Reviewerly/exHarmony huggingface dataset:
CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.
Tourist loyalty in the metaverse: the role of immersive tourism experience and cognitive perceptions

Purpose Metaverse technology is of interest to researchers and practitioners in tourism and hospitality. The metaverse offers tourists the capacity to enjoy unprecedented tourism experiences. These opportunities may radically change the tourism and hospitality landscape. This study aims to examine how metaverse experiences affect tourists' cognitive processing, satisfaction and loyalty toward metaverse tourism using the stimulus-organism-response paradigm and cognitive experiential self-theory. Design/methodology/approach An online survey was undertaken to gather information from 329 respondents who were frequent users of different metaverse applications to meet the study's objectives. Data were evaluated through partial least squares structural equation modelling. Findings The findings revealed that metaverse experiences, such as immersion, escapism and enjoyment, had a significant positive impact on metaverse organismic experience. Additionally, metaverse cognitive processing positively influenced metaverse tourist responses, including satisfaction and loyalty, with tourist involvement acting as a moderator. Originality/value This study provides guidelines for metaverse developers and travel industry professionals to encourage visitor satisfaction and loyalty to metaverse tourism.
Multi-Velocity Neural Networks for Facial Expression Recognition in Videos

We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reached maturity for image understanding tasks, we are still exploring network topologies and features to handle the richer environment of video clips. Here, we tackle the problem of multiple velocities in action recognition, and provide state-of-the-art results for facial expression recognition, on known and new collected datasets. We further provide the training steps for our semi-supervised network, suited to learn from huge unlabeled datasets with only a fraction of labeled examples.
Probability Boltzmann Machine Network for Face Detection on Video

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer's neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.
Fine-tuning Monocular Depth-Estimator Artificial Neural Networks Trained on Synthetic RGB-D Data Sets for Real Scenes

With every new synthetic RGB-D data set, the scenes are becoming more and more photo-realistic. However, the depth-estimator models trained on these sophisticated data sets still fail in generalization to real scenes. Recent attempts to overcome this challenge mainly include transferring style of the RGB images from synthetic to real or vice versa. Such approaches make it possible to train the depth-estimator without the availability of real image-depth pairs. On the other hand, this study considers a case in which a limited number of real RGB-D samples are also available. The real samples' availability allows for fine-tuning as an approach to fill the gap between the style of the real and synthetic samples. Results show that no matter how many real samples are available, to fit the real samples to all the parameters of the pre-trained model (considered for this study) is always better than only fine-tuning the parameters of the encoder of the model. Also, it is in the availability of less than 12.5% of the real data required to achieve the best performance of the model that complete fine-tuning provides results better than training from scratch. This study, also details the process of building a synthetic data for flaying robots in the Unity game Engine.
Brief Announcements: Verifiable Data Sharing in Distributed Computing

When being applied to share data in distributed computing in practice, even the VSS scheme with the highest efficiency and strongest information-theoretic security cannot be totally assumption free and is vulnerable to attack if its secure communication channel is publicly implemented and verified.
TPDP'20: 6th Workshop on Theory and Practice of Differential Privacy

Differential privacy is a rigorous mathematical model of privacy protection that has been the subject of deep theoretical research and also been deployed in real-world systems. This workshop aims to bring together a diverse array of researchers and practitioners to provoke stimulating discussion about the current state of differential privacy, in theory and practice. TPDP aims to be an inclusive forum that seeks to grow and diversify the differential privacy community.
Hands-on Statistical Methods: A Case Study with Hidden Markov Models Using Simulations and Experiments

Biological processes are often very complicated compared with physics and chemistry. One of the newest and most challenging interactions between biology and computational science comes from modern molecular biology and bioinformatics, where Hidden Markov Models (HMM) are widely applied tools. This paper presents the background, theory and HMM algorithms based on examples from Gregor Mendel’s classical plant experiments. This approach aims to achieve some intuitive advantages in a biological and bioinformatical setting, because the pedagogy goes from the known to the unknown. It only presumes basic knowledge of genetics, statistics and matrix algebra. The student may gain insight into the complex HMM methodology by running “experiments” with the application MendelHMM in a kind of “digital laboratory”. The optimal model can only be sought in a certain probabilistic sense. This process is known as machine learning.
Investigation of a Turtle-Shaped Construction Block and Its Relationship with Other Geometric Toys

