Reviewerly/exHarmony huggingface dataset:
Research on the Persuasion Design of the Interactive Interface of the Online Courses of Middle School English

To analyze the user behavior and the corresponding interface in the process of online course of middle school English learning, and summarize the corresponding interface design problems. At the same time, this paper puts forward the design strategy of online English course interface with the theory of Persuasive Design, which helps users to achieve continuous and efficient learning behavior. Based on the previous research, this paper starts with the prompt of the three elements of persuasion design, and combines the corresponding curriculum interface with the characteristic users, this paper puts forward some strategies to design the interface of English online courses, such as competition, individuation, clarity and feedback.
An Improved Traffic Sign Recognition Algorithm Based on Deep Learning

Aiming at the disadvantages of convolutional neural network in traffic sign recognition, such as poor real-time performance and high hardware requirements, an improved network based on lightweight convolutional neural network with real-time performance and high precision is proposed. On the one hand, depthwise separable convolution and activation function Mish are introduced to speed up the network training and recognition speed and reduce the requirements for hardware devices; on the other hand, through the improvement of network architecture and level, the size and number of convolution cores are reasonably changed to enhance the expression and transmission of image features. The experimental results on the traffic sign dataset of Belgium TSC show that the improved network significantly improves the network training speed, and the recognition accuracy is slightly higher than that of the original network, which verifies the effectiveness of the improved method. Compared with other models, this model can complete the task of traffic sign recognition more quickly and accurately, which verifies the feasibility of this method.
Three-dimensional Orderings, Text Representation, and Eye Fixation

The internal representation of a simple text can be analyzed through the construction process or by means of task solving strategies, if hypothetical. In the case of memory scanning, the scanning process is known and requires a listing of elements as internal representation. Potts argues that subjects arrange the terms of the orderings along an internal interval scale and that the greater the distance separating those two terms on the scale the easier it is to compare them. With the interpretation, linear orderings and set inclusion relations are striking examples of how information is integrated. In case of “direct comparison” and “after comparison” the number of fixations is equal. In case of “before comparison” the number of fixations is increased, whereas the number of fixations is decreased in case of “during comparison”. The reduced reaction time in case of large letter or element remoteness might be caused by the reduced number of processing fixation found during comparison.
IMPACT OF BLOCK CHAIN IN COMMUNICATION AND NETWORK

A block chain network is a technical infrastructure that provides ledger and smart contract (chain code) services to applications. Primarily, smart contracts are used to generate transactions which are subsequently distributed to every peer node in the network where they are immutably recorded on their copy of the ledger. The users of applications might be end users using client applications or block chain network administrators. First and foremost, block chain is a public electronic ledger built around a P2P system that can be openly shared among disparate users to create an unchangeable record of transactions, each time-stamped and linked to the previous one. Every time a set of transactions is added, that data becomes another block in the chain.
Lossless compression method for ultraspectral sounder data based on key information extraction and spectral–spatial prediction

Given the unprecedented size of ultraspectral sounder data, there is a special process of radiance thinning in assimilating this data to reduce the data volume with minimal loss of atmospheric information. Considering the potential correlation between the selected data by radiance thinning and the unselected data, a lossless compression method for ultraspectral sounder data is proposed based on key information extraction and spatial–spectral prediction. Sensitive channels are first selected by stepwise iteration based on information entropy to maintain critical atmospheric information, and then auxiliary channels are further selected based on information content and correlation constraints to facilitate prediction. All of the selected channels are spatially thinned to generate key information, which is then used to predict original ultaspectral sounder data by spatially bicubic interpolation and spectrally sparse reconstruction. The residual errors are processed by the least-squares linear prediction to further reduce data redundancy. Together with the key information, the final residual errors are then fed into a range coder after positive mapping and histogram packing. Experimental results with IASI-L1C data show that the proposed method achieves an average compression ratio of 2.68, which is 4.7% higher than that of the typical methods, including JPEG-LS, JPEG-2000, M-CALIC, CCSDS-122.0, CCDS-123.0, and HEVC.
Grave Artifacts in Image Inpainting: Investigating the Causes and Untangling the Factors

