Reviewerly/exHarmony huggingface dataset:
Paragraph Coherence Detection Model Based on Recurrent Neural Networks

Paragraph coherence detection which is to evaluate the semantic correlation and structure coherence between paragraphs in text. Most of previous studies are based on English text, and only few studies are based on Chinese composition. The Chinese compositions emphasize rich expressions and complex transformed sentence structures, these cause the difficulties in Chinese text level analysis. A good paragraph coherence is a reflection of a qualified composition, which it is also important criterion for automatic scoring for compositions. In this paper, we focus on the paragraph coherence detection based on Chinese composition. We propose a paragraph coherence detection model based on recurrent neural networks (RNNs) to detect the coherence between two paragraphs, and then we extended the model to the whole composition. The experimental results show that our model achieves remarkable results with 80% accuracy, for automatically detecting the paragraph coherence of Chinese composition.
INNOVATIVE ONLINE EXPERIMENTS FOR STEM EDUCATION IN CHINA: DESIGN AND DEVELOPMENT AND EVALUATION OF STUDENTS’ LEARNING

INNOVATIVE ONLINE EXPERIMENTS FOR STEM EDUCATION IN CHINA: DESIGN AND DEVELOPMENT AND EVALUATION OF STUDENTS’ LEARNING
UTILIZATION OF EDUCATIONAL HYBRID ROCKET IN AN OUTREACH PROGRAM

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 2261-2267ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0612Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
MobileGyro: Android Application for Bluetooth Gyroscope Tracking With Potential for Impact in Rehabilitative Processes

According to the CDC, 795,000 Americans are diagnosed with a stroke each year. Out of the all of the technology used to track the gait of a stroke patient, the Inertial Measurement Unit (IMU) is growing in recognition and use. These are often used as wearable sensors for rehabilitation, with their small size and cost effectiveness. The purpose of this research is to detail the innovation of an android application with the ability to receive IMU data and present it through medically significant visual displays. The data from a device is received via bluetooth, and is transmitted in terms of angles roll (x axis), pitch (y axis) and yaw (z axis). The application displays this data to allow the user to track the position of the IMU in live time. The medical professional has the ability to input a desired set of angles, and track the proximity of a connected device to the target position. The data is presented visually through the coronal, transverse &amp; sagittal plane. When roll and pitch angles from the app were compared to angular values measured on a protractor, the average difference was +/− 0.366° and +/− 0.436°, in roll and pitch respectively. Along with the average accuracy being less than a 0.5° difference, the application has the ability to connect and track 3 IMU devices simultaneously. The data can be stored and exported. The greatest limitation is angular presentation latency. This creation has the potential to catalyze creation of an app that can track a high number of devices simultaneously, with high accuracy and low signal latency. The ability to track and position wearable IMU sensors using a smartphone or tablet offers an exciting future in rehabilitation, especially for recovering stroke patients.
An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning.

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modeled as a spatially distributed system [e.g., a partial differential equation (PDE)], but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified—to “emerge from”—the data mining process. We will first validate this “emergent space” reconstruction for time series sampled without space labels in known PDEs; this brings up the issue of observability of physical space from temporal observation data and the transition from spatially resolved to lumped (order-parameter-based) representations by tuning the scale of the data mining kernels. We will then present actual emergent space “discovery” illustrations. Our illustrative examples include chimera states (states of coexisting coherent and incoherent dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics, arising in partial differential equations and/or in heterogeneous networks. We also discuss how data-driven “spatial” coordinates can be extracted in ways invariant to the nature of the measuring instrument. Such gauge-invariant data mining can go beyond the fusion of heterogeneous observations of the same system, to the possible matching of apparently different systems.
Universal Framework of Seals Erasing with Generative Adversarial Network

In last decades, methods based on deep learning has dominated many application fields, such as image inpainting, image super high-resolution and image denoising. In the Optical Character Recognition (OCR), we need to erase all seals due to some special reason like improving the performance of invoices and receipts recognition. However, owing to the diversity of the shape, color and position of the seals, it is very difficult to erase all seals without affecting the original information of the invoices and receipts. At present, there are two common methods to erase seals, one is from the perspective of computer graphics, the other is from the perspective of computer software, like PhotoShop. Nevertheless, the common weakness is the poor robustness and inefficient. So, In this paper, We propose an end-to-end network for erasing seals, and we have four contributions. (1) We first propose a universal framework to do the seals erasing by using Generative Adversarial Network (GAN). (2) Training the seals erasing network by the images synthesised by scripts and seals crawled on the website, instead of spending high cost to collect images by ourselves. (3) In terms of speed, due to using end-to-end predict script, using our method to erase all seals on the 512 * 512 RGB image with padding is only 800 ms, much faster than the traditional methods. (4) There is also improvement on the invoices and receipts with the same parameter on the Convolution Recurrent Neural Network (CRNN), the average accuracy of the sequences by using our method is 91.3% higher than the original images is 89.7% on the 800 quota invoice test images.
A Novel Multi-image Encryption Algorithm Based on the RDFrMT and Cascaded Phase Retrieval

