Automated curation of brand-related social media images with deep learning

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.Peer ReviewedPostprint (author's final draft

Association of interleukin-17A polymorphisms with the risk of colorectal cancer: A case-control study

International audienceBackground: Interleukin (IL)-17A is proinflammatory cytokine produced by Th17 cells, which play key, but sometimes inconsistent role in autoimmunity and cancer. Polymorphic variants in IL-17A gene were differentially associated with susceptibility to cancer, including colorectal cancer (CRC). Aim: We investigated the association between six IL-17A gene variants (rs3819024, rs2275913, rs3819025, rs10484879, rs7747909, and rs3748067) with CRC susceptibility in Tunisians. Subjects and Methods: Retrospective case-control study. Study subjects comprised 293 patients with CRC, and 268 age-, gender-, and BMI-matched healthy controls. IL-17A genotyping was done by real-time PCR, with defined clusters. Results: Of the seven tested IL-17A tag-SNPs, minor allele frequency (MAF) of rs10484879 was significantly higher in CRC patients than control subjects. Heterozygous rs10484879 [OR (95% CI) = 2.63 (1.64-4.21)] was associated with higher risk, while carriage of heterozygous rs3748067 genotype was associated with reduced risk of CRC [OR (95% CI) = 0.56 (0.37-0.84)], respectively. Carriage of rs10484879 minor allele correlated with positive family history of CRC and other cancers (P = 0.002), CRC staging (P = 0.044), CRC treatment (P = 0.038), and with chemo body reaction (P = 0.001). Of the 7 IL-17A variants, 4 were in linkage dis-equilibrium, hence allowing for construction of 4-locus haplotypes. Varied linkage disequilibrium (LD) was noted between the even tested IL-17A variants, and further analysis was limited to only 4-locus (rs3819024-rs2275913-rs10484879-rs7747909). Haploview analysis identified the 4-locus IL-17A haplotypes AGTG (P < 0.011), and GATG (P = 0.036) to be positively associated with CRC, after controlling key covariates. Conclusion: IL-17A rs10484879 SNP, and IL-17A haplotypes AGGTG and GAGTG constitute independent factors of CRC susceptibility. We propose that IL-17A may be a target for future CRC immunotherapy

An artificial intelligence based crowdsensing solution for on-demand accident scene monitoring

Road traffic crashes have a devastating impact on societies by claiming more than 1.35 million lives each year and causing up to 50 million injuries. Improving the efficiency of emergency management systems constitutes a key measure to reduce road traffic deaths and injuries. In this work, we propose a comprehensive crowdsensing-based solution for the real-time collection and the analysis of accident scene intelligence as a means to improve the efficiency of the emergency response process and help reduce road fatalities. The solution leverages sensory, mobile, web technologies for the real-time monitoring of accident scenes, employs Artificial Intelligence for the automatic analysis of the accident scene data, to allow the automatic generation of accident intelligence reports. Police officers and rescue teams can use those reports for fast and accurate situational assessment and effective response to emergencies. The proposed system was fully implemented and its operation was successfully tested using a variety of scenarios. This work gives interesting insights into the possibility of leveraging crowdsensing and artificial intelligence for offering emergency situational awareness and improving the efficiency of emergency response operations

Modeling time constraints in inter-organizational workflows

International audienceThis paper deals with the integration of temporal constraints within the context of Inter-Organizational Workflows (IOWs). Obviously, expressing and satisfying time deadlines is important for modern business processes, and need to be optimized for efficiency and extreme competitiveness. In this paper, we propose a temporal extension to CoopFlow, an existing approach for designing and modeling IOWs, based on Time Petri Net models and tools. Methods are given, based on reachability analysis and model checking techniques, for verifying whether or not the added temporal requirements are satisfied, while maintaining the core advantage of CoopFlow; i.e. that each partner can keep the critical parts of its business process privat

