Abstract
Service Design (SD) and Design Thinking (DT) evolved in the last decade and have become popular in the research field of service science. However, the application of SD and DT research outcomes into practice is still scarce. To help understanding the differences between research and practice, we conducted 20 semi-structured interviews with professionals and trainees from four organizations that are involved in service innovation projects. The results reveal several similarities and complementarities, (dis)advantages, requests and obstacles, which hinder companies from implementing and using structured SD and DT approaches. The findings present some challenges for both researchers and practitioners on actions they could take to overcome barriers and foster the SD and DT practice within organizations.

Abstract
We present a solution submitted to the Social Media and Harassment Competition held in collaboration with ECML PKDD 2019 Conference. The dataset used is as set of tweets and the first task was on the detection of harassment tweets. To deal with this problem, we proposed a solution based on a gradient tree-boosting algorithm. The second task was categorization harassment tweets according to the type of harassment, a multiclass classification problem. For this problem we proposed a LSTM network model. The solutions proposed for these tasks presented good predictive accuracy.

Abstract
Harmonic representation models are widely used, notably in speech coding and synthesis. In this paper, we describe two fully parametric harmonic representation and signal reconstruction alternatives that rely on a shift-invariant harmonic phase model and that implement accurate frame-based synthesis in the frequency-domain, and accurate pitch pulse-based synthesis in the time-domain. We use natural spoken and sung voice signals in order to assess the objective and subjective quality of both alternatives when parameters are exact, and when they are replaced by compact and shift-invariant harmonic phase and magnitude approximation models. We highlight the flexibility of these models and present results indicating that not only does the compact shift-invariant phase model cause a smaller impact than that caused by harmonic magnitude modeling, but it also compares favorably to results presented in the literature.

Abstract
In this paper, we present a computationally efficient and fully parametric harmonic speech model that is suitable for real-time flexible frame-based analysis and synthesis implementation in the frequency domain. We carry out a performance comparison between this vocoder and similar ones, such as WORLD and HPMD. Then, a deliberate manipulation of the speaker's fundamental frequency micro-variations is performed in order to understand in which way it conveys prosodic and idiosyncratic information. We conclude our discussion by evaluating the impact of these manipulations through the realization of perceptual tests. © 2020 IEEE.

Abstract
With the advances of the big data era in biology, deep learning have been incorporated in analysis pipelines trying to transform biological information into valuable knowledge. Deep learning demonstrated its power in promoting bioinformatics field including sequence analysis, bio-molecular property and function prediction, automatic medical diagnosis and to analyse cell imaging data. The ambition of this work is to create an approach that can fully explore the relationships across modalities and subjects through mining and fusing features from multi-modality data for cell state classification. The system should be able to classify cell state through multimodal deep learning techniques using heterogeneous data such as biological images, genomics and clinical annotations. Our pilot study addresses the data acquisition process and the framework capable to extract biological parameters from cell images.

Abstract
Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.