Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Preparation and Reduction Technique in Intrusion Detection Systems: ANOVA-PCA

Intrusion detection system plays a main role in detecting anomaly and suspected behaviors in many organization environments. The detection process involves collecting and analyzing real traffic data which in heavy-loaded networks represents the most challenging aspect in designing efficient IDS. Collected data should be prepared and reduced to enhance the classification accuracy and computation performance. In this research, a proposed technique called, ANOVA-PCA, is applied on NSL-KDD dataset of 41 features which are reduced to 10. It is tested and evaluated with three types of supervised classifiers: k-nearest neighbor, decision tree, and random forest. Results are obtained using various performance measures, and they are compared with other feature selection algorithms such as neighbor component analysis (NCA) and ReliefF. Results showed that the proposed method was simple, faster in computation compared with others, and good classification accuracy of 98.9% was achieved.
Named Entity Recognition for Telugu Using Conditional Random Field

Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into predefined categories such as Person names, Organization names , Location names, miscellaneous(Date and others). It is a key technology of Information Extraction, Question Answering system, Machine Translations, Information Retrial etc. This paper reports about the development of a NER system for Telugu using Conditional Random field (CRF). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Telugu languages is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named entities (NE) classes, such as Person name, Location name, Organization name, miscellaneous (Date and others).
Tooth loss early in life induces hippocampal morphology remodeling in senescence-accelerated mouse prone 8 (SAMP8) mice

Long-term tooth loss is associated with the suppression of hippocampal neurogenesis and impairment of hippocampus-dependent cognition with aging. The morphologic basis of the hippocampal alterations, however, remains unclear. In the present study, we investigated whether tooth loss early in life affects the hippocampal ultrastructure in senescence-accelerated mouse prone 8 (SAMP8) mice, using transmission electron microscopy. Male SAMP8 mice were randomized into control or tooth-loss groups. All maxillary molar teeth were removed at 1 month of age. Hippocampal morphologic alterations were evaluated at 9 months of age. Tooth loss early in life induced mitochondrial damage and lipofuscin accumulation in the hippocampal neurons. A thinner myelin sheath and decreased postsynaptic density length were also observed. Our results revealed that tooth loss early in life may lead to hippocampal ultrastructure remodeling and subsequent hippocampus-dependent cognitive impairment in SAMP8 mice with aging.
SIMULTANEOUS TOPOGRAPHICAL AND ELECTROCHEMICAL MAPPING USING SCANNING ION CONDUCTANCE MICROSCOPY

image submission is open until June 30 NANOScientific Publications announces the 2018 NANOSCIENTIFIC SYMPOSIUMS ON SCANNING PROBE MICROSCOPY (SPM) new venues for nanoscience researchers, scientists, and engineers to learn about the latest studies being formed using SPM. Keynote speakers from both academia and industry will talk about cutting-edge work being performed in their laboratories and the hottest topics in nanoscience today sponsored by NanoScientific and Park Systems. Poster and Oral Presentation Opportunities, submit your abstract today. NanoScientific Symposium on SPM in Europe – Oct. 10-12, 2018 at Frieberg University www.parksystems. com/nsfe2018 NanoScientific Symposium on SPM in US – Sept 19-20 at SUNY Polytechnic Institute www.parksystems. com/2018spm
Labor supply and child care costs∗

In Italy the participation of women has not increased very much in the last few decades relative to other developed countries and it is still among the lowest in Europe. The female employment rate stands almost 13 percentage points below the EU average and 22 below the Lisbon target. One of the most important reasons is related to the characteristics of child care system. In this paper we analyze the characteristics of the child care system in Italy and its relationship to the labor market participation decision of mothers. We present a simple discrete choice framework in which the two decisions can be jointly considered, which also allows for simple forms of rationing. We go on to estimate a bivariate probit model of the child care and employment decisions and interpret the results within the framework of the our model. We find evidence that rationing is an important consideration in interpreting price effects on utilization rates. J.E. L Subject Codes: J2, C3, D1.
Growth of Hyperlink Network Formed by Web Pages of Japanese Public Libraries

