Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process

The identification of network attacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the collecion, preprocessing and transformation of the data, as well as the data mining and evaluation methods. We also present the characteristics and motivations behind the use of each of these techniques and propose more adequate and up-to-date taxonomies and definitions for intrusion detectors based on the terminology used in the area of data mining and KDD. Special importance is given to the evaluation procedures followed to assess the detectors, discussing their applicability in current, real networks. Finally, as a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
SDOD: Real-time Segmenting and Detecting 3D Objects by Depth

Most existing instance segmentation methods only focus on 2D objects and are not suitable for 3D scenes such as autonomous driving. In this paper, we propose a model that splits instance segmentation and object detection into two parallel branches. We discretize the objects depth into depth categories (background set to 0, objects set to [1, K]), then the instance segmentation task has been transformed into a pixel-level classification task. Mask branch predicts pixel-level depth categories, 3D branch predicts instance-level depth categories, we produce instance mask by assigning pixels which have same depth categories to each instance. In addition, in order to solve the problem of imbalanced between mask labels and 3D labels in the KITTI dataset (200 for mask, 7481 for 3D), we introduce coarse mask generated by auto-annotation model to increase samples.
The Innovative Role of the Principal as Instructional Leader: A Prerequisite for High Student Achievement?

Principals today face more demands, more complex decisions, and more responsibilities than principals of the past. The principal’s day is filled with activities of management such as handling relations with parents and community, dealing with multiple teacher and student crisis that are inevitable. However principals should facilitate best practices and keep the school focused on curriculum, instruction, and assessment to meet students’ learning needs and improve achievement. The principal should take on an innovative role as an instructional leader who possesses the requisite skills, capacity and commitment to lead an effective school. This study investigates how principals perceive their roles as instructional leaders. Using an open ended questionnaire and personal interviews with eight principals in the Gauteng Province of South Africa, it found that principals who placed importance to instructional matters were managing and leading schools effectively and attaining high student achievement.
Effects of dexmedetomidine on endothelial progenitor cells and its therapeutic application in patients with traumatic brain injury

Objective: To investigate the role of dexmedetomidine (DEX) in the treatment of traumatic brain injury (TBI) and its effect on endothelial progenitor cells (EPCs). Methods: Seventy-six patients with TBI were randomly and equally divided into control and observation groups. Patients in the observation group were intravenously injected with DEX before administering anesthesia and after the surgery, while those in the control group were not given any drug in this prospective study. Prior to and 1 month after the surgery, the levels of serum S100β and neuron specific enolase (NSE) were measured using enzyme-linked immunosorbent assay; the hemodynamic parameters including end-diastolic blood flow velocity (Vd), peak systolic blood flow velocity (Vs), mean blood flow velocity (Vm), pulsatility index (PI), and resistance index (RI) of the middle cerebral artery were measured using transcranial Doppler ultrasound; the number of endothelial microparticles (EMPs) and EPCs in the peripheral blood were measured using flow cytometry; and the proliferation rate and adhesion level of EPCs were also measured. Three months after the surgery, patients’ rehabilitation was evaluated according to Glasgow outcome score (GOS). Results: After surgery, compared to the control group, the levels of serum S100β (P=0.007) and NSE (P=0.031) decreased in the observation group; the hemodynamic parameters Vd (P=0.032), Vs (P=0.003), Vm (P<0.001), and PI (P=0.017) increased, while RI (P=0.033) decreased; the number of EMPs in the peripheral blood decreased (P<0.001); the number of EPCs in the peripheral blood (P<0.001), proliferation (P<0.001) and adhesion level (P<0.001) improved. Three months after surgery, the GOS score of the observation group was significantly higher than that of the control group (P=0.004). Conclusion: Pre-operative and post-operative DEX therapy can reduce the serum levels of S100β and NSE, stabilize the blood flow of patients with TBI, reduce the content of EMPs and improve the number of EPCs in the peripheral blood as well as the function of EPC.
Therapeutic strategies in poorly controlled hypertension in outpatient setting in Poland — POSTER study Strategie terapeutyczne u pacjentów z niekontrolowanym nadciśnieniem tętniczym pozostających w opiece ambulatoryjnej w Polsce — badanie POSTER

