Miracle-dz/UniFAR-FTUs huggingface dataset:
A critical view on moving target defense and its analogies

In the last decade the Moving Target Defense (MTD) has gained popularity as a new cyber security defense paradigm. Moving Target Defense (MTD) intends to change the (presumable) information asymmetry between attacker and defender in favor of the defender by constantly changing a network's appearance as to invalidate previously acquired information. Many papers discussing MTD have been proposed and in recent years MTD techniques for a wide range of applications in enterprise networks, Cloud environments, IoT, automotive CAN buses and smart grids have been proposed. In these papers, MTD is often introduced as a "game changer" and explained with help of nice figurative analogies. Yet, how useful are these repeated changes really to deter the attacker? And even more importantly, could it not be that there are downsides to the constant changes that, ultimately, degrade security? In this position paper we argue that one needs to have a more critical and open minded view on MTD. There are MTD techniques that improve security. But we also provide several examples where they reduce security or where movement does not matter at all and is only introduced as to label a given technique as MTD.
Mind the gap: joining bezel-separated lines in multi-mobile systems

This paper presents a multi-mobile system that allows users to come together with their mobile devices in an ad-hoc manner, and integrates together as one seamless display surface with multi-touch capabilities. Typically, gaps and bezels between the display causes inherent design problems to the multi-display structures. Two user studies have been conducted with two versions of prototypes design by observing groups of students performing an interactive drawing task. Solution for the bezels was implemented into an iterative prototype. The findings show gaps and disjointed objects were observed in the drawing outcomes, with the implementation of the Continuous Spatial Configuration, the gaps and spaces between the screens were eliminated.
Towards analyzing crash events for novice drivers under reduced-visibility settings: A simulator study

Road accidents are one of the primary concerns and critical issues that encounters societies nowadays: Crash events analysis is a key role in improving traffic safety and reducing potential inconveniences to road users. As such, novice drivers continue to be overrepresented in fatalities and injuries arising from crashes, especially in those that occur during nigh times under rainy weather conditions. In this study, we aim to investigate road crash events for novice drivers under reduced-visibility scenarios during multiple night-time driving simulations that have been conducted using a desktop driving simulator. This paper depicted the effect of both light rain and heavy rain on traffic safety by endorsing real-time driver inputs established as throttle pedal position, brake pedal position and wheel angle. To the authors' knowledge, minimal work has been directed to the examination of light and heavy rain on novice drivers crash events based on driver inputs during night times. Artificial Neural Networks (ANN) and Decision Trees (DT) machine learning models have been developed to analyze crash events; results depict that ANN model exhibited the best performances in terms of accuracy and AUC measures during all-weather covariates. Conventionally, and based on the findings, new insights into night-related crash events' assessments for novice drivers could be harnessed to assist enforcement endeavors to design crash avoidance/warning systems under reduced-visibility settings.
Manipulation of Self-Expansion Alters Responses to Attractive Alternative Partners

Past behavioral research has examined relationship infidelity as a potential outcome of focusing on attractive alternative partners when already in a relationship. The extent to which individuals find such alternatives attractive has been shown to be associated with various factors in the relationship, including self-expansion. However, no previous research has tested the role of self-expansion experimentally. This paper presents two experiments that directly manipulate self-expansion to determine the effect of self-expansion on responses to attractive alternative partners. Participants primed to experience a higher need for self-expansion had better memory for attractive alternatives with self-expanding traits dissimilar to their partner’s versus attractive alternatives with self-expanding traits similar to their partner’s. Additionally, participants primed with self-expansion (via a video of their partner discussing ways in which life with one another is exciting, novel, and challenging), had less fMRI BOLD response to attractive alternatives of the opposite sex in regions associated with perception of attractive faces (anterior cingulate and medial prefrontal cortex) relative to when they were primed with love (via a video of their partner discussing times they felt strong feelings of love for one another), or neutral content (via a video of their partner discussing some times in which they engage in mundane, routine activities together). The magnitude of this effect in the ACC correlated with relationship closeness as measured by the inclusion of the other in the self scale.
Seed size and shape predict persistence in soil

An understanding of seed persistence in the soil is important to vegetation management and weed control, but experimental collection of seed bank data is tedious and expensive. We report a rapid, simple method for predicting seed persistence in the soil. The method is tested on a range of British, mostly herbaceous, species. Diaspore (seed or fruit) weight is plotted against variance of the three linear dimensions of the diaspore. All diaspores within an area of the graph defined by a maximum weight and variance are persistent in the soil. The critical weight is the same for fruits and seeds, but the critical variance of diaspore dimensions appears slightly higher for fruits. The great majority of diaspores outside this region are short lived, and the relatively few ambiguous cases can be resolved by reference to habitat (.)
Copper-chelating peptide from salmon by-product proteolysate

