Miracle-dz/UniFAR-FTUs huggingface dataset:
Modular Facial Expression Recognition using Double Channel DNNs

Recognizing human expressions is an important task for machines to understand emotional changes in humans. However, the the accurate features that are closely linked to changes in expression are difficult to extract due to the influence of individual differences and variations in emotional intensity. The modular approach presented here imitates the human being’s ability to identify a person with a limited facial part. In this article, we demonstrate experimentally that certain parts of the face, such as the eyes, nose, lips and forehead, contribute more to the recognition of expressions. A combination of two deep neural networks is also proposed to extract the characteristics of the facial images provided. Two preprocessing approaches are implemented, Histogram Equalization (to handle illumination) and Data Augmentation (increasing number of facial images), to restrict the regions used for recognition of the facial expression. Two-channel architecture used for implementation, one channel accepts input as a grayscale face image, processed by VGG16 ft (fine-tuned VGG16), and another channel accepts input as histograms face image. the second order gradients (HSOG), processed from the proposed CNN model and extracts the characteristics accordingly. Then concatenate the characteristics from the two channels. The final recognition result is calculated using the SVM and KNN classifiers. Experimental results indicate that the proposed algorithm is able to recognize six basic facial expressions (happiness, sadness, anger, disgust, fear and surprise) with great precision. Fine tuning is effective for FER activities with a well-trained model if there are not enough samples to collected.
Investigation of InGaN/GaN quantum wells for polariton laser diodes

III-nitride based microcavities (MCs) appear as one of the most promising candidates for the realization of room temperature (RT) polariton laser diodes. The present work focuses on the properties of low In content InGaN/GaN multiple quantum wells (MQWs) in terms of inhomogeneous broadening, exciton localization energy, and plastic strain relaxation. For a small number of such QWs, an inhomogeneous line broadening of 41 meV is reported, which is compatible with strong coupling regime requirements. By contrast when considering an InGaN/GaN MQW set, a high density of defects is reported, which is ascribed to plastic strain relaxation. From the evolution of the inhomogeneous line broadening as a function of the number of QWs probed by microphotoluminescence measurements, it is concluded that for the realization of polariton light-emitting devices the QW number should not go beyond 30 for MC structures grown on FS-GaN substrate relying on InGaN/GaN QWs with an indium content similar to 12-15% and thicknesses of 1.5-2 nm/3 nm for the wells and the barriers, respectively. (C) 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
The impact and spreading of a small liquid drop on a non-porous substrate over an extended time scale

High-speed imaging has been used to analyse the impact and spreading of sub-30 μm drops of Newtonian fluids (diethyl phthalate and glycerol–water mixture) on smooth glass surfaces with controlled wettabilities at velocities from 3 to 8 m s−1. Data on drop height and spreading diameter were generated with high time and spatial resolution, over eight orders of magnitude in time scale. During the initial kinematic phase, the contact diameter followed a simple power-law independent of impact speed and surface wettability. In the spreading phase there was significant influence of impact speed, with the time taken to reach the maximum spreading diameter increasing with speed. During the wetting phase, for a hydrophilic substrate the drop spreading followed Tanner's law for all impact speeds. Measurements of the maximum spreading factor were compared with the predictions of analytical models based on energy balance, and were in reasonable agreement. The final spreading factor, however, showed better agreement with the value predicted from a volume conservation model, and some confusion has been identified in the previous literature over the distinction between these two measures of spreading. Good correlation was found between the deposition dynamics over the whole range of time scales of these small drops, and the data for the much larger, mm-sized drops studied in much previous work, provided that the values of initial Reynolds and Weber numbers were similar.
Comfort Evaluation from EEG Dipole Imaging

Different people may have different feelings even in the same environment. However, most of the evaluation index in comfort are based on a fixed standard without considering individual differences. In this study, we focus on the preference of comfort, and discussed the dipole imaging of brain waves to evaluate the comfort. The amygdala is said to be one of the parts of the brain related to comfort. In this paper, we stated the relationship between comfort and the area around the amygdala by dipole imaging.
Player Identification in Different Sports

