Predicting discomfort glare in open-plan offices is a challenging problem since most of available glare metrics are developed for cellular offices which are typically daylight dominated. The problem with open-plan offices is that they are mainly dependent on electric lighting rather than daylight even when they have a fully glazed facade. In addition, the contrast between bright windows and the buildings interior can be problematic and may cause discomfort glare to the building occupants. These problems can affect occupant productivity and wellbeing. Thus, it is important to develop a predictive model to avoid discomfort glare when designing open plan offices. To the best of our knowledge, we are the first to adopt Machine Learning (ML) models to predict discomfort glare. In order to develop new glare predictive models for these types of offices, Post-Occupancy Evaluation (POE) and High Dynamic Range (HDR) images were collected from 80 occupants (n=80) in four different open-plan offices. Consequently, various multi-region luminance values, luminance and glare indices were calculated and used as input features to train ML models. The accuracy of the ML model was compared to the accuracy of 24 indices which were also evaluated using a Receiver Operating Characteristic (ROC) analysis to identify the best cutoff values (thresholds) for each index for open-plan configurations. Results showed that the ML glare model could predict glare in open-plan offices with an accuracy of 83.8% (0.80 true positive rate and 0.86 true negative rate) which outperformed the accuracy of the previously developed glare metrics.

Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson-Gamma models are able to derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules for the posterior parameters. They also fail to share the update information to better cope with the data sparsity. Moreover, these models are not endowed with a component that handles the imbalance in count data values. In this paper, we propose a novel variational auto-encoder framework called VAE-BPTF which addresses the above issues. It uses multi-layer perceptron networks to encode and share complex update information. The encoded information is then reweighted per data instance to penalize common data values before aggregated to compute the posterior parameters for the latent factors. Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values. It also outperformed current models in both reconstruction errors and latent factor (semantic) coherence across five real-world datasets. Furthermore, the latent factors inferred by VAE-BPTF are perceived to be meaningful and coherent under a qualitative analysis.

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.

ACCRA - Artificial intelligence took center stage as African female technology experts met at Women in Tech Week in Ghana to promote women's involvement in the field. When Lily Edinam Botsyoe was studying computer science at a university in Ghana, students wrote programming codes on a whiteboard because there were not enough computers. This made it difficult to apply the coding skills they were learning, she says, and the problem continues today. "We have students coming out of schools having the theoretical background -- which is very important because you can't actually appreciate something practical if you don't have the theory. But, the industry-ready skills is lacking because they didn't have the hands-on experience," Botsyoe said.

Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resource-limited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics.

The first decade of the 21st century introduced us to sweeping mobile and social revolutions largely driven by names like Jobs, Zuckerberg and Bezos. In the second decade that's now closing, things got a little more… complicated. During those years, a new collection of faces have joined the earlier tech titans to continue moving us into the future. A person wears a Guy Fawkes mask, which today is a trademark and symbol for the online hacktivist group Anonymous. More a decentralized collective than a personality, Anonymous was the name claimed by the loose affiliation of hackers who brought "hacktivism" into the mainstream. During the first half of the decade, Anonymous launched attacks against targets like ISIS, the governments of the US and Tunisia, and corporations such as Sony and PayPal.

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) to combine a meta-learning strategy into multi-task models. In the first step, it searches for a global pattern by optimising the general parameters with gradient descents under constraints, which is a geological regularizer to enable model learning with less training data. In the second step, we derive an optimised model on each specific task from the global pattern with only a few local training data. Compared with traditional multi-task learning methods, SUM shows advantages of generalisation ability on distant tasks. It can be applied on any multi-task models with the gradient descent as its optimiser regardless if the prediction function is linear or not. Moreover, we can harness the model to enable traditional prediction model to make coKriging. The experiments on public datasets have suggested that our framework, when combined with current multi-task models, has a conspicuously better prediction result when the task number is small compared to low-rank tensor learning, and our model has a quite satisfying outcome when adjusting the current prediction models for coKriging.

As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions. Often these interactions form a multigraph, where we might have multiple interactions between two entities. Such multigraphs tend to be sparse yet structured, and their distribution often evolves over time. Existing statistical models with interpretable parameters can capture some, but not all, of these properties. We propose a dynamic nonparametric model for interaction multigraphs that combines the sparsity of edge-exchangeable multigraphs with dynamic clustering patterns that tend to reinforce recent behavioral patterns. We show that our method yields improved held-out likelihood over stationary variants, and impressive predictive performance against a range of state-of-the-art dynamic graph models.

Jeremy Bloom and his polite' family sometimes tell Amazon Alexa to zip it.' Jeremy Bloom has a polite family. But after a few frustratingly failed attempts in which Bloom politely asked Alexa to turn down the volume at dinner time, he shouted instead, "Alexa, zip it." "To our surprise, the music immediately stopped," says the Pittsburgh-area commercial lender. "We got a huge laugh out of that. And while not the best lesson in manners for the kids, it is common for us to tell Alexa to'zip it' now."

Is Kenya ready for five 5G network? With Asia and other continents of the world taking steps to become a global leader in 5G and the Advanced Intelligence (AI) technology, Kenya will be ranked one of the first East African countries to tap into this, with the completion of the Data Centre by Huawei in Konza Smart City. This would help to revolutionize several industries, including manufacturing, agriculture, transport, health sector, making factory automation, as well as communication between self-driving vehicles to regulate traffic. According to Ms Pamela Tutui, a director at Konza Technopolis Development Authority (KoTDA), Kenya is set to borrow ideas from a campus in China's Shenzhen City to lift Konza Smart City to a technology hub in Africa. "Smart cities is bringing solutions to a city that is not smart. Like in Nairobi what is stopping us from having street lights, from looking at our road networks and to go beyond just taxis and looking into our busing lanes," says Ann Theresse Jatta Ndong – Director UNESCO regional Eastern Africa.