Miracle-dz/UniFAR-FTUs huggingface dataset:
Responding to Sensitive Disclosures on Social Media

When people disclose information on social media that is sensitive or potentially stigmatized (e.g., mental illness, pregnancy loss), how do others decide to respond? We use interviews and vignettes to provide a response decision-making framework (RDM) that explains factors informing whether and how individuals respond to sensitive disclosures from their social media connections. The RDM framework includes factors related to the self, poster, and disclosure context (i.e., relational, temporal, social). Our findings include how people's decisions are complicated by balancing their own needs (e.g., privacy, wellbeing) as well as the posters’ (e.g., support) when seeing what they consider sensitive posts on social media. We identify empirically grounded insights and information that social media designs could surface to support both potential disclosers and responders. We argue that social media sites should provide privacy controls for both disclosers and responders, and facilitate the visibility of network-level support.
EasyFont

Generating personal handwriting fonts with large amounts of characters is a boring and time-consuming task. For example, the official standard GB18030-2000 for commercial font products consists of 27,533 Chinese characters. Consistently and correctly writing out such huge amounts of characters is usually an impossible mission for ordinary people. To solve this problem, we propose a system, EasyFont, to automatically synthesize personal handwriting for all (e.g., Chinese) characters in the font library by learning style from a small number (as few as 1%) of carefully-selected samples written by an ordinary person. Major technical contributions of our system are twofold. First, we design an effective stroke extraction algorithm that constructs best-suited reference data from a trained font skeleton manifold and then establishes correspondence between target and reference characters via a non-rigid point set registration approach. Second, we develop a set of novel techniques to learn and recover users’ overall handwriting styles and detailed handwriting behaviors. Experiments including Turing tests with 97 participants demonstrate that the proposed system generates high-quality synthesis results, which are indistinguishable from original handwritings. Using our system, for the first time, the practical handwriting font library in a user’s personal style with arbitrarily large numbers of Chinese characters can be generated automatically. It can also be observed from our experiments that recently-popularized deep learning based end-to-end methods are not able to properly handle this task, which implies the necessity of expert knowledge and handcrafted rules for many applications.
Overview of the EVALITA 2018 Cross-Genre Gender Prediction (GxG) Task

English. The Gender Cross-Genre ( GxG ) task is a shared task on author proﬁling (in terms of gender) on Italian texts, with a speciﬁc focus on cross-genre performance. This task has been proposed for the ﬁrst time at EVALITA 2018, providing different datasets from different textual genres: Twitter, YouTube, Children writing, Journalism, Personal diaries. Re-sults from a total of 50 different runs show that the task is difﬁcult to learn in itself: while almost all runs beat a 50% base-line, no model reaches an accuracy above 70%. We also observe that cross-genre modelling yields a drop in performance, but not as substantial as one would expect. Italiano.
Merging Datasets for Hate Speech Classification in Italian

This paper presents an approach to the shared task HaSpeeDe within Evalita 2018. We followed a standard machine learning procedure with training, validation, and testing phases. We considered word embedding as features and deep learning for classification. We tested the effect of merging two datasets in the classification of messages from Facebook and Twitter. We concluded that using data for training and testing from the same social network was a requirement to achieve a good performance. Moreover, adding data from a different social network allowed to improve the results, indicating that more generalized models can be an advantage. ll manoscritto presenta un approccio per la risoluzione dello shared task HaSpeeDe organizzato all’interno di Evalita 2018. La classificazione è stata condotta con caratteristiche del testo estratte con word embedding e utilizzando algoritmi di deep learning. Abbiamo voluto sperimentare l’effetto dell’integrazione di messaggi di Facebook e Twitter ha e abbiamo ottenuto due risultati. 1) Addestrare modelli con un dataset integrato migliora le performance di classificazione in datasets provenienti dai singoli social network suggerendo una migliore capacità di generalizzazione del modello. 2) Tuttavia, utilizzare modelli addestrati su datasets provenienti da un social network per classificare messaggi provenienti da un altro social network comporta un peggioramento delle performance indicando che è indispensabile includere nel train set messaggi dello stesso social network che si è interessati a classificare nel test set.
Hate Speech Detection Using Attention-based LSTM

