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Polyvictimization means looking at multiple victimizations of different kinds that one person has experienced. Virtually, all of the work in this field focuses on the effects of childhood trauma and victimization on currently distressed children, and empirical and theoretical work on the intertwining of adult female offline and online abuse experiences is in short supply. Recently, however, some scholars are starting to fill these research gaps by generating data showing that technology-facilitated violence and abuse are part and parcel of women's polyvictimization experiences at institutions of higher education. This chapter provides an in-depth review of the extant social scientific literature on the role technology-facilitated violence and abuse plays in the polyvictimization of female college/university students. In addition to proposing new ways of knowing, we suggest progressive policies and practices aimed at preventing polyvictimization on the college campus.

This chapter reviews successful pedagogical interventions in teaching literacy in languages of teaching and learning in the upper primary grades in low- and middle-income countries (LMICs) and explores how researchers and teachers decolonized classroom spaces to ensure student achievement of learning outcomes. Themes emerging from the analysis of data are biliteracy interventions, interventions in official and national languages, teacher professional development, and ecological interventions. Results of the review indicate that researchers decolonized classroom spaces by using student linguistic repertoires, evidence-based pedagogical strategies student cultural capital and engaging families and communities. The review also reveals that more research needs to be conducted on teaching and learning in upper primary grades. The linguistic diversity of LMICs provides rich contexts for more research in bilingual education and L2 acquisition which could be useful worldwide as stakeholders in the education process explore the best ways to improve learning outcomes in schools.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

While digital technologies have led to many important social and cultural advances worldwide, they also facilitate the perpetration of violence, abuse and harassment, known as technology-facilitated violence and abuse (TFVA). TFVA includes a spectrum of behaviors perpetrated online, offline, and through a range of technologies, including artificial intelligence, livestreaming, GPS tracking, and social media. This chapter provides an overview of TFVA, including a brief snapshot of existing quantitative and qualitative research relating to various forms of TFVA. It then discusses the aims and contributions of this book as a whole, before outlining five overarching themes arising from the contributions. The chapter concludes by mapping out the structure of the book.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.