In this study, we studied the growth of the network of Japanese public libraries formed by hyperlinks of their Web pages by comparing the network in 2007 with that in 2009. By focusing on the locality factor and degree factor, we found that two models in which each factor comes into play individually do not fully fit the hyperlink network and that cliques increase more sharply than in these models. These results indicate that neither of these factors comes into play by themselves in the hyperlink network and that cliques are characteristic of the hyperlink network. We also studied in-prefecture networks, i.e., sub networks formed of all municipal and prefectural libraries within a prefecture and which are one of the most important types of cliques, and found that these networks could be classified into four types: (A) those with few or no links; (B) those with hubs, which are prefectural libraries, but no clique; (C) those with hubs, which are prefectural libraries, and cliques; and (D) those with hubs, which are prefectural libraries, and many links and cliques. These types are also steps of a common growth process that goes from (A) to (B) to (C) to (D). The degree factor mainly comes into play in steps (A) and (B), whereas the locality factor mainly comes into play in steps (C) and (D).
Reeb Graph for Automatic 3 D Cephalometry

The purpose of this study is to present a method of three-dimensional computed tomographic (3D-CT) cephalometrics and its use to study cranio/maxilla-facial malformations. We propose a system for automatic localization of cephalometric landmarks using reeb graphs. Volumetric images of a patient were reconstructed into 3D mesh. The proposed method is carried out in three steps: we begin by applying 3d mesh skull simplification, this mesh was reconstructed from a head volumetric medical image, and then we extract a reeb graph. Reeb graph mesh extraction represents a skeleton composed in a number of nodes and arcs. We are interested in the node position; we noted that some reeb nodes could be considered as cephalometric landmarks under specific conditions. The third step is to identify these nodes automatically by using elastic mesh registration using “thin plate” transformation and clustering. Preliminary results show a landmarks recognition rate of more than 90%, very close to the manually provided landmarks positions made by a medical stuff.
The Effects of Virtual Audience Size on Social Anxiety during Public Speaking

Prior studies have explored the possibility of inducing social anxiety (SA) in virtual reality (VR). Among various existing protocols for this purpose, the Trier Social Stress Test (TSST) has been proven to be robust in evoking SA in the majority of participants in both in vivo as well as VR conditions. The TSST consists of giving a speech and performing mental arithmetic calculations each for five minutes in front of three persons. In this paper, we present an adaptation of TSST to investigate the effects of different numbers of virtual humans (VHs) (i.e., three, six, or fifteen) on perceived SA. In addition, we compare the results with an in vivo TSST with three real persons in the audience. Twenty four participants took part in this experiment. As a result, physiological arousal could be observed with VR inducing SA yet less than in vivo TSST. Furthermore, some of the subjective measures showed a high state of anxiety experienced during the experiment. An effect of the virtual audience size could be observed only in heart rate (HR) as a virtual audience size of three VHs induced the highest HR responses which was significantly different from an audience of size six and fifteen.
DEHN SURGERY

Dehn surgery is a basic method for constructing 3-manifolds. It was introduced by Dehn to construct homology spheres. In the early 1960’s, Lickorish and Wallace showed that all orientable 3-manifolds can be obtained using this construction. Let K be a (tame) knot in S, i.e., an embedding of S into S. We can remove a neighbourhood of this knot, which is a solid torus, and sew it back in a different way. This is the simplest case of a Dehn surgery. More generally, given a link L in a 3-manifold M , we can remove neighbourhoods of each component of the link and sew back the solid tori in a different way. A theorem of Lickorish and Wallace asserts that we get every closed orientable 3manifold by performing this operation on some link in S.
Matrix Padding Method for Sparse Signal Reconstruction

Compressive sensing has been evolved as a very useful technique for sparse reconstruction of signals that are sampled at sub-Nyquist rates. Compressive sensing helps to reconstruct the signals from few linear projections of the sparse signal. This paper presents a technique for the sparse signal reconstruction by padding the compression matrix for solving the underdetermined system of simultaneous linear equations, followed by an iterative least mean square approximation. The performance of this method has been compared with the widely used compressive sensing recovery algorithms such as l1_ls, l1-magic, YALL1, Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit, etc. The sounds generated by 3-blade engine, music, speech, etc. have been used to validate and compare the performance of the proposed technique with the other existing compressive sensing algorithms in ideal and noisy environments. The proposed technique is found to have outperformed the l1_ls, l1-magic, YALL1, OMP, CoSaMP, etc. as elucidated in the results.
A Genetic Algorithm to Find the Minimum Cost Paths Tree with Bandwidth Constraint in the Computer Networks

