Reviewerly/exHarmony huggingface dataset:
Face-Recognition Based Attendance System using One-Shot Learning

An attendance marking system is a part of every institute, college, corporation, be it big or small. With giant strides made in the domain of image processing, we have created a system that uses the latter to simplify the former. Face Recognition is growing increasingly popular over other biometric authentications because of its easy, non-invasive, and contactless approach. The main objective of the system is to detect faces and recognize them in a real-time environment, match them with the data present in the database and record their attendance. This is aimed to ease the hectic manual attendance process and make it more efficient. This further also solves the problem of authentication and proxies as biometrics are unique, and facial features used for Face Recognition being one of them, cannot be duplicated. The designed system uses OpenCV, dlib, Face Recognition libraries, and One-Shot Learning for face detection and recognition which requires only one image per person in the database, thus also being space-optimized as compared to traditional training-testing models.
VR Natural Walking in Impossible Spaces

Locomotion techniques in Virtual Reality (VR) are the means by which users traverse a Virtual Environment (VE) and are considered an integral and indispensable part of user interaction.
ContourCNN: Convolutional neural network for contour data classification

This paper proposes a novel Convolutional Neural Network model for contour data analysis (ContourCNN) and shape classification. A contour is a circular sequence of points representing a closed shape. For handling the cyclical property of the contour representation, we employ circular convolution layers. Contours are often represented sparsely. To address information sparsity, we introduce priority pooling layers that select features based on their magnitudes. Priority pooling layers pool features with low magnitudes while leaving the rest unchanged. We evaluated the proposed model using letters and digits shapes extracted from the EMNIST dataset and obtained a high classification accuracy.
Snapwear: A Snapchat AR filter for the virtual tryon of real clothes

In this paper, we present Snapwear, a Snapchat AR filter for visualizing real-world clothing in a magic mirror-alike experience with the use of a smartphone. Our objective is to document the steps needed to transform a garment designed for production to be ready-for-use in an AR environment. In this direction, we analyse the generation pipeline of an average avatar, the garment export from the fashion design software, its skinning and rigging to the average avatar and the simulation options offered by Snapchat. Finally, we discuss the AR experience creation process ranging from technical challenges such us body tracking and dealing with occlusions, to enabling the user interaction with the filter.
CRAPPY: Command and Real-Time Acquisition in Parallelized Python, a Python module for experimental setups

Performing relevant mechanical tests requires complex experimental setups. CRAPPY is a Python module meant to help researchers develop code for systems involving several sensors and/or actuators. It features a number of advanced tools to perform measurements, drive hardware, and process data. CRAPPY aims at making the design of advanced tests easier and accessible to non-code experts. It is highly modular, and new devices can easily be added to the module as long as they can be interfaced with Python. Users can fully take advantage of Python’s versatility in their experimental scripts, as they are not constrained by any GUI. CRAPPY features a wide variety of tools specific to experimental mechanics as it was first developed for this field, but tools and instruments from other domains can as well be included within its framework. In this paper, the project and its functionalities will be described, illustrated by short examples of code and existing experimental setups using CRAPPY.
NONLOCALITY DYNAMICS INDUCED BY A LAMB–DICKE NONLINEARITY IN TWO DIPOLE-COUPLED TRAPPED IONS UNDER INTRINSIC DECOHERENCE

In this paper, we explore the nonlinear dynamics of two dipole-coupled-trapped ions in the Lamb–Dicke regime. The dynamics of the generated two-trapped-ions correlations under intrinsic decoherence is quantified by Bell function, the uncertainty-induced nonlocality, and the concurrence. We investigate the effects of the Lamb–Dicke nonlinearity, the intrinsic decoherence, and the two-trapped-ions coupling. It is found that the two-trapped-ions state has different nonlocal correlations. These correlations can be controlled. In the absence of decoherence, the nonlocal correlations can be enhanced by the Lamb–Dicke nonlinearity and the two-trapped-ions coupling. The stable value and the sudden death-birth phenomenon of the entanglement can be apperceived due to the increase of the Lamb–Dicke nonlinearity. The intrinsic decoherence reduces and stabilizes the nonlocal correlations. They are more pronounced with large Lamb–Dicke values. The decoherence effects are reduced by the strong Lamb–Dicke nonlinearity.
An Intelligent Video Processing Architecture for Edge-cloud Video Streaming

This work proposes an intelligent video processing architecture for bandwidth-efficient edge-cloud video streaming. On receiving the bandwidth-saving low-quality video streaming in compressed format, the proposed architecture can perform direct DNN-based video enhancement, e.g., super-resolution and motion-compensated frame interpolation (MCFI), on streams. By utilizing the metadata motion vectors and residuals extracted from the encoded video, our workflow will significantly eliminate the unnecessary pixels being processed by the video-enhancing DNNs, and greatly promote the execution efficiency. The evaluation results on popular datasets show that our architecture can reduce the edge-side processing latency of video-enhancing DNNs by 90% compared to the traditional flow while producing accurate and high-quality videos on edge.
A Novel Big Data Approach for Record and Represent Compliance in the Covid-19 Era

