Reviewerly/exHarmony huggingface dataset:
Pose Recognition in Cricket using Keypoints

In the present-day time, there has been a gain in interest in video summarization and highlights generation in football, cricket, basketball, and baseball. Some pose recognition methods for recognizing the pose of an umpire in cricket have been proposed, but none of them leverage the potential of pose estimation and neural networks, which are two of the most powerful tools in Deep learning. In this paper, we work on the dataset termed SNOW, for the detection of umpire pose in the game of cricket. This dataset has been assessed as an introductory aid for pose recognition of the umpire in cricket. The umpire in cricket has the power to give decisions, and these decisions are conveyed using hand signals. On the basis of identifying the umpire's pose from the frames of a cricket video, we try to identify five such signals: NO BALL, SIX, WIDE, OUT, and NO ACTION. This paper discusses a technique for recognition of the gestures and poses of the umpire using keypoints generated using pose estimation. The experimental results show that the accuracy of our proposed technique is 87%, and the evaluation metrics of our technique are quite promising compared to existing state-of-the-art works.
Which notification is better? Comparing Visual, Audio and Tactile Cues for Asynchronous Mixed Reality (MR) Remote Collaboration: A User Study

Although Mixed Reality (MR) has been explored to support scenarios of remote collaboration, the majority of studies reported in literature have focused on synchronous use-cases, where all team-members can interact in real-time. Hence, an opportunity exist to explore asynchronous situations, in which collaborative actions take place at different times. Despite the physical distance and different time zones, remote experts can generate MR-instructions to assist with emerging problems, suggesting to on-site collaborators where to act, and what to do. This work explored three distinct notification methods for such scenarios: C1 - visual cue; C2 - audio cue; C3 - tactile cue, aiming to comprehend which one captures the attention of on-site team-members easily. A user study with 12 participants was conducted to evaluate these conditions during an asynchronous scenario of remote maintenance. We report participants insights, suggesting tactile cues represent the predominant condition given the scenario context.
Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.&#x0D;
High Quality Coal Foreign Object Image Generation Method Based on StyleGAN-DSAD

Research on coal foreign object detection based on deep learning is of great significance to safe, efficient, and green production of coal mines. However, the foreign object image dataset is scarce due to collection conditions, which brings an enormous challenge to coal foreign object detection. To achieve augmentation of foreign object datasets, a high-quality coal foreign object image generation method based on improved StyleGAN is proposed. Firstly, the dual self-attention module is introduced into the generator to strengthen the long-distance dependence of features between spatial and channel, refine the details of the generated images, accurately distinguish the front background information, and improve the quality of the generated images. Secondly, the depthwise separable convolution is introduced into the discriminator to solve the problem of low efficiency caused by the large number of parameters of multi-stage convolutional networks, to realize the lightweight model, and to accelerate the training speed. Experimental results show that the improved model has significant advantages over several classical GANS and original StyleGAN in terms of quality and diversity of the generated images, with an average improvement of 2.52 in IS and a decrease of 5.80 in FID for each category. As for the model complexity, the parameters and training time of the improved model are reduced to 44.6% and 58.8% of the original model without affecting the generated images quality. Finally, the results of applying different data augmentation methods to the foreign object detection task show that our image generation method is more effective than the traditional methods, and that, under the optimal conditions, it improves APbox by 5.8% and APmask by 4.5%.
Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network

The critical structure and nature of different bone marrow cells which form a base in the diagnosis of haematological ailments requires a high-grade classification which is a very prolonged approach and accounts for human error if performed manually, even by field experts. Therefore, the aim of this research is to automate the process to study and accurately classify the structure of bone marrow cells which will help in the diagnosis of haematological ailments at a much faster and better rate. Various machine learning algorithms and models, such as CNN + SVM, CNN + XGB Boost and Siamese network, were trained and tested across a dataset of 170,000 expert-annotated cell images from 945 patients’ bone marrow smears with haematological disorders. The metrics used for evaluation of this research are accuracy of model, precision and recall of all the different classes of cells. Based on these performance metrics the CNN + SVM, CNN + XGB, resulted in 32%, 28% accuracy, respectively, and therefore these models were discarded. Siamese neural resulted in 91% accuracy and 84% validation accuracy. Moreover, the weighted average recall values of the Siamese neural network were 92% for training and 91% for validation. Hence, the final results are based on Siamese neural network model as it was outperforming all the other algorithms used in this research.
Walk/Zeta Correspondence

Our previous work presented explicit formulas for the generalized zeta function and the generalized Ihara zeta function corresponding to the Grover walk and the positive-support version of the Grover walk on the regular graph via the Konno–Sato theorem, respectively. This paper extends these walks to a class of walks including random walks, correlated random walks, quantum walks, and open quantum random walks on the torus by the Fourier analysis.
Current trends in the development of pedagogical systems of ukraine in the conditions of digitalization of society