Through the communication network, several communication applications need a source to transfer information to different destinations. In order to support the applications, it is essential to evaluate the shortest paths tree of minimal cost to connect source to destination nodes with respect to the bandwidth conditions on the information communication. In order to solve the problem, the genetic algorithm is applied. The paper highlights the generic algorithm to manage shortest path tree problem in relation to bandwidth and cost constraints. The aim of the presented algorithm is to determine the group of edges that connecting all nodes, value of bandwidth and sum of costs will be constrained. To demonstrate the efficiency the propose algorithm has applied to two sample networks.
Development of Personal Assistant System with Human Computer Interaction

In recent years, some personal assistant systems have appeared in our daily life. They provided some services such as account management, curriculum management, diary management, financing management and so on. This study presents a design and implementation of a personal assistant system platform especially for students and employee to help to manage their learning, life and work better. One of the highlights of this system has an instant chatting system except for the above functions. This paper indicates not only some beneficial interactive functions but also presented detailed ideas of information system analysis, design, and test. It has a friendly interactive interface and a good manageable system, and experimental studies of these functions are proved to be reliable, efficient, and acceptable for users when applied to learning and working environments.
Improvement of Minimum Tracking in Minimum Statistics Noise Estimation Method

Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper we propose a new method for minimum tracking in Minimum Statistics (MS) noise estimation method. This noise estimation algorithm is proposed for highly nonstationary noise environments. This was confirmed with formal listening tests which indicated that the proposed noise estimation algorithm when integrated in speech enhancement was preferred over other noise estimation algorithms.
Learning Sparse Representative Subsets

Gaussian processes (GPs) are a family of statistical models for inferring distributions over latent functions fs from data. GPs are widely used and can be applied to many machine learning-related tasks such as regression, classification and optimisation. The main drawback of GPs is that inference is performed over high dimensional correlated latent variables which results in complexity of O(N3) and storage of O(N2) for a dataset of size N . Since N in modern datasets can be very large, the cost of using GPs can be prohibitive. There exist a plethora of methods proposed to address this pitfall, most notably sparse approximations methods based on pseudo-points or inducing points (see e.g. Snelson and Ghahramani, 2006; Titsias, 2009). Sparse GPs enables us to trade off accuracy against complexity by approximating the full N -rank posterior with a low M -rank posterior where M is a set of inducing points and M N , which reduces complexity and storage to O(M2N) and O(M2) respectively. Hensman et al. (2013)’s sparse variational Gaussian processes (SVGP) extends Titsias (2009) to very large datasets by opting to not collapse the distribution of the inducing points, thus enabling stochastic optimisation to be used. However, we know of no existing approaches for learning sparse GPs given a fixed subset of points. We propose a framework for learning a sparse representative set of points conditioned on a subset of existing points. Next, we present two methods derived from the framework that enables sparse GPs to be learned given a fixed subset of points. The first straddles the divide between GP regression on a subset and full GP regression while the second learns a transformation from the set of sampled point to a set of representative points. We also explore the use of different priors for our methods and SVGP.
Role of Live-Duck Movement Networks in Transmission of Avian Influenza, France, 2016–2017

The relative roles that movement and proximity networks play in the spread of highly pathogenic avian influenza (HPAI) viruses are often unknown during an epidemic, preventing effective control. We used network analysis to explore the devastating epidemic of HPAI A(H5N8) among poultry, in particular ducks, in France during 2016–2017 and to estimate the likely contribution of live-duck movements. Approximately 0.2% of live-duck movements could have been responsible for between-farm transmission events, mostly early during the epidemic. Results also suggest a transmission risk of 35.5% when an infected holding moves flocks to another holding within 14 days before detection. Finally, we found that densely connected groups of holdings with sparse connections between groups overlapped farmer organizations, which represents important knowledge for surveillance design. This study highlights the importance of movement bans in zones affected by HPAI and of understanding transmission routes to develop appropriate HPAI control strategies.
Agile Knowledge Sharing