Abstract The aims of this study included evaluation of copper-binding capacity (CBC) and amino acid composition of salmon by-product proteolysate and its peptide fractions, optimization of hydrolysis condition, and identification of copper-binding peptides from the proteolysate. The result was that under the ideal hydrolysis (Neutrase, temperature of 45 °C, pH 7, enzyme:substrate (E:S) proportion of 72.24 U/g protein and hydrolysis time of 8.02 h), the proteolysate had the indispensable amino acid content at approximately 38.7% and also displayed the maximal CBC of 15163.6 µg Cu2+/g protein. Besides, four peptide fractions of 10–30 kDa, 3–10 kDa, 1–3 kDa, and <1 kDa were recovered using ultrafiltration, among which the <1 kDa fraction had the highest CBC of 10852.00 ± 895.06 µgCu2+/g protein. A copper-binding peptide, Phe-Ile-Asp-Asp-Asp-Ala-Phe-Ile-Arg (1110 Da), was identified from this fraction using tandem mass spectrometry (MS/MS). As a whole, the proteolysate/peptides could be used for copper enhancement that could shield human body from copper inadequacy disorders.
Practical Issues in Contingency Planning for UAVs with Engine Failures

Unmanned Air Vehicles (UAV), also known as Unmanned Air Systems (UAS), are gaining more attention in recent years. Some potential commercial applications with UAVs may include small cargo transport, search and rescue operations, drought and pest monitoring, etc. It is well-known that UAVs are less reliable as compared to manned aircraft. This is probably one of the consequential reasons that Federal Aviation Administration (FAA) is hesitant to open up the national airspace (NAS) and imposes tight restrictions to UAVs. Reliability of UAVs can be improved using engines and equipment with high quality and fault diagnostic algorithms using machine learning and artificial intelligence techniques, and robust and fault tolerant controllers. Despite the above measures, engine and equipment malfunctions may still appear in various applications. In this paper, we summarize some recent research results by us with respect to engine failures encountered in UAVs. Due to engine failure, there is limited hanging time and the mishap UAV needs to land preferably in an unpopulated area. In particular, we explicitly address some practical issues related to engine failures.
Classifying Alcoholics and Control Patients Using Deep Learning and Peak Visualization Method

A lot of advancements have been made in the field of Brain Computer Interfaces (BCI) using machine and deep learning. This paper presents a novel preprocessing technique to process Electroencephalography (EEG) signals in time domain. The proposed methodology, (Peak Visualization Method) (PVM) is based on selecting peaks with distinctive width and height range in order to perform better classification. PVM uses multiple machine learning techniques such as Random Forest, Logistic Regression and Support Vector Machine (SVM), Naive Bayes in order to find the most discriminating ranges. Moreover, selected range peaks are further used to compute features like indices of peaks, prominence of peak, contour heights, relative maxima, relative minima, local maxima and local minima. The extracted features were used as training and test data for a competitive 5-fold cross-validated analysis with Long Short Term Memory (LSTM) network. A publicly available EEG dataset for alcoholic and non-alcoholic classification was used to compare the proposed technique with state of the art EEG-NET deep learning model. In order to visualize the generalized performance of proposed system we use award winning dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) on the features extracted by EEGLSTM and show how our model's activations are classified in alcoholic and non-alcoholic categories. The reduced features are visualized into two dimensions.Features extracted using PVM gives an average accuracy of 90% on 5 folds beating the current state of the art EEG-NET, which manages to achieve an average accuracy of 88% on this dataset.
Genetic variation within and between subpopulations of the Australian Merino breed

Genetic variation within and between Australian Merino subpopulations was estimated from a large breeding nucleus in which up to 8500 progeny from over 300 sires were recorded at eight sites across Australia. Subpopulations were defined as genetic groups using the Westell–Quaas model in which base animals with unknown pedigree were allocated to groups based on their flock of origin if there were sufficient ‘expressions’ for the flock, or to one of four broad sheep-type groups otherwise (Ultra/Superfine, Fine/Fine-medium, Medium/Strong, or unknown). Linear models including genetic groups and additive genetic breeding values as random effects were used to estimate variance components for 12 traits: yearling greasy and clean fleece weight (ygfw and ycfw), yearling mean and coefficient of variation of fibre diameter (yfd and ydcv), yearling staple length and staple strength (ysl and yss), yearling fibre curvature (ycuv), yearling body wrinkle (ybdwr), post-weaning weight (pwt), muscle (pemd) and fat depth (pfat), and post-weaning worm egg count (pwec). For the majority of traits, the genetic group variance ranged from approximately equal to two times larger than the additive genetic (within group) variance. The exceptions were pfat and ydcv where the genetic group to additive variance ratios were 0.58 and 0.22, respectively, and pwec and yss where there was no variation between genetic groups. Genetic group correlations between traits were generally the same sign as corresponding additive genetic correlations, but were stronger in magnitude (either more positive or more negative). These large differences between genetic groups have long been exploited by Merino ram breeders, to the extent that the animals in the present study represent a significantly admixed population of the founding groups. The relativities observed between genetic group and additive genetic variance components in this study can be used to refine the models used to estimate breeding values for the Australian Merino industry.
New Frequency Reconfigurable Patch Antenna for Wireless Communication

