Miracle-dz/UniFAR-FTUs huggingface dataset:
The 'Teen Triple P' Positive Parenting Program: A Preliminary Evaluation

Adolescents who develop severe conduct disorders are at greater risk of becoming involved in juvenile crime, including property crime, interpersonal violence, theft, arson and illegal substance use. Prior research has found that dysfunctional parenting practices often place children at risk of developing conduct problems and are among the strongest predictors of later delinquent behaviour. Various programs have been developed to assist parents in improving their parenting skills. This paper evaluates one such program, the ‘Teen Triple P’ Positive Parenting Program. Preliminary results suggest positive outcomes for most participating parents. There have been significant reductions in a variety of risk factors, with some evidence of improvements still being maintained after six months. Further extensive evaluations are being undertaken to assess the reliability of these preliminary findings.
Deficiency in Cardiolipin Reduces Doxorubicin-Induced Oxidative Stress and Mitochondrial Damage in Human B-Lymphocytes

Cardiolipin (CL) is an inner mitochondrial membrane phospholipid which plays an important role in mitochondrial function. Perturbation in CL biosynthesis alters mitochondrial bioenergetics causing a severe genetic disorder commonly known as Barth syndrome. Barth syndrome patients are known to have a reduced concentration and altered composition of CL. Cardiolipin is also known to have a high affinity for the chemotherapeutic agent doxorubicin (Dox), resulting in an extensive mitochondrial accumulation of the drug. Our results indicate that B-lymphocytes from healthy individuals are more sensitive to Dox-induced oxidative stress and cellular toxicity compared to the B-lymphocytes from Barth syndrome as indicated by greater cell death and greater level of cleaved caspase-3 following Dox treatment. Barth lymphocytes, when compared to healthy lymphocytes, showed a greater basal level of mitochondrial reactive oxygen species (mito-ROS), yet exhibited a lower level of induced mito-ROS production in response to Dox. Significantly less ATP content and slightly greater OXPHOS protein levels were detected in healthy cells compared to Barth cells after Dox treatment. Consistent with greater mitochondrial ROS, treatment with Dox induced a higher level of lipid peroxidation and protein carbonylation in healthy lymphocytes compared to Barth lymphocytes. The final remodeling of CL during CL synthesis is catalyzed by the tafazzin protein. Knockdown of tafazzin gene in H9c2 cardiomyocytes using siRNA showed decreased oxidant-induced damage, as observed in Barth lymphocytes. Our findings demonstrate that a deficiency in CL might provide a therapeutic advantage in favor of oxidant-induced anticancer activities.
Harmony Search Based Supervised Training of Artificial Neural Networks

This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation, we propose a training technique where two of HS probabilistic parameters are determined dynamically based on the best-to-worst (BtW) harmony ratio in the current harmony memory instead of the improvisation count. This would be more suitable for ANN training since parameters and termination would depend on the quality of the attained solution. We have empirically tested and verified our technique by training an ANN with a benchmarking problem. In terms of overall training time and recognition, our results have revealed that our method is superior to both the original improved HS and standard Backpropagation.
Secure Machine Learning, a Brief Overview

The purpose of this article is to give a brief overview on the current work towards the emerging research problem of secure machine learning. Machine learning technique has been applied widely in various applications especially in spam detection and network intrusion detection. Most existing learning schemes assume that the environment they settle in is benign. However this is not always true in the real adversarial decision-making situations where the future data sets and the training data set are no longer from the same population, due to the transformations employed by the adversaries. As more and more machine learning systems are put into use, it is imperative to consider the security of the machine learning system. As a emerging problem, it is attracting more and more researchers' attention. In this article, we present a brief overview on secure machine learning and current progress on developing secure machine learning algorithms.
System identification and robust anti-windup control for a helium liquifier

This paper addresses the system identification and advanced control of a helium (He) liquifier supplying cooling power at liquid helium (LHe) temperatures (-269 °C). To study the dynamic response to heat load variation, a He liquifier simulation model is utilized. The main focus is to regulate the discharge pressure of the compressor station to guarantee stable system operation. System identification is first conducted to obtain plant models, and a two degree of freedom H∞ controller is designed to achieve regulation. Moreover, saturation of the control valve is compensated via an anti-windup technique, which is suitable for regulation problem with disturbance rejection. The effectiveness of the proposed control designs is demonstrated by dynamical simulations in EcosimPro (EA International) software.
Explaining Political Engagement with Online Panels Comparing the British and American Election Studies

