Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Software Engineering Ontology Tool for Knowledge Sharing

Ontology is an important concept in Computer Science to formally represent knowledge in a way software can process the knowledge and reason about it. The software engineering ontology assists in defining information for the exchange of semantic project information framework. It intends to clear up the ambiguities that occur in the knowledge sharing between the software engineers. This paper presents the basic ontological representations for a given software project that which is well developed using the rudimentary software engineering principles. It draws a bead on an ontology model of software engineering to represent its knowledge. This paper also elicits about the analysis of SE Ontology and its advantages/applications with the example scenarios. Finally, a practical implementation of the in-depth ontological representation is elicited at the terminal of the paper with appropriate illustrations.
Biomarkers of Depression among Adolescent Girls: BDNF and Epigenetics

Alterations in brain-derived neurotrophic factor (BDNF) expression have been suggested to mediate the influence of environmental factors on the emergence of depression through epigenetic modifications. However, research on this subject in the developmental population is lacking and the pathophysiology of adolescent depression remains unclear. We aimed to investigate the alterations in BDNF expression and global DNA methylation in depression among adolescent girls. Thirty female inpatients with the initial diagnosis of depression were assessed before and after the period of antidepressant treatment and compared with thirty age-matched healthy controls. The assessment involved BDNF and proBDNF serum levels, the BDNF gene exon IV promoter methylation, and global DNA methylation. The methylation level in the BDNF gene exon IV promoter was significantly lower in the studied group compared with the control and correlated negatively with the severity of depression. The test distinguished the studied group from the controls with a sensitivity of 37% and specificity of 90%. The differences were no longer present after the period of antidepressant treatment. No differences in the global DNA methylation, BDNF, and proBDNF levels were found. We concluded that decreased methylation in the BDNF exon IV promoter could be considered as a biomarker of a depression state among adolescent girls.
On the 2D Teager-Kaiser Operator

The Teager-Kaiser operator is a discrete version of Teager's energy operator, advanced about 16 years ago. It is a filter of the moving window type and is commonly used as an estimator of the local energy contents of a signal; it is also used as a contrast enhancer of gray level images. We state some properties of a 2D version of the operator and its responses to common images. We characterize some of its root and preconstant images, and consider the case of separable images.
Modeling volcanic ash resuspension dynamics: the Eyjafjallajökull ash deposit case

The eruption of Eyjafjallajökull in early 2010 led to an increase in the frequency of observed PM10 (particulate matter with diameter < 10 μm) resuspension events in the southern region of Iceland, attributed to the deposition of fresh volcanic tephra. Particulate matter under this diametric threshold is readily respirable by humans and livestock, and has been shown to cause adverse effects to human health and the agriculture and aviation industries. Improvements have been made to the processing of observational data from the time period of 23 May–2 July 2010, as well as to the scheme for calibration of the NAME Lagrangian dispersion model. A new method for calibration allowed for the derivation of a timedependent scaling factor, representing the evolution of the source material as a function of time. The evolution of source material, due to various geomorphological and meteorological processes, affects its ability to be readily remobilized. Under the new calibration scheme, ash resuspension events in the immediate deposit proximity are captured 72% of the time, and those in the capital region to the southwest captured 57% of the time. This newly derived scaling factor indicates a rapid decline in volcanic ash resuspension following the end of an eruption event. The eruption signal dominates the record of observed PM10 levels in the immediate weeks following an event end, after which the normal dust resuspension record dust returns, with only minor influence from the remaining volcanic ash. To Clarence Ostwald, who demonstrated the value of hard work and humor in the face of it.
Summation of perceptual cues in natural visual scenes

Natural visual scenes are rich in information, and any neural system analysing them must piece together the many messages from large arrays of diverse feature detectors. It is known how threshold detection of compound visual stimuli (sinusoidal gratings) is determined by their components' thresholds. We investigate whether similar combination rules apply to the perception of the complex and suprathreshold visual elements in naturalistic visual images. Observers gave magnitude estimations (ratings) of the perceived differences between pairs of images made from photographs of natural scenes. Images in some pairs differed along one stimulus dimension such as object colour, location, size or blur. But, for other image pairs, there were composite differences along two dimensions (e.g. both colour and object-location might change). We examined whether the ratings for such composite pairs could be predicted from the two ratings for the respective pairs in which only one stimulus dimension had changed. We found a pooling relationship similar to that proposed for simple stimuli: Minkowski summation with exponent 2.84 yielded the best predictive power (r=0.96), an exponent similar to that generally reported for compound grating detection. This suggests that theories based on detecting simple stimuli can encompass visual processing of complex, suprathreshold stimuli.
Mitigation of SQL Injection Attacks using Threat Modeling

