Reviewerly/exHarmony huggingface dataset:
Design and implementation of teaching analysis system based on data mining

With the development of informatization and data collection, more and more educational process data can be obtained by education practitioners, and these massive data need to be processed in order to be used by people. Based on the classical algorithm and technical principle of data mining, this paper designs and implements a teaching analysis system based on data mining, which mainly provides the related functions of clustering analysis, regression analysis and association analysis for users. According to the K-means algorithm, the students' data are divided into several clusters in order to complete the clustering analysis of the students' scores. FP-Growth algorithm is used to analyze the strong association rules between courses. Through Python's drawing package, the data can be displayed clearly and intuitively, thus completing the data visualization. Finally, a user-friendly interface is built by PyQt5, and the analysis results are visualized and output.
Controlling Formality and Style of Machine Translation Output Using AutoML

An often overlooked difficulty of machine translation is producing a consistent formality (or register) in the target language. This is especially hard when the source language may have fewer levels of formality than the target language. We take a transfer learning approach using Google’s AutoML Translate to train custom neural machine translation (NMT) models to consistently produce a specific formality. We experiment with formality levels for English to Spanish, English to French and English to Czech. This approach makes it possible to have better and more consistent in-context translation while still leveraging the strength of a general purpose machine translation system.
Activity prediction for mapping contextual-temporal dynamics

Existing activity recognition technologies empower the smart home for perceiving the ambient environment. Efficient activity prediction, based on activity recognition, can enable the smart home to provide timely personalized services. However, predicting the next activity and its precise occurrence period are challenging due to the complexity of modelling human behaviour. In this work, we aim to understand whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing. We develop two Long Short-Term Memory (LSTM) models, both with deep contextualized word representation on sensor labels, one with temporal information and one without. Our results highlight that if temporal information is used appropriately, the model with timestamp can outperform the model without this information. While modelling human activity prediction, comprehending the contextual-temporal dynamics is highly important.
Dialectical Models of Deliberation, Problem Solving and Decision Making

Hamblin distinguished between formal and descriptive dialectic. Formal normative models of deliberation dialogue have been strongly emphasized as argumentation frameworks in computer science. But making such models of deliberation applicable to real natural language examples has reached a point where the descriptive aspect needs more interdisciplinary work. The new formal and computational models of deliberation dialogue that are being built in computer science seem to be closely related to some already existing and very well established computing technologies such as problem solving and decision making, but whether or how dialectical argumentation can be helpful to support these systems remains an open question. The aim of this paper is to examine some real examples of argumentation that seem to hover on the borderlines between deliberation, problem solving and decision making.
An Improved Edge Detection Algorithm for Noisy Images

On the basis of classical image edge detection algorithm, a new edge detection algorithm is proposed. The number of pulse noise points in detection window of 5 × 5 is counted. For two subzones in each direction of detection window, the mean gray value of non-noise points in every subzone is calculated respectively. Then, the improved Canny algorithm is used to extract the edge features of the target image. Finally, the improved watershed algorithm is used to segment the edge. The experiment results show that directionality of the image edge detected by the new algorithm is well and the edge is fine, and the algorithm can suppress different degree impulse noise and has a certain suppression effect on Gaussian noise and strong adaptability.
Early Churn User Classification in Social Networking Service Using Attention-Based Long Short-Term Memory

Social networking services (SNSs) see much early churn of new users. SNSs can provide effective interventions by identifying potential early churn users and important factors leading to early churn. The long short-term memory (LSTM) model, whose input is the user behavior event sequence binned at constant intervals, is proposed for this purpose. This model better classifies early churn users than previous machine learning models. We hypothesized that the importance of each temporal part in the event sequence is different for classifying early churn users because user behavior is known to consist of coarse and dense parts and initial behavior influences long-term behavior. To treat this, we proposed attention-based LSTM for classifying early churn users. In an experiment conducted on RoomClip, a general SNS, the proposed model achieved higher classification performance compared to baseline models, thus confirming its effectiveness. We also analyzed the importance of each temporal part and each event. We revealed that the initial temporal part and users' actions have high importance for classifying early churn users. These results should contribute to providing effective interventions for preventing early churn.
A Neural Conversational Model Using MMI-WMD Decoder Based on the Seq2Seq with Attention Mechanism

