Reviewerly/exHarmony huggingface dataset:
Neural Question Generation based on Seq2Seq

Neural Question Generation is the use of deep neural networks to extract target answers from a given article or paragraph and generate questions based on the target answers. There is a problem in the previous NQG(Neural Question Generation) model, and the generated question does not explicitly connect with the context in the target answer, resulting in a large part of the generated question containing the target answer and the accuracy is not high. In this paper, a QG model based on seq2seq is used, which consists of encode and decoder, and adds the attention mechanism and copy mechanism. We use special tags to replace the target answer of the original paragraph, and use the paragraph and target answer as input to reduce the number of incorrect questions, including the correct answer. Through the partial copy mechanism based on character overlap, we can make the generation problem have higher overlap and relevance at the word level and the input document. Experiments show that our proposed model performs better than before.
Lexical Patterns in Adolescents’ Online Writing: The Impact of Age, Gender, and Education

This article examines the impact of the sociodemographic profile (including age, gender, and educational track) of Flemish adolescents (aged 13–20) on lexical aspects of their informal online discourse. The focus on lexical and more “traditional,” print-based aspects of literacy is meant to complement previous research on sociolinguistic variation with respect to the use of prototypical features of social media writing. Drawing on a corpus of 434,537 social media posts written by 1,384 teenagers, a variety of lexical features and related parameters is examined, including lexical richness, top favorite words, and word length. The analyses reveal a strong common ground among the adolescents with respect to some features but divergent writing practices by different groups of teenagers with regard to other parameters. Furthermore, this study analyzes both standardized versions of social media messages and the original utterances (including nonstandard markers of online writing). Strikingly, different results emerge with respect to adolescents’ exploitation of more traditional versus digital literacy skills in relation to their sociodemographic profile, especially with respect to sentiment expression (verbal versus typographic/pictorial). The study suggests that the inclusion of nonverbal communicative strategies, for instance in language teaching, might be a pedagogical asset, since these strategies are eagerly adopted by teenagers who show proof of less developed traditional writing skills.
Fulfilling the Information Need After an Earthquake: Statistical Modelling of Citizen Science Seismic Reports for Predicting Earthquake Parameters in Near Realtime

Summary When an earthquake affects an inhabited area, a need for information immediately arises among the population. In general, this need is not immediately fulfilled by official channels which usually release expert-validated information with delays of many minutes. Seismology is among the research fields where citizen science projects succeeded in collecting useful scientific information. More recently, the ubiquity of smartphones is giving the opportunity to involve even more citizens. This paper focuses on seismic intensity reports collected through smartphone applications while an earthquake is occurring. The aim is to provide a framework for predicting and updating in near realtime earthquake parameters that are useful for assessing the effect of the earthquake. This is done by using a multivariate space–time model based on time-varying coefficients and a spatial latent variable. As a case-study, the model is applied to more than 200000 seismic reports globally collected over a period of around 4 years by the Earthquake Network citizen science project. It is shown how the time-varying coefficients are needed to adapt the model to an information content that changes with time, and how the spatial latent variable can capture the local seismicity and the heterogeneity in the people's response across the globe.
Flippo the Robo-Shoe-Fly: A Foot Dwelling Social Wearable Companion

Here we present Flippo: A social wearable creature prototype. This design is meant to support people to take breaks away from their desks and move, as well as to socialize with others by caring for their creatures. Flippo takes the shape of a soft and fuzzy bug-like creature. It lives on people's shoes and occasionally nudges them when it needs to move and have social interaction with another creature from its species. It does this by making sounds and visual effects and requires that the wearers coordinate shaking their feet and helping the creatures face each other. If Flippo is satisfied with the interaction it displays a light animation and plays 'happy' tunes, and if not it nudges the wearer again. We ran a field study with 13 participants, preliminary results show the potential of the design to encourage and facilitate co-located social interaction.
Autonomous smart robot for path predicting and finding in maze based on fuzzy and neuro‐Fuzzy approaches

Abstract The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as the industrial, agricultural, and military sectors. This paper aims at carefully investigating two advanced types of approaches for guiding a non‐holonomic mobile robot to navigate in an environment area cluttered with static obstacles. Firstly, a Fuzzy logic controller (FLC) was designed, using trapezoidal shape Membership functions (MF's). Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. To validate the feasibility and effectiveness of the proposed models, V‐REP and MATLAB software are used. A comparative evaluation is, then, done on the basis of speed. The simulations results showed that the mobile robot could navigate successfully into maze environment with both proposed approaches but ANFIS controller provided better results in comparison to fuzzy controller.
Personal Loan Fraud Detection Based on Hybrid Supervised and Unsupervised Learning

In recent years, we have been witnessing a dramatic increase on the personal loan for consumption, due to the rapid development of e-services, including e-commerce, e-finance and mobile payments. Resulting from the lack of effective grid verification and supervision, it inevitably leads to largescale losses caused by credit loan fraud [1]. Considering the difficulty of manual inspection and verification on the large amount of credit card transactions, machine learning methods are commonly used to detect fraudulent transactions automatically. This article has applied the Extreme Gradient Boosting(XGBoost) model for data mining and analysis, which is inspired by its brilliant reputation in various data mining contests. With people's growing concern about privacy protection, how can we apply data mining techniques while taking consideration into privacy terms is one problem. Additionally, according to current loan fraud detection studies, some features are considered to contain little information or a bit of redundancy, whereas others hold the critical information which makes things harder when feature engineering. In order to filter useless information and preserve the useful information without knowing the meaning of our data, this paper combines Kernel Principal Component Analysis (Kernel PCA) together with XGBoost algorithm and proposes a new hybrid unsupervised and supervised learning model, KPXGBoost. We use grid search to avoid over-fitting and compare the performance of both XGBoost and P-XGBoost and other classical machine learning methods. It turns out that P-XGBoost outperforms XGBoost in fraud detection, which provides a new perspective to detecting the fraud behaviour while protecting clients' privacy.
Big Data Solutions Proposed for Cluster Computing Systems Challenges

