Reviewerly/exHarmony huggingface dataset:
A constrained extended Kalman filter based on LS-VCE formulated by condition equations with prediction of cross-covariances

A constrained extended Kalman filter (CEKF) based on least-squares variance component estimation (LS-VCE) is generally developed by condition equations since the proper prediction of dispersion matrices is one of the main bottlenecks in the KF algorithms. Here we investigate four problems which have not been simultaneously considered yet. These problems are examination of non-linearty of dynamic model, VCE, general non-linear state constraints and fairly general stochastic model. Although a few contributions proposed some adaptive KF in particular based on Helmert’s VCE method, they developed their filters for special problems with some restrictive conditions such as independence of all variables and/or linearity of the dynamic model. Also some of these filters did not apply VCE methods to all parts of the dynamic model. In this contribution, we try to overcome all of these restrictions. Moreover, LS-VCE method gives some added advantages over other VCE methods. First the new formulation of CEKF is developed by condition equations with prediction of all possible cross-covariances as algorithm 1. Then the LS-VCE method is applied to it after some modifications which results in an adaptive constrained extended Kalman filter (ACEKF) as the second algorithm.
Siamese Network with Channel-wise Attention and Multi-scale Fusion for Robust Object Tracking

Object tracking is an important issue in computer vision, and fully-convolutional Siamese networks have recently received much research attentions. It uses two offline deep convolutional networks with shared parameters to solve the general similarity problem. However, in fully-convolutional Siamese networks, not all channels of the feature map contain useful information for tracking. In this paper, we propose to introduce the channel-wise attention mechanism to help the network learn to select the most informative and discriminative channels in feature map. At the same time, a novel multi-scale feature fusion method is proposed which uses a top-down structure with horizontal connections to construct advanced semantic feature maps at multiple scales. Experiments have shown that the proposed method has achieved remarkable improvement in both successful rate and accuracy in tracking.
El uso de las TIC en los sistemas educativos formales. Propuesta de marco metodológico para su caracterización y análisis

Undoubtably, the rise of technology of the information and communication (ITCs) was what revolutionized the current epoch, giving a radical turn to the way in which we communicate, learn, and work—a situation that led to considering information the most valuable resource toward progress and education the médium through which to produce it and distribute it. This led governments to generate high expectations regarding the use of technologies in education, believing that they offered the possibility of innovating teaching processes and improve learning processes, it being a motive for a UNESCO world declaration. However, recent studies demonstrate that the incorporation and use of ITCs in classrooms have not been easy for various reasons, especially the financial cost they represent, the challenge of digital literacy for teachers and students, and, most importantly, the use that they make of ITCs. In this regard, in this work, a methodological framework is proposed, understood as a set of actions geared toward describing and analyzing an object of research orientadas, in this case, the use of technologies in formal education. For this purpose, a meticulous survey is performed to choose a paradigm, a qualitative-humanistic perspective, a method, an orientation, the scope of the study, and techniques for data gathering and análisis, as well as to determine the population and sample.
Domain-Independent Video Summarization Based on Transfer Learning Using Convolutional Neural Network

Video summarization methods generate a compact representation of the original video preserving the essential content of the input video. Video summaries utilize less storage space compared to the original video. The summaries also facilitate efficient browsing and retrieval of video data. This paper presents a novel method for static video summarization based on deep features extracted using convolutional neural network (CNN) by means of transfer learning. To detect the frames with content change, the Chebyshev distance scores between the feature vectors of consecutive frames are thresholded. Uniform pre-sampling and redundancy elimination are done before summarization to reduce the complexity of the summarization step. Uniform pre-sampling is done by selecting two frames in one second. Redundant frames are eliminated based on flow vectors between consecutive frames using the SIFT flow algorithm. The experimental results on two well-known datasets show that our approach significantly improves the performance of the baseline methods and achieves competitive results compared with other state-of-the-art methods.
Coverage control of multi-agent systems for ergodic exploration

In this paper, according to the ergodic theory, the ergodic metric is defined as the distance of the time average statistics of the agent's trajectories from the spatial distribution probability of the terrain. The feedback control law of the agent's dynamic is designed using the ergodic metric to achieve uniform or non-uniform coverage of a given area. At the same time, in order to expand the detection range, and obtain more complete and accurate environmental information and performed different types of tasks, the multi-agent system covers in the form of formation, and can adapt to move along the boundary. In this paper, the combination of the leader-follower method and the artificial potential field method are used to control the formation of a multi-agent system.
TOPSIS threat assessment based on the coefficient of variation