The purpose of the work is to analyse the current trends in the development of domestic pedagogical systems in the context of digitalization of society.&#x0D; The research methodology consists in the use of methods of scientific analysis, pedagogical positions on the issues of digitalization of education, implementation of systemic and integrative approaches.&#x0D; The scientific novelty consists in highlighting the main trends of the prospective pedagogical system. The main trends include: humanization and humanitarianization; national orientation; openness; science; continuity; digitalization. An analysis of each trend is presented and their role in the development of education is determined. It has been established that the digitalization of education creates prerequisites for the wide implementation of psychological and pedagogical developments in pedagogical practice, which allow to intensify the educational process, implement learning ideas, including within the framework of new models.&#x0D; Conclusions. Summarizing the analysis of the main trends and features of pedagogical systems at the current stage, it is very important to take into account the orientation of global educational processes. This is primarily an orientation to the individual, development of creative potential; openness of education as a condition of professional flexibility, mobility of a specialist, his ability to self-education and professional self-development; the need for interconnection and coordination of various stages of professional training, retraining and advanced training. Therefore, in order to modernize the pedagogical system of Ukraine, the national traditions and prospects of the country should be determined, taking into account the current trends in the development of digital technologies and the experience of foreign education systems.
PENERAPAN TUNING HYPERPARAMETER RANDOMSEARCHCV PADA ADAPTIVE BOOSTING UNTUK PREDIKSI KELANGSUNGAN HIDUP PASIEN GAGAL JANTUNG

Heart failure is the number one cause of death every year. Heart failure is a pathological condition characterized by abnormalities in heart function, which results in the failure of blood to be pumped to supply metabolic needs of tissues. The application of data mining and computational techniques to medical records can be an effective tool to predict each patient's survival who has heart failure symptoms. Data mining is a process of gathering important information from big data. The collection of important information is carried out through several processes, including statistical methods, mathematics, and artificial intelligence technology. The AdaBoost method is one of the supervised algorithms in data mining that is widely applied to make classification models. Hyperparameter Optimization is selecting the optimal set of hyperparameters for a learning algorithm. AdaBoost has hyperparameters requiring a classification process set, namely learning rate and n_estimators. RandomSearchCV is a random combination method of selected hyperparameters used to train the model. This research uses heart failure patient data collected at the Faisalabad Institute of Cardiology and at the Allied Hospital in Faisalabad (Punjab, Pakistan) from April to December 2015. The research uses learning rate: [-2.2] (log scale), n_estimators start from 10 to 776, and Kfold=5 and produces the best hyperparameters in learning rate=0.01 and n_estimators=443 with an accuracy value of 0.85 and AUC value of 0.897.
Multi-rate and multi-protocol continuous-variable quantum key distribution

Quantum key distribution (QKD) is an effective solution to ensure the secure transmission of information. However, for the large-scale application of QKD, the interoperability and flexibility of the transmitter and receiver are urgent issues to be solved. Here, for the first time, to the best of our knowledge, we experimentally verify the feasibility of one continuous-variable (CV) QKD system to achieve multiple protocols and rates. The flexibility of the system comes from the modulator realizing multiple protocols and a broadband coherent detector realizing multiple symbol rates. The results show that this system can switch between different rates and protocols to generate the secure key, and reveal its similarity to classical optical communication. Therefore, It can be adjusted according to user needs and provides a system-level solution for building a flexible quantum network.
CAEclust: A Consensus of Autoencoders Representations for Clustering

The CAEclust Python package implements an original deep spectral clustering in an ensemble framework.Recently, strategies combining classical clustering approaches and deep autoencoders have been proposed, but their effectiveness is impeded by deep network hyperparameters settings.We alleviate this issue with a consensus solution that hinges on the fusion of multiple deep autoencoder representations and spectral clustering.CAEclust offers an efficient merging of encodings by using the landmarks strategy and demonstrates its effectiveness on benchmark data.CAEclust enables to reproduce our experiments and explore novel datasets.
From Word Types to Tokens and Back: A Survey of Approaches to Word Meaning Representation and Interpretation

Abstract Vector-based word representation paradigms situate lexical meaning at different levels of abstraction. Distributional and static embedding models generate a single vector per word type, which is an aggregate across the instances of the word in a corpus. Contextual language models, on the contrary, directly capture the meaning of individual word instances. The goal of this survey is to provide an overview of word meaning representation methods, and of the strategies that have been proposed for improving the quality of the generated vectors. These often involve injecting external knowledge about lexical semantic relationships, or refining the vectors to describe different senses. The survey also covers recent approaches for obtaining word type-level representations from token-level ones, and for combining static and contextualized representations. Special focus is given to probing and interpretation studies aimed at discovering the lexical semantic knowledge that is encoded in contextualized representations. The challenges posed by this exploration have motivated the interest towards static embedding derivation from contextualized embeddings, and for methods aimed at improving the similarity estimates that can be drawn from the space of contextual language models.
Robust federated learning under statistical heterogeneity via hessian-weighted aggregation

