Reviewerly/exHarmony huggingface dataset:
Nonlinear Non-Gaussian Estimation Using Maximum Correntropy Square Root Cubature Information Filtering

This paper concerns the nonlinear filter designing methods in the information space of the nonlinear systems with non-Gaussian noises. Firstly, the prediction information vector is obtained by the traditional square root cubature information filtering algorithm. Then, under the maximum correntropy criterion, the prediction information vector is corrected with the contribution information vector obtained by the non-Gaussian measurement. The information filtering gain is obtained by utilizing the state information correntropy matrix and the measurement information correntropy matrix, in which, the state prediction is taken as the state value. In order to improve the advantage of the above nonlinear non-Gaussian information filter in filtering accuracy, with the help of fixed-point theory, an iterative computation method is further developed to update the estimation information vector and the state estimate. The effectiveness of the two proposed nonlinear non-Gaussian filtering methods is illustrated in final four simulation examples.
An Efficient Elastic-Degenerate Text Index? Not Likely

Elastic-degenerate text provides a novel and effective method for modeling collections of text that have local variations. Due to its applicability in pan-genomics, an index for an elastic-degenerate text which can efficiently report the occurrences of a given query pattern is desirable. This paper attempts to dash our hopes for such an index, one that is deterministic and has good worst-case query time. We do so by providing conditional lower bounds based on the Orthogonal Vectors Hypothesis (OVH) (and hence the Strong Exponential Time Hypothesis). We show that, even with arbitrary polynomial preprocessing time, an index for an elastic-degenerate text with n degenerate letters that can perform queries on a pattern of length m in time Open image in new window for constants \(\alpha \) and \(\beta \) where Open image in new window or Open image in new window would violate OVH. Additionally, we provide an elastic-degenerate text index with query time Open image in new window, which is independent of the size N (distinct from its length) of the elastic-degenerate text. Finally, we investigate the hardness of matching elastic-degenerate text to elastic-degenerate text.
Machine learning model for clinical named entity recognition

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manner
Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

In this paper, a new form of open traveling salesman problem (OTSP) is used for path planning for optimal coverage of a wide area by cooperated unmanned aerial vehicles (UAVs). A hybrid form of particle swarm optimization (PSO) and genetic algorithm (GA) is developed for the current path planning problem of multiple UAVs in the coverage mission. Three path-planning approaches are introduced through a group of the waypoints in a mission area: PSO, genetic algorithm, and a hybrid form of parallel PSO-genetic algorithm. The proposed hybrid optimization tries to integrate the advantages of the PSO, i.e. coming out from local minimal, and genetic algorithm, i.e. better quality solutions within a reasonable computational time. These three approached are compared in many scenarios with different levels of difficulty. Statistical analyses reveal that the hybrid algorithm is a more effective strategy than others for the mentioned problem.
3D Capsule Hand Pose Estimation Network Based on Structural Relationship Information

Hand pose estimation from 3D data is a key challenge in computer vision as well as an essential step for human–computer interaction. A lot of deep learning-based hand pose estimation methods have made significant progress but give less consideration to the inner interactions of input data, especially when consuming hand point clouds. Therefore, this paper proposes an end-to-end capsule-based hand pose estimation network (Capsule-HandNet), which processes hand point clouds directly with the consideration of structural relationships among local parts, including symmetry, junction, relative location, etc. Firstly, an encoder is adopted in Capsule-HandNet to extract multi-level features into the latent capsule by dynamic routing. The latent capsule represents the structural relationship information of the hand point cloud explicitly. Then, a decoder recovers a point cloud to fit the input hand point cloud via a latent capsule. This auto-encoder procedure is designed to ensure the effectiveness of the latent capsule. Finally, the hand pose is regressed from the combined feature, which consists of the global feature and the latent capsule. The Capsule-HandNet is evaluated on public hand pose datasets under the metrics of the mean error and the fraction of frames. The mean joint errors of Capsule-HandNet on MSRA and ICVL datasets reach 8.85 mm and 7.49 mm, respectively, and Capsule-HandNet outperforms the state-of-the-art methods on most thresholds under the fraction of frames metric. The experimental results demonstrate the effectiveness of Capsule-HandNet for 3D hand pose estimation.
Weakly Supervised Deep Learning for Breast Cancer Segmentation with Coarse Annotations

Cancer lesion segmentation plays a vital role in breast cancer diagnosis and treatment planning. As creating labels for large medical image datasets can be time-consuming, laborious and error prone, a framework is proposed in this paper by using coarse annotations generated from boundary scribbles for training deep convolutional neural networks. These coarse annotations include locations of lesions but are lack of accurate information about boundaries. To mitigate the negative impact of annotation errors, we propose an adaptive weighted constrained loss that can change the weight of the task-specific penalty term according to the learning process. To impose further supervision about the boundaries, uncertainty-based boundary maps are generated, which can provide better descriptions for the blurry boundaries. Validation on a dataset containing 154 MRI scans has shown an average Dice coefficient of \(82.25\%\), which is comparable to results from fine annotations, demonstrating the efficacy of the proposed approach.
The Role of Motivation in Second Language Acquisition

