Reviewerly/exHarmony huggingface dataset:
Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans.Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image.Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN).The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature.This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express.Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm.The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel.Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed.The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved.Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.
Memory Graph Networks for Explainable Memory-grounded Question Answering

We introduce Episodic Memory QA, the task of answering personal user questions grounded on memory graph (MG), where episodic memories and related entity nodes are connected via relational edges.We create a new benchmark dataset first by generating synthetic memory graphs with simulated attributes, and by composing 100K QA pairs for the generated MG with bootstrapped scripts.To address the unique challenges for the proposed task, we propose Memory Graph Networks (MGN), a novel extension of memory networks to enable dynamic expansion of memory slots through graph traversals, thus able to answer queries in which contexts from multiple linked episodes and external knowledge are required.We then propose the Episodic Memory QA Net with multiple module networks to effectively handle various question types.Empirical results show improvement over the QA baselines in top-k answer prediction accuracy in the proposed task.The proposed model also generates a graph walk path and attention vectors for each predicted answer, providing a natural way to explain its QA reasoning.
PRACTICE OF INTRODUCING LEAN TECHNOLOGIES IN THE UNIVERSITY MANAGEMENT SYSTEM: A PROJECT APPROACH

Within the modern educational space, in the conditions of transformation of the external environment, tough competition obliges universities to build their own development strategies and trajectories of internal changes using effective management approaches and technologies. The purpose of the article is to describe a new managerial practice in the field of higher education, demonstrating the advantage of using project management when introducing changes to the university management system associated with the transition to the so-called «lean manufacturing» (which takes place, for example, in Belgorod State National Research University and in Belgorod State University). The article as well aims at suggesting practical recommendations on the development of a model for a lean design system project. The author focuses on two managerial problems – the one of resisting changes in the implementation of lean manufacturing and the other of lacking consistency in implementing the corresponding changes. There are found out a number of constraints limiting the use of lean technologies in higher education organizations management. The article analyzes the experience of implementing lean technologies in the scientific, educational and managerial activities of higher education institutions on the example of several Russian and foreign universities. It also reveals the essence of the implemented approaches and technologies. There are used the methods of active comparative analysis and theoretical generalization of scientific and practical experience, content analysis of regulatory acts of the Belgorod Region on the lean management principles implementation in executive bodies, as well as strategic development documents of Belgorod State Research University through the interrelation of lean management and the strategy of university development. Based on generalizing the experience of Belgorod State National Research University, a model of a lean system development project is suggested, recommendations are given on structuring the project architecture and on forming a team of changes and a project management system, which may be of interest to regional universities when introducing lean manufacturing and choosing change management methods.
Rhetorical Structures of English-Major Undergraduate Thesis Introduction Chapters

Undergraduate students are required to write a thesis to obtain a degree. One of the most important chapters in the thesis is introduction. Introduction section plays an important role because it describes what the research is all about. To write an introduction chapter, Swales (1990) proposed Create-a-Research-Space (CARS) model, which include three moves, namely establishing a territory, establishing a niche, and occupying the niche. Every move consists of several steps. This study aims to analyze Swales’ CARS model in the introduction chapters of undergraduate theses written by English major students. Furthermore, the lexical and syntactical signals in each move and step are investigated. This research employed discourse analysis, which focuses on how texts are structured. The researchers analyzed 18 introduction chapters of undergraduate theses written by the English major students of Sanata Dharma University who graduated in 2017. In analyzing the corpus, the researcher used top-down analysis. The results showed that Move 1, Establishing a territory, appeared in the undergraduate theses. However, many of the students did not review the previous research. Move 2, Establishing a niche, was presented in three theses. In this stage, many of the students did not fill in the gap about the previous research. Move 3, Occupying the niche, was frequently used by the students. In this phase, the students outlined the purpose of their research. This study also found that the lexical and syntactical signals used in the theses were quite different from Swales’ CARS model.
Scale-Transferrable Pyramid Network for Multi-Scale Ship Detection in Sar Images

Previous ship detection methods for synthetic aperture radar (SAR) images suffer from an extreme variance of ship scale. The problem of large scale variation across ships lies in the heart of ship detection. In this paper, scale-transferrable pyramid network for multi-scale ship detection in SAR images is proposed. We construct a feature pyramid network by lateral connection, and densely connect each feature maps from top to down using scale-transfer layer. Lateral connection injects more semantic information into feature maps with high resolution. Dense scale-transfer connection can expand the resolution of feature maps and explicitly explore information contained in channels. Finally, we can detect multi-scale ships by combining these multi-scale feature maps. Experimental results demonstrate that our network outperforms the state-of-the-art methods.
Pop Up Factory : Collaborative Design in Mixed Rality Interactive live installation for the makeCity festival, 2018 Berlin

