Reviewerly/exHarmony huggingface dataset:
Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.
Design and in-orbit test of a high accuracy pointing method in satellite-to-ground quantum communication

To meet the requirement of high-accuracy pointing of quantum signals in satellite-to-ground quantum communication, this paper proposes a flexible satellite-based pointing method that changes the fine tracking point to solve the problem from point-ahead angle and ground beacon laser offset. This method does not require the use of a point-ahead mechanism and can detect the pointing angle in real time. Detailed algorithms and analysis are given. The method has been verified in orbit on the quantum science satellite Micius. The satellite-to-ground test results show that the quantum signal pointing accuracy is between 0.5∼1.0 µrad, which meets the efficiency requirements of satellite-to-ground quantum communication.
Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

The meteoric growth of data over the internet from the last few years has created a challenge of mining and extracting useful patterns from a large dataset.In recent years, the growth of digital libraries and video databases makes it more challenging and important to extract useful information from raw data to prevent and detect the crimes from the database automatically.Street crime snatching and theft detection is the major challenge in video mining.The main target is to select features/objects which usually occurs at the time of snatching.The number of moving targets imitates the performance, speed and amount of motion in the anomalous video.The dataset used in this paper is Snatch 101; the videos in the dataset are further divided into frames.The frames are labelled and segmented for training.We applied the VGG19 Convolutional Neural Network architecture algorithm and extracted the features of objects and compared them with original video features and objects.The main contribution of our research is to create frames from the videos and then label the objects.The objects are selected from frames where we can detect anomalous activities.The proposed system is never used before for crime prediction, and it is computationally efficient and effective as compared to state-of-the-art systems.The proposed system outperformed with 81 % accuracy as compared to stateof-the-art systems.
Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

Abstract Artificial bee colony (ABC) optimization and imperialist competitive algorithm (ICA) are two famous metaheuristic methods. In ABC, exploration is good because each bee moves toward random neighbors in the first and second phases. In ABC, exploitation is poor because it does not try to examine a promising region of search space carefully to see if it contains a good local minimum. In this study, ICA is considered to improve ABC exploitation, and two novel swarm-based hybrid methods called ABC–ICA and ABC–ICA1 are proposed, which combine the characteristics of ABC and ICA. The proposed methods improve the evaluations results in both continuous and discrete environments compared to the baseline methods. The second method improves the first optimization method as well. Feature selection can be considered to be an optimization problem because selecting the appropriate feature subset is very important and the action of appropriate feature selection has a great influence on the efficiency of classifier algorithms in supervised methods. Therefore, to focus on feature selection is a key issue and is very important. In this study, different discrete versions of the proposed methods have been introduced that can be used in feature selection and feature scoring problems, which have been successful in evaluations. In this study, a problem called cold start is introduced, and a solution is presented that has a great impact on the efficiency of the proposed methods in feature scoring problem. A total of 16 UCI data sets and 2 Amazon data sets have been used for the evaluation of the proposed methods in feature selection problem. The parameters that have been compared are classification accuracy and the number of features required for classification. Also, the proposed methods can be used to create a proper sentiment dictionary. Evaluation results confirm the better performance of the proposed methods in most experiments.
An Improved Image Dehazing Technique using CLAHE and Guided Filter

Presence of haze components in the atmosphere, degrade the contrast as well as the true color of the real images. As a result of this, images are difficult to comprehend and to be used in several applications like surveillance, navigation, detection etc. Therefore, it is important to remove hazy effect from the real world images to get clear or haze free image. In the present paper, a technique to remove haze is implemented by combining the modified Adaptive Histogram Equalization and Guided Filter approaches. Experiments are carried out using various hazy images collected from Internet and the result reveals that the this method can dehaze the haze affected images and restore the original contrast of the images as well.
A comparison between audio and IMU data to detect chewing events based on an earable device