In this paper, a multi-image encryption method is presented by using the Random Discrete Fractional Mellin Transform (RDFrMT) and the cascaded phase retrieval algorithm (PRA). Firstly, we define the RDFrMT and discuss its properties including nonlinear, multi-parameter, and randomness. Then, based on the RDFrMT, a cascaded multiple-image encryption scheme is proposed. In our algorithm, an image generated by logistic map is transformed to the RDFrMT domain, and the resulting image is fed into the PRA. This nonlinear process makes our cryptosystem robust for potential attacks and ensures its security. Thereafter, the output is cut based on the Hamming distance and the keys are related to multiple input images, which makes our cryptosystem has distinct security level for different images. We repeat the above steps and get a cascaded encryption scheme. Numerical simulations demonstrate the security and feasibility of the proposed scheme.
THE EFFECTIVENESS OF CUBING AND PEER REVIEWING TECHNIQUES IN TEACHING WRITING DESCRIPTIVE TEXT TO THE ELEVENTH GRADE STUDENTS AT SMA NEGERI 1 BANYUASIN I

Abstract This study aimed to find out: (1) whether or not cubing technique was significantly effective in improving students’ writing achievement; (2) whether or not peer reviewing technique was significantly effective in improving students’ writing achievement, and (3) whether or not there were significant differences in writing achievements among the students of SMA Negeri 1 Banyuasin I who were taught through the techniques of cubing and peer reviewing. The method of the study was randomized two-group pretest-posttest design. Data were collected through pretest and posttest of writing descriptive text. The results of pretest and posttest were analyzed by using t-test. The population was all the eleventh grade students of SMA Negeri 1 Banyuasin I in the academic year 2017/2018. The sampling technique used was simple random sampling. The samples were 90 eleventh grade students of SMA Negeri 1 Banyuasin I. The results showed that: (1) Cubing technique was significantly effective in improving students’ writing achievement; (2) Peer reviewing technique was significantly effective in improving students’ writing achievement; (3) There were significant differences in writing achievement among the students of SMA Negeri 1 Banyuasin I who were taught through the techniques of cubing and peer reviewing. Keywords: cubing, peer reviewing, writing descriptive text
Bandwidth Profiling Extensions for MUD

Manufacturer Usage Descriptions (MUD) are a means by which devices canestablish expectations about how they are intended to behave, and howthe network should treat them. Earlier work focused on access control.This draft specifies a means by which manufacturers can express todeployments what form of bandwidth profile devices are expected tohave with respect to specific services.
Laser networks for reservoir computing: How can we optimize the performance?

We explore fundamental aspects of reservoir computing with laser networks from the perspective of nonlinear dynamics. The role of the effective phase-space dimension as well as the information injection routine are investigated with numerical methods.
Switching and partially switching the hypercube while maintaining perfect state transfer

A graph is said to exhibit perfect state transfer (PST) if one of its corresponding Hamiltonian matrices, which are based on the vertex-edge structure of the graph, gives rise to PST in a quantum information-theoretic context, namely with respect to inter-qubit interactions of a quantum system. We perform various perturbations to the hypercube graph---a graph that is known to exhibit PST---to create graphs that maintain many of the same properties of the hypercube, including PST as well as the distance for which PST occurs. We show that the sensitivity with respect to readout time errors remains unaffected for the vertices involved in PST. We give motivation for when these perturbations may be physically desirable or even necessary.
How visual working memory exploits environmental structure

How do people use the structure of items when storing them in visual memory? An iterated learning experiment reveals that people expect visual scenes to be hierarchically organized into self-similar groups, and that this expectation biases visual memory. A series of subsequent experiments on the error structure of recall reveals that the inferred hierarchical structure not only biases what people remember, but also influences how people forget: memory errors can be attributed to different nodes in the hierarchical parse by virtue of the relationships between errors for different objects. Together, these results show not how people use environmental structure to remember displays, but also that the inferred scene hierarchy influences the encoded data structure in memory.
A Study of the Quasi-affix‘秀’

