Reviewerly/exHarmony huggingface dataset:
Stochastic Optimal Control for Multivariable Dynamical Systems Using Expectation Maximization

Trajectory optimization is a fundamental stochastic optimal control (SOC) problem. This article deals with a trajectory optimization approach for dynamical systems subject to measurement noise that can be fitted into linear time-varying stochastic models. Exact/complete solutions to these kind of control problems have been deemed analytically intractable in literature because they come under the category of partially observable Markov decision processes (MDPs). Therefore, effective solutions with reasonable approximations are widely sought for. We propose a reformulation of stochastic control in a reinforcement learning setting. This type of formulation assimilates the benefits of conventional optimal control procedure, with the advantages of maximum likelihood approaches. Finally, an iterative trajectory optimization paradigm called as SOC-expectation maximization (SOC-EM) is put forth. This trajectory optimization procedure exhibits better performance in terms of reduction in cumulative cost-to-go which is proven both theoretically and empirically. Furthermore, we also provide novel theoretical work which is related to uniqueness of control parameter estimates. Analysis of the control covariance matrix is presented, which handles stochasticity through efficiently balancing exploration and exploitation.
Federated Clique Percolation for Overlapping Community Detection on Attributed Networks

Community detection is a popular research topic in complex network analysis, which can be applied in many real-world scenarios such as disease prediction. With the increase of people’s awareness of privacy protection, more and more laws enforce the protection of sensitive information while transferring data. The anonymization-based community detection methods have to sacrifice accuracy for privacy protection. In this paper, we first propose a standalone clique percolation algorithm to detect overlapping communities on attributed networks. A clique similarity metric is designed to percolate cliques accurately. Second, we develop a federated clique percolation algorithm to detect overlapping communities on distributed attributed networks. Perturbation strategy and homomorphic encryption are used to protect network privacy. The experiments on real-world and artificial datasets demonstrate that the federated clique percolation algorithm achieves identical results to the standalone ones and realizes higher accuracy than the simple distributed ones without federating learning.
Self-auxiliary Hashing for Unsupervised Cross Modal Retrieval

Recently, cross modality hashing has attracted significant attention for large scale cross-modal retrieval owing to its low storage overhead and fast retrieval speed. However, heterogeneous gap still exist between different modalities. Supervised methods always need additional information, such as labels, to supervise the learning of hash codes, while it is laborious to obtain these information in daily life. In this paper, we propose a novel self-auxiliary hashing for unsupervised cross modal retrieval (SAH), which makes sufficient use of image and text data. SAH uses multi-scale features of pairwise image-text data and fuses them with the uniform feature to facilitate the preservation of intra-modal semantic, which is generated from Alexnet and MLP. Multi-scale feature similarity matrices of intra-modality preserve semantic information better. For inter-modality, the accuracy of the generated hash codes is guaranteed by the collaboration of multiple inter-modal similarity matrices, which are calculated by uniform features of both modalities. Extensive experiments carried out on two benchmark datasets show the competitive performance of our SAH than the baselines.
A Full Information Enhanced Question Answering System Based on Hierarchical Heterogeneous Crowd Intelligence Knowledge Graph

With the development of deep learning technology, generative question answering models based on neural networks have gradually become a mainstream research direction in academia and industry. The current question answering models fail to make full use of the multi-level knowledge embedded in the learned corpus, and the interpretability and robustness of the models in the face of attack samples have certain shortcomings. From the perspective of information theory, this paper constructs the semantic, pragmatic and syntactic knowledge contained in the large amount of crowd intelligence corpora obtained from the Internet platform into a hierarchical and heterogeneous natural language knowledge graph. The graph-based full information enhanced question answering model (GFIQA) is proposed, and the hierarchical heterogeneous knowledge graph is incorporated in the model. Through the crowd intelligence knowledge interpretation module, knowledge-enhanced generation module and single-layer anisotropic decoder, the relevant knowledge in the crowd intelligence natural language knowledge graph is appropriately selected based on the attention mechanism, and the ability of question understanding and answer generation is improved. The experimental results show that the GFIQA model has a large improvement in PPL, BLEU, and ENC (PPL: −11.76, BLEU: +0.126, ENC: + 0.232) compared with the baseline model, and can generate fluent and smooth answers with reasonable grammatical modifications and rich semantics.
Adaptive Registration for Multi-type Remote Sensing Images via Dynamic Feature Selection

