Miracle-dz/UniFAR-FTUs huggingface dataset:
A System for Reasoning-based Link Prediction in Large Knowledge Graphs

This poster paper presents an efficient method R-Linker for link prediction in large knowledge graphs, based on rule learning. The scalability and efficiency is achieved by a combination of several optimisation techniques. Experimental results show that R-Linker is able to handle KGs with over 10 million of entities and more efficient than existing state-of-the-art methods including RLvLR and AMIE+ in rule learning stage for link prediction.
Flexible Acceleration of Convolutions on FPGAs: NEURAghe 2.0

Convolutional Neural Networks are commonly employed in applications involving Computer Vision tasks like image/video classification/recognition/segmentation. The increasing focus of the community on this topic, has generated a wide scope of approaches that use different kernel shapes and techniques for executing convolutions with respect to the classic one, such as for example separable convolutions, deformable convolutions or deconvolutions ([4, 5]), frequently used in semantic segmentation tasks ([23, 13]). While it is common knowledge that FPGAs can be used to accelerate classic Convolutional layers in CNNs, there is limited literature about FPGA-based accelerators supporting less regular and common processing kernels ([20]). In our research, starting from the previous experience acquired developing NEURAghe, we plan to improve flexibility of CNN accelerators and to study new methodologies to improve efficiency on the previously mentioned use-cases. As a first experiment we focus on layered approaches based on 1D convolutions, that, as indicated by several recent research results, can be effectively used to classify and segment time series and sequences, as well as in tasks involving sequence modeling. In multiple scenarios a convolution approach applied on the time dimension, hereafter called Temporal Convolution Network (TCN) can outperform classic strategies relying on recurrent networks in terms of accuracy and training time. We modified NEURAghe to support TCN and validate results on an ECG-classification benchmark, achieving up to 95% efficiency in terms of GOPS/s with respect to the accelerator peak performance.
Startup workplace, mobile games, and older adults: a practical guide on UX, usability, and accessibility evaluation

Mobile game applications stand as a profitable market, being mostly represented by independent startups striving to overcome their lack of experience. One of their big challenges refers to the adoption of mobile games by older adults. Appropriated methods for usability, accessibility and user experience (UX) evaluation of such domain remains a few in the literature. Engineering methods are still required for startup companies to overcome the challenge. In this paper, we sought to build evaluation methods for the context of startups, mobile games and older adults. This study is an empirical research based on techniques from the constructivist grounded theory methodology. Our findings show a lean framework aiming to guide startup practitioners to diagnose usability, accessibility and UX issues from tests of mobile games with older adults. We also provide a guide to usability and accessibility heuristics that can be employed to inspect the domain.
Querying Large-scale RDF Datasets Using the SANSA Framework

In this paper we present Sparklify: a scalable software component for eﬃcient evaluation of SPARQL queries over distributed RDF datasets. In particular, we demonstrate a W3C SPARQL endpoint powered by our SANSA framework’s RDF partitioning system and Apache Spark for querying the DBpedia knowledge base. This work is motivated by the lack of Big Data SPARQL systems that are capable of exposing large-scale heterogeneous RDF datasets via a Web SPARQL endpoint.
People's perception of others' risk preferences

Our everyday decisions are driven by costs, risk, and reward. How do people take these factors into account when they predict and explain the decisions of others? In a two-part exper- iment, we assessed people’s perceptions of other people’s risk preferences, relative to their own. In Part 1, participants re- ported their relative preference between a guaranteed payout and lotteries with various probabilities and payouts, and made predictions about other people’s preferences. In Part 2, participants estimated the lottery payout that generated a given rela- tive preference between a guaranteed payout and a lottery, both for themselves and others. We found considerable individual variability in how people perceive the risk preferences of others relative to their own, and consistency in people’s percep- tions across our two measures. Future directions include for-mal computational models and developmental studies of how we think about our own and each other’s decision-making.
Applying Deep Language Understanding to Open Text: Lessons Learned

