Miracle-dz/UniFAR-FTUs huggingface dataset:
Title Image correlation spectroscopy of multiphoton images correlates with collagen mechanical properties Permalink

Multiphoton microscopy (MPM) holds promise as a noninvasive imaging technique for characterizing collagen structure, and thus mechanical properties, through imaging second harmonic generation (SHG) and two-photon fluorescence in engineered and real connective tissues. Controlling polymerization pH to manipulate collagen gel microstructure, we quantified pore and fiber dimensions using both standard methods and image correlation spectroscopy (ICS) on MPM, scanning electron, and darkfield microscopy images. The latter two techniques are used to confirm microstructural measurements made from MPM images. As polymerization pH increased from 5.5 to 8.5, mean fiber diameter decreased from 3.76 0.7 mm to 1.66 0.3 mm, the average pore size decreased from 81.7 6 3.7 mm to 7.8 6 0.4 mm, and the pore area fraction decreased from 56.8% 6 0.8% to 18.0% 6 1.3% (measured from SHG images), whereas the storage modulus G9 and loss modulus G$, components of the shear modulus, increased ;33-fold and ;16-fold, respectively. A characteristic length scale measured using ICS, WICS, correlates well with the mean fiber diameter from SHG images (R 1⁄4 0.95). Semiflexible network theory predicts a scaling relationship of the collagen gel storage modulus (G9) depending upon mesh size and fiber diameter, which are estimated from SHG images using ICS. We conclude that MPM and ICS are an effective combination to assess bulk mechanical properties of collagen hydrogels in a noninvasive, objective, and systematic fashion and may be useful for specific in vivo applications.
Oil and Gas Pipeline Third Party Damage ( TPD )-A New Way to Model External Hazard Failure

Oil and gas pipelines incidents resulting from damage from third parties – collectively referred to as Third Party Damage (TPD) caused 141 deaths, 440 injuries and incurred $ 369 million in property losses between 1993 and 2012. Unlike failure causes such as internal and external corrosion, TPD cannot easily be deterministically modelled. Understanding TPD is more a human versus a hardware reliability problem. Prediction requires some deterministic or mechanistic basis. The aim of creating a sophisticated model is to understand causality – what influences the outcome of a process in a way that allows engineers to do something about it. Most literature involves simple statistical analysis of TPD, identifying basic trends and, but this is not sophisticated or analytic. This paper outlines a new modelling approach based on a Bayesian Belief Network (BBN) that can incorporate more information sources than those provided empirically by databases. Instead of modelling TPD based on pipeline characteristics, the decision-making process of the third party is examined in more detail than any previous modelling initiative has attempted. This process is informed by expert opinion, and allows things like training, awareness, organization nature and so on to be modelled in a useful way.
Dose–response and transmission: the nexus between reservoir hosts, environment and recipient hosts

Dose is the nexus between exposure and all upstream processes that determine pathogen pressure, and is thereby an important element underlying disease dynamics. Understanding the relationship between dose and disease is particularly important in the context of spillover, where nonlinearities in the dose–response could determine the likelihood of transmission. There is a need to explore dose–response models for directly transmitted and zoonotic pathogens, and how these interactions integrate within-host factors to consider, for example, heterogeneity in host susceptibility and dose-dependent antagonism. Here, we review the dose–response literature and discuss the unique role dose–response models have to play in understanding and predicting spillover events. We present a re-analysis of dose–response experiments for two important zoonotic pathogens (Middle East respiratory syndrome coronavirus and Nipah virus), to exemplify potential difficulties in differentiating between appropriate models with small exposure experiment datasets. We also discuss the data requirements needed for robust selection between dose–response models. We then suggest how these processes could be modelled to gain more realistic predictions of zoonotic transmission outcomes and highlight the exciting opportunities that could arise with increased collaboration between the virology and epidemiology disciplines. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
STAMAD: a STAtic MAlware Detector

One of the main challenges in malware detection is the discovery of malicious behaviors. This task requires a huge amount of engineering and manual study of the code. To avoid this tedious manual task, we propose in this paper a tool, called STAMAD, that, given a training set of known malwares and benign programs, (1) either automatically extracts malicious behaviors using Information Retrieval techniques, or (2) applies machine learning techniques to automatically learn malwares. Then, in both cases, STAMAD can classify a new given unseen program as malicious or benign.
Automated Pattern Inference Based on Repeatedly Observed Malware Artifacts

