Digital Literacy
Grace Llego 1, Jiawei Zhang2 Xin Zhang3 Xinyin Mia1 Senior Investment Analyst, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA 2 Data Scientist, PRA Group (Nasdaq: PRAA), Norfolk, Virginia, USA3 Senior Data Analyst, American Airlines Group Inc (Nasdaq: AAL), Dallas, Texas,USA
This paper provides an innovative methodology of partial penalty on machine learning models to handle the data imbalance scenario occurring in credit card fraud detection implementation. Unlike the normal over-sampling or under-sampling methodologies, partial penalty directs the machine learning model to focus on learning the minor class of target variable even when the class distribution is extremely imbalanced. Besides comparing the partial penalty approach with over-sampling and under-sampling approaches to handle data imbalance scenario, we’ve implemented this new approach under five machine learning classification models, including Logistic Regression, Random Forest, kNN, Decision Tree, and Light Gradient Boosting Model. The new partial penalty approach realizes a performance of 88.35% F1 score and 98.79% AUC score with Light GBM, higher than either over-sampling or under-sampling approaches.
Partial Penalty, Gradient Boosting, Data Imbalance, Credit Card Fraud Detection, SMOTE
Ashwin Geeni 1, Independent Researcher, USA
AI and deep learning workloads rely heavily on matrix multiplication, which demands both high speed and numerical accuracy. Traditional processors use fixed arithmetic precision, forcing a global trade-off between performance and accuracy. This paper presents the Mixed Precision Pipeline (MPP), a five-stage RISC-V processor pipeline that dynamically switches between INT8, FP16, and FP32 precisions based on runtime numerical stability analysis..
Mixed-precision computing, RISC-V, Matrix multiplication, Hardware acceleration, Numerical stability
Guowei Xiao 1, Ping Wang21 Faculty of Automation Dept, Guangdong University of Technology, Guangzhou, 510006,China 2 Jinqi,Micro Electronics,China
This paper proposes the multirate Convolutional Neural Networks (mCNN) algorithms for an efficient implementation of the 2-Dimensional (2-D) CNN circuits implementation. During the rapid growth in computation power, Deep Learning (DL) using CNN has widened the areas of the Artificial Intelligent (AI) applications. For the layers of the convolution with pooling operation in CNN the work (Franca et al., 1985) has initially applied the multirate algorithms[1-3] to the traditional (non-multirate) convolutional kernel operation of using polyphase architectures resulting in the more efficient implementation of the multirate filtering. In this work we extend it into 2-D CNN by using time-varying coefficient to achieve an efficient implementation with reduced memory(i.e. the line-buffer) size by M-fold(the pooling factor) and the MACs at 1/M of clock running rate. A design example of the first stage of CNN system will be provided. Its results are verified with the Matlab CNN-based digit recognition tool.
CNN, ML, DL, AI, IC, Multirate, 2-D, Signal Processing,DSP, AISC, Filter