Human-level natural language understanding (NLU) of open text is far beyond the current state of the art. In practice, if deep NLU is attempted at all, it is within narrow domains. We report a program of R&D on cognitively modeled NLU that works toward depth and breadth of processing simultaneously. The current contribution describes lessons learned – scientifically and methodologically – from an exercise in applying deep NLU to open-domain texts. An overarching lesson was that although learning to compute sentence-level semantics seems like a natural step toward computing full, contextsensitive, semantic and pragmatic meaning, corpus evidence underscores just how infrequently semantics can be cleanly separated from pragmatics. We conclude that a more comprehensive methodology for automatic example selection and result validation is needed as prerequisite for success in developing NLU applications operating on open text.
Epileptic Seizure Classification based on the Combined Features

Electroencephalography (EEG) can provide a wealth of valuable information to help understand the mechanism of seizures. The automatic classification of EEG signals can help clinicians make effective judgments on whether seizures occur. In this work, a method based on combined features is proposed to classify epilepsy seizures. Firstly, discrete wavelet transform is applied to the signal, and the line length features, energy distribution proportion and approximate entropy of each sub-band signal are extracted. Then the statistical features of the raw signal are extracted, including mean, standard deviation, coefficient of variation, median absolute deviation (MAD) and interquartile range (IQR). All the features are combined and the dimension of the combined feature vector is reduced by the principal component analysis (PCA). Finally, the support vector machine (SVM) is used to classify the epileptic seizure. The dataset is from the epilepsy laboratory of the University of Bonn, Germany. The accuracy of 98.40% proves the validity of this method.
A Kalman-Based Approach With EM Optimization for Respiratory Motion Modeling in Medical Imaging

Respiratory motion degrades quantitative and qualitative analysis of medical images. Estimation and, hence, correction of motion commonly uses static correspondence models between an external surrogate signal and internal motion. This paper presents a patient specific respiratory motion model with the ability to adapt in the presence of irregular motion via a Kalman filter with expectation maximization for parameter estimation. The adaptive approach introduces generalizability allowing the model to account for a broader variety of motion. This may be required in the presence of irregular breathing and with different sensors monitoring the external surrogate signal. The motion model framework utilizing an adaptive Kalman filter approach is tested on dynamic magnetic resonance imaging data of nine volunteers and compared to a state-of-the-art static total least squares approach. Results demonstrate the framework is capable of reducing motion to the order of <3 mm and is significantly ( ${p} < 0.001$ ) more effective in the presence of irregular motion, assessed using the ${F}$ -test for model comparison. Utilizing the total sum of squares of estimated vector field error from the calculated ground truth, we observe approximately a fifty percent reduction in root mean square error and thirty percent reduction in standard deviation utilizing the Kalman model (EKF) in comparison to a static counterpart.
Polysemy and Verb Mutability: Differing Processes of Semantic Adjustment for Verbs and Nouns

Previous research has found that verbs are more likely to adapt their meaning to the semantic context provided by a noun than the reverse (verb mutability). One possible explanation for this effect is that verbs are more polysemous than nouns, allowing for more sense-selection. We investigated this possibility by testing polysemy as a predictor of semantic adjustment. Our results replicated the verb mutability effect. However, we found no evidence that polysemy predicts meaning adjustment in verbs. Instead, polysemy was found to predict meaning adjustment in nouns, while semantic strain was found to predict meaning adjustment in verbs (but not nouns). This suggests that processes of meaning adjustment may be different for nouns vs verbs.
Using a Knowledge Graph of Scenes to Enable Search of Autonomous Driving Data

With the recent push to develop autonomous driving technologies, automotive companies are investing heavily into machine learning and AI. Training machine learning models for this task requires access to lots of data. In this talk, we discuss our experience in using semantic technologies to organize and manage this data within a large enterprise company. More specifically, we have developed a knowledge graph of driving scenes and will demonstrate its utility for representing, integrating, and querying large amounts of autonomous driving data.
Feeding Machine Learning with Knowledge Graphs for Explainable Object Detection

