Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupled Recurrent Network (CRN)

Many semantic video analysis tasks can benefit from multiple, heterogenous signals. For example, in addition to the original RGB input sequences, sequences of optical flow are usually used to boost the performance of human action recognition in videos. To learn from these heterogenous input sources, existing methods reply on two-stream architectural designs that contain independent, parallel streams of Recurrent Neural Networks (RNNs). However, two-stream RNNs do not fully exploit the reciprocal information contained in the multiple signals, let alone exploit it in a recurrent manner. To this end, we propose in this paper a novel recurrent architecture, termed Coupled Recurrent Network (CRN), to deal with multiple input sources. In CRN, the parallel streams of RNNs are coupled together. Key design of CRN is a Recurrent Interpretation Block (RIB) that supports learning of reciprocal feature representations from multiple signals in a recurrent manner. Different from RNNs which stack the training loss at each time step or the last time step, we propose an effective and efficient training strategy for CRN. Experiments show the efficacy of the proposed CRN. In particular, we achieve the new state of the art on the benchmark datasets of human action recognition and multi-person pose estimation.
Spotting Micro-Expressions on Long Videos Sequences

This paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i.e. SAMM and CAS(ME)2. First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-χ2-distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-χ2-distance method is to compare the feature difference by calculating χ2 distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-χ2-distance method in terms of F1-score on both databases.
An Efficient L-Shape Fitting Method for Vehicle Pose Detection with 2D LiDAR

Detecting vehicles with strong robustness and high efficiency has become one of the key capabilities of fully autonomous driving cars. This topic has already been widely studied by GPU-accelerated deep learning approaches using image sensors and 3D LiDAR, however, few studies seek to address it with a horizontally mounted 2 $D$ laser scanner. 2 $D$ laser scanner is equipped on almost every autonomous vehicle for its superiorities in the field of view, lighting invariance, high accuracy and relatively low price. In this paper, we propose a highly efficient search-based L-Shape fitting algorithm for detecting positions and orientations of vehicles with a 2D laser scanner. Differing from the approach to formulating L-Shape fitting as a complex optimization problem, our method decomposes the L-Shape fitting into two steps: L-Shape vertexes searching and L-Shape corner localization. Our approach is computationally efficient due to its minimized complexity. In on-road experiments, our approach is capable of adapting to various circumstances with high efficiency and robustness.
Goal-based Course Recommendation

With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on the findings and methodologies of a quickly developing literature around prediction and recommendation in higher education and develop a novel recurrent neural network-based recommendation system for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. We validate the model using tests of grade prediction and the ability to recover prerequisite relationships articulated by the university. In the third validation, we run the fully personalized recommendation for students the semester before taking a historically difficult course and observe differential overlap with our would-be suggestions. While not proof of causal effectiveness, these three evaluation perspectives on the performance of the goal-based model build confidence and bring us one step closer to deployment of this personalized course preparation affordance in the wild.
Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images

Sublingual vein is commonly used to diagnose the health status. The width of main sublingual veins gives information of the blood circulation. Therefore, it is necessary to segment the main sublingual veins from the tongue automatically. In general, the dataset in the medical field is small, which is a challenge for training the deep learning model. In order to train the model with a small data set, the proposed method for automatically segmenting the sublingual veins is to re-train U-net model with different sets of the limited number of labels for the same training images. With pre-knowledge of the segmentation, the loss of the trained model will be convergence easier. To improve the performance of the segmentation further, a novel strategy of data augmentation was utilized. The operation for masking output of the model with the input was randomly switched on or switched off in each training step. This approach will force the model to learn the contrast invariance and avoid overfitting. Images of dataset were taken with the developed device using eight near infrared LEDs. The final segmentation results were evaluated on the validation dataset by the IoU metric.
Precision Highway for Ultra Low-Precision Quantization

Neural network quantization has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To resolve this problem, we propose precision highway, which forms an end-to-end high-precision information flow while performing the ultra low-precision computation. First, we describe how the precision highway reduce the accumulated quantization error in both convolutional and recurrent neural networks. We also provide the quantitative analysis of the benefit of precision highway and evaluate the overhead on the state-of-the-art hardware accelerator. In the experiments, our proposed method outperforms the best existing quantization methods while offering 3-bit weight/activation quantization with no accuracy loss and 2-bit quantization with a 2.45 % top-1 accuracy loss in ResNet-50. We also report that the proposed method significantly outperforms the existing method in the 2-bit quantization of an LSTM for language modeling.
Nonlinear Robust Filtering of Sampled-Data Dynamical Systems