Remote sensing image registration (RSIR) has been performed in various RS applications for decades. However, how to adaptively register multi-type (multi-view, multi-temporal, and multi-sensor) RS images remains a challenging problem due to different degrees of local nonrigid distortions, rotation angles, and nonlinear intensity differences between such images. This paper presents a general RSIR method for multi-type RS images. The multi-type mixed feature descriptor (MMFD) is first constructed by combining the respective advantages of channel features of orientated gradients (CFOG) descriptor, speeded up robust features (SURF) local distance, and neighbouring structure descriptor. According to prematching of SURF local distance and CFOG descriptor, a dynamic threshold adjusts the feature points number adaptively. Finally, the dynamic feature selection strategy is implemented to adjust weight parameters of MMFD by the thresholding techniques. Extensive experiments on proposed method are performed over satellite and UAV datasets, and results show that the proposed method provides favorable performance (RSME of 1.59761 and 1.0811) with respect to eight state-of-the-art methods.
Integration of Depth Normal Consistency and Depth Map Refinement for MVS Reconstruction

To address the problem of incomplete Multi-view Stereo (MVS) reconstruction, the initial depth and loss function of the depth residual iterative network are investigated, and a new multi-view stereo reconstruction network integrating depth normal consistency and depth map thinning is presented. Firstly, downsampling the input image to create an image pyramid and extracting a feature map from the image pyramid; Then, constructing a cost volume from the 2D feature map, adding the depth normal consistency to the initial cost volume to optimize the depth map. On the DTU data set, the network is tested and compared to traditional reconstruction approaches and MVS networks based on deep learning. The experimental results show that the proposed MVS reconstruction network was produced the better results in completeness and increased the quality of MVS reconstruction.
DOCEM: A Domain-Embedding-Based Open-Source Community Event Monitoring Model

Open source software (OSS) development has become a trend and many popular software systems are developed and maintained by open source communities. In open source communities, developers are loosely organized which brings difficulties to the management of OSS projects. The events happening in the open source community may affect the development of the OSS project, which should be taken care of by the project organizers. Unfortunately, to identify these events from large amounts of messages generated in the community, especially from multiple sources, is still a big challenge. In this paper, a Domain-embedding-based Open-source Community Event Monitoring Model (DOCEM) is proposed to identify events from multiple sources. Specifically, DOCEM is based on DualGAN and StarGAN. An event dataset for Tensorflow project is constructed to train and test DOCEM. The experiment results show that DOCEM has much better performance than counterparts.
Modern algorithms for interpreting the history of music: an epistemological discourse

In the article a historically significant situation is considered, when with the revival in the last third of the twentieth century of B. Asafyev's ideas on music as a sound intonation environment and an art of intoned meaning, as well as because of the expansion of modern humanitarian knowledge due to close interdisciplinary links, the musical criticism underwent a process of active development, synthesizing the subject field of historical and theoretical musicology on the basis of culturology and philosophy. The achievements of this process have been analyzed, which demonstrate a radical renovation of the conceptual and semantic content of the leading categories of musicology, in particular, in relation to the interpretation of historical phenomena in music in view only of their inherent worldview paradigms. Analytical algorithms for detecting epistemologically reliable data on the style of epochs and criteria for compiling their generalized "image" under the guise of an invariant model are specified. The expediency of a monadological (nonlinear) approach to the history of music, which methodologically corresponds to the idea of "philosophy of integrity", is proposed and substantiated.
Detection of Abnormal Crowd Behavior Based on ViBE and Optical Flow Methods

With the rapid development of computer vision and image processing technology, the detection of abnormal behavior in video has gradually attracted more and more scholars’ attention. This paper proposes a fast method to detect abnormal behavior in surveillance videos. Firstly, the Visual Background Extraction (ViBE) method was used to extract the foreground object. Then, the image speed was separated according to the optical flow method. Finally, the Otsu method was used to binarize the image, the detection effect was evaluated by defining the density of moving pixels in a single frame of image relative to all pixels. In this paper, an experiment with the sudden scattering of concentrated crowds was conducted to simulate crowd abnormal behavior, the results show that the experimental effect is in line with expectations. Compared with other commonly used machine learning methods, the proposed method can be quickly used to detect crowd abnormal behavior in a surveillance video, the detection results can also be quickly evaluated, which has the advantages of low cost and convenience.
Mediation effect of collapse avoidance assurance on behavioural intention to use forensic accounting