Identifying players through jersey numbers in sports videos is a challenging task. Jersey number can be distorted and deformed due to variation of the player’s posture and the camera’s view. Moreover, it varies in font and size due to the different sports fields. In this paper, we present a deep learning-based framework to address these challenges of jersey number recognition. Our framework has three main parts. Firstly, it detects players on the court using state of the art object detector YOLO V4. Secondly, each jersey number per detected player bounding boxes is localized. Then a four-stage scene text recognition is employed for recognizing detected number regions. A benchmark dataset consists of three subsets is collected. Two subsets include player images from different fields in basketball sport and the third includes player images from ice hockey sport. Experiments show that the proposed approach is effective compared to state-of-the-art jersey number recognition methods. This research makes the automation of player identification applicable across
A note on Post Quantum Onion Routing

Even though the currently used encryption and signature schemes are well tested and secure in a classical computational setting, they are not quantum-resistant as Shor’s work proves. Taking this into account, alternatives based on hard mathematical problems that cannot be solved using quantum methods are needed, and lattice-based cryptography oﬀers such solutions. The well-known GGH and NTRUEncrypt encryption schemes are proven secure, but their corresponding signature schemes are ﬂawed in their design approach. Once introducing the computationally hard problems like Ring-LWE, elegant and eﬃcient cryptographic primitives could be built.
Towards Collaborative Analysis of Computational Fluid Dynamics using Mixed Reality

: Computational fluid dynamics is an important subtopic in the field of fluid mechanics. The associated workflow includes post processing simulation data which can be enhanced using Mixed Reality to provide an intuitive and more realistic three-dimensional visualization. In this paper we present a cloud-based proof of concept Mixed Reality system to accomplish collaborative post processing and analysis of computational fluid dynamics simulation data. This system includes an automated data processing pipeline with a ML-based 3D mesh simplification approach and a collaborative environment using current head mounted Mixed Reality displays. To prove the effectiveness and accordingly support the workflow of engineers in the field of fluid mechanics we will evaluate and extend the system in future work.
I still See You! Inferring Fitness Data from Encrypted Traffic of Wearables

In this paper we describe a cyberattack against 2 well-known wearable devices. The attacker presented in this paper is an “honest but curious” Internet Service Provider (ISP) sitting somewhere in the path between the device and the cloud. The ISP launches the attack when the smartbands try to synchronize their data with the permanent cloud storage. By launching its attack, this “honest but curious” ISP is able to learn much personal information about the users of the smartbands, including the frequency of measuring the users’ heart rate and weight; the number and duration of workouts; as well as whether (i) sleep or (ii) steps were recorded on a given day. We show that privacy leaks might occur even when the transferred data are fully encrypted, and the representative mobile application utilizes state-of-the-art security mechanisms: certificate pinning, and source
Mesh Animation Compression using Skinning and Multi-chart Displacement Texture

Realistic animations of 3D models are currently very complex, and to stream them over a network in real-time, it is necessary to use compression. Currently, many different methods can be used. Some of them use skeleton transformation as an approximation of the animation, others use remeshing for decreasing mesh complexity. In this paper, a novel method of 3D animation compression is presented. It is based on reconstructing a 3D mesh from a 2D displacement texture and subsequent skeletal skinning enhanced by a surface tuning. The tuning is performed by displacing the skinned vertices according to high-frequency details encoded in a Differential Texture since the output of the skinning is just an approximation of the original animation. Skinning weights are encoded in another new texture type Skinning Map. In each frame of the streaming animation, just a new skeleton pose and the Differential Texture are needed to be sent. The proposed method has a high compression ratio for various animations because the pose is a small data structure and the Differential Texture contains just high-frequency details and on top of that, it can be compressed as video. Furthermore, differences between original and reconstructed animation are minimal, as evidenced by visual and numerical comparisons.
Multimodal Neural Network for Sentiment Analysis in Embedded Systems

: Multimodal neural network in sentiment analysis uses video, text and audio. Processing these three modalities tends to create computationally high models. In the embedded context, all resources and speciﬁcally computational resources are restricted. In this paper, we design models dealing with these two antagonist issues. We focused our work on reducing the numbers of model input features and the size of the different neural network architectures. The major contribution in this paper is the design of a speciﬁc 3D Residual Network instead of using a basic 3D convolution. Our experiments are focused on the well-known dataset MOSI (Multimodal Corpus of Sentiment Intensity). The objective is to perform similar results as the state of the art. Our best multimodal approach achieves a F1 score of 80% with a number of parameters reduced by 2.2 and the memory load reduced by a factor 13.8, compared to the state of the art. We designed ﬁve models, one for each modality (i.e video, audio and text) and one for each fusion technique. The two high-level multimodal fusions presented in this paper are based on the evidence theory and on a neural network approach.
Thermal Evolution of Band Edge States in ZnO Film as a Function of Annealing Ambient Atmosphere

