On-device modeling of user's social context and familiar places from smartphone-embedded sensor data

Context modeling and recognition are crucial for adaptive mobile and ubiquitous computing. Context-awareness in mobile environments relies on prompt reactions to context changes. However, current solutions focus on limited context information processed on centralized architectures, risking privacy leakage and lacking personalization. On-device context modeling and recognition are emerging research trends, addressing these concerns. Social interactions and visited locations play significant roles in characterizing daily life scenarios. This paper proposes an unsupervised and lightweight approach to model the user's social context and locations directly on the mobile device. Leveraging the ego-network model, the system extracts high-level, semantic-rich context features from smartphone-embedded sensor data. For the social context, the approach utilizes data on physical and cyber social interactions among users and their devices. Regarding location, it prioritizes modeling the familiarity degree of specific locations over raw location data, such as GPS coordinates and proximity devices. The effectiveness of the proposed approach is demonstrated through three sets of experiments, employing five real-world datasets. These experiments evaluate the structure of social and location ego networks, provide a semantic evaluation of the proposed models, and assess mobile computing performance. Finally, the relevance of the extracted features is showcased by the improved performance of three machine learning models in recognizing daily-life situations. Compared to using only features related to physical context, the proposed approach achieves a 3% improvement in AUROC, 9% in Precision, and 5% in Recall

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

ContextLabeler Dataset: physical and virtual sensors data collected from smartphone usage in-the-wild

This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment

Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks

Wi-Fi Direct is a promising technology for the support of device-to-device communications (D2D) on commercial mobile devices. However, the standard as-it-is is not sufficient to support the real deployment of networking solutions entirely based on D2D such as opportunistic networks. In fact, WiFi Direct presents some characteristics that could limit the autonomous creation of D2D connections among users' personal devices. Specifically, the standard explicitly requires the user's authorization to establish a connection between two or more devices, and it provides a limited support for inter-group communication. In some cases, this might lead to the creation of isolated groups of nodes which cannot communicate among each other. In this paper, we propose a novel middleware-layer protocol for the efficient configuration and management of WiFi Direct groups (WiFi Direct Group Manager, WFD-GM) to enable autonomous connections and inter-group communication. This enables opportunistic networks in real conditions (e.g., variable mobility and network size). WFD-GM defines a context function that takes into account heterogeneous parameters for the creation of the best group configuration in a specific time window, including an index of nodes' stability and power levels. We evaluate the protocol performances by simulating three reference scenarios including different mobility models, geographical areas and number of nodes. Simulations are also supported by experimental results related to the evaluation in a real testbed of the involved context parameters. We compare WFD-GM with the state-of-the-art solutions and we show that it performs significantly better than a Baseline approach in scenarios with medium/low mobility, and it is comparable with it in case of high mobility, without introducing additional overhead.Comment: Accepted by the IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 201

3D printed clotrimazole intravaginal ring for the treatment of recurrent vaginal candidiasis

Vulvovaginal candidiasis is a vaginal infection caused by the fungal pathogen Candida albicans that, most commonly, affects women of reproductive age. Its first-line treatment consists in topical applications of conventional drug formulations (e.g., creams, gels, tablets) containing imidazole drugs. The treatment involves single or multiple daily applications and, in the case of recurrences, daily administration of oral antifungal drugs for up to one month. Intravaginal rings are flexible, biocompatible medical devices that, compared to conventional drug formulations, offer the possibility of a controlled vaginal drug delivery over a determined period with a single application, thus increasing patient compliance. Among innovative manufacturing techniques, in recent years, fused deposition modeling 3D printing has emerged in the pharmaceutical field to produce different therapeutics combining drugs and polymers. This technique allows to print objects layer by layer with many different thermoplastic materials after a computer-assisted design. Thermoplastic polyurethanes are flexible and biocompatible materials that can be efficiently employed for the manufacturing of drug release systems, already utilized to prepare vaginal devices. In this work, we produced a clotrimazole-loaded intravaginal ring by fused deposition modeling 3D printing combining the drug with thermoplastic polyurethane using hot melt extrusion. The rings were computer-designed and then printed with two different drug concentrations (i.e., 2% and 10% w/w). The intravaginal rings were first tested in an agar-diffusion test to evaluate their effectiveness against C. albicans; and the 10% loaded ring was selected for further studies. Drug release was evaluated in two different media (i.e., 50% ethanol and vaginal fluid simulant) showing a sustained release over a period of seven days. Next, in vitro time-kill analysis against C. albicans in simulated vaginal fluid was performed and displayed a complete growth inhibition after 5 days, compared to the control. These results suggest a potential application of these 3D printed intravaginal rings for the treatment of vulvovaginal candidiasis and for the long-time treatment of recurrences

Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices. The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%

Towards a Comparative Index Assessing Mechanical Performance, Material Consumption and Energy Requirements for Additive Manufactured Parts

The increasing use of Additive Manufacturing technologies and systems in several industrial sectors and their numerous applications turn the attention of scientists and investigators to studying and evaluating the environmental impacts of these processes. Additive Manufacturing generally allows for a reduction of raw material consumption and waste generation. On the other hand, the need for long processing times and the necessary thermal conditioning of the manufacturing chamber to avoid product defects, lead to a considerable amount of consumed energy per produced item. Energy consumption has been a primary concern of the research on the sustainability of Additive Manufacturing indeed. More recent studies extended the analysis through more complete evaluation methods such as the Life Cycle Assessment. This approach allows a detailed description of environmental impacts but is affected by some concerns about the need for an interpretation of the final results, which can be non-univocal. This fact is particularly critical when the assessment is intended to be used for comparison between alternative solutions. In this study, a novel index is introduced including three main aspects: material consumption, energy requirements and mechanical performance. The proposed formulation makes the index immediately usable for comparing alternative solutions. Within the scope of this study, the index has been applied to one of the most widespread Additive Manufacturing processes, namely Fused Filament Fabrication. The presented case study demonstrates the suitability of the proposed method to compare and identify the optimal choice among alternative manufacturing scenarios

PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks

In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic complexity and the level of personalization of recommended items. To evaluate PLIERS, we performed a set of experiments on real OSN datasets, demonstrating that it outperforms state-of-the-art solutions in terms of personalization, relevance, and novelty of recommendations.Comment: Published in SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computin

Effects of short‐loop material recycling on mechanical properties of parts by Arburg Plastic Freeforming

Arburg Plastic Freeforming allows for transforming granulated thermoplastics with variable shapes and sizes. This opens marvellous opportunities for in-place recycling of process waste and auxiliary structures. The present study investigates for the first time the effects of recycled material on the mechanical properties of manufactured parts. To this end, the mechanical, thermomechanical and rheological properties of parts produced with different contents of recycled material are investigated. Findings demonstrate that a balanced mixture of primary and secondary material determines a drop in mechanical performances due to a less accurate deposition. A higher percentage of recycled material determines a sharp decrease in viscosity, leading to a more homogeneous layer and tensile properties similar to those of the virgin polymer. The drop in viscosity also affects the accuracy of deposition, determining a worse definition of sharp edges

A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images

In recent months, the monkeypox (mpox) virus -- previously endemic in a limited area of the world -- has started spreading in multiple countries until being declared a ``public health emergency of international concern'' by the World Health Organization. The alert was renewed in February 2023 due to a persisting sustained incidence of the virus in several countries and worries about possible new outbreaks. Low-income countries with inadequate infrastructures for vaccine and testing administration are particularly at risk. A symptom of mpox infection is the appearance of skin rashes and eruptions, which can drive people to seek medical advice. A technology that might help perform a preliminary screening based on the aspect of skin lesions is the use of Machine Learning for image classification. However, to make this technology suitable on a large scale, it should be usable directly on mobile devices of people, with a possible notification to a remote medical expert. In this work, we investigate the adoption of Deep Learning to detect mpox from skin lesion images. The proposal leverages Transfer Learning to cope with the scarce availability of mpox image datasets. As a first step, a homogenous, unpolluted, dataset is produced by manual selection and preprocessing of available image data. It will also be released publicly to researchers in the field. Then, a thorough comparison is conducted amongst several Convolutional Neural Networks, based on a 10-fold stratified cross-validation. The best models are then optimized through quantization for use on mobile devices; measures of classification quality, memory footprint, and processing times validate the feasibility of our proposal. Additionally, the use of eXplainable AI is investigated as a suitable instrument to both technically and clinically validate classification outcomes.Comment: Submitted to Pervasive and Mobile Computin