Online surveys have seen a rapid growth in the past decade and are now frequently being used for electoral research. Although they have obvious advantages, it is unclear whether the data produce inferences similar to more traditional face-to-face surveys, particularly when response to the survey is correlated with the survey variables of interest. Drawing on data from the latest American National Election Study and British Election Study, we examine how age affects political engagement, comparing responses between face-to-face and online surveys. The results indicate that online surveys, particularly those where respondents have opted in, reduce variance and overestimate the proportion of those who are politically engaged, which produces different conclusions about what motivates citizens to vote. These findings suggest a need to acknowledge selection bias when examining questions about political engagement, particularly when it comes to election surveys that rely on opt-in panels that are more likely to attract those who are interested in the subject matter and thus more politically engaged.
Simulation Study and Implementation on Routing Protocols in MANET

Ad-hoc networks are characterized by a lack of infrastructure, and by a random and quickly changing network topology; thus the need for a robu st dynamic routing protocol that can accommodate such an environment. Consequently, many routing algorithms have come in to existence to satisfy the needs of communications in such networks. This article presents a simulation study and comparison the performance between two categories of routing protocols, table-driven (Proactive) and on-demand (Reactive) routing protocols, this two categories were illustrated by using two different examples o f routing protocols, first example is DSDV (Destination Sequenced Distance-Vector) from the Proactive family and the second example is AODV (Ad Hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing Protocol) from the Reactive family. Both protocols were simulated by using NS-2 (network simulator-2) package. Both routing protocols were compared in te rms of average throughput (packets delivery ratio) while varying data rate, TCP types ,maxi mum packets in queue and number of packet drop while varying data rate by using the Trace fil e. For the same queue length DSDV performs better than AODV. AODV is preferred due to its more efficient use of bandwidth.
Analysis of attackers against windows emulating honeypots in various types of networks and regions

The paper is devoted to an analysis of a one-year-long period of operation of a honeynet composed of 6 Dionaea honeypots emulating Windows services. The analysis focused on the frequency of attacks according to the location of individual honeypots (sensors) as well as to the geographical location of attackers. From the statistical processing of the results, it was demonstrated that the most frequently attacking malware was well-known Conficker worm. Moreover, attacking OS were studied with the conclusion that Windows is the most frequent OS. Regarding the geographical location of the attackers, several non-western countries and autonomous systems were indicated as being the most frequent origin of the attacks.
Microbial Community in a Biofilter for Removal of Low Load Nitrobenzene Waste Gas

To improve biofilter performance, the microbial community of a biofilter must be clearly defined. In this study, the performance of a lab-scale polyurethane biofilter for treating waste gas with low loads of nitrobenzene (NB) (< 20 g m-3 h-1) was investigated when using different empty bed residence times (EBRT) (64, 55.4 and 34 s, respectively). In addition, the variations of the bacterial community in the biofilm on the longitudinal distribution of the biofilters were analysed by using Illumina MiSeq high-throughput sequencing. The results showed that NB waste gas was successfully degraded in the biofilter. High-throughput sequencing data suggested that the phylum Actinobacteria and genus Rhodococcus played important roles in the degradation of NB. The variations of the microbial community were attributed to the different intermediate degradation products of NB in each layer. The strains identified in this study were potential candidates for purifying waste gas effluents containing NB.
Using bdds to verify multipliers

Bryant’s Binary Decision Diagrams (BDDs) [6] have been successfully used for verifying combinational circuits [12, 181. However, multiplier circuits are difficult to verify using BDDs since the size of the BDD representing multiplication grows exponentially in the number of input bits [6, 71. This paper presents a method for using BDDs to verify multipliers that avoids this exponential complexity. The method has been used to verify a 16 by 16 bit combinational multiplier, the C6288 circuit from the ISCAS85 benchmarks [5]. This is the only ISCAS85 benchmark circuit that could not be verified using the techniques described by Fujita et al. [12] and Malik et al. [la].
Type-2 Fuzzy System Based Blood Pressure Parameters Estimation

