Reviewerly/exHarmony huggingface dataset:
Comparing models of learning and relearning in large-scale cognitive training data sets

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.
Static Hand Gesture Recognition for ASL Using MATLAB Platform

Generally, communication with people in our daily life is by speaking with voice but some communications can be possible with body language, facial expressions, and hand signs. We can also communicate with others without voice. Apart from that, hand gestures are playing very important role in communication. Here, we developed a gesture identification system which interprets the American Sign Language. This system helps the people who are deaf and dumb. This system leads them to understand and communicate as like normal people. Lot of proposals are introduced on gestures specified with their languages like ASL, ISL, etc. Here, we are introducing new static gestures using MATLAB on bases of existing systems. Our input is captured from camera; then, system applies the pre-processing on captured image. The set of features are retrieved using PCA. Comparison of the features is done using Euclidean distance with the help of training sets. Finally, optimal gestures identify and produce the output inwards of text or voice.
ACQC: Apollonius Circle‐based Quantum Clustering

The Quantum Clustering (QC) method begins by generating a wave function for estimating the data points' density distribution, which is the sum of Gaussian kernels to the data points' centers. It then forms the corresponding potential function and finally locates clusters, emphasizing the potential function's minimums. This process is highly sensitive to the kernel bandwidth in the Schrödinger equation that controls the shape of the Gaussian kernel and affects the clustering result. This paper proposes an Apollonius Circle-based Quantum Clustering (ACQC) method, which adaptively and automatically sets the kernel bandwidth without any prior knowledge regarding the data points and clusters. ACQC is the first attempt to achieve such an adaptive kernel bandwidth through the Apollonius region's neighborhood construction. The wave function is estimated based on data points in the neighborhood group constructed by Apollonius circles to optimize ACQC calculations. The experimental results of ACQC compared to the original QC method indicate an improvement in calculation efficiency by approximately a 38.3% reduction in terms of running time and a 41.17% improvement in detecting the correct number of clusters. Extensive experiments on four synthetic and 20 real-world datasets show that ACQC outperforms state-of-the-art methods significantly. All the implementation source codes of ACQC are made publicly available at https://github.com/NAbdolmaleki/ACQC-Apollonius-Circle-based-Quantum-Clustering.
Adopting an immersive virtual reality system to enhance Pre-service teachers’ communicative competence

The use of virtual simulations is increasingly seen as an opportunity to provide safe training environments. It could provide pre-service teachers with unique opportunities to experience examples of classroom life in a controlled and structured. However, some authors pointed that usability tests are necessary to achieve effective e-learning systems. This paper presents the usability testing of Didascalia Virtual-ClassRoom, an immersive virtual reality system to enhance preservice Secondary school teachers’ communicative competence in classroom climate management. Using the technology acceptance model, we analyzed the responses of 62 users (schoolteachers, counselors and preservice teachers) from Madrid and Barcelona that participated on the experiment. Qualitatively, participants discussed perceived usefulness, as well as positive and negative emotional reactions, which may act as additional facilitators and inhibitors to technology adoption. Based on our findings, we considered that this tool could provide a safe scenario to implement the much-needed practical training, in which preservice teachers can experiment and reflect on their ability to manage the classroom climate. Improvements to the tool are suggested to optimize its functionality, as well as some ideas for its introduction in preservice teacher training.
The Idea and Becoming of a University Across Time and Space: Ivory Tower, Factory and Network

Abstract The modern university has grown from small scale, elite access institution, growing out of the Enlightenment period in Europe in the early nineteenth century. Freedom to pursue knowledge and ‘dare to know’ was a key characteristic of the Enlightenment university, conceptualised here as Mode 1 Elite Ivory Tower University . The twentieth century saw a rapid rise in national government involvement, funding and regulating universities as a way of nation state building. This developed within social contexts of neoliberal knowledge and information economies. Market demand and regulation, seeing teaching and research as products saw huge growth of universities in size and number globally. More citizens accessed and engaged with universities as large institutions, conceptualised here as a Mode 2 Mass Access Factory University . Globalisation, digital technologies and a move away from big organisations and states is seeing an emergence of a Mode 3 Universal Network University which is universal and unbundled in complex technological and social network relationships, often underpinned by a form of capitalism which is developing neoliberal approaches aided by data collection and information networks. These three modes are developed and explored here through a postdigital lens across time (genealogically) and space (residual, dominant and emerging discourses and cultures remaining in and between institutions) as a theoretical framework with which to research and envisage the characteristics, discourse, perceptions and becoming of the current and future university.
Non-periodic input-driven magnetization dynamics in voltage-controlled parametric oscillator

