Reviewerly/exHarmony huggingface dataset:
Predicting Length of Stay in Hospital Using Electronic Records Available at the Time of Admission.

Predicting a patient's hospital length of stay (LoS) can help manage staffing. In this paper, we explore LoS prediction for a large group of patients admitted non-electively. We use information available at admission, including demographics, acute and long-term diagnoses and physiological tests results. Data were extracted from the electronic health records (EHR), so that the LoS prediction would not require additional data entry. Although the data can be accessed, the system does not present a unified view of the data for one patient: to resolve this we designed a process of cleaning and combining data for each patient. The data was used to fit semi-parametric, parametric and competing outcomes survival models. All models performed similarly, with concordance of approximately 0.7. Calibration results showed underestimation of predicted discharges for patients with high discharge probabilities and overestimation of predicted discharges for those with low discharge probabilities. The main challenges in operationalizing LoS predictions are delays in entering admissions data into EHR and absent data about non-medical factors determining discharges.
Joint Contrastive Learning for Unsupervised Domain Adaptation

Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination. In particular, the ideal joint hypothesis error in the target error upper bound, which was previously considered to be minute, has been found to be significant, impairing its theoretical guarantee. In this paper, we propose an alternative upper bound on the target error that explicitly considers the joint error to render it more manageable. With the theoretical analysis, we suggest a joint optimization framework that combines the source and target domains. Further, we introduce Joint Contrastive Learning (JCL) to find class-level discriminative features, which is essential for minimizing the joint error. With a solid theoretical framework, JCL employs contrastive loss to maximize the mutual information between a feature and its label, which is equivalent to maximizing the Jensen-Shannon divergence between conditional distributions. Experiments on two real-world datasets demonstrate that JCL outperforms the state-of-the-art methods.
Comparison of sensitivity to beam collimation of the holographic shearing interferometer with the wedge plate shearing interferometer and the Talbot shearing interferometer

High sensitivity of collimation testing equipment is desirable where collimated beams are used for precise and accurate measurements. Precision in the setting of collimation depends on the sensitivity of the testing equipment. In the present work, sensitivity to beam collimation of the recently reported holographic shearing interferometer (HSI) [ J. Opt. 20 , 055603 ( 2018 ) JOOPDB 0150-536X 10.1088/2040-8986/aab6dc ] is measured and compared with sensitivities of other collimation testing techniques based on the wedge plate shearing interferometer and the Talbot shearing interferometer. For a test beam of diameter 25 mm from an He–Ne laser and displacement of the collimating lens by 1 mm from the collimation position, the Talbot shearing interferometer shows a rotation of interference fringes from the horizontal direction by 2°, the wedge plate shearing interferometer shows 20°, and the HSI shows 25°. Sensitivity is also presented in terms of measured slopes of phase maps of the recorded interferograms for a 1 mm displacement of the collimating lens and is obtained as 0.98 mrad, 15 mrad, and 19 mrad corresponding to the Talbot shearing interferometer, the wedge plate shearing interferometer, and HSI, respectively. The effect of decollimation of the laser beam on the interference fringes of diffraction of the Lloyd mirror interferometer is also demonstrated. Theoretical concepts and experimental results are presented and discussed for the above-mentioned beam collimation testing techniques.
SSC: Clustering Of Turkish Texts By Spectral Graph Partitioning

There is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.
Presenting a new fuzzy system for web service selection aimed at dynamic software rejuvenation

Abstract As an effective technique to counteract software aging, software rejuvenation is applied in continuously running applications such as web service-based systems. In such systems, web services are allocated depending on the requirements of receivers and the facilities of servers. One of the challenging issues during assignment of web services is how to select the appropriate server to minimize faults. In this paper, we proposed dynamic software rejuvenation in the form of a proactive fault-tolerance technique based on fuzzy system. While including a threshold for the rejuvenation of each web service, we carried out the training phase based on the features of the service providers as well as the receivers’ requirements. The results of simulations revealed that our strategy can mitigate the failure rate of web services by 45, 40, 23, and 12% in comparison with the non-fuzzy, regression-based, Markov-based, and ACOGELS-based web service rejuvenation strategies, respectively.
General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains

The process of transporting and synthesizing experimental findings from heterogeneous data collections to construct causal explanations is arguably one of the most central and challenging problems in modern data science. This problem has been studied in the causal inference literature under the rubric of causal effect identifiability and transportability (Bareinboim and Pearl 2016). In this paper, we investigate a general version of this challenge where the goal is to learn conditional causal effects from an arbitrary combination of datasets collected under different conditions, observational or experimental, and from heterogeneous populations. Specifically, we introduce a unified graphical criterion that characterizes the conditions under which conditional causal effects can be uniquely determined from the disparate data collections. We further develop an efficient, sound, and complete algorithm that outputs an expression for the conditional effect whenever it exists, which synthesizes the available causal knowledge and empirical evidence; if the algorithm is unable to find a formula, then such synthesis is provably impossible, unless further parametric assumptions are made. Finally, we prove that do-calculus (Pearl 1995) is complete for this task, i.e., the inexistence of a do-calculus derivation implies the impossibility of constructing the targeted causal explanation.
AACS: Attribute-Based Access Control Mechanism for Smart Locks

This article researched the security and application of smart locks in Internet of Things environments in the domain of computer and engineer science and symmetry. Smart locks bring much convenience for users. However, most smart lock systems are cloud-based and it is problematic managing and enforcing the permissions of an authorized device if the device is offline. Moreover, most smart lock systems lack fine-grained access control and cascading removal of permissions. In this paper, we leverage attribute-based access control mechanisms to manage the access of visitors with different identities. We use identity-based encryption to verify the identity of the visitor. In our proposed system, the administrator uses the policy set in the smart lock to implement access control on the device side, which reduces the dependence of access control on the server. We set attributes such as role, time, date, and location to have fine-grained control over access to different permissions and roles that might appear in the house. And the scheme provides the cascading delete function while providing the group access function. Our solution considers multiple roles in the home as well as hierarchical management issues, and improves the applicability of the smart lock system in complex residential and commercial situations. In the experimental section, we show that our system can be applied to premises with many different inhabitant identities.
Evaluating the robustness of image matting algorithm

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.
Learning personalized ADL recognition models from few raw data

Recognition of activities of daily living (ADL) is an essential component of assisted living systems based on actigraphy. This task can nowadays be performed by machine learning models which are able to automatically extract and learn relevant features but, most of time, need to be trained with large amounts of data collected on several users. In this paper, we propose an approach to learn personalized ADL recognition models from few raw data based on a specific type of neural network called matching network. The interest of this few-shot learning approach is three-fold. Firstly, people perform activities their own way and general models may average out important individual characteristics unlike personalized models that could thus achieve better performance. Secondly, gathering large quantities of annotated data from one user is time-consuming and threatens privacy in a medical context. Thirdly, matching networks are by nature weakly dependent on the classes they are trained on and can generalize easily to new activities without needing extra training, thus making them very versatile for real applications. Our results show the effectiveness of the proposed approach compared to general neural network models, even in situations with few training data.
Hybrid Models for Learning to Branch

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to high-end GPUs. In this work, we ask two key questions. First, in a more realistic setting where only a CPU is available, is the GNN model still competitive? Second, can we devise an alternate computationally inexpensive model that retains the predictive power of the GNN architecture? We answer the first question in the negative, and address the second question by proposing a new hybrid architecture for efficient branching on CPU machines. The proposed architecture combines the expressive power of GNNs with computationally inexpensive multi-layer perceptrons (MLP) for branching. We evaluate our methods on four classes of MILP problems, and show that they lead to up to 26% reduction in solver running time compared to state-of-the-art methods without a GPU, while extrapolating to harder problems than it was trained on. The code for this project is publicly available at https://github.com/pg2455/Hybrid-learn2branch.
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification

Complex classification performance metrics such as the F${}_\beta$-measure and Jaccard index are often used, in order to handle class-imbalanced cases such as information retrieval and image segmentation. These performance metrics are not decomposable, that is, they cannot be expressed in a per-example manner, which hinders a straightforward application of M-estimation widely used in supervised learning. In this paper, we consider linear-fractional metrics, which are a family of classification performance metrics that encompasses many standard ones such as the F${}_\beta$-measure and Jaccard index, and propose methods to directly maximize performances under those metrics. A clue to tackle their direct optimization is a calibrated surrogate utility, which is a tractable lower bound of the true utility function representing a given metric. We characterize sufficient conditions which make the surrogate maximization coincide with the maximization of the true utility. Simulation results on benchmark datasets validate the effectiveness of our calibrated surrogate maximization especially if the sample sizes are extremely small.
A New Hybrid Binary Algorithm of Bat Algorithm and Differential Evolution for Feature Selection and Classification

