Miracle-dz/UniFAR-FTUs huggingface dataset:
WhisperNet: Deep Siamese Network For Emotion and Speech Tempo Invariant Visual-Only Lip-Based Biometric

In the recent decade, the field of biometrics was revolutionized thanks to the rise of deep learning. Many improvements were done on old biometric methods which reduced the security concerns. Before biometric people verification methods like facial recognition, an imposter could access people's vital information simply by finding out their password via installing a key-logger on their system. Thanks to deep learning, safer biometric approaches to person verification and person re-identification like visual authentication and audio-visual authentication were made possible and applicable on many devices like smartphones and laptops. Unfortunately, facial recognition is considered to be a threat to personal privacy by some people. Additionally, biometric methods that use the audio modality are not always applicable due to reasons like audio noise present in the environment. Lip-based biometric authentication (LBBA) is the process of authenticating a person using a video of their lips' movement while talking. In order to solve the mentioned concerns about other biometric authentication methods, we can use a visual-only LBBA method. Since people might have different emotional states that could potentially affect their utterance and speech tempo, the audio-only LBBA method must be able to produce an emotional and speech tempo invariant embedding of the input utterance video. In this article, we proposed a network inspired by the Siamese architecture that learned to produce emotion and speech tempo invariant representations of the input utterance videos. In order to train and test our proposed network, we used the CREMA-D dataset and achieved 95.41 % accuracy on the validation set.
Dream11 IPL Team Recommendation using Machine Learning and Skill-Based Ranking of Players

Cricket is one of the most popular watched and played sports all over the world. This has also led to the rise of fantasy cricket sports like Dream11. Fantasy players create teams of their choice and get rewards on the team's merits. People strive to create the optimal team of 11 players and earn maximum points. Most existing studies quantify player performance on the basis of batting and bowling skills only. Skills like fielding and wicket keeping are not taken into account at all. In this paper we implemented a team recommendation system where a players’ performance is quantified on the basis of different player roles like batsman, bowler, wicket-keeper and all-rounder and skills like fielding, bowling type, etc. Other factors like pitch type, weather, etc, are also taken into account.
Higher-Order Dynamic Mode Decomposition and Multichannel Singular Spectrum Decomposition Hybridization for BCI Feature Extraction

A Brain-Computer Interface (BCI) communicates between the human brain and the different external intelligent devices. The main objective of the BCI system is to translate human intentions into computer-based control commands. This paper demonstrates that a hybrid feature engineering approach is more efficient in characterizing human intentions than features extracted from single-mode algorithms. We used a randomized amalgamation process with variance maximization to merge features obtained from two recently developed feature extraction techniques: (1) Multichannel singular spectrum decomposition and (2) Higher-order dynamic mode decomposition. The Extreme Learning Machine (ELM) classifier is used with a five-fold cross-validation technique to discriminate multiclass Motor Imagery (MI) patterns. The proposed model achieves a superior Classification Accuracy (CA) of 80.66% compared to the state-of-the-art classification models. Additionally, our method obtains the highest kappa score, which is more robust than existing classification models.
Internet Gaming Obsession: An Explorative Study of Connotation between Massively Multiplayer Online Role-Playing Games and Social Health

The last decade has witnessed an extensive surge in the online gaming industry with new players on board, across different age groups. The number of people who play and become potentially obsessed with them has also increased. This is due to the fact of internet availability and increasing bandwidth over the years. The recently imposed lockdowns have also multiplied the outcomes. The objective of the research is to study the physical, behavioral effects and psychosocial influences caused by obsession with Massively Multiplayer Online Role-Playing Games (MMORPGs) and also to suggest solutions to decrease their negative influence. The quantitative method has been used to collect and examine the data for this study. The questions are shared on popular online role-playing game communities and the data is acquired from them. Incentives were given to the participants of this study and their consent was obtained while also maintaining their privacy. The findings show that not all players tend to develop an obsession, but for a good percentage of users, online games can hurt their personal and social lives. The findings reveal the type of people vulnerable to online game obsession besides other causes. The results show conclusive evidence of obsession developed through online role-playing games. The conclusion of this research interprets the connotation between online role-playing games and social health. The major highlight of this study is that it investigates and understands the characteristics of obsessed gamers. To the readers, this article provides insights on regulating the time spent on online games to overcome an unhealthy and distracted life.
A 3D Mouse Design based on A Three-axis Acceleration Gyroscope

In the use of contemporary electronic computer, the traditional mouse has become an indispensable auxiliary equipment in the process of operation, but the use of the traditional two-dimensional mouse also has limitations: more harsh use conditions (smooth, smooth), wrist nerve compression formed "mouse hand" health problems, use way and use scene narrow, can not meet different needs. Based on the above problems, we designed a three-dimensional mouse motion based on three-axis acceleration gyroscope, it from the traditional two-dimensional use plane, the problems existing in two dimensions in three-dimensional improvement, expand the use scene, more accurately realized the professional and personalized mouse, brought considerable technical change benefits and market development benefits.
A 3D-CNN Framework for Hyperspectral Unmixing with Spectral Variability