The feasibility of collecting various data from built-in wearable sensors has enticed many researchers to use these devices for analyzing human activities and behaviors. In particular, audio, video, and motion data have been utilized for automatic dietary monitoring. In this paper, we investigate the feasibility of detecting chewing activities based on audio and inertial sensor data obtained from an ear-worn device, eSense. We process each sensor data separately and determine the accuracy of each sensing modality for chewing detection when using MFCC and Spectral Centroid as features and Logistic Regression, Decision Tree, and Random Forest as classifiers. We also measure the performance of chewing detection when fusing features extracted from both audio and inertial sensor data. We evaluate the chewing detection algorithm by running a pilot study inside a lab environment on a total of 5 participants. This consists of 130 minutes audio and inertial measurement unit (IMU) data. The results of this study indicate that an in-ear IMU with an accuracy of 95% outperforms audio data in detecting chewing and fusing both modalities improves the accuracy to 97%.
R2LSH: A Nearest Neighbor Search Scheme Based on Two-dimensional Projected Spaces

Locality sensitive hashing (LSH) is a widely practiced c-approximate nearest neighbor (c-ANN) search algorithm because of its appealing theoretical guarantee and empirical performance. However, available LSH-based solutions do not achieve a good balance between cost and quality because of certain limitations in their index structures. In this paper, we propose a novel and easy-to-implement disk- based method named R2LSH to answer ANN queries in high-dimensional spaces. In the indexing phase, R2LSH maps data objects into multiple two-dimensional projected spaces. In each space, a group of B+-trees is constructed to characterize the corresponding data distribution. In the query phase, by setting a query-centric ball in each projected space and using a dynamic counting technique, R2LSH efficiently determines candidates and returns query results with the required quality. Rigorous theoretical analysis reveals that the proposed algorithm supports c-ANN search for arbitrarily small c ≥ 1 with probability guarantee. Extensive experiments on real datasets verify the superiority of R2LSH over state-of-the-art methods.
ATE-SPD: simultaneous extraction of aspect-term and aspect sentiment polarity using Bi-LSTM-CRF neural network

Aspect-based sentiment analysis is one of the challenging problems among the various type of tasks in sentiment analysis. Sequential models specifically deep neural networks (like Recurrent Neural Networks) have been found to handle this problem in an efficient way. This paper presents a deep neural network model named ATE-SPD for aspect-based sentiment analysis that simultaneously extracts aspect-terms and their corresponding polarities in review sentences. This problem can be solved as a sequence labelling problem. We are using Bi-LSTM hybridised with CRF as both of these approaches are state-of-the-art approaches for sequence labelling tasks. It was observed that CRF is able to improve the performance of the traditional Bi-LSTM model. Another important contribution of this paper is that it provides a novel set of sequential tags for extracting aspect-terms along with their sentiment polarities. Aspect-terms and their polarities are determined without explicitly labelling the sentiment terms. The ATE-SPD is evaluated using a benchmark dataset of SemEval’14-Task4 and obtains state-of-the-art performance.
Angular Visual Hardness

Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not always reflecting the underlying true ambiguity and hardness. In this paper, we propose angular visual hardness (AVH), a score given by the normalized angular distance between the sample feature embedding and the target classifier to measure sample hardness. We validate this score with an in-depth and extensive scientific study, and observe that CNN models with the highest accuracy also have the best AVH scores. This agrees with an earlier finding that state-of-art models improve on the classification of harder examples. We observe that the training dynamics of AVH is vastly different compared to the training loss. Specifically, AVH quickly reaches a plateau for all samples even though the training loss keeps improving. This suggests the need for designing better loss functions that can target harder examples more effectively. We also find that AVH has a statistically significant correlation with human visual hardness. Finally, we demonstrate the benefit of AVH to a variety of applications such as self-training for domain adaptation and domain generalization.
Coherence effects of the strongly-coupled optical polaron-level qubit in a quantum well with asymmetrical semi-exponential potential

A theoretically investigation of the effects of barrier height of an asymmetrical semi-exponential confinement potential (ASECP) quantum well (QW) and the range of the ASECP on vibration frequency, first excited state (FES) energy, excitation energy and coherence time in the ASECP QW is performed. In order to determine the relationship between these factors, the linear combination operator, twice unitary transformations and Fermi Golden Rule methods are used. The vibration frequency, FES energy and excitation energy of the strong-coupled polaron are derived, as well as the qubit coherence time in the ASECP QW when the barrier height of the QW and the ASECP range are varied. It is found that these physical quantities are increasing functions of the barrier height, whereas they are decaying functions of the range. Furthermore, the coherence time is a decaying function with increased barrier height and an increasing function with enhanced range.
Predicting Most Effective Software Development Teams by Mapping MBTI Personality Traits with Software Lifecycle Activities