Sequence-to-sequence neural conversational model generates responses tend to be safe and common-place(e.g., I don't know, I'm not sure, I'm sorry), meanwhile the more diversity-promoting and conform responses to queries are not scored higher probably in the N-best lists. We suggest the Maximum Mutual Information(MMI) for diversifying the responses combing with Word Mover's Distances(WMD) for relating the responses to queries as the objective function to rerank N-best lists based on the Seq2Seq with attention mechanism. Experimental results prove that the MMI-WMD model generates more diverse, meaningful, relevant and appropriate responses to queries. The model performs well in human evaluations and BLEU scores in cornell movie dialogue datasets.
Relation Extraction Based on Dual Attention Mechanism

The traditional deep learning model has problems that the long-distance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information processing on the sentence set is still insufficient. Aiming at the above problems, a relation extraction method combining bidirectional GRU network and multi-attention mechanism is proposed. The word-level attention mechanism was used to extract the word-level features from the sentence, and the sentence-level attention mechanism was used to focus on the characteristics of sentence sets. The experimental verification in the NYT dataset was conducted. The experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.
Echo State Network with Adversarial Training

Reservoir Computing (RC) is a high-speed machine learning framework for temporal data processing. Especially, the Echo State Network (ESN), which is one of the RC models, has been successfully applied to many temporal tasks. However, its prediction ability depends heavily on hyperparameter values. In this work, we propose a new ESN training method inspired by Generative Adversarial Networks (GANs). Our method intends to minimize the difference between the distribution of teacher data and that of generated samples, and therefore we can generate samples that reflect the dynamics in the teacher data. We apply a feedforward neural network as a discriminator so that we don't need to use backpropagation through time in training. We justify the effectiveness of the proposed method in time series prediction tasks.
Nonclassicality revival of photon-added displaced thermal states in the amplitude decay channel

In this paper, the decoherence features of the photon-added displaced thermal state (PADTS) in the amplitude decay channel were studied by analytically and numerically investigating the time evolution of the Wigner function. The normal product representation of density for the PADTS is presented. The results indicate that the nonclassicality of PADTS can revive after disappearing with time. This kind of revival can maintain for a period of time and inevitably disappear eventually. However, we find that the operation of adding photons has the effect of reducing the decoherence rate of PADTS in the amplitude decay channel, although it also can decrease the PADTS’s nonclassicality markedly.
Artificial bee colony algorithm with tree-seed searching for modeling multivariable systems using GRNN

In this paper, an artificial bee colony algorithm with tree- seed searching (tsABC) is proposed for modeling multivariable systems using GRNN. In the tsABC, the local search operator of tree-seed algorithm is added to the onlooker bee phase of ABC. The optimization results with some benchmark functions show the effectiveness of tsABC. tsABC based GRNN (tsABC-GRNN) is adopted to model the crude oil fractionation process. The simulation results show that the proposed modeling method can reflect the characteristics of the real process.
Continual Learning Exploiting Structure of Fractal Reservoir Computing

Neural network has a critical problem, called catastrophic forgetting, where memories for tasks already learned are easily overwritten with memories for a task additionally learned. This problem interferes with continual learning required for autonomous robots, which learn many tasks incrementally from daily activities. To mitigate the catastrophic forgetting, it is important for especially reservoir computing to clarify which neurons should be fired corresponding to each task, since only readout weights are updated according to the degree of firing of neurons. We therefore propose the way to design reservoir computing such that the firing neurons are clearly distinguished from others according to the task to be performed. As a key design feature, we employ fractal network, which has modularity and scalability, to be reservoir layer. In particular, its modularity is fully utilized by designing input layer. As a result, simulations of control tasks using reinforcement learning show that our design mitigates the catastrophic forgetting even when random actions from reinforcement learning prompt parameters to be overwritten. Furthermore, learning multiple tasks with a single network suggests that knowledge for the other tasks can facilitate to learn a new task, unlike the case using completely different networks.
Exploring Pie Menus for System Control Tasks in Virtual Reality

The growing popularity of Virtual Reality (VR) makes it possible to address a growing user base that can draw on extensive prior knowledge of traditional, desktop-based applications and related two-dimensional interaction techniques using menus. The transfer of these techniques into the three-dimensional interaction space benefits VR applications, in particular those that are characterized by extensive system control options. The main focus of this work is on the adaptation of pie menus for VR. Four different implementations have been developed: Pick-Ray (PR) and Pick-Hand (PH) each supporting six degrees of freedom (6-DoF) for selection as well as Hand-Rotation (HR) and Stick-Rotation (SR) each supporting one degree of freedom (1-DoF). To examine the influence of the four different implementations on selection time, error rate, user experience, usability and presence we propose a corresponding user study.
Short-term forecast methods of electricity generation by solar power plants and its classification