Input-driven dynamical systems have attracted attention because their dynamics can be used as resources for brain-inspired computing. The recent achievement of human-voice recognition by spintronic oscillator also utilizes an input-driven magnetization dynamics. Here, we investigate an excitation of input-driven chaos in magnetization dynamics by voltage controlled magnetic anisotropy effect. The study focuses on the parametric magnetization oscillation induced by a microwave voltage and investigates the effect of random-pulse input on the oscillation behavior. Solving the Landau-Lifshitz-Gilbert equation, temporal dynamics of the magnetization and its statistical character are evaluated. In a weak perturbation limit, the temporal dynamics of the magnetization are mainly determined by the input signal, which is classified as input-driven synchronization. In a large perturbation limit, on the other hand, chaotic dynamics are observed, where the dynamical response is sensitive to the initial state. The existence of chaos is also identified by the evaluation of the Lyapunov exponent.
Modeling the behavior of mobile robots using genetic algorithms

The article is devoted to the analysis of the behavior of a mobile robot using finite state machine algorithms in order to find a way to the goal and avoid obstacles. After justifying the use of such methods, the analysis of a standard deterministic finite automaton is done. Further, the theory of Markov processes is applied to this algorithm, as a result of which the state machine becomes part of the hidden Markov model. This allows you to apply probabilistic methods to modeling the behavior of the robot. This probabilistic behavior is most promising in complex environments with unpredictable obstacle configurations. To compare the efficiency of deterministic and probabilistic finite state machine, we applied a genetic algorithm. In the numerical experiment that we conducted in the Scilab software, we considered two main types of environments in which a mobile robot can move - an office-type environment and a polygonal-type environment. For each type of environment, we alternately applied each of the indicated behavior algorithms. For the genetic algorithm, we used one hundred individuals who were trained over 1000 generations to find the most optimal path in the specified environments. As a result, it was found that the deterministic finite state machine algorithm is the most promising for movement in an office-type environment, and the probabilistic finite state machine algorithm gives the best result in a complex polygonal environment.
Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach

Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble learning method. Ensemble learning methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose of improving the stability and accuracy of machine learning algorithms. This study presented REMARS (Random Ensemble MARS), a new MARS model selection approach obtained using the Random Forest (RF) algorithm. 200 training and test data set generated via the Bagging method were analysed in the MARS analysis engine. At the end of the analysis, two different MARS model sets were created, one yielding the smallest Mean Square Error for the test data (Test MSE) and the other yielding the smallest Generalised Cross-Validation (GCV) value. The best model was estimated for both Test MSE and GCV criteria by examining the error of measurement criteria, variable importance averages, and frequencies of the knot values for each model. Eventually, a new model was obtained via the ensemble learning method, i.e., REMARS, that yields result as good as the MARS model obtained from the original data set. The MARS model, which works better in the larger data set, provides more reliable results with smaller data sets utilising the proposed method.
Adioc loss: An Auxiliary descent IoC loss function

Object detection based on deep learning has progressed significantly hitherto. Intersection over Union (IoU) is a wildly adopted evaluation metric in this field, which is also used as a loss function to constrain training. To address the problem of IoU zero gradient under the non-overlap circumstance, most improved loss functions tend to change penalty terms while few loss functions tried to improve IoU itself. In this paper, we proposed an intersection over convex (IoC) algorithm via analysis of IoU series loss functions. IoC can provide a gradient when a bounding box (also called a predicted box) and a ground truth (also called a target box) share no region. This characteristic will accelerate the training phase. In consideration of the comprehensiveness of the loss function, we constructed Auxiliary descent intersection over convex (Adioc) loss. Adioc loss function was tested on a one-stage network named Yolov5 and a two-stage network named Faster-RCNN. For the one-stage network, the results showed that under the same training batch, the accuracy of the Yolov5s network on VOC datasets increased by 0.1% ∼ 0.4%, and the accuracy of the Yolov5s network on COCO datasets increased by 0.1% ∼ 0.4% The relative improvement of Yolov5 m is 0.6% ∼ 0.7% on COCO dataset. For the two-stage network, the improvement of Faster-RCNN on COCO datasets is about 0.3% ∼ 0.5%.
Large scale instance segmentation of outdoor environment based on improved YOLACT

