Miracle-dz/UniFAR-FTUs huggingface dataset:
DEAF-BSL: Deep lEArning Framework for British Sign Language recognition

The recent development of disability studies in academic bodies has expedited the promotion of investigation on disability. With computer-aided tools, communication between the impaired person and someone who does not understand sign language could be accessible. A large number of people across the world are using sign language (e.g., British Sign Language (BSL), Asian Sign Language (ASL), Indian Sign Language (ISL), etc.) with hand gestures for communication. In BSL recognition, the involvement of both hands overlapping each other becomes the main challenge. Moreover, BSL comprises ambiguous signs concerning viewpoint. However, existing traditional techniques seem in-stable, less accurate, and inefficient. In this work, the BSL fingerspelling alphabet recognition problem explores using a Deep learning framework to address the above-mentioned concerns. Convolutional Neural Network (CNN) is employed to detect and recognize for classification of 26 alphabets after being trained on the BSL corpus dataset. The proposed work outperforms the existing works with better precision (6%), recall (4%), and F-measure (5̃%). It reported better results on the BSL corpus dataset and webcam videos. The model achieved better accuracy (98.0%) for a large lexicon of words than previous models (Goh & Holden [6]: 69.5%, Rambhau [9]: 79.2%, and Liwicki et al. [8]: 92.5%). The 3D CNN-based proposal performs robust hand detection, much more accurate sign recognition, more scalability, and less ambiguity in BSL finger-spelling recognition.
Non-Dyadic Interaction: A Literature Review of 15 Years of Human-Robot Interaction Conference Publications

Going beyond dyadic (one-to-one) interaction has been increasingly explored in HRI. Yet we lack a comprehensive view on non-dyadic interaction research in HRI. To map out 15 years of works investigating non-dyadic interaction, and thereby identifying the trend of the field and future research areas, we performed a literature review containing all 164 publications (2006-2020) from the HRI conference investigating non-dyadic interaction. Our approach is inspired by the 4C framework, an interaction framework focusing on understanding and categorising different types of interaction between humans and digital artefacts. The 4C framework consists of eight interaction principles for multi-user/multi-artefact interaction categorised into four broader themes. We modified the 4C framework to increase applicability and relevance in the context of non-dyadic human-robot interaction. We identify an increasing tendency towards non-dyadic research (36% in 2020), as well as a focus on simultaneous studies (85% from 2006-2020) over sequential. We also articulate seven interaction principles utilised in non-dyadic HRI and provide specific examples. Last, based on our findings, we discuss several salient points of non-dyadic HRI, the applicability of the modified 4C framework to HRI and potential future topics of interest as well as open-questions for non-dyadic research.
BERIS: An mBERT-based Emotion Recognition Algorithm from Indian Speech

Emotions, the building blocks of the human intellect, play a vital role in Artificial Intelligence (AI). For a robust AI-based machine, it is important that the machine understands human emotions. COVID-19 has introduced the world to no-touch intelligent systems. With an influx of users, it is critical to create devices that can communicate in a local dialect. A multilingual system is required in countries like India, which has a large population and a diverse range of languages. Given the importance of multilingual emotion recognition, this research introduces BERIS, an Indian language emotion detection system. From the Indian sound recording, BERIS estimates both acoustic and textual characteristics. To extract the textual features, we used Multilingual Bidirectional Encoder Representations from Transformers. For acoustics, BERIS computes the Mel Frequency Cepstral Coefficients and Linear Prediction coefficients, and Pitch. The features extracted are merged in a linear array. Since the dialogues are of varied lengths, the data are normalized to have arrays of equal length. Finally, we split the data into training and validated set to construct a predictive model. The model can predict emotions from the new input. On all the datasets presented, quantitative and qualitative evaluations show that the proposed algorithm outperforms state-of-the-art approaches.
The Determinants of Labour Demand in the Lubelskie Voivodship

The aim of this study is to characterise labour demand in the Lubelskie region and some of its determinants. The first part briefly discusses the theoretical aspects of labour-demand, including its measurement methods and main determinants. The second part offers a statistical analysis of labour demand in the Lubelskie Voivodship against national trends. It proves that labour demand in the Lubelskie Voivodship demonstrates slower growth and a less growth-favourable structure, and that the chances to find work there are smaller than in the country as a whole. However, an analysis of the main determinants of labour demand conducted in the next part of the study suggests that lower labour-demand growth in comparison to the country as a whole can be more attributable to factors related to the product-market situation rather than excessive increase in wages. It was also indicated that an important reason for such disproportions might lie in the relatively high significance of rural areas and agriculture, and also in the weaker position of the region’s business sector compared to the country as a whole.
Combining DNN partitioning and early exit

DNN inference is time-consuming and resource hungry. Partitioning and early exit are ways to run DNNs efficiently on the edge. Partitioning balances the computation load on multiple servers, and early exit offers to quit the inference process sooner and save time. Usually, these two are considered separate steps with limited flexibility. This work combines partitioning and early exit and proposes a performance model to estimate both inference latency and accuracy. We use this performance model to offer the best partitioned/early exit DNN based on deployment information and user preferences. Our experiments show that the flexibility in number and position of partitioning points and placement on available devices plays an important role in deciding the best output. In the future, we plan to turn this work into a "one-click" system to train and optimize models for edge computing.
Air-Puff Tonometry in population research - a comparison with Goldmann tonometer in individuals with suspected ocular hypertension

Objetivo: Avaliar a utilidade do tonometro de ar (TA) em estudos populacionais em individuos suspeitos de hipertensao ocular, comparando os valores com os fornecidos pelo tonometro de aplanacao de Goldmann (TG). Metodos: Estudo transversal, de amostra probabilistica, composta por 11.452 individuos e"20 anos, compondo-se uma subamostra dos que apresentaram valores de pressao intraocular (PIO) obtidos com o TA e"20mmHg, nos quais a PIO foi repetida com o TG. Os resultados dos dois tonmetros foram comparados considerando sexo, cor da pele referida, lateralidade, relacao escavacao/disco (Â0,6; entre e"0,6 e <0,8; e"0,8) e diagnostico. Foram consideradas tres situacoes: nao-portadores de glaucoma (NG), suspeitos (SG) e portadores de glaucoma (CG). Para comparacao entre as medidas foi utilizado o teste t de Student para amostras pareadas e o teste de correlacao de Pearson para avaliar a associacao entre PIO, idade e tonometria. Resultados: Foram detectados 198 individuos (339 olhos) com PIOe"20mmHg com o TA, que tiveram a medida repetida com o TG. Foram considerados 233 olhos como NG, 47 olhos como SG e 19 olhos como CG. Em olhos com escavacao e"0,8, a medida com TA e TG foram semelhantes. Nos NG e SG, o TA superestimou os valores. Houve associacao entre aumento da PIO e aumento da idade com os dois tonmetros. Conclusao: Valores de PIO sao superiores com TA comparados ao TG, principalmente quando a PIO e normal. Ha concordância entre os metodos quando a PIO e alta e a escavacao do nervo optico e aumentada, o que valida a aplicacao do TA em campanhas populacionais.
SNR-multiconditon Approaches of Robust Speaker Model Compensation Based on PLDA in Practical Environment

In this paper, we focus on the research of robust speaker verification system in adverse noise conditions. To improve the performance of conventional single PLDA model in the practical environment, two SNR-multicondition strategies are adopted to solve the SNR mismatch problem. The first approach uses different PLDA models to match the relative SNR conditions of training and testing speech utterances. The other uses one PLDA model trained by speech utterances with different SNR and a multicondition score normalization method to adjust the score distribution. The performance of the two systems are demonstrated on a 120speakers test set for each gender. According to the result of the experiment, the proposed approaches are proof to reduce the EER to 7.39%, 7.17% for male and 10.22%, 10.06% for female respectively in the random SNR noise condition, which means the SNRmulticondition systems are more robust in the real noisy environment.
Shape-Preserving Accelerating Electromagnetic Wavepackets in Curved Space

We present shape-preserving spatially accelerating electromagnetic wavepackets in curved space: wavepackets propagating along non-geodesic trajectories while recovering their structure periodically. These wavepackets are solutions to the paraxial and non-paraxial wave equation in curved space. We analyze the dynamics of such beams propagating on surfaces of revolution, and find solutions that carry finite power. These solutions propagate along a variety of non-geodesic trajectories, reflecting the interplay between the curvature of space and interference effects, with their intensity profile becoming narrower (or broader) in a scaled self-similar fashion Finally, we extend this concept to nonlinear accelerating beams in curved space supported by the Kerr nonlinearity. Our study concentrates on optical settings, but the underlying concepts directly relate to General Relativity.
Aggregating the Gaussian Experts' Predictions via Undirected Graphical Models

Distributed Gaussian process (DGP) is a popular approach to scale Gaussian processes to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction. To combine the local predictions, the conditional independence assumption is used which basically means there is a perfect diversity between the subsets. Although it keeps the aggregation tractable, it is often violated in practice and generally yields poor results. In this paper, we propose a novel approach for aggregating the Gaussian experts' predictions by Gaussian graphical model (GGM) where the target aggregation is defined as an unobserved latent variable and the local predictions are the observed variables. We first estimate the joint distribution of la-tent and observed variables using the Expectation-Maximization (EM) algorithm. The interaction between experts can be en-coded by the precision matrix of the joint distribution and the aggregated predictions are obtained based on the property of conditional Gaussian distribution. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art DGP approaches.
Analysis on Media Data and its Optimal Process in Communication Network

With the rapid development of mobile communication and the widespread use of wireless networks, the demand of user data service is in a rapidly growth stage. Media data, which is usually accompanied by a large amount of communication information, requires high communication speed and low latency, and will also result in much more energy consumption on data processing. In view of the above issue, this paper analyzed the media data in the communication network, introduce a series of distinct processing metrics for specific applications, media data Computing Sub-Tasks Optimal Processing scheme (CSOP) based on cloud computing technology is proposed which achieves the tradeoff between user experience and energy consumption. In the proposed scheme, the interests of communication operators and power grid need to be considered during the assignment of sub-tasks, which forms a nested optimization problem. This 0–1 programming is solved using the genetic algorithm (GA). Through the analysis of the results for case study, the proposed method can effectively improve the energy efficiency of the data processing at the expense of limited communication delay.
Comparative Research on Reactive Power Optimization of Distribution Network Based on Ant Colony and Bee Colony Algorithm

Reactive power optimization of distribution network is an effective means to ensure the safe and economic operation of distribution network, and it is an important measure to improve the voltage quality of distribution network. The reactive power optimization of distribution network can improve the voltage distribution of distribution network system, improve the voltage quality of users, reduce the power loss of power transmission, reduce the power cost, and improve the power transmission and distribution capacity and stable operation level of distribution network. This paper studies the optimality of ant colony algorithm and artificial bee colony algorithm for distribution network reactive power optimization. Taking the minimum system active power loss as the objective function, the mathematical model of distribution network reactive power optimization is established. Based on IEEE33 node system, the simulation experiments of distribution network reactive power optimization based on ant colony algorithm and artificial bee colony algorithm are carried out respectively, and the experimental results are compared and analyzed. The experimental results show that both algorithms can improve the system node voltage, the artificial bee colony algorithm has better performance in reducing the efficiency of system active power loss, and the ant colony algorithm runs faster.
Design of Intelligent Sorting Trash Can System Based on MobileNet Model

In order to realize resource recycling and reduce the workload of garbage sorting personnel, this paper proposes a design scheme of image recognition classification trash bin based on MobileNet model. The system combines image recognition and voice recognition with dual-modal recognition, Increase the accuracy and reliability of garbage identification. The garbage recognition model is based on MobileNet-V1 training a MobileNet model. The training results show that the model has a training accuracy rate of 99.8% and a test accuracy rate of 98%. The recognition results show that the MobileNet model has better performance and higher accuracy, which provides a reference for intelligent garbage classification.
Swing-up Control of a Double Inverted Pendulum by Combination of Q-Learning and PID Algorithms

The double inverted pendulum is a nonlinear system with unstable balance and rapid response. When two pendulums are allocated vertically, and there is no movement, the system is stable. In this article, we propose a control mechanism based on reinforcement learning. The system can improve its performance and balance by implementing the Q-Learning techniques besides the PID control scheme. The Q-Learning algorithm balances the pendulum to its inverted vertical position in the first step. In the second step, the Q-Learning algorithms are combined with classical PID to balance the double inverted pendulum and perform swing-up control. Eventually, simulation findings showed the efficiency of the proposed controller.
Visual Recognition Method for Deformable Wires in Aircrafts Assembly based on Sequential Segmentation and Probabilisitic Estimation

Manipulation object recognition is necessary for robotic manipulation planning and control. However, wire harnesses to be automatically assembled in aircrafts have small radial scale and complex structure which make them hard to be recognized. A method in order to accomplish multi-branch wire harness objects recognition is presented, which involves segmentation and estimation. Segmentation is performed in a sequential approach based on Cartesian distance, color similarity and bending continuity, among which the bending character considering rigidity diminishing thesis provides the segmentation result with physical interpretability. Gaussian Mixture Model is employed to making an estimation to complete the missing wire parts resulted from occlusion and omission on the segmented results. Effects of the method have been verified on a point cloud dataset with over 200 aviation wire objects. It has been demonstrated that aviation wire harnesses with multi-branches and occlusion can be distinguished and completed with a success rate over 96%.
Droplet Coalescence is Initiated by Thermal Motion ( Supplemental Material )

This Supplemental Material contains technical details that are complementary to the Letter. We discuss (i) the molecular dynamics simulation details and initial conditions, (ii) thermal fluctuations on liquid surfaces, (iii) the probable region of the onset of coalescence, (iv) bridge growth in the thermal regime, (v) calculations of thermal fluctuations on the surface of a thin cylinder, and (vi) the role of van der Waals interactions between the droplets.
Automatic Segmentation of brain tumors based on DFP-UNet

Brain tumor segmentation plays an important role in computer-aided diagnosis and treatment. Manually segmenting brain tumors is time-consuming and highly dependent on the expertise and experience of radiologists. In this paper, we propose an automatic brain tumor segmentation network named DFP-UNet to improve the accuracy of segmentation. Two improvements are made with respect to the UNet model: 1) use the pre-trained DenseNet121 model based on the ImageNet dataset as the encoder, 2) design a feature pooling block (FPB) as the decoder. In addition, some data preprocessing operations are carried out to alleviate the problems of low image contrast and limited training data for medical images, including image enhancement, data augmentation and normalization. Five-fold cross-validation is performed on the brain CE-MRI dataset to evaluate the effectiveness of the proposed method. The results show that DFP-UNet can achieve an average dice score of 81.69±1.14%, which outperforms the state-of-the-art method by a large margin on the same dataset.
Simulating effects of a wind‐turbine array using LES and RANS

Growth in wind power production has motivated investigation of wind‐farm impacts on in situ flow fields and downstream interactions with agriculture and other wind farms. These impacts can be simulated with both large‐eddy simulations (LES) and mesoscale wind‐farm parameterizations (WFP). The Weather Research and Forecasting (WRF) model offers both approaches. We used the validated generalized actuator disk (GAD) parameterization in WRF‐LES to assess WFP performance. A 12‐turbine array was simulated using the GAD model and the WFP in WRF. We examined the performance of each scheme in both convective and stable conditions. The GAD model and WFP produced qualitatively similar wind speed deficits and turbulent kinetic energy (TKE) production across the array in both stability regimes, though the magnitudes of velocity deficits and TKE production levels were underestimated and overestimated, respectively. While wake growth slowed in the latter half of the WFP array as expected, wakes did not approach steady state by the end of the array as simulated by the GAD model. A sensitivity test involving the deactivation of explicit TKE production by the WFP resulted in turbulence levels within the array well that were below those produced by the GAD in both stable and unstable conditions. Finally, the WFP overestimated downwind power production deficits in stable conditions because of the lack of wake stabilization in the latter half of the array.
Two new polypodane-type bicyclic triterpenoids from mastic

Abstract Pistacia lentiscus L. is an evergreen shrub belonging to the Anacardiaceae family, cultivated exclusively in the southern area of Chios Island. Mastic gum as a unique natural resin of the tree Pistacia lentiscus L. has been used extensively in functional foods and traditional medicine. The biological properties of Pistacia resins may be influenced by different chemical constituents. Herein the present work is aimed to further explore the diverse triterpenoids of mastic, and evaluate their anti-inflammatory activity. Two undescribed polypodane-type bicyclic triterpenoids were isolated from the Pistacia resins, their structures were elucidated using ultraviolet, infrared, high resolution electrospray ionization mass spectroscopy (HRESIMS), and nuclear magnetic resonance data. LPS-stimulated RAW264.7 macrophages were incubated with various concentrations of isolated compounds, and results showed that compounds 1 and 2 inhibited nitric oxide production in LPS-induced RAW264.7 cells with IC50 values of 28.1 and 32.6 µM, respectively.
Sustainability Status of Solar PV Projects in Nepal

DOI: 10.21276/sjet.2018. 6.4. 2 Abstract: Harsh topographic conditions are a huge challenge for rural electrification in Nepal through national grid extension. Hence off-grid electrification has a huge potential and is gaining huge popularity in Nepal. Recognizing the significance of renewable energy in rural areas, with the support of various development partners, Government of Nepal considers rural electrification through renewable energy technologies as an appropriate means to enhance rural livelihoods and conserve environment in rural areas. As the intention of the installation is to increase access to solar energy services for livelihood enhancement, it has been realized to assess whether these systems installed and/being installed across different parts of Nepal is sustainable and contributing for socio-economic growth of society. The overall objective of this study is to measure and eventually assess the sustainability of solar energy projects in Nepal that have been installed for a year through a developed framework. The sustainability frame work has been developed which measures sustainability in general and technical, economic, social, and institutional and environment sustainability in particular for renewable projects implemented. Testing with framework, 74 percentages system is weakly sustainable, 10 percentages are not sustainable and only 16 percentage system is sustained which raises a question on sustainability of renewable energy projects in rural Nepal. At 70 percentages subsidy policy from Government of Nepal, there are no much changes in sustainability status of solar PV projects. 21 percentages system are sustainable, 70 percentages are weakly sustainable whereas 9 percentage system are still not sustainable. Decreasing trend on PV panel cost and increasing the electricity tariff rate might increase the sustainable status of renewable systems installed.