Reviewerly/exHarmony huggingface dataset:
Recognition of Urdu sign language: a systematic review of the machine learning classification

Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date.All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review.This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language.Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language.
Applicability of the Van Cittert–Zernike theorem in a Ronchi shearing interferometer

Ronchi shearing interferometry is a promising technique for in situ wavefront aberration measurement of the projection lens in advanced photolithography systems. The Van Cittert–Zernike theorem has been used to analyze the interference signal of a Ronchi shearing interferometer in the effective interference area (overlapping area of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mrow> </mml:math> st diffraction orders produced by the image grating). However, the applicability of this theorem has not been systematically studied. In this work, the analytical expression of the Ronchigram in this area is derived based on the theory of scalar diffraction and incoherent imaging. The results show that only the object and image grating with infinite diffraction orders can fully satisfy the Van Cittert–Zernike theorem. In the finite diffraction orders case, the theorem can be considered approximately applicable in the overlapping area of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> </mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> rd diffraction orders produced by the image grating. The applicable area extends to the overlapping area of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>±<!-- ± --></mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:mrow> </mml:math> nd diffraction orders under a shear ratio of less than 1%, which accounts for 97% of the effective interference area. The theoretical analysis has been verified by simulation and fundamental experiments.
Gray-Related Support Vector Machine Optimization Strategy and Its Implementation in Forecasting Photovoltaic Output Power

Reliable and accurate photovoltaic (PV) output power projection is critical for power grid security, stability, and economic operation. However, because of the indirectness, unpredictability, and solar energy volatility, predicting precise and reliable photovoltaic output power is a complicated subject. The photovoltaic output power variable is evaluated in this study using a powerful machine learning approach called the support vector machine model based on gray-wolf optimization. A vast dataset of previously published papers was compiled for this purpose. Several studies were carried out to assess the suggested model. The statistical evaluation revealed that this model predicts absolute values with reasonable accuracy, including <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> and RMSE values of 0.908 and 74.6584, respectively. The practical input data were also subjected to sensitivity analysis. The results of this analysis showed that the air temperature parameter has a greater effect on the target parameter than the solar irradiance intensity parameter (relevancy factor equal to 0.75 compared to 0.49, respectively). The leverage approach was also used to test the accuracy of actual data, and the findings revealed that the vast majority of data is accurate. This basic but accurate model may be quite effective in predicting target values and could be a viable substitute for laboratory data.
Decay of a single photon in a cavity with atomic mirrors

We study the decay of a single-photon pulse inside a cavity consisting of two atomic mirrors coupled to a one-dimensional waveguide. The finesse of the cavity can be increased with proper choice of coupling strength, cavity length, spontaneous decay rate, and separation between atoms. The loss rate of a single-photon pulse inside the cavity can be decreased with narrower spectrum width and smaller detuning between the photon pulse and the atomic resonance. The decay of a single-photon pulse in the atomic cavity can be significantly slowed down with proper parameters and the increase of the number of atoms within the atomic mirrors.
Parallel Algorithm of Improved FunkSVD Based on GPU

The increasing amount of network data and the development of big data technology have brought development opportunities and challenges to recommendation systems.Model-based collaborative filtering has become a mainstream algorithm in recommendation systems.For example, the Funk singularvalue decomposition (FunkSVD) algorithm.However, in big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm.Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is ineffective.Therefore, we propose an improved FunkSVD algorithm (IFABG) based on the root-mean-square prop (RMSProp) and graphics processing unit (GPU) to solve this problem.First, we used RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm.Next, we implemented parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm.Finally, we verify the IFABG algorithm using the Movielens dataset.The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock.The prediction accuracy rate is approximately 30% higher than that of the traditional FunkSVD algorithm.The experimental results also demonstrate that the IFABG algorithm has a good acceleration effect.For the same size dataset, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.
Multi-label feature selection method based on dynamic weight

