Last week's column – headlined "Hospital hazard management" – was written to try to change the direction of the conversation about the non-compliance with fire-safety rules by some public institutions that The Sunday Gleaner started. Today's will attempt to alter the direction of the discussion that Prime Minister Andrew Holness amplified last Tuesday in Parliament about the financial impact of the heavy rainfall that has been affecting the island during the latter part of October. Estimates suggest that the costs will rise sharply above the initial $2.7 billion. It is raining heavily at the time of writing. Water is accumulating in my backyard.

The reinforcement techniques enhance combat training for both virtual … Integration of machine learning and geospatial analysis with the military's …

INTRODUCTION: This was a feasibility pilot study aiming to develop and validate an activity recognition system based on a custom-made body-fixed sensor and driven by an algorithm for recognising basic kinetic movements in military personnel. The findings of this study are deemed essential in informing our development process and contributing to our ultimate aim which is to develop a low-cost and easy-to-use body-fixed sensor for military applications. METHODS: Fifty military participants performed a series of trials involving walking, running and jumping under laboratory conditions in order to determine the optimal, among five machine learning (ML), classifiers. Thereafter, the accuracy of the classifier was tested towards the prediction of these movements (15 183 measurements) and in relation to participants' gender and fitness level. RESULTS: Random forest classifier showed the highest training and validation accuracy (98.5% and 92.9%, respectively) and classified participants with differences in type of activity, gender and fitness level with an accuracy level of 83.6%, 70.0% and 62.2%, respectively.

Artificial intelligence has penetrated almost all civilian industries that one can think of. It has transformed the way individuals and businesses work, and now it is quickly making its way in becoming a critical part of modern warfare. The strength of its army is one of the factors indicating how powerful the country is. In some of the most developed nations, investment in this sector is the highest as compared to other sectors. A major part of this investment goes towards rigorous research and development in modern technology such as AI in military applications.

We live in a world in which threats are constantly growing and morphing. This empowers leaders to understand what is happening, where it's happening, and why it's happening -- so they can take decisive action to protect citizens. Advancements in artificial intelligence (AI) are driving unprecedented change and opportunity in this space. The ability to merge physical models with digital content and conduct analysis at extraordinary speed is a game-changer for the GEOINT workforce -- but such a major industry shakeup also presents new challenges. A new study by the United States Geospatial Intelligence Foundation (USGIF) and MeriTalk, "Mapping AI to the GEOINT Workforce," shows that 91% of geospatial intelligence stakeholders believe AI has the potential to greatly improve the discipline -- particularly in the areas of national security, emergency response, and urban planning and development.

Nearly 90% of organizations say malicious bots are proving increasingly elusive to identify and ... [ ] destroy. These and many other fascinating findings are from Kount's 2020 Bot Landscape & Impact Report published earlier this week. The report's methodology is based on interviews with online retail and eCommerce business employees with full-time roles related to fraud prevention, customer experience, payments and management. Please see page 3 of the study for additional details on the methodology. The findings bring to light new insights into how businesses are using good bots, the breadth of the threat posed by different types of malicious bots and the state of bot mitigation and management.

Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant. Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess, such as sentiment, tense, emotion, political position, etc. It has a long history in Natural Language Processing but recently gains much more attention thanks to the promising performance brought by deep learning models. In this article, we present a systematic survey on these works for neural text attribute transfer. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze around 65 representative works in a comprehensive way. Overall, we have covered the task formulation, existing datasets and metrics for model development and evaluation, and all methods developed over the last several years. We reveal that existing methods are indeed based on a combination of several loss functions with each of which serving a certain goal. Such a unique perspective we provide could shed light on the design of new methods. We conclude our survey with a discussion on open issues that need to be resolved for better future development.

The stochastic block model is one of the most studied network models for community detection. It is well-known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al.(2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and is not well suited for small- or medium- scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of the communities in a stochastic block model. As demonstrated in simulation studies, the proposed method outperforms the pseudo-likelihood approach in terms of both estimation accuracy and computation efficiency, especially for large sparse networks. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties.

Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.

It is a statement that has been broadcasted and heard around the world: China intends to be the global leader of artificial intelligence by 2030. The country is putting its money where its mouth is, officials and analysts say, and making investments in AI that could threaten the United States and erode Washington's advantages in the technology. "The Chinese Communist Party recognizes the transformational power of AI," Defense Secretary Mark Esper recently said during remarks at the Defense Department's AI Symposium and Exposition. Beijing views the technology as a critical component to its future military and industrial power, said the Pentagon's recently released "Military and Security Developments Involving the People's Republic of China 2020" annual report to Congress. The country's "Next Generation AI Development Plan" details Beijing's strategy to employ commercial and military organizations to achieve major breakthroughs by 2025 and become the world leader by 2030, the report said.