The present work shows the importance of the Big Data Analytics (Anisetti et al., 2018) scenario applied to corporate COVID-19 compliance, following the concept of "accountability" related privacy issues introduced by the new General Data Protection Regulation. This compliance approach is very useful for territorial security, environmental monitoring and safety prevention (in today pandemic context) and it is developed by the use of technologies (RFID, QR-CODE, NFC, blockchain) and HMI (Human Machine Interface) that it is fundamental core for data monitoring and information representation. The paper shows some new approaches about user HMI interface developed to collect big data about compliance and an innovative data representation approach using audio spectrum waves to record, share and evaluate compliance through an audio representation following a new process mapping approach.
Automatic Human Fall Detection Using Multiple Tri-axial Accelerometers

Accurately detecting human falls of elderly people at an early stage is vital for providing early alert and avoid serious injury. Towards this purpose, multiple triaxial accelerometers data has been used to uncover falls based on an unsupervised monitoring procedure. Specifically, this paper introduces a one-class support vector machine (OCSVM) scheme into human fall detection. The main motivation behind the use of OCSVM is that it is a distribution-free learning model and can separate nonlinear features in an unsupervised way need for labeled data. The proposed OCSVM scheme was evaluated on fall detection databases from the University of Rzeszow's. Three other promising classification algorithms, Mean shift, Expectation-Maximization, k-means, were also assessed based on the same datasets. Their detection performances were compared with those obtained by the OCSVM algorithm. The results showed that the OCSVM scheme outperformed the other methods.
Fusing sufficient dimension reduction with neural networks

Neural networks are combined with sufficient dimension reduction methodology in order to remove the limitation of small p and n of the latter. NN-SDR applies when the dependence of the response Y on a set of predictors X is fully captured by the regression function g(B′X), for an unknown function g and low rank parameter B matrix. It is shown that the proposed estimator is on par with competing sufficient dimension reduction methods, such as minimum average variance estimation and conditional variance estimation, in small p and n settings in simulations. Its main advantage is its scalability in regressions with large data, for which the other methods are infeasible.
A novel improved cubature Kalman filter with adaptive generation of cubature points and weights for target tracking

Abstract In nonlinear state estimation, the generation method of cubature points and weights of the classical cubature Kalman filter (CKF) limits its estimation accuracy. Inspired by this, in this paper, a novel improved CKF with adaptive generation of the cubature points and weights is proposed. Firstly, to improve the accuracy of the classical CKF while considering the calculation efficiency, we introduce a new high-degree cubature rule combining the third-order spherical rule and the sixth-degree radial rule. Next, in the new cubature rule, a novel method that can adaptively generate cubature points and weights based on the distance between the points and center point in the sense of the inner product is designed. We use the cosine similarity to quantify the distance. Then, based on that, a novel high-degree CKF (HCKF) is derived that uses much fewer points than other HCKFs. In the proposed filter, based on the actual dynamic filtering process, the simultaneous adaptive generation of cubature points and weight can make the filter reasonably distribute the cubature points and allocate the corresponding weights, which can obviously improve the approximate accuracy of the one-step state and measurement prediction. Finally, the superior performance of the proposed filter is demonstrated in a benchmark target-tracking model.
Deep Learning Based Video Captioning in Bengali

Generating meaningful textual descriptions from visual contents having the context in consideration is very challenging in terms of Natural Language Processing (NLP) and Computer Vision (CV) and video captioning adds more to it. Currently, the field of video captioning is highly explored in different languages except for Bengali, where no work is available so far. In this paper, we have proposed a model to generate meaningful captions describing the activities of a video in Bengali. The proposed model is an encoder-decoder-based novel deep architecture that incorporates a combination of different Convolutional Neural Networks (CNNs), like 2D-CNN and 3D-CNN with Bidirectional Long Short Term Memory (Bi-LSTM) as the encoder and two-layer Long Short Term Memory (LSTM) as the decoder. Lack of proper concern in this field left us with no video dataset with proper Bengali captions. For this reason, all the English captions of the MSVD dataset have been translated to Bengali language using the Google Translate API. In this work, we have trained and evaluated our model on the MSVD dataset and achieved a 32.6%, and 51.2% score on BLEU and CIDEr, respectively, which is currently the state-of-the-art result even compared with closely related Bengali image captioning works.
PETRI: Reducing Bandwidth Requirement in Smart Surveillance by Edge-Cloud Collaborative Adaptive Frame Clustering and Pipelined Bidirectional Tracking

Neural networks running on cloud servers have been widely used in smart surveillance, but they require high bandwidth to upload videos. Edge-cloud collaborative encoding based on ROI (Region-Of-Interest) can reduce bandwidth requirement, but it suffers from inaccurate ROI detection due to feedback latency and undetected new targets. To address the above challenges, we propose an object detection system named PETRI. It adopts a latency-hiding pipeline workflow with adaptive keyframe interval selection for different input videos, and utilizes a retro-tracking method to find undetected targets. While achieving negligible impact on model accuracy, the proposed PETRI can save up to 66.44% and 30.25% bandwidth compared with the cloud only method and the previous state-of-art work respectively.
Simple But Effective: Upper-Body Geometric Features for Traffic Command Gesture Recognition