In today's economy, enterprises require knowledge more than ever before. Employees are being classified through their skill set and experience, where the tacit knowledge of individuals is the key factor. The effect of knowledge hunger can be easily seen in agile software development teams. To sustain the quality permanence of software development, it is essential to transform individuals' tacit knowledge to core organizational knowledge. To achieve this goal, every software development process utilizes different knowledge sharing and creation approaches. In this paper, knowledge sharing issues are surveyed and categorized into: 1) sociological issues, 2) documentation issues, and 3) implementation issues with/without pair programming. Finally, a proposed technique, Knowledge Temple, is introduced as feasible improvement to well-known knowledge sharing problems for small agile software development teams.
EMIGRATION FORCED BY CLIMATE CHANGE IMPACTS ON THE HYDROLOGY DUE TO THEIR RELIANCE UPON AGRICULTURE AS LIVELIHOOD

The vertiginous and unquestionable climate change could alter the delicate balance that exists between human beings and nature. Countries struggle to reach international agreements to set mitigation measures or adaptation to changing climatic condition; otherwise, autochthonous population in certain vulnerable regions could be forced to search better life conditions. They are poorest communities settled on ecologically fragile areas with a lack of livelihood alternatives, low income and their greater reliance on climate-sensitive sectors like an agriculture that is relied on low technology. The loss of hydrology resources decreases the agricultural productivity due to climate change. Several coupled systems, briefly mentioned climatology, livelihood resources and the individual dignity concern are studied in the MEDCHANGe project. In this context, this paper only tries the methodology to tackle one of the echelons of this complex causality chain. The connection between climatology and hydrology and consequently agriculture has been well studied in the literature. Some methods have been applied in numerous areas of the world where exogenous and endogenous variables have different weights according to the particular circumstances, which are determined by the soil, local climate, market economy involvement, among other factors. The objective is to advance in this causality chain, to connect with the social researching, by integrating the results in a mixed method (qualitative and quantitative). This is in order to find a more suitable model that could best reveal the causality or correlation between climate change and agriculture in the Marrakesh region. Agriculture is in fact the basis of the economy and the population welfare and, consequently, it can determine the proper conditions and hence evaluate the life dignity threshold that forces people to the exodus. The findings will be useful in the MEDCHANGe context, to confirm the hypothesis of a possible relationship between clime change and the people movement in the Mediterranean area. Therefore, among the factors intervening in the decision to emigrate, climate change phenomena should be considered in some measure. By isolating the factor impact, the method to mitigate the effects would be encouraging under this approach.
Epstein-Barr Virus in Oral Cavity Squamous Cell Carcinoma of Chilean Patients

Background: Squamous cell carcinoma is the most frequent malignancy in the oral cavity. Although the role of tobacco and alcohol in the process of carcinogenesis is widely considered, viral participation in carcinogenesis is scarcely studied. Our aim was to detect the Epstein-Barr virus presence in fresh tissue samples from adult patients with oral cavity Squamous Cell Carcinoma and its relationship with clinical and socio-epidemiological variables. Methods: A cross-sectional study was carried out for two years in all untreated patients derived to the Chilean National Cancer Institute. A fresh sample of the tumour was obtained for real time-polymerase chain reaction. Clinical and sociodemographic variables were recorded. To explore statistical associations, the p-value of 0.05 was used. Results: Twenty-two patients acceded to be enrolled, 54.5% of men. Fifteen biopsies (68%) were positive for the Epstein-Barr virus and 56% were located on the left edge of the tongue. No statistical association was found between the viral presence and the recorded variables. Male sex was associated with alcohol consumption, and the female sex was associated with tongue localization. Our mortality rate was 59%. Conclusion: This study showed a high prevalence of EBV in fresh Squamous Cell Carcinoma tumour tissue of the oral cavity of Chilean patients and a low survival rate. Oral cavity cancer is not comparable to pharyngeal. The lack of statistical association could be due to the sample size. Consequently, larger and multicentre studies are needed in our region. A worldwide common methodology as well as a new separate classification is desirable.
Natural Text Anonymization Using Universal Transformer with a Self-attention∗

The paper focuses on the anonymization of natural language text in Russian. The problem of anonymization is topical in connection with the need to conduct studies aimed at assessing the effectiveness and stability of methods of attribution of the text to its intentional distortion by various techniques of anonymization. The paper presents a technique for anonymizing a Russian text based on a fast correlation filter, dictionary synonymization and a universal transformer model with a self-attention mechanism. The automated system developed on its basis is tested on an experimental corpus of Russian texts. The texts obtained with its help are analyzed by the authorship identification system. The effectiveness of attribution of anonymous texts by a specialized software system was reduced to a level of random guessing, which allows to name the proposed methodology effective.