A GROUNDED THEORY STUDY OF MATERNAL ENGAGEMENT OF LOW INCOME, YOUNG RURAL MOTHERS IN HOME-BASED, EARLY INTERVENTION SERVICES

Home-based, early intervention programs as a preferred delivery model are widely endorsed, heavily funded and highly utilized as prevention and remediation initiatives for families with young children (Eckenrode, et al., 2010; Karoly, Killion, & Cannon, 2005). Of concern, is that while an estimated 40 billion dollars are spent annually between federally funded programs and private foundations, a significant number of families disengage from services before the end of a child’s eligibility period (Stevens, Ammerman, Putnam, Gannon, & van Ginkel, 2005). Several meta-analyses indicate only modest effectiveness of home-based services (Tandon, et al., 2008). It is estimated that well over 500,000 families enroll in home-based services each year; however, retaining these enrollees in consistent and prolonged intervention is a definite challenge (Ammerman et al, 2006). While home-based services are widely recommended to families, the families’ perspective about having program personnel come to their home several times per month has not been well represented in the literature on home-based services. The purpose of this qualitative, grounded theory study was to discover a central theory that explains the decisions young, low-income, rural mothers make about engagement in home-based, early intervention services. Nine women who were custodial parents of children enrolled in an early literacy, home-based program participated in in-depth interviews conducted over multiple sessions. A semi-structured interview and graphical interview elicitation method of drawing a timeline were used to collect data. Line by line coding using participants’ words was utilized during open coding. Axial coding helped make apparent 69 categories. Using selective coding, five primary themes and a core category emerged. Verification of findings was accomplished by use of multiple sources of data, a clear audit trail and thick, rich description. The data revealed that young, low income mothers may not be prepared for the responsibilities that come with assuming the mothering role and are ambivalent about letting strangers into their homes. As the home visitors formed positive relationships with the child and the mother, the women in this study made the decision to continue with services because the child enjoyed the home visitor and the activities and because the home visitor also fulfilled the mothers’ needs for social contact and a connection to community resources. By interacting and partnering with home visitors, the mothers came to believe that being a mother helped them grow into a better person. Mothers expressed a desire for a better future for their children than they themselves were currently experiencing. Part of carving out that better future for the children necessitated that they allow home visitors help with the education of their children even though some mothers did not necessarily like making their home space more public. The results indicate for these mothers, part of becoming a mother entailed delaying their own dreams and goals until their children were older but that they also held onto hope for a future more focused on themselves

Intelligent Detection for Cyber Phishing Attacks using Fuzzy rule-Based Systems

Cyber phishing attacks are increasing rapidly, causing the world economy monetary losses. Although various phishing detections have been proposed to prevent phishing, there is still a lack of accuracy such as false positives and false negatives causing inadequacy in online transactions. This study constructs a fuzzy rule model utilizing combined features based on a fuzzy inference system to tackle the foreseen inaccuracy in online transactions. The importance of the intelligent detection of cyber phishing is to discriminate emerging phishing websites with a higher accuracy. The experimental results achieved an excellent accuracy compared to the reported results in the field, which demonstrates the effectiveness of the fuzzy rule model and the feature-set. The findings indicate that the new approach can be used to discriminate between phishing and legitimate websites. This paper contributes by constructing a fuzzy rule model using a combined effective feature-set that has shown an excellent performance. Phishing deceptions evolve rapidly and should therefore be updated regularly to keep ahead with the changes

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward

Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available

Live video transmission over data distribution service with existing low-power platforms

This paper investigates video transmission over a middleware layer based on the Object Management Group’s Data-Distribution Service (DDS) standard, with a focus on low power platforms. Low power platforms are being widely utilised to implement IoT devices. One important type of IoT application is live video sharing which requires higher bandwidth than current typical applications. However, only limited research has been carried out on quality of services of data distribution utilising low end platforms. This paper discusses the development of prototypes that consist of both a Raspberry Pi 2 and an Android smartphone with client applications using Prismtech’s Vortex line of DDS middleware. Experiments have yielded interesting performance results: DDS middleware implementations that run on low power hardware with native code can provide sufficient performance. They are efficient enough to consistently handle high bandwidth live video with the network performance proving to be the bottleneck rather than the processing power of the devices. However, virtual machine implementations on an Android device did not achieve similar performance levels. These research findings will provide recommendations on adopting low power devices for sharing live video distribution in IoT over DDS middleware

