Engineering Commons^™

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Efficient Training On Alzheimer’S Disease Diagnosis With Learnable Weighted Pooling For 3d Pet Brain Image Classification, Xin Xing, Muhammad Usman Rafique, Gongbo Liang, Hunter Blanton, Zu Zhang, Chris Wang, Nathan Jacobs, Ai-Ling Lin

Computer Science Faculty Publications

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By …

Dfhic: A Dilated Full Convolution Model To Enhance The Resolution Of Hi-C Data, Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Hi-C technology has been the most widely used chromosome conformation capture(3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods.

Results: In this work, we propose …

Mitigating Anomalous Electricity Consumption In Smart Cities Using An Ai-Based Stacked-Generalization Technique, Arshid Ali, Laiq Khan, Nadeem Javaid, Safdar Hussain Bouk, Abdulaziz Aldegheishem, Nabil Alrahjeh

Computer Science Faculty Publications

Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the …

Segmenting Technical Drawing Figures In Us Patents, Md Reshad Ul Hoque, Xin Wei, Muntabir Hasan Choudhury, Kehinde Ajayi, Martin Gryder, Jian Wu, Diane Oyen

Computer Science Faculty Publications

Image segmentation is the core computer vision problem for identifying objects within a scene. Segmentation is a challenging task because the prediction for each pixel label requires contextual information. Most recent research deals with the segmentation of natural images rather than drawings. However, there is very little research on sketched image segmentation. In this study, we introduce heuristic (point-shooting) and deep learning-based methods (U-Net, HR-Net, MedT, DETR) to segment technical drawings in US patent documents. Our proposed methods on the US Patent dataset achieved over 90% accuracy where transformer performs well with 97% segmentation accuracy, which is promising and computationally …

See-Trend: Secure Traffic-Related Event Detection In Smart Communities, Stephan Olariu, Dimitrie C. Popescu

Computer Science Faculty Publications

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed …

Influence Spread In Two-Layer Interdependent Networks: Designed Single-Layer Or Random Two-Layer Initial Spreaders?, Hana Khamfroush, Nathaniel Hudson, Samuel Iloo, Mahshid R. Naeini

Computer Science Faculty Publications

Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model …

X-Search: An Open Access Interface For Cross-Cohort Exploration Of The National Sleep Research Resource, Licong Cui, Ningzhou Zeng, Matthew Kim, Remo Mueller, Emily Ruth Hankosky, Susan Redline, Guo-Qiang Zhang

Computer Science Faculty Publications

Background: The National Sleep Research Resource (NSRR) is a large-scale, openly shared, data repository of de-identified, highly curated clinical sleep data from multiple NIH-funded epidemiological studies. Although many data repositories allow users to browse their content, few support fine-grained, cross-cohort query and exploration at study-subject level. We introduce a cross-cohort query and exploration system, called X-search, to enable researchers to query patient cohort counts across a growing number of completed, NIH-funded studies in NSRR and explore the feasibility or likelihood of reusing the data for research studies.

Methods: X-search has been designed as a general framework with two loosely-coupled components: …

Query-Constraint-Based Mining Of Association Rules For Exploratory Analysis Of Clinical Datasets In The National Sleep Research Resource, Rashmie Abeysinghe, Licong Cui

Computer Science Faculty Publications

Background: Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics.

Methods: We introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on …

Scheduling Based On Interruption Analysis And Pso For Strictly Periodic And Preemptive Partitions In Integrated Modular Avionics, Hui Lu, Qianlin Zhou, Zongming Fei, Rongrong Zhou

Computer Science Faculty Publications

Integrated modular avionics introduces the concept of partition and has been widely used in avionics industry. Partitions share the computing resources together. Partition scheduling plays a key role in guaranteeing correct execution of partitions. In this paper, a strictly periodic and preemptive partition scheduling strategy is investigated. First, we propose a partition scheduling model that allows a partition to be interrupted by other partitions, but minimizes the number of interruptions. The model not only retains the execution reliability of the simple partition sets that can be scheduled without interruptions, but also enhances the schedulability of the complex partition sets that …

Gpu-Optimized Code For Long-Term Simulations Of Beam-Beam Effects In Colliders, Y. Roblin, V. Morozov, B. Terzić, M. Aturban, D. Ranjan, M. Zubair

Computer Science Faculty Publications

We report on the development of the new code for long-term simulation of beam-beam effects in particle colliders. The underlying physical model relies on a matrix-based arbitrary-order symplectic particle tracking for beam transport and the Bassetti-Erskine approximation for beam-beam interaction. The computations are accelerated through a parallel implementation on a hybrid GPU/CPU platform. With the new code, a previously computationally prohibitive long-term simulations become tractable. We use the new code to model the proposed medium-energy electron-ion collider (MEIC) at Jefferson Lab.

Special Issue On Wireless Sensor And Actuator Networks, Jiming Chen, Karl H. Johanson, Stephan Olariu, Ioannis Ch. Paschalidis, Ivan Stojmenovic

Computer Science Faculty Publications

( First paragraph) WIRELESS Sensor Networks (WSNs), in their various shapes and forms, have greatly facilitated and enhanced the automated, remote, and intelligent monitoring of a large variety of physical systems. These networks consist of a large number of typically small devices, each incorporating sensing, processing, and wireless communications capabilities. Their use has penetrated a plethora of application domains from industrial and building automation, to environmental, wildlife, and health monitoring.

Structure Prediction For The Helical Skeletons Detected From The Low Resolution Protein Density Map, Kamal Al Nasr, Weitao Sun, Jing He

Computer Science Faculty Publications

Background: The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.

Results: We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density …

Zsbt: A Novel Algorithm For Tracing Dos Attackers In Manets, Xin Jin, Yaoxue Zhang, Yi Pan, Yuezhi Zhou

Computer Science Faculty Publications

Denial of service (DoS) attack is a major class of security threats today. They consume resources of remote hosts or network and make them deny or degrade services for legitimate users. Compared with traditional Internet, the resources, such as bandwidth, memory, and battery power, of each node are more limited in mobile ad hoc networks (MANETs). Therefore, nodes in MANETs are more vulnerable to DoS attacks. Moreover, attackers in MANETs cannot only use IP spoofing to conceal their real identities but also move arbitrarily, which makes it a challenging task to trace a remote attacker in MANETs. In this paper, …