Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevance of inflammatory and complement activation biomarkers profiling in antiphospholipid syndrome patients outside acute thrombosis.

OBJECTIVES To evaluate whether inflammatory and complement biomarkers are associated with specific characteristics of antiphospholipid syndrome (APS). METHODS Serum levels of interleukin (IL)-1β (IL-1β), IL-6, IL-8, IL-10, tumour necrosis factor (TNF)-α, interferon-α (IFN)-α, IFN-γ, vascular endothelial growth factor (VEGF), intercellular adhesion molecule 1 (ICAM-1), E-selectin, and vascular cell adhesion molecule (VCAM)-1, and plasma levels of soluble C5b-9 (sC5b-9), C3a, C4a, Bb fragment were measured in unselected APS patients. Twenty-five healthy blood donors were included as controls. RESULTS Between January 2020 and April 2021, 98 APS patients were included outside acute thrombosis (median time from the last APS manifestation: 60 (23;132) months). Levels of IL6, VCAM-1, sC5b-9, C3a, C4a, and Bb were significantly increased in APS patients compared to controls. A cluster analysis allowed to divide patients into two clusters: "inflammatory" (higher levels of IL-6 and VCAM-1) and "complement". In APS, elevated IL-6 was associated with hypertension, diabetes, BMI, and hypertriglyceridaemia. 85% of our APS patients had elevated levels of at least one complement biomarker. Elevated Bb (34%) was associated with aPL positivities, especially with triple aPL positivity (50% vs. 18%, p<0.001). 7/8 patients with history of catastrophic APS had elevated levels of complement biomarkers. CONCLUSIONS Our findings suggested that APS patients outside acute thrombosis might be divided into two clusters: "inflammatory" and "complement". Elevated IL-6 was associated with cardiovascular risk factors and metabolic parameters, whereas Bb fragments, a marker of alternative pathway complement activation, was strongly associated with aPL profile at highest risk of severe disease.
Elastic properties of suspended black phosphorus nanosheets

The mechanical properties of black phosphorus (BP) nanosheets suspended over circular holes were measured by an atomic force microscope nanoindentation method. The continuum mechanic model was introduced to calculate the elastic modulus and pretension of BP nanosheets with thicknesses ranging from 14.3 to 34 nm. Elastic modulus of BP nanosheets declines with thickness, and the maximum value is 276 ± 32.4 GPa. Besides, the effective strain of BP ranges from 8 to 17% with a breaking strength of 25 GPa. Our results show that BP nanosheets serve as a promising candidate for flexible electronic applications.
D-test: A New Test for Analyzing Scale Invariance Using Symbolic Dynamics and Symbolic Entropy

The aim of this study is to improve measurement in marketing research by constructing a new, simple, nonparametric, consistent, and powerful test to study scale invariance. The test is called D-test. D-test is constructed using symbolic dynamics and symbolic entropy as a measure of the difference between the response patterns which comes from two measurement scales. We also give a standard asymptotic distribution of our statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. We applied D-test to a real marketing research to study if scale invariance holds when measuring service quality in a sports service. We considered a free-scale as a reference scale and then we compared it with three widely used rating scales: Likert-type scale from 1 to 5 and from 1 to 7, and semantic-differential scale from −3 to +3. Scale invariance holds for the two latter scales. This test overcomes the shortcomings of other procedures for analyzing scale invariance; and it provid.
Experimental Demonstration of In‐Memory Computing in a Ferrofluid System

Magnetic fluids are excellent candidates for several important research fields including energy harvesting, biomedical applications, soft robotics, and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computing capability, including the emulation of synaptic functions, which requires complex non‐linear dynamics. Here, it is experimentally demonstrated that a Fe3O4 water‐based ferrofluid (FF) can perform electrical analogue computing and be programmed using quasi direct current (DC) signals and read at radio frequency (RF) mode. Features have been observed in all respects attributable to a memristive behavior, featuring both short and long‐term information storage capacity and plasticity. The colloid is capable of classifying digits of a 8 × 8 pixel dataset using a custom in‐memory signal processing scheme, and through physical reservoir computing by training a readout layer. These findings demonstrate the feasibility of in‐memory computing using an amorphous FF system in a liquid aggregation state. This work poses the basis for the exploitation of a FF colloid as both an in‐memory computing device and as a full‐electric liquid computer thanks to its fluidity and the reported complex dynamics, via probing read‐out and programming ports.
Treating Role Assertions as First-class Citizens in Repair and Error-tolerant Reasoning

