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Abstract Background Itch is known to have a particularly high impact on psoriasis (PsO) and atopic dermatitis (AD) patients' quality of life. Although AD therapies have exhibited a high efficacy when it comes to itch control, itch control with PsO therapies is not as well documented. Objectives The aim of this post‐hoc analysis is to better understand the impact of itch on the patients' quality of life in PsO as well as AD by providing a pairwise correlation between itch improvement and patients' quality of life and determine the predictive factors in patients achieving Dermatology Life Quality Index score of 0 or 1 [DLQI (0/1)]. Methods Three phase III clinical studies, one in PsO and two in AD, were assessed. Pairwise correlations between objective improvement of visible signs of disease, quality of life, and itch intensity were investigated at 16 weeks of treatment. Predictive analyses methods were applied on the data to assess the impact of clinical and itch improvement on the DLQI improvement. Results This study shows that change in itch from baseline in AD and PsO patients correlate to change in DLQI from baseline. Change in itch from baseline was found to be the most important factor in predicting DLQI (0/1). Conclusions These results highlight the necessity to study itch in both PsO and AD clinical trials, and it is recommended that itch may be considered a coprimary or at minimum a secondary efficacy end‐point in all such clinical studies.

Web services play an important role in our daily lives. They are used in a wide range of activities, from online banking and shopping to education, entertainment and social interactions. Therefore, it is essential to ensure that they are kept as secure as possible. However – as is the case with any complex software system – creating a sophisticated software free from any security vulnerabilities is a very challenging task. One method to enhance software security is by employing static code analysis. This technique can be used to identify potential vulnerabilities in the source code before they are exploited by bad actors. This approach has been instrumental in tackling many vulnerabilities, but it is not without limitations. Recent research suggests that static code analysis can benefit from the use of large language models (LLMs). This is a promising line of research, but there are still very few and quite limited studies in the literature on the effectiveness of various LLMs at detecting vulnerabilities in source code. This is the research gap that we aim to address in this work. Our study examined five notable LLM chatbot models: ChatGPT 4, ChatGPT 3.5, Claude, Bard/Gemini1, and Llama-2, assessing their abilities to identify 104 known vulnerabilities spanning the Top-10 categories defined by the Open Worldwide Application Security Project (OWASP). Moreover, we evaluated issues related to these LLMs’ false-positive rates using 97 patched code samples. We specifically focused on PHP vulnerabilities, given its prevalence in web applications. We found that ChatGPT-4 has the highest vulnerability detection rate, with over 61.5% of vulnerabilities found, followed by ChatGPT-3.5 at 50%. Bard has the highest rate of vulnerabilities missed, at 53.8%, and the lowest detection rate, at 13.4%. For all models, there is a significant percentage of vulnerabilities that were classified as partially found, indicating a level of uncertainty or incomplete detection across all tested LLMs. Moreover, we found that ChatGPT-4 and ChatGPT-3.5 are consistently more effective across most categories, compared to other models. Bard and Llama-2 display limited effectiveness in detecting vulnerabilities across the majority of categories listed. Surprisingly, our findings reveal high false positive rates across all LLMs. Even the model demonstrating the best performance (ChatGPT-4) notched a false positive rate of nearly 63%, while several models glaringly under-performed, hitting startlingly bad false positive rates of over 90%. Finally, simultaneously deploying multiple LLMs for static analysis resulted in only a marginal enhancement in the rates of vulnerability detection. We believe these results are generalizable to most other programming languages, and hence far from being limited to PHP only.

