Reviewerly/exHarmony huggingface dataset:
Effectiveness of Role Play in Improving Speaking Skill

This study was carried out to find out the effectiveness of role play technique in improving speaking skill in English. The population of the study was the grade X students of a public school from Lamjung district. The experimental group was taught through role play while the control class was taught through the traditional grammar based techniques. The total population of this study was 40 students who were enrolled in the academic year 2018/2019. The study was experimental approach. The tools applied in this research were observation sheet and speaking test. After 20 lessons of the teaching, the post-test of speaking was conducted in which the students in both groups were asked to answer. The results showed that there is a significant improvement in speaking skill of experimental group. It can be concluded that role play have significant effect on students’ speaking skill.
Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition

Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&amp;T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.
Taxonomy-Oriented Domain Analysis of GIS: A Case Study for Paleontological Software Systems

Documenting the paleontological process includes data produced by different techniques and protocols, which are used by paleontologists to prospect and eventually find a new fossil. Nowadays, together with the aforementioned data, a great amount of information is also available in terms of georeferenced systems, including contextual as well as descriptive information. However, the use of this information into a model capable of recognizing similarities and differences is still an open issue within the Natural Heritage community. From the software engineering field, software product lines are models that focus on reusing common assets, in such a way that new software developments are only concern on differentiation relying on already modeled (and implemented) systems. This synergy leads us to apply our taxonomy-oriented domain analysis for Software Product Line (SPL) development, when building systems for documenting the paleontological process. In this paper, we introduce the approach for building such software systems, and illustrate its use through a case study in North Patagonia. Findings show promissory results in terms of reuse.
Quadtree-Based Coding Framework for High-Density Camera Array-Based Light Field Image

The size of a high-density-camera-array (HDCA)-based light field image (LFI) is usually very large, containing hundreds of high-resolution views. Therefore, there is an urgent need to efficiently compress it. Currently, no compression algorithms, specially, for the HDCA-based LFI have been designed. In this paper, we propose an algorithm based on a quadtree-based 2D hierarchical coding framework for the HDCA-based LFI data compression. The proposed framework has the following contributions. First, we organize the views of the HDCA-based LFI into a quadtree-based coding structure. Under this structure, all of the views are divided into four quadrants at the first level. Each quadrant is further sub-divided into four quadrants at each subsequent level. The process continues until the desired depth is reached. This quadtree-based coding structure can make full use of the strong inter-view correlations to improve the coding efficiency. In addition, the proposed quadtree-based structure can be easily extended to a general 2D hierarchical structure with variable group of pictures (GOP) sizes to adapt to the reference frame buffer constraint. Second, we try to improve the performance of the 2D hierarchical coding framework using the distance-based criteria for both the reference frame selection and motion vector scaling. Third, a one-pass optimal bit allocation scheme is proposed to further optimize the performance by taking the quality dependencies among various views into consideration. The proposed framework is implemented in the newest video coding standard, high efficiency video coding (HEVC). The experimental results show that the proposed quadtree-based 2D hierarchical coding framework can achieve an average of over 25% bitrate saving compared with the 1D hierarchical coding structure.
Fixing Gaussian Mixture VAEs for Interpretable Text Generation

Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the meaning of the latent variable. To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data. In this paper, we generalize the practice and introduce DEM-VAE, a class of models for text generation using VAEs with a mixture distribution of exponential family. Unfortunately, a standard variational training algorithm fails due to the mode-collapse problem. We theoretically identify the root cause of the problem and propose an effective algorithm to train DEM-VAE. Our method penalizes the training with an extra dispersion term to induce a well-structured latent space. Experimental results show that our approach does obtain a meaningful space, and it outperforms strong baselines in text generation benchmarks. The code is available at this https URL.
Deep Intrinsic Image Decomposition Using Joint Parallel Learning

Intrinsic image decomposition is a highly ill-posed problem in computer vision referring to extract albedo and shading from an image. In this paper, we regard it as an image-to-image translation issue and propose a novel thought, which makes use of parallel convolutional neural networks (ParCNN) to learn albedo and shading with different spatial features and data distributions, respectively. At the same time, the energy is preserved as much as possible under the constraint of image reconstruction loss shared by the two networks. Moreover, we add the gradient prior based on the traditional image formation process into the loss function, which can lead to a performance improvement of our basic learning model by jointing advantages of the physically-based method and the data-driven method. We choose MPI Sintel dataset for model training and testing. Quantitative and qualitative evaluation results outperform the state-of-the-art methods.
Predicting Human Activities from User-Generated Content