Errors in Description Logic (DL) ontologies are often detected when reasoning yields unintuitive consequences. The question is then how to repair the ontology in an optimal way, i.e., such that the unwanted consequences are removed, but a maximal set of the unobjected consequences is kept. Error-tolerant reasoning does not commit to a single optimal repair: brave reasoning asks whether the consequence is entailed by some repair and cautious reasoning whether it is entailed by all repairs. Previous research on repairing ABoxes w.r. t. TBoxes formulated in the DL EL has developed methods for computing optimal repairs, and has recently also determined the complexity of error-tolerant reasoning: brave reasoning is in P and cautious reasoning is in coNP. However, in this work the unwanted consequences were restricted to being EL instance assertions. In the present paper, we show that the mentioned results can be extended to a setting where also role assertions can be required to be removed. Our solution is based on a two-stage approach where first the unwanted role assertions and then the unwanted concept assertions are removed. We also investigate the complexity of error-tolerant reasoning w.r. t. classical repairs, which are maximal subsets of the ABox that do not have the unwanted consequences, and show that, in this setting, brave reasoning is NP-complete and cautious reasoning is coNP-complete.
Detecting Suspicious Conditional Statement using App Execution Log

Because1 the logic bomb performs malicious behaviors only within the branch that triggers the malicious behaviors, if the branch can be easily found, malicious behaviors can be detected efficiently. Existing malicious app analysis tools look for branches that trigger malicious behaviors based on static analysis, so if reflection is used in the app, this branch statement cannot be found properly. Therefore, in this paper, we propose an app execution log-based suspicious conditional statement detection tool that can detect suspicious conditional statements even when reflection is used. The proposed detection tool which is implemented on the android-10.0. 0_r47 version of AOSP(Android Open Source Project) can check the branch statement and information about called method while the app is executing, including the method called by reflection. Also, since suspicious conditional statements are detected by checking the method call flow related to branch statements in the execution log, there is no need to examine all branch statements in the app. Experimental results show that the proposed detection tool can detect suspicious conditional statements regardless of the use of reflection.
On the Measurement of Performance Metrics for Virtualization-Enhanced Architectures

Cloud computing facilitates resource sharing among multiple users. Although sharing leads to the effective utilization of resources, it raises many security concerns. Hence, virtualization-enabled processors, which are used in cloud computing, are enhanced to provide security. This has put a pressing demand for the measurement of performance metrics for virtualization-enhanced architectures. However, this is a challenging task. Most of the existing simulators do not simulate security-related enhancements in virtualzation-enhanced architectures. Also, software-based simulators are slow. This paper proposes an approach for hardware-based simulation of security-related enhancements in virtualization-enhanced architectures. This is illustrated through the simulation of rollback-sensitive memory architecture like Extended-HyperWall. The results demonstrate that Extended-HyperWall introduces only one extra memory access per memory read/write by the hypervisor or direct memory access controller. Also, it incurs only three additional memory accesses per page during checkpointing and rollback. The paper concludes that virtualization-enabled processors can be efficiently simulated in hardware.
The subgrid modeling of propagation of acoustic waves in heterogeneous media with multiscale isotropic random elastic stiffness and density

The effective coefficients in the problem of the acoustic wave propagation have been calculated for a multiscale 3D isotropic medium using a subgrid modeling approach. The density and the elastic stiffness have been represented mathematically by the Kolmogorov multiplicative cascades, which, to date, appear to be the only mechanisms for generating a stationary multifractal fields with a log-stable probability distribution. The fields with the stable distribution are described with the help of linear combination random values , and weight coefficients , , which satisfy certain conditions in the nodes of spatial grid . The parameters of the stable distribution of the random values , are equal: , , , . The wavelength is assumed to be large as compared with the scale of heterogeneities of the medium. We consider the regime in which the waves propagate over a distance of the typical wave length in source. The theoretical results obtained in this paper are compared with the results of a direct 3D numerical simulation.
ARGANIDS: a novel Network Intrusion Detection System based on adversarially Regularized Graph Autoencoder

Machine Learning (ML) algorithms are largely adopted in modern Network Intrusion Detection Systems (NIDS). The most recent researches propose the use of Graph Neural Networks (GNN) to improve the detection performance. Instead of analyzing each network flow independently, these novel algorithms operate over a graph representation of the data that can take into account the network topology. This paper presents a novel NIDS based on the Adversarially Regularized Graph Autoencoder (ARGA) algorithm. Unlike existing proposals, ARGA offers several advantages as it encodes both the topological information of the graph and the node features in a compact latent representation through an un-supervised autoencoder. Moreover, it derives robust embedding through an additional regularization phase based on adversarial training. We consider also two ARGA variants, namely ARVGA for variational autoencoder and ARVGA_AX for content information reconstruction. A large experimental campaign using two public datasets demonstrates that our proposals are able to outperform other state-of-the-art GNN-based algorithms that already provide good results for network intrusion detection.
Optimization of the Product Configuration System of Renault

