MIMIC: a Multi Input Micro-Influencers Classifier

Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performance of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.98 with our best performing model

A multi-methodological protocol to characterize PDO olive oils

An analytical approach including Panel Test, Isotope Ratio Mass Spectrometry (IRMS) and Nuclear Magnetic Resonance (NMR) spectroscopy was proposed to characterize Italian “Colline Pontine” PDO olive oils (40 samples) of two consecutive crop years. Our approach has evidenced the high quality of these olive oils. Only 6 of 40 olive oils samples were defined as “defective” by the official Panel Test due to the detection of negative sensory attributes. The low variability of isotopic data monitored by IRMS confirmed that the olive oil samples all came from a limited geographical area. NMR spectra did not evidence any chemical composition anomaly in the investigated samples. In order to assess the influence of harvesting year over the olive oil chemical composition, the NMR analysis was extended to other 22 olive oil samples of a third harvesting year. NMR data were submitted to two different statistical methods, namely, analysis of variance (ANOVA) and principal component analysis (PCA) allowing olive oils of three consecutive harvesting years to be grouped

Automated Test Selection for Android Apps Based on APK and Activity Classification

Several techniques exist for mobile test automation, from script-based techniques to automated test generation based on GUI models. Most techniques fall short in being adopted extensively by practitioners because of the very costly definition (and maintenance) of test cases. We present a novel testing framework for Android apps that allows a developer to write effective test scripts without having to know the implementation details and the user interface of the app under test. The main goal of the framework is to generate adaptive tests that can be executed on a significant number of apps, or different releases of the same app, without manual editing of the tests. The frameworks consists of: (1) a Test Scripting Language, that allows the tester to write generic test scripts tailored to activity and app categories; (2) a State Graph Modeler, that creates a model of the app’s GUI, identifying activities (i.e., screens) and widgets; (3) an app classifier that determines the type of application under test; (4) an activity classifier that determines the purpose of each screen; (5) a test adapter that executes test scripts that are compatible with the specific app and activity, automatically tailoring the test scripts to the classes of the app and the activities under test. We evaluated empirically the components of our testing framework. The classifiers were able to outperform available approaches in the literature. The developed testing framework was able to correctly adapt high-level test cases to 28 out of 32 applications, and to reduce the LOCs of the test scripts of around 90%. We conclude that machine learning can be fruitfully applied to the creation of high-level, adaptive test cases for Android apps. Our framework is modular in nature and allows expansions through the addition of new commands to be executed on the classified apps and activities

Visual exploration and retrieval of XML document collections with the generic system X2

This article reports on the XML retrieval system X2 which has been developed at the University of Munich over the last five years. In a typical session with X2, the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed

Safety assessment of cosmetic products, with emphasis on the ocular area: regulatory aspects and validation processes

Before marketing a cosmetic product, a series of biological assays, such as ocular irritation tests, must be conducted in order to prove that the product is safe. However, a few scientific articles mention the discussion and evolution of cosmetic products testing performed in the eyes area. the aim of this study was to review the available literature on the evolution of tests carried out with cosmetics, in the ocular area, as well as to describe the methodologies that have been used and that are currently accepted. in Brazil, tests performed on animals are still allowed. However, the international laws strongly recommend the use of alternative methods for evaluating the risk of cosmetic ingredients and products. Regulatory requirements involving the registration of these products also request safety support of them in human beings. To perform ocular tests in human beings, it is necessary to involve an ophthalmologist for conducting clinical protocols. These protocols signed by the expert physician are sent to the National Health Surveillance Agency in order to endorse the product manufacturer concerning its safety. the safety support of a cosmetic product is very important, taking into account that the consumer has free access to these products of widespread use in today's society.Universidade Federal de São Paulo, Inst Environm Chem & Pharmaceut Sci, BR-09913030 Diadema, SP, BrazilUniversidade Federal de São Paulo, Dept Med, São Paulo, SP, BrazilGrp Invest, Campinas, SP, BrazilTRIDSKIN Labs Ltda, Grp Invest, Campinas, SP, BrazilUniversidade Federal de São Paulo, Inst Environm Chem & Pharmaceut Sci, BR-09913030 Diadema, SP, BrazilUniversidade Federal de São Paulo, Dept Med, São Paulo, SP, BrazilWeb of Scienc

Mining micro-influencers from social media posts

Micro-influencers have triggered the interest of commercial brands, public administrations, and other stakeholders because of their demonstrated capability of sensitizing people within their close reach. However, due to their lower visibility in social media platforms, they are challenging to be identified. This work proposes an approach to automatically detect micro-influencers and to highlight their personality traits and community values by computationally analyzing their writings. We introduce two learning methods to retrieve Five Factor Model and Basic Human Values scores. These scores are then used as feature vectors of a Support Vector Machines classifier. We define a set of rules to create a micro-influencer gold standard dataset of more than two million tweets and we compare our approach with three baseline classifiers. The experimental results favor recall meaning that the approach is inclusive in the identification

Indole and p-cresol in feces of healthy subjects: Concentration, kinetics, and correlation with microbiome