Summary Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.
An interpretable semi-supervised framework for patch-based classification of breast cancer

Developing effective invasive Ductal Carcinoma (IDC) detection methods remains a challenging problem for breast cancer diagnosis. Recently, there has been notable success in utilizing deep neural networks in various application domains; however, it is well-known that deep neural networks require a large amount of labelled training data to achieve high accuracy. Such amounts of manually labelled data are time-consuming and expensive, especially when domain expertise is required. To this end, we present a novel semi-supervised learning framework for IDC detection using small amounts of labelled training examples to take advantage of cheap available unlabeled data. To gain trust in the prediction of the framework, we explain the prediction globally. Our proposed framework consists of five main stages: data augmentation, feature selection, dividing co-training data labelling, deep neural network modelling, and the interpretability of neural network prediction. The data cohort used in this study contains digitized BCa histopathology slides from 162 women with IDC at the Hospital of the University of Pennsylvania and the Cancer Institute of New Jersey. To evaluate the effectiveness of the deep neural network model used by the proposed approach, we compare it to different state-of-the-art network architectures; AlexNet and a shallow VGG network trained only on the labelled data. The results show that the deep neural network used in our proposed approach outperforms the state-of-the-art techniques achieving balanced accuracy of 0.73 and F-measure of 0.843. In addition, we compare the performance of the proposed semi-supervised approach to state-of-the-art semi-supervised DCGAN technique and self-learning technique. The experimental evaluation shows that our framework outperforms both semi-supervised techniques and detects IDC with an accuracy of 85.75%, a balanced accuracy of 0.865, and an F-measure of 0.773 using only 10% labelled instances from the training dataset while the rest of the training dataset is treated as unlabeled.
Accelerated Quantum Adiabatic Transfer in Superconducting Qubits

Quantum adiabatic transfer is widely used in quantum computation and quantum simulation. However, the transfer speed is limited by the quantum adiabatic approximation condition, which hinders its application in quantum systems with a short decoherence time. Here we demonstrate quantum adiabatic state transfers that jump along geodesics in one-qubit and two-qubit superconducting transmons. This approach possesses the advantages of speed, robustness, and high-fidelity compared with the usual adiabatic process. Our protocol provides feasible strategies for improving state manipulation and gate operation in superconducting quantum circuits.
Detection of Breast Cancer Using Machine Learning and Deep Learning Methods

The most common reason for women&#x2019;s deaths worldwide is Breast Cancer, which can be controlled if detected at an early stage. There are numerous techniques based on Machine Learning as well as Deep Learning can be used for the diagnoses of the cancer. This paper employs Convolutional Neural Network (CNN), which also performs feature extraction for the Machine Learning algorithms in comparison with Support Vector Machine (SVM), Na&#x00EF;ve Bayes classifier (NB), K-Nearest Neighbor (KNN), Logistic Regression (LR) and Random Forest (RF). The system is experimented on BreaK-Hist Dataset obtained from Kaggle. The train_test_split method is used to bifurcate the raw data to distinct sets in order to achieve appropriate results. Softmax activation function predicts the outcomes based on probabilities. The main objective is to correctly classify the data and asses the performance of the models in comparison based on precision and recall rate. Further it also asses based on f1- score and as well as accuracy. When experimented the results show that CNN gives maximum accuracy with the lower most loss/error rate.
GeometryMotion-Transformer: An End-to-End Framework for 3D Action Recognition