Forensic Accounting is a widely known technique for fraud prevention and detection. To date, the demand for forensic accounting is increasing, as well as the fraud level. However, some companies are reluctant to use forensic accounting to find guilty and face legal consequences such as liquidation or winding up. The two main theories of this study are the Protection Motivation Theory (PMT) and Forensic Accounting Theory. The two theories incorporate fraud treat appraisal and collapse avoidance assurance in forensic accounting to determine the behavioural intention to use forensic accounting. This study is expected to generate the best model to address the unwillingness to adopt forensic accounting services by industrial products and services companies. Therefore, this study may significantly contribute to the increase in companies' awareness and willingness to use forensic accounting in mitigating fraud or unethical activities.
Sentiment Analysis on Bangla Financial News

Sentiment analysis of news and media material can provide important details about events and causal relationships. Bangla text sentiment analysis is getting increasingly popular. As a market indicator, financial market participants use financial sentiment analysis. In Bangla, financial sentiment analysis is a less-explored research field. In this research, we attempt to analyze the emotion of Bangla financial news items. In this study, we created a dataset with titles of news articles classified as positive or negative from a well-known online web portal that provides business and financial news. We employ vector-based feature encoding and different machine learning approaches to illustrate the method&#x2019;s effectiveness. Our findings are encouraging, and they suggest the way forward for Bengali sentiment analysis research in the financial sector.
LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration

In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms. Our approach has shown a competitive performance with respect to benchmark supervised and model-based methods.
THE USE OF YOUTUBE TUTORIAL VIDEOS TO IMPROVE STUDENTS’ SPEAKING SKILL AT THE SECOND GRADE STUDENTS OF SMA NEGERI 4 BULUKUMBA

The objective of the research is to find out does the use of YouTube tutorial videos can improve the students’ speaking skill at the second grade students of SMA Negeri 4 Bulukumba. The researcher applied pre-experimental with pre-test and post-test design. The population of this research was taken by purposive sampling technique from the second grade students’ of SMA Negeri 4 Bulukumba in academic year 2020/2021. This population consists of 11 students.&#x0D; The result of the data analysis showed that there is a significant difference between pre-test and post-test of the students’ speaking skill especially in students’ fluency and pronunciation after giving treatment. The students’ mean score in pre-test was 42,72 and post-test was 59,09. It showed that the students’ speaking skill in post-test was higher than the students’ pre-test. The t-test value was 4,50 and it was greater than t-table 2,228 at he level of significance (α) = 0,05 and degree of freedom (df) =11-1=10. It was found that the result of t-test value was greater than t-table (4,50&gt;2,228). It means that hypothesis H0 was rejected and H1 was accepted.&#x0D; This research finds that the use of YouTube tutorial video could improve students’ speaking skill in procedure text especially in students’ fluency and pronunciation. The researcher expexcts that the use of YouTube tutorial video could be used by the teacher as one of the media in teaching and learning process especially in teaching speaking.
Multi-Contextual Analysis of Internet Security Perception and Behavior

Perception has been found to influence thought patterns and human behavior in other academic spheres. However, the impact of perception on technology adoption and use in the field of Information Systems is virtually nonexistent. This study sought to examine the internet security perception of internet users and consequential effect on their online behavior. The study used language (French vs English), as a differentiation tool for the respondents. 101 nationals from different African countries who reside in Ghana were sampled to participate in the study. The study found strong negative security perception, low level of knowledge regarding cyber awareness and a lax attitude in cyber hygiene practices to ensure personal safety when online. Impact of culture on online security perception was not identified, and also respondents were not collectivist in their cultural orientation, a marked shift from long held opinion of strong collectivism in Africa. Implications are also discussed.
Features of Teaching Analytical and Accounting Disciplines in Higher Education Institutions

The article describes the main features of curricula of accounting and analytical orientation and aspects of teaching accounting and analytical disciplines: lectures and practical classes using multimedia technologies, applications for accounting and financial analysis, the organization of distance learning, which requires access to modern software products. also contains specific suggestions for improving the perception of accounting disciplines.
PH Computation to Growth Prediction