Recognizing traffic command gestures with high accuracy and quick response at a low computational cost is a requisite for driver assistance or autonomous driving. However, it has been understudied for a long time. Existing research takes advantage of increasing development in human action recognition but pays little attention to onboard conditions. In this article, we propose a simple but effective recognition model based on human upper-body geometric features and a long short-term memory (LSTM) network. The handcrafted geometric features can easily be calculated with estimated 2-D human keypoints at a low computational cost but are discriminative and sufficient in classification. Offline and online inferences are implemented to comprehensively evaluate the proposed model. For the sake of robustness required in the automotive domain, dual voting is designed to filter the output in online inference. On the recently published Chinese traffic police gesture (CTPG) dataset, the presented approach is the best with a remarkable improvement of approximately 8% compared to previous LSTM-based methods with handcrafted spatial features and is competitive with advanced GCN-based deep learning methods. The tradeoff pattern is explored to demonstrate how accuracy and response time alter with different training and inference strategies so that a balanced setup can be manually chosen under various application scenarios. Field tests are also carried out with an experimental vehicle, and the results uncover the present gap between research and practical application to some extent, moving a step closer to real-life traffic command gesture recognition.
Influence of COVID-19 Epidemic on Dark Web Contents

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA) in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.
Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations

Mean square error matrices belong to key concepts in decentralised estimation. They assess the quality of estimates and are essential for the optimisation of the estimation fusion. In the case of a missing knowledge, sets of admissible matrices are replaced by their bounds and a robust fusion is applied. This paper prospects a specific partial knowledge of the sets of matrices. Upper bounds are constructed first. Then, the stress is laid on non-zero lower bounds, which do not exist in the fusion under completely unknown correlation. Limit cases are discussed on numerical examples and graphical illustrations are given.
Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of ArtificialNeural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models.
An Enhanced Naked Mole Rat Algorithm for Optimal Cross-Layer Solution for Wireless Underground Sensor Networks

Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.
MAT: Processing In-Memory Acceleration for Long-Sequence Attention

Attention-based machine learning is used to model long-term dependencies in sequential data. Processing these models on long sequences can be prohibitively costly because of the large memory consumption. In this work, we propose MAT, a processing in-memory (PIM) framework, to accelerate long-sequence attention models. MAT adopts a memory-efficient processing flow for attention models to process sub-sequences in a pipeline with much smaller memory footprint. MAT utilizes a reuse-driven data layout and an optimal sample scheduling to optimize the performance of PIM attention. We evaluate the efficiency of MAT on two emerging long-sequence tasks including natural language processing and medical image processing. Our experiments show that MAT is $2.7 \times$ faster and $3.4 \times$ more energy efficient than the state-of-the-art PIM acceleration. As compared to TPU and GPU, MAT is $5.1 \times$ and $16.4 \times$ faster while consuming $27.5 \times$ and $41.0 \times$ less energy.
Teaching System of Undergraduate Entrepreneurship Education under the Background of Internet of Things

This article mainly introduces the research on the teaching system of entrepreneurship education for college students under the background of the Internet of Things. On the basis of extensive investigation and analysis, this article combines the reality of entrepreneurship education in our country’s universities to study how to construct an entrepreneurial education system. This paper designs the social support guarantee and specific implementation plan for corporate education by constructing the corporate goal system, curriculum system, practical curriculum system, training team, and other systems, defines the relevant concepts of the innovation and entrepreneurship education system, and analyzes the related concepts of innovation and entrepreneurship education. Theories provide a theoretical basis for constructing college students’ innovation and entrepreneurship education system. Based on the analysis and construction of the innovation and entrepreneurship education system for college students in our country, the necessity and feasibility of the innovation and entrepreneurship education system for college students are further elaborated. The experimental results of this paper show that after the theoretical viewpoints and operational content of the EE system are put forward, the entrepreneurial enthusiasm of college students has increased by 15%, the learning ability has been significantly improved, and it has also fully demonstrated the teaching effect of the entrepreneurial education teaching system for college students under the background of the Internet of Things. In addition, their understanding of EE and teaching has also improved significantly. This structure provides an important reference for further research and practice of undergraduate EE.
Competencies of the Future: Transforming Education in the Digital Economy

The global community has been «taken over» by global digitalization. Businesses have been digitally reshaped by the complex nature of Industry 4.0 technology. Industry and entrepreneurship are transforming business processes. The diffusion of “end-to-end” digital technologies into the global economy also requires a reconsideration of the role and place of humans in this process. In this regard, the article considers a comprehensive understanding of the necessary skills of the digital future, analyzes their various aspects, defines the conceptual apparatus of digital skills development problems. The problems faced by business and the modern education system at different levels of digital skills training are highlighted. Existing and nascent but promising approaches to solving educational problems are summarized and analyzed. The tools of competence forecasting in accordance with the future market conditions, taking into account international factors of change—innovation and technological progress—are considered. The author's position on the formation of the HR model of the near future is formulated.