In the face of multi-target attacks, it is very important to evaluate the threat degree of unknown targets reasonably and accurately and provide data support for subsequent command decision-making. In the traditional calculation of threat degree, the weight coefficient is obtained by expert scoring. This method has strong subjectivity and is far from suitable for urban anti-terrorism operations. Therefore, this paper proposes a TOPSIS threat degree ranking method based on the coefficient of variation, which combines the variation coefficient method with the TOPSIS method, and uses the non-dimensional variable variation coefficient to obtain the weight coefficient, reducing the influence of human factors, and obtains the threat through matrix operation. The results show that the method is objective, accurate and practical.
An image super-resolution method for better cognition of images in cognition computing system

Data cognition plays an important role in cognitive computing. Cognition of low-resolution (LR) image is a long-stand problem because LR images have insufficient information about objects. For better cognition of LR images, a multi-resolution residual network (MRRN) is proposed to improve image resolution in this paper for cognitive computing systems. In MRRN, a multi-resolution feature learning (MRFL) strategy is introduced to achieve satisfying performance with low computational costs. Inspired by image pyramids, a feature pyramid is designed to implement multi-resolution feature learning in the building unit of the proposed MRRN. Specifically, multi-resolution residual units (MRRUs) are introduced as the building units of the proposed network, which consist of a feature pyramid decomposition stage and a feature reconstruction stage. To obtain informative features, transferred skip links (TSLs) are utilized to transfer fine-grain residual features in the pyramid decomposition stage to the reconstruction stage. The effectiveness of MRFL and TSL is demonstrated by ablation experiments. Also, the tests on standard benchmarks indicate the superiority of the proposed MRRN over other state-of-the-art methods.
Proactive Network Maintenance using Fast, Accurate Anomaly Localization and Classification on 1-D Data Series

Proactive network maintenance (PNM) is the concept of using data from a network to identify and locate network faults, many or all of which could worsen to become service failures. The separation between the network fault and the service failure affords early detection of problems in the network to allow PNM to take place. Consequently, PNM is a form of prognostics and health management (PHM).The problem of localizing and classifying anomalies on 1-dimensional data series has been under research for years. We introduce a new algorithm that leverages Deep Convolutional Neural Networks to efficiently and accurately detect anomalies and events on data series, and it reaches 97.82% mean average precision (mAP) in our evaluation.
Simulation of athletes’ motion detection and recovery technology based on monocular vision and biomechanics

Athletes’ sports detection has a heavy pressure on athletes’ training and post-injury rehabilitation. In the traditional mobilization test, there is no effective combination of exercise and rehabilitation, which directly leads to the athlete’s physical health cannot be guaranteed. Based on this, this study combines the current situation of the athletes’ field and the training ground, and uses monocular vision as the video input interface, and combines the monocular vision technology in the research. Moreover, in the research, this paper combines the human body model to construct an athlete’s human body model that adapts to monocular vision. At the same time, this paper combines the image processing technology to transform the image of the monocular visual athlete into a skeleton model, so as to realize the modeling of the athlete’s movement. In addition, this paper combines the model to explore the indoor and outdoor athlete recovery techniques and validates the model by experiment. The research shows that the research model has certain effects, which can meet the actual needs, and can provide theoretical reference for subsequent related research.
Robust Convex Clustering with Spectral Analysis-based Feature Selection

Clustering is a fundamental problem in many scientific applications. Traditional methods, such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Furthermore recently introduced convex relaxations of methods average the weight of cluster assignment, which may lead performance deterioration. To address these issues, this paper presents a novel approach for robust convex clustering. In contrast to previously considered algorithms, the formulation utilize spectral analysis-based feature selection for alternating between minimization algorithm and multipliers. Rather than focusing on local features and their consistencies, our method aims at extracting sufficient information about the structure of the target concept. The experimental results demonstrate the effectiveness of our method in a variety of contexts on a real-world dataset.
Incomplete high dimensional data streams clustering

Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.
‘La ballata dell’amore cieco’: a case study on the use of songs in Italian language learning

The song ‘La ballata dell’amore cieco (o della vanità)’ (1966), by Italian singer-songwriter Fabrizio De André (1940-1999), offers students opportunities to practise their language skills, stimulate their cultural appreciation, and expand their knowledge of a musical and poetic form (the ballad), while reflecting on the close connection between poetry and dance. Moving from receptive to productive skills, from gist to detail, learning activities are staged over three sessions, where students examine the song’s musical features and lyrics and then produce their own ballads in writing. The paper first discusses the benefits and pitfalls of using songs as a tool to develop linguistic skills and raise cultural awareness. It then describes the learning opportunities offered by De André’s song and concludes with an evaluation of the effectiveness of these activities, examining the potential for further research.
Efficient Image Dehazing by Improving Retinex Algorithm