Threat Intelligence comprises the concept of Indicators of Compromise, which are commonly used similar to classical intrusion detection signatures. However, data quality is often of limited quality with regard to this use case. The quality of these Indicators of Compromise can be increased by deriving patterns form repeated observations. A method is introduced which is capable to derive patterns from these observations automatically. Employing automatically derived pattern increases detection quality significantly. Moreover, it lead to the discovery of a previously unfamiliar type of patterns; inter-observable patterns, which capture relationships between patterns. An approach to address them in a fully STIX™ compliant fashion is proposed.
HEHLKAPPE: Utilizing Deep Learning to Manipulate Surveillance Camera Footage in Real-Time

Image analysis and manipulation have always been active topics, both in practice and academia. Driven by the progress in the field of deep learning, significant advances have been achieved in recent years. This causes even complex image manipulation and analysis tasks to be easily applicable by a wide audience. Combined with traditional attack methods, this results in new attack vectors. In this paper, we present HEHLKAPPE, an application to hide persons from real-time video streams while keeping other movement untouched. The application is fully automated and does not require any domain knowledge in deep learning, image manipulation or other related areas in order to use it. In addition, we present 2 attack vectors to access the video stream to enable the manipulation. Our evaluation shows that HEHLKAPPE works well with static camera positions, is able to adapt to background changes and therefore is suitable to deceive observers. The discussion of the results discovers potential for improvement by using even more sophisticated techniques. We are confident that these techniques will be applicable in real-time in the near future. Appropriate countermeasures to mitigate our attack include improving the state of IoT security and verifying the authenticity of each frame using a blockchain-linke structure.
IoT Ignorance is Digital Forensics Research Bliss: A Survey to Understand IoT Forensics Definitions, Challenges and Future Research Directions

Interactions with IoT devices generates vast amounts of personal data that can be used as a source of evidence in digital investigations. Currently, there are many challenges in IoT forensics such as the difficulty in acquiring and analysing IoT data/devices and the lack IoT forensic tools. Besides technical challenges, there are many concepts in IoT forensics that have yet to be explored such as definitions, experience and capability in the analysis of IoT data/devices and current/future challenges. A deeper understanding of these various concepts will help progress the field. To achieve this goal, we conducted a survey which received 70 responses and provided the following results: (1) IoT forensics is a sub-domain of digital forensics, but it is undecided what domains are included; (2) practitioners are already having to examine IoT devices even though they felt undertrained; (3) requirements for technical training, software and education are non-existent; (4) high priority on research should be to develop IoT forensic tools, how to preserve volatile data and methods to identify and acquire data from the cloud; (5) improvements to forensic tools should be aimed at data acquisition (imaging) and device disassembly / forensic process; (6) practitioners' perspectives on research direction differ slightly to non-practitioners in that the focus should be on breaking encryption on IoT devices rather than focus on cloud data forensics; (7) future research should focus on developing initiatives and strategies to overcome data encryption and trail obfuscation in the cloud and ongoing development of IoT forensic tools. The responses to the survey question on the definition of IoT forensics helped us formulate a working definition. This has provided a clearer understanding of the subject, which will help further advance the research area.
Location Tracking Using Smartphone Accelerometer and Magnetometer Traces

We demonstrate a breach in smartphone location privacy through the accelerometer and magnetometer's footprints. The merits or otherwise of explicitly permissioned location sensors are not the point of this paper. Instead, our proposition is that other non-location-sensitive sensors can track users accurately when the users are in motion, as in travelling on public transport, such as trains, buses, and taxis. Through field trials, we provide evidence that high accuracy location tracking can be achieved even via non-location-sensitive sensors for which no access authorisation is required from users on a smartphone.
Energy Attack in LoRaWAN: Experimental Validation

Myriads of new devices take their places around us every single day, making a decisive step towards bringing the concept of the Internet of Things (IoT) in reality. The Low Power Wide Area Networks (LPWANs) are today considered to be one of the most perspective connectivity enablers for the resource and traffic limited IoT. In this paper, we focus on one of the most widely used LPWAN technologies, named LoRaWAN. Departing from the traditional data-focused security attacks, in this study we investigate the robustness of LoRaWAN against energy (depletion) attacks. For many IoT devices, the energy is a limited and very valuable resource, and thus in the near future the device's energy may become the target of an intentional attack. Therefore, in the paper, we first define and discuss the possible energy attack vectors, and then experimentally validate the feasibility of an energy attack over one of these vectors. Our results decisively show that energy attacks in LoRaWAN are possible and may cause the affected device to lose a substantial amount of energy. Specifically, depending on the device's SF (Spreading Factor), the demonstrated attack increased the total energy consumption during a single communication event 36% to 576%. Importantly, the shown attack does not require the attacker to have any keys or other confidential data and can be carried against any LoRaWAN device. The presented results emphasize the importance of energy security for LPWANs in particular, and IoT in general.
Productivity and Patterns of Activity in Bug Bounty Programs: Analysis of HackerOne and Google Vulnerability Research