Hyperspectral unmixing plays an important role in hyperspectral image processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. The hyperspectral image contains spatial information in neighborhood regions, and spectral signatures existing in the region also have high correlation. However, most autoencoder (AE) based unmixing methods are pixel-to-pixel methods and ignore these priors. It is helpful to add spectral-spatial information into unmixing methods. A recent trend to deal with this problem is to use convolutional neural networks (CNNs). Our proposed framework uses 3D-CNN based networks to jointly learn spectral-spatial priors. Moreover, previous AE-based unmixing methods use fixed spectral signatures for each pure material. In our work, we use a carefully designed decoder to cope with the endmember variability issue, and variational inference strategy is applied to add uncertainty property into endmembers. To avoid over-fitting, we use structured sparsity regularizers to the encoder networks, and ℓ2,1-loss is added to the estimated abundances to guarantee the sparseness. Experimental results on both simulated and real data demonstrate the effectiveness of our proposed method.
Stability Analysis of Electromagnetic Levitation System Using Lyapunov-Krasovskii's Method

The stability analysis of an electromagnetic levitation system is presented in this paper. With the technique of feedback linearization, a transformed matrix is obtained with the control input. An optimal control method is adopted to calculate the value of the external input. The stability analysis is studied using Lyapunov-Krasvoskii's stability criteria. In this article, stability analysis of autonomous and non - autonomous system has been carried out. Hence, the stability analysis is studied and validated for both the systems. The validation of the stability analysis is deliberated through simulation experiments.
Automated Sleep Stage Scoring Using Brain Effective Connectivity and EEG Signals

Sleep staging is necessary for the diagnosis of sleep disorders and evaluating the quality of sleep. Scoring of sleep stages is mainly done manually by a specialist based on Polysomnography data and mainly EEG, which is very time consuming and costly. Hence, it is essential to provide an automated method. This paper proposes an automatic sleep staging method based on brain effective connectivity. In this study, using the Granger causality criterion, causality matrices for each epoch of EEG data sampled from 10 healthy individuals were extracted as features. Then, the Gaussian SVM classifier has been employed to classify sleep stages using extracted features. For feature reduction, two algorithms, PCA and RSFS, were assessed, but we did not apply feature reduction in the final method due to the insignificant effect on classification accuracy. Finally, we were able to classify sleep stages with 72.7% accuracy and Cohen's Kappa Coefficient of 0.65. The experimental results demonstrate that the combination of Granger causality features and SVM can be used as an efficient framework for automated sleep stage scoring with regard to promising classification performance in terms of accuracy and Cohen's Kappa coefficient.
A technique to improve the displaying quality of skin deformation caused by external force

This article continues the research on simulation of preclinical medical practice started by Do Nang Toan and Nguyen Duc Hoang [1]. In previous research, a technique to present colour of the human skin was introduced and in this rearch we continue improve the displaying quality of skin deformation cause by external force. Pressing on the skin and observing changes in capillary refill time (CRT) of human skin to evaluate the patient’s health though terms of blood pressure, heart rate, etc. is an indispensable operation in medical examination and treatment. This articale mentions a technique to improve the detail of the deformation of the shape of human skin affected by external force without increasing the detail of the entire object. The proposed technique is applied on a 3D model simulating emergency medical practice of children in Vietnam and proved to be effective in displaying the deformation of the shape of human skin caused by the external force.
Hybrid Deep Learning Method Based on LSTM-Autoencoder Network for Household Short-term Load Forecasting

Energy prediction is an essential task in smart homes for demand-side management and energy consumption reduction. Therefore, an intelligent forecasting model is necessary for predicting demand-side energy in residential buildings. Recent studies have shown that deep learning networks have higher performance than traditional machine learning methods in short-term load forecasting. In this paper, a new hybrid network is proposed that consists of Auto-Encoder LSTM layer, Bi-LSTM layer, stack of LSTM layer, and finally Fully connected layer. The experiments are conducted on an individual household electric power consumption dataset and the results demonstrate that the proposed network has the smallest value in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in comparison with other state-of-the-art approaches.
Indoor Localization With Bluetooth: a Framework for Modelling Errors in AoA and RSSI

Ubiquitous connectivity among objects is the already expected future of the Internet of Things ages we are living today. Technologies are competing fiercely to fulfill this goal. Still, none of them has been proven as the one-size-fits-all solution for any application scenarios. Indoor Positioning System, and Direction Finding problems, represent an interesting playground for Internet of Things technologies. In this challenge, one of the major stakeholders is Bluetooth: initially conceived as a short-range solution for Personal Area Networks, it has now evolved to version 5, which natively supports both Angle of Departure and Angle of Arrival techniques. In this work, a Connection-Oriented Real-Time Locating System is realized to deeply investigate the newly added features of Bluetooth, thanks to a dedicated framework that evaluates gathered data and their reliability. A thorough experimental campaign has been carried out in both indoor and outdoor conditions, with interesting results. Overall, the main outcome is that the Angle of Arrival is not sufficient to solve Direction Finding problems and a more precise estimation of both directions and distances requires other quality indexes. In particular, the Received Signal Strength Indicator is proposed to be used in conjunction with the Angle of Arrival as part of the measurement framework.
RSS-Based Fusion of UWB and WiFi-Based Ranging for Indoor Positioning

WiFi positioning with estimated ranges using Round Trip Time (RTT) measurements based on IEEE 802.11 Wireless Local Area Network (WLAN) has become well known since Fine Timing Measurement (FTM) protocol has been characterized. However, the multipath effect is one of the barriers to accurate time-based range measurement. On the other hand, Ultra Wide Band (UWB)-based range measurement has fair resistance to multipath effects but its accuracy is highly dependant on the orientation of the antennas in the transmitter and the receiver and its transmit power is also limited due to the applied regulations. This paper utilizes a Received Signal Strength (RSS)-based fusion of both UWB and WiFi-based range measurements to increase the indoor positioning accuracy. The proposed method takes the advantage of WiFi FTM protocol as well as Two-Way Ranging (TWR) for UWB devices. The empirical range measurement campaign is done at the University of Helsinki premises. Test points with known positions are considered as the ground truth to evaluate the results. The outcome proves that fusing UWB and WiFi ranges for indoor positioning, improves the accuracy in comparison with using the UWB or WiFi alone.
A Proposal on Stampede Detection in Real Environments

It is a fact that the world population has grown in recent decades, as well as the number of social and tourism events, generating situations of agglomerations where different problems may lead to generate bottlenecks stampedes or falls, that can be a risk for people. Thus, the study of the behaviour of crowds is a relevant research topic. In this context, this paper presents and approach for real-time stampede detection from images, in low and medium crowd scenarios. The proposal is based on a feature vector extracted from the optical flow entropy, and this does not require the use of thresholds. Instead of that, it includes a a Stacking classifier, based on the union of a random forest with ten estimators and an support vector classifier, that works properly in the different analyzed scenarios. The proposal has been evaluated in UMN and PETS 2009 datasets and compared to other state-of-the-art proposals in terms of accuracy and computational cost. However, since the provided ground-truth was not accurate, a new manually-labelled ground-truth has been generated and make publicly available to the scientific community. The obtained results allows validating the proposal, outperforming the state-of-the-art methods both in terms of accuracy and computational cost in all the evaluated scenarios.
Optimización del despacho de llamadas mediante la inclusión de la consideración del tiempo de espera relativo Optimal Dispatching of Elevators employing the consideration of relative time

The article puts forward the idea of a novel elevator group control system that takes into account not only the time a lift spends responding to a call but the impact this allocation option have over all the dispatching options. Firstly the proposed al- gorithm is justified in mathematical and theoretical terms and finally its performance is demonstrated via computer simulation.
Oil boom, job prospects and schooling decisions : Evidence from Chad

This paper examines the effect of oil-induced employment opportunities for natives on secondary schooling decisions in the oil-rich region of Chad. Using a synthetic control method and a differencein-differences approach, I find that these opportunities increased secondary school attendance in the region. Concerning the mechanism, the results suggest, on the one hand, a decrease in school dropouts, and on the other hand, that the observed effect does not come from an increase in parental income but incentives related to employment opportunities. Finally, I find similar results when I use survey data instead of regional administrative data.
Research on high-speed synchronous motor direct torque control

High-speed motor is increasingly used in ultra-high precision machining and high-performance machinery. It is necessary to achieve closed-loop control of high-speed motor in order to realize highperformance motor control. The high-frequency and high-speed characteristics of high-speed motor have brought great difficulties for its realization of closed-loop control. The performance of the hexagonal flux direct torque control (DTC) method that can reduce the switching frequency of power devices is researched for high-speed synchronous motor in this paper. After adopting this control method, the harmonics in the motor current and torque can be suppressed by adopting an appropriate filtering method. The same effect as the conventional DTC can still be achieved. It provides a basis for the application realization of the highspeed synchronous motor control system.
AN UNBALANCED EQUATION: INCREASING PARTICIPATION OF WOMEN IN STEM IN LAC

