SIMCO: SIMilarity-based object COunting

We present SIMCO, the first agnostic multi-class object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (circle, square, rectangle, etc.). Each object detected is described by a low-dimensional embedding, obtained from a novel similarity-based head branch; this latter implements a triplet loss, encouraging similar objects (same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this embedding for clustering, so that different types of objects can emerge and be counted, making SIMCO the very first multi-class unsupervised counter. Experiments show that SIMCO provides state-of-the-art scores on counting benchmarks and that it can also help in many challenging image understanding tasks

Computational Aesthetics for Fashion

The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately

POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion

We propose a data-centric pipeline able to generate exogenous observation data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e., predicting the performance of a brand-new clothing probe with no available past observations. Our pipeline manufactures the missing past starting from a single, available image of the clothing probe. It starts by expanding textual tags associated with the image, querying related fashionable or unfashionable images uploaded on the web at a specific time in the past. A binary classifier is robustly trained on these web images by confident learning, to learn what was fashionable in the past and how much the probe image conforms to this notion of fashionability. This compliance produces the POtential Performance (POP) time series, indicating how performing the probe could have been if it were available earlier. POP proves to be highly predictive for the probe's future performance, ameliorating the sales forecasts of all state-of-the-art models on the recent VISUELLE fast-fashion dataset. We also show that POP reflects the ground-truth popularity of new styles (ensembles of clothing items) on the Fashion Forward benchmark, demonstrating that our webly-learned signal is a truthful expression of popularity, accessible by everyone and generalizable to any time of analysis. Forecasting code, data and the POP time series are available at: https://github.com/HumaticsLAB/POP-Mining-POtential-PerformanceComment: ECCV 202

Mentoring Style, Self-Description, and Academic Achievement in English Class

The intention of the study was to examine student mentoring, self-description, and academic achievement in a selected private university in Jakarta, Indonesia. There were 150 respondents in the study. The 2 instruments used for collecting data were adopted from Cohen (1995) for identifying the mentoring style of the mentors of the students, and from Marsh (1999) for identifying self-description of the students. The analysis of data employed descriptive statistics (independent t-test) as well as Chisquare, One-way ANOVA, and Two-way ANOVA. The research inquiries focused on the following issues: (1) identifying the mentoring style, self-description, and academic achievement of the students; (2) the relationship of mentoring style, self-description, academic achievement, and demographic profiles; and (3) the interactive effects—individual and joint—of mentoring style, self-description, and student academic achievement.  The findings of the study showed that 2 mentoring styles were predominant among their mentors: relationship emphasis and mentor model; students perceived themselves with a self-description focused on spiritual values; and students had high academic performance. Both male and female students perceived similar mentoring styles among their mentors, while, 1st year and 2nd year students perceived mentoring style to be different among their mentors. In self-description, differences were found between genders while there was no difference found between 1st and 2nd year students. There was no difference found between gender and year of study in the academic achievement, the students showed high performance. Mentoring style and self-description did not have a significant individual or joint difference on academic achievement. Since the students, as a whole had high academic achievement, this study seemed to suggest that the different mentoring style did not have a difference on their academic achievement. However, that did not mean that mentoring did not work. On the contrary, it seemed that mentoring, regardless of style—based on the high academic achievement scores—did work. However, there was also the possibility that high achieving students might not need mentoring for improving their academic achievement. &nbsp

On the use of learning-based forecasting methods for ameliorating fashion business processes: A position paper

The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of business processes are involved in this large-scale industry, but due to the generally short life-cycle of clothing items, supply-chain management and retailing strategies are crucial for good market performance. Correctly understanding the wants and needs of clients, managing logistic issues and marketing the correct products are high-level problems with a lot of uncertainty associated to them given the number of influencing factors, but most importantly due to the unpredictability often associated with the future. It is therefore straightforward that forecasting methods, which generate predictions of the future, are indispensable in order to ameliorate all the various business processes that deal with the true purpose and meaning of fashion: having a lot of people wear a particular product or style, rendering these items, people and consequently brands fashionable. In this paper, we provide an overview of three concrete forecasting tasks that any fashion company can apply in order to improve their industrial and market impact. We underline advances and issues in all three tasks and argue about their importance and the impact they can have at an industrial level. Finally, we highlight issues and directions of future work, reflecting on how learning-based forecasting methods can further aid the fashion industry.Comment: 2nd International Workshop on Industrial Machine Learning @ ICPR 202