Indole and p-cresol are precursors of the most important uremic toxins, generated from the fermentation of amino acids tryptophan and tyrosine by the proteolytic community of intestinal bacteria. The present study focused on the relationship between the microbiome composition, the fecal levels of indole and p-cresol, and their kinetics of generation/degradation in fecal cultures. The concentration of indole and p-cresol, the volatilome, the dry weight, and the amount of ammonium and carbohydrates were analyzed in the feces of 10 healthy adults. Indole and p-cresol widely differed among samples, laying in the range of 1.0–19.5 μg/g and 1.2–173.4 μg/g, respectively. Higher fecal levels of indole and p-cresol were associated with lower carbohydrates and higher ammonium levels, that are markers of a more pronounced intestinal proteolytic metabolism. Positive relationship was observed also with the dry/wet weight ratio, indicator of prolonged intestinal retention of feces. p-cresol and indole presented a statistically significant negative correlation with OTUs of uncultured Bacteroidetes and Firmicutes, the former belonging to Bacteroides and the latter to the families Butyricicoccaceae (genus Butyricicoccus), Monoglobaceae (genus Monoglobus), Lachnospiraceae (genera Faecalibacterium, Roseburia, and Eubacterium ventriosum group). The kinetics of formation and/or degradation of indole and p-cresol was investigated in fecal slurries, supplemented with the precursor amino acids tryptophan and tyrosine in strict anaerobiosis. The presence of the precursors bursted indole production but had a lower effect on the rate of p-cresol formation. On the other hand, supplementation with indole reduced the net rate of formation. The taxa that positively correlated with fecal levels of uremic toxins presented a positive correlation also with p-cresol generation rate in biotransformation experiments. Moreover other bacterial groups were positively correlated with generation rate of p-cresol and indole, further expanding the range of taxa associated to production of p-cresol (Bacteroides, Alistipes, Eubacterium xylanophylum, and Barnesiella) and indole (e.g., Bacteroides, Ruminococcus torques, Balutia, Dialister, Butyricicoccus). The information herein presented contributes to disclose the relationships between microbiota composition and the production of uremic toxins, that could provide the basis for probiotic intervention on the gut microbiota, aimed to prevent the onset, hamper the progression, and alleviate the impact of nephropaties

Isolation of carotenoid-producing yeasts from an alpine glacier

Cold-adapted yeasts are increasingly being isolated from glacial environments, including Artic, Antarctic, and mountain glaciers. Psychrophilic yeast isolates mostly belong to Basidiomycota phylum, such as Cryptococcus, Mrakia, and Rhodotorula, and represent an understudied source of biodiversity for potential biotechnological applications. Since some basidiomycetous yeast genera (e.g. Rhodotorula, Phaffia, etc.) were demonstrated to produce commercially important carotenoids (e.g. β-carotene, torulene, torularhodin and astaxanthin), the present study aimed to obtain psychrophilic yeast isolates from the surface ice of two Italian glaciers to identify new pigment-producers. 23 yeast isolates were obtained. Among them, three isolates giving pigmented colonies was subjected to ITS1/ITS2 sequencing and were attributed to the Basidiomycetous yeasts Dioszegia sp., hodotorula mucilaginosa, and Rhodotorula laryngis. The strains were cultured batchwise in a carbon-rich medium at 15°C until the stationary phase was reached, then the pigments were extracted from freeze-dried biomass using DMSO:acetone mixture. Visible spectrum and HPLC-DAD analysis revealed the presence of carotenoid pigments. In batch cultures of Dioszegia sp., carotenoid production was growth-associated and yielded up to 3.4 mg/L of a molecule exhibiting an m/z ratio (568) consistent with the molecular weight of xanthophylls bearing 2 OH groups

Automated Test Selection for Android Apps Based on APK and Activity Classification

Several techniques exist for mobile test automation, from script-based techniques to automated test generation based on GUI models. Most techniques fall short in being adopted extensively by practitioners because of the very costly definition (and maintenance) of test cases. We present a novel testing framework for Android apps that allows a developer to write effective test scripts without having to know the implementation details and the user interface of the app under test. The main goal of the framework is to generate adaptive tests that can be executed on a significant number of apps, or different releases of the same app, without manual editing of the tests. The frameworks consists of: (1) a Test Scripting Language, that allows the tester to write generic test scripts tailored to activity and app categories; (2) a State Graph Modeler, that creates a model of the app's GUI, identifying activities (i.e., screens) and widgets; (3) an app classifier that determines the type of application under test; (4) an activity classifier that determines the purpose of each screen; (5) a test adapter that executes test scripts that are compatible with the specific app and activity, automatically tailoring the test scripts to the classes of the app and the activities under test. We evaluated empirically the components of our testing framework. The classifiers were able to outperform available approaches in the literature. The developed testing framework was able to correctly adapt high-level test cases to 28 out of 32 applications, and to reduce the LOCs of the test scripts of around 90%. We conclude that machine learning can be fruitfully applied to the creation of high-level, adaptive test cases for Android apps. Our framework is modular in nature and allows expansions through the addition of new commands to be executed on the classified apps and activities

Anura, Hylidae, Dendropsophus nahdereri (Lutz and Bokermann, 1963): distribution extension and new state record

Dendropsophus nahdereri is included in the Dendropsophus marmoratus group. Its distribution is known from the Brazilian states of Paraná and Santa Catarina. Here we report new records of this species and briefly describe the habitat of calling males. We found new localities of occurrence of D. nahdereri in Brazilian states of Santa Catarina and Rio Grande do Sul. We collected calling males in temporary lentic water bodies surrounded by arboreal vegetation, inside and on the border of native forest, and inside Pinus plantations near native forest