It is crucial for organizations to ensure that their business processes are executed accurately and comply with internal policies and requirements. Process mining is a discipline of data science that exploits business process execution data to analyze and improve business processes. It provides a data-driven approach to understanding how processes actually work in practice. Conformance checking is one of the three most relevant process mining tasks. It consists of determining the degree of correspondence or deviation between the expected (or modeled) behavior of a process vs the real one observed and revealed from the historical events recorded in an event log during the execution of each instance of the process. Under a big data scenario, traditional conformance checking methods struggle to analyzing the instances or traces in large event logs, increasing the associated computational cost. In this article, we study and address the conformance-checking task supported by a traces selection approach that uses representative sample data of the event log and thus reduces the processing time and computational cost without losing confidence in the obtained conformance value. As main contributions, we present a novel conformance checking method that (i) takes into account the data dispersion that exists in the event log data using a statistic measure, (ii) determines the size of the representative sample of the event log for the conformance checking task, and (iii) establishes selection criteria of traces based on the dispersion level. The method was validated and evaluated using fitness, precision, generalization, and processing time metrics by experiments on three actual event logs in the health domain and two synthetic event logs. The experimental evaluation and results revealed the effectiveness of our method in coping with the problem of conformance between a process model and its corresponding large event log.

Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.

Abstract Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin‐film wing interference signals (WISs) can discriminate free‐flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave‐infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS‐based surveillance is therefore a potent tool for remote insect identification and surveillance.

Abstract Monitoring insects of different species to understand the factors affecting their diversity and decline is a major challenge. Laser remote sensing and spectroscopy offer promising novel solutions to this. Coherent scattering from thin wing membranes also known as wing interference patterns (WIPs) have recently been demonstrated to be species specific. The colors of WIPs arise due to unique fringy spectra, which can be retrieved over long distances. To demonstrate this, a new concept of infrared (950–1650 nm) hyperspectral lidar with 64 spectral bands based on a supercontinuum light source using ray‐tracing and 3D printing is developed. A lidar with an unprecedented number of spectral channels, high signal‐to‐noise ratio, and spatio‐temporal resolution enabling detection of free‐flying insects and their wingbeats. As proof of principle, coherent scatter from a damselfly wing at 87 m distance without averaging (4 ms recording) is retrieved. The fringed signal properties are used to determine an effective wing membrane thickness of 1412 nm with ±4 nm precision matching laboratory recordings of the same wing. Similar signals from free flying insects (2 ms recording) are later recorded. The accuracy and the method's potential are discussed to discriminate species by capturing coherent features from free‐flying insects.

The increasing dominance of Android smartphones for everyday communication and data processing makes long-term stealthy malware an even more dangerous threat. Recent malware campaigns like Flubot demonstrate that by employing stealthy malware techniques even at minimal capacity, malware is highly effective in making its way to millions of devices with little resistance from existing detection mechanisms. Consequential late detection demands comprehensive forensic timelines to reconstruct all malicious activities. However, the reduced forensic footprint of stealthy attacks with minimal malware involvement leaves investigators little evidence to work with even when utilising state-of-the-art digital forensics tools. Volatile memory forensics can be effective in such scenarios since app execution of any form is always bound to leave a trail of evidence in memory, even if it is short-lived. In this work, we motivate the need for JIT-MF (Just-in-time Memory Forensics), a technique that aims to address the challenges that arise with timely collection of short-lived evidence in volatile memory to solve the stealthiest of Android attacks. By taking an incident-response-centric approach, focused on protecting stock Android device users rather than treating them as potential adversaries, we show that JIT-MF tools can collect elusive attack steps in volatile memory without requiring device rooting. Furthermore, we build MobFor, a JIT-MF based tool focusing on capturing evidence related to messaging hijack attacks. This tool provides a context to explore solutions for JIT-MF implementation challenges, aiming to render JIT-MF tools practical for real-world requirements. Finally, we demonstrate that when compared to state-of-the-art digital forensic tools Belkasoft and XRY in a realistic attack scenario involving an enhanced version of the WhatsApp Pink malware and stock Android devices, only MobFor can recover the contents of messages sent by the malware, hence decisively contributing to an enriched forensic timeline.