In this paper, a new frequency reconfigurable patch antenna is proposed. The planned antenna is designed on the dielectric substrate of FR4-epoxy with 4.4 relative permittivity, with a compact volume of 25 × 21 × 1.67mm3. The proposed antenna is formed of a Positive Intrinsic Negative diode (PIN diode), placed at the radiating patch to achieve frequency reconfigurability. Based on the switching state of the PIN diode, the antenna is capable of operating at two different frequency ranges. The simulation of the proposed antenna is implemented using CST microwave studio, it was re-simulated in Ansys HFSS software to confirm the results obtained. The performance of the antenna is analysed from the reflection coefficient, the voltage standing wave ratio(VSWR), the surface current distribution and the radiation pattern. The antenna has two resonant frequencies: 3.44 GHz (Worldwide Interoperability for Microwave Access (WiMAX)) and 5.26 GHz (Wireless Local Area Network (WLAN)).
Polish City as Perceived by a US Grown Urban Planner

Abstract This article describes the differences in perception of urban space, functional relations and needs of the inhabitants, observed in Polish cities and compared to American cities, focusing on the current approach to new development in Kraków and Phoenix in Arizona. The article particularly articulates such problems as transit and parking in view of the city’s functionality as well as their ecological and esthetic impact.
Effects of Age and Gender in Emotion Regulation of Children and Adolescents

Emotional regulation, understood as the skills and strategies needed to influence and/or modify the emotional experiences, has a very remarkable implication within numerous emotional and behavioral disorders in childhood and adolescence. In recent years there has been a significant increase in research on emotional regulation, however, the results are still divergent in terms of differences in emotional regulation in relation to age and gender. This study aimed to assess emotional regulation in adolescents in relation to their age and gender. Two hundred and fifty-four adolescents from eight schools in the Valencian Community and aged between 9 and 16 years participated in the study. The adolescents completed the Cognitive Emotion Regulation Questionnaire and the FEEL-KJ questionnaire. We analyzed the differences in emotional regulation strategies and a latent emotional regulation variable in two age groups (9–12 years and 13–16 years) and by gender. The results suggested that children and pre-adolescents in the 9–12 year group obtained lower scores in the emotional regulation strategies than the 13–16 year group. Girls reported higher scores on the use of emotional regulation strategies when experiencing sadness, anxiety and anger than boys, and on the overall average of regulation according to these specific emotions. Age, but not gender, had a major effect on scores for the latent variable of emotion regulation. An interaction effect between age and gender was identified in the latent emotion regulation scores. Girls tended to have higher scores than boys when they were younger and lower scores than boys when they were older. These results could be relevant for designing prevention and intervention programs for adolescents and at different ages.
Fog Computing for Smart Healthcare data Analytics: An Urgent Necessity

The advent of Internet of Things (IoT) technologies in the 21st century has led to exciting achievements. IoT smart medical devices along with wearables are generating and communicating high volume and high-velocity of heterogeneous data. Consequently, services provided by traditional methods such as cloud computing to manage these data become insufficient. Fog computing is seen as being the most effective solution for IoT-based healthcare. The stringy demand of this model was principally driven by a growing need for real-time analysis and low latency. It is apparently envisioned that the medicine field expects to draw enormous interest in combining IoT and fog computing. This in turn, improved the patient treatment, staff satisfaction, and operational efficiency and showed positive influence on all aspects of healthcare. This paper has reviewed fog computing in the context of medical IoT while comparing it with cloud computing and has attempted to present the three-layer architecture of fog-based healthcare system containing: edge, fog and cloud. It has also highlighted challenges facing IoT-driven healthcare in an ever-changing world.
The second out-neighborhood for local tournaments

Abstract Sullivan stated the conjectures: (1) every oriented graph has a vertex x such that d ++(x) ≥ d −(x) and (2) every oriented graph has a vertex x such that d ++(x) + d + (x) ≥ 2d −(x). In this paper, we prove that these conjectures hold for local tournaments. In particular, for a local tournament D, there are at least two vertices satisfying (1) and either there exist two vertices satisfying (2) or there exists a vertex v satisfying d ++(v) + d + (v) ≥ 2d −(v) + 2 if D has no vertex of in-degree zero.
The Relationship between Growth and Consumption: Comparisons across Fish Populations