Image Inpainting, the task of recovering missing information in an image, is of great practical interest to users who wish to recover damaged photographs or remove unwanted parts of images. Advances in Convolutional Neural Networks and Adversarial Networks have produced large gains in this problem. Nevertheless, Inpainting algorithms may fail to recover a decent image, and even produce jarring artifacts. In this work, we seek to uncover what causes such issues. We investigate the relationship between the artifacts and the type of degradation mask, the size of the mask, and the algorithms used. To that end, we employ rectangular, free-form and segmentation-based degradation masks, of varying sizes. We find that large, continuous regions of missing information are often to blame for the most egregious failures, and not really how complex the shape of the region is. Moreover, we observe that no algorithm is completely immune to these failure modes.
Smart Attendance Recording System using RFID and e-Certificate using QR Code-based Digital Signature

Nowadays, the development of Information Technology is growing very rapidly. The term smart is widely used in the field of Information Technology as a smart system such as a smart library, smart campus. There are many components of technology associated with smart campuses, one of which is the Internet of Things. RFID is one of the Internet of Things solutions that will have a positive impact on universities. This paper will discuss how to implement a smart attendance recording system using RFID and integrate the e-Certificate system for organizing activities at Maranatha Christian University, by a QR Code-based digital signature as a guarantee of the authenticity of the e-Certificate generated.
An Improved Audio Based Key Frame Detection Model for Movie Summarization

In the recent scenario, the amount of multimedia content mainly videos is increasing at a high rate. This makes the browsing, retrieval and delivery of videos a very slow and difficult task. Video summarization helps in faster browsing and content indexing of large videos by producing an abstract overview of the whole video. Movie summarization condenses a full length movie into a summary which includes the relevant contents in the movie. The proposed framework implements a movie summarization method based on the audio and video features of a movie. The method comprises of three phases. In the pre-processing phase, audio and frames of the video are extracted and stored. Next in the audio feature analysis phase, audio data having high frequency value are extracted. Three techniques are proposed for audio frame analyses which are high frequency method (HFM), Low frequency method (LFM) and threshold frequency method (TFM). In summary generation phase, key frames are extracted which is further refined in the post processing phase. The experimental results indicate that in majority of the cases HFM achieves better performance than LFM and TFM in both qualitative and quantitative analysis.
Impact of Text Length for Information Retrieval Tasks based on Probabilistic Topics

This work is supported by the project KnowledgeSpaces with reference PID2020-118274RB-I00, financed by the Spanish Ministry of Science and Innovation.
A complete robust control network based on skewed temporal logic

The robust control network for nonlinear large-scale systems with parametric uncertainties also considers the uncertain robust stabilization problem for controlled networks. In heterogeneous populations, hybrid regression models are the most important statistical analysis tools. To aim of the study is to conduct a more in-depth analysis of the existing completive robust control networks relying on biased temporal logic. Compared with the symmetric distribution, the skewed distribution can obtain accurate and effective information. Therefore, a time-series logic model under skewed distribution is proposed. The temporal logic under skew state is applied to describe the normative language of fuzzy systems. Firstly, the mixed nonlinear regression model under skewed distribution data is introduced to test whether the temporal logic formula can be realized under the skew state. Secondly, through the method of reduction, the control flow interval logic CFITL is studied, and the time series logic sequence is used to describe the measurement output loss. The sufficient conditions for the control network system to satisfy the exponential stability and H ∞ performance index are given. The linear matrix inequality obtains the completeness control network to be designed, and the effectiveness of the proposed method is verified by stochastic simulation experiments. Finally, the method is verified to be practical and feasible based on actual data. The maximum recognition rates of nearest neighbor classification, nearest subspace classification and biased distribution temporal logic classification reached 0.9019, 0.9622 and 0.9304, respectively.
Deep Recommendation Model Based on Local Attention and GRU

The recommendation systems are dedicated to providing people with valuable information by information filtering. Although the recommendation models based on matrix decomposition are widely used, they perform poorly in the case of sparse data. The review-based recommendation models use reviews to extract user preferences and item features which can alleviate the data sparsity problem, but the improvement of recommendation quality is limited because these models didn't consider the weights of words or phrases in the reviews. This paper proposes a Deep recommendation model based on Local Attention and GRU (DLAG) for reviews level explanation. In DLAG, we first use the local attention to assign the weight to a word or phrase in the reviews. Then, the convolutional neural networks (CNNs) extract complex features after local attention. Finally, the bidirectional gated recurrent unit (Bi-GRU) can extract deep levels non-linear features from user's reviews and item's reviews, and generate latent hidden vectors. Multi-layer perceptron training is used to predict user's rating for an item. This model alleviates the problem of data sparseness by weighting words in reviews and extracting serializable features. Through training on three different datasets, we found that DLAG achieves better performances than others models.
Preparedness of Russian Schools, Teachers and Students to Distance Teaching

