ABSTRACT Assessing collision risk is a critical challenge to effective traffic safety management. The deployment of unmanned aerial vehicles (UAVs) to address this issue has shown much promise, given their wide visual field and movement flexibility. This research demonstrates the application of UAVs and V2X connectivity to track the movement of road users and assess potential collisions at intersections. The study uses videos captured by UAVs. The proposed method combines deeplearning based tracking algorithms and time-to-collision tasks. The results not only provide beneficial information for vehicle's recognition of potential crashes and motion planning but also provided a valuable tool for urban road agencies and safety management engineers. INTRODUCTION It has been prognosticated that unmanned aerial vehicles (UAVs) will play a vital role in various application or context areas of transportation systems management. This is motivated by the success of UAVs in other domains including photography, photogrammetry, agriculture, terrain mapping, monitoring, disaster relief and rescue operations, and recreational purposes (1). Due to these applications, the emerging global market for drone-enabled services has been valued by the 2016 Middle East and North Africa Business Report at over $127B (2).

The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteorology, epidemiology) or finance (trades, stocks, or sales and inventory). The efforts for time-series narration hitherto are domain-specific and use predefined templates that offer consistency but lead to mechanical narratives. We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models). TCube's design primarily addresses the challenge that lies in building a neural framework in the complete paucity of annotated training data for time-series. The design incorporates knowledge graphs as an intermediary for the representation of essential time-series elements which can be linearized for textual translation. To the best of our knowledge, TCube is the first investigation of the use of neural strategies for time-series narration. Through extensive evaluations, we show that TCube can improve the lexical diversity of the generated narratives by up to 65.38% while still maintaining grammatical integrity. The practicality and deployability of TCube is further validated through an expert review (n=21) where 76.2% of participating experts wary of auto-generated narratives favored TCube as a deployable system for time-series narration due to its richer narratives. Our code-base, models, and datasets, with detailed instructions for reproducibility is publicly hosted at https://github.com/Mandar-Sharma/TCube.

Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this does not guarantee that generated responses are factually correct. In this paper, we examine factual correctness in knowledge-grounded neural response generation models. We present a human annotation setup to identify three different response types: responses that are factually consistent with respect to the input knowledge, responses that contain hallucinated knowledge, and non-verifiable chitchat style responses. We use this setup to annotate responses generated using different stateof-the-art models, knowledge snippets, and decoding strategies. In addition, to facilitate the development of a factual consistency detector, we automatically create a new corpus called Conv-FEVER that is adapted from the Wizard of Wikipedia dataset and includes factually consistent and inconsistent responses. We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data. We will release the Conv-FEVER dataset and the human annotated responses.

The complete freedom of expression in social media has its costs especially in spreading harmful and abusive content that may induce people to act accordingly. Therefore, the need of detecting automatically such a content becomes an urgent task that will help and enhance the efficiency in limiting this toxic spread. Compared to other Arabic dialects which are mostly based on MSA, the Tunisian dialect is a combination of many other languages like MSA, Tamazight, Italian and French. Because of its rich language, dealing with NLP problems can be challenging due to the lack of large annotated datasets. In this paper we are introducing a new annotated dataset composed of approximately 10k of comments. We provide an in-depth exploration of its vocabulary through feature engineering approaches as well as the results of the classification performance of machine learning classifiers like NB and SVM and deep learning models such as ARBERT, MARBERT and XLM-R.

Many problems in RL, such as meta RL, robust RL, and generalization in RL, can be cast as POMDPs. In theory, simply augmenting model-free RL with memory, such as recurrent neural networks, provides a general approach to solving all types of POMDPs. However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs. This paper revisits this claim. We find that careful architecture and hyperparameter decisions yield a recurrent model-free implementation that performs on par with (and occasionally substantially better than) more sophisticated recent techniques in their respective domains. We also release a simple and efficient implementation of recurrent model-free RL for future work to use as a baseline for POMDPs. Code is available at https://github.com/twni2016/pomdp-baselines

Stochastic differential equations provide a rich class of flexible generative models, capable of describing a wide range of spatio-temporal processes. A host of recent work looks to learn data-representing SDEs, using neural networks and other flexible function approximators. Despite these advances, learning remains computationally expensive due to the sequential nature of SDE integrators. In this work, we propose an importance-sampling estimator for probabilities of observations of SDEs for the purposes of learning. Crucially, the approach we suggest does not rely on such integrators. The proposed method produces lower-variance gradient estimates compared to algorithms based on SDE integrators and has the added advantage of being embarrassingly parallelizable. Stochastic differential equations (SDEs) are a natural extension to ordinary differential equations which allows modelling of noisy and uncertain driving forces. These models are particularly appealing due to their flexibility in expressing highly complex relationships with simple equations, while retaining a high degree of interpretability. Much work has been done over the last century focussing on understanding and modelling with SDEs, particularly in dynamical systems and quantitative finance (Pavliotis, 2014; Malliavin & Thalmaier, 2006).

The world's first recorded case of an autonomous drone attacking humans took place in March 2020, according to a United Nations (UN) security report detailing the ongoing Second Libyan Civil War. Libyan forces used the Turkish-made drones to "hunt down" and jam retreating enemy forces, preventing them from using their own drones. The field report (via New Scientist) describes how the Haftar Affiliated Forces (HAF), loyal to Libyan Field Marshal Khalifa Haftar, came under attack by drones from the rival Government of National Accord (GNA) forces. After a successful drive against HAF forces, the GNA launched drone attacks to press its advantage. The report says Turkey supplied the drones to Libyan forces, which is a violation of a UN arms embargo slapped on combatants in the conflict.

Researchers from Estonia and Italy developed an innovative method by combining neural networks and statistics. Using this newly developed method, they refined the "Out of Africa" scenario. The researchers claimed that the African dynamics around the time of the Out of Africa expansion are more complex than previously thought. Archaeologists and geneticists agree that all modern humans originated somewhere in Africa around 300 thousand years ago. The population movement that colonized the rest of the globe occurred approximately 60-70 thousand years ago.

On the 12th of October, the world will celebrate Ada Lovelace Day to honor the achievements of women in science, technology, engineering and maths (STEM). In Finding Ada (the main network supporting Ada Lovelace Day), there will be three free webinars that you can enjoy in the comfort of your own home. There will also be loads of events happening around the world, so you have a wide range of content to celebrate Ada Lovelace Day 2021! Engineering is the science of problem solving, and we have some pretty big problems in front of us. So how are engineers tackling the COVID-19 pandemic and climate change?

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. After a 2019 research paper demonstrated that commercially available facial analysis tools fail to work for women with dark skin, AWS executives went on the attack. Instead of offering up more equitable performance results or allowing the federal government to assess their algorithm like other companies with facial recognition tech have done, AWS executives attempted to discredit study coauthors Joy Buolamwini and Deb Raji in multiple blog posts. More than 70 respected AI researchers rebuked this attack, defended the study, and called on Amazon to stop selling the technology to police, a position the company temporarily adopted last year after the death of George Floyd. But according to the Abuse and Misogynoir Playbook, published earlier this year by a trio of MIT researchers, Amazon's attempt to smear two Black women AI researchers and discredit their work follows a set of tactics that have been used against Black women for centuries.