Reviewerly/exHarmony huggingface dataset:
THE OUTBREAK’S MODELING OF CORONAVIRUS (COVID-19) USING THE MODIFIED SEIR MODEL IN INDONESIA

Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by a new type of virus called SARS-CoV-2, and by the beginning of 2020 had spread throughout the world, including Indonesia. A high rate of spread of COVID-19 causes the number of patients that infected increase significantly. In this study, mathematical modeling was carried out to predict the number of COVID-19 patients and the duration of the COVID-19 pandemic in Indonesia. The model used is a modified SEIR model (Susceptible, Exposed, Infected, Recovered) with several assumptions such as a constant and homogeneous population, patients who have recovered can not be infected, and the spread only occurs from human to human. In addition, it is assumed that there are individuals who carry out quarantine and isolation. Modeling is done using the help of MATLAB R2015a. The modeling results show that the peak of the COVID-19 pandemic in Indonesia will occur in the middle of May 2020, and the number of infected patients will be about 15000 people. This amount can be reduced if the quarantine and self-isolation process is carried out optimally.
ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients’ disease history

Abstract Given the rapid rate at which text data are being digitally gathered in the medical domain, there is growing need for automated tools that can analyze clinical notes and classify their sentences in electronic health records (EHRs). This study uses EHR texts to detect patients’ disease history from clinical sentences. However, in EHRs, sentences are less topic-focused and shorter than that in general domain, which leads to the sparsity of co-occurrence patterns and the lack of semantic features. To tackle this challenge, current approaches for clinical sentence classification are dependent on external information to improve classification performance. However, this is implausible owing to a lack of universal medical dictionaries. This study proposes the ETM (enrichment by topic modeling) algorithm, based on latent Dirichlet allocation, to smoothen the semantic representations of short sentences. The ETM enriches text representation by incorporating probability distributions generated by an unsupervised algorithm into it. It considers the length of the original texts to enhance representation by using an internal knowledge acquisition procedure. When it comes to clinical predictive modeling, interpretability improves the acceptance of the model. Thus, for clinical sentence classification, the ETM approach employs an initial TFiDF (term frequency inverse document frequency) representation, where we use the support vector machine and neural network algorithms for the classification task. We conducted three sets of experiments on a data set consisting of clinical cardiovascular notes from the Netherlands to test the sentence classification performance of the proposed method in comparison with prevalent approaches. The results show that the proposed ETM approach outperformed state-of-the-art baselines.
Machine Learning Model for Movie Recommendation System

The primary aim of recommendation systems is to recommend applicable objects to a consumer-based totally on ancient data.If a movie is rated excessive by means of a consumer who also watched the movie you are watching now, it's miles possibly to show up inside the recommendations.The films with the highest overall scores are in all likelihood to be enjoyed by way of nearly everyone.The algorithm which does all these features is called CineMatch.For personal users, it also learns from the conduct of the person to higher expect a movie the consumer is anticipated to be fascinated in.Here we have to increase our CineMatch algorithm 10% by using fashionable collaborative filtering techniques.
SPST-CNN: Spatial pyramid based searching and tagging of liver’s intraoperative live views via CNN for minimal invasive surgery

Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images. In this research, we have proposed spatial pyramid based searching and tagging of liver’s intraoperative views using convolution neural network (SPST-CNN). By exploiting a hybrid combination of an image pyramid at input and spatial pyramid pooling layer at deeper stages of SPST-CNN, we reveal the gains of full-image representations for searching and tagging variable scaled liver live views. SPST-CNN provides pinpoint searching and tagging of intraoperative liver views to obtain up-to-date information about the location and shape of the area of interest. Downsampling input using image pyramid enables SPST-CNN framework to deploy input images with a diversity of resolutions for achieving scale-invariance feature. We have compared the proposed approach to the four recent state-of-the-art approaches and our method achieved better mAP up to 85.9%.
1GHz clocked distribution of electrically generated entangled photon pairs