The personality traits of software developers can be exploited to co-relate their intellectual capabilities with work tasks in accordance with their job descriptions in industry. A software engineer may be required to have different levels and types of soft/hard skills during the different stages of software development activities. It would be more beneficial to select most appropriate software development personalities as team developers by considering their personality traits. Personality traits may play a vital role in selecting the right people for right jobs and hence may contribute in improvement of quality of software development process and its outcomes. This article presents a systematic mapping of MBTI personality traits of software developers with their assigned job cards/roles to allocate them with more appropriately associated software development tasks during different stages of software lifecycle. In this way, the article contributes in the improvement of overall software development process by finding most appropriate workers for each stage of software development activities. The article is assumed to serve as an asset for software development companies to predict most effective software development teams for their projects.
Deep Learning for Cardiovascular Risk Stratification

Abstract Purpose of review Although deep learning represents an exciting platform for the development of risk stratification models, it is challenging to evaluate these models beyond simple statistical measures of success, which do not always provide insight into a model’s clinical utility. Here we propose a framework for evaluating deep learning models and discuss a number of interesting applications in light of these rubrics. Recent findings Data scientists and clinicians alike have applied a variety of deep learning techniques to both medical images and structured electronic medical record data. In many cases, these methods have resulted in risk stratification models that have improved discriminatory ability relative to more straightforward methods. Nevertheless, in many instances, it remains unclear how useful the resulting models are to practicing clinicians. Summary To be useful, deep learning models for cardiovascular risk stratification must not only be accurate but they must also provide insight into when they are likely to yield inaccurate results and be explainable in the sense that health care providers can understand why the model arrives at a particular result. These additional criteria help to ensure that the model can be faithfully applied to the demographic for which it is most accurate.
Guided exploration of user groups

Finding a set of users of interest serves several applications in behavioral analytics. Often times, identifying users requires to explore the data and gradually choose potential targets. This is a special case of Exploratory Data Analysis (EDA), an iterative and tedious process. In this paper, we formalize and solve the problem of guided exploration of user groups whose purpose is to find target users. We model exploration as an iterative decision-making process, where an agent is shown a set of groups, chooses users from those groups, and selects the best action to move to the next step. To solve our problem, we apply reinforcement learning to discover an efficient exploration strategy from a simulated agent experience, and propose to use the learned strategy to recommend an exploration policy that can be applied to the same task for any dataset. Our framework accepts a wide class of exploration actions and does not need to gather exploration logs. Our experiments show that the agent naturally captures manual exploration by human analysts, and succeeds to learn an interpretable and transferable exploration policy.
Medical Time-Series Data Generation Using Generative Adversarial Networks

Medical data is rarely made publicly available due to high de-identification costs and risks. Access to such data is highly regulated due to it’s sensitive nature. These factors impede the development of data-driven advancements in the healthcare domain. Synthetic medical data which can maintain the utility of the real data while simultaneously preserving privacy can be an ideal substitute for advancing research. Medical data is longitudinal in nature, with a single patient having multiple temporal events, influenced by static covariates like age, gender, comorbidities, etc. Extending existing time-series generative models to generate medical data can be challenging due to this influence of patient covariates. We propose a workflow wherein we leverage existing generative models to generate such data. We demonstrate this approach by generating synthetic versions of several time-series datasets where static covariates influence the temporal values. We use a state-of-the-art benchmark as a comparative baseline. Our methodology for empirically evaluating synthetic time-series data shows that the synthetic data generated with our workflow has higher resemblance and utility. We also demonstrate how stratification by covariates is required to gain a deeper understanding of synthetic data quality and underscore the importance of including this analysis in evaluation of synthetic medical data quality.
Multi‐channel Sliced Deep RCNN with Residual Network for Text Classification