39% of parents consider distance learning as holidays. Nearly half of students (49%) share the same point of view. Moreover, 62% of students and 56% of their families do not consider that the online format can be considered as a full-fledged alternative to live classroom studies, which was revealed by the survey carried out by Maximum Education Co. Schools must be ready for that they will have to study again the material which was taught online during the transition to customary forms of education.
INCREASING STUDENTS’ VOCABULARY BY USING MIND MAPPING TECHNIQUE AT SMP N 2 BITUNG

The purpose of the study was to find out whether using mind mapping technique can increase students’ vocabulary or not. It was conducted at SMP N 2 Bitung. The subject in this study was Class VII-4, academic year 2017/2018, which consisted of 35 students. This research was a pre-experimental design with one group pre-test and post-test. The data were analyzed statistically by comparing the mean score of the tests. The findings showed that the students' score in posttest were higher than the pre-test. The mean score of post-test (T2) was 82, and the mean score of the pre-test (T1) was 49. This indicated that the use of mind mapping technique increased students’ vocabulary.
ANALYSIS OF WOMEN SAFETY USING MACHINE LEARNING ON TWEETS

Ladies and young ladies have been encountering a ton of viciousness and badgering in broad daylight places in different urban communities beginning from following and leading to abuse harassment or abuse assault. In this we basically focuses on the job of online media in advancing the well being of ladies in Indian urban areas with exceptional reference to the job of web-based media sites and applications including Twitter stage Facebook and Instagram. This also additionally centers around how an awareness of certain expectations on piece of Indian culture can be fostered the normal Indian individuals so we should zero in on the wellbeing of ladies encompassing them. Tweets on Twitter which normally contains pictures and message and furthermore composed messages and statements which center around the wellbeing of ladies in Indian urban areas can be utilized to peruse a message among the Indian Youth Culture and instruct individuals to make a severe move and rebuff the individuals who irritate the ladies. Twitter and other Twitter handles which incorporate hash label messages that are broadly spread across the entire globe sir as a stage for ladies to communicate their perspectives regarding how they feelwhile we go out for work or travel in a public vehicle and what is the condition of their psyche when they are encircled by obscure men and if these ladies have a sense of security?
Modeling visual estimation of the centers of symmetric distributions

Estimating the summary statistics of arrays of items is a key visual operation (see Jacoby, Kamke & Mattingley, JEP:HPP, 2013). In previous work (Ota et al., VSS, 2020), we presented human observers with 9-point samples (points on a computer screen) drawn from three families of symmetric population density functions (Gaussian, Laplace, and Uniform) and asked them to estimate the center (axis of symmetry) of the population given only the sample and knowledge of distributional family from which it had been drawn. We tested whether observers estimate the center differently if they know that the points are drawn from, for example, a Uniform. We found that the visual system treated samples from three different populations differently but that no obvious normative criterion (e.g., minimum variance) captured what observers had done. Here we describe a process model intended to capture human performance for all three distributions. We propose that the center estimate is a weighted linear combination of the nine sample points but that the weights assigned to each point depend on two factors. The first is a global agreement of each sample point with the others (a measure based on likelihood) and the second depends upon the clusters to which the point belongs. Intuitively, points within a cluster are assigned more weight in estimation. These two steps can be interpreted as two opposite constraints the visual system has to optimize, the integration of the points (likelihood) and their segmentation (clusters). We show that this two-step model can account for observed quantitative differences in performance with the three distributions. These results illustrate that our visual system is sensitive to the distributional family when estimating the summary statistics of a sample drawn from the family, and that the center estimates of symmetric distributions can be described by a single model of perceptual organization.
Scaling by Optimising: Modularisation of Data Curation Services in Growing Organisations

After a century of theorising and applying management practices, we are in the middle of entering a new stage in management science: digital management. The management of digital data submerges in traditional functions of management and, at the same time, continues to recreate viable solutions and conceptualisations in its established fields, e.g. research data management. Yet, one can observe bilateral synergies and mutual enrichment of traditional and data management practices in all fields. The paper at hand addresses a case in point, in which new and old management practices amalgamate to meet a steadily, in part characterised by leaps and bounds, increasing demand of data curation services in academic institutions. The idea of modularisation, as known from software engineering, is applied to data curation workflows so that economies of scale and scope can be used. While scaling refers to both management science and data science, optimising is understood in the traditional managerial sense, that is, with respect to the cost function. By means of a situation analysis describing how data curation services were applied from one department to the entire institution and an analysis of the factors of influence, a method of modularisation is outlined that converges to an optimal state of curation workflows.
A workbench for corpus linguistic discourse analysis