The word ‘秀’ originally derived from ‘talk show(脱口秀)’ in English. This study is about analyzing the characteristics of quasi-affix ‘秀’ and ‘X秀’. The results of the analysis are as follows.<BR> First, we found that ‘秀’ is grammaticalized with in meaning but refers to show and the degree of grammaticalization is measured to be weak. Second, When a quasi-affix ‘秀’ composes a word, ‘秀’ is located at the back of the word. It is confirmed that it has the structure of ‘X秀’. Third, ‘秀’ is a quasi-suffix which considered to have a high vocabulary productivity.<BR> In conclusion, ‘秀’ will have a rich vocabulary productivity and will continue to derive words. Through this study, we will be able to understand the word-formation and format rules of ‘X秀’, it could be a word that can express chinese’s various culture phenomena.
Co-creation Design Mechanism for the Online Entrepreneur Community Building

In recent years, Innovation and entrepreneurship have been a heatedly discussed topic and become a development philosophy of many cities and regions. Maker spaces and incubators are blossoming all over the world. However, many obstacles emerge from the mechanism, such as lacking vitality, limiting resource sharing and knowledge flow, hindering interdisciplinary collaboration. This paper proposed a hypothesis that online co-creation platform can help bridge these gaps. Then we use qualitative research methods to discuss the issues. A preliminary conclusion was drawn that the online co-creation design method can link stakeholders, establish sharing mechanisms and promote entrepreneurial communities building. This study proposed that an online entrepreneurial community with a co-creation approach has a good effect on breaking through information barriers and improving the full flow of knowledge, technology, human resources, and investment. Finally, based on all the research findings and analysis, a set of design strategies are proposed that the online entrepreneurial community should follow.
Surprisal and Interference Effects of Case Markers in

Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi. Our first hypothesis is that Hindi tends to optimize for processing efficiency at both lexical and syntactic levels. We quantify the role of case markers in this process. For the task of predicting the reference sentence occurring in a corpus (amidst meaning-equivalent grammatical variants) using a machine learning model, surprisal estimates from an artificial version of the language (i.e., Hindi without any case markers) result in lower prediction accuracy compared to natural Hindi. Our second hypothesis is that Hindi tends to minimize interference due to case markers while ordering preverbal constituents. We show that Hindi tends to avoid placing next to each other constituents whose heads are marked by identical case inflections. Our findings adhere to PDC assumptions and we discuss their implications for language production, learning and universals.
Strategic Steps Towards The Open University GraduatesDevelopment Of The Competitiveness Of A Solution To FaceIndustrial Revolution 4.0 Era

Demanding needs of today's workforce and the future has begun to see thechanges as a result of the industrial revolution to four (i4.0). The industrialrevolution to 4 have the characteristics that blend technology with society andthe human body, robotics, quantum computing, biotechnology, 3D printing,vehicle automation, internet, virtual and physical systems cooperate globally.Currently, UT intensify the use of information technology (IT) to improve theacademic quality of support services mainly in the study and evaluation oflearning outcomes. It also carried out an increase in the accessibility ofstudents to UT teaching materials online through a digital library and ebookstore.Therefore, in 2008, UT to improve service by offering face-to-facetutorials, online tutorials, e-book store to all students and provide freecatalogs to all students who register with an online exam at UPBJJ-UT. FromUT diverse range of services and has been based IT are expected to bedirectly proportional to the competitiveness that has been generated mainlylocated in this i4.0 era, but in fact the competitiveness of some of the alumnistill not optimally adapted to the development of science and technology,especially alumni UPBJJ corners. These challenges must be anticipatedquickly is by taking a strategic step towards the construction of an openuniversity graduates competitiveness, namely: 1. Revise the curriculum tounderstand the needs of people in the digital era, 2. Stay focused oninnovation in teaching and learning, 3.Keywords: strategic, competitiveness, industrial revolution
Effect of head movement using HMD on visually induced motion sickness

Our previous reports indicate that the severity of visually induced motion sickness, (VIMS), can be predicted, to some extent, by a total amount of visual rotation, such as yaw, pitch, and roll, in a limited period in a consistent viewing condition. The visual rotation, however, is not uncertain as to whether it is in terms of optic flow, a pattern of apparent motion of a visual scene caused by relative motion between an observer and a scene, or retinal flow. To clarify this, we conducted an experiment, in which five-minute CG moving image with a first person point of view was presented three times with five-minute break between them using head-mounted display (Oculus VR, Oculus Rift cv1). There were two conditions: (i) Head-movement condition, in which observers move their heads so that visual target moving right/left or upward/downward relative to the display was aligned with display-fixed visual alignment, and (ii) Head-stationary condition, in which observers fixed their heads so that visual target being stationary relative to the display was, again, aligned with display-fixed visual alignment. Observers answered Simulator Sickness Questionnaire, (SSQ), four times: just before viewing the moving image, at the first and second break, and just after finishing the viewing. The results showed that SSQ total score significantly increase every measurement time (p < 0.01), while there’s no significant difference between the conditions (p < 0.1). This suggests that the severity of VIMS can be predicted in terms of optic flow, but not retinal flow.
THE MANIFESTATION OF TEACHING AND LEARNING ASSESSMENT IN THE STRATEGIC DOCUMENTS OF SECONDARY SCHOOLS

