Abstract--Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance. Over the last two decades, Road Traffic Accidents (RTAs) are increasingly being recognised as a growing public health such as Global Positioning System (GPS), accelerometers, problem.

Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts. We propose a new framework for this key problem, context-informed dynamics adaptation (CoDA), which takes into account the distributional shift across systems for fast and efficient adaptation to new dynamics. CoDA leverages multiple environments, each associated to a different dynamic, and learns to condition the dynamics model on contextual parameters, specific to each environment. The conditioning is performed via a hypernetwork, learned jointly with a context vector from observed data. The proposed formulation constrains the search hypothesis space to foster fast adaptation and better generalization across environments. It extends the expressivity of existing methods. We theoretically motivate our approach and show state-ofthe-art generalization results on a set of nonlinear dynamics, representative of a variety of application domains. We also show, on these systems, that new system parameters can be inferred from context vectors with minimal supervision.

In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy a model to get feedback about the distribution it induces. We study the problem of finding near-optimal models under performativity while maintaining low regret. On the surface, this problem might seem equivalent to a bandit problem. However, it exhibits a fundamentally richer feedback structure that we refer to as performative feedback: after every deployment, the learner receives samples from the shifted distribution rather than only bandit feedback about the reward. Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. The construction only relies on smoothness of the shifts and does not assume convexity. More broadly, our work establishes a conceptual approach for leveraging tools from the bandits literature for the purpose of regret minimization with performative feedback.

Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully-connected network has so far proven elusive. Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localised, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task. By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognised as the hallmark of natural images. We provide an analytical and numerical characterisation of the pattern-formation mechanism responsible for this phenomenon in a simple model, which results in an unexpected link between receptive field formation and the tensor decomposition of higher-order input correlations. These results provide a new perspective on the development of low-level feature detectors in various sensory modalities, and pave the way for studying the impact of higher-order statistics on learning in neural networks.

Representing words by vectors, or embeddings, enables computational reasoning and is foundational to automating natural language tasks. For example, if word embeddings of similar words contain similar values, word similarity can be readily assessed, whereas judging that from their spelling is often impossible (e.g. cat /feline) and to predetermine and store similarities between all words is prohibitively time-consuming, memory intensive and subjective. We focus on word embeddings learned from text corpora and knowledge graphs. Several well-known algorithms learn word embeddings from text on an unsupervised basis by learning to predict those words that occur around each word, e.g. word2vec and GloVe. Parameters of such word embeddings are known to reflect word co-occurrence statistics, but how they capture semantic meaning has been unclear. Knowledge graph representation models learn representations both of entities (words, people, places, etc.) and relations between them, typically by training a model to predict known facts in a supervised manner. Despite steady improvements in fact prediction accuracy, little is understood of the latent structure that enables this. The limited understanding of how latent semantic structure is encoded in the geometry of word embeddings and knowledge graph representations makes a principled means of improving their performance, reliability or interpretability unclear. To address this: 1. we theoretically justify the empirical observation that particular geometric relationships between word embeddings learned by algorithms such as word2vec and GloVe correspond to semantic relations between words; and 2. we extend this correspondence between semantics and geometry to the entities and relations of knowledge graphs, providing a model for the latent structure of knowledge graph representation linked to that of word embeddings.

Earth could have as many as 73,000 tree species, a new first-of-its-kind study has estimated, including some 9,200 that are yet to be discovered. Most of these undiscovered species are likely to be rare, in very low numbers and at threat from human-driven changes in land use and climate, researchers said. South America contains about 43 per cent of the world's tree species and the highest number of rare ones. The findings suggest the continent should be the focus of conservation efforts, along with global tropical and subtropical forests, which also likely harbour many rare, undiscovered species, according to researchers. The study is the outcome of a three-year international project that involved almost 150 scientists and led to the identification of approximately 40 million trees belonging to 64,000 species.

Flying into Dallas Fort Worth International Airport from Mexico in December, I queued in the immigration line for US citizens and was taken aback when – rather than request my passport – the Customs and Border Protection (CBP) agent simply instructed me to look at the camera and then pronounced my first name: "Maria?" Feeling an abrupt violation of my entire bodily autonomy, I nodded – and reckoned that it was perhaps easy to lose track of the rapid dystopian devolution of the world when one had spent the past two years hanging out on a beach in Oaxaca. A CBP poster promoting the transparent infringement on privacy was affixed to the airport wall, and featured a grey-haired man smiling suavely into the camera along with the text: "Our policies on privacy couldn't be more transparent. In my case, the process was not so fast, as I had to hand over my passport for physical scrutiny after I raised the agent's suspicions by being unable to answer in any remotely coherent fashion the ...

In industries from healthcare to education to finance to manufacturing, quarantine and extended work-from-home forced companies to use technology to reimagine nearly every facet of their operations. As the world reopens in fits and starts, we analyze the industries poised to thrive in a post-Covid world. As the Covid-19 pandemic has charted its unprecedented path around the world, it's carried with it the question: What will Covid-19's legacy be? From healthcare to education to entertainment to manufacturing, technology innovators are stepping forward to help answer that question. "Crisis can be… a catalyst or can speed up changes that are on the way -- it almost can serve as an accelerant." In the wake of the outbreak, everything from doctors appointments to schooling to workouts went online. As more people have worked, learned, banked, exercised, relaxed, and even sought medical care from home during Covid-19, they have gotten a crash course in just how much can be accomplished at ...

Dubai has emerged as a leading centre for research and development in emerging industries with a primary focus on embracing new technologies. Digital transformation in the government and private sector across the United Arab Emirates (UAE) is further strengthening Dubai's position as a global tech hub. The Emirate is building momentum as a knowledge-based economy by large-scale adoption of Artificial Intelligence (AI) which is benefiting businesses in multiple ways. "AI is a game-changer for businesses since it improves productivity, lowers cost, unlocks job expansion and creates growth opportunities," states Omar Bin Sultan Al Olama, the UAE Minister of State for Artificial Intelligence. Al Olama, in fact, is the first minister dedicated to AI by any government in the world.

Chatbot trends 2022: This year, we reported AI trends in the Middle East, North Africa, and Southeast Asia. We were all in for them, so we decided to peer into our looking glass once more and give it another try. In summary, the year 2021 was a good one for conversational automation. AI became a useful tool for many internet enterprises once the pandemic shifted everyone to the Internet's side. Today's chatbots are significantly more intelligent than those of a year ago.