In this paper, we introduce the Swiss-AL workbench, an online tool for corpus linguistic discourse analysis. The workbench enables the analysis of Swiss-AL, a multilingual Swiss web corpus with sources from media, politics, industry, science, and civil society. The workbench differs from other corpus analysis tools in three characteristics: (1) easy access and tidy interface, (2) focus on visualizations, and (3) wide range of analysis options, ranging from classic corpus linguistic analysis (e.g., collocation analysis) to more recent NLP approaches (topic modeling and word embeddings). It is designed for researchers of various disciplines, practitioners, and students.
Neural Knowledge Base Repairs

The curation of a knowledge base is a crucial but costly task. In this work, we suggest to make use of the advances in neural network research to improve the automated correction of constraint violations. Our method is a deep learning refinement of Learning how to correct a knowledge base from the edit history, and similarly uses the edits that solved some violations in the past to infer how to solve similar violations in the present. Our system makes use of the graph content, literal embeddings, and features extracted from Web pages to improve its performance. The experimental evaluation on Wikidata shows significant improvements over baselines.
Semi-supervised learning of pose-specific detector for human lying-pose detection

Under the superlow-altitude aerial image, human lying-pose detection is an important problem in object detection. This paper is mainly focused on the application study of an Unmanned Aerial Vehicle...
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery

Cattle farming is responsible for 8.8\% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive process, the growing need for grazing areas is an important driver of deforestation. While some regulations are in place for preserving the Amazon against deforestation, these are being flouted in various ways, hence the need to scale and automate the monitoring of cattle ranching activities. Through a partnership with \textit{Global Witness}, we explore the feasibility of tracking and counting cattle at the continental scale from satellite imagery. With a license from Maxar Technologies, we obtained satellite imagery of the Amazon at 40cm resolution, and compiled a dataset of 903 images containing a total of 28498 cattle. Our experiments show promising results and highlight important directions for the next steps on both counting algorithms and the data collection process for solving such challenges. The code is available at \url{this https URL}.
Is Hidden Safe? Location Protection against Machine-Learning Prediction Attacks in Social Networks

User privacy protection is a vital issue in online social networks (OSNs). Even though users often intentionally hide their private information in OSNs, adversaries may conduct prediction attacks to predict the hidden information with advanced machine learning techniques. The private information that users intend to hide may still be at the stake of being exposed. By taking current city on Facebook as a case, we propose a solution to estimating and managing the exposure risk of users’ hidden information. First, we simulate an aggressive prediction attack by proposing a new current city prediction framework that integrates location indications from users’ various published information including demographic attributes, behaviors and relationships with advanced state-of-the-art machine learning algorithms. Second, we study on the prediction attack results to model the pattern of prediction correctness (as correct predictions lead to information exposures), and construct an exposure risk estimator. The proposed exposure risk estimator can not only notify users of the exposure risks of their hidden current city, but also help them mitigate the exposure risk by overhauling and selecting countermeasures. Moreover, it can improve OSNs’ privacy management by facilitating OSNs’ empirical studies on the exposure risks of collective users. While taking the current city as a case, this work sheds light on the protection of other hidden information against machine-learning prediction attacks, and reveals several insightful implications for both practice management and future research.
Emotion Recognition With Feature Extracted From the Manifold of Brain Networks

Transforming neural activities into language is revolutionary for human–computer interaction as well as functional restoration of aphasia. Present rapid development of artificial intelligence makes it feasible to decode the neural signals of human visual activities. In this paper, a novel Progressive Transfer Language Decoding Model (PT-LDM) is proposed to decode visual fMRI signals into phrases or sentences when natural images are being watched. The PT-LDM consists of an image-encoder, a fMRI encoder and a language-decoder. The results showed that phrases and sentences were successfully generated from visual activities. Similarity analysis showed that three often-used evaluation indexes BLEU, ROUGE and CIDEr reached 0.182, 0.197 and 0.680 averagely between the generated texts and the corresponding annotated texts in the testing set respectively, significantly higher than the baseline. Moreover, we found that higher visual areas usually had better performance than lower visual areas and the contribution curve of visual response patterns in language decoding varied at successively different time points. Our findings demonstrate that the neural representations elicited in visual cortices when scenes are being viewed have already contained semantic information that can be utilized to generate human language. Our study shows potential application of language-based brain–machine interfaces in the future, especially for assisting aphasics in communicating more efficiently with fMRI signals.