RF-sputtered ZnO films were annealed under various annealing ambient atmospheres, including a vacuum, air, and water vapor. The physical and electrical properties of ZnO films annealed in various ambient atmospheres, were studied as a function of annealing temperature. The carrier concentration was dramatically increased, and the mobility was decreased when the films were annealed in a vacuum or water vapor. Even though the annealing ambient atmosphere and temperature were different, the preferred orientation and crystallization of the annealed ZnO films are maintained. However, two distinct band edge states below the conduction band, observed by spectroscopic ellipsometry measurement, undergo a thermal change as a function of annealing ambient atmosphere and these changes are correlated to changes in carrier concentration and mobility.
Word-sized Visualizations for Exploring Discussion Diversity in Social Media

In this paper, we explore the design space of word-sized visualizations—small graphics, usually the same size as a word, that visualize data in or related to a text—for displaying and exploring categories in social media feeds such as Twitter streams. Social media contributions are typically microposts, which allow us to attach word-sized visualizations to show category assignment, diversity, or development. We consider and combine word-sized visualizations made up of basic marks and visual variables, existing word-sized visualization concepts, as well as large text visualizations. In an application example we show how wordsized visualizations can evince context changes within a discussion on Twitter and reveal topic diversity.
A Multi-level Rank Correlation Measure for Image Retrieval

Accurately ranking the most relevant elements in a given scenario often represents a central challenge in many applications, composing the core of retrieval systems. Once ranking structures encode relevant similarity information, measuring how correlated are two rank results represents a fundamental task, with diversified applications. In this work, we propose a new rank correlation measure called Multi-Level Rank Correlation Measure (MLCM), which employs a novel approach based on a multi-level analysis for estimating the correlation between ranked lists. While traditional weighted measures assign more relevance to top positions, our proposed approach goes beyond by considering the position at different levels in the ranked lists. The effectiveness of the proposed measure was assessed in unsupervised and weakly supervised learning tasks for image retrieval. The experimental evaluation considered 6 correlation measures as baselines, 3 different image datasets, and multiple features. The results are competitive or, in most of the cases, superior to the baselines, achieving significant effectiveness gains.
Object based Hybrid Video Compression

Standard video compression techniques have provided pixel-based solutions that have achieved high compression performance. However, with new application areas such as streaming, ultra-high definition TV(UHDTV) etc., expectations of end user applications are at an all-time high. Never the less, the issue of stringent memory and bandwidth optimization remains. Therefore, there is a need to further optimize the performance of standard video codecs to provide more flexibility to content providers on how to encode video. In this paper, we propose replacing pixels with objects as the unit of compression while still harnessing the advantages of standard video codecs thereby reducing the bits required to represent a video scene while still achieving suitable visual quality in compressed videos. Test results indicate that the proposed algorithm provides a viable hybrid video coding solution for applications where pixel level precision is not required.
Interpretation of Human Behavior from Multi-modal Brain MRI Images based on Graph Deep Neural Networks and Attention Mechanism

Interpretation of human behavior by exploiting the complementarity of the information offered by multimodal functional magnetic resonance imaging (fMRI) data is a challenging task. In this paper, we propose to fuse task-fMRI for brain activation and rest-fMRI for functional connectivity with the incorporation of structural MRI (sMRI) as an adjacency matrix to maintain the rich spatial structure between voxels of the brain. We consider then the structural-functional brain connections (3D mesh) as a graph. The aim is to quantify each subject’s performance in voice recognition and identification. More specifically, we propose an advanced multi-view graph auto-encoder based on the attention mechanism called MGATE, which seeks at learning better representation from both modalities taskand rest-fMRI using the Brain Adjacency Graph (BAG), which is constructed based on sMRI. It yields a multi-view representation learned at all vertices of the brain, which be used as input to our trace regression model in order to predict the behavioral score of each subject. Experimental results show that the proposed model achieves better prediction rates, and reaches competitive high performances compared to various existing graph representation learning models in the state-
PIU-Net: Generation of Virtual Panoramic Views from Colored Point Clouds

