Sentiment Recognition in Egocentric Photostreams

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral)

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Understanding the role played by genetic variations in diseases, exploring genomic variants, and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, the authors propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis

Spectroscopic and morphological data assessing the apatite forming ability of calcium hydroxide-releasing materials for pulp capping

A pulp capping material must perform as a barrier and protect the dental pulpal complex by inducing the formation of a new dentin bridge or dentin-like tissue. Being a favorable condition for the healing process, the apatite forming ability of TheraCal (light-curable Portland-dimethacrylate cement) and Dycal (calcium hydroxide-based) pulp capping materials was studied in two simulated body fluids, i.e. Dulbecco's Phosphate Buffered Saline (DPBS) and Hank's Balanced Salt Solution (HBSS). The cements were analyzed before and after soaking in these media for different times (1\u201328 days) by ESEM-EDX, micro-Raman and IR spectroscopy. This data article refers to \u201cAn in vitro study on dentin demineralization and remineralization: collagen rearrangements and influence on the enucleated phase\u201d (Di Foggia et al., 2019)

Few-shot re-identification of the speaker by social robots

Nowadays advanced machine learning, computer vision, audio analysis and natural language understanding systems can be widely used for improving the perceptive and reasoning capabilities of the social robots. In particular, artificial intelligence algorithms for speaker re-identification make the robot aware of its interlocutor and able to personalize the conversation according to the information gathered in real-time and in the past interactions with the speaker. Anyway, this kind of application requires to train neural networks having available only a few samples for each speaker. Within this context, in this paper we propose a social robot equipped with a microphone sensor and a smart deep learning algorithm for few-shot speaker re-identification, able to run in real time over an embedded platform mounted on board of the robot. The proposed system has been experimentally evaluated over the VoxCeleb1 dataset, demonstrating a remarkable re-identification accuracy by varying the number of samples per speaker, the number of known speakers and the duration of the samples, and over the SpReW dataset, showing its robustness in real noisy environments. Finally, a quantitative evaluation of the processing time over the embedded platform proves that the processing pipeline is almost immediate, resulting in a pleasant user experience

Vibrational Raman and IR data on brown hair subjected to bleaching

Brown human hair was bleached three times (45 min × 3) and four times (45 min × 3 + 15 min) with commercial formulations containing persulfate salts and hydrogen peroxide. The hair samples were characterized by Raman and IR spectroscopy in the Attenuated Total Reflectance (ATR) mode to gain more insights into the possible secondary structure and Cα-Cβ-S-S-Cβ-Cα conformational changes induced by bleaching. The latter were evaluated through band-fitting procedures; the relative content of the disulfide bridges and oxidized sulfur species (cysteic acid, Bunte salt, cystine oxides) was assessed. The observed conformational changes could be significant in developing restoring agents to be used after hair decoloration. The use of two different spectroscopic techniques allowed to discriminate the information coming from the cortical region of hair (Raman) and the cuticle (ATR/IR). This article refers to “Structural investigation on damaged hair keratin treated with α,β-unsaturated Michael acceptors used as repairing agents” (Di Foggia et al., Int. J. Biol. Macromol. 167 (2021) 620–632 https://doi.org/10.1016/j.ijbiomac.2020.11.194)

The Influence of the Matrix on the Apatite-Forming Ability of Calcium Containing Polydimethylsiloxane-Based Cements for Endodontics

This study aimed to characterize the chemical properties and bioactivity of an endodontic sealer (GuttaFlow Bioseal) based on polydimethylsiloxane (PDMS) and containing a calcium bioglass as a doping agent. Commercial PDMS-based cement free from calcium bioglass (GuttaFlow 2 and RoekoSeal) were characterized for comparison as well as GuttaFlow 2 doped with dicalcium phosphate dihydrate, hydroxyapatite, or a tricalcium silicate-based cement. IR and Raman analyses were performed on fresh materials as well as after aging tests in Hank's Balanced Salt Solution (28 d, 37 degrees C). Under these conditions, the strengthening of the 970 cm(-1) Raman band and the appearance of the IR components at 1455-1414, 1015, 868, and 600-559 cm(-1) revealed the deposition of B-type carbonated apatite. The Raman I-970/I-638 and IR A(1010)/A(1258) ratios (markers of apatite-forming ability) showed that bioactivity decreased along with the series: GuttaFlow Bioseal > GuttaFlow 2 > RoekoSeal. The PDMS matrix played a relevant role in bioactivity; in GuttaFlow 2, the crosslinking degree was favorable for Ca2+ adsorption/complexation and the formation of a thin calcium phosphate layer. In the less crosslinked RoekoSeal, such processes did not occur. The doped cements showed bioactivity higher than GuttaFlow 2, suggesting that the particles of the mineralizing agents are spontaneously exposed on the cement surface, although the hydrophobicity of the PDMS matrix slowed down apatite deposition. Relevant properties in the endodontic practice (i.e., setting time, radiopacity, apatite-forming ability) were related to material composition and the crosslinking degree

On the Uniform Random Generation of Non Deterministic Automata Up to Isomorphism

In this paper we address the problem of the uniform random generation of non deterministic automata (NFA) up to isomorphism. First, we show how to use a Monte-Carlo approach to uniformly sample a NFA. Secondly, we show how to use the Metropolis-Hastings Algorithm to uniformly generate NFAs up to isomorphism. Using labeling techniques, we show that in practice it is possible to move into the modified Markov Chain efficiently, allowing the random generation of NFAs up to isomorphism with dozens of states. This general approach is also applied to several interesting subclasses of NFAs (up to isomorphism), such as NFAs having a unique initial states and a bounded output degree. Finally, we prove that for these interesting subclasses of NFAs, moving into the Metropolis Markov chain can be done in polynomial time. Promising experimental results constitute a practical contribution.Comment: Frank Drewes. CIAA 2015, Aug 2015, Umea, Sweden. Springer, 9223, pp.12, 2015, Implementation and Application of Automata - 20th International Conferenc

A Method for Detecting Long Term Left Baggage based on Heat Map

n/

Subgraph spotting in graph representations of comic book images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset.University of La Rochelle (France