Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decision-making that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wireless-based human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics.

The Chase is a fixpoint algorithm enforcing satisfaction of data dependencies in databases. Its execution involves the insertion of tuples with possible null values and the changing of null values which can be made equal to constants or other null values. Since the chase fixpoint evaluation could be non-terminating, in recent years the problem know as chase termination has been investigated. It consists in the detection of sufficient conditions, derived from the structural analysis of dependencies, guaranteeing that the chase fix-point terminates independently from the database instance. Several criteria introducing sufficient conditions for chase termination have been recently proposed [9, 8, 13, 12]. The aim of this paper is to present more general criteria and techniques for chase termination. We first present extensions of the well-known stratification conditions and introduce a new criterion, called local stratification (LS), which generalizes both super-weak acyclicity and stratification-based criteria (including the class of constraints which are inductively restricted). Next the paper presents a rewriting algorithm, whose structure is similar to the one presented in [10]; the algorithm takes as input a set of tuple generating dependencies and produces as output an equivalent set of dependencies and a boolean value stating whether a sort of cyclicity has been detected. The output set, obtained by adorning the input set of constraints, allows us to perform a more accurate analysis of the structural properties of constraints and to further enlarge the class of tuple generating dependencies for which chase termination is guaranteed, whereas the checking of acyclicity allows us to introduce the class of acyclic constraints (AC), which generalizes LS and guarantees chase termination.

Recently there has been an increasing interest in frameworks extending Dung’s abstract Argumentation Framework (AF). Popular extensions include bipolar AFs and AFs with recursive attacks and necessary supports. Although the relationships between AF semantics and Partial Stable Models (PSMs) of logic programs has been deeply investigated, this is not the case for more general frameworks extending AF.In this paper we explore the relationships between AF-based frameworks and PSMs. We show that every AF-based framework Δ can be translated into a logic program P_Δ so that the extensions prescribed by different semantics of Δ coincide with subsets of the PSMs of P_Δ. We provide a logic programming approach that characterizes, in an elegant and uniform way, the semantics of several AF-based frameworks. This result allows also to define the semantics for new AF-based frameworks, such as AFs with recursive attacks and recursive deductive supports.