The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework.

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities), and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator (TMLE) to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and post-baseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while other common CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for cluster-level covariates, after controlling for missingness on individual-level outcomes.

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by training two models that learn visual features via cooperative reconstruction, but one model is adversarially dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks where natural and unconstrained visual distractions pose a formidable challenge.

Conversational machine reading (CMR) tools allow users to give a description of their scenario and pose a question to them [1] [2]. The CMR tool then processes the rule text in relation to the user scenario and question and either picks an appropriate answer from the set of possible answers A {Yes, No, Irrelevant} or chooses to seek futher clarification before giving an answer from the set A [3]. A number of systems [2] [3] [4] [5] have been developed with a goal to improve the precision of the answers given to the user. However, all the existing tools apply supervised learning technique which require manually labeled dataset. For every new rule text, the supervised techniques will require that a labeled dataset be created. The task of manually labeling dataset is tedious and error prone [6].

ZzappMalaria, a Jerusalem-based startup whose mobile app aims to help identify potential sources of malaria, has won a first prize of $3 million in the IBM Watson AI XPRIZE competition. The firm was also selected as the "Most Inspiring Team" in the People's Choice Award. The IBM Watson AI XPRIZE Challenge was launched in 2016 to promote the use of AI to solve the world's most pressing problems. Aifred Health, a Montreal-based digital health company focused on providing support for clinical decisions for mental health, won second place, getting a $1 million prize. Marinus Analytics, a Pittsburg, US-based firm that uses AI to quickly turn big data into actionable intelligence, helps fight human trafficking by saving hours and sometimes days of investigative time to find traffickers and recover victims.

U.S. fighter jets dropped bombs on Iranian-backed militias in Iraq and Syria. The strike was in response to Iranian-backed militias firing armed drones against U.S. troops in Iraq, which was a response to a U.S. attack in February, which was a response to a militia attack days earlier. A Pentagon spokesman justified the most recent U.S. airstrikes as "necessary, appropriate, and deliberate action designed to limit the risk of escalation--but also to send a clear and unambiguous deterrent message." This may be true, but similar statements have followed similar strikes for years, even decades; yet counter-attacks nonetheless follow (the "deterrent message" doesn't get through), and so it's possible that we are heightening the "risk of escalation," not limiting it. President Joe Biden finds himself in a jam.

Suzuki Motor Corp. warned that the supply of semiconductors and batteries may be tight into the foreseeable future as automakers shift toward electric vehicles, and is planning to increase its stockpiles as a result. "We will be using many batteries and various chips, more rapidly," Osamu Honda, Suzuki's representative director, said at a shareholder meeting on Friday. "We expect chips, batteries and others to always be in tight supply in that case." A global shortage of chips sparked by a range of factors including the coronavirus pandemic, a factory fire in Japan and frigid weather in the U.S. has limited production of everything from cars to game consoles and sent governments scrambling to bolster domestic supply. Japan, which heavily relies on imports, is now eyeing investing trillions of yen to revive its semiconductor manufacturing industry.

Lehmann and Magidor's rational closure is acknowledged as a landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program (dl-program).

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as: "the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual pattern and it may take the form: "the more X, the more Y, almost 3 months later". In our third contribution, we propose a data crossing model that allows for integration of mostly gradual pattern mining algorithm implementations into a Cloud platform. This contribution is motivated by the proliferation of IoT applications in almost every area of our society and this comes with provision of large-scale time-series data from different sources.

Africa has over 2000 languages, but these languages are not well-represented in the existing Natural Language Processing ecosystem. One challenge is the lack of useful African language datasets that we can use to solve different social and economic problems. In this article, I have compiled a list of African language datasets from across the web. You can use these datasets in various NLP tasks such as text classification, named entity recognition, machine translation, sentiment analysis, speech recognition, and topic modeling. I've made this collection of datasets public to give you an opportunity to use your skills and help solve different challenges.