As VR-systems become more and more widespread, the interest in high-quality content increases drastically. One way of generating such data is by digitizing real world environments using SLAM-based mapping devices, which capture both the geometry of the environment (typically as point clouds) and its appearance (typically as a small set of RGB panoramas). However, when creating such digital representations of real-world spaces, artifacts and missing data cannot be fully avoided. Furthermore, free movement is often restricted. In this paper, we introduce a technique, which allows for the generation of high quality panoramic views at any position within a captured real world scenario. Our method consists of two steps. First, we render virtual panoramas from the projected point cloud data. Those views exhibit imperfections in comparison to the real panoramas, which can be corrected in the second step by an inpainting neural network. The network itself is trained using a small set of panoramic images captured during the mapping process. In order to take full advantage of the panoramic information, we use a U-Net-like structure with circular convolutions. Further, a custom perceptual panoramic loss is applied. The resulting virtual panoramas show high quality and spatial consistency. Furthermore, the network learns to correct erroneous point cloud data. We evaluate the proposed approach by generating virtual panoramas along novel trajectories where the panorama positions deviate from the originally selected capturing points and observe that the proposed approach is able to generate smooth and temporally consistent walkthrough sequences.
In vitro antimicrobial activity of mineral trioxide aggregate, Biodentine, and calcium-enriched mixture cement against Enterococcus faecalis, Streptococcus mutans, and Candida albicans using the agar diffusion technique

Background: This study assessed the antimicrobial activity of Biodentine, mineral trioxide aggregate (MTA), and calcium-enriched mixture (CEM) cement against Enterococcus faecalis, Streptococcus mutans , and Candida albicans. Materials and Methods: In this in vitro study, microbial suspensions were inoculated onto agar plates. The antimicrobial effects of MTA, Biodentine and CEM cement were assessed against E. faecalis, S. mutans, and C. albicans by the agar diffusion test. In each experimental group, 7 plates containing 3 wells were prepared and immediately filled with freshly mixed cements. Positive and negative control plates were prepared with/without the bacterial suspension, respectively. After 2 h of preincubation at room temperature, the plates were incubated at 37°C for 24 h. The diameter of growth inhibition zones was measured after 24 h. Data were analyzed using ANOVA and Tukey's test (α = 0.05). Results: Biodentine showed strong antimicrobial activity against all three microorganisms with an average inhibition zone of 9.10 mm. The inhibitory effect of Biodentine on E. faecalis and C. albicans was significantly superior to that of the other two cements (P < 0.05). MTA and CEM cement showed significantly higher antimicrobial activity against S. mutans (P < 0.05). The antimicrobial effects of Biodentine on S. mutans and E. faecalis were significantly greater than on C. albicans (P < 0.05). Conclusion: All cements revealed antimicrobial properties against the tested microbial strains. Biodentine had stronger antimicrobial effects against E. faecalis and C. albicans compared with MTA and CEM cement. Furthermore, the largest inhibition zones around all three cements belonged to S. mutans.
Molecular Typing of Community-acquired Methicillin-Resistant Staphylococcus aureus Isolated from 2- to 6-year old Children by Staphylococcal Protein A and Agr Typing in Isfahan, Iran

Background: Methicillin - resistant Staphylococcus aureus (MRSA) has become a considerable public health concern in the entire world due to the rapid spread of this bacterium in human community; also the epidemiology of MRSA has changed, as the isolation of MRSA strains from healthy and non-healthy patients. Therefore, the objective of this study is to determine the genetic diversity and antibiotic resistance profile of community-acquired (CA)-MRSA nasal carriage in the Iranian samples. Materials and Methods: A total of 25 CA-MRSA were isolated from the anterior nares of 410 healthy preschool children. All MRSA isolates were characterized by the detection of the toxic shock syndrome toxin-1 (TSST-1) and typed by γ-hemolysin genes, agr groups, and staphylococcal protein A (spa) typing. Kirby-Buyer antibiotic susceptibility testing was performed and interpreted as per the standard guidelines. Results: A total of 25 (6.1%) MRSA isolates were recovered from the anterior nares of 410 preschool children. Sixteen isolates (64%) were positive for the TSST-1 gene. Three agr specificity groups were determined, as follows: eight (32%) isolates belonged to agr Group I, five (20%) isolates belonged to agr Group II, and 12 (48%) isolates belonged to agr Group III. The repeated profiles of these spa types of 25 isolates were organized into eight different lineages groups. Five of lineages contained a single strain, three of lineages contained two strains, and three of lineages consisted of more than three strains. Conclusions: The results of our study show that the rate of MRSA in our region is significantly high. Additionally, spa type t037 was the predominant type among CA S. aureus.