Zayed University, College of Technological Innovation
University of Nebraska-Lincoln
Old Dominion University
Oregon Graduate Institute of Science & Technology
Nanjing University
University of Massachusetts Amherst
University of Connecticut
Bluetooth Low Energy Indoor Positioning System,
2023
Whittier College
Bluetooth Low Energy Indoor Positioning System, Jackson T. Diamond, Jordan Hanson Dr
Whittier Scholars Program
Robust indoor positioning systems based on low energy bluetooth signals will service a wide range of applications. We present an example of a low energy bluetooth positioning system. First, the steps taken to locate the target with the bluetooth data will be reviewed. Next, we describe the algorithms of the set of android apps developed to utilize the bluetooth data for positioning. Similar to GPS, the algorithms use trilateration to approximate the target location by utilizing the corner devices running one of the apps. Due to the fluctuating nature of the bluetooth signal strength indicator (RSSI), we used an averaging …
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps),
2023
Southern Methodist University
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Multipath Tcp, And New Packet Scheduling Method,
2023
University of Minnesota, Morris
Multipath Tcp, And New Packet Scheduling Method, Cole N. Maxwell
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal
Today many devices contain hardware to transmit data across the internet via cellular, WiFi, and wired connections. Many of these devices communicate by using a protocol known as Transmission Control Protocol (TCP). TCP was developed when network resources were expensive, and it was rare for a typical network-aware device to have more than one connection to a network. An extension to TCP known as Multipath TCP (MPTCP) was developed to leverage the multiple network connections to which devices now have access. While the MPTCP extension has been successful in its goal of using multiple network connections to send data simultaneously, …
Generalizing Graph Neural Network Across Graphs And Time,
2023
Singapore Management University
Generalizing Graph Neural Network Across Graphs And Time, Zhihao Wen
Research Collection School Of Computing and Information Systems
Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. …
Effective Graph Kernels For Evolving Functional Brain Networks,
2023
Singapore Management University
Effective Graph Kernels For Evolving Functional Brain Networks, Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu
Research Collection School Of Computing and Information Systems
The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel …
Finding Forensic Evidence In The Operating System's Graphical User Interface,
2023
Louisiana State University and Agricultural and Mechanical College
Finding Forensic Evidence In The Operating System's Graphical User Interface, Edward X. Wilson Mr.
LSU Master's Theses
A branch of cyber security known as memory forensics focuses on extracting meaningful evidence from system memory. This analysis is often referred to as volatile memory analysis, and is generally performed on memory captures acquired from target systems. Inside of a memory capture is the complete state of a system under investigation, including the contents of currently running as well as previously executed applications. Analysis of this data can reveal a significant amount of activity that occurred on a system since the last reboot. For this research, the Windows operating system is targeted. In particular, the graphical user interface component …
A Novel Parking Management In Smart City Vehicular Datacenters,
2023
Old Dominion University
A Novel Parking Management In Smart City Vehicular Datacenters, Syed Rizvi, Susan Zehra, Steven Olariu
College of Sciences Posters
Researchers have shown that most vehicles spend the majority of their time parked in parking garages, lots, or driveways. During this time, their computing resources are unused and untapped. This has led to substantial interest in Vehicular Cloud, an area of research in which each vehicle acts as a computation node. The main difference between traditional cloud computing and vehicular cloud computing is the availability of nodes. In traditional clouds, nodes are available 24/7, while in vehicular clouds, nodes (vehicles) are only available while parked in parking lots. This creates a dynamic environment as vehicles enter and exit parking garages …
Unmasking Deception In Vanets: A Decentralized Approach To Verifying Truth In Motion,
2023
Old Dominion University
Unmasking Deception In Vanets: A Decentralized Approach To Verifying Truth In Motion, Susan Zehra, Syed R. Rizvi, Steven Olariu
College of Sciences Posters
VANET, which stands for "Vehicular Ad Hoc Network," is a wireless network that allows vehicles to communicate with each other and with infrastructure, such as Roadside Units (RSUs), with the aim of enhancing road safety and improving the overall driving experience through real-time exchange of information and data. VANET has various applications, including traffic management, road safety alerts, and navigation. However, the security of VANET can be compromised if a malicious user alters the content of messages transmitted, which can harm both individual vehicles and the overall trust in VANET technology. Ensuring the correctness of messages is crucial for the …
Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization,
2022
Kennesaw State University