Quantum networks are essential for realising distributed quantum computation and quantum communication. Entangled photons are a key resource, with applications such as quantum key distribution, quantum relays, and quantum repeaters. All components integrated in a quantum network must be synchronised and therefore comply with a certain clock frequency. In quantum key distribution, the most mature technology, clock rates have reached and exceeded 1GHz. Here we show the first electrically pulsed sub-Poissonian entangled photon source compatible with existing fiber networks operating at this clock rate. The entangled LED is based on InAs/InP quantum dots emitting in the main telecom window, with a multi-photon probability of less than 10% per emission cycle and a maximum entanglement fidelity of 89%. We use this device to demonstrate GHz clocked distribution of entangled qubits over an installed fiber network between two points 4.6km apart.
A network model of speaker identification with new feature extraction methods and asymmetric BLSTM

Speaker identification has recently attracted considerable attention in speaker recognition. Environmental noise and short utterance pose two challenges for accurate speaker identification. In this paper, a network model with new feature extraction methods and a new bi-directional long short-term memory network is proposed to identify the speaker. Specifically, this paper proposes to combine the mel-spectrogram and cochleagram to generate two new features, named MC-spectrogram and MC-cube. They have stronger robustness and can obtain more abundant voiceprint feature in the short utterance. Then, multi-dimensional CNNs are applied to process MC-spectrogram and MC-cube features correspondingly. They contain multi-dimensional convolution kernels, which can learn the voiceprint features more efficiently. In addition, the context information is ignored by CNN. And the forward voiceprint features are more crucial because the voiceprint features concentrate on the back part in the short utterance. Asymmetric bi-directional long short-time memory network (ABLSTM) is proposed to further learn the voiceprint features in global feature learning. It can improve the accuracy of speaker identification. According to the diverse dimension of input, the proposed network model can manifest diverse patterns, which are named Audio-1DCNN-ABLSTM, MCS(MC-spectrogram)-2DCNN-ABLSTM and MCC(MC-cube)-3DCNN-ABLSTM. From the experimental results, it is shown that the diverse patterns can achieve superior accuracy and robustness in the short utterance with extra environmental noise. Furthermore, the proposed network model provides a reliable solution in text-independent speaker identification.
A Tensor Foreground-Background Separation Algorithm Based on Dynamic Dictionary Update and Active Contour Detection

Foreground-background separation of surveillance video, that models static background and extracts moving foreground simultaneously, attracts increasing attentions in building a smart city.Conventional techniques towards this always consider the background as primary target and tend to adopt low-rank constraint as its estimator, which provides finite (equal to the value of rank) alternatives when constructing the background.However, in practical missions, although general sketch of background is stable, some details change constantly.Aimed at this, we propose to represent the general background by a linear combination of some atoms and record the detailed background by spatiotemporal clustered patches.Then, the moving foreground is considered as a mixture of active contours and continuous contents.Eventually, joint optimization is conducted under a unified framework, i.e., alternating direction multipliers method (ADMM), and produces our tensor model for hierarchical background and hierarchical foreground separation (THHS).The employed tensor space, which agrees with the instinct structure of video data, benefits all the spatiotemporal designs in both background modular and foreground part.Experimental results show that THHS is more adaptive to the dynamic background and produces more accurate foreground when compared against current state-of-the-art techniques.
Incorporating Dependencies in Spectral Kernels for Gaussian Processes

Gaussian processes (GPs) are an elegant Bayesian approach to model an unknown function. The choice of the kernel characterizes one’s assumption on how the unknown function autocovaries. It is a core aspect of a GP design, since the posterior distribution can significantly vary for different kernels. The spectral mixture (SM) kernel is derived by modelling a spectral density - the Fourier transform of a kernel - with a linear mixture of Gaussian components. As such, the SM kernel cannot model dependencies between components. In this paper we use cross convolution to model dependencies between components and derive a new kernel called Generalized Convolution Spectral Mixture (GCSM). Experimental analysis of GCSM on synthetic and real-life datasets indicates the benefit of modeling dependencies between components for reducing uncertainty and for improving performance in extrapolation tasks.
Global Aggregation then Local Distribution in Fully Convolutional Networks.