Machine Learning (ML), as one of the key driver of Artificial Intelligence, has demonstrated disruptive results in numerous industries. However one of the most fundamental problem of applying ML, and particularly Artificial Neural Network models, in critical systems is its inability to provide a rational of their decisions. For instance a ML system recognizes an object to be a warfare mine through comparison with its similar observations. No human-transposable rationale is given, mainly because common sense knowledge or reasoning is out-of-scope of ML systems. We developed an asset, combining ML and knowledge graphs to expose a human-like explanation when recognizing an object of any class in a knowledge graph of 4,233,000 resources.
Population Pharmacokinetics of the Antituberculosis Agent Pretomanid

A population pharmacokinetic (PopPK) model for pretomanid was developed using data from 14 studies in the pretomanid development program: six phase 1 studies, six phase 2 studies, and two phase 3 studies. The final analysis data set contained 17,725 observations from 1,054 subjects, including healthy subjects and subjects with drug-sensitive, multidrug-resistant, or extensively drug-resistant pulmonary tuberculosis dosed pretomanid in monotherapy or combination therapy for up to 6 months. ABSTRACT A population pharmacokinetic (PopPK) model for pretomanid was developed using data from 14 studies in the pretomanid development program: six phase 1 studies, six phase 2 studies, and two phase 3 studies. The final analysis data set contained 17,725 observations from 1,054 subjects, including healthy subjects and subjects with drug-sensitive, multidrug-resistant, or extensively drug-resistant pulmonary tuberculosis dosed pretomanid in monotherapy or combination therapy for up to 6 months. Pretomanid pharmacokinetic behavior was described by a one-compartment model that at a given dose was linear in its absorption and clearance processes but where the rate of absorption and extent of bioavailability changed with dose. Clearance and volume of distribution scaled allometrically with weight. Apparent clearance in females was 18% less than in males. Among HIV-positive subjects, absent the effect of CYP3A4-inducing antiretrovirals, apparent clearance was 6% higher. Some effects of total bilirubin and albumin were found, but the impacts on exposure were small. Bioavailability in the fasted condition was about half that in the fed condition. Relative bioavailability decreased with increasing dose in the fasted condition, but not for doses of ≤200 mg in the fed condition. HIV-positive subjects taking efavirenz and lopinavir/ritonavir had exposures that were reduced by 46 and 17%, respectively. There was little evidence for noteworthy effects of regimen partners on pretomanid. Standard diagnostics indicated that the model described the voluminous, diverse data well, so that the model could be used to generate exposure metrics for exposure/response analyses to be reported elsewhere.
The effect of elevational gradient on alpine gingers (Roscoea alpina and R. purpurea) in the Himalayas

There is currently enormous interest in how morphological and physiological responses of herbaceous plants may be affected by changing elevational gradient. Mountain regions provide an excellent opportunity to understand how closely related species may adapt to the conditions that rapidly change with elevation. We investigated the morphological and physiological responses of two Himalayan alpine gingers (Roscoea alpina and R. purpurea) along two different vertical transects of 400 m, R. purpurea between 2,174–2,574 m a.s. l and R. alpina between 2,675–3,079 m a.s. l. We measured the variables of plant height, leaf length, leaf area, specific leaf area, and stomata density at five plots, along the vertical transect at an elevational gap of ca. 100 m. Results revealed that with increased elevation plant height, and leaf area decreased while stomata density increased, whereas changes in specific leaf area, were not correlated with the elevation. Our results reveal that these alpine gingers undergo local adaptation by modifying their plant height, leaf area and stomata density in response to the varying selection pressure associated with the elevational gradient. Thus, the findings of this research provide valuable information on how a narrow range of elevational gradient affects the herbaceous plants at the alpine habitat of the Himalayas.
EDTA-modified mesoporous silica as supra adsorbent of copper ions with novel approach as an antidote agent in copper toxicity

