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The Internet of Things is ushering in the era of connected environments where the number and diversity of data sources (devices and sensors) are inevitably increasing the size of the data that need to be stored locally (at the edge device level) and transmitted to base storages (at the sink level) of the network. This huge amount of data highlights several challenges including network bandwidth, consumption of network energy, cloud storage, and I/O throughput. These call for data pre-processing and filtering solutions to reduce the amount of data being handled and transmitted over the network. In this study, we investigate data deduplication as a prominent pre-processing method that can be used and adapted to address such challenges. Data deduplication techniques have been traditionally developed for data storage and data warehousing applications and aim at identifying and eliminating redundant data items. Few recent approaches have been designed for connected environments, yet they share various limitations, including: (i) detecting duplicates at one level only of the network (either edge or sink exclusively), (ii) overlooking the context and dynamicity of the network (disregarding device mobility and overlooking boundary separations and sensor coverage areas), (iii) relying on crisp thresholds and providing minimum-to-no expert control over the deduplication process (disregarding the domain expert's needs in defining redundancy). In this study, we propose FREDD, a new approach for Fuzzy Redundancy Elimination for Data Deduplication in a connected environment. FREDD uses simple natural language rules to represent domain knowledge and expert preferences regarding data duplication boundaries. It then applies pattern codes and fuzzy reasoning to detect duplicates at both the edge level and the sink level of the network. This reduces the time required to hard-code the deduplication process, while adapting to the domain expert's needs for different data sources and applications. Moreover, FREDD is adapted for multiple scenarios, considering both static and mobile devices, with different configurations of hard-separated and soft-separated zones, and different sensor coverage areas in the connected environment. Experiments on a real-world dataset highlight FREDD's potential and improvement compared with existing solutions. [ABSTRACT FROM AUTHOR]

Establishing a healthy lifestyle has become a very important aspect in people's lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, allowing to regulate one's intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts which services are costly, time-consuming, and not readily available. While various e-solutions have been developed to perform meal planning, yet most of them lack a completely automated process and require domain expert intervention at different stages of the recommendation process (e.g., identifying macronutrient distribution, providing pre-defined meal plans, or combining recommended foods into meal structures). In addition, most solutions focus on fulfilling the patients' nutrition requirements (in terms of caloric intake and macronutrients) while disregarding other relevant factors such as patient food preferences, food variety, food-meal compatibility, and inter-food compatibility. Hence, there is a need for an automated solution to produce a full-fledged meal plan from scratch, based on a recommended caloric intake and considering multiple factors. In this study, we introduce a novel solution titled MPG for automated Meal Plan Generation recommendations, designed based on an adaptation of the transportation optimization problem to simulate the "human thought process" involved in generating daily meal plans. MPG allows to: (i) generate plans which fulfill a recommended caloric intake, given a set of available foods, while (ii) personalizing the plans following patient chosen factors (e.g., food preferences, variety, and compatibility), and (iii) evaluating the relevance of the produced plans following patient preferences. We have conducted various experiments involving 9 human testers and 124 meal plans to test the performance of MPG. Results highlight MPG's effectiveness in producing "healthy" and personalized meal plans while complying with the testers' preferences. [ABSTRACT FROM AUTHOR]

Over the past years, several approaches have been developed to create algorithmic music composers. Most existing solutions focus on composing music that appears theoretically correct or interesting to the listener. However, few methods have targeted sentiment-based music composition: generating music that expresses human emotions. The few existing methods are restricted in the spectrum of emotions they can express (usually to two dimensions: valence and arousal) as well as the level of sophistication of the music they compose (usually monophonic, following translation-based, predefined templates or heuristic textures). In this paper, we introduce a new algorithmic framework for autonomous music sentiment-based expression and composition, titled MUSEC, that perceives an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new polyphonic (pseudo) thematic, and diversified musical pieces that express these emotions. Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. We conducted a large battery of tests to evaluate MUSEC's effectiveness and efficiency in both sentiment analysis and composition. It was trained on a specially constructed set of 120 MIDI pieces, including 70 sentiment-annotated pieces: the first significant dataset of sentiment-labeled MIDI music made available online as a benchmark for future research in this area. Results are encouraging and highlight the potential of our approach in different application domains, ranging over music information retrieval, music composition, assistive music therapy, and emotional intelligence. [ABSTRACT FROM AUTHOR]