This work is concerned with robust filtering of nonlinear sampled-data systems with and without exact discrete-time models. A linear matrix inequality (LMI) based approach is proposed for the design of robust $H_{\infty}$ observers for a class of Lipschitz nonlinear systems. Two type of systems are considered, Lipschitz nonlinear discrete-time systems and Lipschitz nonlinear sampled-data systems with Euler approximate discrete-time models. Observer convergence when the exact discrete-time model of the system is available is shown. Then, practical convergence of the proposed observer is proved using the Euler approximate discrete-time model. As an additional feature, maximizing the admissible Lipschitz constant, the solution of the proposed LMI optimization problem guaranties robustness against some nonlinear uncertainty. The robust H_infty observer synthesis problem is solved for both cases. The maximum disturbance attenuation level is achieved through LMI optimization. At the end, a path to extending the results to higher-order approximate discretizations is provided.
Towards resilient machine learning for ransomware detection

There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ML in threat detection has demonstrated to be a good cop to guard platform security. However it is imperative to evaluate - is ML-powered security resilient enough? In this paper, we juxtapose the resiliency and trustworthiness of ML algorithms for security, via a case study of evaluating the resiliency of ransomware detection via the generative adversarial network (GAN). In this case study, we propose to use GAN to automatically produce dynamic features that exhibit generalized malicious behaviors that can reduce the efficacy of black-box ransomware classifiers. We examine the quality of the GAN-generated samples by comparing the statistical similarity of these samples to real ransomware and benign software. Further we investigate the latent subspace where the GAN-generated samples lie and explore reasons why such samples cause a certain class of ransomware classifiers to degrade in performance. Our focus is to emphasize necessary defense improvement in ML-based approaches for ransomware detection before deployment in the wild. Our results and discoveries should pose relevant questions for defenders such as how ML models can be made more resilient for robust enforcement of security objectives.
Training Deep Capsule Networks

The capsules of Capsule Networks are collections of neurons that represent an object or part of an object in a parse tree. The output vector of a capsule encodes the so called instantiation parameters of this object (e.g. position, size, or orientation). The routing-by-agreement algorithm routes output vectors from lower level capsules to upper level capsules. This iterative algorithm selects the most appropriate parent capsule so that the active capsules in the network represent nodes in a parse tree. This parse tree represents the hierarchical composition of objects out of smaller and smaller components. In this paper, we will show experimentally that the routing-by-agreement algorithm does not ensure the emergence of a parse tree in the network. To ensure that all active capsules form a parse tree, we introduce a new routing algorithm called dynamic deep routing. We show that this routing algorithm allows the training of deeper capsule networks and is also more robust to white box adversarial attacks than the original routing algorithm.
Graph-Based Algorithm for a User-Aware SaaS Approach: Computing Optimal Distribution

As a tool to exploit economies of scale, Software as a Service cloud models promote Multi-Tenancy which is the notion of sharing instances among a large group of tenants. However, Multi-Tenancy only satisfies requirements that are common to all tenants as well as the fact that tenants themselves hesitate about sharing. In a try to solve this problem, the present paper propose a User-Aware approach for Software as a Service models using Rich-Variant Components. The main contribution of this approach is a framework summarized in a graph-based algorithm enabling deduction of an optimal distribution of instances on application's tenants. To illustrate and evaluate the framework, the approach is applied on a Software as a Service Application for private school management.
L’envelliment en persones amb trastorn mental sever