This paper examines a novel, integrated and collaborative approach to designand fabrication, enabled through Mixed Reality. In a bespoke fabrication process,the design is controlled and altered by users in holographic space, through acustom, multi-modal interface. Users input is live-streamed and channeled to 3Dmodelling environment,on-demand robotic fabrication and AR-guided assembly.The Holographic Interface is aimed at promoting man-machine collaboration. Abespoke pipeline translates hand gestures and audio into CAD and numericfabrication. This enables non-professional participants engage with a plethora ofnovel technology. The feasibility of Mixed Reality for architectural workflow wastested through an interactive installation for the makeCity Berlin 2018 festival.Participants experienced with on-demand design, fabrication an AR-guidedassembly. This article will discuss the technical measures taken as well as thepotential in using Holographic Interfaces for collaborative design and on-sitefabrication.Please write your abstract here by clicking this paragraph.
Detection of Image Seam Carving Using a Novel Pattern

Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.
Neural Unsupervised Parsing Beyond English

Recently, neural network models which automatically infer syntactic structure from raw text have started to achieve promising results.However, earlier work on unsupervised parsing shows large performance differences between non-neural models trained on corpora in different languages, even for comparable amounts of data.With that in mind, we train instances of the PRPN architecture (Shen et al., 2018a)-one of these unsupervised neural network parsers-for Arabic, Chinese, English, and German.We find that (i) the model strongly outperforms trivial baselines and, thus, acquires at least some parsing ability for all languages; (ii) good hyperparameter values seem to be universal; (iii) how the model benefits from larger training set sizes depends on the corpus, with the model achieving the largest performance gains when increasing the number of sentences from 2,500 to 12,500 for English.In addition, we show that, by sharing parameters between the related languages German and English, we can improve the model's unsupervised parsing F1 score by up to 4% in the low-resource setting.
Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task

The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al. (2019).For the MT track, we submitted Transformerbased Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora.Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.
A method for feature matching in images using descriptor structures

A method of feature matching in images using descriptor structures is considered in the work. The descriptors in the developed method can be any known solutions in the field of computer vision. However, inaccuracies can occur when matching image pairs. It is proposed that descriptor structures should be compared to eliminate the “outliers”. Descriptor structures are described using graphs. An Umeyama method is used to find matching features using descriptor structures. The method is based on the decomposition of matrices into eigenvalues and eigenvectors for weighted graph matching problems. Thus, matches are based on the descriptor at the initial stage. Two graphs are then constructed for each image based on the resulting sets of mapped features. The weights of the graph are distances between all image features, calculated using the Gauss function. Weight matrices are built for each graph. Matrix decomposition is carried out into eigenvalues and eigenvectors. The resulting matrix is calculated based on the Umeyama method and correct matches are found. Thus, false matches are excluded from the set of matches obtained using descriptors by comparing structures. The method is invariant to zoom and in-plane image rotation. The method leads to correct results only if the number of correct matches is greater than the number of false matches. The complexity of the developed algorithm is proportional to the number of matches found.
Securing Smart Offices Through an Intelligent and Multi-device Continuous Authentication System

Smart Offices promise the improvement of working conditions in terms of efficiency, productivity and facility. However, new cybersecurity challenges arise associated with the new capabilities of Smart Cities. One of the key challenges is the utilisation of continuous and non-invasive authentication mechanisms since traditional authentication methods have important limitations. Thus, to cover these limitations, the main contribution of this paper is the design and deployment of a continuous and intelligent authentication architecture oriented to Smart Offices. The architecture is oriented to the cloud computing paradigm and considers Machine Learning techniques to authenticate users according to their behaviours. Some experiments demonstrated the suitability of the proposed solution when recognising and authenticating different users using a classification algorithm.
Automatic Classification of Academic and Vocational Guidance Questions using Multiclass Neural Network

The educational and professional orientation is an essential phase for each student to succeed in his life and his curriculum.In this context, it is very important to take into account the interests, occupations, skills, and the type of each student's personalities to make the right choice of training and to build a solid professional outline.This article deals with the problematic of educational and vocational orientation and we have developed a model for automatic classification of orientation questions."E-Orientation Data" is a machine learning method based on John L. Holland's Theory of RIASEC typology that uses a multiclass neural network algorithm.This model allows us to classify the questions of academic and professional orientation according to their four categories, thus allows automatic generation of questions in this area.This model can serve E-Orientation practitioners and researchers for further research as the algorithm gives us good results.
Bengali Information Retrieval System(BIRS)

