Reviewerly/exHarmony huggingface dataset:
Online Teaching Gestures Recognition Model Based on Deep Learning

The development of deep learning technology has promoted the application of artificial intelligence in the field of education, and teaching gestures are widely used by teachers in the teaching process. In order to further analyze how teachers' gestures can improve teaching effects and quality, and to make up for the lack of teaching gestures image recognition function and action anthropomorphism, which are currently lacking in educational robots, it is necessary to analyze the different gestures used by teachers in teaching. The key point is how scientific and efficient to recognize all kinds of gestures used by the teachers. Based on the unique convolutional layer and pooling layer structure of convolutional neural network, this paper analyzed five types of online teaching gestures images marked by the human key points detection, including indicative gestures, one-hand beat gestures, two-hand beat gestures, frontal habitual gestures, and lateral habitual gestures. An online teaching gestures recognition model with a ten-layer convolutional neural network is established. The error of this model is minimized by error back propagation and gradient descent algorithms, and the corresponding model parameters are obtained. The model is verified by the test set, and the training accuracy of the model is as high as 96%. The model can be applied to analyze the influence of the teachers' teaching gestures on the teaching effect and quality, at the same time, it can improve the intelligence and anthropomorphism of educational robots in the field of computer vision and its human-computer interaction.
Sanitization using Hexacopter Autonomous Drone

Abstract Multicopter are slowly finding its way into various industrial sector applications including security, oil &amp; gas, public health, etc. Unmanned Aerial Vehicles provides faster and reliable solutions for many real time challenges. Spraying sanitizers using drone is one such solution which is effectively implemented during COVID-19 pandemic situation. Hexacopter is designed to carry up to 2 litres of sanitizer in its reservoir and the sanitizer spray system is designed to dispense the liquid as droplets at a constant flow rate. Hexacopter is made of carbon fibre and equipped with Pixhawk autonomous module as flight controller. The UAV can be operated in both manual and autonomous modes to increase the ease of operation for indoors as well as in outdoors sanitization process.
Deep Learning-Based 3D Face Recognition Using Derived Features from Point Cloud

With developing technology and urbanization, smart city applications have increased. Accordingly, this development brought some difficulties such as public security risk. Identifying people’s identities is a requirement in both smart city challenges and smart environment or smart interaction difficulties. Face recognition has a huge potential for people’s identification. It was possible to perform face recognition applications in larger databases and different situations with the development of deep learning methods. 2D images are usually used for face recognition applications. However, different challenges such as pose change and illumination cause difficulties in 2D facial recognition applications. Laser scanning technology has provided the production of 3D point clouds, including the geometric information of the faces. When the point clouds are combined with deep learning techniques, 3D face recognition has great potential. In the study, 2D images were created for facial recognition using feature maps obtained from 3D point clouds. ResNet-18, ResNet-50 and ResNet-101 architectures, which are different versions of ResNet architecture, were used for classification purposes. Bosphorus database was used in the study. 3D Face recognition was performed with different facial expressions and occlusions based on the data of 105 people. As a result of the study, overall accuracy was obtained with ResNet-18, ResNet-50, and ResNet-101 architectures at 77.36%, 77.03% and 81.54% respectively.
Efficient image segmentation method based on an adaptive selection of Gabor filters

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre-setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker-based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks.
Influence-aware Memory Architectures for Deep Reinforcement Learning

Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally. In such cases, it is important to keep track of the observation history to uncover hidden state. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize past observations. However, these models are expensive to train and have convergence difficulties, especially when dealing with high dimensional input spaces. In this paper, we propose influence-aware memory (IAM), a theoretically inspired memory architecture that tries to alleviate the training difficulties by restricting the input of the recurrent layers to those variables that influence the hidden state information. Moreover, as opposed to standard RNNs, in which every piece of information used for estimating Q values is inevitably fed back into the network for the next prediction, our model allows information to flow without being necessarily stored in the RNN's internal memory. Results indicate that, by letting the recurrent layers focus on a small fraction of the observation variables while processing the rest of the information with a feedforward neural network, we can outperform standard recurrent architectures both in training speed and policy performance. This approach also reduces runtime and obtains better scores than methods that stack multiple observations to remove partial observability.
Research on the Mode of Innovative Talent Cultivation in the Multi-synergy Integrated Circuit Industry Based on the PDCA Cycle Theory

Abstract Based on the PDCA cycle theory, the technology in the research of the innovative talent training mode of the multi-collaborative integrated circuit industry has effectively solved the cultivation of innovative talents. Other solutions for the cultivation of innovative talents in the integrated circuit industry cannot effectively solve the innovation model. The successful development of the research on the innovative talent training model of the multi-collaborative integrated circuit industry based on the PDCA cycle theory will lead to the interaction of educational innovation, which is omnidirectionally related, thus benefiting everyone in the world.
Refining Deep Generative Models via Discriminator Gradient Flow

Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-world data. However, generation quality is generally inconsistent for any given model and can vary dramatically between samples. We introduce Discriminator Gradient flow (DGflow), a new technique that improves generated samples via the gradient flow of entropy-regularized f-divergences between the real and the generated data distributions. The gradient flow takes the form of a non-linear Fokker-Plank equation, which can be easily simulated by sampling from the equivalent McKean-Vlasov process. By refining inferior samples, our technique avoids wasteful sample rejection used by previous methods (DRS & MH-GAN). Compared to existing works that focus on specific GAN variants, we show our refinement approach can be applied to GANs with vector-valued critics and even other deep generative models such as VAEs and Normalizing Flows. Empirical results on multiple synthetic, image, and text datasets demonstrate that DGflow leads to significant improvement in the quality of generated samples for a variety of generative models, outperforming the state-of-the-art Discriminator Optimal Transport (DOT) and Discriminator Driven Latent Sampling (DDLS) methods.
Knowing me knowing you: Understanding user involvement in the design process

Design thinking is an emerging concept gaining much traction in practice and more recently in academic debate. While prior work has examined the characteristics and processes of design thinking, there is an absence of studies that attempt to measure the extent of its use and effectiveness in industry. This paper reports on the preliminary results of a study targeted at designers operating in dynamic project environments. The aim of this exploratory research is to identify who designers collaborate with, the extent to which users are involved in the design process in practice and the perceived importance of understanding the emotional needs of the user. The study also sought to ascertain whether user interaction is associated with performance. To do this, data was collected from 131 experienced designers in Ireland. Initial findings from this analysis are presented and discussed. This study answers calls for further work in a neglected but essential area and helps to bridge the gap between theory and practice.
A generalized ordinal finite mixture regression model for market segmentation

Model-based market segmentation analyses often involve an ordinal dependent variable as ordinal responses are frequently collected in marketing research. In the Bayesian segmentation literature, there are models for an interval- or ratio-scaled dependent variable but there is not any general model for an ordinal dependent variable. In this manuscript, the authors propose a new Bayesian procedure to simultaneously perform segmentation and ordinal regression with variable selection within each derived segment. The procedure is robust to outliers and it also provides an option to include concomitant variables that allows the simultaneous profiling of the derived segments. The authors demonstrate that the practice of treating ordinal responses as interval- or ratio-scales to apply existing Bayesian segmentation procedures can lead to very misleading results and conclusions. Through simulation studies, the authors show that the proposed procedure outperforms several benchmark Bayesian segmentation models in parameter recovery, segment retention, and segment membership prediction for such data. Finally, they provide a commercial business customer satisfaction empirical application to illustrate the usefulness of the proposed model.
The concealment of accelerated information is possible

The possibility of masking an accelerated two-qubit system by using a minimum number of qubits is discussed. It is shown that the information may be masked in either entangled local states or product non-local separable states. We examine that each partition of these states satisfies the masking conditions. Due to the presence of non-local separable partition, one may consider that it is a type of quantum data hiding scheme. The local/non-local information encoded in the masked entangled state is robust against the decoherence of the acceleration process. The possibility of estimating the acceleration parameter via the entangled/separable masked state increases as the initial entanglement value increases. The efficiency of the masking process is examined by quantifying the fidelity of the accelerated state and its subsystems. It is shown that the fidelity of the masked state is maximum at small initial acceleration, while the minimum fidelity is more than 96%.
Professional competencies as a key factor in the competitiveness of specialists in the labor market

The article considers the factors of formation of professional competencies of university graduates and their influence on the construction of personal trajectories of professional activity of young specialists in the labor market.
Novel Deep Neural Network Model for Handwritten Digit Classification and Recognition

Deep neural network is a technique of deep learning, where deep neural network model have multiple hidden layers with input and output layer, but artificial neural network have single hidden layer between input and output layer. The use of multiple hidden layers in deep neural network is to improve the performance of model and achieving the higher accuracy compare to machine learning models and their accuracy. The field of pattern recognition is mostly used by the researchers for their research work. There are lots of pattern are available in the field of pattern recognition like: handwritten digits, characters, images, faces, sound, speech etc. In this paper we have concentrated on handwritten digits classification and recognition. For handwritten digit datasets, we have used commonly known Arkiv Digital Sweden (ARDIS) [1] dataset and United State postal service (USPS) [7] dataset. ARDIS dataset is a collection of 7600 samples, where 6600 used as training samples and 1000 used as testing samples. USPS dataset is a collection of 10000 image samples where 7291 samples are used as training sample and 2007 samples are used as testing samples. In this paper we have implemented the proposed deep neural network technique for the classification and recognition of the ARDIS and USPS dataset. The proposed model has collection of 6 layers with relu and softmax activation function. After implementing model, 98.70% testing and 99.76% training accuracy for ARDIS samples achieved, which is higher than previous research accuracy. Also 98.22% training and 93.01%testing accuracy with USPS samples dataset has been achieved. The results represents the performance of deep neural networks have been outstanding compare to other previous techniques.
Predictive visual control network for occlusion solution in human-following robot