English. This paper describes the system we developed for EVALITA 2018, the 6th evaluation campaign of Natural Language Processing and Speech tools for Italian, on Hate Speech Detection (HaSpeeDe). The task consists in automatically annotating Italian messages from two popular microblogging platforms, Twitter and Facebook, with a boolean value indicating the presence or not of hate speech. We propose an Attention-based in Long Short-Term Memory Recurrent Neural Network where the attention layer helps to calculate the contribution of each part of the text towards targeted hateful messages. Italiano. In questo articolo descriviamo il sistema che abbiamo sviluppato per il task di Hate Speech Detection (HaSpeeDe), presso EVALITA 2018, la sesta campagna di valutazione dellelaborazione del linguaggio naturale. Il task consiste nellannotare automaticamente testi italiani da due popolari piattaforme di microblogging, Twitter e Facebook, con un valore booleano indicando la presenza o meno di incitamento allodio. Il nostro approccio usa una rete neurale ricorrente LSTM attention-based, in cui il layer di attenzione aiuta a calcolare il contributo di ciascuna porzione del testo verso messaggi di odio mirati.
A Comparison of Regularization Techniques in Deep Neural Networks

Artificial neural networks (ANN) have attracted significant attention from researchers because many complex problems can be solved by training them. If enough data are provided during the training process, ANNs are capable of achieving good performance results. However, if training data are not enough, the predefined neural network model suffers from overfitting and underfitting problems. To solve these problems, several regularization techniques have been devised and widely applied to applications and data analysis. However, it is difficult for developers to choose the most suitable scheme for a developing application because there is no information regarding the performance of each scheme. This paper describes comparative research on regularization techniques by evaluating the training and validation errors in a deep neural network model, using a weather dataset. For comparisons, each algorithm was implemented using a recent neural network library of TensorFlow. The experiment results showed that an autoencoder had the worst performance among schemes. When the prediction accuracy was compared, data augmentation and the batch normalization scheme showed better performance than the others.
Detect User's Rating Characteristics by Separate Scores for Matrix Factorization Technique

A recommender system can effectively solve the problem of information overload in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance. However, these methods do not pay attention to the influence of a user’s rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, we propose a novel method based on matrix factorization. We consider that the user’s rating score is composed of two parts: the real score, which is decided by the user’s preferences; and the bias score, which is decided by the user’s rating characteristics. We then analyze the user’s historical behavior to find his rating characteristics by using the matrix factorization technique and use them to adjust the final prediction results. Finally, by comparing with the latest algorithms on the open datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieve the best performance in terms of prediction accuracy criterion over other state-of-the-art methods.
Non-circular multi-core fibers for super-dense SDM

: Non-circular multi-core fibers (NC-MCFs) to accommodate more than 100 cores per fiber are proposed for super-dense space-division multiplexing. The proposed MCFs have the potential for easier splicing/connection and their applicability to highly-dense ribbon MCFs for ultra-high capacity optical transport systems.
Detecting Hate Speech for Italian Language in Social Media

English. In this report we describe the hate speech detection system for the Italian language developed by a joint team of researchers from the two universities of Perugia (University for Foreigners of Perugia and University of Perugia). The experimental results obtained in the HaSpeeDe task of the Evalita 2018 evaluation campaign are analyzed. Finally, a suggestion for future research directions is provided in the conclusion. Italiano. In questo documento descriviamo il sistema di hate speech detection per la lingua Italiana sviluppato da una squadra di ricercatori dell’Università per Stranieri di Perugia e dell’Università degli Studi di Perugia. I risultati sperimentali ottenuti nel task HaSpeeDe, organizzato nell’ambito di Evalita 2018, sono riportati e analizzati. Infine, una possibile direzione di ricerca è fornita nelle conclusioni.
Novel Parameterized Distance Measures on Hesitant Fuzzy Sets with Credibility Degree and Their Application in Decision-Making

The subject of this study is to explore the role of cardinality of hesitant fuzzy element (HFE) in distance measures on hesitant fuzzy sets (HFSs). Firstly, three parameters, i.e., credibility factor, conservative factor, and a risk factor are introduced, thereafter, a series of novel distance measures on HFSs are proposed using these three parameters. These newly proposed distance measures handle the relationship between the cardinal number and the element values of hesitant fuzzy set well, and are suitable to combine subjective and objective decision-making information. When using these functions, decision makers with different risk preferences are allowed to give different values for these three parameters. In particular, this study transfers the hesitance degree index to a credibility of the values in HFEs, which is consistent with people’s intuition. Finally, the practicability of the newly proposed distance measures is verified by two examples.
Personal verification based on multi-spectral finger texture lighting images