The Open Science Forum in Latin America and the Caribbean -CILAC- is conceived as a space that contributes to the implementation of the 2030 Agenda endorsed by the General Assembly of the United Nations. It is aimed at becoming a platform for reflection and interaction in relation to the issues of science, technology and innovation management. Therefore, for the consortium of CILAC organizing institutions, it is important that the debates and reflections are not limited to the organization of the Forums every two years, but kept alive over time.
Fabricate It or Render It? Digital Fabrication vs. Virtual Reality for Creating Objects Instantly

In the technical human-computer interaction (HCI) community, two research fields that gained significant popularity in the last decade are digital fabrication and augmented/virtual reality (AR/VR). Although the two fields deal with different technical challenges, both aim for a single end goal: creating ”objects” instantly – either by fabricating them physically or rendering them virtually. In this panel, we will discuss the pros and cons of both approaches, discuss which one may prevail in the future, and what opportunities exist for closer collaboration between researchers from the two research fields.
Microsoft Word-2777490-file00

Acute lung injury (ALI) is characterized by lung inflammation and diffuse infiltration of neutrophils into the alveolar space. The inhibition of alveolar neutrophil apoptosis has been implicated in the pathogenesis of ALI. Although sphingolipids may regulate cell apoptosis, the role of sphingolipids in activated neutrophils during ALI is not clear. In this study, we test the hypothesis that sphingolipids would attenuate neutrophil apoptosis which contributes to the development of ALI. Lipopolysaccharide (LPS)-stimulated human neutrophils, with or without inhibitor treatment, were analyzed for apoptosis. We found that the inhibitory effect of LPS on neutrophil apoptosis was blocked by treatment with neutral sphingomyelinase (nSMase) inhibitor sphingolactone-24 (Sph-24), sphingosine kinase inhibitor (SKI)-II, and p38 mitogen-activated protein kinase (MAPK) inhibitor SB203580, but not by acidic sphingomyelinase inhibitor chlorpromazine. LPS-activated phosphorylation of p38 MAPK was also attenuated by treatment with Sph-24 and SKI-II. Furthermore, mice with LPS-induced lung injury were treated with nSMase inhibitor Sph-24 to evaluate its impact on lung injury and survival. The severity of LPS-induced ALI was reduced and the survival rate was increased in mice treated with Sph-24 as compared with those given LPS alone. Intracellular levels of sphingolipids in alveolar neutrophils from patients with acute respiratory distress syndrome (ARDS) were also This article has not been copyedited and formatted. The final version may differ from this version. JPET Fast Forward. Published on July 1, 2011 as DOI: 10.1124/jpet.111. 181560 at A PE T Jornals on M arch 1, 2022 jpet.asjournals. org D ow nladed from
Unsupervised Digit Recognition Using Cosine Similarity In A Neuromemristive Competitive Learning System

This work addresses how to naturally adopt the l2-norm cosine similarity in the neuromemristive system and studies the unsupervised learning performance on handwritten digit image recognition. Proposed architecture is a two-layer fully connected neural network with a hard winner-take-all (WTA) learning module. For input layer, we propose single-spike temporal code that transforms input stimuli into the set of single spikes with different latencies and voltage levels. For a synapse model, we employ a compound memristor where stochastically switching binary-state memristors connected in parallel, which offers a reliable and scalable multi-state solution for synaptic weight storage. Hardware-friendly synaptic adaptation mechanism is proposed to realize spike-timing-dependent plasticity learning. Input spikes are sent out through those memristive synapses to each and every integrate-and-fire neuron in the fully connected output layer, where the hard WTA network motif introduces the competition based on cosine similarity for the given input stimuli. Finally, we present 92.64% accuracy performance on unsupervised digit recognition with only single-epoch MNIST dataset training via high-level simulations, including extensive analysis on the impact of system parameters.
The challenges experienced by mothers with children suffering from cerebral palsy: A study conducted at Mutale Municipality, South Africa

Abstract Cerebral palsy (CP) is a developmental disorder, characterized by movement disorders that appear in early childhood. Children with CP experience poor motor coordination and have problems with sensation, vision, hearing, swallowing and speaking. A qualitative study was conducted at Mutale Municipality to ascertain the challenges faced by mothers with children who have CP. Unstructured interviews were conducted with 12 participants and data were analysed using interpretative data analysis. The results showed that the challenges faced by mothers, with cerebral palsy children were social and psychological, hence, the study concluded that these specific types of support should be given to these mothers. The findings also indicated that cerebral palsy is condition that requires a lot of care from parents and that there is lack of support and understanding of the condition from family and friends. Parents raising children with cerebral palsy require the support of friends and family members, therefore, the lack of that support leads to psychological problems, such as feelings of isolation, worries and stress. The researchers recommend that campaigns must be undertaken to help the society to understand cerebral palsy, as this might enhance the societal acceptance and motivate support for those with the condition.