27 papers · ranked by Valyu relevance
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for emission event detection, localization, and quantification on oil and gas production sites that uses concentration data collected by pointin-space continuous monitoring systems (CMS). The framework uses a gradient-based spike detection algorithm to estimate emission start and…
Yulin Zhang, Yong Hu, Xiao Chen, Claudio Savaglio
With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we…
Duraid Thamer Salim, Manmeet Mahinderjit Singh, Pantea Keikhosrokiani
Advancements in computing technology and the growing number of devices (e.g., computers, mobile) connected to networks have contributed to an increase in the amount of data transmitted between devices. These data are exposed to various types of cyberattacks, one of which is advanced persistent threats (APTs). APTs are…
Meng Jia, Troy Sorensen, Dorit Hammerling
We propose a generic, modular framework to optimize the placement of continuous monitoring sensors on oil and gas sites aiming to maximize the methane emissions detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different…
Shi Jin, Zhaofeng Guo, Dongli Liu, Yanhua Yang
In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly widespread trend, and its development has brought many negative impacts to…
Basheer Husham Ali, Nasri Sulaiman, Syed Abdul Rahman Al-Haddad, Rodziah Atan + 5 more
'Rodziah Atan' 'Siti Lailatul Mohd Hassan' 'Mokhalad Alghrairi' 'Hamed Badihi' 'Tao Chen' 'Ningyun Lu'] One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable…
Thomas Pircher, Bianca Pircher, Andreas Feigenspan
Spontaneous synaptic activity is a hallmark of neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it…
Zhuolun Meng, Liam White, Pengfei Xie, S. Reza Mahmoodi + 5 more
This work explores label-free biosensing as an effective method for biomolecular analysis, ensuring the preservation of native conformation and biological activity. The focus is on a novel electronic biosensing platform utilizing micro-fabricated nanowell-based impedance sensors, offering rapid, point-of-care diagnosis…
Mehdi Lotfi, Lars Kaderali
Change point detection is critical for identifying structural transitions in time series data. While most existing methods focus on changes in statistical properties of the data such as the mean or variance, many real-world systems are governed by dynamical models in which changes occur in model parameters. We…
Marcin Niemiec, Rafał Kościej, Bartłomiej Gdowski, Amelia Carolina Sparavigna + 2 more
'Amelia Carolina Sparavigna' 'Héctor D. Menéndez' 'Guillermo Suárez-Tangil'] The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for…
Abdul Razak M. S., Nirmala C. R., Sreenivasa B. R., Husam Lahza + 1 more
'Hassan Fareed M. Lahza'] Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The…
Akila Subasinghe, Jagath Samarabandu, Yanxin Li, Ruth Wilkins + 3 more
Accurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and…
Nathan Gold, Martin G Frasch, Christophe Herry, Bryan S Richardson + 2 more
Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, non-stationary time series. This noise, due to natural variability and measurement error, poses a challenge to conventional changepoint detection methods. We proposed a novel, real-time…
Md Moman Ul Haque Khan, Samira Sadaoui
Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift. The presence of concept drift poses a major challenge for many real-world applications because it can severely degrade their predictive performance…
Mita Banik, Ken Kreutz-Delgado, Ishan Mohanty, James B. Brown + 1 more
Understanding the decision-making process of black-box neural network classifiers is crucial for their adoption in medical applications, including histopathology and cancer diagnostics. An approach of increasing interest is to clarify how the decisions of neural networks compare to, and perform parallel to, those of…
Authors not listed
Localized detection of hydrogen permeation in steel membranes is crucial for practical applications but remains challenging. We present a reflective microscopy (RM) approach combined with machine learning (ML)-driven image analysis to address this issue. Hydrogen permeation in press-hardened steel alters the…
William Daniels, Meng Jia, Dorit Hammerling
We propose a generic, modular framework for methane emission event detection, localization, and quantification on oil and gas production sites that uses concentration and wind data collected by point-in-space continuous monitoring systems. The framework uses a gradient-based spike detection algorithm to estimate…
Arwa AlKhonaini, Tarek Sheltami, Ashraf Mahmoud, Muhammad Imam + 1 more
Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate…
Heena Heena
Malware has become a widely used means in cyber attacks in recent decades because of various new obfuscation techniques used by malwares. In order to protect the systems, data and information, detection of malware is needed as early as possible. There are various studies on malware detection techniques that have been…
Liu Hua Yeo, Xiangdong Che, Shalini Lakkaraju
Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are classified, and the different algorithms used to detect anomalous activities. It attempts…
Harjinder Kaur, Gurpreet Singh, Jaspreet Minhas
Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are…
Mohamed Abushwereb, Muhannad Mustafa, Mouhammd Alkasassbeh, Malik Qasaimeh
'Malik Qasaimeh'] Abstract — One of the most common internet attacks causing significant economic losses in recent years is the Denial of Service (DoS) flooding attack. As a countermeasure, intrusion detection systems equipped with machine learning classification algorithms were developed to detect anomalies in network…
Denice van Herwerden, Jake O'Brien, Phil Choi, Kevin Thomas + 2 more
Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula…
Vahid Hashemi, Jan Křetínský, Sabine Rieder, Jessica Schmidt
> Abstract. Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a…
Jonathan A Fine, Judy Kuan-Yu Liu, Armen Beck, Kawthar Alzarieni + 4 more
Diagnostic ion-molecule reactions using tandem mass spectrometry can differentiate between isomeric compounds unlike a popular collision-activated dissociation methodology for the identification of previously unknown mixtures. Selected neutral reagents, such as 2-methoxypropene (MOP) are introduced into an ion trap…
Jorge Maestre Vidal, Marco Antonio Sotelo Monge, Luis Javier García Villalba
'Luis Javier García Villalba'] In this paper, a malware detection system for smartphones based on the study of the dynamic behavior of suspicious applications is proposed. The main goal is to prevent the installation of the malicious software on the victim systems. Due to its popularity, the approach focuses on the…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…