Finger Texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the Surrounded Patterns Code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. The SPC approach proposes using a single texture descriptor for FT images captured under multispectral illuminations, where this reduces the cost of employing different feature extraction methods for different spectral FT images. Furthermore, a novel classifier termed the Re-enforced Probabilistic Neural Network (RPNN) is proposed. It enhances the capability of the standard Probabilistic Neural Network (PNN) and provides better recognition performance. Two types of FT images from the Multi-Spectral CASIA (MSCASIA) database were employed as two types of spectral sensors were used in the acquiring device: the White (WHT) light and spectral 460 nm of Blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the Equal Error Rates (EERs) at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilizing the RPNN.
A Novel Vertical Fragmentation Method for Privacy Protection Based on Entropy Minimization in a Relational Database

Many scholars have attempted to use an encryption method to resolve the problem of data leakage in data outsourcing storage. However, encryption methods reduce data availability and are inefficient. Vertical fragmentation perfectly solves this problem. It was first used to improve the access performance of the relational database, and nowadays some researchers employ it for privacy protection. However, there are some problems that remain to be solved with the vertical fragmentation method for privacy protection in the relational database. First, current vertical fragmentation methods for privacy protection require the user to manually define privacy constraints, which is difficult to achieve in practice. Second, there are many vertical fragmentation solutions that can meet privacy constraints; however, there are currently no quantitative evaluation criteria evaluating how effectively solutions can protect privacy more effectively. In this article, we introduce the concept of information entropy to quantify privacy in vertical fragmentation, so we can automatically discover privacy constraints. Based on this, we propose a privacy protection model with a minimum entropy fragmentation algorithm to achieve minimal privacy disclosure of vertical fragmentation. Experimental results show that our method is suitable for privacy protection with a lower overhead.
Adaptive total sliding mode control for the current of power factor correction circuit

: The design of an adaptive total sliding mode control (ATSMC) system for the current control of power factor correction (PFC) is addressed in this paper. The designed ATSMC can achieve elegant performance under ideal condition as well as the existence of parameters variations, moreover, the designed controller parameter is adjusted adaptively to overcome the potential chattering. The merits of the designed ATSMC is veri ﬁ ed by a boost type PFC prototype, the simulation and experiment results show that the PFC circuit can achieve unity power factor and sinusoidal grid current, simultaneously, the output voltage has well dynamic and steady state performance.
Overview of the EVALITA 2018 Hate Speech Detection Task

English. The Hate Speech Detection (HaSpeeDe) task is a shared task on Italian social media (Facebook and Twitter) for the detection of hateful content, and it has been proposed for the first time at EVALITA 2018. Providing two datasets from two different online social platforms differently featured from the linguistic and communicative point of view, we organized the task in three tasks where systems must be trained and tested on the same resource or using one in training and the other in testing: HaSpeeDe-FB, HaSpeeDeTW and Cross-HaSpeeDe (further subdivided into Cross-HaSpeeDe FB and Cross-HaSpeeDe TW sub-tasks). Overall, 9 teams participated in the task, and the best system achieved a macro F1score of 0.8288 for HaSpeeDe-FB, 0.7993 for HaSpeeDe-TW, 0.6541 for CrossHaSpeeDe FB and 0.6985 for CrossHaSpeeDe TW. In this report, we describe the datasets released and the evaluation measures, and we discuss results. Italiano. HaSpeeDe è la prima campagna di valutazione di sistemi per l’identificazione automatica di discorsi di incitamento all’odio su social media (Facebook e Twitter) in lingua italiana, proposta nell’ambito di EVALITA 2018. Fornendo ai partecipanti due insiemi di dati estratti da due piattaforme differenti dal punto di vista linguistico e della comunicazione, abbiamo articolato HaSpeeDe in tre compiti in cui i sistemi sono addestrati e testati sulla stessa tipologia di dati oppure addrestrati su una tipologia e testati sull’altra: HaSpeeDe-FB, HaSpeeDe-TW e Cross-HaSpeeDe (a sua volta suddiviso in Cross-HaSpeeDe FB e Cross-HaSpeeDe TW). Nel complesso, 9 gruppi hanno partecipato alla campagna, e il miglior sistema ha ottenuto un punteggio di macro F1 pari a 0,8288 in HaSpeeDe-FB, 0,7993 in HaSpeeDe-TW, 0,6541 in Cross-HaSpeeDe FB e 0.6985 in Cross-HaSpeeDe TW. L’articolo descrive i dataset rilasciati e le modalità di valutazione, e discute i risultati ottenuti.
Extendible slot-type wireless power transfer system with load-independent output voltage based on solenoid coil