Day after day, SQL Injection (SQLI) attack is consistently proliferating across the globe. According to Open Web Application Security Project (OWASP) Top Ten Cheat Sheet-2014, SQLI is at top in the list of online attacks. The cause of spread of SQLI is thought to be Unsecure Software Engineering. The Software Development process itself appears to look at security as an add-on to be checked and deployed towards the end of the software development lifecycle which leads to vulnerabilities in web applications. This paper is an attempt to integrate security in early stages of SDLC i.e. in design phase to mitigate SQLI vulnerability. How SQLI attack happens is illustrated. Threat Modeling is performed to mitigate the SQLI vulnerability.
Out of reach? — A novel AR interface approach for motor rehabilitation

Mixed reality rehabilitation systems and games are demonstrating potential as innovative adjunctive therapies for health professionals in their treatment of various hand and upper limb motor impairments. Unilateral motor deficits of the arm, for example, are commonly experienced post stroke. Our TheraMem system provides an augmented reality game environment that contributes to this increasingly rich area of research. We present a prototype system which “fools the brain” by visually amplifying users' hand movements — small actual hand movements lead to perceived larger movements. We validate the usability of our system in an empirical study with forty-five non-clinical participants. In addition, we present early qualitative evidence for the utility of our approach and system for stroke recovery and motor rehabilitation. Future uses of the system are considered by way of conclusion.
Picosecond amorphization of SiO2 stishovite under tension

Quantum simulation reveals that fast tensile amorphization is key to hard and tough ceramics made of Earth-abundant silica. It is extremely difficult to realize two conflicting properties—high hardness and toughness—in one material. Nano-polycrystalline stishovite, recently synthesized from Earth-abundant silica glass, proved to be a super-hard, ultra-tough material, which could provide sustainable supply of high-performance ceramics. Our quantum molecular dynamics simulations show that stishovite amorphizes rapidly on the order of picosecond under tension in front of a crack tip. We find a displacive amorphization mechanism that only involves short-distance collective motions of atoms, thereby facilitating the rapid transformation. The two-step amorphization pathway involves an intermediate state akin to experimentally suggested “high-density glass polymorphs” before eventually transforming to normal glass. The rapid amorphization can catch up with, screen, and self-heal a fast-moving crack. This new concept of fast amorphization toughening likely operates in other pressure-synthesized hard solids.
Ground substrate classification for adaptive quadruped locomotion

In order to realize adaptive quadruped locomotion on terrains with different properties (such as surface friction or elasticity modulus), we plan to collect the foot-ground contact force and gyroscope information during locomotion on different ground substrates, then classify the ground substrates with the feature vector extracted from the collected data using Support Vector Machine (SVM) algorithm. However, the quadruped walk gait generated by Central Pattern Generators (CPGs) does not perform well on certain ground substrates, e.g., robots may be stuck in the soft ground substrates with small elasticity modulus. Therefore, for one thing, we present a Center of Gravity (COG) adjustment method to eliminate the offset between the control signal generated by CPGs and the actual phase of the quadruped limb, so the limb in theoretical swing phase is able to lift off the ground. For another, we combine CPGs with a foot path planning method to make the lift height controllable. Using these methods, the quadruped robot Biodog realizes the sensor data collection on five different ground substrates. Then we train and classify the sensor data with the SVM. About 99.33% of the five ground substrates can be classified correctly.
An Evaluation of Design Rule Spaces as Risk Containers

It is well understood that software development can be a risky enterprise and industrial projects often overrun budget and schedule. Effective risk management is, therefore, vital for a successful project outcome. Design Rule Spaces (DRSpaces) have been used by other researchers to understand why implemented software is error-prone [1, 2]. This industrial case study evaluates whether such spaces are durable, meaningful, and isolating risk containers. DRSpaces were created from UML class diagrams of architectural design artefacts. In our study, object orientated metrics were calculated from the UML diagrams, and compared to the error-proneness of the DRSpace implementation, to determine whether architectural coupling translated into implementation difficulties. A correlation between architectural coupling and error-proneness of DRSpaces was observed in the case study. Software developers were asked to identify DRSpaces they found difficult to implement, in order to understand which factors, other than architectural coupling, were also important. The qualitative results show agreement between the code areas developers found difficult to implement and the error-prone DRSpaces. However, the results also show that architectural coupling is just one risk factor of many. The case study suggests that architectural DRSpaces can be used to facilitate a targeted risk review prior to implementation and manage risk.
Gradient Estimation in Volume Data using 4D Linear Regression