The stability issues assessment by the impedance-based method demands the computation of accurate small-signal models. However, obtaining impedance models can be a time-consuming task if analytical models or the perturbation-based method are used. Especially in large-scale, poorly damped, distributed, and frequency-dependent parameter systems. A third approach, which has been less explored, is based on numerical derivative approximations, rather than analytical equations or time-domain simulations. This approach works with large-scale systems, avoiding tedious mathematical expressions and large recursive time-domain simulations. As a step-forward in this approach, this paper proposes a numerical-oriented method, based on the finite-difference method, to compute impedance models of time-delay power-electronics-based power systems. An outstanding feature of the proposal is the capability to incorporate the exact delays into the numerical models of impedance without resorting to approximations nor increasing the size of the system model. We compare the proposed method with the analytical and perturbation methods using a grid-connected microgrid with two power electronic inverters as a test system. The results confirm the correct performance of the proposal.

This work presents a hardware-based digital emulator capable of digitally driving a permanent magnet synchronous machine electronic setup. The aim of this work is to present a high-performance, cost-effective, and portable complementary solution when new paradigms of electronic drive design are generated, such as machine early failure detection, fault-tolerant drive, and high-performance control strategy implementations. In order to achieve the high performance required by the digital emulator, the electronic drive models (permanent-magnet synchronous machine, voltage-source inverter, motor-control strategy) are digitally described in Verilog hardware description language and implemented on a field programmable gate array (FPGA) digital platform using two approaches: parallel and sequential methods. The results obtained show the effectiveness of the digital emulator design, and the resources used by the solution presented can be implemented on a low-cost digital platform that reveals a cost-effective operation of the solution presented.

The deep web is a huge source of domain-specific information (sale of houses, medical information, e-commerce, science, etc) stored in database servers accessible through HTML forms called web query interfaces (WQIs). Information in the deep web is retrieved by querying one database server at a time, which results inefficient. A more attractive approach is to create an integrated WQI (IWQI) that acts as single entry point to query several database servers at a time for a given domain. Schema matching and string (labels in WQIs) comparison have been the most popular techniques to create IWQIs. In this work, we propose a new method for the integration of web forms based on linked data and the VDIS (View-based Data Integration System) architecture. We present WebQuIn-LD, an alternative and novel approach relying on linked data principles to combine individual WQIs into a single IWQI for a given domain is presented. WebQuIn-LD follows a data integration system architecture, starting from the wrapping of domain-specific WQIs until the creation of the IWQI. A domain-independent ontology is created to describe WQI elements as linked data resources and to exploit semantic integration between the WQI’s elements. WebQuIn-LD was evaluated on performance metrics (precision, recall, and F1) using the state-of-the-art WQIs datasets for different domains (airfares, books, autos, jobs, music, movies, hotels, jobs). The obtained results demonstrate the effectiveness of the linked data approach presented in this work for the WQI integration problem.

This paper is dedicated to presenting control-tuning methodologies for the rotor-side converter (RSC) and grid-side converter (GSC) of series-compensated DFIG-based wind farms (WF) and to determining the origin of subsynchronous resonance (SSR). Unlike conventional approaches to re-tuning controller gains to achieve desirable performance, these methodologies only consider the mathematical models of the states variables to be controlled. A PI cascade topology is used to control the RSC and GSC. An inner loop is required for current control and an outer loop for voltage control. Special consideration is taken for tuning the RSC because this converter is coupled with the mechanical variables as mechanical rotor speed of the DFIG. Compared with standard tuning, a better system behavior during a resonance condition is achieved with the proposed tuning methodology. Regarding the analysis of the SSR origin, the modal impedance (MI) technique is used as a tool to study SSR issues; its advantages compared to the driving point impedance technique are highlighted. In this context, the small-signal-stability (SSS) analysis is performed to evaluate the overall system. With the combination of MI and SSS, the phenomena of induction generator effect, torque interaction, and torque amplification are analyzed. As a result, the modes involved in the SSR phenomenon are identified and a discrimination procedure is described to determine the origin of the SSR.

