Miracle-dz/UniFAR-FTUs huggingface dataset:
Children With Dyslexia and Familial Risk for Dyslexia Present Atypical Development of the Neuronal Phonological Network

Learning to read changes the brain language system. Phonological processing is the language domain most crucial for reading, but it is still unknown how reading acquisition modifies the neural phonological network in children who either develop dyslexia or are at risk of dyslexia. For the two first years of formal education, we followed 90 beginning readers with (n = 55) and without (n = 35) familial risk of dyslexia who became typical readers (n = 70) or developed dyslexia (n = 20). We used functional magnetic resonance imaging to identify the neural correlates of phonological awareness using an auditory rhyme judgment task. This task was applied when participants were starting formal education, and repeated 2 years later. By applying two alternative group splits, we analyzed the effects of dyslexia and the effects of familial risk of dyslexia separately. We found that the phonological brain network undergoes reorganization during the first 2 years of formal education. This process proceeds differently depending on the presence of a familial history of dyslexia and reading impairment. Typical readers without risk for dyslexia activate structures responsible for phonological processing already at the beginning of literacy. This group shows reduced brain activation over time during phonological processing, perhaps due to automatization of phonological skills. Children who develop reading impairment present a delay in the development of phonological structures such as the bilateral superior temporal gyri, left middle temporal gyrus, right insula and right frontal cortex, where we observed time and group interaction. Finally, typical readers with familial risk of dyslexia also present an atypical development of the neural phonological structures, visible both at the beginning of reading instruction and 2 years later. These children used a presumably efficient neural mechanism of phonological processing, based on the activation of the precentral and postcentral gyri, and achieved a typical level of phonological awareness.
Manipulation and Malicious Personalization: Exploring the Self-Disclosure Biases Exploited by Deceptive Attackers on Social Media

In the real world, the disclosure of private information to others often occurs after a trustworthy relationship has been established. Conversely, users of Social Network Sites (SNSs) like Facebook or Instagram often disclose large amounts of personal information prematurely to individuals which are not necessarily trustworthy. Such a low privacy-preserving behavior is often exploited by deceptive attackers with harmful intentions. Basically, deceivers approach their victims in online communities using incentives that motivate them to share their private information, and ultimately, their credentials. Since motivations, such as financial or social gain vary from individual to individual, deceivers must wisely choose their incentive strategy to mislead the users. Consequently, attacks are crafted to each victim based on their particular information-sharing motivations. This work analyses, through an online survey, those motivations and cognitive biases which are frequently exploited by deceptive attackers in SNSs. We propose thereafter some countermeasures for each of these biases to provide personalized privacy protection against deceivers.
Combined Optokinetic Treatment and Vestibular Rehabilitation to Reduce Visually Induced Dizziness in a Professional Ice Hockey Player After Concussion: A Clinical Case

Background: The appropriate detection and therapy of concussion symptoms are of great importance to avoid long-term impairment and absence from pre-concussive activities, such as sport, school or work. Post-traumatic headache and dizziness are known as risk factors of persistent symptoms after a concussion. Dizziness has even been classified as a predictor for symptom persistence. One type of dizziness, which has never been considered is visually induced dizziness (VID) often develops as a consequence of vestibular impairment. This manuscript presents the clinical case of a 25-year-old male, professional ice hockey player, whereby a therapeutic approach to VID after concussion is demonstrated. Case: A detailed interdisciplinary clinical and laboratory-assisted neurological, neurovestibular and ocular-motor examination was performed 20 days post-concussion, which indicated VID symptoms. Thus, the player qualified for a 5-day combined vestibular, balance and optokinetic therapy, which aimed to reduce the player's increased sensitivity to visual information. Each treatment day consisted of two sessions: vestibular/ocular-motor training and exposure to optokinetic stimuli combined with postural control exercises. The optokinetic stimulus was delivered in the form of a rotating disk. VID symptoms were recorded daily via posturography and a visual analog scale prior to the optokinetic sessions. The player improved over the course of each treatment day and was able to return to ice hockey 15 days after the final treatment session. Three months later the player reported no symptoms in the follow up questionnaire. Conclusion: The combination of vestibular, balance and optokinetic therapy led to remission of VID symptoms in a professional ice hockey player after multiple concussions, within a short time frame after his last concussion. Thus, this case study highlights the significant benefit of treating post-concussive VID symptoms utilizing a multi-modal approach.
An Initial Computational Model for Musical Schemata Theory