The traditional Retinex hazy image enhancement algorithm using a fixed-scale parameter filter without considering the disadvantages of hazy density, so we propose an improved Retinex dehazing algorithm based on the dark channel prior. According to the correspondence between filter scale parameters and mean values of image sub-block, this algorithm uses Gaussian filters with different scale parameters for different haze concentration regions. Secondly, this algorithm enhanced image performance with the help of Retinex. Finally, optimized details by restoring color information and enhancing image edges with improved fractional differentiation. Experiments prove that this algorithm improves the sharpness and contrast of the image, makes the image richer in detail, has a stronger edge retention ability, and also restores the image with better layering and global effects.
Distributed Hypergraph Processing Using Intersection Graphs

The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named <inline-formula><tex-math notation="LaTeX">$Hyraph$</tex-math> </inline-formula> that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the <inline-formula><tex-math notation="LaTeX">$Hyraph$</tex-math></inline-formula> framework.
A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.
Medical Transcriptions and UMLS-Based Disease Inference and Risk Assessment Using Machine Learning

An extensive set of applications and approaches for automatic disease inference with risk assessment system in medical fields objected to improve the efficacy and efficiency of patient care. Recent research studies are getting done in the area to predict patients’ future health conditions based on the medical records, particularly full clinical texts, medical transcriptions, clinical measurements, or medical codes. Healthcare data are highly complex, multi-dimensional, and heterogeneous in nature which are the key challenges. In this paper, a system has been proposed for disease inference by extracting symptoms and mapping the metadata using Unified Medical Language System (UMLS) to have the disease code. The symptoms and metadata are extracted from medical transcripts using natural language processing (NLP) and the extracted information has been categorized into chief complaints, present illness, and past history. These extracted symptoms are mapped using UMLS for disease code inference. Based on the disease code, the risk classifier classifies the level of risk. The proposed system based on deep learning and UMLS for disease inference resulted with significant improvement in accuracy.
Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15% for the first model and 11% for second model.
Reducing Energy Consumption of HMAC Applications on Heterogeneous Platforms

Hash-based message authentication code (HMAC) is a form of cryptographic checksum which is frequently used in SSL/TLS. The IETF standard permits several hash function families with several possible checksum lengths. Thus, if checksums must be computed for a number of messages, a multi-dimensional optimization problem arises, especially if a heterogeneous platform like ARM big.LITTLE is used. We formulate the optimization problem as an energy minimization problem with constraints given by deadlines and the different power and performance profiles of different hash functions on different core types. Furthermore, we show that with the help of measurements done in previous work this optimization problem can be mapped to a known scheduling problem for big.LITTLE, i.e., that a scheduling algorithm for that problem can be adapted to our scheduling problem. Thus, a solution can be computed that, given deadline as well as message sizes and hash functions for the HMAC computations, minimizes energy consumption on the big.LITTLE, and takes profit from the heterogeneity. We compare the scheduling results from an optimal and a heuristic scheduler and find that the heuristic on average produces schedules with 4% higher energy consumption, with small variance.
Dynamics of quantum Fisher information in the two-qubit systems constructed from the Yang–Baxter matrices

By using the quantum Yang–Baxterization approach to the three different Hamiltonians, we investigate the behavior of the quantum Fisher information (QFI) under the actions of these Hamiltonians on the different two-qubit input states and by estimating the meaningful parameter $$\varphi $$ . We address the overall estimation properties by evaluating the QFI for the whole system undergone different unitary evolution. The results show that the behavior of the QFI depends on the choice of the initial states. Choosing the optimal input states can improve the precision of quantum parameter estimation. On the other hand, we also focus on the dynamical evolution of QFI to distinguish Markovianity and non-Markovianity of the process by adopting the flow of QFI as the quantitative measure for the information flow. We show that the Hamiltonians constructed with Yang–Baxter matrices influence the dynamics of the system in the sense of the Markovianity and non-Markovianity. In certain ranges of parameters, we observe that dynamical evolutions of the systems show non-Markovian behavior in which the information flows from the environment to the system.
Covariance Intersection Kalman Fuser with Time-delayed Measurements

For a two-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained, and then using the covariance intersection (CI) fusion method, the covariance intersection steady-state Kalman fuser is presented. It can handle the estimation fusion problem between local estimation errors for the system with unknown cross-covariances and avoid a large computed burden and computational complexity of cross-covariances. It is proved that its accuracy is higher than that of each local estimator, and is lower than that of optimal Kalman fuser weighted by matrices with known cross-covariances. A Monte-Carlo simulation example shows the above accuracy relation, and indicates that its actual accuracy is close to that of the Kalman fuser weighted by matrices, hence it has good performances.
An Investigation on Indoor Navigation Systems

The development of navigation system for visually impaired people in an indoor environment has been a challenge to researchers for more than a decade. This paper presents a review of the literature on recent techniques and methodologies for indoor navigation system. A detailed review on each phase in the navigation system along with the various techniques used, cost involved, feasibility level along with the performance of path selection algorithms has been analyzed. The paper also presents the demerits of the existing approaches for navigation in indoors.