Blood pressure parameters; Systolic, Diastolic and Mean Blood Pressure, have high correlation relationship with heart rate, but this relationship is nonlinear, multimode, and ambiguity. Type-2 Fuzzy is an intelligent technique and it has high capability to deal with the problem with high uncertainty that blood pressure parameters with heart rate relationship can be classify under them. This paper presents an Interval Type-2 Fuzzy System to estimate Systolic, Diastolic and Mean blood pressure; based on the relationship between systolic, diastolic and mean blood pressures and heart rate. The Interval Type-2 Fuzzy System is used to estimate systolic, diastolic and mean blood pressures values of thirty cases. The results are compared with real reading of systolic, diastolic and mean blood pressure and very encouraging results are achieved; the absolute value of mean of difference between real systolic, diastolic and mean blood pressures and estimated systolic, diastolic and mean blood pressures are less than 5 mmHg.
Vigilance analysis based on fractal features of EEG signals

Fatigue driving is an important factor leading to fatal traffic accidents. Many different approaches have been studied to detect low level vigilance of drivers. Electroencephalogram (EEG) has been approved an effective medium to measure human vigilance. Fractal dimension (FD) is considered as a useful indicator of the complexity of physiological signal, and maximum fractal length (MFL) is reported to be a practical indicator of the level of human activity. In this paper, we extract above fractal features and linear features from each epoch of EEG data into feature vectors, and then apply Random Forest to the feature reduction and the classification of three different vigilance levels. The result shows that fractal features are more powerful than linear features, the classification accuracy of fractal features reaches 92% on average.
Euro Crisis and plurality: Does the political orientation of newspapers determine the selection and spin of information?

This article studies the impact of right and left moderate political orientation of newspapers on the levels of plurality in the news coverage of the Euro Crisis in 20 newspapers from 10 European countries through a methodology based on Simpson’s D index. The expectation of finding distinct patterns of coverage leading to high levels of plurality was not fully supported and the results have shown that national frames influence levels of overall plurality more than political ideology.
The Controversial Clinicobiological Role of Breast Cancer Stem Cells

Breast cancer remains a leading cause of morbidity and mortality in women mainly because of the propensity of primary breast tumors to metastasize. Growing experimental evidence suggests that cancer stem cells (CSCs) may contribute to tumor progression and metastasis spread. However, despite the tremendous clinical potential of such cells and their possible therapeutic management, the real nature of CSCs remains to be elucidated. Starting from what is currently known about normal mammary stem/progenitor cells, to better define the cell that originates a tumor or is responsible for metastatic spread, this review will discuss experimental evidence of breast cancer stem cells and speculate about the clinical importance and implications of their evaluation.
Variable momentum factor algorithm for nonlinear principle component analysis

In this paper, a variable momentum factor algorithm is presented for improving the performance of the momentum term based nonlinear principle component analysis (PCA). Firstly, a smoothed error function is defined to describe the estimation error between the estimated separating matrix and its optimal value. Then, using a nonlinear function, the variable momentum factor is obtained according to the smoothed error function. Computer simulation results of adaptive blind source separation demonstrate that the proposed approach leads to faster convergence rate and lower misadjustment error than the momentum nonlinear PCA just with small increase in computational complexity.
Functional Assays and Metagenomic Analyses Reveals Differences between the Microbial Communities Inhabiting the Soil Horizons of a Norway Spruce Plantation

In temperate ecosystems, acidic forest soils are among the most nutrient-poor terrestrial environments. In this context, the long-term differentiation of the forest soils into horizons may impact the assembly and the functions of the soil microbial communities. To gain a more comprehensive understanding of the ecology and functional potentials of these microbial communities, a suite of analyses including comparative metagenomics was applied on independent soil samples from a spruce plantation (Breuil-Chenue, France). The objectives were to assess whether the decreasing nutrient bioavailability and pH variations that naturally occurs between the organic and mineral horizons affects the soil microbial functional biodiversity. The 14 Gbp of pyrosequencing and Illumina sequences generated in this study revealed complex microbial communities dominated by bacteria. Detailed analyses showed that the organic soil horizon was significantly enriched in sequences related to Bacteria, Chordata, Arthropoda and Ascomycota. On the contrary the mineral horizon was significantly enriched in sequences related to Archaea. Our analyses also highlighted that the microbial communities inhabiting the two soil horizons differed significantly in their functional potentials according to functional assays and MG-RAST analyses, suggesting a functional specialisation of these microbial communities. Consistent with this specialisation, our shotgun metagenomic approach revealed a significant increase in the relative abundance of sequences related glycoside hydrolases in the organic horizon compared to the mineral horizon that was significantly enriched in glycoside transferases. This functional stratification according to the soil horizon was also confirmed by a significant correlation between the functional assays performed in this study and the functional metagenomic analyses. Together, our results suggest that the soil stratification and particularly the soil resource availability impact the functional diversity and to a lesser extent the taxonomic diversity of the bacterial communities.
Risk Management for IT Security: When Theory Meets Practice