The problem of configuring a variability model is widespread in many different domains. Renault has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles' range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space. A new method describes how these symmetries are exploited and integrated. The extensive experiments we conducted on datasets from the automobile manufacturer show our approach's robustness and effectiveness: the achieved gain is a reduction of 52.13% in space representation on average.
SLResNet: Neural-Network-Based End-to-End Structure Light 3D Reconstruction for Endoscope

Three-dimensional endoscopes have been widely studied and developed for video-guided mini-invasive surgery to improve depth perception. Modern 3D endoscopes still cannot provide accurate depth readings. Moreover, the users may suffer from symptoms such as dizziness and nausea resulting from the vergence-accommodation conflict on stereo endoscopes. To resolve the aforementioned challenges, this work takes advantage of both structured light techniques and neural-network-based methods to reconstruct depth information on endoscopic images. The developed method includes SLResNet, a neural network model for end-to-end structure light pattern decoding, and a coordinate refinement algorithm. To focus on algorithm design, this work evaluated the algorithms on the projector-camera system. Using a metal gauge block as the targeted object, the maximum relative depth error is 0.396mm. This method can reconstruct at steepness up to 70 degrees stably. The errors in reconstructing the human upper jaw are less than 1mm in depth.
Traffic Intersections as Agents: A model checking approach for analysing communicating agents

The analysis of traffic policies, for instance, the duration of green and red phases at intersections, can be quite challenging. While the introduction of communication systems can potentially lead to better solutions, it is important to analyse and formulate policies in the presence of potential communication failures and delays. Given the stochastic nature of traffic, posing the problem as a model checking problem in probabilistic epistemic temporal logic seems promising. In this work, we propose an approach that uses epistemic modalities to model the effect of communication between multiple intersections and temporal modalities to model the progression of traffic volumes over time. We validate our approach in a non-stochastic setting, using the tool Model Checker for Multi-Agent Systems (MCMAS). We develop a Statistical Model Checking module and use it in conjunction with a tool chain that integrates a traffic simulator (SUMO) and a network simulator (OMNeT++/Veins) to study the impact of communications on traffic policies.
Image4Assess: Automatic learning processes recognition using image processing

Recently, there has been a growing interest in improving students' competitiveness in STEM education. Self-reporting and observation are the most used tools for the assessment of STEM education. Despite their effectiveness, such assessment tools face several challenges, such as being labor-intensive and time-consuming, prone to subjective awareness, depending on memory limitations, and being influenced due to social expectations. To address these challenges, in this research, we propose an approach called Image4Assess that---by benefiting from state-of-the-art machine learning like convolutional neural networks and transfer learning---automatically and uninterruptedly assesses students' learning processes during STEM activities using image processing. Our findings reveal that the Image4Assess approach can achieve accuracy, precision, and recall higher than 85% in the learning process recognition of students. This implies that it is feasible to accurately measure the learning process of students in STEM education using their imagery data. We also found that there is a significant correlation between the learning processes automatically identified by our proposed approach and students' post-test, confirming the effectiveness of the proposed approach in real-world classrooms.
Robust DeepFake Detection Method based on Ensemble of ViT and CNN

With the development of convolutional neural networks (CNN) and generative adversarial networks (GAN) in recent years, classifying fake videos produced through DeepFake has become a very difficult task. Most previous studies on DeepFake Detection were focused on finding DeepFake artifacts through CNN. DeepFake detection using CNN has high accuracy, but is vulnerable to noisy inputs such as side faces, shadowed faces, and low-quality images. In addition, although it has the advantage of being able to learn quickly through inductive bias, it tends to be overfitted to specific datasets, showing low accuracy in manipulated videos created with a different type of DeepFake from training datasets. In this study, we propose the robust DeepFake detection method, which combines vision transformer(ViT) and CNN models. We found through the experiments that the ViT model was highly effective in processing side faces and low-quality videos. Our method where the ResNeSt269 model was combined with the DeiT model using a weighted majority voting ensemble(WMVE) approach had 97.66% accuracy, which outperformed the results of the existing DeepFake Detection Challenge(DFDC)'s state-of-the-art model, which achieved 96.78% accuracy. In addition, when benchmarking is performed on a dataset that is completely different from the training dataset, Our method has the robustness to new dataset, showing more than 10% higher accuracy than the CNN model due to the high generalization performance of ViT.
Technology training for first-year students: An answer to questions asked at library service points

Abstract Library service points continue to be one of the few places for in-person contact where patrons can freely ask questions, including those involving technology. This is especially true in the college setting. Recognition of this need led to the creation of a hands-on Tech Academy for first-year, first-generation college students. When the pandemic hit, it became necessary to take the Academy online and expand it to the wider University student body. This article outlines the process used to rapidly develop, implement, and assess this technology mini-course. In addition, it considers the sustainability of such a program beyond the pandemic environment.
Designing for impact: DEIA social media posts for library resources