In this work, we considered two well-known bug bounty programs - HackerOne and Google Vulnerability Research - with the goal of investigating patterns of activity and comparing productivity of security researchers. HackerOne and Google's programs differ in many ways. HackerOne is one of the largest and most successful bug bounty programs, with heterogeneous membership of security researchers and software producers. Google Vulnerability Research, instead, is a closed program for selected Google employees working on a more homogeneous range of software. For the analysis, we introduced three productivity metrics, which let us study the performance of researchers under different perspectives and possible patterns of activity. A contribution of this work is to shed new light on the yet not well understood environment represented by bug bounties and software vulnerability discovery initiatives. The low-hanging fruits approach adopted by unexperienced researchers in open bug bounties has been often discussed, but less is known about the approach adopted by more experienced participants. Another result is to have shown that a generic comparison between different bug bounty programs may lead to wrong conclusions. Bug bounty programs could exhibits large variations in researcher profiles and software characteristics, which make them not comparable without a careful examination of homogeneous subsets of participants and incentive mechanisms.
Adversarial Communication Networks Modeling for Intrusion Detection Strengthened against Mimicry

The rapid evolution of the emerging communication landscape prompted the rise of never seen before threats, in this way encouraging the development of more effective Network-based Intrusion Detection Systems (NIDS) able to recognize outlying behaviors. But despite the theoretical effectiveness of the existing state-of-the-art, the in-depth review of the bibliography suggests the need for their constant adaptation to the changes in their operational environment and preventing being evaded by mimicry methods. The latest threats attempt to hide the malicious actions in a tangle of statistical features that simulate the normal use of the protected network, so they acquire a greater chance of avoiding the defensive actuators. In order to contribute to their mitigation, this paper introduces a novel intrusion detection strategy resistant against mimicry. The proposal constructs models of the network usage and from them, analyzes the binary contents of the traffic payload looking for outlying patterns that may evidence malicious contents. In contrast to most previous solutions, our research overcomes the traditional strengthening via randomization, by taking advantage of scoring the suspicious packet similarity between legitimate and previously built adversarial models. Its effectiveness was evaluated on the public datasets DARPA'99 and UCM 2011, where its ability to recognize attacks obfuscated by imitation was proven.
How to Avoid Customer Churn in Health Insurance/Plans? A Machine Learn Approach

In a Health Plan, beneficiaries can cancel their contracts at any given time. For that reason, Health Insurance/Plan Providers (HIP) need to avoid optional contract cancellations to keep their financial operations stable. This work's main purpose is to develop an approach to predict the optional contract cancellation in a Private HIP and help them to prevent those cancelations.
Zero Residual Attacks on Industrial Control Systems and Stateful Countermeasures

In this paper, we discuss the practical implementation of stealthy attacks on industrial control systems. We start by reviewing the attacks proposed in prior works. Then, we offer Zero-Residual Attacks (ZeRA), which allow the attacker to launch stealthy attacks leveraging estimation of the stateful anomaly detector and matching of residuals as a fraction of actual estimation residual. To perform the zero residual attack, the attacker will require the use of two state estimators each for the physical system state and the detector system state, adding complexity that was so far not discussed. We implement ZeRA and demonstrate its efficacy. Then, we propose to use a Stateful Detector (SD) to precisely detect such stealthy attacks. We design and implement the SD detector. The obtained results from the performance evaluation demonstrate that we can detect stealthy attacks such as the ZeRA, with precision above 99%, sensitivity above 99%, and Matthews correlation coefficient above 0.98.
Highly Stable Li Metal Anode Interface via Molecular Layer Deposition Zircone Coatings for Long Life Next-Generation Battery Systems.

Herein, molecular layer deposition is used to form a nanoscale "zircone" protective layer on Li metal to achieve stable and long life Li metal anodes. The zircone-coated Li metal shows enhanced air stability, electrochemical performance and high rate capability in symmetrical cell testing. Moreover, as a proof of concept, the protected Li anode is used in a next-generation Li-O2 battery system and is shown to extend the lifetime by over 10-fold compared to the batteries with untreated Li metal. Furthermore, in-situ synchrotron X-ray absorption spectroscopy is used for the first time to study an artificial SEI on Li metal, revealing the electrochemical stability and lithiation of the zircone film. This work exemplifies significant progress towards the development and understanding of MLD thin films for high performance next-generation batteries.
Early alpha‐foetoprotein response associated with treatment efficacy of immune checkpoint inhibitors for advanced hepatocellular carcinoma