A Layer-Based Risk Tool (LBRT) for IT security management in a corporate environment is presented and discussed. The Risk-Rank algorithm is modified for implementation in this tool by taking practical considerations into account. The focus is shifted to a security requirement-based approach during actual assessment of operational risk in the organization and absolute risk values are computed instead of relative risk probabilities. In addition, a risk mitigation algorithm is proposed to find the optimum set of measures under certain budget constraints. A dynamic programming formulation is presented and a shortest path solution is obtained based on Dijkstra's algorithm. The risk assessment and mitigation algorithms are illustrated and evaluated with numerical examples.
Students’ ethical and professional perceptions of earnings management

We examine the influence of two treatments on students’ perceptions of earnings management. We find that student reading assignments on earnings management and professionalism topics followed by individual testing do not alter student-reported beliefs. However, when the same students process and report their beliefs about earnings management practices in a group setting, the results are different from their initially reported individual beliefs. These statistically significant results suggest that a group dynamic that involves students in the learning process, may be effective in influencing the ethical judgments and perceptions of our future business professionals.
Defining Synphenotype Groups in Xenopus tropicalis by Use of Antisense Morpholino Oligonucleotides

To identify novel genes involved in early development, and as proof-of-principle of a large-scale reverse genetics approach in a vertebrate embryo, we have carried out an antisense morpholino oligonucleotide (MO) screen in Xenopus tropicalis, in the course of which we have targeted 202 genes expressed during gastrula stages. MOs were designed to complement sequence between −80 and +25 bases of the initiating AUG codons of the target mRNAs, and the specificities of many were tested by (i) designing different non-overlapping MOs directed against the same mRNA, (ii) injecting MOs differing in five bases, and (iii) performing “rescue” experiments. About 65% of the MOs caused X. tropicalis embryos to develop abnormally (59% of those targeted against novel genes), and we have divided the genes into “synphenotype groups,” members of which cause similar loss-of-function phenotypes and that may function in the same developmental pathways. Analysis of the expression patterns of the 202 genes indicates that members of a synphenotype group are not necessarily members of the same synexpression group. This screen provides new insights into early vertebrate development and paves the way for a more comprehensive MO-based analysis of gene function in X. tropicalis.
Business process management in small- and medium-sized enterprises: an empirical study

This study aims to focus on the examination of business process management (BPM) maturity levels within small-and medium-sized enterprises (SME) and their relationship with the company's economic success. A further aim is to evaluate the understanding of the term BPM in the field. The findings result from a mixed-method approach combining qualitative and quantitative empirical methods. The qualitative study collected data carrying out 20 interviews in 11 enterprises. On the basis of these findings, a web-questionnaire for the quantitative study was developed. In order to evaluate the maturity levels, an adapted version of Hammers Process and Enterprise Maturity Model (PEMM) had to be developed. Data collected within quantitative research suggests higher maturity levels in comparison with data collected within qualitative research. A weak statistical connection between maturity level and economic success can be found. However, the data suggest that there is a lack in common understanding of the term business process management in praxis. Due to the reactivity of qualitative interviews a different sample may provide other findings. Whilst accessibility is a considerable advantage of web-surveys, a disadvantage of web-surveys is the lack of regulation of the sample. Hence, the findings of this study are not statistically representative for all SMEs.
Multi-engine ASP solving with policy adaptation

The recent application of Machine Learning techniques to the Answer Set Programming (ASP) field proved to be effective. In particular, the multi-engine ASP solver ME-ASP is efficient: it is able to solve more instances than any other ASP system that participated to the 3rd ASP Competition on the "System Track" benchmarks. In the ME-ASP approach, classification methods inductively learn off-line algorithm selection policies starting from both a set of features of instances in a training set, and the solvers performance on such instances. In this paper we present an improvement to the multi-engine framework of ME-ASP , in which we add the capability of updating the learned policies when the original approach fails to give good predictions. An experimental analysis, conducted on training and test sets of ground instances obtained from the ones submitted to the "System Track" of the 3rd ASP Competition, shows that the policy adaptation improves the performance of ME-ASP when applied to test sets containing domains of instances that were not considered for training.