Reviewerly/exHarmony huggingface dataset:
Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.
Two-Stream Convolutional Neural Network for Video Action Recognition

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis.However, video action recognition can be disturbed by many factors, such as background, illumination and so on.Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end.The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action.Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight.It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video.This way also greatly improves the accuracy and reduces the training time.What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model.In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.
A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.
Realidades y retos en el uso de las TIC en educación, por la emergencia sanitaria provocada por el COVID-19

The paper first addresses the educational policies dictated at the international and national levels to deal with the health emergency caused by COVID-19. The main objective of these policies has been not to stop the teaching-learning processes, moving from face-to-face and blended modalities to an online modality, trying not to neglect aspects such as educational quality. Afterwards, those dictated and implemented by the University Network of the University of Guadalajara (U. de G.) are analyzed. Then, the reality faced by the professors of the Centro Universitario del Norte (CUNorte) to continue their teaching work is described. Finally, the challenges in the short, medium and long term, related to the use of information and communication technologies (ICT), such as the lack of training in digital platforms and tools, computer equipment for work, obsolescence of the same and problems of Internet connectivity, among others.
Emotion Classification and Facial Key point detection using AI

Facial expressions are not only the natural form of human emotions display but also a key nonverbal type of communication. Face detection and emotion classification is an area which is widely used and has been at the center of the on-going research now. The rapid development in this area has allowed to use the technology in applications such as living assistance, real time monitoring of health, surveillance systems, authentication, access control and many more. As most of the focus for emotion detection lies around mouth and eyes, extracting and categorizing the gestures is very important. Neural networks and machine learning has demonstratedits use in obtaining good results in emotion recognition and classification. This paper discusses a combined model for facial key point detection and facial expression detection model.
Toolbox for non-classical state calculations

Abstract Computational challenges associated with the use of Wigner functions to identify non-classical properties of states are addressed with the aid of generating functions. It allows the computation of the Wigner functions of photon-subtracted states for an arbitrary number of subtracted photons. Both the formal definition of photon-subtracted states in terms of ladder operators and the experimental implementation with heralded photon detections are analyzed. These techniques are demonstrated by considering photon subtraction from squeezed thermal states as well as squeezed Fock states. Generating functions are also used for the photon statistics of these states. These techniques reveal various aspects of the parameter dependences of these states.
Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault

In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.
A Bayesian Adaptive Unscented Kalman Filter for Aircraft Parameter and Noise Estimation

This paper proposes a new algorithm for the aerodynamic parameter and noise estimation for aircraft dynamical systems. The Bayesian inference method is combined with an unscented Kalman filter to estimate the augmented states and the unknown noise covariance parameters jointly. A Gauss-Newton method is utilized to sequentially maximize the posterior likelihood function for the noise unknown parameter estimation. The performance of the proposed algorithm is evaluated and compared with two other UKFs via a flight scenario of a given aircraft. The results indicate that the proposed algorithm has equivalent performance to the simplified UKF with prior noise information and slightly outperforms the parallel UKF on precision and efficiency in this flight scenario assessment. Then, the consistency and accuracy of the algorithm are further validated by a Monte Carlo simulation with random process noise covariance. This adaptive algorithm provides another feasible and effective way for estimating aerodynamic parameters from the aircraft real flight data with unknown noise characteristics.
Polarimetric Dehazing Method Based on Image Fusion and Adaptive Adjustment Algorithm

To improve the robustness of current polarimetric dehazing scheme in the condition of low degree of polarization, we report a polarimetric dehazing method based on the image fusion technique and adaptive adjustment algorithm which can operate well in many different conditions. A splitting focus plane linear polarization camera was employed to grab the images of four different polarization directions, and the haze was separated from the hazy images by low-pass filtering roughly. Then the image fusion technique was used to optimize the method of estimating the transmittance map. To improve the quality of the dehazed images, an adaptive adjustment algorithm was introduced to adjust the illumination distribution of the dehazed images. The outdoor experiments have been implemented and the results indicated that the presented method could restore the target information obviously, and both the visual effect and quantitative evaluation have been enhanced.
Fake News Detection using RNN-LSTM

