The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings.

Israel killed three Palestinians in a drone strike on Thursday in Tulkarem, a city and refugee camp in the occupied West Bank. That was during an Israeli raid – a near-daily occurrence in the West Bank – on the Tulkarem refugee camp, during which Israeli troops clashed with fighters from the Qassam Brigades, the military wing of Hamas, according to fighters in the city. Here's all you need to know about Israeli raids on Tulkarem: News reports say Israeli soldiers were deployed on rooftops and sent bulldozers into the camp to destroy large residential areas. Israel also reportedly set fire to people's homes and prevented local relief workers from putting the fires out. Experts say Israel's tactics during its raids appear to be part of a broader doctrine to collectively punish the population, ostensibly because pockets of armed resistance are fighting back against Israel's ever-entrenching occupation. Israel claims that it is conducting "counter-terrorism" operations.

The emerging field of \emph{value awareness engineering} claims that software agents and systems should be value-aware, i.e. they must make decisions in accordance with human values. In this context, such agents must be capable of explicitly reasoning as to how far different courses of action are aligned with these values. For this purpose, values are often modelled as preferences over states or actions, which are then aggregated to determine the sequences of actions that are maximally aligned with a certain value. Recently, additional value admissibility constraints at this level have been considered as well. However, often relaxed versions of these constraints are needed, and this increases considerably the complexity of computing value-aligned policies. To obtain efficient algorithms that make value-aligned decisions considering admissibility relaxation, we propose the use of learning techniques, in particular, we have used constrained reinforcement learning algorithms. In this paper, we present two algorithms, $\epsilon\text{-}ADQL$ for strategies based on local alignment and its extension $\epsilon\text{-}CADQL$ for a sequence of decisions. We have validated their efficiency in a water distribution problem in a drought scenario.

Mid-level ontologies are used to integrate terminologies and data across disparate domains. There are, however, no clear, defensible criteria for determining whether a given ontology should count as mid-level, because we lack a rigorous characterization of what the middle level of generality is supposed to contain. Attempts to provide such a characterization have failed, we believe, because they have focused on the goal of specifying what is characteristic of those single ontologies that have been advanced as mid-level ontologies. Unfortunately, single ontologies of this sort are generally a mixture of top- and mid-level, and sometimes even of domain-level terms. To gain clarity, we aim to specify the necessary and sufficient conditions for a collection of one or more ontologies to inhabit what we call a mid-level architecture.

A smart home is realized by setting up various services. Several methods have been proposed to create smart home services, which can be divided into knowledge-based and data-driven approaches. However, knowledge-based approaches usually require manual input from the inhabitant, which can be complicated if the physical phenomena of the concerned environment states are complex, and the inhabitant does not know how to adjust related actuators to achieve the target values of the states monitored by services. Moreover, machine learning-based data-driven approaches that we are interested in are like black boxes and cannot show the inhabitant in which situations certain services proposed certain actuators' states. To solve these problems, we propose a hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system), where knowledge-based and machine learning-based data-driven services are profitably integrated. The principal advantage is that it inherits the explicability of knowledge-based services and the dynamism of data-driven services. We compare HKD-SHO with several systems for creating dynamic smart home services, and the results show the better performance of HKD-SHO.

Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, ABMs typically can not estimate agent-specific (or "micro") variables: this is a major limitation which prevents ABMs from harnessing micro-level data availability and which greatly limits their predictive power. In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data. The first step of our protocol is to reduce an ABM to a probabilistic model, characterized by a computationally tractable likelihood. This reduction follows two general design principles: balance of stochasticity and data availability, and replacement of unobservable discrete choices with differentiable approximations. Then, our protocol proceeds by maximizing the likelihood of the latent variables via a gradient-based expectation maximization algorithm. We demonstrate our protocol by applying it to an ABM of the housing market, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We demonstrate that the obtained model allows accurate estimates of the latent variables, while preserving the general behavior of the ABM. We also show that our estimates can be used for out-of-sample forecasting. Our protocol can be seen as an alternative to black-box data assimilation methods, that forces the modeler to lay bare the assumptions of the model, to think about the inferential process, and to spot potential identification problems.

The number of elderly people has increased significantly in recent decades due to various factors, such as increased life expectancy and improved health services [1]. As more people require care, there is also a need for more caregivers to be involved in this field, which means higher costs and workloads for caregivers. However, while a growing number of older people prefer to stay at home for as long as possible rather than go to a care home [2], most of the population cannot afford the expense of having a carer at home. In this sense, smart environments have emerged as a solution to help elderly people to live safely, comfortably and independently in their homes, while reducing the toll on healthcare systems. The combination and use of different types of sensors to recognise Activities of Daily Living (ALD) is becoming increasingly common [3] and includes vision sensors, audio sensors, wearables, binary sensors, RFID, PIR... under sensor fusion approaches [4].

Designing smart home services is a complex task when multiple services with a large number of sensors and actuators are deployed simultaneously. It may rely on knowledge-based or data-driven approaches. The former can use rule-based methods to design services statically, and the latter can use learning methods to discover inhabitants' preferences dynamically. However, neither of these approaches is entirely satisfactory because rules cannot cover all possible situations that may change, and learning methods may make decisions that are sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic Based Rule Extractor) is proposed to extract rules from learning methods to realize dynamic rule generation for smart home systems. The expected advantage is that both the explainability of rule-based methods and the dynamicity of learning methods are adopted. We compare PBRE with an existing rule extraction method, and the results show better performance of PBRE. We also apply PBRE to extract rules from a smart home service represented by an NRL (Neural Network-based Reinforcement Learning). The results show that PBRE can help the NRL-simulated service to make understandable suggestions to the inhabitant.

We provide here a dataset for tasks related to natural language understanding and natural language inference. The dataset contains logical puzzles in natural language from three domains: comparing puzzles, knighs and knaves, and zebra puzzles. Each puzzle is associated with the entire set of atomic questions that can be generated based on the relations and individuals occurring in the text. For each question we provide the correct answer: entailment, contradiction or ambiguity. The answer's correctness is verified against theorem provers. Good puzzles have two properties: (i) each piece of information is necessary and (ii) no unnecessary information is provided. These properties make puzzles interesting candidates for machine comprehension tasks.

Technological progress has enabled unprecedented developments in the field of research. This is how the world of biology and medicine benefit from technological innovation. The application of computer science to the world of biology and medicine has been absolutely revolutionary for both branches and has helped significantly in the improvement of treatments. As indicated by the Instituto de Salud Carlos III, bioinformatics has been fundamental in the analysis and interpretation of SARS-CoV-2 data. During 2020, the research carried out by the Bioinformatics Unit of the aforementioned centre was essential, since it shed light on such important issues as the sequencing of the genome of the new coronavirus and the automation of diagnostic tests.