Son moltes les investigacions que han aprofundit en l’estudi de l’esquizofrenia i algunes aporten resultats de persones que estan a la tercera edat. L’objectiu principal d’aquest estudi es descriure alguns aspectes medics i de gestio de casos en poblacio gran afectada de trastorn mental. Un segon objectiu es observar si hi ha un major risc cardiovascular en edat avancada en comparacio amb persones de menor edat. Es tracta d’un estudi descriptiu i transversal amb una mostra de 220 subjectes afectats de trastorn mental sever, 33 dels quals sobrepassen els 60 anys. Es van utilitzar els registres sobre les dades mediques, cliniques i de funcionament social durant l’any 2011. Entre els resultats principals s’ha trobat una bona adherencia al tractament i al seguiment(95,3%-90% respectivament) una bona participacio familiar en el tractament(90%), nivells mes elevats de dislipemies(colesterol 70,6%; triglicerids 64,3%), menor nombre de fumadors(28%). Les valoracions psicopatologiques no mostren significacio estadistica respecte al grup mes jove (p=.468) ni en reingressos ni urgencies(p=.181;.420 respectivament). Les dades que es presenten en aquest estudi fan emfasi en la necessitat de tenir cura dels malalts mentals tambe a la tercera edat, on la fragilitat fisica i psicosocial s’accentua a nivell de comorbilitat amb altres malalties mediques. Paraules clau: tercera edat, trastorn mental sever, malalties mediques
Fixed Points, Induction, and Coinduction in Order Theory, Set Theory, (PL) Type Theory, Category Theory, and Logic: A Concise Summary

In this note we present the formulation of the induction and coinduction principles and related notions, such as fixed points, using the language (a.k.a., jargon) and conventions of each of order theory, set theory, (first-order) logic, category theory, and the theory of types in (object-oriented and functional) programming languages, for the purpose of examining some of the similarities and dissimilarities between these six mathematical subdisciplines. As a side-benefit that is of relevance to researchers of programming languages in particular, our comparison demonstrates one of the fundamental differences between structural typing (dominant in FP) and nominal typing (dominant in mainstream OOP). 1 Order Theory (using ≤, for ‘is less than or equal to’) Order theory is the first branch of mathematics where the notions fixed point, pre-fixed point, post-fixed point, least fixed point, greatest fixed point, induction, and coinduction were formulated and the relations between them were proven [29]. Order theory seems to be the simplest and most natural setting in which these notions can be defined and studied. We thus start this note by presenting the order-theoretic formulation of these notions in this section. Let ≤ be an ordering relation (also called a partial order) over a set O and let F : O→ O be an endofunction over O (sometimes also called a self-map over O, i.e., a function whose domain and codomain are the same). An element/point P ∈ O is called a pre-fixed point of F if its F -image—the image of P under F , namely F (P )—is less than or equal to it, i.e., if
Two algorithms for the package-exchange robot-routing problem

We present and analyze two new algorithms for the package-exchange robot-routing problem (PERR): restriction to inidividual paths (RIP) and bubbletree. RIP provably produces a makespan that is $O(\text{SIC}+k^2)$, where SIC is the sum of the lengths of the individual paths and $k$ is the number of robots. Bubbletree produces a makespan that is $O(n)$, where $n$ is the number of nodes. With optimizations bubbletree can also achieve a makespan of $O((k+l)\text{log}k)$, where $l$ is the longest path from start to goal in the bubbletree subgraph.
Constructing Faithful Homomorphisms over Fields of Finite Characteristic

We study the question of algebraic rank or transcendence degree preserving homomorphisms over finite fields. This concept was first introduced by Beecken et al. [3] and exploited by them, and Agrawal et al. [2] to design algebraic independence–based identity tests using the Jacobian criterion over characteristic zero fields. An analogue of such constructions over finite characteristic fields was unknown due to the failure of the Jacobian criterion over finite characteristic fields. Building on a recent criterion of Pandey et al. [15], we construct explicit faithful maps for some natural classes of polynomials in the positive characteristic field setting, when a certain parameter called the inseparable degree of the underlying polynomials is bounded (this parameter is always 1 in fields of characteristic zero). This presents the first generalisation of some of the results of Beecken et al. [3] and Agrawal et al. [2] in the positive characteristic setting.
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages

Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU—the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.https://github.com/siddiquelatif/URDU-Dataset.
The Study of Regional Structures Identification by Using Remote Sensing Images in Metallogenic Deposit Prognosis

As an effective assistant method, remote sensing technology has been used in metallogenic deposit prognosis for many years. One key application is to identify regional structures by using remote sensing images. However, there is not a very effective way to do so yet. This paper presents a regional structures identification model by using remote sensing images, and the method of automatic extracting feature information from remotesensing images, and recognizing regional structures through spatial correlative analysis with gravitation, magnetic information, which includes remote sensing data processing, linear and circular structures extraction, and spatial correlative analysis of gravitation, magnetic and remote sensing information.
KAYIN, OKUME VE KAVAK SOYMA KAPLAMALARINDAN FARKLI KOMBİNASYONLARDA ÜRETİLEN KONTRPLAKLARIN BAZI TEKNİK ÖZELLİKLERİNİN KARŞILAŞTIRILMASI

Bu calismada kayin ( Fagus orientalis Lipsky), okume ( Aucoumea klaineana ) ve melez kavak ( Populus x eureamericana I 45/51) soyma kaplamalarindan farkli kombinasyonlarda, ure formaldehit tutkali kullanilarak uretilen uc tabakali kontrplaklarin, yogunluk, cekme-makaslama direnci, egilme direnci ve egilmede elastiklik modulu degerleri belirlenmistir. Sonuc olarak kayin kontrplaklarin yogunlugu, cekme-makaslama ve egilme direncleri ile elastiklik modulu, kavak ve okume kontrplaklarinkinden, kavak kontrplaklarin cekme makaslama direnci ise okume kontrplaklarin cekme-makaslama direncinden buyuk bulunmustur. Okume ve kavak kontrplaklarinin egilme direncleri arasindaki fark ise belirgin degildir. Dis tabakasi kayin olanlarin cekme-makaslama direnci daha yuksek, okume olanlarin ise en dusuktur. Orta tabakalari okume ve kavak olan kontrplaklarin cekme-makaslama direncleri arasinda fark gorulmezken, orta tabakasi kayin olan kontrplaklarin cekme-makaslama direnci digerlerinden daha yuksek bulunmustur. Orta tabakasi kayin ve okume kaplamalardan uretilmis kontrplaklarin egilme direncleri ve egilmede elastiklik modulu degerleri arasindaki fark onemsiz cikmistir.
Danse exotique, danse érotique.: Perspectives géographiques sur la mise en scène du corps de l'Autre

EnglishSince the 19th century, Occidental males have been mesmerized by exotic dancing. The colonial and patriarchal society made of this exotic show an erotic one, and, in the 1950's, exotic dance came to mean strip tease in the USA. Exotic/ erotic dance is not just a symptom: it is also a metonymy and a process of colonization. Exotic dance is a way to control the Other's body, and to put it/her at the colonizer's disposal. Nevertheless, it gave to some dancers the opportunity to challenge colonial and gender stereotypes' and their white male public. francaisLa danse exotique, qui fascine les hommes occidentaux des le XIX e siecle, s'inscrit dans le cadre d'une societe coloniale et patriarcale. Elle possede une dimension plus ou moins explicitement erotique, consubstantielle a son exotisme. C'est dans cette logique que l'expression exotic dance en vient a designer le strip-tease dans les annees 1950 aux Etats-Unis. La danse exotique/erotique n'est pas que la manifestation secondaire de la colonisation : elle en est la metonymie, voire le vecteur. Le processus colonial est lie a la domination et a la mise a disposition du corps de l'autre, dont la danse exotique est une forme importante. Elle peut aussi etre l'occasion pour la danseuse de transgresser les normes et de s'imposer a travers le role qui lui est donne.
Genetik algoritmalar ile perdeli yapı sisteminin maliyet optimizasyonu

Bu calismada, perdeli betonarme yapi sistemlerinin Genetik Algoritma Yontemi ile maliyet optimizasyonu yapilmaktadir. Tasarim degiskenleri perde boyutlaridir ve yapinin toplam malzeme maliyetini minimize eden perde boyutlari aranmaktadir. “ABYYHY” ve “TS 500” de perde boyutlari ve donati ile ilgili kosullar problemin kisitlamalari olarak alinmistir. Optimizasyon probleminin cozumunde yapiyi olusturan diger tasiyici sistem eleman boyutlarinin sabit kaldigi kabul edilmistir. Sayisal uygulamalarda kullanilmak uzere, yapisal sistemlerin minimum maliyet tasarimi icin gerekli olan optimum perde boyutlarini belirleyen bir bilgisayar programi gelistirilmistir. Orneklerin cozumunde, minimize edilmek istenen maliyet fonksiyonu beton ve donati maliyetlerini icermektedir. Sunulan calismada, sayisal uygulama olarak, 8 katli bir perde yapinin Genetik Algoritma ile optimizasyonu ayrintili olarak verilmektedir. Anahtar Kelimeler : Genetik algoritma, perdeli sistemler, yapisal optimizasyon, yapay zeka.