Purpose The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control. Design/methodology/approach This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments. Findings The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture. Originality/value The proposed method can effectively solve the occlusion problem in visual servo control.
VOICE Actuated Control Systems (VACS) for Accessible and Assistive Smart Homes. A Preliminary Investigation on Accessibility and User Experience with Disabled Users

Recently, mobile and in-home Voice Actuated Control Systems (VACs) have become affordable and reliable, allowing the control of several IoT devices from a distance. Individuals with motor impairments could benefit from VACS installed in smart domestic environments to operate home appliances and other devices. The present paper explores the potentials of using such systems in terms of accessibility and user experience by involving a group of individuals with motor disabilities and mild speech impairments. Participants were invited to directly try out the voice-controlled assistant by operating different smart devices (e.g., lights, fan) in a friendly living-room like environment. Results show that all participants were able to perform the proposed tasks after a brief practice section and few failures, and that the interaction experience was well received by participants.
An outlier detection algorithm for categorical matrix-object data

Outlier detection is a significant problem in data mining and machine learning which aims to discover objects in a data set that do not conform to well-defined notions of expected behavior. Generally, the input of the existing outlier detection algorithms is a collection of n objects and each object is described by a feature vector. However, in many real world applications, an object is not only described by one feature vector, but a number of feature vectors. In this paper, we define an object described by more than one feature vector as a matrix-object. Inspired by the concepts of cohesion and coupling in software engineering, we define the coupling of a matrix-object based on the average distance between it and other matrix-objects, and define its cohesion based on information entropy and mutual information. On this basis, the outlier factor of a matrix-object is given, and an outlier detection algorithm for categorical matrix-object data is proposed. The experimental results on real and synthetic data sets have shown that the proposed outlier detection algorithm can effectively detect outliers for the matrix-object data set compared with other algorithms.
Online Voting system

Our paper deals with online voting system that facilitates user(voter), candidate and administrator (who will be in charge and will verify all the user and information) to participate in online voting. our online voting system is highly secured, and it has a simple and interactive user interface. The proposed online portal is secured and have unique security feature such as unique id generation that adds another layer of security (except login id and password) and gives admin the ability to verify the user information and to decide whether he is eligible to vote or not. It also creates and manages voting and an election detail as all the users must login by user name and password and click on candidates to register vote. Our system is also equipped with a chat bot that works as a support or guide to the voters, this helps the users in the voting process.
Learning to Use Narrative Function Words for the Organization and Communication of Experience

How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation (the situation model) is built up from experienced events, and enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction.
Identification of structural geology at the Tangkuban Parahu geothermal area, West Java based on remote sensing and gravity data

Tangkuban Parahu is located in the north of Bandung, the capital province of West Java. Several hydrothermal manifestations, such as hot and warm springs with a temperature of 29°C-80°C, indicate that the Tangkuban Parahu has potential as a geothermal prospect. In such a case, understanding the surface and subsurface geological structures in the prospect area is needed as the first step in geothermal exploration. This study identifies geological structures based on remote sensing and gravity datasets. Structural lineaments were extracted manually using GIS software to produce fault, and fracture density (FFD) maps from the DEMNAS dataset along with IFSAR and ALOS PALSAR images. Lineament extraction was performed at eight different sun azimuth angles (hillshade) to reduce non-meaningful lines that may appear. The FFD maps show that most of the surface manifestation correlates with the high FFD zones. Gravity method is one of the first steps in geothermal exploration to provide an overview of the distribution of rocks and subsurface geological structures. Gravity data were analyzed using the parasnis method to reflect the average of rock density. The derivative technique for gravity method is common to be applied in order to delineate subsurface geological structures [1]. In this research, we used First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) to determine the presence and the type of major fault. The results of this research indicate that most of the high FFD zones are located in the peak maximum of FHD, which has SVD=0. Nevertheless, there are 3 locations of the high FFD zone that have no peak maximum FFD. Integration of this analysis mostly shows that the existence of geological structures is related to surface hydrothermal manifestations.
A Trustworthy Blockchain-based Decentralised Resource Management System in the Cloud

Quality Critical Decentralised Applications (QC-DApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers to deliver their business value. The development of the QCDApp has to tackle the low-performance challenge of the current blockchain technologies due to the low collaboration efficiency among distributed peers for consensus. On the other hand, the resilience of the Cloud has enabled significant advances in software-defined storage, networking, infrastructure, and every technology; however, those rich programmabilities of infrastructure (in particular, the advances of new hardware accelerators in the infrastructures) can still not be effectively utilised for QCDApp due to lack of suitable architecture and programming model.