We propose a multi-channel sliced deep Recurrent convolutional neural network (RCNN) with a residual network. We expand the RCNN into a deep neural network. Our proposed model can directly learn to extract bigram features and other features from sentences where other machine learning methods cannot. The experimental results indicate that our model outperforms the traditional methods.
Memory-Efficient Automatic Kidney and Tumor Segmentation Based on Non-local Context Guided 3D U-Net

Automatic kidney and tumor segmentation from CT volumes is essential for clinical diagnosis and surgery planning. However, it is still a very challenging problem as kidney and tumor usually exhibit various scales, irregular shapes and blurred contours. In this paper, we propose a memory efficient automatic kidney and tumor segmentation algorithm based on non-local context guided 3D U-Net. Different from the traditional 3D U-Net, we implement a lightweight 3D U-Net with depthwise separable convolution (DSC), which can not only avoid over fitting but also improve the generalization ability. By encoding long range pixel-wise dependencies in features and recalibrating the weight of channels, we also develop a non-local context guided mechanism to capture global context and fully utilize the long range dependencies during the feature selection. Thanks to the non-local context guidance (NCG), we can successfully complement high-level semantic information with the spatial information simply based on a skip connection between encoder and decoder in the 3D U-Net, and finally realize a more accurate 3D kidney and tumor segmentation network. Our proposed method was validated and evaluated with KiTS dataset, including various 3D kidney and tumor patient cases. Convincing visual and statistical results verified effectiveness of our method. Comparisons with state-of-the-art methods were also conducted to demonstrate its advantages in terms of both efficiency and accuracy.
Cloud Resource Management Optimization: Taxonomy and Research Challenges

The idea of cloud computing has reshaped the field of distributed systems, as well as changed how organizations utilizing computing today. Because of the accessibility of limited resources it is exceptionally challenging for cloud service providers to give all the requested resources. Resource management in cloud environment focus upon computing the workload intensive applications that have that have diverse optimization parameters. This study exhibits a complete resource management survey and elaborates broad scientific taxonomy based on the distinct highlights. The scientific taxonomy analyzed most encouraging existing research in resource management and observed monitored values and significant criteria for assessing the viability of several resource management (RM) techniques with their performance evaluation. This paper shows a consecutive study on cloud resource management with broad investigation of particular RM strategies with their evaluation parameters and research issues that should be taken into consideration while suggesting unique RM techniques.
Convergent learning–based model for leukemia classification from gene expression

Microarray data analysis is a major challenging field of research in recent days. Machine learning-based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning techniques to mitigate these types of problems. Due the presence of more number of hidden layers, it can easily handle the big amount of data. We have presented a method of classification to understand the convergence of training deep neural network (DNN). The assumptions are taken as the inputs do not degenerate and the network is over-parameterized. Also the number of hidden neurons is sufficiently large. Authors in this piece of work have used DNN for classifying the gene expressions data. The dataset used in the work contains the bone marrow expressions of 72 leukemia patients. A five-layer DNN classifier is designed for classifying acute lymphocyte (ALL) and acute myelocytic (AML) samples. The network is trained with 80% data and rest 20% data is considered for validation purpose. Proposed DNN classifier is providing a satisfactory result as compared to other classifiers. Two types of leukemia are classified with 98.2% accuracy, 96.59% sensitivity, and 97.9% specificity. The different types of computer-aided analyses of genes can be helpful to genetic and virology researchers as well in future generation.
Toward Communication Efficient Adaptive Gradient Method

In recent years, distributed optimization is proven to be an effective approach to accelerate training of large scale machine learning models such as deep neural networks. With the increasing computation power of GPUs, the bottleneck of training speed in distributed training is gradually shifting from computation to communication. Meanwhile, in the hope of training machine learning models on mobile devices, a new distributed training paradigm called "federated learning'' has become popular. The communication time in federated learning is especially important due to the low bandwidth of mobile devices. While various approaches to improve the communication efficiency have been proposed for federated learning, most of them are designed with SGD as the prototype training algorithm. While adaptive gradient methods have been proven effective for training neural nets, the study of adaptive gradient methods in federated learning is scarce. In this paper, we propose an adaptive gradient method that can guarantee both the convergence and the communication efficiency for federated learning.