In this work, we propose a new end-to-end optimized two-stream framework called GeometryMotion-Transformer (GMT) for 3D action recognition. We first observe that the existing 3D action recognition approaches cannot well extract motion representations from point cloud sequences. Specifically, when extracting motion representations, the existing approaches do not explicitly consider one-to-one correspondence among frames. Besides, the existing methods only extract the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single-scale</i> motion representations, which cannot well model the complex motion patterns of moving objects in point cloud sequences. To address these issues, we first propose the feature extraction module (FEM) to generate one-to-one correspondence among frames without using the voxelization process, and explicitly extract both geometry and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-scale</i> motion representations from raw point clouds. Moreover, we also observe the existing two-stream 3D action recognition approaches simply concatenate or add the geometry and motion features, which cannot well exploit the relationship between two-steam features. To this end, we also propose an improved transformer-based feature fusion module (FFM) to effectively fuse the two-stream features. Based on the proposed FEM and FFM, we build our GMT for 3D action recognition. Extensive experimental results on four benchmark datasets demonstrate the effectiveness of our backbone GMT.
Novel robust minimum error entropy wasserstein distribution kalman filter under model uncertainty and non-gaussian noise

Recently, the divergence-based minimax approach was proposed and introduced to robust Kalman filtering under model uncertainty and Gaussian noise, and it significantly outperformed the standard Kalman filter. In this study, we consider a new Wasserstein-distribution based Kalman filter in which model uncertainty and non-Gaussian noise exist simultaneously. First, we construct a joint Gaussian distribution of states and observations in state space. Then, we set the equivalent estimator joint Gaussian distribution as a Wasserstein ambiguity set allowable neighborhood centered on the true distribution. Thus, the estimator distribution problem is transformed into a minimax problem, and the model uncertainty is tolerated accordingly. Subsequently, we introduce minimum error entropy to optimize the minimax problem based on Wasserstein ambiguity sets, so as to handle the influence of non-Gaussian noise. The minimax problem constrained by the minimum error entropy is transformed into a semi-positive definite convex optimization problem. By constructing two iterative sub-problems that are mutually conditional, the nonlinear semi-definite program finite convex optimization problem is solved. Finally, the minimum error entropy Wasserstein distribution Kalman filter algorithm is proposed. Additionally, the convergence of the proposed algorithm is clarified, and its effectiveness verified by comparing a series of algorithms in typical simulation scenarios.
Proof of Stake Voting in Multiple Entry PBFT System

In this work we integrate Proof of Stake (PoS) consensus technique with multiple entry Practical Byzantine Fault Tolerance voting in a permissioned blockchain network. We introduce several PoS classes based on stake and truthfulness of voting. Each class is modeled using Semi Markov Process (SMP). We derive probability of reaching the two-thirds majority of total number of votes, and highlight the impact of the populations of individual stake/priority classes on achieving consensus. We have also connected stake classes with Enhanced Distribution Coordination Function EDCA for leader selection, which enables nodes from high stake classes to have higher frequency of leader role and gain more revenue for block handling.
Teaching Practice of Engineering Management Course for Engineering Education Certification under Background of Artificial Intelligence

With the advancement of China’s industrial construction, the field of engineering management has also attracted more attention. However, China’s engineering management major is currently in a growing stage due to the issue of the opening years, and the teaching and practice setting of each course is also in an immature stage, which makes China’s engineering management majors present more and more problems. The truancy rate has been increasing year by year, the students’ dominant position in the class has become objectified, and their trust in teachers has decreased. Students’ learning shows the characteristics of individualization and diversity. Higher requirements are put forward for teachers’ teaching quality, and schools lack an effective supervision mechanism. In order to solve these problems better, it is imperative to reform and innovate the course teaching of engineering management majors. The core of engineering education accreditation is to confirm that engineering graduates meet established quality standards recognized by the industry. It is a unique method to test whether the course teaching of engineering management majors is qualified and attracts many scholars to discuss it. Engineering education accreditation has attracted many scholars to discuss it because it is a unique means to test the qualifications of engineering management students’ course teaching. This study was based on an in-depth exploration of the teaching practice of engineering management courses and combines artificial intelligence with an engineering education certification. Through the research and analysis of colleges and universities, the research finally showed that the engineering management professional course teaching of engineering education certification under the background of artificial intelligence can promote the attendance of students in school by about 20%. The achievement of course teaching objectives has increased by about 13% and the comprehensive ability level of graduates has increased by about 8%. It improved the overall level of students and the teaching quality and efficiency of engineering management courses and also promoted the development of college education so that today’s engineering management graduates can better meet the needs of today’s society.