Bacterial cellulose is a bio self-assembled organic material with unique features such as great tensile strength, biodegradability, and renewable potential that has made it worthwhile for different fields of industrial development research.Since the past decade, in the field of architecture also, enormous efforts were done to reach the desired guided shape of bacterial cellulose with optimized structural features.However, all these efforts are in their infancy.To reach the adaptive architectural bio-component, we need something beyond static prototyping.Therefore, we investigate the specific type ``Bacterium Glucoacetobacter xylinus(BC)'' cellulose growth procedure by syncing the culture medium (cellulose growth environment) to a virtual stimulating environment to introduce the computational architectural design process based on dynamic biological structures.This research presents the smart design process via the syncing of CAD environment and growth environment to create a framework that provides data analysis that the implementation of its outcomes can revolutionize the bio-digital fabrication process.
Corrigendum to: “Rashba control to minimize circuit cost of quantum Fourier algorithm in ballistic nanowires” [Phys. Lett. A 383 (2019) 1247]

Eq. (11) is a normal Schrödinger's equation with a negative sign [1] and not an adjoint Schrödinger's equation, so there is a misprint in Eq. (11).
Session-Based Graph Attention POI Recommendation Network

Point-of-interest (POI) recommendation which aims at predicting the locations that users may be interested in has attracted wide attentions due to the development of Internet of Things and location-based services. Although collaborative filtering based methods and deep neural network have gain great success in POI recommendation, data sparsity and cold start problem still exist. To this end, this paper proposes session-based graph attention network (SGANet for short) for POI recommendation by making use of regional information. Specifically, we first extract users’ features from the regional history check-in data in session windows. Then, we use graph attention network to learn users’ preferences for both POI and regional POI, respectively. We learn the long-term and short-term preferences of users by fusing the user embedding and POI ancillary information through gate recurrent unit. Finally, we conduct experiments on two real world location-based social network datasets Foursquare and Gowalla to verify the effectiveness of the proposed recommendation model and the experiments results show that SGANet outperformed the compared baseline models in terms of recommendation accuracy, especially in sparse data and cold start scenario.
Research and algorithm design of higher vocational teacher evaluation system based on big data

With the advancement of science and technology, especially the advent of the information age, concepts such as cloud computing, big data, the Internet of Things and mobile Internet have been deeply embedded in people's daily lives. Combining big data technology with the characteristics of higher vocational education and the school's The requirements of teachers' professional development and clarifying that higher vocational colleges have an urgent need for an effective teaching quality(TQ) evaluation system(ES) in today's era. The purpose of this paper is to study the ES and algorithm of higher vocational teachers based on big data. In this paper, the association rules algorithm is used to solve the irrationality and subjectivity of the current TQ evaluation, and a TQ evaluation algorithm based on association rules is proposed to make the TQ ES of higher vocational education institutions rational and efficient. Provide scientific and objective decision-making analysis materials for managers of higher vocational education institutions. The experimental study shows that the evaluation level of teachers with doctoral degrees is good with 14% support and 70% confidence. On the whole, the higher vocational teacher ES proposed in this paper is in line with the current teaching laws in colleges and universities, the evaluation results better reflect the current teaching status, and the reliability of the data is high.
Job Analyses of Earth Science Data Managers: A Survey Validation of Competencies to Inform Curricula in Research Data Management Education

The expansion of research data availability and production has highlighted the need for proper training in data stewardship. Recent years have seen both incentivized and mandated research data management plans enacted by institutions and funding agencies as a way of supporting more open and integrated data-intensive research. Despite the advancement of data management as a practice and profession, those charged with maintaining and curating research data remain understudied. By investigating what and how data management is realized and who is involved, a discussion on the current educational needs, the trajectory of professions, and an assessment of potential educational needs of those entering the workforce can progress. A job analysis survey was developed to validate the tasks identified from two prior interview studies of Earth Science data managers. This survey was distributed to the Federation of Earth Science Information Partners membership and responses validate prior job analyses work as well as provide other educational consideration related to tools and necessary skills to conduct frequent job tasks. The most frequent activities and necessary knowledge, skills, and abilities of current data managers will inform future avenues of inquiry, provide context for current research, and direct research data management education.
THE MAIN DIRECTIONS OF STANDARDIZATION IN MEDICAL INFORMATICS

The application of the medical standard HL7 CDA for the tasks of creating clinical documents and digital signature means suitable for use in a single information space is described. A new specification (FHIR standard) is presented, based on the latest approaches in the field of e-health, but taking into account all the accumulated experience (real needs, successful solutions and typical difficulties). The structure of the electronic medical map (EMC), the conceptual, logical and physical model of the database for the needs of the cardiological service, which is the core of the unified information space of the RSSPMCC, is developed.