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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]