In this paper a new gradient estimation method is presented which is based on linear regression. Previous contextual shading techniques try to fit an approximate function to a set of surface points in the neighborhood of a given voxel. Therefore a system of linear equations has to be solved using the computationally expensive Gaussian elimination. In contrast, our method approximates the density function itself in a local neighborhood with a 3D regression hyperplane. This approach also leads to a system of linear equations but we will show that it can be solved with an efficient convolution. Our method provides at each voxel location the normal vector and the translation of the regression hyperplane which are considered as a gradient and a filtered density value respectively. Therefore this technique can be used for surface smoothing and gradient estimation at the same time.
Implementation, validation and application of the 3GPP 3D MIMO channel model in open source simulation tools

Beamforming and Multiple Input Multiple Output (MIMO) have been identified as key technologies to meet the ever increasing capacity demands in future mobile cellular networks. So far, these features have mainly been investigated in the azimuth dimension, considering one-dimensional antenna arrays. Recently, the 3rd Generation Partnership Project has released a new 3-dimensional (3D) spatial channel model that also accounts for the elevation. It supports two-dimensional antenna arrays and enables to scrutinize concepts such as elevation beamforming and Full Dimension-Multiple Input Multiple Output. However, existing studies have mainly been carried out with commercial tools, thus largely limiting their reproducibility. This paper provides a guideline for the practical implementation of the 3D channel model into existing link- and system level simulation tools. Considering the complexity of the model itself, our main focus is on computational efficiency. We validate our approach with the Vienna LTE-A Downlink System Level Simulator and present simulation examples with various planar antenna arrays and polarization schemes.
Modelling of sugarcane yield using LISS-IV data based on ground LAI and yield observations

Abstract Crop acreage estimation and forecasting the yield of agricultural crops before harvest is of prime importance to a nation with a well-organized food and agricultural economy. Discrimination vigor assessment and yield estimation are some of the main concern of remote sensing applications. Sugarcane in India is a high priority crop for the Government. The aim of this study is to discuss the possibility of sugarcane plant yield estimation using an empirical relationship derived leaf area index (LAI) and farm scale sugarcane plant yield. The ground measurements of sugarcane LAI have been taken with the help of Accupar LP-80 Ceptometer instrument. A strong exponential relation (R 2 = 0.861) is observed between ground measured LAI and LISS-IV sensor NDVI. The yield model is developed using regression analysis between plot-wise yield data with LISS-IV LAI data. This empirical yield model has been found to give a reasonably fair indication (R 2 = 0.714) of the expected yield of sugarcane in advance.
Cullin4 Is Pro-Viral during West Nile Virus Infection of Culex Mosquitoes

Although mosquitoes serve as vectors of many pathogens of public health importance, their response to viral infection is poorly understood. It also remains to be investigated whether viruses deploy some mechanism to be able to overcome this immune response. Here, we have used an RNA-Seq approach to identify differentially regulated genes in Culex quinquefasciatus cells following West Nile virus (WNV) infection, identifying 265 transcripts from various cellular pathways that were either upregulated or downregulated. Ubiquitin-proteasomal pathway genes, comprising 12% of total differentially regulated genes, were selected for further validation by real time RT-qPCR and functional analysis. It was found that treatment of infected cells with proteasomal inhibitor, MG-132, decreased WNV titers, indicating importance of this pathway during infection process. In infection models, the Culex ortholog of mammalian Cul4A/B (cullin RING ubiquitin ligase) was found to be upregulated in vitro as well as in vivo, especially in midguts of mosquitoes. Gene knockdown using dsRNA and overexpression studies indicated that Culex Cul4 acts as a pro-viral protein by degradation of CxSTAT via ubiquitin-proteasomal pathway. We also show that gene knockdown of Culex Cul4 leads to activation of the Jak-STAT pathway in mosquitoes leading to decrease viral replication in the body as well as saliva. Our results suggest a novel mechanism adopted by WNV to overcome mosquito immune response and increase viral replication.
Performance Modelling of Peer-to-Peer Routing

We propose several models based on discrete-time Markov chains for the analysis of distributed hash tables (DHTs). Specifically, we examine the Pastry routing protocol, as well as a Stealth DHT adaptation of Pastry to compute their exact expressions for average number of lookup hops. We show that our analytical models match with the protocols' simulation results almost perfectly, making them ideal for rapid evaluation.
Treatment strategies for obsessive-compulsive disorder