Secure Cloud-Based Iot Water Quality Gathering For Analysis And Visualization, Soin Abdoul Kassif Baba M Traore
Symposium of Student Scholars
Water quality refers to measurable water characteristics, including chemical, biological, physical, and radiological characteristics usually relative to human needs. Dumping waste and untreated sewage are the reasons for water pollution and several diseases to the living hood. The quality of water can also have a significant impact on animals and plant ecosystems. Therefore, keeping track of water quality is a substantial national interest. Much research has been done for measuring water quality using sensors to prevent water pollution. In summary, those systems are built based on online and reagent-free water monitoring SCADA systems in wired networks. However, centralized servers, transmission …
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization,
2022
Singapore Management University
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a …
Continual Learning With Neural Networks,
2022
Singapore Management University
Continual Learning With Neural Networks, Pham Hong Quang
Dissertations and Theses Collection (Open Access)
Recent years have witnessed tremendous successes of artificial neural networks in many applications, ranging from visual perception to language understanding. However, such achievements have been mostly demonstrated on a large amount of labeled data that is static throughout learning. In contrast, real-world environments are always evolving, where new patterns emerge and the older ones become inactive before reappearing in the future. In this respect, continual learning aims to achieve a higher level of intelligence by learning online on a data stream of several tasks. As it turns out, neural networks are not equipped to learn continually: they lack the ability …
Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability,
2022
Singapore Management University
Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang
Research Collection School Of Computing and Information Systems
Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how easy it …
Explanation Guided Contrastive Learning For Sequential Recommendation,
2022
Singapore Management University
Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Research Collection Lee Kong Chian School Of Business
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations …
Qvip: An Ilp-Based Formal Verification Approach For Quantized Neural Networks,
2022
Singapore Management University
Qvip: An Ilp-Based Formal Verification Approach For Quantized Neural Networks, Yedi Zhang, Zhe Zhao, Guangke Chen, Fu Song, Min Zhang, Taolue Chen, Jun Sun
Research Collection School Of Computing and Information Systems
Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs) are increasingly being deployed in practice, but are limited on resource-constrained devices owing to their demand for computational power. Quantization has emerged as a promising technique to reduce the size of DNNs with comparable accuracy as their floating-point numbered counterparts. The resulting quantized neural networks (QNNs) can be implemented energy-efficiently. Similar to their floating-point numbered counterparts, quality assurance techniques for QNNs, such as testing and formal verification, are essential but are currently less explored. In …
Stitching Weight-Shared Deep Neural Networks For Efficient Multitask Inference On Gpu,
2022
Singapore Management University
Stitching Weight-Shared Deep Neural Networks For Efficient Multitask Inference On Gpu, Zeyu Wang, Xiaoxi He, Zimu Zhou, Xu Wang, Qiang Ma, Xin Miao, Zhuo Liu, Lothar Thiele, Zheng. Yang
Research Collection School Of Computing and Information Systems
Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resourceconstrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN graph rewriters are blind for multiDNN optimization, while GPU vendors provide inefficient APIs for parallel multi-DNN execution at runtime. A few prior graph rewriters suggest cross-model graph fusion for low-latency multiDNN execution. Yet they request duplication of the shared …
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives,
2022
The University of Western Ontario
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Electronic Thesis and Dissertation Repository
This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect …
Holistic Performance Analysis And Optimization Of Unified Virtual Memory,
2022
Clemson University
Holistic Performance Analysis And Optimization Of Unified Virtual Memory, Tyler Allen
All Dissertations
The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that …
Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning,
2022
East Tennessee State University
Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt
Electronic Theses and Dissertations
Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …
Verifying Neural Networks Against Backdoor Attacks,
2022
Singapore Management University
Verifying Neural Networks Against Backdoor Attacks, Pham Hong Long, Jun Sun
Research Collection School Of Computing and Information Systems
Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is …
Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment,
2022
Singapore Management University
Self-Checking Deep Neural Networks For Anomalies And Adversaries In Deployment, Yan Xiao, Ivan Beschastnikh, Yun Lin, Rajdeep Singh Hundal, Xiaofei Xie, David S. Rosenblum, Jin Song Dong
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt with in deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model …
