Can New Modes of Digital Learning Help Resolve the Teacher Crisis in Sub-Saharan Africa?

Sub-Saharan Africa, more than any other part of the world, is experiencing a crisis in finding sufficiently qualified teachers to meet the needs of expanding school systems. The professional development support provided to serving teachers is also inadequate in most countries. The most recent data on learner outcomes has revealed a worrying picture of significant under-achievement across the region. This paper argues that the teacher education and training structures of the last century will never be able to meet urgent contemporary needs. Given population growth, especially among the young, large-scale expansion of the teaching force and the associated teacher education systems will be the norm through to the middle years of the century and beyond. In this context the paper argues for a significant policy shift to expand quality teacher education and professional support at scale through a more school-based and digitally supported network model of provision. Examples of current digital programmes within the region are considered as well as the new technologies that are emerging with relevance to teacher education. The paper suggests a three-phase process through which national governments might move in making the necessary changes in policy and practice

Neural processing of criticism and positive comments from relatives in individuals with schizotypal personality traits

Objectives. High negative expressed emotion by family members towards schizophrenia patients increases the risk of subsequent relapse. The study aimed to determine whether individuals with high schizotypy (HS) and low schizotypy (LS) would differ in activation of brain areas involved in cognitive control when listening to relative criticism

Functional connectivity changes and their relationship with clinical disability and white matter integrity in patients with relapsing-remitting multiple sclerosis

Background and objective: To define the pathological substrate underlying disability in multiple sclerosis by evaluating the relationship of resting-state functional connectivity with microstructural brain damage, as assessed by diffusion tensor maging, and clinical impairments. Methods: Thirty relapsing–remitting patients and 24 controls underwent 3T-MRI; motor abilities were evaluated by using measures of walking speed, hand dexterity and balance capability, while information processing speed was evaluated by a paced auditory serial addiction task. Independent component analysis and tract-based spatial statistics were applied to RS-fMRI and diffusion tensor imaging data using FSL software. Group differences, after dual regression, and clinical correlations were modelled with GeneralLinear-Model and corrected for multiple comparisons. Results: Patients showed decreased functional connectivity in 5 of 11 resting-state-networks (cerebellar, executive-control, medial-visual, basal ganglia and sensorimotor), changes in inter-network correlations and widespread white matter microstructural damage. In multiple sclerosis, corpus callosum microstructural damage positively correlated with functional connectivity in cerebellar and auditory networks. Moreover, functional connectivity within the medial-visual network inversely correlated with information processing speed. White matter widespread microstructural damage inversely correlated with both the paced auditory serial addiction task and hand dexterity. Conclusions: Despite the within-network functional connectivity decrease and the widespread microstructural damage, the inter-network functional connectivity changes suggest a global brain functional rearrangement in multiple sclerosis. The correlation between functional connectivity alterations and callosal damage uncovers a link between functional and structural connectivity. Finally, functional connectivity abnormalities affect information processing speed rather than motor abilities

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

A Computerized Tomographic Data Analysis System to Evaluate the Dental Implant Surface Roughness

AbstractDental implants have been progressively used in the recent years to support and retain dental prosthesis. Implant surface roughness has been suggested as a crucial factor in implant osseointegration and long term survival of the implant and prosthesis, where a key factor for the success or failure of dental implants is the manner in which stresses are transferred to surrounding bone. In this study completely edentulous patients were rehabilitated by implant retained over denture in which two implant systems with different surface roughness were used. Peri implant bone density in Hounsfield Units (HU) was evaluated by analyze Computerized Tomographic (CT) images to judge the behavior of an implant system under functional loading, where DICOM raw data was imported into the analysis proposed system to correlate the bone density regarding to the HU values. Results are compared with clinical readings and previous findings, which it showed that there is a difference in peri implant bone density around regularly patterned and randomly patterned implant surfaces

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models.Comment: Under Review IEEE TM

Changes in brain activity in response to problem solving during the abstinence from online game play

Background and aims: Several studies have suggested that addictive disorders including substance abuse and pathologic gambling might be associated with dysfunction on working memory and prefrontal activity. We hypothesized that excessive online game playing is associated with deficits in prefrontal cortex function and that recovery from excessive online game playing might improve prefrontal cortical activation in response to working memory stimulation. Methods: Thirteen adolescents with excessive online game playing (AEOP) and ten healthy adolescents (HC) agreed to participate in this study. The severity of online game play and playing time were evaluated for a baseline measurement and again following four weeks of treatment. Brain activation in response to working memory tasks (simple and complex calculations) at baseline and subsequent measurements was assessed using BOLD functional magnetic resonance imaging (fMRI). Results: Compared to the HC subjects, the AEOP participants exhibited significantly greater activity in the right middle occipital gyrus, left cerebellum posterior lobe, left premotor cortex and left middle temporal gyrus in response to working memory tasks during baseline measurements. After four weeks of treatment, the AEOP subjects showed increased activity within the right dorsolateral prefrontal cortex and left occipital fusiform gyrus. After four weeks of treatment, changes in the severity of online game playing were negatively correlated with changes in the mean β value of the right dorsolateral prefrontal cortex in response to complex stimulation. Conclusions: We suggest that the effects of online game addiction on working memory may be similar to those observed in patients with substance dependence