It has been widely proven that modelling long-range dependencies in fully convolutional networks (FCNs) via global aggregation modules is critical for complex scene understanding tasks such as semantic segmentation and object detection. However, global aggregation is often dominated by features of large patterns and tends to oversmooth regions that contain small patterns (e.g., boundaries and small objects). To resolve this problem, we propose to first use \emph{Global Aggregation} and then \emph{Local Distribution}, which is called GALD, where long-range dependencies are more confidently used inside large pattern regions and vice versa. The size of each pattern at each position is estimated in the network as a per-channel mask map. GALD is end-to-end trainable and can be easily plugged into existing FCNs with various global aggregation modules for a wide range of vision tasks, and consistently improves the performance of state-of-the-art object detection and instance segmentation approaches. In particular, GALD used in semantic segmentation achieves new state-of-the-art performance on Cityscapes test set with mIoU 83.3\%. Code is available at: \url{this https URL}
An Improved RRT* Path Planning Algorithm for Service Robot

The space environment of path planning for service robots is complex. In this environment, the traditional Rapid-exploring Random Tree * (RRT*) algorithm guarantees the completeness and asymptotic optimality of global search probability, but it has high memory occupancy, slow convergence speed, large sampling space and long time-consuming. Therefore, a fast extended random tree star trajectory planning algorithm is proposed in this paper. R-RRT* improves the above problems through three novel strategies: connected domain sampling, elliptic modeling sampling and path optimization strategy. Two kinds of complex map scenarios are simulated and compared. The simulation results show that the method can locate the target area quickly and improve the computational efficiency. Compared with the traditional RRT* method, it has the advantages of low memory occupancy, fast convergence speed and short time-consuming.
Gan-Based Effective Bit Depth Adaptation for Perceptual Video Compression

Resolution and effective bit depth (EBD) adaptation have been recently utilised in video compression to improve coding efficiency. This type of approach dynamically reduces spatial/temporal resolutions and effective bit depth at the encoder and restores the original video formats during decoding. In this paper, a convolutional neural networks (CNN) based EBD adaptation method is presented for perceptual video compression, in which the employed CNN models are trained using a generative adversarial network (GAN), with perception-based loss functions. This method was integrated into the HEVC HM 16.20 reference software and fully evaluated on test sequences from the JVET Common Test Conditions using the Random Access configuration. The results show significant coding gains achieved on all test sequences with an overall bit rate saving of 24.8% (Bjøntegaard Delta measurement) based on a perceptual quality metric, VMAF.
A Hybrid-Domain Framework for Secure Gradient Tree Boosting.

Gradient tree boosting (e.g. XGB) is one of the most widely usedmachine learning models in practice. How to build a secure XGB inface of data isolation problem becomes a hot research topic. However, existing works tend to leak intermediate information and thusraise potential privacy risk. In this paper, we propose a novel framework for two parties to build secure XGB with vertically partitioneddata. Specifically, we associate Homomorphic Encryption (HE) domain with Secret Sharing (SS) domain by providing the two-waytransformation primitives. The framework generally promotes theefficiency for privacy preserving machine learning and offers theflexibility to implement other machine learning models. Then weelaborate two secure XGB training algorithms as well as a corresponding prediction algorithm under the hybrid security domains.Next, we compare our proposed two training algorithms throughboth complexity analysis and experiments. Finally, we verify themodel performance on benchmark dataset and further apply ourwork to a real-world scenario.
Coherence Equality and Communication in a Quantum Superposition