Musical schemata theory entails the classiﬁcation of subphrase-length progressions in melodic, harmonic and metric feature-sets as named entities (e.g., ‘Romanesca’, ‘Meyer’, ‘Cadence’, etc.), where a musical schema is characterized by factors such as music content and form, position and tonal function within phrase structure, and in-terrelation with other schemata. To examine and auto-mate the task of musical schemata classiﬁcation, we developed a novel musical schemata classiﬁer. First, we tested methods for exact and approximate matching of user-deﬁned schemata prototypes, to establish the notions of identity and similarity between composite music patterns. Next, we examined methods for schemata prototype extraction from collections of same-labelled annotated examples, performing training and testing sessions similar to supervised learning approaches. The performance of the above tasks was veriﬁed using the same annotated dataset of 40 keyboard sonata excerpts from pre-Classical and Classical periods. Our evaluation of the classiﬁer sheds light on: (a) ability to parse and interpret music information, (b) similarity methods for composite music patterns, (c) categorization methods for polyphonic music.
Fake and Hyper-partisan News Identification

Fake news represent a phenomenon that has widened over the last period of time. Their spreading is facilitated by social media platforms, which have a major influence on people’s lives. In this context, automated solutions are sought to detect the fake news circulating online. In this paper, we developed models designed to detect fake and hyper-partisan news using various machine learning algorithms. The paper contains a description of the attributes used and their importance in identifying fake news, and also different trained classifiers using more dataset distributions in order to see the differences between their results. In addition to the results analyzed on existing datasets in the field, we tested the ability of models to work in a real environment. Thus, we collected URLs from Twitter, tested the models on these data and made statistics on the number of fake news articles found by each classifier. We also gave some examples detected by models on articles from Twitter which we considered fake news or hyper-partisan news by manual analyzing their content. Author
Statistical Music Structure Analysis Based on a Homogeneity-, Repetitiveness-, and Regularity-Aware Hierarchical Hidden Semi-Markov Model

This paper describes a music structure analysis method that splits music audio signals into meaningful segments such as musical sections and clusters them. In this task, how to model the four fundamental aspects of musical sections, i.e. , homogeneity, repetitiveness, novelty, and regularity, in a uniﬁed way is still an open problem. Here we pro-pose a solid statistical approach based on a homogeneity, repetitiveness-, and regularity-aware hierarchical hidden semi-Markov model. The higher-level semi-Markov chain represents a sequence of sections that tend to have regularly spaced boundaries. The timbral features in each section are assumed to follow emission distributions that are homogeneous over time. The lower-level left-to-right Markov chain in each section represents a chord sequence whose sequential order is constrained to be a repetition of a chord sequence in another section of the same cluster. The whole model can be trained unsupervisedly based on Bayesian sparse learning where unnecessary sections automatically degenerate. The proposed method outperformed representative methods in segmentation and clustering accuracies with estimated sections having similar statistical properties as the ground truth data.
Fast and Flexible Neural Audio Synthesis

Autoregressive neural networks, such as WaveNet, have opened up new avenues for expressive audio synthesis. High-quality speech synthesis utilizes detailed linguistic features for conditioning, but comparable levels of con-trol have yet to be realized for neural synthesis of musical instruments. Here, we demonstrate an autoregressive model capable of synthesizing realistic audio that closely follows ﬁne-scale temporal conditioning for loudness and fundamental frequency. We ﬁnd the appropriate choice of conditioning features and architectures improves both the quantitative accuracy of audio resynthesis and qualitative responsiveness to creative manipulation of conditioning. While large autoregressive models generate audio much slower than real-time, we achieve these results with a more efﬁcient WaveRNN model, opening the door for exploring real-time interactive audio synthesis with neural networks.
bertZH at GermEval 2019: Fine-Grained Classification of German Offensive Language using Fine-Tuned BERT

The bertZH system for abusive tweet detection in the GermEval 2019 competition is a neural classiﬁer based on BERT (Devlin et al., 2018). We describe our submission runs for subtask 2 on ﬁne-grained classiﬁ-cation of tweets. We used the pretrained German language model from deepset.ai 1 implemented in pytorch and ﬁne-tuned it to the data of the task, before then using it to train on the classiﬁcation task. We also experimented with the pretrained multilingual BERT model from Google Research implemented in keras , but it resulted in a worse score than with the German model. We have found that a language-speciﬁc BERT model outperforms a multilingual model and that ﬁne-tuning a BERT model to the tasks domain achieves a small gain in performance.
Combining Simulation and FPGA Based Verification to an Affordable and Ultra-Fast Multi-Billion-Gate Verification System

Many traditional technologies exist in the dynamic verification field. These are for instance virtual prototyping, simulation, emulation, FPGA based in-system verification and FPGA based system prototyping. Many attempts have been made in the past to find hybrid solutions which are supposed to be superior to these basic technologies. The goal is to find the right trade-off between cost, bring-up time, compile/synthesis time, design capacity, execution speed and their possibilities to be used in-system. This paper proposes a novel many-core hardware system and a design compilation flow, which combines most advantages of the aforementioned methodologies. Most noteworthy are the low cost, the high signal visibility, the fast execution speed and its extremely high capacity of multi-billion gate designs.
DEMOGRAPHIC FINDINGS OF TRAMADOL POISONED WOMEN ADMITTED TO AYATOLLAH TALEGHANI HOSPITAL, URMIA, IRAN FROM JAN 2012 TO JAN 2013

Background & Aims: Tramadol poisoning has dangerous side effects. Considering limited researches on tramadol poisoning, especially in women population, this study tries to investigate the demographic specifications of tramadol intoxicated women in Urmia. Materials & Methods: In this cross-sectional descriptive study, all tramadol intoxicated women admitted in Ayatollah Taleghani teaching hospital in 2013 were included in the study. The data were collected using a researches’ made checklist. After data analysis, the results were reported as frequency, percent, mean and standard deviation, and frequency tables. Results: Among 175 all the patients referring in 2013, 38 patients (21.7%) were women. The mean age of tramadol intoxicated patients was 22.5 ± 6.9 years. Twenty patients (52.6%) were single. The level of educations in 35 patients (92.1%), were less than or equal to high school diploma. Thirty patients (78.9%) had ingested drugs for suicidal attempt. Conclusion: Tramadol is an opioid drug which is easily accessible and misused in the community. Based on results of the current study, the young and low educated women are also victims of tramadol abuse. Therefore, increasing the awareness of the community about adverse effects of tramadol abuse, and limiting the ease accessibility of patients to the drug may decrease the number and side effects of tramadol intoxication.
Data Usage in MIR: History & Future Recommendations

The MIR community faces unique challenges in terms of data access, due in large part to country-speciﬁc copy-right laws. As a result, there is an emerging divide in the MIR research community between labs that have access to music through large companies with abundant funds, and independent labs at smaller institutions who do not have such expansive access. This paper explores how independent researchers have worked to overcome limitations of access to music data without contributing to the crisis of reproducibility. Acknowledging that there is no single solution for every data access problem that smaller labs face, we propose a number of possibilities for how the MIR community can bridge the gap between advancements from large companies and those within academia. As MIR looks towards the next 20 years, democratizing and expanding access to MIR research and music data is critical. Future solutions could include a distributed MIREX system, an API designed for MIR researchers, and community-led advocacy to stakeholders.
Toward Modeling Cache-Miss Ratio for Dense-Data-Access-Based Optimization

Adapting a source code to the specificity of its host hardware represents one way to implement software optimization. This allows to benefit from processors that are primarily designed to improve system performance. To reach such a software/hardware fitting without narrowing the scope of the optimization to few executions, one needs to have at his disposal relevant performance models of the considered hardware. This paper proposes a new method to optimize software kernels by considering their data-access mode. The proposed method permits to build a data-cache-miss model of a given application regarding its specific memory-access pattern. We apply our method in order to evaluate some custom implementations of matrix data layouts. To validate the functional correctness of the generated models, we propose a reference algorithm that simulates a kernel's exploration of its data. Experimental results show that the proposed data alignment permits to reduce the number of cache misses by a factor up to 50%, and to decrease the execution time by up to 30%. Finally, we show the necessity to integrate the impact of the Translation Lookaside Buffers (TLB) and the memory prefetcher within our performance models.
The Relationship between Emotional Intelligence and Mental Health of Students of different field at IAU, Qaemshahr Branch, Iran

The present study evaluated the relationship between emotional intelligence and mental health of high school students in different field of Islamic Azad University of Qaemshahr. This study is a descriptive-survey research and the method of data collection is library study and questionnaires. Statistical population of research includes 3672 people and statistical sample includes 350 people of students. Descriptive and inferential statistics were applied to analyze data using SPSS software. Bar-On Emotional Intelligence and the Goldberg and Hillier's mental health (GHQ-28) questionnaires were used to measure emotional intelligence and mental health, respectively. Results from Pearson correlation coefficient test showed a significant relationship between emotional intelligence and mental health for different field. Findings showed that there is significant relationship between emotional intelligence and components of mental health of student in deferent field.
Efficiently Realizing Interval Sequences

We consider the problem of realizable interval-sequences. An interval sequence comprises of $n$ integer intervals $[a_i,b_i]$ such that $0\leq a_i \leq b_i \leq n-1$, and is said to be graphic/realizable if there exists a graph with degree sequence, say, $D=(d_1,\ldots,d_n)$ satisfying the condition $a_i \leq d_i \leq b_i$, for each $i \in [1,n]$. There is a characterisation (also implying an $O(n)$ verifying algorithm) known for realizability of interval-sequences, which is a generalization of the Erdos-Gallai characterisation for graphic sequences. However, given any realizable interval-sequence, there is no known algorithm for computing a corresponding graphic certificate in $o(n^2)$ time. In this paper, we provide an $O(n \log n)$ time algorithm for computing a graphic sequence for any realizable interval sequence. In addition, when the interval sequence is non-realizable, we show how to find a graphic sequence having minimum deviation with respect to the given interval sequence, in the same time. Finally, we consider variants of the problem such as computing the most regular graphic sequence, and computing a minimum extension of a length $p$ non-graphic sequence to a graphic one.
Guitar Tablature Estimation with a Convolutional Neural Network