European and Italian regulation on orphan medicinal products

The paper presents an overview of the European and Italian Regulation on Orphan Medicinal Products (OMPs), along with some data on the OMPs licensed in the EU from 2000 to 2012. The EU legislation encourages pharmaceutical companies to develop drugs for rare diseases, so-called “orphan drugs”. The European Medicine Agency recognizes orphan drug status mainly on the basis of the prevalence of the disease (≤ 5/10,000), and potential benefit. Orphan status implies incentives for pharmaceutical companies. From 2000 up to 2012 890 candidate orphan drug designations received a positive opinion and the marketing authorization was granted to 72 OMPs corresponding to 80 different indications. Currently, 59 OMPs are available to Italian patients either because licensed to the market by the AIFA or included in the list of the L. 648/96. Despite of an encouraging regulation nearly all the currently estimated rare diseases still await treatments

Mathematic Skill, Verbal Skill, Mathematic Achievement, and English Achievement

This descriptive quantitative study was intended to analyze and describe the respondents’ description of mathematic skills, verbal skills, Mathematic and English Language achievements. This study was primarily meant to find significant difference in mathematic skills and verbal skills, Mathematic and English Language for female and male respondents. Correlations of both mathematic and verbal skills with both Mathematics and English achievements were investigated along with their effect sizes. This study investigated 90 male and 90 female students a private high school in Tomohon City. The findings showed that the students had average level of mathematic skills, average level of verbal skills, average level of Mathematic achievement and good level of English achievement. No significant difference was found in the self-description of verbal skill, self-description of mathematic skill, and their achievements in Mathematic subject for female and male students. Male students, however, had significantly higher score for their self-description of verbal skill. The students were more likely to have high scores in Mathematic subject as they gave high score on self-description of mathematic skill, even when self-description of verbal skill was controlled statistically. They were also more likely to have high score in achievement in English subject as they had high score on self- description of verbal skill verbal, even when self-description of mathematic skill was average

STRATEGI PENGEMBANGAN KAWASAN WISATA LIKUPANG KABUPATEN MINAHASA UTARA

This study aims to evaluate the condition of tourist areas in Likupang, to know the internal factors and external factors Likupang tourist location development and its strategies of development. The type of this research is descriptive qualitative. Selection of sample research used purposive sampling method. The data collections in this research are primary and secondary data. Primary data were collected through interviews, observations and discussions and documentation. The interview was based on the questionnaire. The discussion was conducted by conducting Focus Group Discussion (FGD) Activities. While secondary data collected from various agencies, among others, Central Bureau of Statistics and North Minahasa District Office. Data were analyzed using SWOT analysis and Development Strategy. The result of research shows that the development strategy of Likupang tourism area based on SWOT analysis result lies in position of Quadrant I which is located between external opportunity and internal strength where the result of analysis obtained by total score of IFAS (Internal Strategic Factor Summary Analysis) is 3,498 and EFAS (External Strategic Factor Summary Analysis) is 3,854. The research concludes that Likupang tourism area development strategy can be done by maintaining panorama and sustainability of beach and Marine Park and also trying to complete infrastructure facilities especially the availability of electricity, water and internet network. Further accelerate the construction of new connecting road from the airport to Likupang so as to shorten the distance and travel time and also set up tourist information center for tourists

Learning Styles and English Learning Achievement

The purpose of this study was to determine the relationship between the learning styles and English achievement of a selected private Junior High School students in Manado. This research also showed the description of the student learning styles and English learning achievement.  An adapted questionnaire was used to gather data from 182 student participants, wherein 55 students participated in the pilot study and 127 students participated in the actual study.  The data were analyzed and interpreted with a statistical software which included the use of both descriptive and inferential statistics. The descriptive statistic showed that the students had high level of all the six learning styles and a very good level of English achievement.  Analysis of inferential statistics revealed that there was no significant relationship between each of the six learning styles and English achievement.  Keywords—learning style, English learning achievement   Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara gaya belajar dan prestasi bahasa Inggris siswa salah satu SMP swasta yang dipilih di Manado. Penelitian ini juga menunjukkan gambaran gaya belajar siswa dan prestasi belajar bahasa Inggris. Kuesioner yang disesuaikan digunakan untuk mengumpulkan data dari 182 peserta siswa, di mana 55 siswa berpartisipasi dalam studi uji coba dan 127 siswa berpartisipasi dalam studi yang sebenarnya. Data dianalisis dan diinterpretasikan dengan perangkat lunak statistik yang meliputi penggunaan statistik deskriptif dan inferensial. Statistik deskriptif menunjukkan bahwa siswa memiliki gaya belajar yang tinggi dan tingkat pencapaian bahasa Inggris yang sangat baik. Analisis statistik inferensial mengungkapkan bahwa tidak ada hubungan yang signifikan antara masing-masing dari enam gaya belajar dan prestasi bahasa Inggris.  Kata kunci—gaya belajar, prestasi belajar bahasa Inggris &nbsp