The security of financial apps on smartphones is threatened by a class of advanced and persistent malware that can bypass all existing security measures. Strong cryptography and trusted on-chip hardware modules are powerless against sophisticated attacks that supplant device owners through device input record/replay functionality, effectively hijacking their credentials, privileges, and actions. In this paper, we introduce Proof-of-Presence and Locality (PoPL), a new security measure that tackles this threat by leveraging sensors to prove the physical presence of device owners and therefore discriminate between malware-initiated transaction requests and legitimate ones. Moreover, PoPL neither imposes the expense of additional hardware nor compromises app usability. In order to demonstrate PoPL’s practicality, we developed PoPLar, a challenge puzzle implementation of the PoPL concept that ensures usability even on limited screen sizes by the use of a dendrogram. We have made it available as an open-source library ready to be integrated with minimal effort with existing apps. We demonstrate PoPLar’s effectiveness and ease of integration through case studies involving apps from the three top cryptocurrency exchanges and an open-source crypto wallet.

Fog is challenging to predict, and the accuracy of fog prediction may depend on location and time of day. Furthermore, accurate detection of fog is difficult, since, historically, it is often carried out based on visual observations which can be biased and are often not very frequent. Furthermore, visual observations are more challenging to make during the night. To overcome these limitations, we detected fog using FM-120 instruments, which continuously measured liquid water content in the air in the Monterey, California (USA), area. We used and compared the prediction performance of logistic regression (LR) and random forest (RF) models each evening between 5 pm and 9 pm, which is often the time when advection fog is generated in this coastal region. The relative performances of the models depended on the hours between 5 pm and 9 pm, and the two models often generated different predictions. In such cases, a consensus approach was considered by revisiting the past performance of each model and weighting more heavily the more trustworthy model for a given hour. The LR resulted in a higher sensitivity (hit rate) than the RF model early in the evening, but the overall performance of the RF was usually better than that of the LR. The consensus approach provided more robust prediction performance (closer to a better accuracy level between the two methods). It was difficult to conclude which of the LR and RF models was superior consistently, and the consensus approach provided robustness in 3 and 2 h forecasts.

This work presents an analysis of the existing dependencies between the tests of the FIPS 140-2 battery. Two main analytical approaches are utilized, the first being a study of correlations through the Pearson’s correlation coefficient that detects linear dependencies, and the second one being a novel application of the mutual information measure that allows detecting possible non-linear relationships. In order to carry out this study, the FIPS 140-2 battery is reimplemented to allow the user to obtain p-values and statistics that are essential for more rigorous end-user analysis of random number generators (RNG).

Android malware is increasing in spread and complexity. Advanced obfuscation, emulation detection, delayed payload activation or dynamic code loading are some of the techniques employed by the current malware to hinder the use of reverse engineering techniques and anti-malware tools. This growing complexity is particularly noticeable in the evolution of different strands of the same malware family. Over the years, these families mature to become more effective by incorporating new and enhanced techniques. In this paper, we focus on a particular Android ransomware family named Jisut, and perform a thorough technical analysis. We also provide a detailed overall perspective, which will hopefully help to create new tools and techniques to tackle more effectively the threat posed by ransomware.

This work reports the development of arrays of infrared sensors (microbolometers) using a hydrogenated polymorphous silicon–germanium alloy (pm-SixGe1-x:H). Basically, polymorphous semiconductors consist of an amorphous semiconductor matrix with embedded nanocrystals of about 2–3 nm. The pm-SixGe1-x:H alloy studied has a high temperature coefficient of resistance (TCR) of 4.08%/K and conductivity of 1.5 × 10−5 S∙cm−1. Deposition of thermosensing film was made by plasma-enhanced chemical vapor deposition (PECVD) at 200 °C, while the area of the devices is 50 × 50 μm2 with a fill factor of 81%. Finally, an array of 19 × 20 microbolometers was packaged for electrical characterization. Voltage responsivity values were obtained in the range of 4 × 104 V/W and detectivity around 2 × 107 cm∙Hz1/2/W with a polarization current of 70 μA at a chopper frequency of 30 Hz. A minimum value of 2 × 10−10 W/Hz1/2 noise equivalent power was obtained at room temperature. In addition, it was found that all the tested devices responded to incident infrared radiation, proving that the structure and mechanical stability are excellent.