Given that the meaning of an image is rarely self-evident using traditional keyword and/or content-based descriptions, the general goal of this study is to convert, with minimal human intervention, a stream of web vector graphics into a searchable knowledge graph structure that encodes semantically relevant image contents. To do so, we introduce an original framework titled SSG which automatically converts a stream of SVG images and objects into a semantic graph. We introduce an incremental clustering approach to semantically annotate SVG images and their constituent objects in a fast and efficient manner, using an aggregation of shape, area, color, and location similarity measures. We then produce an RDF graph representation of the input image and integrate it in a reference knowledge graph, incrementally extending its semantic expressiveness to improve future annotation tasks. This achieves semantization of vector image contents with minimum human effort and training data, while complying with native Web standards (i.e., SVG and RDF) to preserve transparency in representing and searching images using Semantic Web stack technologies. Our solution is of linear complexity in the number of images and clusters used. We have conducted a large battery of experiments to test and evaluate our approach. We have created a labelled SVG dataset consisting of 22,553 objects from 750 images based on panoramic dental X-ray images. To our knowledge, it is the first significant dataset of labelled SVG objects and images, which we make available online as a benchmark for future research in this area. Results underline our approach's effectiveness, and its applicability in a practical application domain. [ABSTRACT FROM AUTHOR]

Merging XML documents can be of key importance in several applications. For instance, merging the RSS news from same or different sources and providers can be beneficial for end-users in various scenarios. In this paper, we address this issue and explore the relatedness measure between RSS elements. We show here how to define and compute exclusive relations between any two elements and provide several predefined merging operators that can be extended and adapted to human needs. We also provide a set of experiments conducted to validate our approach. [ABSTRACT FROM AUTHOR]

Major advances in the fields of Internet and Communication Technology (ICT), data modeling/processing, and sensing technology have rendered traditional environments (e.g., cities, buildings) more connected. Although the sensed data could be useful for various applications (e.g., event detection in cities, energy management in commercial buildings), it first requires pre-processing to clean various inconsistencies (e.g., anomalies, redundancies, missing values). In this work, we focus on managing data redundancies in connected environments. Existing approaches suffer from (i) disregarding edge data redundancies either at the edge or at the core of the network; (ii) disregarding sensor mobility and the dynamicity of the network; (iii) disregarding the limited resources of edge devices; (iv) disregarding network/infrastructure resources; and (v) disregarding data consumer needs/requirements when cleaning the data redundancies. To address these limitations, we propose here DRMF: Data Redundancy Management for leaF-edges allowing to identify and remove data redundancies in connected environments at the device level. DRMF considers both static and mobile edge devices, and provides two algorithms for temporal and spatio-temporal redundancy detection. Once redundancies are identified, DRMF performs data deduplication taking into account the dynamic requirements of data consumers and device resources (e.g., processing, battery, memory). Experimental results highlight the performance and accuracy of our solution in detecting and eliminating edge data redundancies. [ABSTRACT FROM AUTHOR]

During the past two decades, 3D simulation models have gained importance in the development of software solutions that aim to mimic real-world events and phenomena with increasing levels of accuracy and detail. In this context, knowledge representation and processing have recently shown a significant contribution to the simulation modeling domain, where knowledge graphs have been used in different fields to build knowledge representations for multiple purposes. In this paper, we introduce VOWES, a Virtual Outdoor Weather Event Simulator to replicate and measure outdoor weather events in vivid 3D visualizations. We design and implement an integrated knowledge graph (KG) representation for VOWES, by creating two constituent KGs: (i) Weather KG describing weather data and events, and (ii) Simulator KG describing 3D simulation components and properties, and connecting them with the (iii) Semantic Sensor Network (SSN) KG to form an integrated structure serving as the knowledge backbone of the VOWES simulation environment. We make use of the Unity 3D engine to build and design the simulator environment and its virtual sensors, and integrate the Mapbox SDK and the WeatherStack API for realistic real-world weather mapping. We have conducted qualitative evaluations involving 13 expert and 30 non-expert testers, to assess the quality of VOWES' KGs and its simulation environment. Results show that more than 80% of the testers gave a combined quality score ≥ 3 out of 4 on most evaluation criteria. We have also conducted performance evaluations to test VOWES loading, execution, and data search time, among other features. Results show that most operations require almost instantaneous or linear time, where search, refresh, and export operations share almost identical performance levels, with execution time increasing by approximately 179 μs for every added game object. This highlights the simulation tool's time performance in running large simulation projects, and its ability to simulate complex weather environments with large numbers of sensors and weather phenomena. [ABSTRACT FROM AUTHOR]