Abstract: Fake news distribution is a social phenomenon that can't be avoided on a personal level or through web-based social media like Facebook and Twitter. We're interested in counterfeit news because it's one of many sorts of double dealing in online media, but it's a more severe one because it's designed to deceive people. We're concerned about this now that we've seen what's going on. We are concerned about this issue because we have seen how, through the usage of social correspondence, this marvel has recently caused a shift in the direction of society and people groupings, as well as their opinions. Along these lines, we chose to confront and decrease this wonder, which is as yet the principal factor to pick a large portion of our choices. Our objective in this study is to develop a detector that can predict if a piece of news is false based just on its content, and then attack the problem using RNN method models LSTMs and Bi-LSTMs to tackle the problem from a basic deep learning viewpoint. Keywords: RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), Fake news detection, Deep learning
ONLINE LEARNING AT HIGHER EDUCATION INSTITUTIONS IN UKRAINE: ACHIEVEMENTS, CHALLENGES, AND HORIZONS

Online learning has shown considerable growth over the last decade, as the Internet and education were combined to serve millions of people to gain new skills. This paper outlines benchmarking studies of the current online learning trends. We have demonstrated the weak points in organizing and conducting online classes, identified the shortcomings of excessive digitalization of education, and suggested several problem-solving alternatives. We strongly believe that the appropriate arrangement of distance learning is impossible without getting feedback from all participants of the learning process. Our study represents the benefits and challenges of distance learning noticed by students. The given study is the initial stage of the multi-level research. To receive a qualitative and valid result, we developed a questionnaire with cross-sectional questions, which allowed analysing each question from multiple perspectives, namely we were interested in the opinion of students on the following issues: transition to entirely distance learning; the role of a teacher in distance learning; the influence of distance learning format on the education quality (perception, understanding, learning, and mastering the material); difficulties and challenges. After examining the data obtained, we have managed to single out the following aspects: the most advisable forms of ICT use in the learning organization, balanced learning workload, and communication between teacher and student; co-ordination within the institution to avoid students' overload, which leads to demotivation and physical fatigue to cope with the scope of learning materials. The findings of this study indicate weak aspects in the organization and use of ICT in education. Based on these findings, we offered strategies for successful distance and blended learning. Besides, our findings must be taken into account when organizing blended learning.
Generation of entangled photons via parametric down-conversion in semiconductor lasers and integrated quantum photonic systems

We propose and design a high-brightness, ultracompact electrically pumped GaSb-based laser source of entangled photons generated by mode-matched intracavity parametric down-conversion of lasing modes. To describe the nonlinear mixing in highly dispersive and dissipative waveguides, we develop a nonperturbative quantum theory of parametric down-conversion of waveguide modes which takes into account the effects of modal dispersion, group and phase mismatch, propagation, dissipation, and coupling to noisy reservoirs. We extend our theory to the regime of quantized pump fields with an approach based on the propagation equation for the state vector which solves the nonperturbative boundary-value problem of the parametric decay of a quantized single-photon pump mode and can be generalized to include the effects of dissipation and noise. Our formalism is applicable to a wide variety of three-wave mixing propagation problems. It provides convenient analytic expressions for interpreting experimental results and predicting the performance of monolithic quantum photonic systems.
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories.While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents.We applied our model to classify clinical trial (CT) protocols into completed and terminated categories.We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores 0.85 on a publicly available large scale CT registry of around 360K protocols.We further demonstrate how the selective pooling can add insights into the CT termination status prediction.We make the source code and dataset splits accessible.
A Brain-Inspired Dynamic Environmental Emergency Response Framework for Sudden Water Pollution Accidents

Sudden water pollution accidents happen frequently in China, and the number of treated accidents is low, due to the slow response speed. In addition, there is a lack of decision support systems that can follow up the whole process instead of just giving a one-time method. This study constructs a framework suitable for China that has both the ability of quick responses and full-time dynamic decision support, such as an experienced expert, while not being affected by pressure, to be used an emergency response for sudden water pollution accidents. To allow new decisionmakers to integrate into this professional decision-making role more quickly, a brain-inspired system is realized through combining the machine learning algorithm KNN and the idea of iteration and dynamic programming. The feasibility of our framework is further tested through a major water pollution happened recently. The results show that this framework can be well connected with the emergency response technology system that has been completed before, while also supporting the rapid and robust decision making such as the decisionmaker’s second brain, reducing the demand for professional background and experience of emergency decisionmakers, thus effectively shorten the waiting period for response.
Broadband-laser-diode pumped periodically poled potassium titanyl phosphate-Sagnac polarization-entangled photon source

