Physical Sciences and Mathematics Commons^™

Software Training In Hep, Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel H. Villanueva, Daniel S. Katz, Graeme A. Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber

Faculty and Student Publications

The long-term sustainability of the high-energy physics (HEP) research software ecosystem is essential to the field. With new facilities and upgrades coming online throughout the 2020s, this will only become increasingly important. Meeting the sustainability challenge requires a workforce with a combination of HEP domain knowledge and advanced software skills. The required software skills fall into three broad groups. The first is fundamental and generic software engineering (e.g., Unix, version control, C++, and continuous integration). The second is knowledge of domain-specific HEP packages and practices (e.g., the ROOT data format and analysis framework). The third is more advanced knowledge involving …

Automated Classification Model With Otsu And Cnn Method For Premature Ventricular Contraction Detection, Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm …

Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin

Research Collection School Of Computing and Information Systems

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all …

A Roadmap For Building Data Science Capacity For Health Discovery And Innovation In Africa, Joseph Beyene, Solomon W. Harrar, Mekibib Altaye, Tessema Astatkie, Tadesse Awoke, Ziv Shkedy, Tesfaye B. Mersha

Statistics Faculty Publications

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In …

Gamification Platform For Social Engineering Training And Awareness, Rui Xu

Electronic Theses and Dissertations

Almost every type of cybersecurity incident leverages one or more social engineering attacks. Nowadays social engineering attack is considered one of the most significant threats to individuals and organizations. It is an attacking technique that manipulates and deceives users to access or gain privileged information. Cybersecurity training is an effective defense method to enhance people's awareness of social engineering attacks, especially training through game playing or educational games. However, fewer tools can customize social engineering simulations based on user's characteristics and needs. Some social engineering training tools are lack motivation, engagement, and interaction.

Gamification is the use of game elements …

Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …

Deeprepair: Style-Guided Repairing For Deep Neural Networks In The Real-World Operational Environment, Bing Yu, Hua Qi, Guo Qing, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are continuously expanding their application to various domains due to their high performance. Nevertheless, a well-trained DNN after deployment could oftentimes raise errors during practical use in the operational environment due to the mismatching between distributions of the training dataset and the potential unknown noise factors in the operational environment, e.g., weather, blur, noise, etc. Hence, it poses a rather important problem for the DNNs' real-world applications: how to repair the deployed DNNs for correcting the failure samples under the deployed operational environment while not harming their capability of handling normal or clean data with limited …

An Exploratory Study Of Mode Efficacy In Cybersecurity Training, Michael D. Workman

Journal of Cybersecurity Education, Research and Practice

Cybersecurity capabilities in organizations and governmental agencies continue to lag behind the threats. Given the current environment, these entities have placed renewed emphasis on cybersecurity education. However, education appears to lack its full potential in most settings. Few empirical studies have systematically tested the efficacy of various training methods and modes, and those that have been conducted have yielded inconsistent findings. Recent literature on the use of gamified simulations have suggested that they may improve cybersecurity behaviors. Similarly, live activities such as hackathons and capture the flag events have been surmised to augment learning and capabilities. We conducted an exploratory …

Classification And Analysis Of Android Malware Images Using Feature Fusion Technique, Jaiteg Singh, Deepak Thakur, Tanya Gera, Babar Shah, Tamer Abuhmed, Farman Ali

All Works

The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android …

The Effects Of Advanced Analytics And Machine Learning On The Transportation Of Natural Gas, Bj Stigall

Doctoral Dissertations and Projects

This qualitative single case study describes the effects of an advanced analytic and machine learning system (AAML) has on the transportation of natural gas pipelines and the causes for failure to fully utilize the advanced analytic and machine learning system. This study's guiding theory was the Unified Theory of Acceptance and Use of Technology (UTAUT) model and Transformation Leadership. The factors for failure to fully utilize AAML systems were studied, and the factors that made the AAML system successful were also examined. Data were collected through participant interviews. This study indicates that the primary factors for failure to fully utilize …

Counterfactual Zero-Shot And Open-Set Visual Recognition, Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often out of the true distribution, which causes severe recognition rate imbalance between the seen-class (high) and unseen-class (low). We show that the key reason is that the generation is not Counterfactual Faithful, and thus we propose a faithful one, whose generation is from the sample-specific counterfactual question: What would the sample look like, if we set its …

Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism;, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Achananuparp Palakorn, Ee Peng Lim, Steven Hoi

Research Collection School Of Computing and Information Systems

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these …

Learning To Fuse Asymmetric Feature Maps In Siamese Trackers, Wencheng Han, Xingping Dong, Fahad Shahbaz Khan, Ling Shao, Jianbing Shen

Computer Vision Faculty Publications

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on …

Literature Review And Comparative Analysis Of Existing Certification And Training Programs Applicable To Clean Water Project Operations And Maintenance, Marc Companion, Anna Hildebrand, Kristine Stepenuck

Lake Champlain Sea Grant Institute

Stormwater runoff that carries sediments and nutrients is a primary pollutant entering surface waters in the State of Vermont. Phosphorus pollution is driving cyanobacteria blooms in many of our lakes including Lake Champlain, Lake Carmi, and Lake Memphremagog, especially in the warmer months. Warmer weather patterns and an increased frequency of extreme storms are predicted with climate change. As such, there is critical need to take action on the land to minimize and treat stormwater runoff on-site.

The State adopted a Clean Water Act in 2015, which was swiftly followed by a Total Maximum Daily Load (TMDL) for Lakes Champlain …

Law Library Blog (January 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov

All Works

The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model’s dependency on the training data by leveraging many related but different data …

Efficientnet-Lite And Hybrid Cnn-Knn Implementation For Facial Expression Recognition On Raspberry Pi, Mohd Nadhir Ab Wahab, Anthony Tan Zhen Ren, Amril Nazir, Mohd Halim Mohd Noor, Muhammad Firdaus Akbar, Ahmad Sufril Azlan Mohamed

All Works

Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …