Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Eye Tracking Data using Symbolic Aggregate Approximation

Oculomotor disturbance (OMD) is a common vision problem, meaning that the left and right eye do not cooperate properly, i.e., by having a common gaze point. Eye tracking technology (ET) promises support for identifying problematic eye coordination. Data from the eye tracker is based on time series of screen positions and the stimuli movements, which are recorded, following a structural pattern. A vision specialist can analyze and interpret the graphical plots of the time series visually to get a better understanding of the problems related to gaze movements. However, this is tedious and time consuming due to the huge amount of data collected by ETs, or the necessity for replaying the tests. This paper explores a method to automatically analyzing the results of a screening to indicate potential OMD problems by applying Symbolic Aggregate Approximation (SAX) and pinpointing relevant features for OMD. The potential benefits of the method are investigated via examples considering the distance between left and right gaze points. This indicates promising results for faster examining large data sets and discussing possibilities for future extensions for considering eye movement parameters based on real-time measurements of the distance between the stimuli and the gaze points.
The Justice Access Game: Crowd-Sourced Evaluation of Systems for Pro Se Litigants

A variety of computational tools and systems have become available in recent years to assist pro se (self-represented) litigants in formulating claims and defenses in legal forums. However, there has been little exploration of techniques for evaluation of these systems to establish their relative effectiveness and identify specific strengths and weaknesses of each approach. This paper proposes the Justice Access Game , a crowd-sourced method for comparative evaluation of legal information systems. The Justice Access Game is a framework applicable to wide range of domains and systems.
Third Person First Person ? " Cleaning Dishes " Learned Joint Representation Transfer "

Several theories in cognitive neuroscience suggest that when people interact with the world, or simulate interactions, they do so from a first-person egocentric perspective, and seamlessly transfer knowledge between third-person (observer) and first-person (actor). Despite this, learning such models for human action recognition has not been achievable due to the lack of data. This paper takes a step in this direction, with the introduction of Charades-Ego, a large-scale dataset of paired first-person and third-person videos, involving 112 people, with 4000 paired videos. This enables learning the link between the two, actor and observer perspectives. Thereby, we address one of the biggest bottlenecks facing egocentric vision research, providing a link from first-person to the abundant third-person data on the web. We use this data to learn a joint representation of first and third-person videos, with only weak supervision, and show its effectiveness for transferring knowledge from the third-person to the first-person domain. ∗Work was done while Gunnar was at Inria. †Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France.
Generative Quantile Regression with Variability Penalty

Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this paper, we introduce a deep learning generative model for joint quantile estimation called Penalized Generative Quantile Regression (PGQR). Our approach simultaneously generates samples from many random quantile levels, allowing us to infer the conditional distribution of a response variable given a set of covariates. Our method employs a novel variability penalty to avoid the problem of vanishing variability, or memorization, in deep generative models. Further, we introduce a new family of partial monotonic neural networks (PMNN) to circumvent the problem of crossing quantile curves. A major benefit of PGQR is that it can be fit using a single optimization, thus bypassing the need to repeatedly train the model at multiple quantile levels or use computationally expensive cross-validation to tune the penalty parameter. We illustrate the efficacy of PGQR through extensive simulation studies and analysis of real datasets. Code to implement our method is available at https://github.com/shijiew97/PGQR.
Iteratively Training Look-Up Tables for Network Quantization

Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values. We show that this method is very flexible and that many other techniques can be seen as special cases of LUT-Q. For example, we can constrain the dictionary trained with LUT-Q to generate networks with pruned weight matrices or restrict the dictionary to powers-of-two to avoid the need for multiplications. In order to obtain fully multiplier-less networks, we also introduce a multiplier-less version of batch normalization. Extensive experiments on image recognition and object detection tasks show that LUT-Q consistently achieves better performance than other methods with the same quantization bitwidth.
A COMPARATIVE STUDY FOR BREAST CANCER DETECTION BY NEURAL APPROACH FOR DIFFERENT CONFIGURATIONS OF THE MICROWAVE IMAGING SYSTEM

The study done in this paper focuses on the detection of breast cancer by neuronal approach, by rotating the transmitting antenna from 15◦, 30◦, 45◦, 60◦, 75◦, to 90◦ relative to its initial position which is of 0◦ (i.e., to the opposite of the receiving antenna). We have generated our database by using a CST electromagnetic simulator for each antenna location. Then the learning and test phases of our artificial neural network (ANN) are done for seven antennae locations using two learning algorithms which are: the Scaled Conjugate Gradient Back-propagation (Trainscg) and the Gradient Descent with Momentum (Traingdm). A comparative study was conducted for all the seven cases. The results obtained are very satisfying and show that the best location of the transmitter antenna is at 60◦ and that the learning algorithm Trainscg gives better results than Traingdm.
Multi-Task Variational Information Bottleneck

—Multi-task learning (MTL) is an important subject in machine learning and artiﬁcial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks.
CURE : An Efficient Clustering Algorithm for Large Databases

Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering algorithm called CURE that is more robust to outliers, and identifies clusters having non-spherical shapes and wide variances in size. CURE achieves this by representing each cluster by a certain fixed number of points that are generated by selecting well scattered points from the cluster and then shrinking them toward the center of the cluster by a specified fraction. Having more than one representative point per cluster allows CURE to adjust well to the geometry of non-spherical shapes and the shrinking helps to dampen the effects of outliers. To handle large databases, CURE employs a combination of random sampling and partitioning. A random sample drawn from the data set is first partitioned and each partition is partially clustered. The partial clusters are then clustered in a second pass to yield the desired clusters. Our experimental results confirm that the quality of clusters produced by CURE is much better than those found by existing algorithms. Furthermore, they demonstrate that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality.
Large Copy-Number Variations in Patients With Statin-Associated Myopathy Affecting Statin Myopathy-Related Loci

Summary Some patients are susceptible to statin-associated myopathy (SAM) either because of genetic variations affecting statin uptake and metabolism, or because they predispose their carriers to muscular diseases. Among the frequent variants examined using the genome-wide association study approach, SLCO1B1 c.521T>C represents the only validated predictor of SAM in patients treated with high-dose simvastatin. Our aim was to ascertain the overall contribution of large copy-number variations (CNVs) to SAM diagnosed in 86 patients. CNVs were detected by whole genome genotyping using Illumina HumanOmni2.5 Exome BeadChips. Exome sequence data were used for validation of CNVs in SAM-related loci. In addition, we performed a specific search for CNVs in the SLCO1B region detected recently in Rotor syndrome subjects. Rare deletions possibly contributing to genetic predisposition to SAM were found in two patients: one removed EYS associated previously with SAM, the other was present in LARGE associated with congenital muscular dystrophy. Another two patients carried deletions in CYP2C19, which may predispose to clopidogrel-statin interactions. We found no common large CNVs potentially associated with SAM and no CNVs in the SLCO1B locus. Our findings suggest that large CNVs do not play a substantial role in the etiology of SAM.
Char Emb Feature Embedding Layer RNN Layer Attention Layer Answer Layer Shared Highway BiLSTM BiLSTMBiLSTM Attention Self-Attention Self-Attended Question Attention Choice-Attended Text Attention Choice-Attended Question Match Elem-wise Weighted Sum Aspect _ CT Match Aspect _ CQ Final Answer Elem-wi

This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neural network called Hybrid Multi-Aspects (HMA) model, which mimic the human’s intuitions on dealing with the multiple-choice reading comprehension. In this model, we aim to produce the predictions in multiple aspects by calculating attention among the text, question and choices, and combine these results for final predictions. Experimental results show that our HMA model could give substantial improvements over the baseline system and got the first place on the final test set leaderboard with the accuracy of 84.13%.
Is that an opportunity? Global versus local processing of technological and socioeconomic constraints

ABSTRACT Opportunity beliefs lead entrepreneurs to explore or walk away from opportunities. The dominant process for explaining opportunity beliefs is structural alignment theory’s analogical problem solving of information. Information can be conceptualized according to its structure with some information presented as separate pieces of information (local) and others as aggregated information (global). We conducted an experiment with 116 upper-level managers and engineers, and found that structural and procedural similarities between technologies and socioeconomic conditions of markets drive opportunity beliefs. We found that the constraining effects of technological and socioeconomic differences on opportunity beliefs are contingent on individuals’ global versus local processing.
Time-crystalline behavior in central-spin models with Heisenberg interactions

Time-crystalline behavior has been predicted and observed in quantum central-spin systems with periodic driving and Ising interactions. Here, we theoretically show that it can also arise in central-spin systems with Heisenberg interactions. We present two methods to achieve this: application of a sufficiently large Zeeman splitting on the central spin compared to the satellite spins, or else by applying additional pulses to the central spin every Floquet period. In both cases, we show that the system exhibits a subharmonic response in spin magnetizations in the presence of disorder for both pure Heisenberg and XXZ interactions. Our results pertain to any XXZ central-spin system, including hyperfine-coupled electron-nuclear systems in quantum dots or color centers.
PRNN: Recurrent Neural Network with Persistent Memory

Although Recurrent Neural Network (RNN) has been a powerful tool for modeling sequential data, its performance is inadequate when processing sequences with multiple patterns. In this paper, we address this challenge by introducing an external memory and constructing a novel persistent memory augmented RNN (term as PRNN) model. The PRNN model captures the principle patterns in training sequences and stores them in the explicit memory. By leveraging the persistent memory, the proposed method can adaptively update states according to the similarities between encoded inputs and memory slots, leading to a stronger capacity in assimilating sequences with multiple patterns. Content-based addressing is suggested in memory accessing, and gradient descent is utilized for implicitly updating the memory. Experiments on several datasets demonstrate the effectiveness of the proposed method.
Similar but different : revealing the relative roles of species-traits versus biome 9 properties structuring genetic variation in South American marsh rats 10 11

This article is protected by copyright. All rights reserved 1 MS. JOYCE RODRIGUES PRADO (Orcid ID : 0000 02-2025-5479) 2 DR ALEXANDRE REIS PERCEQUILLO (Orcid ID : 0000 002-7892-8912) 3 4 5 Article type : Research Paper 6 7 8 Title: Similar but different: revealing the relative roles of species -traits versus biome 9 properties structuring genetic variation in South American marsh rats 10 11 Joyce R. Prado , Alexandre R. Percequillo , Andréa T. Thomaz , L. Lacey Knowles 13 3 12
Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multi-Spectral MR Image Using Convolutional Neural Network

The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While a quantitative evaluation of the stroke lesions on the magnetic resonance images (MRIs) is effective in clinical diagnosis, manually segmenting the stroke lesions is commonly used, which is, however, a tedious and time-consuming task. Therefore, how to segment the stroke lesions in a fully automated manner has recently extracted extensive attentions. Considering that the clinically acquired MRIs usually have thick slices, we propose a 2D-slice-based segmentation method. In particular, we use multi-spectral MRIs, i.e., diffusion weighted image, apparent diffusion coefficient, and T2-weighted image, as input, and propose a residual-structured fully convolutional network (Res-FCN). The proposed Res-FCN is trained and evaluated on a large data set with 212 clinically acquired MRIs, which achieves a mean dice coefficient of 0.645 with a mean number of false negative lesions of 1.515 per subject. The proposed Res-FCN is further evaluated on a public data set, i.e., ISLES2015-SISS, which presents a very competitive result among all 2D-slice-based segmentation methods.
Klasifikasi Sentimen Pada Movie Review Dengan Metode Multinomial Naïve Bayes

Opini orang lain terhadap suatu movie review di media sangat penting dalam membuat suatu keputusan. Untuk mengetahui opini seseorang terhadap suatu movie review di media diperlukan sistem yang dapat mempermudah dalam mengetahui opini seseorang. Klasifikasi sentimen dapat membantu dalam membangun sistem untuk mengetahui opini seseorang terhadap movie review. Dataset yang digunakan dalam proses klasifikasi sentimen ini adalah Internet Movie Database (IMDb). Namun yang menjadi permasalahan dalam mengetahui polaritas suatu opini dalam proses klasifikasi sentimen pada dataset movie review adalah adannya data yang tidak terstuktur, atribut data yang begitu banyak serta adannya negasi yang menyebabkan polaritas suatu kata akan berbeda pada konteks teks yang berbeda. Degan permasalahan tersebut maka proses klasifikasi pada dataset tersebut akan di klasifikasikan ke dalam dua kelas polaritas yaitu positif dan negatif. Metode klasifikasi yang digunakan adalah dengan menggunakan metode multinomial naive bayes. Untuk meningkatkan nilai akurasi metode multinomial naive bayes dilakukan dengan memecahkan masalah diatas. Dalam memecahkan permasalahan tersebut yang dilakukan adalah pertama, akan dilakukan proses preprocessing untuk menangani data noisy. Kedua, dilakukan penanganan negasi, adapun lingkup permasalah negasi yang akan dilakukan adalah negasi dengan kata “not”, “n’t”, “no”. Ketiga, dilakukan penghitungan bobot setiap kata dengan menggunakan TF-IDF. Berdasarkan hasil pengujian yang telah dilakukan diperoleh nilai akurasi terbesar 85,16%. Hal tersebut dikarenakan multinomial naive bayes dengan negation handling berdasarkan punctuation, preprocessing dan TF-IDF dapat meningkatkan nilai akurasi terhadap metode multinomial naive bayes. Kata kunci : Multinomial Naive Bayes, TF-IDF, Preprocessing, Negasi
INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY: APPLIED BUSINESS AND EDUCATION RESEARCH

This study investigated the effect of the directive and non-directive school inspection approaches on teacher instructional effectiveness. While teacher instructional effectiveness was studied in terms of reporting on pupil academic achievements, teacher attitudes, effective planning, attendance and pedagogical approaches, the directive School inspection approach was studied in terms of directing and evaluation of teacher knowledge yet the non-directive approach was studied in terms of team planning and consultations. A cross-sectional survey mixed research design using the quantitative and qualitative approaches on a sample of 178 people namely teachers, head teachers, school inspectors and education officers was adopted. While quantitative data was collected using survey questionnaires and analysed using inferential analysis, qualitative data was collected through interviews, focus group discussions and analysed by grouping the responses in themes which were merged with quantitative findings. The findings showed that all the attributes of directive inspection approach were positively insignificant to teacher instructional effectiveness explaining that they had no impact on teacher instructional effectiveness. Similarly, team planning had a positive and insignificant relationship meaning that it had no effect on teacher instructional effectiveness. In conclusion, only team planning had effect on teacher instructional effectiveness and consultation did not have any effect. It was recommended that school inspectors should use the non-directive inspection approach because of sub-variable team planning that had a positive effect on teacher instructional effectiveness.
A Key to Your Heart : Biometric Authentication Based on ECG Signals

Over the recent years, biometric authentication has become ubiquitous. We make use of biometric authentication when we unlock our smartphones using fingerprint scanners, access bank services using voice recognition and enter other countries using automated border control gates with facial recognition technology. In this project, I investigated a biometric based on the electrical activity of the human heart as recorded by the electrocardiogram (ECG) signals. In order to design an ECGbased biometric system, I started by creating a dataset containing records of 55 users. The data collection was performed over a period of four months, which allowed me to investigate the stability of ECG-based biometrics. After collecting the ECG data, I designed and fully implemented the biometric authentication system, which included signal processing, feature extraction and ECG trace classification. The obtained biometric system was evaluated using two different approaches, and compared with the results from existing studies. The results from this work support the claim that ECG-based biometrics can be successfully used for personal authentication. Nevertheless, more research needs to be done in order to improve the long-term performance of ECG-based biometric systems.
The Truth, The Whole Truth, and Nothing But the Truth

An unsound claim can misdirect a field, encouraging the pursuit of unworthy ideas and the abandonment of promising ideas. An inadequate description of a claim can make it difficult to reason about the claim, for example, to determine whether the claim is sound. Many practitioners will acknowledge the threat of unsound claims or inadequate descriptions of claims to their field. We believe that this situation is exacerbated, and even encouraged, by the lack of a systematic approach to exploring, exposing, and addressing the source of unsound claims and poor exposition. This article proposes a framework that identifies three sins of reasoning that lead to unsound claims and two sins of exposition that lead to poorly described claims and evaluations. Sins of exposition obfuscate the objective of determining whether or not a claim is sound, while sins of reasoning lead directly to unsound claims. Our framework provides practitioners with a principled way of critiquing the integrity of their own work and the work of others. We hope that this will help individuals conduct better science and encourage a cultural shift in our research community to identify and promulgate sound claims.
The Truth, the Whole Truth, and Nothing But the Truth

Truth maintenance is a collection of techniques for doing belief revision. A truth maintenance system's task is to maintain a set of beliefs in such a way that they are not known to be contradictory and no belief is kept without a reason. Truth maintenance systems were introduced in the late seventies by Jon Doyle and in the last five years there has been an explosion of interest in this kind of systems. In this paper we present an annotated bibliography to the literature of truth maintenance systems, grouping the works referenced according to several classifications.