This article reports a geometric investigation. The object under study is the main piece of the construction toy set called ReptanglesTM, created by Jonathan Walker Stapleton and manufactured by Fat brain Toy Co. In the form of a stylized turtle, this plastic block holds a series of relationships with well-known polyhedra, starting from the cube. Inspired by a video entitled Contortionist Cubes, performed by the mathematician Burkard Polster, and the animation entitled Ars Qubica, created by the graphic designer Cristóbal Vila, both available on the Internet, we also compare other toys and puzzles related to the first one by their polyhedral nature. Similarities between the turtle-shaped piece from ReptanglesTM and the parts from Tri MagsTM (former MagBlocksTM), GeoBender®, the Star Burr Puzzle, and the Yoshimoto Cubes are noticed.
Numeric CNNs: CNNs for Reading Numeric Characters on Meters

Nowadays, Internet of Things (IoT) is becoming an irreplaceable role in human life. Moreover, the meter reading is becoming the procedure composed of picture collection and image recognition. Previous works treat meter reading as a problem of image classification. They only focus on the accuracy of classification but ignore numerical accuracy, which is the measurement’s essential performance. In this paper, we address that the meter reading is a hybrid regression and classification (HRC) problem. Under this definition, the resulting algorithm considers the targets of both measurement and digits recognition. To solve the HRC problem, we designed a hybrid regression and classification loss function and a multi-branch convolutional neural networks for numbers (N-CNNs). To further verify the effectiveness of the model’s classification and regression, we constructed two kinds of datasets: standard dataset and carry dataset. The N-CNNs establishes new state-of-the-art metrics both on regression and classification. Notably, the numerical precision of N-CNNs outperforms the classification-based methods. The numerical accuracy of the model has one order of magnitude higher than other models. Furthermore, we deployed N-CNNs in a realistic meter reading system based on smart meter shells and cloud computing.
A Memory and Computation Efficient Local Stereo Algorithm Suited for Mobile Robot Applications

Range sensing is a key component for a mobile robot to interact with the environment. This paper proposes a memory and computation efficient local stereo algorithm for mobile robot applications. The memory reduction is done by enforcing local smoothness constraint during the weighted cost aggregation to make the difference between neighboring aggregated matching cost small and then applying predictive coding scheme to compress the original representations. In addition, by performing cost aggregation only at the sampled positions, the run time is largely reduced and the memory requirement is further decreased. Finally, a high resolution disparity map is derived by an efficient disparity upsampling algorithm utilizing approximated geodesic distance. The tradeoff between matching accuracy, memory cost and computational complexity is extensively investigated using Kitti dataset. Experimental results demonstrate that our method provides high-quality disparity maps with low memory cost and computational effort.
Channel Mismatch Speaker Verification Based on Deep Learning and PLDA

Abstract At present, speaker recognition technology is developing continuously, but in real world, existing speaker recognition algorithms are generally disturbed by channel factors. With the increase of current data volume, neural network has also become a key technology for speaker recognition. This paper introduction a speaker recognition method based on supervised training and a channel compensation algorithm. The experimental results show that this method combined with channel compensation algorithm can remove the influence of channel noise and has a good speaker recognition effect. The experimental results show that the combination of x-vector and PLDA channel compensation algorithm can obtain better recognition rate and ensure the robustness of the algorithm.
Classifiers for Regulatory Feedback Networks Using AB-Divergences

Humans have the ability to learn and reason about a new concept from just one or a handful of examples, incorporate new information and modify existing patterns quickly. In this paper, we attempt to develop flexible and robust one-shot classifiers with such properties. First, we consider the biologically motivated Regulatory Feedback Networks (RFNs) as the solution. Second, we reformulate the recognition problem of RFNs as the minimization of the difference between the reconstruction of the internal copy of the inputs on hand and the inputs from the environment. Then generalized AB-divergence is introduced as a scalar measure of that difference. Based on the divergence, a set of Multiplicative Update Rules are formally derived. With the flexibility and robustness of generalized AB-divergence, these algorithms achieve strong results for one-shot learning and statistically significantly outperformed the most popular classifiers, Support Vector Machine (SVN) and K-Neares Neighbors(KNN) for few-shots learning in face and character recognition over LFW and Omniglot datasets.
Active Learning for Video Description with Cluster-Regularized Ensemble Ranking