Guitar tablature is a popular notation guitarists use to learn and share music. As it stands, most tablatures are created by an experienced guitarist taking the time and effort to annotate a song. As the process is time consuming and requires expertise, we are interested in automating this task. Previous approaches to automatic tablature transcription break the problem into two steps: 1) polyphonic pitch estimation, followed by 2) tablature ﬁngering arrangement. Using a convolutional neural network (CNN) model, we can jointly solve both steps by learning a mapping directly from audio data to tablature. The model can simultaneously leverage physical playability constraints and differences in string timbres implicit in the data to determine the actual ﬁngerings being used by the guitarist. We propose TabCNN, a CNN for estimating guitar tablature from audio of a solo acoustic guitar performance. We train and test our network using microphone recordings from the GuitarSet dataset [24], and TabCNN outperforms a state-of-the-art multipitch estimation algorithm. We also introduce a set of metrics to evaluate guitar tablature estimation.
Can We Listen To It Together?: Factors Influencing Reception of Music Recommendations and Post-Recommendation Behavior

Few prior studies on music recommendations investigate the context in which users receive the recommendations, and what impact the recommendation has on the user. In this paper, we aim to better understand the factors that affect people’s decisions as to whether they choose to listen to music recommendations and how the recommendations impact their music-listening behaviors. We conducted an online survey asking about people’s past experiences on giving and receiving music recommendations. We found that in addition to the aesthetic qualities of music and the respondent’s taste, expectations regarding the delivery (e.g., timing, persistence) of the recommendations, familiarity, trust in the recommender’s abilities, and the rationale for suggestions were important factors. We discuss the implications for the design of music recommenders based on the findings, including better rationale for and accessibility of recommended music, improved saving options, and more targeted delivery at specific times. The data also suggests disparities in how people wish to receive music recommendations and what will influence them to listen to recommendations, versus how they would like to offer recommendations to others. In addition, the findings highlight the importance of music recommendations in people’s existing social relationships and their role in building/improving new relationships.
Detecting the boundaries of sentence-like units in spoken German

Automatic division of spoken language transcripts into sentence-like units is a challenging problem, caused by disﬂuencies, ungrammatical structures and the lack of punctuation. We present experiments on dividing up German spoken dialogues where we investigate the impact of task setup and data representation, encoding of context information as well as different model architectures for this task
An Exploration of Commuter Travel Time Through Social Media Analytics on the Cloud

The population of Melbourne is growing at over 100,000 every year. This is impacting on all aspects of society: house prices, health, multi-ethnic society, and transport amongst many others. Social media data is a hugely popular data source for research in many domains from understanding urban environments to predicting election results. In this paper we present an approach to identify commuter travel patterns and calculate the average travel time of commuters around Melbourne using social media data from Twitter, Instagram, FourSquare and Flickr. At present there is no other technology or system that captures this information other than by randomly sampling subsets of the population. To achieve this, the social media data was transformed into travel vectors and subsequently filtered in order to facilitate analysis and reduce noise in the data. Travel patterns were then learned using K-means clustering and use of force-directed graphing techniques. Travel pattern identification was based on social media user movement between suburb boundaries in the city of Melbourne. The calculated travel time was compared to the expected travel time by train. A web based platform was developed to allow analysis of the results. The results show that social media can indeed be used to better understand commuting behaviours, although it is highly dependent on the amount of data and especially geo-coded data from individual users.
Automatic Choreography Generation with Convolutional Encoder-decoder Network

Automatic choreography generation is a challenging task because it often requires an understanding of two abstract concepts - music and dance - which are realized in the two different modalities, namely audio and video, respectively. In this paper, we propose a music-driven choreography generation system using an auto-regressive encoder-decoder network. To this end, we ﬁrst collected a set of multimedia clips that include both music and corresponding dance motion. We then extract the joint coordinates of the dancer from video and the mel-spectrogram of music from audio and train our network using music-choreography pairs as input. Finally, a novel dance motion is generated at the inference time when only music is given as an input. We performed a user study for a qualitative evaluation of the proposed method, and the results show that the proposed model is able to generate musi-cally meaningful and natural dance movements given an unheard song. We also revealed through quantitative evaluation that the network has created a movement that correlates with the beat of music.