Abstract Comparisons of growth rates and consumption rates among fish populations are not straightforward and are difficult to interpret in the absence of other information about the populations. Differences in temperature regimes, reproductive timing, activity cost, prey availability, caloric densities of predator and prey, and allometric effects of body size on metabolism are factors that can alter the direct effect of consumption rate on growth rate, We developed a method to elucidate relations between growth and consumption by correcting for allometric weight effects. Bioenergetics model simulations showed that when growth rate is regressed against consumption rate (on either an absolute or a relative basis), the relationship is confounded by the allometric effects of differences in body weight among the populations being compared. Regressing growth rates on the difference between actual and maintenance consumption rate corrects for most of the allometric effects. That regression can be used to determ.
UV PROTECTION IN CYANOBACTERIA

If the depletion of the stratospheric ozone shield continues, the influx of solar UV-B radiation (280–320 nm) will increase in the near future. In photosynthetic organisms there are several targets for the deleterious UV-B radiation, such as proteins, DNA and membranes. Cyanobacteria use three different types of strategies to counteract UV damage: (i) stress avoidance by gliding mechanisms, (ii) stress defence by synthesis of UV-absorbing compounds, antioxidants and extracellular polysaccharides, and (iii) repair mechanisms including DNA repair and resynthesis of UV-sensitive proteins. In the past, most studies concentrated on the physiological aspects of UV tolerance whereas the molecular basis of UV tolerance in cyanobacteria is poorly understood. We will summarize the effects of and responses to UV-B at the physiological and molecular levels and present data on the influence of UV-B irradiation on the proteome of the terrestrial cyanobacterium Nostoc commune. SDS-PAGE and high-resolution two-dimensiona.
Abscisic acid in lichens: variation, water relations and metabolism

For the first time the well known drought stress hormone abscisic acid, which is involved in regulating processes increasing desiccation tolerance in many plant systems, was analysed in lichens. ABA was detected in all 26 species investigated. In contrast to higher plants and liverworts, the ABA content increased after hydration of air dry lichen thalli and decreased in desiccating lichen material. Experiments with Baeomyces rufus (Huds.) Rebent indicated that the mycobiont might be the major site of ABA biosynthesis. After incubation of hydrated lichen thalli with radioactive ABA for up to 72 h no metabolism to phaseic acid and dihydrophaseic acid could be detected. Fluctuations of internal ABA might be a result of ABA release to the external medium.
An efficient Algorithm for medical image classification using Deep Convolutional Network: Case of Cancer Pathology

Automatic classification of medical images especially of tissue images is an important task in computer aided diagnosis (CAD) systems. Deep learning methods such as convolutional networks (ConvNets) outperform other state of-the-art methods in images classification tasks. This article describes an accurate and efficient algorithms for this challenging problem, and aims to present different convolutional neural networks to classify the tissue images. first, we built a model that consist of feature extraction and the classification with simple CNN, the second model consist of a CNN as feature extractor by removing the classification layers and using the activations of the last fully connected layer to train Random Forest, and the last one using transfer learning --Fine-Tuning-- pre-trained CNN "DenseNet201". Finally, we have evaluated our models using three metrics: accuracy, Precision and F1 Score.
Comparative Analysis of the Coarse Aggregate Shapes used to Manufacturing High Performance Self-Compacting Concrete

Abstract The influence of the shape of coarse aggregate on the properties of fresh concrete mixes, and the strength of high-performance self-compacting concrete (HPSCC) is important issue. In this study, irregular and regular grains were separated from the basalt, porphyry and granite coarse aggregate. The shape of these grains was determining using digital image analysis and was in accordance with the European Standard [19]. The aspect ratio (AR) and roundness (R) were ascertained in order to highlight the differences in the coarse aggregate shape used the design HPSCC. The study indicates that using the same crushing system, varied parameters of the shape of coarse aggregates were obtained. It was determined that the best fitting distribution for aspect ratio and roundness at a 95% confidence level is the generalised extreme value distribution.
Passive Target Detection and Location using UAV-borne RF Sensors

The detection and location of uncooperative Radio Frequency (RF) emitters are both important capabilities for Electronic Surveillance (ES) of adversarial assets for national security and defense purposes. In this paper, we present a concept for an application of Unmanned Aerial Vehicles (UAVs) with on-board ES payload/sensors to conduct tactical surveillance, target detection and location. This UAV-borne sensor configuration can be operated in cooperation with surface-based systems/platforms (ship- or ground-based systems). By collaborating, the spatially separated UAV-borne sensor and surface-based sensors/systems can provide better geometry and diversification in time and space to increase coverage and improve detection and location of any RF targets.