Information Retrieval System is an effective process that helps a user to trace relevant information by Natural Language Processing (NLP).In this research paper, we have presented present an algorithmic Information Retrieval System(BIRS) based on information and the system is significant mathematically and statistically.This paper is demonstrated by two algorithms for finding out the lemmatization of Bengali words such as Trie and Dictionary Based Search by Removing Affix (DBSRA) as well as compared with Edit Distance for the exact lemmatization.We have presented the Bengali Anaphora resolution system using the Hobbs' algorithm to get the correct expression of information.As the actions of questions answering algorithms, the TF-IDF and Cosine Similarity are developed to find out the accurate answer from the documents.In this study, we have introduced a Bengali Language Toolkit (BLTK) and Bengali Language Expression (BRE) that make the easiest implication of our task.We have also developed Bengali root word's corpus, synonym word's corpus, stop word's corpus and gathered 672 articles from the popular Bengali newspapers 'The Daily Prothom Alo' which is our inserted information.For testing this system, we have created 19335 questions from the introduced information and got 97.22% accurate answer.
An Attempt to Read Network Traffic with Doc2vec

Detecting new malicious traffic is a challenging task. There are many behavior-based detection methods which extract the features of malicious traffic. However, many previous methods require knowledge of how to extract feature vectors. If attackers modify the attack techniques, these previous methods may have to extract new feature representation to detect them. To address this problem, neural networks can be applied to perform feature learning. Doc2vec is one of these models that learn fixed-length feature representation from variable-length documents and has been applied to proxy logs. However, some attackers still use protocols other than http or https. In this paper, we extend the previous method to a generic detection method which supports any protocol. The key idea of this research is reading network packets as a natural language. In our method, a protocol analyzer reads network packets, and summarizes the traffic. Our method extracts the feature representation from the summary with Doc2vec. We apply several classifiers to the automatically extracted feature representation, and classify traffic into benign and malicious traffic. In the fundamental experiment, the best F-measure achieves 0.98 in the timeline analysis and 0.97 in the cross-dataset validation. Furthermore, we generate imbalanced datasets which simulate actual network traffic. In the practical experiment, the best F-measure achieves 0.82 in the timeline analysis and 0.73 in the cross-dataset validation.
Dimension Reduction Using Quantum Wavelet Transform on a High-Performance Reconfigurable Computer

The high resolution of multidimensional space-time measurements and enormity of data readout counts in applications such as particle tracking in high-energy physics (HEP) is becoming nowadays a major challenge. In this work, we propose combining dimension reduction techniques with quantum information processing for application in domains that generate large volumes of data such as HEP. More specifically, we propose using quantum wavelet transform (QWT) to reduce the dimensionality of high spatial resolution data. The quantum wavelet transform takes advantage of the principles of quantum mechanics to achieve reductions in computation time while processing exponentially larger amount of information. We develop simpler and optimized emulation architectures than what has been previously reported, to perform quantum wavelet transform on high-resolution data. We also implement the inverse quantum wavelet transform (IQWT) to accurately reconstruct the data without any losses. The algorithms are prototyped on an FPGA-based quantum emulator that supports double-precision floating-point computations. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. The experimental results show that the proposed concepts represent a feasible approach to reducing dimensionality of high spatial resolution data generated by applications such as particle tracking in high-energy physics.
The influence of using Sparkol videoscribe’s learning media to increase science literacy on pressure concept

This research related to determining the influence of using Sparkol videoscribe’s learning media to increase students’ science literacy. The research was conducted on one of junior high schools at Cilegon City in years of academy 2017/2018. The research method was Pre-Experimental Designs (non-designs), with One Group Pretest-Posttest Design. The sample of this research consisted of 30 students from 2nd grade. The instrument used was science literacy tests. The results of this study showed an increase in students’ science literacy based on the pre-test with average score of 10.33 and the post-test with average score was 16.63. N-gain value (increase in value after learning) students’ literacy skills showed the significant result on 0.74 which is high category, with the results of paired t-test obtained significance value of 0.00 <0.05, H0 is rejected. Based on the results of the study, it can be concluded that the use of Sparkol videoscribe’s learning media influences the increase of science literacy.