In this Letter, we introduce a ``coherence equality'' that is satisfied by any classical communication---i.e., conveyed by a localized carrier traveling along well defined directions. In contrast, this equality is violated when the carrier is prepared in coherent quantum superposition of communication directions. This is phrased in terms of the success probability of a certain communication task, which is always constant and equal to $1/2$ in the classical case. On the other hand, we develop two simple quantum schemes that deviate systematically from the classical value, thus, violating the coherence equality. Such a violation can also be exploited as an operational way to witness spatial quantum superpositions without requiring us to recombine the modes in a standard interferometer, but only by means of spatially separated local measurements.
A Fuzzy Analytic Hierarchy Process and Cooperative Game Theory Combined Multiple Mobile Robot Navigation Algorithm

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout.
High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance

Currently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution in the dataset, the class imbalance issue is also gradually highlighted. The traditional machine learning algorithms lack of abilities for handling the aforementioned issues so that the classification efficiency and precision may be significantly impacted. Therefore, this paper presents an improved artificial neural network in enabling the high-performance classification for the imbalanced large volume data. Firstly, the Borderline-SMOTE (synthetic minority oversampling technique) algorithm is employed to balance the training dataset, which potentially aims at improving the training of the back propagation neural network (BPNN), and then, zero-mean, batch-normalization, and rectified linear unit (ReLU) are further employed to optimize the input layer and hidden layers of BPNN. At last, the ensemble learning-based parallelization of the improved BPNN is implemented using the Hadoop framework. Positive conclusions can be summarized according to the experimental results. Benefitting from Borderline-SMOTE, the imbalanced training dataset can be balanced, which improves the training performance and the classification accuracy. The improvements for the input layer and hidden layer also enhance the training performances in terms of convergence. The parallelization and the ensemble learning techniques enable BPNN to implement the high-performance large-scale data classification. The experimental results show the effectiveness of the presented classification algorithm.
Peregrine preying pattern based differential evolution for robot path planning

Abstract Differential evolution (DE) is a simple and robust evolutionary algorithm. DE has three simple steps and it is easy to implement. It is a popular meta-heuristics that efficiently solved various complex optimization problems. This paper addressed the robot path planning problem. The robot path planning problem is considered as one of the complex optimization problem. Here a new variant of DE with new scaling factor inspired by peregrine preying pattern. The performance of anticipated algorithm is equated with basic DE and other competitive algorithms. Results proves that new variant perform well for considered problem.
Disentangling in Latent Space by Harnessing a Pretrained Generator.

Learning disentangled representations of data is a fundamental problem in artificial intelligence. Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process. Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality. In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks. Our key insight is to decouple the processes of disentanglement and synthesis, by employing a leading pre-trained unconditional image generator, such as StyleGAN. By learning to map into its latent space, we leverage both its state-of-the-art quality, and its rich and expressive latent space, without the burden of training it. We demonstrate our approach on the complex and high dimensional domain of human heads. We evaluate our method qualitatively and quantitatively, and exhibit its success with de-identification operations and with temporal identity coherency in image sequences. Through extensive experimentation, we show that our method successfully disentangles identity from other facial attributes, surpassing existing methods, even though they require more training and supervision.
Recognition of Human Activities Using Depth Maps and the Viewpoint Feature Histogram Descriptor

In this paper we propose a way of using depth maps transformed into 3D point clouds to classify human activities. The activities are described as time sequences of feature vectors based on the Viewpoint Feature Histogram descriptor (VFH) computed using the Point Cloud Library. Recognition is performed by two types of classifiers: (i) k-NN nearest neighbors’ classifier with Dynamic Time Warping measure, (ii) bidirectional long short-term memory (BiLSTM) deep learning networks. Reduction of classification time for the k-NN by introducing a two tier model and improvement of BiLSTM-based classification via transfer learning and combining multiple networks by fuzzy integral are discussed. Our classification results obtained on two representative datasets: University of Texas at Dallas Multimodal Human Action Dataset and Mining Software Repositories Action 3D Dataset are comparable or better than the current state of the art.
Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.