: An innovative extendible wireless power transfer system with load independent output voltage is developed in this paper. This system eliminates the interference caused by the cross coupling between nonadja-cent coils and unrelated circuit networks among the receiving coils. The wireless power transfer system consists of a full-bridge converter power supply, transmitting resonator, relay resonators, and multiple receiving resonators. The theory of load independent output voltage is demonstrated in detail in this paper. The structure and features of the system has also been carefully illustrated. The simulation and experimental results show that the multiple receiving resonators do not a ﬀ ect each other when obtaining power and that the system can obtain load-independent output voltage. Experimental results and simulation analysis are highly consistent and the integrity of the theory is veri ﬁ ed.
The Impact of a 3D Visual Programming Tool on Students' Performance and Attitude in Computer Programming: A Case Study in Jordan

Learning programming can be challenging, particularly object-oriented programming (OOP). However, using visualization could be useful in enhancing students' learning OOP concepts. In this study, the impact of using a 3D visual programming tool – Alice 2 – on student performance and attitude was explored in an introductory computer programming course using Java. Research participants were undergraduate computing students at Arab Open University – Jordan branch. Quasi-experimental design was adopted in this research, where two groups of students were chosen. The findings of this research showed that using Alice has positively impacted on students' performance and attitude towards computer programming and learning OOP concepts. The study suggests the incorporation of Alice in teaching introductory programming courses.
Characterization of HIV-2 Protease Structure by Studying Its Asymmetry at the Different Levels of Protein Description

HIV-2 protease (PR2) is a homodimer, which is an important target in the treatment of the HIV-2 infection. In this study, we developed an in silico protocol to analyze and characterize the asymmetry of the unbound PR2 structure using three levels of protein description by comparing the conformation, accessibility, and flexibility of each residue in the two PR2 chains. Our results showed that 65% of PR2 residues have at least one of the three studied asymmetries (structural, accessibility, or flexibility) with 10 positions presenting the three asymmetries in the same time. In addition, we noted that structural and flexibility asymmetries are linked indicating that the structural asymmetry of some positions result from their large flexibility. By comparing the structural asymmetry of the crystallographic and energetically minimized structures of the unbound PR2, we confirmed that the structural asymmetry of unbound PR2 is an intrinsic property of this protein with an important role for the PR2 deformation upon ligand binding. This analysis also allowed locating asymmetries corresponding to crystallization artefacts. This study provides insight that will help to better understand the structural deformations of PR2 and to identify key positions for ligand binding.
Temperature and Water Availability During Maturation Affect the Cytokinins and Auxins Profile of Radiata Pine Somatic Embryos

Somatic embryogenesis (SE) provides us a potent biotechnological tool to manipulate the physical and chemical conditions (water availability) along the process and to study their effect in the final success in terms of quantity of somatic embryos produced. In the last years, our research team has been focused on the study of different aspects of the SE in Pinus spp. One of the main aspects affecting SE is the composition of culture media; in this sense, phytohormones play one of the most crucial roles in this propagation system. Many studies in conifers have shown that different stages of SE and somatic embryo development are correlated with distinct endogenous phytohormone profiles under the stress conditions needed for the process (i.e., cytokinins play a regulatory role in stress signaling, which it is essential for radiata pine SE). Based on this knowledge, the aim of this study was to test the effect of different temperatures (18, 23, and 28°C) and gelling agent concentrations (8, 9, and 10 gL-1) during the maturation stage of Pinus radiata SE in maturation and germination rates. Parallel, phytohormone profile of somatic embryos developed was evaluated. In this sense, the highest gellan gum concentration led to significantly lower water availability. At this gellan gum concentration and 23°C a significantly higher number of somatic embryos was obtained and the overall success of the process increased with respect to other treatments assayed. The somatic embryos produced in these conditions showed the highest concentration of iP-type cytokinins and total ribosides. Although, the different conditions applied during maturation of somatic embryos led to different hormonal profiles, they did not affect the ex vitro survival of the resulting somatic plants, where no significant differences were observed.