Automatic video captioning aims to train models to generate text descriptions for all segments in a video, however, the most effective approaches require large amounts of manual annotation which is slow and expensive. Active learning is a promising way to efficiently build a training set for video captioning tasks while reducing the need to manually label uninformative examples. In this work we both explore various active learning approaches for automatic video captioning and show that a cluster-regularized ensemble strategy provides the best active learning approach to efficiently gather training sets for video captioning. We evaluate our approaches on the MSR-VTT and LSMDC datasets using both transformer and LSTM based captioning models and show that our novel strategy can achieve high performance while using up to 60% fewer training data than the strong state of the art baselines.
A “Bright-on-Dark, Dark-on-Bright” Approach to Multi-lingual Scene Text Detection

Detecting texts from natural scene images is currently becoming a popular trend in the field of information retrieval. Researchers find it interesting due to the challenges faced while processing an image. In this paper, a relatively simple but effective approach is proposed where bright texts on a dark background and dark texts on a bright background are detected in natural scene images. This approach is based on the fact that there is usually stark contrast between the background and foreground. Hence, K-means clustering algorithm is applied on the gray levels of the image where bright and dark gray level clusters are generated. Each of these clusters are then analyzed to extract the text components. This method proves to be robust compared to the existing methods, giving reasonably satisfactory results when evaluated on Multi-lingual standard datasets like KAIST and MLe2e, and an in-house dataset of images having Multi-lingual texts written in English, Bangla and Hindi.
Implementation Of Convolutional Neural Network (CNN) Algorithm For Classification Of Human Facial Expression In Indonesia

Emotional expression is an effort made by someone to communicate the status of feelings or emotions in response to certain situations both internal and external as seen from biological changes, physiological and a series of actions like attitudes and behaviors oriented toward goal-oriented. Although humans can recognize expressions very well, facial recognition research is continuing to improve the quality of expression recognition in human and computer interactions. In this study discusses the detection of human facial expressions using the Convolution Neural Network (CNN) method with the Indonesian Mixed Emotion Dataset (IMED), in this algorithm there are two methods in a series namely convolution as feature extraction and neural network as classification. To facilitate the extraction of features, the researcher does preprocessing. The preprocessing stage is face detection, cropping, resizing and grayscaling. To overcome overfitting, in this study, data augmentation was performed on training data and also test data. The results of experiments in this study that the Convolution Neural Network (CNN) algorithm can recognize human facial expressions with an accuracy rate of 93.63% of the 110 expressions tested.
Remote sensing image registration based on fusion of spatial transformation and dense convolution

In order to achieve high-precision registration of remote sensing images, this paper proposes a remote sensing image registration method based on spatial transformation and dense convolution fusion. First, the dense convolutional network improved by the spatial transformation module is used for feature extraction to improve the robustness of feature points; then use improved Grid-based Motion Statistics (GMS) algorithm for feature matching, and use the homography matrix-based method to eliminate mismatched point pairs to improve matching accuracy; finally, the transformed model is used to solve the transformed model to achieve accurate registration of remote sensing images. Experiments show that the proposed method can effectively improve the correct matching rate of feature points, has higher registration accuracy and stronger robustness.
TAC-GAIL: A Multi-modal Imitation Learning Method

