Text simplification is concerned with reducing the language complexity and improving the readability of professional content so that the text is accessible to readers at different ages and educational levels. As a promising practice to improve the fairness and transparency of text information systems, the notion of text simplification has been mixed in existing literature, ranging all the way through assessing the complexity of single words to automatically generating simplified documents. We show that the general problem of text simplification can be formally decomposed into a compact pipeline of tasks to ensure the transparency and explanability of the process. In this paper, we present a systematic analysis of the first two steps in this pipeline: 1) predicting the complexity of a given piece of text, and 2) identifying complex components from the text considered to be complex. We show that these two tasks can be solved separately, using either lexical approaches or the state-of-the-art deep learning methods, or they can be solved jointly through an end-to-end, explainable machine learning predictor. We propose formal evaluation metrics for both tasks, through which we are able to compare the performance of the candidate approaches using multiple datasets from a diversity of domains.

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in next-generation wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

In recent years, multi-dimensional online decision making has been playing a crucial role in many practical applications such as online recommendation and digital marketing. To solve it, we introduce stochastic low-rank tensor bandits, a class of bandits whose mean rewards can be represented as a low-rank tensor. We propose two learning algorithms, tensor epoch-greedy and tensor elimination, and develop finite-time regret bounds for them. We observe that tensor elimination has an optimal dependency on the time horizon, while tensor epoch-greedy has a sharper dependency on tensor dimensions. Numerical experiments further back up these theoretical findings and show that our algorithms outperform various state-of-the-art approaches that ignore the tensor low-rank structure.

We consider structural impulse response analysis for sparse high-dimensional vector autoregressive (VAR) systems. Since standard procedures like the delta-method do not lead to valid inference in the high-dimensional set-up, we propose an alternative approach. First, we directly construct a de-sparsified version of the regularized estimators of the moving average parameters that are associated with the VAR process. Second, the obtained estimators are combined with a de-sparsified estimator of the contemporaneous impact matrix in order to estimate the structural impulse response coefficients of interest. We show that the resulting estimator of the impulse response coefficients has a Gaussian limiting distribution. Valid inference is then implemented using an appropriate bootstrap approach. Our inference procedure is illustrated by means of simulations and real data applications.

Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a $5\times5$ grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfall

Distinguishing between individual animals is important for long-term monitoring of populations and protecting species from pressures such as climate change. However, it is also one of the most expensive, troublesome, and time-consuming aspects of animal behaviour research. While some creatures such as leopards have unique markings which allow humans to recognise individuals by eye, most species require additional visual identifiers such as coloured bands to be distinguished. Attaching bands to birds' legs can be stressful and disruptive to the animals, limiting the scope of research. Seeking an alternative method for distinguishing between individual birds, researchers from institutes in France, Germany, Portugal, and South Africa developed the first AI bird identification tool of its kind.

If you need a reason to feel good about the direction technology is going, look up Dell Technologies CTO John Roese on Twitter. The handle he composed back in 2006 is @theICToptimist. ICT stands for information and communication. This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review's editorial staff. "The reason for that acronym was because I firmly believed that the future was not about information technology and communication technology independently," says Roese, president and chief technology officer of products and operations at Dell Technologies. "It was about them coming together." Close to two decades later, it's hard not to call him right. Organizations are looking to the massive amounts of data they're collecting and generating to become fully digital, they're using the cloud to process and store all that data, and they're turning to new wireless technologies like 5G to power data-hungry applications such as artificial intelligence (AI) and machine learning. In this episode of Business Lab, Roese walks through this confluence of technologies and its future outcomes. For example, autonomous vehicles are developing fast, but fully driverless cars aren't plying are streets yet. And they won't until they tap into a "collaborative compute model"--smart devices that plug into a combination of cloud and edge-computing infrastructure to provide "effectively infinite compute." "One of the biggest problems isn't making the device smart; it's making the device smart and efficient in a scalable system," Roese says. So big things are ahead, but technology today is making huge strides, Roese says. He talks about machine intelligence, which taps AI and machine learning to mimic human intelligence and tackle complex problems, such as speeding up supply chains, or in health care, more accurately detecting tumors or types of cancer.

Huawei has launched a South African-based Cloud and Artificial Intelligence (AI) Innovation Centre to drive innovation, knowledge transfer and economic growth through app development in the AI industry. The announcement was made by Ray Rui, president of Huawei Cloud Africa region, during the Huawei Cloud Summit Africa 2020, an online event to unpack the opportunities of cloud computing for African business under the theme "Building an Intelligent Africa". "AI will be critical to social evolution and industrial growth in future," said Rui. "We also believe that when you grow economic opportunities, everyone benefits. For this reason, we are opening the Huawei Cloud & AI Innovation Centre to application developers across all economic sectors." The new centre will be based at Huawei's South African headquarters in Woodmead, Johannesburg, but developers across Africa will be able to access the centre remotely. It will teach AI application best practice, link developers to markets, support AI supply chains, develop talent and support application innovation.

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of COVID-19. Prediction of the spread can allow counter measures and actions to be implemented to mitigate the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models were trained and tested on novel coronavirus 2019 dataset, which contains 19.53 Million confirmed cases of COVID-19. Our proposed models were evaluated by using Mean Absolute Error and compared with baseline method. Our experimental results, both quantitative and qualitative, demonstrate the superior prediction performance of the proposed models.

With histograms as its foundation, we develop Categorical Exploratory Data Analysis (CEDA) under the extreme-$K$ sample problem, and illustrate its universal applicability through four 1D categorical datasets. Given a sizable $K$, CEDA's ultimate goal amounts to discover by data's information content via carrying out two data-driven computational tasks: 1) establish a tree geometry upon $K$ populations as a platform for discovering a wide spectrum of patterns among populations; 2) evaluate each geometric pattern's reliability. In CEDA developments, each population gives rise to a row vector of categories proportions. Upon the data matrix's row-axis, we discuss the pros and cons of Euclidean distance against its weighted version for building a binary clustering tree geometry. The criterion of choice rests on degrees of uniformness in column-blocks framed by this binary clustering tree. Each tree-leaf (population) is then encoded with a binary code sequence, so is tree-based pattern. For evaluating reliability, we adopt row-wise multinomial randomness to generate an ensemble of matrix mimicries, so an ensemble of mimicked binary trees. Reliability of any observed pattern is its recurrence rate within the tree ensemble. A high reliability value means a deterministic pattern. Our four applications of CEDA illuminate four significant aspects of extreme-$K$ sample problems.