Abstract The article provides a classification of existing forecast models the generation of electricity by solar power plants and discusses various options for forecast methods for each of the selected models. As a result of the study, it was concluded that the most promising forecast methods are hybrid statistical-adaptive methods.
Computational opposition analysis using word embeddings: A method for strategising resonant informal argument

In informal argument, an essential step is to ask what will “resonate” with a particular audience and hence persuade. Marketers, for example, may recommend a certain colour for a new soda can because it “pops” on Instagram; politicians may “fine-tune” diction for different social demographics. This paper engages the need to strategise for such resonance by offering a method for automating opposition analysis (OA), a technique from semiotics used in marketing and literary analysis to plot objects of interest on oppositional axes. Central to our computational approach is a reframing of texts as proxies for thought and opposition as the product of oscillation in thought in response to those proxies, a model to which the contextual similarity information contained in word embeddings is relevant. We illustrate our approach with an analysis of texts on gun control from ProCon.org, implementing a three-step method to: 1) identify relatively prominent signifiers; 2) rank possible opposition pairs on prominence and contextual similarity scores; and 3) derive plot values for proxies on opposition pair axes. The results are discussed in terms of strategies for informal argument that might be derived by those on each side of gun control.
Clustering of Intercriteria Analysis Data Using a Health-Related Quality of Life Data

Determination of Inter Criteria Analysis (ICA) dependence very often uses large amounts of data. In this paper, the large amount of data is reduced using the Self Organizing Map Neural Networks to use only the cluster representative vector. The data used are intuitionistic fuzzy estimations of quality of life. To obtain the data, a population study on health-related quality of life is used.
Fast salient object detection based on multi-scale feature aggression

Since the current CNN-based salient object detection algorithms are of slow speed, as well as fail to preserve rich information of object boundaries, which makes the regions along object contours blurred and inaccurate, a fast salient object detection algorithm with multi-scaled features aggression was proposed. Based on deep ResNet-50, four kinds of features in various resolution levels are extracted separately and then aggregated, which can make the output of network preserve more detailed information about object boundaries, so that the blurry salient maps can be solved via the proposed method. We trained an end-to-end model on THUS10K database, the resulting network can produce a saliency map with pixel-level accuracy from an input image. Extensive experiments on PascalS and DUTOMRON confirm that the proposed method achieves higher AUC and F-measure value while processing images at a rate of 15 fps, which is dramatically faster than any other eight existing methods.
A Self-reflection Teaching-Learning-Based Optimization Algorithm and Its Application

This paper proposes a self-reflection teaching-learning-based (STLBO) algorithm to improve the optimization performance of the TLBO algorithm. A new learning mechanism is employed to enhance the search ability of TLBO algorithm. In the learner phase, a no-best student learns from a random selected outstanding student or an outstanding student learns from teacher again according the fitness of the two mutual learners. After the interaction learning with other students, a self-reflection learning strategy is carried out to realize more detailed search for local information and jump out the local optimal. To evaluate the performance of STLBO algorithm, a comparison with other TLBO variants is presented to address the global optimization problem for robust pole assignment in linear discrete time systems with poles restrict in a circular region, and a robust test with LMI method is carried out. Finally, the simulation results demonstrate the effectiveness and merits of the proposed method.
Application of an Improved and Unscented Kalman Filtering Algorithm in Target Tracking

The paper introduces and adopts an improved and unscented Kalman filtering algorithm to track moving targets. For issues like large calculation amount, unfavorable real-time performance and non-local effects of samples of this algorithm, scale factors are adaptive selected to simplex sampling with minimum skewness. According to simulation results, on one hand, introduction of the algorithm can reduce calculation amount and increase arithmetic speed. On the other hand, it can decrease errors of non-local effects and high order, and enhance the accuracy of target tracking.
HERO: Maximizing Student Potential to Mobilize Community Empowerment Activities Around Campus

The Indonesian government continues to strive to create a community empowerment programs, but it is still not evenly distributed and not on target because the needs between organizers and community are not well communicated. Cooperation is needed by all parties for a precise and evenly distributed program. This is what underlies the HERO mobile application built, researchers have conducted surveys on fact that impoverished communities living next to campus but untouched by empowerment program. The HERO application is a form of concern from students who want to bring change to their surroundings. HERO is made with a two-way communication method between students and society. The empowerment program can be initiated by the community directly through the application and students with the potential to help will register, then the scheduled program is carried out by mutual agreement, and vice versa. This method is expected increase empathy among students. This application can accommodate a lot of aspirations from both parties. According to the questionnaire's result, as a program was held, the MOS value of the program participants was 4.62. The functional analysis is stated as passed the test, while compatibility test results, it does not burden the performance of the user's device.
Optimization of the randomness in Einstein which based on Monte Carlo Algorithms