Time-Resolved Studies of a Rolled-Up Semiconductor Microtube Laser

We report on lasing in rolled-up microtube resonators. Time-resolved studies on these semiconductor lasers containing GaAs quantum wells as optical gain material reveal particularly fast turn-on-times and short pulse emissions above the threshold. We observe a strong red-shift of the laser mode during the pulse emission which is compared to the time evolution of the charge-carrier density calculated by rate equations

Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support

Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level

Kodierung von Gaußmaßen

Es sei $gamma$ ein Gaußmaß auf der Borelschen $sigma$-Algebra $mathcal B$ des separablen Banachraums $B$. Für $X:Omega o B$ gelte $P_X=gamma$. Wir untersuchen den mittleren Fehler, der bei Kodierung von $gamma$ respektive $X$ mit $Ninmathbb N$ Punkten entsteht, und bestimmen untere und obere Abschätzungen für die Asymptotik ($N oinfty$) dieses Fehlers. Hierbei betrachten wir zu $r>0$ Gütekriterien wie folgt: Deterministische Kodierung $delta_2(N,r) := inf_{y_1,ldots,y_Nin B}Emin_{k=1,ldots,N}X-y_k^r.$ Zufällige Kodierung $delta_3(N,r) := inf_ u Emin_{k=1,ldots,N}X-Y_k^r.$ Die $(Y_k)$ seien hierbei i.i.d., unabhängig von $X$, und nach $u$ verteilt. Das Infimum wird über alle Wahrscheinlichkeitsmaße $u$ gebildet. Für das Gütekriterium $delta_4(cdot,r)$ wird ausgehend von der Definition von $delta_3(cdot,r)$ $u$ nicht optimal, sondern $u=gamma$ gewählt. Das Gütekriterium $delta_1(cdot,r)$ ergibt sich aus der Quellkodierungstheorie nach Shannon. Es gilt $delta_1(cdot,r) le delta_2(cdot,r) le delta_3(cdot,r) le delta_4(cdot,r).$ Wir stellen folgenden Zusammenhang zwischen der Asymptotik von $delta_4(cdot,r)$ und den logarithmischen kleinen Abweichungen von $gamma$ her: Es gebe $kappa,a>0$ und $binR$ mit psi(varepsilon) := -log P{X1.Let $gamma$ be a Gaussian measure on the Borel $sigma$-algebra $mathcal B$ of the separable Banach space $B$. Let $X:Omega o B$ with $P_X=gamma$. We investigate the average error in coding $gamma$ resp. $X$ with $Ninmathbb N$ points and obtain lower and upper bounds for the error asymptotics ($N oinfty$). We consider, given $r>0$, fidelity criterions as follows: Deterministic Coding $delta_2(N,r) := inf_{y_1,ldots,y_Nin B}Emin_{k=1,ldots,N}X-y_k^r.$ Random Coding $delta_3(N,r) := inf_ u Emin_{k=1,ldots,N}X-Y_k^r.$ The $(Y_k)$ above are i.i.d., independent of $X$, and distributed according to $u$. The infimum is taken with respect to all probability measures $u$. For the fidelity criterion $delta_4(cdot,r)$, starting from the definition of $delta_3(cdot,r)$, $u$ is not chosen optimal, but as $u=gamma$. The fidelity criterion $delta_1(cdot,r)$ is given according to the source coding theory of Shannon. The fidelity criterions are connected through $delta_1(cdot,r) le delta_2(cdot,r) le delta_3(cdot,r) le delta_4(cdot,r).$ We obtain the following connection between the asymptotics of $delta_4(cdot,r)$ and the den logarithmic small deviations of $gamma$: Let $kappa,a>0$ and $binR$ with psi(varepsilon) := -log P{X1