Introduction. Hypertension is one of the most common risk factors of cardiovascular disease, which remains the major cause of death in Poland. Number of patients with controlled hypertension remains low. The aim of the study was to evaluate strategies of hypotensive pharmacotherapy, to determine prevalence of poorly controlled hypertension and of cardiovascular risk factors and comorbidities in these patients. Material and methods. Non-interventional, observational study was conducted in patients treated in various outpatient care settings. The study had two stages: 1) screening, with collection of baseline medical data in 50 patients with hypertension attending one physician; 2) collection of additional medical data concerning 10 patients with poorly controlled hypertension. Results. The total of 42,338 patients was included in the screening stage, and of those 54.3% had poorly controlled hypertension. In the latter group, the most prevalent cardiovascular risk factor was dyslipidemia (77.8%) and abdominal obesity (68.3%). More than 25% respondents were smokers. Ischaemic heart disease and diabetes were the most common comorbidities (33.4% and 31%, respectively). As to pharmacotherapy, 73% of patients received more than one antihypertensive agent (angiotensin converting enzyme inhibitor 59.7%, beta-blocker 45%, and diuretic 38.7%), and 10.6% subjects were treated with fixed dose combinations. In 38 patients antihypertensive therapy was modified more than a year before. Conclusions. Of all hypertensive patients in outpatient care setting, still more than half do not reach the target blood pressure values. In this group, the most common risk factors and comorbidities are dyslipidemias, obesity, ischaemic heart disease and diabetes. Nearly three-quarters of patients receive more than one hypotensive agent (usually angiotensin-converting enzyme inhibitor, beta-blocker, and diuretic). Antihypertensive therapy was not modified for more than six months in over 60% of patients.
SOME COMBINATORICS OF RATIONAL BASE REPRESENTATIONS

We investigate representations of natural numbers in terms of rational bases. We then define a collection of combinatorial data associated with a rational number and describe the properties of these data. We use this combinatorial data to construct an infinite, labeled, rooted tree and prove that it contains many of the interesting features of the relevant rational base representations of natural numbers. We also explore some of the combinatorics of these trees as well as the rational base representations of interest. Our main result is an analog of Kummer’s theorem for rational bases representations in terms of generalized binomial coefficients defined in terms of sequences generated from the previously mentioned rooted trees.
Down Syndrome Research and Practice Volume 7 Issue 3

Language development in children with Down syndrome is delayed, on average, relative to general cognitive, motor and social development, and there is also evidence for specifi c delays in morphology and syntax, with many adults showing persistent problems in these areas. It appears that the combined use of signed and spoken input can boost early language development signifi cantly, this evidence coming initially from single case-studies, and more recently from larger scale controlled studies. Research with typically developing hearing and deaf children, as well as children with Down syndrome, has demonstrated the importance of establishing joint attention for vocabulary development. Furthermore, studies carried out with children with Down syndrome indicate that reducing attentional demands may be especially important in scaffolding language development in this group. The use of signing strategies which have been found to facilitate language development in deaf children when signing to children with Down syndrome is discussed, as is the need for further research on this topic and on the importance of joint attention for the use of other augmentative and alternative communication systems, such as graphic symbol and picture systems.
A Prospective Study of Neonatal Pneumothorax in Holy Karbala

