Fiammetta Marulli

Bio: Fiammetta Marulli is an academic researcher from Seconda Università degli Studi di Napoli. The author has contributed to research in topics: Computer science & Cultural heritage. The author has an hindex of 11, co-authored 48 publications receiving 432 citations. Previous affiliations of Fiammetta Marulli include University of Sannio & University of Naples Federico II.

Computer Network Simulation with ns-3: A Systematic Literature Review

Abstract: Complexity of current computer networks, including e.g., local networks, large structured networks, wireless sensor networks, datacenter backbones, requires a thorough study to perform analysis and support design. Simulation is a tool of paramount importance to encompass all the different aspects that contribute to design quality and network performance (including as well energy issues, security management overheads, dependability), due to the fact that such complexity produces several interactions at all network layers that is not easily modellable with analytic approaches. In this systematic literature review we aim to analyze, basing our investigation on available literature, the adoption of a popular network simulator, namely ns-3, and its use in the scientific community. More in detail, we are interested in understanding what are the impacted application domains in which authors prefer ns-3 to other similar tools and how extensible it is in practice according to the experience of authors. The results of our analysis, which has been conducted by especially focusing on 128 papers published between 2009 to 2019, reveals that 10% of the evaluated papers were discarded because they represented informal literature; most of the studies presented comparisons among different network simulators, beyond ns-3 and conceptual studies related to performance assessment and validation and routing protocols. Only about 30% of considered studies present extensions of ns-3 in terms of new modules and only about 10% present effective case studies demonstrating the effectiveness of employing network simulator in real application, except conceptual and modeling studies.

SmARTweet: A Location-Based Smart Application for Exhibits and Museums

Abstract: In this paper, we present "Smartweed", a Locationbased application developed within DATABENC, a high technology district for Cultural Heritage management. In particular, the project aims at exploiting several location-based services and technologies to realize a smart multimedia guide system able to detect the closest artworks to an user, make them able to "tweet" and "talk" during tourists' visit and capable of automatically telling their stories using multimedia facilities. Moreover, we have deployed and tested the installation of some sensors that, using Wi-Fi technology, allow to the users' mobile devices to detect the closest artwork in a museum environment. Once an artwork has been detected, the related identifier is retrieved and a multimedia story is delivered to user by means of proper multimedia delivery and user-profiling techniques, in order to facilitate and make more stimulating the visit. The artworks detection was performed by a localization algorithm that we designed and tested in our laboratory rooms. As case of study, we show an example of "tweeting and talking artworks" as a location-based application of a sculptures' art exhibition within the Maschio Angioino castle, in Naples - Italy.

Multilingual POS tagging by a composite deep architecture based on character-level features and on-the-fly enriched Word Embeddings

Abstract: Natural Language Processing (NLP) field is taking great advantage from adopting models and methodologies from Artificial Intelligence. In particular, Part-Of-Speech (POS) tagging is a building block for many NLP applications. In this paper, a POS tagging system based on a deep neural network is proposed. It is made of a static and task-independent pre-trained model for representing words semantics enriched by morphological information, by approximating the Word Embedding representation learned from an unlabelled corpus by the fastText model, so as to handle consistently common and known words as well as rare and Out-of-Vocabulary words. A character-level representation of words is dynamically learned according to the POS tagging task, and is concatenated to the previous one. This joint representation is fed to the main network, comprising a Bi-LSTM layer, trained to associate a sequence of tags to a sequence of words. The effectiveness of the contributions of the proposed system with respect to the state-of-the-art is proven by an extensive experimental campaign, which provides evidence that improvements are gained in POS tagging accuracy by using Word Embeddings enriched with morphological information, by estimating embeddings for both known and unknown words, and by concatenating Word Embeddings with character-level information of the same size. Similar trends are obtained for two languages of different characteristics, namely English and Italian: in both cases, the overall accuracy on the POS tagging test set was increased with respect to the most advanced existing systems, with particular improvements on the accuracy of Out-of-Vocabulary words. Finally, the method has a general basis, and could be proficiently used for all languages, particularly for those showing a wide morphological richness.

Deep Learning in Mobile and Wireless Networking: A Survey

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Deep Learning for Anomaly Detection: A Survey.

Abstract: Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning in Mobile and Wireless Networking: A Survey

Abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

Abstract: | All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.