The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future.In this paper, we explore the task of predicting human activities from user-generated content.We collect a dataset containing instances of social media users writing about a range of everyday activities.We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities.We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and selfdescription.Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.Given that the space of possible phrases describ-
Deep Learning-Based Interference Fringes Detection Using Convolutional Neural Network

The interference fringes of interferometry are the key to reconstruct a threedimensional topography.But currently the adjustment of the fringes is done by manual, which is time-consuming and lack of quantitative control.Due to the complexity of the fringes, the traditional methods have low recognition rates and are only suitable for the ideal fringes in specific cases.Therefore, an interference fringes discovery (IFD) model consisting of "fringes region proposal network" (FRPN) and "fringes stitching" (FS) model is proposed.The FRPN, a deep convolutional neural network modified on Faster R-CNN, accurately recognizes the fringes with identification boxes.By integrating the feature maps of multiple layers and fine-tuning, the ability of the modified network to extract more complicated fringes is improved.Based on the identification boxes generated by FRPN, the FS restores the fringe shape and generates a complete recognition area.The IFD model achieves 98% accuracy on our testing set.The experimental results confirm that our model has the excellent performance on fringes-detection.It provides important support for the automation and the precise measurement of interferometers.
Controlling Grammatical Error Correction Using Word Edit Rate

When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods.The correction method depends on how much corrections a learner requires.In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction.We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate.Thereby, diverse corrected sentences is obtained from a single erroneous sentence.Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.
Improved time quantum length estimation for round robin scheduling algorithm using neural network

In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue.
Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment

The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects' motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients.
A Two-Level Lifelog Search Engine at the LSC 2019

The rise of affordable wearable devices makes visual lifelog data easier to collect. This data can then be retrieved as an aside memory usable as well for people with memory issues or in daily life to be more efficient. This paper presents the retrieval system designed for the Lifelog Search Challenge 2019 competition. This retrieval system offers two search levels: a semantic one and an image-based one. The data used for the indexing for both levels (semantic data for the text-based method or combinations of HSV Histogram and BRIEF features for the example-based one) as well as the architecture of the overall system is described. Results from our experiments are promising. The text-based search engine has disparate results that strongly depend on the keywords used for the query. For the example search engine, the best features are composed of both HSV histogram and BRIEF features. Despite encouraging results, the processing time appears to be a limitation.
Enhanced Superdense Coding Over Correlated Amplitude Damping Channel

Quantum channels with correlated effects are realistic scenarios for the study of noisy quantum communication when the channels are consecutively used. In this paper, superdense coding is reexamined under a correlated amplitude damping (CAD) channel. Two techniques named as weak measurement and environment-assisted measurement are utilized to enhance the capacity of superdense coding. The results show that both of them enable us to battle against the CAD decoherence and improve the capacity with a certain probability. Remarkably, the scheme of environment-assisted measurement always outperforms the scheme of weak measurement in both improving the capacity and successful probability. These notable superiorities could be attributed to the fact that environment-assisted measurement can extract additional information from the environment and thus it performs much better.
Bot Detection: Will Focusing on Recall Cause Overall Performance Deterioration?

Social bots are an effective tool in the arsenal of malicious actors who manipulate discussions on social media. Bots help spread misinformation, promote political propaganda, and inflate the popularity of users and content. Hence, it is necessary to differentiate bot accounts and human users. There are several bot detection methods that approach this problem. Conventional methods either focus on precision regardless of the overall performance or optimize overall performance, say $$F_1$$ , without monitoring its effect on precision or recall. Focusing on precision means that those users marked as bots are more likely than not bots but a large portion of the bots could remain undetected. From a user’s perspective, however, it is more desirable to have less interaction with bots, even if it would incur a loss in precision. This can be achieved by a detection method with higher recall. A trivial, but useless, solution for high recall is to classify every account (human or bot) as bot, hence, resulting in poor overall performance. In this work, we investigate if it is feasible for a method to focus on recall without considerable loss in overall performance. Extensive experiments with recall and precision trade-off suggest that high recall can be achieved without much overall performance deterioration. This research leads to a recall-focused approach to bot detection, REFOCUS, with some lessons learned and future directions.
Improving generalization ability of instance transfer-based imbalanced sentiment classification of turn-level interactive Chinese texts