ackground: Pneumothorax is the accumulation of air in the space between the parietal and visceral pleura, and is potentially a very serious problem, especially if it occurs in the neonatal population. Patients and Method: A prospective data of sixty newborn infants with pneumothoraces were observed and treated at the Neonatal Care Units in teaching pediatric hospital and gynecological and obstetric teaching hospital in karbala, Iraq, between January 2012 and January 2014 are included in this descriptive study. Patients were evaluated for: sex, gestational age, birth weight; method of delivery; time of pneumothorax development; localization of pneumothorax and the presence of concurrent lung pathology. Diagnosis of pneumothorax done by clinical assessment, and chest X-rays. symptomatic patients were treated with a tube thoracostomy and closed underwater drainage. Results: Sixty neonates with pneumothoraces were included in this study. Sixty three percent of patients were male. Fifty five percent of patients were preterm babies. Fifty three percent were delivered by caesarean section. Seventy seven percent of pneumotoraces were on the right side. Seventy percent cases of pneumothoraces were presented within the first 48 hours of life. Seventy three percent diagnosed as respiratory distress syndrome which is the most common pathology. Mechanical ventilation was performed in 8 patients, including four of the six fatal cases. Tube thoracostomy and closed underwater drainage were performed in 56 patients (93%), while peumothorax resolved spontaneously in four cases. Mortality rate was 10%. Conclusion: pneumothorax may develop during the neonatal period, especially in the presence of risk factors. Treatment is effective, if the disease is recognized and treated as early as possible.
Effect of five plant essential oils as natural antimicrobial agents against Listeria monocytogenes in sausages

The antimicrobial activity of five plant essential oils were evaluated against Listeria monocytogenes, both in vitro and in a food system. Essential oils of thyme, clove, and pimenta were found to be most effective, based on disc diffusion experiments. Thyme and clove proved to be highly effective against L. monocytogenes in peptone water (1g/l) and reduced the bacterial population below detection limits at concentrations of 1 ml/l. Experiments were also carried out in sausage of different fat content (zero-, low-, and full-fat) to evaluate the antimicrobial activity of essential oils against L. monocytogenes. Thyme essential oil reduced bacterial populations significantly (P≤0.05) at 1 ml/l level in zeroand low-fat sausage, but not in full-fat sausages. At 10 ml/l level it reduced the bacterial population >1.3log10 cfu/g in zero-fat sausages, but was less effective in lowand full-fat sausages. Clove essential oil also exhibited antimicrobial activity at 1 ml/l in all sausages, and was more effective than thyme at 5 ml/l. However, increasing concentration to 10 ml/l did not result in significant (P≤0.05) reduction of bacterial population. It is concluded that efficacy of essential oils was reduced in a food system due to interaction with food components.
PJ PALGO JOURNAL OF EDUCATION RESEARCH

The purpose of this research was to study age and gender effects on young children’s The sample consisted of 280 Taiwanese boys and 316 Taiwanese girls, aged five 596 young Taiwanese children. They were given a crea study combined both quantitative analyses with the qualitative content analyses. The results showed that six children scored significantly higher than five creativity. Also, girls in both age groups, scored significantly higher than boys on people drawings and free drawings, but not overall creativity. The qualitative analyses showed different children’s free-drawings and peopledrawings between boys and girls.
Exact rate of convergence of the expected $W_{2}$ distance between the empirical and true Gaussian distribution

We study the Wasserstein distance $W_2$ for Gaussian samples. We establish the exact rate of convergence $\sqrt{\log\log n/n}$ of the expected value of the $W_2$ distance between the empirical and true $c.d. f. $'s for the normal distribution. We also show that the rate of weak convergence is unexpectedly $1/\sqrt{n}$ in the case of two correlated Gaussian samples.
Learning Preference-Based Similarities from Face Images using Siamese Multi-Task CNNs

Online dating has become a common occurrence over the last few decades. A key challenge for online dating platforms is to determine suitable matches for their users. A lot of dating services rely on self-reported user traits and preferences for matching. At the same time, some services largely rely on user images and thus initial visual preference. Especially for the latter approach, previous research has attempted to capture users' visual preferences for automatic match recommendation. These approaches are mostly based on the assumption that physical attraction is the key factor for relationship formation and personal preferences, interests, and attitude are largely neglected. Deep learning approaches have shown that a variety of properties can be predicted from human faces to some degree, including age, health and even personality traits. Therefore, we investigate the feasibility of bridging image-based matching and matching with personal interests, preferences, and attitude. We approach the problem in a supervised manner by predicting similarity scores between two users based on images of their faces only. The ground-truth for the similarity matching scores is determined by a test that aims to capture users' preferences, interests, and attitude that are relevant for forming romantic relationships. The images are processed by a Siamese Multi-Task deep learning architecture. We find a statistically significant correlation between predicted and target similarity scores. Thus, our results indicate that learning similarities in terms of interests, preferences, and attitude from face images appears to be feasible to some degree.
FakeLocator: Robust Localization of GAN-Based Face Manipulations

Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel approach, termed FakeLocator, to obtain high localization accuracy, at full resolution, on manipulated facial images. To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map that preserves more information of fake regions. To improve the universality of FakeLocator across multifarious facial attributes, we introduce an attention mechanism to guide the training of the model. To improve the universality of FakeLocator across different DeepFake methods, we propose partial data augmentation and single sample clustering on the training images. Experimental results on popular FaceForensics++, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method. Compared with the baselines, our method performs better on various metrics. Moreover, the proposed method is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.
How Molecular Chiralities of Bis(mandelato)borate Anions Affect Their Binding Structures With Alkali Metal Ions and Microstructural Properties in Tetraalkylphosphonium Ionic Liquids

Spiroborate anion-based inorganic electrolytes and ionic liquids (ILs) have fascinating electrochemical and tribological properties and have received widespread attention in industrial applications. The molecular chiralities of spiroborate anions have a significant effect on the microstructures and macroscopic functionalities of these ionic materials in application and thus deserve fundamental consideration. In the current work, we performed quantum chemistry calculations to address the binding strength and coordination structures of chiral bis(mandelato)borate ([BMB]) anions with representative alkali metal ions, as well as the electronic properties of alkali metal ion-[BMB] ion pair complexes. The optimized [BMB] conformers are categorized into V-shaped, bent, and twisted structures with varied electrostatic potential contours and conformational energies and distinct alkali metal ion-[BMB] binding structures. Alkali metal ions have additional associations with phenyl groups in V-shaped [BMB] conformers owing to preferential cation-π interactions. Furthermore, the effects of the molecular chiralities of [BMB] anions on the thermodynamics and microstructural properties of tetraalkylphosphonium [BMB] ILs were studied by performing extensive atomistic interactions. Oxygen atoms in [BMB] anions have competitive hydrogen bonding interactions with hydrogen atoms in cations depending on the molecular chiralities and steric hindrance effects of [BMB] anions. However, the molecular chiralities of [BMB] anions have a negligible effect on the liquid densities of tetraalkylphosphonium [BMB] ILs and the spatial distributions of boron atoms in anions around phosphorous atoms in cations. Enlarging tetraalkylphosphonium cation sizes leads to enhanced cation-anion intermolecular hydrogen bonding and Coulombic interactions due to enhanced segregation of polar groups in apolar networks in heterogeneous IL matrices, as verified by scattering structural functions.
Estimating heterogeneous treatment effects with right-censored data via causal survival forests

Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.
Asymptotic expansion for the Hartman-Watson distribution

The Hartman-Watson distribution with density $f_r(t)$ is a probability distribution defined on $t \geq 0$ which appears in several problems of applied probability. The density of this distribution is expressed in terms of an integral $\theta(r,t)$ which is difficult to evaluate numerically for small $t\to 0$. Using saddle point methods, we obtain the first two terms of the $t\to 0$ expansion of $\theta(\rho/t,t)$ at fixed $\rho >0$. As an application we derive, under an uniformity assumption in $\rho$, the leading asymptotics of the density of the time average of the geometric Brownian motion as $t\to 0$. This has the form $\mathbb{P}(\frac{1}{t} \int_0^t e^{2(B_s+\mu s)} ds \in da) = (2\pi t)^{-1/2} g(a,\mu) e^{-\frac{1}{t} J(a)} (1 + O(t))$, with an exponent $J(a)$ which reproduces the known result obtained previously using Large Deviations theory.
Measurements-Based Channel Models for Indoor LiFi Systems

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.