Imitation learning provides a family of promising frameworks that learn policies from expert demonstrations directly. However, most imitation learning methods assume that the expert demonstrations come from the same expert and have a single modality. In fact, the expert demonstrations may be generated by different experts in different modalities. Auxiliary classifier generative adversarial imitation learning (AC-GAIL) uses an auxiliary classifier to classify samples according to modalities, so that the generator can perform different actions according to different modalities, and obtain a multi-modal policy. However, we find that AC-GAIL's objective function missing a conditional entropy, and this conditional entropy cannot be calculated directly. Missing the conditional entropy can result in a decrease in the performance of the learned policy. In this paper, we propose a method that can deal with the problem of missing conditional entropy in AC-GAIL, named twin auxiliary classifiers GAIL (TAC-GAIL). Specifically, we add another auxiliary classifier to the framework of AC-GAIL, which is used to classify the generated samples. We theoretically prove the effectiveness of this method, and the experimental results on MuJoCo tasks show that TAC-GAIL can effectively improve the performance of the learned multi-modal policy.
The overlap phases of SIC-POVMs

Abstract A symmetric informationally complete positive-operator-valued measure (SIC-POVM) is a special type of generalized quantum measurements that possesses a high degree of symmetry. It plays an important role in quantum tomography, quantum cryptography and foundations of quantum mechanics. The most important and difficult problem about SIC-POVM is to show its existence in every dimension n, which is suggested by many numerical evidences. In this paper, this existence problem of SIC-POVMs is investigated by considering their overlap phases. Specifically, the symmetric requirements of SIC-POVMs are encoded into the overlap phases between the fiducial project and the n 2 displacement operators. In this way, an equivalent condition of the existence of Weyl-Heisenberg group covariant SIC-POVMs is obtained in terms of these phases, so that a set of complex equations is established, which enables one to study the existence problem in a more explicit and direct fashion. In particular, we show that all the numbers appearing in the overlap phases have to be algebraic numbers. The significance of Zauner’s conjecture in our equivalent condition is also discussed, and therefore the SIC-POVMs in dimension four is constructed explicitly. Actually, our system of equations always admits a solution, the real difficulty lies in how to ensure these complex number solutions are of norm one, and therefore indeed phases. Our result shows that constructing SIC-POVMs from the overlap phases is possible, and symmetries can be applied directly to reduce the number of equations, so that it provide new insights into the difficult problem of SIC-POVMs, especially about the algebraic properties of these overlap phases.
An ordered search with a large margin classifier for feature selection

In this paper we propose a novel feature selection method for binary classification problems based on an ordered search process to explore the space of feature subsets. The method, called Admissible Ordered Search (AOS), uses a monotone evaluation function based on margin values calculated by a large margin classifier with an arbitrary Lp norm. This includes large margin classifiers with the L∞ norm, which minimize the L1 norm and are very useful in feature selection, since they produce sparse solutions. An important contribution of this paper is the development of the projected margin concept. This value is computed as the maximal margin vector projected into a lower-dimensional subspace and it is used as an upper bound for the hypothesis value. This enables great economy in runtime and consequently efficiency in the search process as a whole. AOS was tested on several problems and its results were compared to other feature selection methods. The experiments demonstrate the competitive performance of the proposed method in terms of generalization power and computational efficiency.
Application of Eye-Tracking Technology in Humanities, Social Sciences and Geospatial Cognition

Eyes are the windows to the mind. Eye movement experiments are objective, real-time and dynamic, which can discover eye movement patterns and analyze the brain thinking process by recording eye movement with the aid of eye-tracking instruments.
THE IMPACT OF TRANSFORMATIONAL LEADERSHIP IN THE ORGANIZATIONAL DEVELOPMENT PROCESS AND ITS ROLE IN THE PROGRESS OF THE INDUSTRIAL SECTOR IN THE UAE

Business does not exist in an isolated condition. There are several factors, which combined the overall effectiveness in order to evaluate all the facts and findings, where the dynamic activities of the business proceedings maintain a huge impact on the overall managerial activities. Therefore, the entire study proved to be very much effective, while describing the roles as well as responsibilities about the process, which maintains a good impact of the transformational leadership in an organizational development process measured within the industrial sector in UAE. Change is one of the most integrated factors associated with any business perspectives. The concept behind evolving the overall effectiveness towards analyzing the facts and findings of the transformational leadership aspects determined some qualities such as leading by an example, making all the employees in the place of work feel that they are contributing to common goals defined the characteristics. Therefore, the combination of these points proved to be very much effective in determining the process of organizational development and its role in the Progress of the industrial sector in the UAE.