Abstract The rise of interest in civil rights during the summer of 2020 created a need for resources that featured Diversity, Equity, Inclusion, and Accessibility (DEIA). Using social media and principles of marketing through graphic design, Wolfgram Memorial Library provided those resources despite being fully online. Examples are given for the use of image selection, color psychology, and visual hierarchy to promote DEIA resources.
A Drosophila model of chemotherapy-related cognitive impairment

Chemotherapy-related cognitive impairment (CRCI) is a common adverse effect of treatment and is characterized by deficits involving multiple cognitive domains including memory. Despite the significant morbidity of CRCI and the expected increase in cancer survivors over the coming decades, the pathophysiology of CRCI remains incompletely understood, highlighting the need for new model systems to study CRCI. Given the powerful array of genetic approaches and facile high throughput screening ability in Drosophila, our goal was to validate a Drosophila model of CRCI. We administered the chemotherapeutic agents cisplatin, cyclophosphamide, and doxorubicin to adult Drosophila. Neurocognitive deficits were observed with all tested chemotherapies, especially cisplatin. We then performed histologic and immunohistochemical analysis of cisplatin-treated Drosophila tissue, demonstrating neuropathologic evidence of increased neurodegeneration, DNA damage, and oxidative stress. Thus, our Drosophila model of CRCI recapitulates clinical, radiologic, and histologic alterations reported in chemotherapy patients. Our new Drosophila model can be used for mechanistic dissection of pathways contributing to CRCI and pharmacologic screens to identify novel therapies to ameliorate CRCI. Summary Statement We present a Drosophila model of chemotherapy-related cognitive impairment, which recapitulates neurocognitive and neuropathologic changes observed in cancer patients treated with chemotherapy.
Comprehensive serial study of dynamic remodeling of atherosclerotic coronary arteries using IVUS

We present a semi-automated approach to comprehensively examine coronary remodeling over the entire length of intravascular ultrasound (IVUS) imaged vessels. Serial measurements at baseline and 12-month follow-up are analyzed rather than static data obtained at a single time point. Every IVUS pullback is segmented automatically, and then reviewed and algorithmically refined by an expert using a computer-aided just-enough-interaction approach. Subsequently, pairs of serial IVUS pullbacks are registered automatically using 3D graph optimization approach. Based on plaque volume increases or decreases over time, pullback frames are divided into two groups — progression and regression. It is shown that plaque progression rates are positively correlated with percent stenosis (PS) indices (p≪0.01) while plaque regression rates are negatively correlated with percent stenosis indices (p≪0.01). Moreover, for the progression group, adventitia area increases in direct relation with the baseline percent stenosis (p=0.007) when PS is less than 50%. Significance of such a correlation is not observed when percent stenosis exceeds 50%. Conversely, for the regression group, change of adventitia area is relatively constant for percent stenosis <50%; but decreases in direct relation with baseline stenosis (p≪0.01) when stenosis > 50%. This strongly suggests that lipid lowering treatment may effectively suppress plaque progression and accelerate plaque regression, especially for larger values of percent stenosis, and further accelerate the corresponding adventitia-remodeling process.
Extended Reality: Exploring End User Development Capabilities

The paper outlines the initial efforts of the Author, who is focused on developing solutions to assist end-users in generating Extended Reality experiences, even if they lack programming or 3D modelling expertise. The current research of the Author involves creating a system for vocational training creators to produce processes that function as either training or guidance tools for various Extended Reality devices, as well as an execution engine that can adjust the process based on the environment context of the user.
Routine Creation in Multi-User Contexts: Improving the Quality of Life through Conflict Resolution

This paper presents an approach to detecting and resolving the conflicts that may arise when different users create routines for a shared smart home. By interacting with a virtual assistant, end users can create routines, namely sequences of actions operating on smart devices, which are triggered by a specific event. The virtual assistant is able to detect a possible conflict between a routine under creation and an existing routine, and guide the user to its resolution. The final aim is to prevent household conflicts and lead to more peaceful and harmonious coexistence among smart home users living under the same roof.
Proomting is Computational Thinking

Proompting, a term informally introduced into the English language to describe the iterative process of writing or modifying a prompt to an AI system such as ChatGPT or Midjourney, could be considered a new kind of Computational Thinking. Computational Thinking describes a process of convergence combining human ability with computer affordances. In essence, Computational Thinking is an iterative process of humans engaging in problem-solving by thinking with computers. In the current literature Computational Thinking is often associated with programming. However, as we are trying to demonstrate here, the perspective that “Proompting is Computational Thinking” can help to elevate the notion of Computational Thinking to become a useful framework to promote the convergence of Human Intelligence and Artificial Intelligence.