In federated learning, client models are often trained on local training sets that vary in size and distribution. Such statistical heterogeneity in training data leads to performance variations across local models. Even within a model, some parameter estimates can be more reliable than others. Most existing FL approaches (such as FedAvg), however, do not explicitly address such variations in client parameter estimates and treat all local parameters with equal importance in the model aggregation. This disregard of varying evidential credence among client models often leads to slow convergence and a sensitive global model. We address this gap by proposing an aggregation mechanism based upon the Hessian matrix. Further, by making use of the first-order information of the loss function, we can use the Hessian as a scaling matrix in a manner akin to that employed in Quasi-Newton methods. This treatment captures the impact of data quality variations across local models. Experiments show that our method is superior to the baselines of Federated Average (FedAvg), FedProx, Federated Curvature (FedCurv) and Federated Newton Learn (FedNL) for image classification on MNIST, Fashion-MNIST, and CIFAR-10 datasets when the client models are trained using statistically heterogeneous data.
Indoor and outdoor in-flight odometry based solely on optic flows with oscillatory trajectories

Estimating distance traveled is a frequently arising problem in robotic applications designed for use in environments where GPS is only intermittently or not at all available. In UAVs, the presence of weight and computational power constraints makes it necessary to develop odometric strategies based on minimilastic equipment. In this study, a hexarotor was made to perform up-and-down oscillatory movements while flying forward in order to test a self-scaled optic flow based odometer. The resulting self-oscillatory trajectory generated series of contractions and expansions in the optic flow vector field, from which the flight height of the hexarotor could be estimated using an Extended Kalman Filter. For the odometry, the downward translational optic flow was scaled by this current visually estimated flight height before being mathematically integrated to obtain the distance traveled. Here we present three strategies based on sensor fusion requiring no, precise or rough prior knowledge of the optic flow variations generated by the sinusoidal trajectory. The “rough prior knowledge” strategy is based on the shape and timing of the variations in the optic flow. Tests were performed first in a flight arena, where the hexarotor followed a circular trajectory while oscillating up and down over a distance of about [Formula: see text] m under illuminances of [Formula: see text] lux and [Formula: see text] lux. Preliminary field tests were then performed, in which the hexarotor followed a longitudinal bouncing [Formula: see text]-long trajectory over an irregular pattern of grass.
Hyperparameter Optimization for Brain Tumor Classification with Hyperspectral Images

Hyperspectral (HS) imaging (HSI) techniques have demonstrated to be useful in the medical field to characterize tissues without any contact and without ionizing the patient. Besides, HSI combined with supervised machine learning (ML) algorithms have proven to be an effective technique to assist neurosurgeons to resect brain tumors. This research looks at the effects of hyperparameter optimization on two common supervised ML algorithms used for brain tumor classification: support vector machines (SVM) and random forest (RF). Correctly classifying brain tumor with HS data containing low spatial and spectral information can be challenging. To tackle this problem, this study has applied hyperparameter optimization techniques on SVM and RF with 10 brain images of patients suffering from glioblastoma multiforme (GBM) with non-mutated isocitrate dehydrogenase (IDH) enzymes. These captures have 409x217 spatial resolution and 25 normalized reflectance wavelengths gathered from 665 to 960 nm with a HS snapshot camera. Results show how this work has been able to obtain 98,60% of weighted area under the curve (AUC) on the test score by employing naive optimizations like grid search (GS) or random search (RS) and even more complex methods based on Bayesian optimization (BO). Not only the weighted AUC of SVM has been improved by 8%, but BO have also enhanced the AUC of the tumor class by 22.50% in comparison with non-optimized SVM models in the state-of-the-art, achieving AUC values of 95,49% on the tumor class. Furthermore, these improvements have been illustrated with classification maps to demonstrate the importance of hyperparameter optimization on SVM to clearly classify brain tumor, whereas non-optimized models from previous studies are unable to detect the tumor.
A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer

The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data.A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes.The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC.The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.
A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection

For a task-oriented dialogue system to provide appropriate answers to and services for users’ questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most appropriate knowledge snippet from the knowledge base, where external unstructured knowledge is used to respond to user requests that cannot be solved by the internal knowledge addressed by the database or application programming interface. Therefore, this paper constructs a three-step knowledge-grounded task-oriented dialogue system with knowledge-seeking-turn detection, knowledge selection, and knowledge-grounded generation. In particular, we propose a hierarchical structure of domain-classification, entity-extraction, and snippet-ranking tasks by subdividing the knowledge selection step. Each task is performed through the pre-trained language model with advanced techniques to finally determine the knowledge snippet to be used to generate a response. Furthermore, the domain and entity information obtained because of the previous task is used as knowledge to reduce the search range of candidates, thereby improving the performance and efficiency of knowledge selection and proving it through experiments.
Characterization of two-photon interference between a weak coherent state and a heralded single photon state

We experimentally investigate two-photon interference between a weak coherent state and a heralded single-photon state, producing from a spontaneous parametric down conversion. Both the unequal spectrum linewidth and average photon number ratio in a given time interval are considered in our model. We obtained excellent agreement between our experimental data and prediction from our model. Furthermore, the range of observing high visibility two-photon interference is significantly extended by isolating coincidence events from two-photon contributions in the weak coherent state. These results may throw some new light on quantum information technology when the two-photon interference with independent sources is required.