The Web has become the main source of information in the digital world, expanding to heterogeneous domains and continuously growing. By means of a search engine, users can systematically search over the web for particular information based on a text query, on the basis of a domain-unaware web search tool that maintains real-time information. One type of web search tool is the semantic focused web crawler (SFWC); it exploits the semantics of the Web based on some ontology heuristics to determine which web pages belong to the domain defined by the query. An SFWC is highly dependent on the ontological resource, which is created by domain human experts. This work presents a novel SFWC based on a generic knowledge representation schema to model the crawler’s domain, thus reducing the complexity and cost of constructing a more formal representation as the case when using ontologies. Furthermore, a similarity measure based on the combination of the inverse document frequency (IDF) metric, standard deviation, and the arithmetic mean is proposed for the SFWC. This measure filters web page contents in accordance with the domain of interest during the crawling task. A set of experiments were run over the domains of computer science, politics, and diabetes to validate and evaluate the proposed novel crawler. The quantitative (harvest ratio) and qualitative (Fleiss’ kappa) evaluations demonstrate the suitability of the proposed SFWC to crawl the Web using a knowledge representation schema instead of a domain ontology.

The forensic analysis of digital images from mobile devices is particularly important given their quick expansion and everyday use in the society. A further consequence of digital images' widespread use is that they are used today as silent witnesses in legal proceedings, as crucial evidence of the crime. This study specifically addresses the description of a technique that allows the identification of the image source acquisition, for the specific case of mobile devices images. This approach is to extract wavelet‐based features from sensor pattern noise which are then classified using a support vector machine. Moreover, there are a number of parameters that allows the authors to adapt the execution of the algorithm to specific situations desired for the forensic analyst (a variety of types and sizes of image or optimising the average accuracy rate in terms of processing time). This article describes a set of experiments with the same set of images that can obtain general conclusions for the different configurations.

This paper tries to tackle the modern challenge of practical steganalysis over large data by presenting a novel approach whose aim is to perform with perfect accuracy and in a completely automatic manner. The objective is to detect changes introduced by the steganographic process in those data objects, including signatures related to the tools being used. Our approach achieves this by first extracting reliable regularities by analyzing pairs of modified and unmodified data objects; then, combines these findings by creating general patterns present on data used for training. Finally, we construct a Naive Bayes model that is used to perform classification, and operates on attributes extracted using the aforementioned patterns. This technique has been be applied for different steganographic tools that operate in media files of several types. We are able to replicate or improve on a number or previously published results, but more importantly, we in addition present new steganalytic findings over a number of popular tools that had no previous known attacks.

In the last few years, the world has witnessed a ground-breaking growth in the use of digital images and their applications in the modern society. In addition, image editing applications have downplayed the modification of digital photos and this compromises the authenticity and veracity of a digital image. These applications allow for tampering the content of the image without leaving visible traces. In addition to this, the easiness of distributing information through the Internet has caused society to accept everything it sees as true without questioning its integrity. This paper proposes a digital image authentication technique that combines the analysis of local texture patterns with the discrete wavelet transform and the discrete cosine transform to extract features from each of the blocks of an image. Subsequently, it uses a vector support machine to create a model that allows verification of the authenticity of the image. Experiments were performed with falsified images from public databases widely used in the literature that demonstrate the efficiency of the proposed method.

Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences in the distribution of acquired and interpolated pixels. The experimental evidence supports the capabilities of the proposed method for detecting a broad range of manipulations, e.g., copy-move, resizing, rotation, filtering and colorization. This technique exhibits tampered areas by computing the probability of each pixel of being interpolated and then applying the DCT on small blocks of the probability map. The value of the coefficient for the highest frequency on each block is used to decide whether the analyzed region has been tampered or not. The results shown here were obtained from tests made on a publicly available dataset of tampered images for forensic analysis. Affected zones are clearly highlighted if the method detects CFA inconsistencies. The analysis can be considered successful if the modified zone, or an important part of it, is accurately detected. By analizing a publicly available dataset with images modified with different methods we reach an 86% of accuracy, which provides a good result for a method that does not require previous training.

In this work, we developed an automated system to detect potentially illegal elephant ivory items for sale on eBay. Two law enforcement experts, with specific knowledge of elephant ivory identification, manually classified items on sale in the Antiques section of eBay UK over an 8 week period. This set the “Gold Standard” that we aim to emulate using data-mining. We achieved close to 93% accuracy with less data than the experts, as we relied entirely on metadata, but did not employ item descriptions or associated images, thus proving the potential and generality of our approach. The reported accuracy may be improved with the addition of text mining techniques for the analysis of the item description, and by applying image classification for the detection of Schreger lines, indicative of elephant ivory. However, any solution relying on images or text description could not be employed on other wildlife illegal markets where pictures can be missing or misleading and text absent (e.g., Instagram). In our setting, we gave human experts all available information while only using minimal information for our analysis. Despite this, we succeeded at achieving a very high accuracy. This work is an important first step in speeding up the laborious, tedious and expensive task of expert discovery of illegal trade over the internet. It will also allow for faster reporting to law enforcement and better accountability. We hope this will also contribute to reducing poaching, by making this illegal trade harder and riskier for those involved.

Steganography is the art and science of concealing information in such a way that only the sender and intended recipient of a message should be aware of its presence. Digital steganography has been used in the past on a variety of media including executable files, audio, text, games and, notably, images. Additionally, there is increasing research interest towards the use of video as a media for steganography, due to its pervasive nature and diverse embedding capabilities. In this work, we examine the embedding algorithms and other security characteristics of several video steganography tools. We show how all feature basic and severe security weaknesses. This is potentially a very serious threat to the security, privacy and anonymity of their users. It is important to highlight that most steganography users have perfectly legal and ethical reasons to employ it. Some common scenarios would include citizens in oppressive regimes whose freedom of speech is compromised, people trying to avoid massive surveillance or censorship, political activists, whistle blowers, journalists, etc. As a result of our findings, we strongly recommend ceasing any use of these tools, and to remove any contents that may have been hidden, and any carriers stored, exchanged and/or uploaded online. For many of these tools, carrier files will be trivial to detect, potentially compromising any hidden data and the parties involved in the communication. We finish this work by presenting our steganalytic results, that highlight a very poor current state of the art in practical video steganography tools. There is unfortunately a complete lack of secure and publicly available tools, and even commercial tools offer very poor security. We therefore encourage the steganography community to work towards the development of more secure and accessible video steganography tools, and make them available for the general public. The results presented in this work can also be seen as a useful resource for forensic examiners to determine the existence of any video steganography materials over the course of a computer forensic investigation.

This paper presents a finite-difference-based method for numerically deriving the DQ impedance model of power electronics-based power systems, specifically tailored for stability analysis. The proposed method offers a computationally efficient alternative to traditional approaches by directly applying finite-difference approximations to the large-signal dynamic system, without relying on repetitive time-domain simulations or small-signal analytical models. This method eliminates the need for additional models or complex procedures to compute the steady-state solution, streamlining the impedance modeling process. The accuracy, efficiency, and precision of the proposed method are evaluated through comparative studies with analytical and time-domain perturbation methods. Results demonstrate that the proposed approach provides accuracy comparable to analytical models while significantly reducing computational effort, outperforming perturbation methods in both speed and precision. These findings highlight the practical value of the proposed method for real-time and large-scale system analysis, making it a robust tool for power systems stability assessment.