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Indoor air pollutants e.g., Carbon dioxide (CO2), Particulate Matter(PM)2.5, PM10, Total Volatile Organic Compounds (TVOC), etc. have a serious impact on human health. Out of these pollutants, CO2 is one of the most dominant one. Hence, proper monitoring and control of this pollutant is an important part of maintaining a healthy indoor. To make this happen, it is required to predict the next moment's indoor pollutant level at an acceptable accuracy range that ensures necessary steps can be taken beforehand to avoid a rise in the indoor pollution level for maintaining a healthy indoor all the time. It also helps people plan ahead, decreases the adverse effects on health and the costs associated. For this experiment, we have collected three months of real-life time-series data along with proper context information and have gone through feature engineering and feature selection process to create model ready data. Now, since the indoor CO2 concentration is dependent on multiple external factors (context data) which in turn is dependent on time, makes it a time-dependent function. Hence, to predict the indoor pollutant CO2, here we have used the time series forecasting model based on our collected data nature. This is a powerful tool and used in a wide range of research domains for predicting the next moment's target value. This model ready data is utilized in forecasting different time series models. According to our findings, among the selected popular time series models, the SARIMAX time series model is best suited for this forecasting problem which is utilizing indoor context information along with historical data (with 10 Fold Time-Series Split Cross-Validation score 0.907). We have achieved an average of RMSE 26.45 ppm (i.e., 97.36% accuracy) level based on a three day average for indoor pollutant prediction which is outperforming other relevant models in this domain. [ABSTRACT FROM AUTHOR]

Since its invention, the Web has evolved into the largest multimedia repository that has ever existed. This evolution is a direct result of the explosion of user-generated content, explained by the wide adoption of social network platforms. The vast amount of multimedia content requires effective management and retrieval techniques. Nevertheless, Web multimedia retrieval is a complex task because users commonly express their information needs in semantic terms, but expect multimedia content in return. This dissociation between semantics and content of multimedia is known as the semantic gap. To solve this, researchers are looking beyond content-based or text-based approaches, integrating novel data sources. New data sources can consist of any type of data extracted from the context of multimedia documents, defined as the data that is not part of the raw content of a multimedia file. The Web is an extraordinary source of context data, which can be found in explicit or implicit relation to multimedia objects, such as surrounding text, tags, hyperlinks, and even in relevance-feedback. Recent advances in Web multimedia retrieval have shown that context data has great potential to bridge the semantic gap. In this article, we present the first comprehensive survey of context-based approaches for multimedia information retrieval on the Web. We introduce a data-driven taxonomy, which we then use in our literature review of the most emblematic and important approaches that use context-based data. In addition, we identify important challenges and opportunities, which had not been previously addressed in this area. [ABSTRACT FROM AUTHOR]

Health systems around the world are currently facing a challenge to fight the tremendous growth of non-transmissible chronic diseases (NTCD), especially cardiovascular diseases. These diseases can be prevented if adoption of healthy habits and lifestyles are adopted by people, specifically by increasing their physical activity and having a healthy nutrition. The objective of this article is to describe the modeling process of a contextaware system with the purpose of supporting interventions to promote physical activity and healthy nutrition, duly adjusted to the characteristics of the user's context. The main results of this article are: a) a classification model of health context and a context adaptability and personalization process model; b) a proposal of a context model for a context-aware system, which supports the promotion of physical activity and healthy nutrition; c) a reference architecture and a prototype of the system developed on such architecture, which consists of a mobile application supported by NFC and GPS technologies; and d) an evaluation of the solution usability. [ABSTRACT FROM AUTHOR]

Occupancy detection and prediction are two well-established problems which can be improved further to achieve higher accuracy in both cases than the existing solutions. To achieve the desired higher accuracy, proposed OccupancySense model detects human presence and predicts indoor occupancy count by the fusion of Internet of Things (IoT) based indoor air quality (I A Q) data along with static and dynamic context data which is a unique approach in this domain. This data fusion helps us to achieve higher forecasting accuracy along with the integration of state of the art gradient boosting based categorical features supported CatBoost algorithm. For comparison, other commonly used machine learning classification and regression algorithms, e.g., Multiple Linear Regression (MLR), Decision Tree (DT), Random Forests (RF) and Support Vector Machine (SVM) for regression and Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF), Support Vector Machine (SVM) for classification, were also assessed during this experiment. Out of these, CatBoost outperformed other models when considered in terms of accuracy. Hence, CatBoost is used as the core of the OccupancySense design and we have validated the proposed model by a real-world case study with continuous 91 days of indoor data, having 33 unique external features. These features are collected directly as well as derived from the collected data. To handle these features, feature engineering plays a key role in the OccupancySense model. The speciality of this model is, it is non-intrusive one but have high predictive power. It can detect occupancy and predicts headcount along with occupancy density of the room pretty accurately with 99.85%, 93.2% and 95.6% respectively (with 10 fold cross-validation) which outperforms other state of the art models. • Non-intrusive occupancy detection and prediction model based on Indoor Air Quality and context data. • Sensor data fusion with the context information increases performance. • Feature engineering for dealing with real-world data is one of the cores of this research. • Class-leading prediction performance is achieved for both occupancy detection and prediction. [ABSTRACT FROM AUTHOR]

Abstract: A science process is a process to solve complex scientific problems which usually have no mature solving methods. Science processes if modeled in workflow forms, i.e. scientific workflows, can be managed more effectively and performed more automatically. However, most current workflow models seldom take account of specific characteristics of science processes and are not very suitable for modeling scientific workflows. Therefore, a new workflow model named problem-based scientific workflow model (PBSWM) is proposed in this paper to accommodate those specific characteristics. Corresponding soundness verification and dynamic modification are discussed accordingly based on the new modelling method. This paper makes three main contributions: (1) three new constructs are proposed for special logic semantics in science processes; (2) verification is deployed with the consideration from both data-specific perspective and control-specific perspective; and (3) a set of rules are provided to automatically infer passive modifications caused by other modifications. [Copyright &y& Elsevier]