Purpose Mesoporous silica (MS) have been considered as a biocompatible compound and found to have various pharmaceutical applications. Recently, novel approaches in applications of MS as antidote agents were introduced. In this study, the capacity of ethylenediaminetetraacetic acid modified mesoporous silica (MS-EDTA) was evaluated in in vitro and in vivo adsorption of copper (Cu). Methods The MS-EDTA was characterized by fourier transform infrared (FT-IR) and X-ray diffraction, while surface area was determined by N2 adsorption–desorption technique. Morphological studies were observed by high resolution-transmission electron microscopy and field emission-scanning electron microscopy and the sizes were determined by dynamic light scattering. The capacity of these particles for copper adsorption was investigated in vitro in both 1.2 and 7.2 pH. In in vivo animal study, the Cu adsorption efficiency of MS-EDTA in Cu-overdosed mice was evaluated. In this case, an animal model of acute copper poisoning was prepared. Results The MS-EDTA with surface area of 352.35 was synthesized. Scanning electron microscope showed spherical particle formation with less than 500 nm in size. Transmission electron microscope images showed porous and honeycomb structure. FT-IR spectroscopy showed an appropriate formation of functional groups. Particle efficiency was investigated for Cu adsorption. MS-EDTA in both media showed a high adsorption capability for Cu (II) adsorption in pH=1.2 and pH=7.2. In addition, the study of Langmuir, Freundlich, and Redlich–Peterson adsorption models showed that copper adsorption by MS-EDTA followed the Freundlich model with multi-layer adsorption. In vivo evaluation showed that MS-EDTA could alleviate the symptoms of acute copper poisoning by lowering Cu plasma levels. Conclusion Structural evaluation showed successful formation of MS-EDTA. In vitro analysis demonstrated that supreme Cu adsorption occurs in both pH conditions (7.2 and 1.2), and was especially more favorable in simulated intestinal pH (7.2). The in vivo studies in animal models with acute Cu poisoning showed that MS-EDTA could be a potent antidote agent.
Gold nanoparticles and angiogenesis: molecular mechanisms and biomedical applications

Abstract Angiogenesis is the formation of new blood vessels from pre-existing vessels. It is a highly regulated process as determined by the interplay between pro-angiogenic and anti-angiogenic factors. Under certain conditions the balance between angiogenesis stimulators and inhibitors is altered, which results in a shift from physiological to pathological angiogenesis. Therefore, the goal of therapeutic targeting of angiogenic process is to normalize vasculature in target tissues by enhancing angiogenesis in disease conditions of reduced vascularity and blood flow, such as tissue ischemia, or alternatively to inhibit excessive and abnormal angiogenesis in disorders like cancer. Gold nanoparticles (AuNPs) are special particles that are generated by nanotechnology and composed of an inorganic core containing gold which is encircled by an organic monolayer. The ability of AuNPs to alter vasculature has captured recent attention in medical literature as potential therapeutic agents for the management of pathologic angiogenesis. This review provides an overview of the effects of AuNPs on angiogenesis and the molecular mechanisms and biomedical applications associated with their effects. In addition, the main synthesis methods, physical properties, uptake mechanisms, and toxicity of AuNPs are briefly summarized.
Clostridium butyricum alleviates intestinal low-grade inflammation in TNBS-induced irritable bowel syndrome in mice by regulating functional status of lamina propria dendritic cells