Importance of the field: In the last decades, the treatment of obsessive-compulsive disorder (OCD) has improved significantly with the introduction into the clinical practice of the selective serotonin reuptake inhibitors (SSRIs): this has led to a renewed interest in this condition and development of appropriate therapeutic strategies, including non-pharmacological techniques. Areas covered in this review: The aim of this paper is to present a comprehensive and critical review of pharmacological and non-pharmacological treatments usually adopted in the management of OCD. MEDLINE and PubMed (1980 – 2009) databases were searched for English-language articles by using the following keywords: OCD, clomipramine, SSRIs, resistance, predictor of response, cognitive-behavioral therapy, exposure and response prevention, repetitive transcranial magnetic stimulation, deep brain stimulation, neurosurgery, electroconvulsive therapy. What the reader will gain: The reader will gain an exhaustive review of the advantages and disadvantages of the treatments usually adopted in OCD, with a special focus on resistance and clinical features that can be used as predictors of response. Take home message: Obsessive-compulsive disorder is a common psychiatric condition that can be adequately managed with the currently available therapeutic options. Although a certain percentage of patients do not achieve complete clinical remission, improvement of some of their core symptoms permits them to live more satisfying lives and to reach a better social adjustment.
Highly Sensitive Microwave Sensors Based on Open Complementary Square Split-Ring Resonator for Sensing Liquid Materials

This paper presents high-sensitivity sensors based on an open complementary square split-ring resonator and a modified open complementary split-ring resonator operating at 4.5 GHz and 3.4 GHz, respectively. The sensors are designed for the detection of multiple liquid materials, including distilled water, methanol, and ethanol. The liquid under test is filled in a glass container loaded using a pipette. Compared to the conventional OCSSRR, the modified OCSSRR with multiple rings exhibits a higher frequency shift of 1200 MHz, 1270 MHz, and 1520 MHz for ethanol, methanol, and distilled water, respectively. The modified sensor also demonstrates a high sensitivity of 308 MHz/RIU for ethanol concentration which is the highest among the existing microwave sensors. The sensors in this manuscript are suitable for multiple liquid-material-sensing applications.
Glueball operators and the microscopic approach to N=1 gauge theories

We explain how to generalize Nekrasov's microscopic approach to = 2 gauge theories to the = 1 case, focusing on the typical example of the U(N) theory with one adjoint chiral multiplet X and an arbitrary polynomial tree-level superpotential Tr W(X). We provide a detailed analysis of the generalized glueball operators and a non-perturbative discussion of the Dijkgraaf-Vafa matrix model and of the generalized Konishi anomaly equations. We compute in particular the non-trivial quantum corrections to the Virasoro operators and algebra that generate these equations. We have performed explicit calculations up to two instantons, that involve the next-to-leading order corrections in Nekrasov's Ω-background.
High-Throughput Proteomic Approaches to the Elucidation of Potential Biomarkers of Chronic Allograft Injury (CAI)

This review focuses on the role of OMICs technologies, concentrating in particular on proteomics, in biomarker discovery in chronic allograft injury (CAI). CAI is the second most prevalent cause of allograft dysfunction and loss in the first decade post-transplantation, after death with functioning graft (DWFG). The term CAI, sometimes referred to as chronic allograft nephropathy (CAN), describes the deterioration of renal allograft function and structure as a result of immunological processes (chronic antibody-mediated rejection), and other non-immunological factors such as calcineurin inhibitor (CNI) induced nephrotoxicity, hypertension and infection. Current methods for assessing allograft function are costly, insensitive and invasive; traditional kidney function measurements such as serum creatinine and glomerular filtration rate (GFR) display poor predictive abilities, while the current “gold-standard” involving histological diagnosis with a renal biopsy presents its own inherent risks to the overall health of the allograft. As early as two years post-transplantation, protocol biopsies have shown more than 50% of allograft recipients have mild CAN; ten years post-transplantation more than 50% of the allograft recipients have progressed to severe CAN which is associated with diminishing graft function. Thus, there is a growing medical requirement for minimally invasive biomarkers capable of identifying the early stages of the disease which would allow for timely intervention. Proteomics involves the study of the expression, localization, function and interaction of the proteome. Proteomic technologies may be powerful tools used to identify novel biomarkers which would predict CAI in susceptible individuals. In this paper we will review the use of proteomics in the elucidation of novel predictive biomarkers of CAI in clinical, animal and in vitro studies.
Metabonomics in toxicology: a review.

Metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past 2 to 3 years. Nowhere has the impact been more profound than within the toxicology community. Within this community there exists a great deal of uncertainty about whether metabonomics is something to count on or just the most recent technological flash in the pan. Much of the uncertainty is due to unfamiliarity with analytical and chemometric facets of the technology and the attendant fear of any "black-box. " With those fears in mind, metabonomics technology is reviewed with particular emphasis on toxicologic applications in preclinical drug development. The jargon, logistics, and applications of the technology are covered in some detail with emphasis on recent work in the field.