Post‐treatment decline in serum alpha‐foetoprotein (AFP) levels has been shown to predict the treatment efficacy of antiangiogenic therapy for advanced hepatocellular carcinoma (HCC). We explored whether a decline in AFP levels was also associated with treatment outcomes of immune checkpoint inhibitors (ICIs) in patients with advanced HCC.
Diffraction paradox: An unusually broad diffraction background marks high quality graphene

The realization of the unusual properties of 2-d materials requires the formation of large domains of single layer thickness, extending over the mesoscale. It is found that the formation of ideal graphene on SiC, contrary to textbook diffraction , is signaled by a strong bell-shaped-component (BSC) around the (00) and G(10), but not the SiC(10) spots.The BSC is also seen for graphene on metals, because single layer uniform graphene with large lateral size can also be this http URL is only seen by electron diffraction and not with X-ray or He-scattering experiments. Most likely it originates from the spatial confinement of the graphene electrons within a single layer.This leads to a large spread in their wavevector, which is transferred through electron-electron interactions to the elastically scattered electrons to generate the BSC.
A Preliminary Study on A Physical Model Oriented Learning Algorithm with Application to UAVs

This paper provides a preliminary study for an efficient learning algorithm by reasoning the error from first principle physics to generate learning signals in near real time. Motivated by iterative learning control (ILC), this learning algorithm is applied to the feedforward control loop of the unmanned aerial vehicles (UAVs), enabling the learning from errors made by other UAVs with different dynamics or flying in different scenarios. This learning framework improves the data utilization efficiency and learning reliability via analytically incorporating the physical model mapping, and enhances the flexibility of the model-based methodology with equipping it with the self-learning capability. Numerical studies are performed to validate the proposed learning algorithm.
Relation-Aware Pyramid Network (RapNet) for temporal action proposal

In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019. First, we fine-tune a ResNet-50-C3D CNN on ActivityNet v1.3 based on Kinetics pretrained model to extract snippet-level video representations and then we design a Relation-Aware Pyramid Network (RapNet) to generate temporal multiscale proposals with confidence score. After that, we employ a two-stage snippet-level boundary adjustment scheme to re-rank the order of generated proposals. Ensemble methods are also been used to improve the performance of our solution, which helps us achieve 2nd place.
Fibre based hyperentanglement generation for dense wavelength division multiplexing

Entanglement is a key resource in quantum information science and associated emerging technologies. Photonic systems offer a large range of exploitable entanglement degrees of freedom (DOF) such as frequency, time, polarization, and spatial modes. Hyperentangled photons exploit multiple DOF simultaneously to enhance the performance of quantum information protocols. Here, we report a fully guided-wave approach for generating polarization and energy-time hyperentangled photons at telecom wavelengths. Moreover, by demultiplexing the broadband emission spectrum of the source into five standard telecom channel pairs, we demonstrate compliance with fibre network standards and improve the effective bit rate capacity of the quantum channel up to one order of magnitude. In all channel pairs, we observe a violation of a generalised Bell inequality by more than 27 standard deviations, underlining the relevance of our approach.
Linear regression in the Bayesian framework

These notes aim at clarifying different strategies to perform linear regression from given dataset. Methods like the weighted and ordinary least squares, ridge regression or LASSO are proposed in the literature. The present article is my understanding of these methods which are, according to me, better unified in the Bayesian framework. The formulas to address linear regression with these methods are derived. The KIC for model selection is also derived in the end of the document.
Gradient boosting survival tree with applications in credit scoring

Abstract Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm. The survival tree ensemble is learned by minimising the negative log-likelihood in an additive manner. The proposed model optimises the survival probability simultaneously for each time period, which can reduce the overall error significantly. Finally, as a test of the applicability, we apply the GBST model to quantify the credit risk with large-scale real market datasets. The results show that the GBST model outperforms the existing survival models measured by the concordance index (C-index), Kolmogorov–Smirnov (KS) index, as well as by the area under the receiver operating characteristic curve (AUC) of each time period.
Tracking Temporal Evolution of Network Activity for Botnet Detection

Botnets are becoming increasingly prevalent as the primary enabling technology in a variety of malicious campaigns such as email spam, click fraud, distributed denial-of-service (DDoS) attacks, and cryptocurrency mining. Botnet technology has continued to evolve rapidly making detection a very challenging problem. There is a fundamental need for robust detection methods that are insensitive to characteristics of a specific botnet and are generalizable across different botnet types. We propose a novel supervised approach to detect malicious botnet hosts by tracking a host's network activity over time using a Long Short-Term Memory (LSTM) based neural network architecture. We build a prototype to demonstrate the feasibility of our approach, evaluate it on the CTU-13 dataset, and compare our performance against existing detection methods. We show that our approach results in a more generalizable, botnet-agnostic detection methodology, is amenable to real-time implementation, and performs well compared to existing approaches, with an overall accuracy score of 96.2%.