BACKGROUND Irritable bowel syndrome (IBS) is one of the most common functional gas-troenterological diseases characterized by abnormal visceral sensitivity and low-grade inflammation. The role of Clostridium butyricum (C. butyricum) in reducing intestinal low-grade inflammation via immune pathways has been well defined. However, the detailed mechanisms of the effects of C. butyricum on intestinal mucosal immunity, especially on immune cells of the lamina propria, remain unclear. Dendritic cells (DCs), which are important immune cells, secrete proinflammatory cytokines (IL-1β, IL-6, and others) and express T cell immuno-globulin and mucin domain-3 (TIM3), promoting proliferation and activation of DCs, and mediating Th1 and Th17 inflammatory responses. AIM To investigate the role of DCs in the development of IBS in a rat model and to understand the regulation of DCs after C. butyricum intervention. METHODS An IBS animal model was established using C57BL/6 mice, and C. butyricum was continuously administered via the intragastric route to simulate different intestinal immune states. Intestinal visceral hypersensitivity and histopathology were assessed using the abdominal withdrawal reflex (AWR) test and hematoxylin & eosin (H&E) staining, respectively. The expression of proinflammatory cytokines (IL-1β and IL-6) and TIM3 was analyzed by Western blot analysis and real-time PCR. Flow cytometry was applied to analyze the quantity, function, and membrane molecule TIM3 of the lamina propria dendritic cells (LPDCs). The regulatory effect of C. butyricum was verified in bone marrow-derived dendritic cells by in vitro experiments. RESULTS The secretion of proinflammatory cytokines (IL-1β and IL-6) in mice with IBS was significantly increased compared with that of the control group, which suggested that the intestinal mucosa in mice with IBS was in a low-grade inflammatory state. The expression of CD11C+CD80+ and CD11c+TIM3+ in intestinal LPDCs in mice with IBS increased significantly. Meanwhile, the cytokines (IL-1β and IL-6) were significantly reduced after the intervention with probiotic C. butyricum. The amount and function of LPDCs and the TIM3 on the surface of the LPDCs were decreased with the alleviation of the intestinal inflammatory response. CONCLUSION The results suggest that C. butyricum regulates the amount and functional status of LPDCs in the intestinal mucosa of mice with IBS, and therefore modulates the local immune response in the intestine.
A research on HDR and low illumination imaging based on the dual channel monochrome sCMOS

Due to the impact of urban lighting in low illumination environment, the images that collected by the low illumination sensor may appear “local saturation” phenomenon. When the traditional image sensor captures high dynamic range (HDR) of the sense, either the brighter part of the generated image is over exposure that may lost many details, or the dark part of the generated image is underexposure which cannot be resolved. In order to overcome this problem, it makes a research on the dual channel monochrome scientific complementary metal oxide semiconductor (sCMOS) sensor, which can generate both a high gain (HG) channel image and a low gain (LG) channel image in a same frame exposure. Take the way of image fusion by using these two images; it can get the HDR image in low illumination environment. Comparing with the traditional image sensor, the dynamic range of dual channel monochrome sCMOS sensor is obviously extended. The scheme based on dual channel sCMOS can effectively conquer the defects of low dynamic range bring by traditional image sensor, and it can overcome the problem of insufficient real time performance due to multiframe exposure. For dual channel sCMOS has the features of HDR, it can operate at a lower illuminative condition and will have a wide application area.
Pilot-Scale Study Of Human Plasma Proteomics Identifies ApoE And IL33 As Markers In Atopic Asthma

Background The pathobiology of atopic asthma is complex and the symptoms similar to other respiratory diseases. As such, identification of biomarkers of atopic asthma is of prime importance for better diagnosis and control of the disease. Objectives We sought to study the changes in plasma proteome and cytokine-expression profile across healthy and atopic asthmatics for identifying biomarkers and exploring aberrant pathways for atopic asthma. Methods A pilot-scale study in humans was performed to identify differentially expressed proteins in blood plasma of healthy controls (n=5) and treatment-naïve atopic asthma patients (n=5) using quantitative label-free liquid chromatography–tandem mass spectrometry proteomics and ELISA. Results Mass spectrometry–based proteomic analysis revealed ApoE to be significantly downregulated in atopic asthmatics compared to healthy volunteers. Decreased expression of ApoE in atopic asthmatics was validated by immunoblotting (50.74% decrease). Comparison with atopic asthmatics and COPD patients showed that ApoE was decreased (36.33%) in atopic asthma compared to COPD. IL33 was significantly upregulated in atopic asthmatics compared to healthy subjects (3.84-fold). Conclusion ApoE was downregulated and IL33 upregulated in atopic asthma patients compared to healthy volunteers. These two proteins' profiles were distinct in atopic asthma from healthy and COPD plasma samples. Differential expression of these proteins could serve as a probable candidate for a two-protein classifier–based prognostic biomarker of atopic asthma.