Multi-label feature selection attracts considerable attention from multi-label learning. Information theory-based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information of labels. Previous methods regard the uncertain amount of information of labels as constant. In fact, as the classification information of the label set is captured by features, the remaining uncertainty of each label is changing dynamically. In this paper, we categorize labels into two groups: One contains the labels with few remaining uncertainty, which means that most of classification information with respect to the labels has been obtained by the already-selected features; another group contains the labels with extensive remaining uncertainty, which means that the classification information of these labels is neglected by already-selected features. Feature selection aims to select the new features that are highly relevant to the labels in the second group. Existing methods do not distinguish the difference between two label groups and ignore the dynamic change amount of information of labels. To this end, a Relevancy Ratio is designed to clarify the dynamic change amount of information of each label under the condition of the already-selected features. Afterward, a Weighted Feature Relevancy is defined to evaluate the candidate features. Finally, a new multi-label feature selection method based on Weighted Feature Relevancy (WFRFS) is proposed. The experiments obtain encouraging results of WFRFS in comparison with six multi-label feature selection methods on thirteen real-world data sets.
Automated Kidney Tumor Segmentation with Convolution and Transformer Network

Kidney cancer is one of the most common malignancies worldwide. Early diagnosis is an effective way to reduce the mortality and automated segmentation of kidney tumor in computed tomography scans is an important way to assisted kidney cancer diagnosis. In this paper, we propose a convolution-and-transformer network (COTRNet) for end to end kidney, kidney tumor, and kidney cyst segmentation. COTRNet is an encoder-decoder architecture where the encoder and the decoder are connected by skip connections. The encoder consists of four convolution-transformer layers to learn multi-scale features which have local and global receptive fields crucial for accurate segmentation. In addition, we leverage pretrained weights and deep supervision to further improve segmentation performance. Experimental results on the 2021 kidney and kidney tumor segmentation (kits21) challenge demonstrated that our method achieved average dice of 61.6%, surface dice of 49.1%, and tumor dice of 50.52%, respectively, which ranked the $$22_{th}$$ place on the kits21 challenge.
Deterministic linear-optical quantum control gates utilizing path and polarization degrees of freedom

We present a general scheme for a family of linear-optical quantum control gates, including controlled-not and controlled-swap gates for two or three qubits. Our approach utilizes polarization-path-entangled pairs of photons and encodes qubits in mixed degrees of freedom with the control qubit specifically occupying the polarization degree of freedom. By exploiting multiple degrees of freedom and initial state entanglement of the two photons, the proposed control gates do not require any ancilla photons or measurement-induced nonlinearities. Since our gates are purely linear and we implicitly use nonlinearities in a standard manner to create entanglement via parametric nonlinear process, our work demonstrates that a need to have nonlinearities in the photonic gates can be shifted to the state preparation stage; the cost of such shift is that the construction of a certain class of single-qubit operations, such as Hadamard, needs to be probabilistic. In particular, we focus on a deterministic linear-optical quantum Fredkin (controlled-swap) gate and perform a full characterization of the gate performance with a high fidelity typically well above $99%$ under realistic conditions. The proposed control gates rely on simple linear-optical elements and polarization-entangled photon pairs readily generated from ubiquitous sources, making the gates experimentally feasible with current technologies.
Improving Entity Linking by Introducing Knowledge Graph Structure Information

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance.
Listening to bluetooth beacons for epidemic risk mitigation

The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address these limitations. Rather than capturing pairwise encounters between user devices as done by existing systems, our system captures encounters between user devices and beacons placed in strategic locations where infection clusters may originate. Epidemiological simulations using an agent-based model demonstrate that, by utilizing location and environmental information and interoperating with manual contact tracing, our system can increase the accuracy of contact tracing actions and may help reduce epidemic spread already at low adoption.
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift

Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful training with amounts of expensive and time-consuming labeled data. To alleviate labeled data scarcity, self-training methods have been widely adopted on graphs by labeling high-confidence unlabeled nodes and then adding them to the training step. In this line, we empirically make a thorough study for current self-training methods on graphs. Surprisingly, we find that high-confidence unlabeled nodes are not always useful, and even introduce the distribution shift issue between the original labeled dataset and the augmented dataset by self-training, severely hindering the capability of self-training on graphs. To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset. Specifically, we first prove the equality of loss function in self-training framework under the distribution shift case and the population distribution if each pseudo-labeled node is weighted by a proper coefficient. Considering the intractability of the coefficient, we then propose to replace the coefficient with the information gain after observing the same changing trend between them, where information gain is respectively estimated via both dropout variational inference and dropedge variational inference in DR-GST. However, such a weighted loss function will enlarge the impact of incorrect pseudo labels. As a result, we apply the loss correction method to improve the quality of pseudo labels. Both our theoretical analysis and extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed DR-GST, as well as each well-designed component in DR-GST.
Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning

Artificial intelligence and machine learning techniques have proved fertile methods for attacking difficult problems in medicine and public health. These techniques have garnered strong interest for the analysis of the large, multi-domain open science datasets that are increasingly available in health research. Discovery science in large datasets is challenging given the unconstrained nature of the learning environment where there may be a large number of potential predictors and appropriate ranges for model hyperparameters are unknown. As well, it is likely that explainability is at a premium in order to engage in future hypothesis generation or analysis. Here, we present a novel method that addresses these challenges by exploiting evolutionary algorithms to optimize machine learning discovery science while exploring a large solution space and minimizing bias. We demonstrate that our approach, called integrated evolutionary learning (IEL), provides an automated, adaptive method for jointly learning features and hyperparameters while furnishing explainable models where the original features used to make predictions may be obtained even with artificial neural networks. In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. We apply IEL to three gold standard machine learning algorithms in challenging, heterogenous biobehavioral data: deep learning with artificial neural networks, decision tree-based techniques and baseline linear models. Using our novel IEL approach, artificial neural networks achieved ≥ 95% accuracy, sensitivity and specificity and 45–73% R 2 in classification and substantial gains over default settings. IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. This approach offers significant flexibility, enlarges the solution space and mitigates bias that may arise from manual or semi-manual hyperparameter tuning and feature selection and presents the opportunity to select the inner machine learning algorithm based on the results of optimized learning for the problem at hand.
A Deep Learning Model for the Automatic Recognition of Aplastic Anemia, Myelodysplastic Syndromes, and Acute Myeloid Leukemia Based on Bone Marrow Smear

Bone marrow biopsy is essential and necessary for the diagnosis of patients with aplastic anemia (AA), myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML). However, the convolutional neural networks (CNN) model that automatically distinguished AA, MDS, and AML based on bone marrow smears has not been reported.Image-net pretrained model of CNN was used to construct the recognition model. Data extracted from the American Society of Hematology (ASH) Image Bank were utilized to develop the model and data extracted from the clinic were used for external validation. The model had two output layers: whether the patient was MDS (two-classification) and which of AA, MDS, and AML the patient was (three-classification). Different outcome weights (two-classification/three-classification = 5:5, 2:8, 1:9) and epochs (30, 50, 200) were used to select the optimal model. The model performance was evaluated by the Accuracy-Loss curves and calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).A total of 115 bone marrow smears from the ASH Image Bank and 432 bone marrow smears from the clinic were included in this study. The results of Accuracy-Loss curves showed that the best model training effect was observed in the model with the outcome weight and epoch of 1:9 and 200. Similarly, this model also performed well performances in the two-classification of MDS and the three-classification of AA, MDS, AML. The AUC, accuracy and sensitivity of the MDS two-classification model in the testing set were 0.985 [95% confidence interval (CI), 0.979-0.991], 0.914 (95%CI, 0.895-0.934), and 0.992 (95%CI, 0.980-1.000), respectively. The AUC, accuracy and sensitivity of the AA, MDS, AML three-classification model in the testing set were 0.968 (95%CI, 0.960-0.976), 0.929 (95%CI, 0.916-0.941), and 0.857 (95%CI, 0.828-0.886), respectively.The image-net pretrained model was able to obtain high accuracy AA, MDS, AML distinction, and may provide clinicians with a convenient tool to distinguish AA, MDS, and AML.
Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to formulate responses to communicable diseases. Unfortunately, determining the causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and experts are still investigating COVID-related complications. To assist physicians in accurately reporting causes of death, an advanced Artificial Intelligence (AI) approach is presented to determine a chronically ordered sequence of conditions that lead to death (named as the causal sequence of death), based on decedent's last hospital discharge record. The key design is to learn the causal relationship among clinical codes and to identify death-related conditions. There exist three challenges: different clinical coding systems, medical domain knowledge constraint, and data interoperability. First, we apply neural machine translation models with various attention mechanisms to generate sequences of causes of death. We use the BLEU (BiLingual Evaluation Understudy) score with three accuracy metrics to evaluate the quality of generated sequences. Second, we incorporate expert-verified medical domain knowledge as constraints when generating the causal sequences of death. Lastly, we develop a Fast Healthcare Interoperability Resources (FHIR) interface that demonstrates the usability of this work in clinical practice. Our results match the state-of-art reporting and can assist physicians and experts in public health crisis such as the COVID-19 pandemic.