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1. Supervised machine learning approaches for leak localization in water distribution systems: impact of complexities of leak characteristics

   Basnet, Lochan; Bril, Downey E; Ranjithan, Ranji S; Mahinthakumar, Kumar (May 2023, Journal of water resources planning and management)

   Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP. 

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2. Pressure Sensor Placement in Water Distribution Systems Using Leak Resolution–Specific Surrogates for Leak Localization

   https://doi.org/10.1061/JWRMD5.WRENG-6715  

   Basnet, Lochan; Brill, Downey; Ranjithan, Ranji; Mahinthakumar, Kumar (July 2025, Journal of Water Resources Planning and Management)

   The effectiveness of model-based leak localization methods in water distribution systems (WDSs), including optimization-based and machine learning approaches, significantly depends on the quality and quantity of input data. Pressure data, easily accessible due to nonintrusive sensor installation and maintenance, are commonly used. However, economic constraints limit the number of sensors in WDSs, highlighting the need for strategic sensor placement to enhance data quality. This study introduces a novel, method-independent sensor placement strategy that integrates cluster definitions (leak resolution) with intuitive surrogates for localization performance, addressing the limitations of existing methods reliant on complex, nonintuitive metrics. We propose the Euclidean cluster-based optimal placement of sensors (ECOPS) approach, which employs sensitivity and uniqueness as fundamental signal properties to guide sensor placement. Validation tests within a comprehensive real-world WDS demonstrate that ECOPS outperforms existing surrogate-based approaches and improves the performance of current sensors installed for leak characterization. These findings provide compelling evidence of ECOPS’s potential for enhancing pressure sensor placement, thereby improving leak localization in WDS applications. 

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3. Lightweight and Modular Resource Leak Verification

   https://doi.org/10.1145/3468264.3468576  

   Kellogg, Martin; Shadab, Narges; Sridharan, Manu; Ernst, Michael D. (August 2021, Proceedings of the 2021 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE))
4. NETA: when IP fails, secrets leak

   https://doi.org/10.1145/3287624.3288739  

   Meade, Travis; Portillo, Jason; Zhang, Shaojie; Jin, Yier (January 2019, Proceedings of the 24th Asia and South Pacific Design Automation Conference)

   Assuring the quality and the trustworthiness of third party resources has been a hard problem to tackle. Researchers have shown that analyzing Integrated Circuits (IC), without the aid of golden models, is challenging. In this paper we discuss a toolset, NETA, designed to aid IP users in assuring the confidentiality, integrity, and accessibility of their IC or third party IP core. The discussed toolset gives access to a slew of gate-level analysis tools, many of which are heuristic-based, for the purposes of extracting high-level circuit design information. NETA majorly comprises the following tools: RELIC, REBUS, REPCA, REFSM, and REPATH. The first step involved in netlist analysis falls to signal classification. RELIC uses a heuristic based fan-in structure matcher to determine the uniqueness of each signal in the netlist. REBUS finds word groups by leveraging the data bus in the netlist in conjunction with RELIC's signal comparison through heuristic verification of input structures. REPCA on the other hand tries to improve upon the standard bruteforce RELIC comparison by leveraging the data analysis technique of PCA and a sparse RELIC analysis on all signals. Given a netlist and a set of registers, REFSM reconstructs the logic which represents the behavior of a particular register set over the course of the operation of a given netlist. REFSM has been shown useful for examining register interaction at a higher level. REPATH, similar to REFSM, finds a series of input patterns which forces a logical FSM initialize with some reset state into a state specified by the user. Finally, REFSM 2 is introduced to utilizes linear time precomputation to improve the original REFSM. 

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5. Privacy Leak Classification from Mobile Devices

   Shuba, A.; Bakopoulou, E; Markopoulou, A (June 2018, In Proc. of SPAWC (19th IEEE Int’l Workshop in Signal Processing Advances in Wireless Communications)

   Mobile devices have access to personal, potentially sensitive data, and there is a growing number of mobile apps that have access to it and often transmit this personally identifiable information (PII) over the network. In this paper, we present an approach for detecting such PII “leaks” in network packets going out of the device, by first monitoring network packets on the device itself and then applying classifiers that can predict with high accuracy whether a packet contains a PII leak and of which type. We evaluate the performance of our classifiers using datasets that we collected and analyzed from scratch. We also report preliminary results that show that collaboration among users can further improve classification accuracy, thus motivating crowdsourcing and/or distributed learning of privacy leaks. 

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