We experimentally demonstrate a polarization-entangled photon source at 810 nm using a type-II phase-matched PPKTP crystal pumped by a low-cost, broadband laser diode with a central wavelength of 405 nm and a typical bandwidth of 0.53 nm. The PPKTP crystal is placed in a Sagnac-loop to realize the compact size and high stability. The downconverted biphotons, the signal and the idler, have typical bandwidths of 5.57 nm and 7.32 nm. We prepare two Bell states |Psi+> and |Psi-> with the fidelities of 0.948+-0.004 and 0.963+-0.002. In polarization correlation measurement, the visibilities are all higher than 96.2%, and in the Bell inequality test, the S value can achieve 2.78+-0.01. To our knowledge, this experiment is the first to combine a multi-mode pump laser with a Sagnac-loop configuration. This high-quality and low-cost entangled photon source may have many practical applications in quantum information processing.
Micromachined two-axis water-immersible scanning mirror using torsional and bending hinges

We report a two-axis water-immersible microscanning mirror using torsional and bending biaxially oriented polyethylene terephthalate hinges. Two different designs based on a four- or single-coil electromagnetic actuator are investigated. A micromachining-based fabrication process is developed to enable high patterning resolution and alignment accuracy and to reduce the amount of manual assembly. With a torsional hinge, the fast axis has a resonance frequency of 300 to 500 Hz in air and 200 to 400 Hz in water. With a bending hinge, the slow axis has a resonance frequency of 60 to 70 Hz in air and 20 to 40 Hz in water. 2D B-scan and 3D volumetric ultrasound microscopy are demonstrated using the hybrid-hinge scanning mirror. The ability of scanning the slow axis at DC or very low frequencies allows a dense raster scanning pattern to be formed for improving both the imaging resolution and field of view.
Dupuytren's Contracture: The Readability of Online Information

Dupuytren's contracture is a common hand pathology for which consultation and treatment are largely at the patient's discretion. The objective of this study was to evaluate the readability of current online patient information regarding Dupuytren's contracture. The largest public search engines (Google, Yahoo, and Bing) were queried using the search terms "Dupuytren's contracture," "Dupuytren's disease," "Viking's disease," and "bent finger." The first 30 unique websites by each search were analyzed and readability assessed using five established algorithms: Flesch Reading Ease, Gunning-Fog Index, Flesch-Kincaid Grade level, Coleman-Liau index, and Simple Measure of Gobbledygook grade level. Analysis of 73 websites demonstrated an average Flesch Reading Ease score of 48.6 ± 8.0, which corresponds to college reading level. The readability of websites ranged from 10.5 to 13.3 reading grade level. No article was written at or below the recommended sixth grade reading level. Information on the internet on Dupuytren's contracture is written at higher than recommended reading grade level. There is a need for high-quality patient information on Dupuytren's contracture at appropriate reading grade levels for patients of various health literacy backgrounds. Hospitals, universities, and academic organizations focused on the development of readable online information should consider patients' input and preferences.
Application Strategies of Cloud Computing Intelligent Optimization Algorithms in English Translation Major Teaching

With the development of social economy, English is still the main language of global communication, and China’s demand for English translators is also higher and higher. In college education, English translation is a very popular and important subject. Although the teaching mode is constantly changing, it still lags behind the speed of social and economic development. Traditional English translation teaching has many disadvantages in teaching theory and methods. This paper discusses the problems existing in translation teaching for English majors and then puts forward a new teaching mode of educational multiattitude resources, to really improve the quality of translation teaching for English majors. The resource pool of cloud computing is composed of a large number of resource nodes with different performances, but as the number of users gradually grows, the demand also increases. How to efficiently allocate large-scale cloud tasks to limited resource nodes and to achieve load balancing is an important issue that needs to be studied in cloud computing. This paper first uses the literature method, in-depth study of the theoretical development, and application bottleneck of education multiposture teaching mode at the same time to understand the problems existing in the current English translation teaching and design a new teaching mode for experimental application research and finally analyzes the comparison of the English translation ability of the control group and the experimental group without teaching method. After a period of experiment, the average score of the control group is 70.53, and the average score of the experimental group is 75.89. It can be seen that the average score of the experimental group is about 5 points higher than that of the control group; this shows that the intelligent optimization algorithm of cloud computing is combined with translation teaching that can improve the students’ overall English translation level to a certain extent and is more conducive to the students’ comprehensive mastery of knowledge.
Approach of immersive adaptive learning for Virtual Reality simulator

The need to improve teaching-learning processes promotes the development of Virtual Reality applications with adaptive learning approaches. Adaptive learning considers the premise that each person learns differently, at their own pace, considering the characteristics of the student. This paper describes the main components of an immersive adaptive learning approach for Virtual Reality simulators.