This paper aims to explore the role of motivation in second language acquisition. Motivation is one of the most important factors for learning a second language proficiently. Motivation is unavoidable linked with language achievement in the sense that language achievement can not happen without motivation. In short, motivation is used as a concept for explaining the success or failure of a language learner. There are many advantages for knowing other languages but they are not absolutely necessary, and as a consequence, motivation can play an important role in learning second language. And there are many factors that can affect this motivation.
Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine

Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.
Comprehensive Evaluation of Music Course Teaching Level Based on Improved Multi-attribute Fuzzy Evaluation Model

Aiming at the problems of multi-attribute fuzzy information and imperfect evaluation system existing in the current evaluation process of music course teaching level, the paper studies the multi-attribute fuzzy evaluation of music course teaching level, analyzes the influencing factors of music course teaching level, and establishes an improved music course teaching level evaluation system; then on this basis, combining with the fuzzy system theory, grey system theory and entropy weight method, it proposes a multi-attribute fuzzy evaluation model of music course teaching level to realize the quantitative analysis of music course teaching level, which has good engineering application value. At the same time, the paper also puts forward some strategies and suggestions to improve the teaching level of music courses, which are of good guidance and reference significance for improving the teaching quality of music courses.
Education for Sustainable Development in the Value System of Teachers

Еducation for sustainable development shows all the necessary from sustainable development concept in unity of ecologic, social-cultural, and economic spheres. The reduction of education for sustainable development to the issue of environmental education poses a threat to the values ​​associated with the humanistic interpretation of education. Firstly, it is the absolutization of environmental values and the lack of effective synthesis of these values with social values, and economic values partly. Secondly, there is a lack of needs and values of teachers and value-methodological principles of integration of environmental and humanistic values. Thirdly, to a large extent on the periphery of education for sustainable development are the problems of transformation of society itself, social relations, culture, ways of communication, interaction of people with each other, as well as the emotional component of such interaction. One of the possible ways to solve these problems can be considered the formation of new, more adapted to the needs of sustainable development of value-cognitive characteristics of teachers. The new model of education should provide a focus on people as the main object of sustainable development, subordinating value changes to the task of socialization and humanization of social development, taking into account environmental constraints. Based on the results of the study, it is necessary to pay attention to the development of social intelligence, emotional intelligence, communicative intelligence and practical intelligence. For more adequate training of professionals in the field of education for sustainable development, it is advisable to expand cooperation with governmental and non-governmental organizations.&#x0D; Key words: education, sustainable development, humanism, ecology, training, values, social intelligence
Design for Sustained Wellbeing through Positive Activities—A Multi-Stage Framework

In this paper, we introduce a framework that conceptualizes a multi-stage process through which technology can promote sustained wellbeing. Intentional wellbeing-enhancing activities form the centerpiece linking direct product interaction to, ultimately, wellbeing. The framework was developed following a bottom-up–top-down approach by integrating theoretical knowledge from positive psychology, behavioral science and human–computer interaction (HCI)/design with empirical insights. We outline (a) the framework, (b) its five main stages including their multidisciplinary theoretical foundations, (c) relations between these stages and (d) specific elements that further describe each stage. The paper illustrates how the framework was developed and elaborates three major areas of application: (design) research, design strategies and measurement approaches. With this work, we aim to provide actionable guidance for researchers and IT practitioners to understand and design technologies that foster sustained wellbeing.
Extrinsic Calibration of a Camera and a 2D LiDAR Using a Dummy Camera With IR Cut Filter Removed

Extrinsic calibration of a camera and a LiDAR is necessary to fuse information from each sensor. The real trajectory of the LiDAR is not visible on an image, therefore the accuracy of the extrinsic calibration is usually checked by evaluating residuals of constraints. In this paper, we present an improved extrinsic calibration algorithm of a camera and a 2D LiDAR using an additional dummy camera removing IR cut filter, which make it possible to observe the real trajectory of LiDAR. Some previous algorithms used the real trajectory of LiDAR for the extrinsic calibration. However, they used IR filter directly on the calibrating camera by adjusting exposure time, which can affect the result of the extrinsic calibration. We use an initial solution using the Hu algorithm which makes extrinsic calibration possible by using just one shot of data. The Hu algorithm gives a sensitive result according to pose variation between a system consisted of a camera and a LiDAR and a calibration structure, which is verified using the real trajectory of LiDAR. We cope with this problem by refining the initial solution through nonlinear minimization in a 3D space using the real trajectory of LiDAR. Experimental results show that the proposed algorithm gives an improved solution.
Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method

This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.
Transition to distance learning during the COVID-19 pandemic: Efforts within the Higher Education sector in the United Arab Emirates