User-generated content curation with deep convolutional neural networks

In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy.This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051) of the Catalan Government.Peer Reviewe

Automated curation of brand-related social media images with deep learning

This paper presents a work consisting in using deep convolutional neural networks (CNNs) to facilitate the curation of brand-related social media images. The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. When appropriate, we also apply object detection, usually to discover images containing logos. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy. We examine the impact of different configurations and derive conclusions aiming to pave the way towards systematic and optimized methodologies for automatic UGC curation.Peer Reviewe

Evaluation of anti-diabetic and anti-tumoral activities of bioactive compounds from Phoenix dactylifera L’s leaf: In vitro and in vivo approach

International audienceAmong various chronic disorders, cancer and diabetes mellitus are the most common disorders. This study was designed to evaluate the effectiveness of hydroalcoholic extract of Phoenix dactylifera L. leaves (HEPdL) in animal models of type II diabetes in vitro/in vivo and in a human melanoma-derived cell line (IGR-39). A liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis was also performed to determine the amount of phenolic and flavonoid compounds in this plant. The physicochemical results by LC–MS/MS analysis of HEPdL showed the presence of 10 phenolic compounds. The in vitro study showed that the extract exhibited a more specific and potent inhibitor of α-glucosidase than α-amylase with an IC50 value of 20 ± 1 μg/mL and 30 ± 0.8 μg/mL, respectively. More importantly, the in vivo study of the postprandial hyperglycemia activity with (20 mg/kg) of HEPdL showed a decrease in plasma glucose levels after 60 min in resemblance to the glucor (acarbose) (50 mg/kg) effect. The oral administration of HEPdL (20 mg/kg) in alloxan-induced diabetic mices for 28 days showed a more significant anti-diabetic activity than that of the drug (50 mg/kg). Moreover, cytotoxicity effects of HEPdL in IGR-39 cancer cell lines were tested by MTT assay. This extract was effective in inhibiting cancer cells growth (IGR-39) at dose 35 and 75 μg/mL. These results confirm ethnopharmacological significance of the plant and could be taken further for the development of an effective pharmaceutical drug against diabetes and cance

User-generated content curation with deep convolutional neural networks

In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call visual brand identity recognition. We report experiments with 5 real brands in which more than 1 million real images were analyzed. In order to speed-up the training of custom CNNs we applied a transfer learning strategy.This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the SGR programme (2014-SGR-1051) of the Catalan Government.Peer Reviewe

Phylogeny and Classification of Human Papillomavirus (HPV)16 and HPV18 Variants Based on E6 and L1 genes in Tunisian Women with Cervical Lesions

International audienceBACKGROUND:High-risk human papillomavirus (HPV) types are the main etiological factors for cervical cancer. HPV16 and HPV18 are generally the most common forms associated with development of high-grade cervical lesions. This study was undertaken to identify intratypic variants of HPV16 and HPV18 among women with cervical lesions in Tunisia.MATERIALS AND METHODS:DNA was extracted from cervical samples collected from 49 women. using a PureLinkTM Genomic DNA mini Kit (Invitrogen). E6 and L1 open reading frames (ORF) were amplified by PCR and viral DNA amplicons were subjected to automated sequencing using Big Dye Terminators technology (Applied Biosystems). The obtained sequences were analyzed using an appropriate software program to allow phylogenetic trees to be generated.RESULTS:HPV16 and HPV18 were detected in 15 and 5 cases, respectively. HPV16 E6 sequences clustered with the European German lineage (A2) whereas one isolate diverged differently in the L1 region and clustered with the African sub-lineage (B1). HPV 18 E6 sequences clustered with the European sub-lineage (A1) but L1 sequences clustered as a new clade which diverged from A1-A5.CONCLUSIONS:Our results suggest that the distribution of HPV16 and HPV18 sequences in women with cervical lesions in Tunisia is mainly related to European epidemiological conditions and point to the presence of recombinant HPV forms