As an incomplete information game, Einstein's unique process of rolling the dice brings great randomness. The randomness of its chess moving makes it difficult to apply direct reference to other relatively mature chess algorithms. With the accumulation and development of competition, the existing Einstein program has already had preliminary intelligence. The existing Einstein algorithm is mainly based on Minimax algorithm with static evaluation and Monte Carlo with pruning algorithm. However, its large randomness and limitation of pruning constrain improving. This paper which based on Monte Carlo analyses the evaluation and its corresponding simulation. From the perspective of the data, we can explore the advantages and disadvantages of Monte Carlo and improve the algorithm to judge the current situation and choose the best move, so that providing different ideas of pruning to optimize Monte Carlo program.
Modified Adaptive Color Plane Based Color Interpolation Algorithm for Digital Image Sensors

At present, there is only one image sensor and a color filter array covering its surface to collect color images in most imaging devices. Each pixel of the collected color image can only obtain one color component from digital image sensor directly, and the other two color missing components need to be calculated through color interpolation. Since Bayer-type color filter array widely used in various kinds of sensors has good color signal sensitivity and color recovery characteristic, we reconstruct the two missing color components in Bayer mode images in this paper according to it. Two Color Interpolation algorithms widely used in digital imaging devices are introduced in detail in this paper, Gradient Edge-oriented (GEO) Interpolation Algorithm and Adaptive Color Plane (ACP) Interpolation Algorithm, and then we present a modified adaptive Color layer Interpolation Algorithm based on them. In the experiments, 10 images are selected from the IVC database. 40 sets of data are generated from the images and then they are used to analyze from Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM) and Computing Time quality metrics to study the quality of the 10 images after the processing of interpolation. This four metrics are the basis of judging the processing effect of MACP, GEO and ACP algorithms. The results of the experiments show that Modified adaptive Color plane (MACP) algorithm can solve the contradiction effectively between the reconstruction of high-quality color images and reduction of computational complexity, and improve the processing speed of color image reconstruction exactly.
Incremental Learning of GAN for Detecting Multiple Adversarial Attacks

Neural networks are vulnerable to adversarial attack. Carefully crafted small perturbations can cause misclassification of neural network classifiers. As adversarial attack is a serious potential problem in many neural network based applications and new attacks always come up, it’s urgent to explore the detection strategies that can adapt new attacks quickly. Moreover, the detector is hard to train with limited samples. To solve these problems, we propose a GAN based incremental learning framework with Jacobian-based data augmentation to detect adversarial samples. To prove the proposed framework works on multiple adversarial attacks, we implement FGSM, LocSearchAdv, PSO-based attack on MNIST and CIFAR-10 dataset. The experiments show that our detection framework performs well on these adversarial attacks.
Digital video watermarking algorithm robust against video container format changes

Abstract A digital watermark embedding algorithm robust against any changes of a video container format with the use of a three-dimensional (3D) discrete cosine transform is proposed. For embedding the three-dimensional (3D) method based on Koch and Zhao’s two-dimensional (2D) method is applied. The image of a digital watermark and its digital hologram as embedded information are used. Embedding is carried out in a YUV color scheme. Computer-simulation experiments are made. The proposed method proved to be robust against attacks of video container format changes.
Enhancing Information Retrieval System Using Change-Prone Classes

In today's competitive world, each company is required to change software to meet changing customer requirements. At the same time, an efficient information retrieval system is required as changes made to software in different versions can lead to complicated retrieval systems. This research aims to find the association between changes and object-oriented metrics using different versions of open source software. Earlier researchers have used various techniques such as statistical methods for the prediction of change-prone classes. This research uses execution time, frequency, run time information, popularity, and class dependency in prediction of change-prone classes. For evaluating the performance of the prediction model, sensitivity, specificity, and ROC curve are used. Higher values of AUC indicate the prediction model gives accurate results. Results are validated in two phases: Experimental Analysis I validates results using OpenClinic software and OpenHospital software and Experimental Analysis II validates result using Neuroph 2.9.2 and Neuroph 2.6.