THE MANIFESTATION OF TEACHING AND LEARNING ASSESSMENT IN THE STRATEGIC DOCUMENTS OF SECONDARY SCHOOLS
System for Automating the Search for Coloured Cells in Images

The strategy of Industry 4.0 brings numerous tasks for automating activities until now performed by humans. Those activities mainly include tasks based on image analysis and computer vision. Our workplace deals with many tasks of this type. The paper presents results of a specific task - automating the search for coloured cells in images. The task takes its grounds from cooperation in biochemical research projects. Such research requires analysing a huge amount of cell samples, some of which are variously coloured in order to determine their ratio on the total number of cells. This analysis has been done by manual counting so far, which is very time-consuming and whose accuracy has not been verified. Basic used algorithms are image segmentation, colour thresholding, and active contour analysis. Several methods have been tested with achieved detection accuracy of 414 cells out of 470. Further solution will deal with the division of the remaining cells. This process will take advantage of an artificial intelligence method and machine learning, most likely a neural network.
Temporal Super-Pixel Based Convolutional Neural Network (TS-CNN) for Human Activity Recognition in Unconstrained Videos

Human activity recognition and video analysis is quite an interesting field and is relevant to a number of research areas. A plethora of applications like (active) video surveillance systems, human-computer interaction, medical analysis, sports (video) interpretation, and video retrieval for content-based search engines depend on extensive video understanding. To determine human actions in highly controlled environments is quite simple and solved; however, this task still remains to be a challenge in an unconstrained environment. In this paper, a temporal super-pixel based convolutional neural network (TS-CNN) is proposed to recognize human activity for enabling real-time monitoring of patients, children, and elderly persons in an unconstrained environment. For every video segment, temporal super-pixels are extracted and output of these pathways are combined with CNN architecture to correctly recognize the human activity. The performance of the proposed method (measured by metrics like accuracy and confusion matrix) is compared with the existing model.
Verification Learning for Robust Visual Object Tracking

Recent years have witnessed the rapid advances of Siamese Networks in visual tracking. Nevertheless, most Siamese-based trackers have poor generalization capability and merely focus on local region, leading to fail to handle hard cases such as out of view, occlusion and so on. In this paper, we employ verification learning method into Siamese-based trackers, by taking advantage of the accurate tracking performance of Siamese-based trackers and leveraging the fast speed of yolov3 network. Firstly, we adopt a Siamese-based tracker to locate the target and output tracking results. Then, a verifier is introduced to determine whether it is a failure and generate the validation result. Simultaneously, feedback mechanism will be stimulated, continuously adjusting and robustifying tracking results. The experiments are performed on VOT2018 benchmark, showing that our method achieves state-of-the-art performance.
Real Time Hand Gesture Recognition for Differently-Abled Using Deep Learning

In the field of Computer science, Gesture recognition is an alternative user interface for providing real-time data to a computer. This paper focuses on a system which provides the communication between the vocally impaired people and the normal people of the society by translating Sign Language system into Text and Speech in English. The system can recognize one handed sign representation of the standard alphabets (A–Z) and numeric values (0–9). In this paper we used OpenCV for gesture capture through web camera and deep learning algorithms to train and recognize the gesture. The system is very efficient and consistent with respect to output.
Multi-task audio-driven facial animation

We propose an effective method to solve multiple characters audio-driven facial animation (ADFA) problem in an end-to-end fashion via deep neural network. In this paper each character's ADFA considered as a single task, and our goal is to solve ADFA problem in multi-task setting. To this end, we present MulTaNet for multi-task audio-driven facial animation (MTADFA), which learns a cross-task unified feature mapping from audio-to-vertex that capture shared information across multiple related tasks, while try to find within-task prediction network encoding character-dependent topological information. Extensive experiments indicate that MulTaNet generates more natural-looking and stable facial animation, meanwhile shows better generalization capacity to unseen languages compare to previous approaches.