In this chapter, a new hybrid binary version of the bat algorithm (BA) is suggested to solve feature selection problems. In particular, BA is integrated with an enhanced version of the differential evolution algorithm (DE). In the proposed algorithm, the BA with its capacity for echolocation to explore the feature space is combined with DE and its ability to converge to the best global solution in the search space. The general performance of the proposed algorithm is investigated by comparing it with the original optimizers and the other optimizers that have been used for feature selection in the literature. The proposed algorithm and various t optimizers are applied over datasets obtained from the UCI repository. The results prove the ability of the proposed algorithm to search the feature space for optimal feature combinations.
Learning Fair Representations for Kernel Models.

Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techniques for learning these representations are typically model-agnostic, as they preprocess the original data such that the output satisfies some fairness criterion, and can be used with arbitrary learning methods. In contrast, we demonstrate the promise of learning a model-aware fair representation, focusing on kernel-based models. We leverage the classical Sufficient Dimension Reduction (SDR) framework to construct representations as subspaces of the reproducing kernel Hilbert space (RKHS), whose member functions are guaranteed to satisfy fairness. Our method supports several fairness criteria, continuous and discrete data, and multiple protected attributes. We further show how to calibrate the accuracy tradeoff by characterizing it in terms of the principal angles between subspaces of the RKHS. Finally, we apply our approach to obtain the first Fair Gaussian Process (FGP) prior for fair Bayesian learning, and show that it is competitive with, and in some cases outperforms, state-of-the-art methods on real data.
Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving

An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kubernetes workloads. Using the AaaS as a foundation, the execution of automatic video annotation workflows is addressed in the broader context of a semi-automatic video annotation business logic for ground truth generation for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). The document presents design decisions, innovative developments, and tests conducted to provide scalability to this cloud-native ecosystem for semi-automatic annotation. The solution has proven to be efficient and resilient on an AD/ADAS scale, specifically in an experiment with 25 TB of input data to annotate, 4000 concurrent annotation jobs, and 32 worker nodes forming a high performance computing cluster with a total of 512 cores, and 2048 GB of RAM. Automatic pre-annotations with the proposed strategy reduce the time of human participation in the annotation up to 80% maximum and 60% on average.
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algorithm used for an anomaly detection, and we extend its deep learning variant to the patch-based method using self-supervised learning. This extension enables anomaly segmentation and improves detection performance. As a result, anomaly detection and segmentation performances measured in AUROC on MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the previous state-of-the-art methods. Our results indicate the efficacy of the proposed method and its potential for industrial application. Detailed analysis of the proposed method offers insights regarding its behavior, and the code is available online.
Uncertainty Quantification for Sparse Deep Learning.

Deep learning methods continue to have a decided impact on machine learning, both in theory and in practice. Statistical theoretical developments have been mostly concerned with approximability or rates of estimation when recovering infinite dimensional objects (curves or densities). Despite the impressive array of available theoretical results, the literature has been largely silent about uncertainty quantification for deep learning. This paper takes a step forward in this important direction by taking a Bayesian point of view. We study Gaussian approximability of certain aspects of posterior distributions of sparse deep ReLU architectures in non-parametric regression. Building on tools from Bayesian non-parametrics, we provide semi-parametric Bernstein-von Mises theorems for linear and quadratic functionals, which guarantee that implied Bayesian credible regions have valid frequentist coverage. Our results provide new theoretical justifications for (Bayesian) deep learning with ReLU activation functions, highlighting their inferential potential.
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the \textit{original graph convolution} in GCN and propose a \textbf{L}ow-pass \textbf{C}ollaborative \textbf{F}ilter (\textbf{LCF}) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on \url{https://github.com/Wenhui-Yu/LCFN}.
Scheme for the generation of hybrid entanglement between time-bin and wavelike encodings

We propose a scheme for the generation of hybrid states entangling a single-photon time-bin qubit with a coherent-state qubit encoded on phases. Compared to other reported solutions, time-bin encoding makes hybrid entanglement particularly well adapted to applications involving long-distance propagation in optical fibers. This makes our proposal a promising resource for future out-of-the-laboratory quantum communication. In this perspective, we analyze our scheme by taking into account realistic experimental resources and discuss the impact of their imperfections on the quality of the obtained hybrid state.
Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using k-anonymization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.