In the UAE, the Ministry of Education (MOE) ordered the closure of higher education institutions’ facilities in March 2020 as a precautionary measure to contain COVID-19 and allowed online distance learning to take place. In this paper, a study on the readiness of higher education institutions in the UAE to transition to distance learning during the COVID-19 pandemic is conducted, and the preparedness of several institutions is discussed. In addition, this study highlights the foundations of the distance learning system adopted by higher education institutions along with the adopted educational platforms. MOE played a crucial role in this crisis by governing, monitoring and supporting the transition among universities to assure business continuity in higher education institutions. Moreover, higher education institutions encountered several challenges while implementing a distance learning system which is presented in this paper along with possible solutions and scenarios. Furthermore, multiple initiatives were launched by the UAE government to support distance learning that resulted in maintaining learning continuity among higher education students and faculty.
Pedagogical Tests As An Element Of Types Of Pedagogical Technologies

The question of the learning objectives of the new pedagogical technology should be considered in conjunction with the problem of transferring the learning objective to the test task, and it is necessary to determine the level of achievement of the goal.Positive qualities in testing can be demonstrated only under the conditions in which qualitative tests are used.However, there are certain difficulties in creating quality tests.This is primarily a process that stems from the experience and knowledge of designing quality tests among teachers.
Bayesian Optimized Monte Carlo Planning

Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. The performance of progressive widening search is dependent upon the action sampling policy, often requiring problem-specific samplers. In this work, we present a general method for efficient action sampling based on Bayesian optimization. The proposed method uses a Gaussian process to model a belief over the action-value function and selects the action that will maximize the expected improvement in the optimal action value. We implement the proposed approach in a new online tree search algorithm called Bayesian Optimized Monte Carlo Planning (BOMCP). Several experiments show that BOMCP is better able to scale to large action space POMDPs than existing state-of-the-art tree search solvers.
Development and Application of a Human–Machine Interface Using Head Control and Flexible Numeric Tables for the Severely Disabled

The human–machine interface with head control can be applied in many domains. This technology has the valuable application of helping people who cannot use their hands, enabling them to use a computer or speak. This study combines several image processing and computer vision technologies, a digital camera, and software to develop the following system: image processing technologies are adopted to capture the features of head motion; the recognized head gestures include forward, upward, downward, leftward, rightward, right-upper, right-lower, left-upper, and left-lower; corresponding sound modules are used so that patients can communicate with others through a phonetic system and numeric tables. Innovative skin color recognition technology can obtain head features in images. The barycenter of pixels in the feature area is then quickly calculated, and the offset of the barycenter is observed to judge the direction of head motion. This architecture can substantially reduce the distraction of non-targeted objects and enhance the accuracy of systematic judgment.
TMaR: a two-stage MapReduce scheduler for heterogeneous environments

Abstract In the context of MapReduce task scheduling, many algorithms mainly focus on the scheduling of Reduce tasks with the assumption that scheduling of Map tasks is already done. However, in the cloud deployments of MapReduce, the input data is located on remote storage which indicates the importance of the scheduling of Map tasks as well. In this paper, we propose a two-stage Map and Reduce task scheduler for heterogeneous environments, called TMaR. TMaR schedules Map and Reduce tasks on the servers that minimize the task finish time in each stage, respectively. We employ a dynamic partition binder for Reduce tasks in the Reduce stage to lighten the shuffling traffic. Indeed, TMaR minimizes the makespan of a batch of tasks in heterogeneous environments while considering the network traffic. The simulation results demonstrate that TMaR outperforms Hadoop-stock and Hadoop-A in terms of makespan and network traffic and achieves by an average of 29%, 36%, and 14% performance using Wordcount, Sort, and Grep benchmarks. Besides, the power reduction of TMaR is up to 12%.
A predictive noise correction methodology for manufacturing process datasets

Abstract In manufacturing processes, datasets intended for data driven decisions are majorly generated from time-sequenced sensor readings. Industrial sensor systems are prone to transmit inaccurate readings, which result in noisy datasets. Noisy datasets inhibit machine learning and knowledge discovery. Using a multi-stage, multi-output process dataset as an experimental case, this article reports a methodology for replacing erroneous sensor values with their predicted likely values. In the methodology, invalid values specified by process owners are first converted to missing values. Then, ReliefF algorithm is used to select the most relevant features to progress for prediction modelling, and also to boost the performance of the prediction model. A Random Forest classifier model is built to predict replacement values for the missing values. Finally, predicted values are inserted into the dataset to fill in the missing entries. With many attributes having a significant number of erroneous values, the invalid values replacement is done one attribute at a time. To do this systematically, the process flow direction and stages in the manufacturing process are exploited to partition the dataset into subsets for model building. The results indicate that the methodology is able to replace erroneous values with likely true values, to a very high degree of accuracy. There is a paucity of this type of methodology for dealing with invalid entries in process datasets. The methodology is useful for both missing and invalid value correction in process datasets. In the future, the plan is to inject the prediction models into streaming data to simultaneously enable erroneous value correction and predictive process monitoring in real-time.