Generally, a classification model achieving better generalization ability means the model performs better on the future incoming data, otherwise the history dataset. Increasing the generalization ability of multi-domain and imbalanced multi-class emotion classification of turn-level interactive Chinese texts poses the challenges due to its high dimension and sparse feature values in its feature space. Moreover, the properties of different feature spaces or diverse data distributions in various domains of target dataset (T) and source dataset (S) make it difficult to employ multi-class and multi-domain instance transfer. To address these challenges, we propose a data-level sampling approach for multi-class and multi-domain instance transfer which is inspired by transfer learning. To verify the validity of our proposed method, an imbalanced dataset is taken as target dataset, while three datasets, one collected from Bulletin Board System of Xi’an Jiaotong University and other two datasets collected from China microblog platform Weibo, as source datasets. The experimental results show that the proposed approach outperforms classic algorithms by alleviating the imbalanced problem in interactive texts effectively. Moreover, a classification model that is trained on immigrated datasets produced by employing our proposed method achieves the best ability of generalization.
Simulation design of trajectory planning robot manipulator

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
Text Classification Based on Conditional Reflection

Text classification is an essential task in many natural language processing (NLP) applications; we know each sentence may have only a few words that play an important role in text classification, while other words have no significant effect on the classification results. Finding these keywords has an important impact on the classification accuracy. In this paper, we propose a network model, named RCNNA, recurrent convolution neural networks with attention (RCNNA), which models on the human conditional reflexes for text classification. The model combines bidirectional LSTM (BLSTM), attention mechanism, and convolutional neural networks (CNNs) as the receptors, nerve centers, and effectors in the reflex arc, respecctively. The receptors get the context information through BLSTM, the nerve centers get the important information of the sentence through the attention mechanism, and the effectors capture more key information by CNN. Finally, the model outputs the classification result by the softmax function. We test our NLP algorithm on four datasets containing Chinese and English for text classification, including a comparison of random initialization word vectors and pre-training word vectors. The experiments show that the RCNNA achieves the best performance by comparing with the state-of-the-art baseline methods.
Building Reading Fluency with Mobile Assisted Extensive Reading

&lt;p&gt;&lt;span&gt;The study aims at investigating students' reading fluency through mobile assisted extensive reading in effect and responses. Forty-five students (14 males and 31 females) were involved in the study. The participants were asked to read the materials of graded reading in electronic books by using their mobile phone. To measure their reading fluency, reading rate was counted in word per minute. The results show that mobile assisted extensive reading outperformed the students' reading fluency. &lt;/span&gt;&lt;span&gt;Some participants commented that mobile based extensive reading was likely to build their reading fluency.&lt;/span&gt;&lt;span&gt; It indicated that mobile might have the potential way not only for reading comprehension but also for reading extensively. &lt;span&gt; &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Wavelength-Tunable Chaotic Signal Generation With On-Chip O/E Conversion

A wavelength-tunable chaotic optical signal generation and on-chip O/E conversion scheme based on an integrated laser-photodiode chip subject to filtered optical feedback is proposed and experimentally demonstrated. The integrated chip consists of a Fabry-Perot (FP) gain section and a photodiode, which was fabricated on an n-InP substrate with the same active material. By applying a filtered feedback signal to the FP gain section, wavelength-tunable chaotic carrier ranging from 1576 to 1587 nm was achieved. On-chip microwave chaotic signal generation with a standard bandwidth over 5.5 GHz was obtained using the integrated photonic chip.
Some cryptanalytic and coding-theoretic applications of a soft stern algorithm

Using the class of information set decoding algorithms is the best known way of decoding general codes, i.e. codes that admit no special structure, in the Hamming metric. The Stern algorithm is the origin of the most efficient algorithms in this class. We consider the same decoding problem but for a channel with soft information. We give a version of the Stern algorithm for a channel with soft information that includes some novel steps of ordering vectors in lists, based on reliability values. We demonstrate how the algorithm constitutes an improvement in some cryptographic and coding theoretic applications. We also indicate how to extend the algorithm to include multiple iterations and soft output values.
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching

We present a latent variable model for predicting the relationship between a pair of text sequences.Unlike previous auto-encodingbased approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence.We further extend the cross-sentence generating framework to facilitate semi-supervised training.We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences.We demonstrate the effectiveness of the proposed model from quantitative and qualitative experiments, while achieving state-of-the-art results on semi-supervised natural language inference and paraphrase identification.