CCS (Cluster Computing System) is coming to solve the problems of standard technology. Whose, objective is to improve the performance/power efficiency of a single processor for storing and mining the large data sets, using the parallel programming to read and process the massive data sets on multiple disks and CPUs. The thing which makes these systems somewhat performant than the standard technology is the physical organization of computing nodes in the cluster. Currently, this kind of cluster does not entirely solve the problem because it comes with its challenges, which are Node failures, Computations, Network Bottleneck, and Distributed programming. All these problems are coming when we are mining and storing the massive volume of data using cluster computing. To solve these challenges, Google invented a new Big Data framework of data processing called MapReduce, to manage large scale data processing across large clusters of commodity servers. The paper outlines the running of CCS and presents its challenges in this era of Big Data. Moreover, it introduces the most popular Big Data solutions proposed to overcome the CCS challenges. Also, it shows how Big Data technologies solve CCS issues. Generally, the main goal of this work is to provide a better understanding of the challenges of CCS and identify the essential big data solutions in this increasingly important area.
A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm

Abstract The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the inter-connections, decisions, and interactions among the users in e-commerce or recommendation systems. Many normative algorithms are developed to calculate the trust which most of them are complicated, depend on the network structure, and need lots of critical information that makes them hard to use. The aim of this paper is proposing a descriptive, simple and effective method for calculating the maximal trust and the trust route between any two users of an Online Social Network (OSN). For this purpose, four new models for estimating the trust mechanism of the users are proposed and analyzed using Kolmogorov–Smirnov and Anderson–Darling statistical hypothesis tests to identify and validate the best-fitted model based on 20,613 empirical results gathered from 4552 social network volunteers. Due to the time–complexity of the problem, a meta-heuristic algorithm based on the Artificial Bee Colony (ABC) optimization method is also developed for solving the best-fitted model. The proposed algorithm is simulated in Matlab ® over six larger test cases adopted from the Facebook dataset. In order to evaluate the performance of the developed algorithm, the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) based meta-heuristics are also simulated on the same test cases. The comparison of the computational results shows that the ABC approach performs better than the ACO and GA as the size of the network increases.
Case Study: Creating Embodied Interaction with Learning Intangible Cultural Heritage through WebAR

This paper presents a case study using a WebAR-based application for learning intangible cultural heritage. This study highlights the embodied interaction perspective for assisted learning of intangible cultural heritage. A practice-based qualitative analysis contributed to the human-computer interaction and suggests further design methodologies for holistic collaborative design.
Rotational-XOR Cryptanalysis of Simon-Like Block Ciphers

Rotational-XOR cryptanalysis is a cryptanalytic method aimed at finding distinguishable statistical properties in ARX-C ciphers, i.e., ciphers that can be described only by using modular addition, cyclic rotation, XOR, and the injection of constants. In this paper we extend RX-cryptanalysis to AND-RX ciphers, a similar design paradigm where the modular addition is replaced by vectorial bitwise AND; such ciphers include the block cipher families Simon and Simeck. We analyze the propagation of RX-differences through AND-RX rounds and develop closed form formula for their expected probability. Finally, we formulate an SMT model for searching RX-characteristics in Simon and Simeck . Evaluating our model we find RX-characteristics of up to 20, 27, and 35 rounds with respective probabilities of $$2^{-26}, 2^{-42}$$ , and $$2^{-54}$$ for versions of Simeck with block sizes of 32, 48, and 64 bits, respectively, for large classes of weak keys in the related-key model. In most cases, these are the longest published distinguishers for the respective variants of Simeck. Interestingly, when we apply the model to the block cipher Simon, the best characteristic we are able to find covers 11 rounds of Simon32 with probability $$2^{-24}$$ . To explain the gap between Simon and Simeck in terms of the number of distinguished rounds we study the impact of the key schedule and the specific rotation amounts of the round function on the propagation of RX-characteristics in Simon-like ciphers.
Multi-Class Text Classification of Uzbek News Articles using Machine Learning

Abstract A large amount of online news on various topics is being posted on the Internet. One of the tasks of processing this data is to provide the user with appropriate methods and tools for quick and easy search for important and interesting news. An approach to solve this problem is the reasonable distribution of news into respective classes. This increases the importance of automated classification of an electronic document section. In this paper, we consider the task of multi-class text classification for the texts written in Uzbek. The articles on ten categories were selected from the Uzbek “Daryo” online news edition and a dataset was developed for them. When performing multi-class text classification for this dataset, the following 6 different machine learning algorithms were used: Support Vector Machines (SVM), Decision Tree Classifier (DTC), Random Forest (RF), Logistic Regression (LR) and Multinomial Naïve Bayes (MNB). A detailed technological description of the stages of the proposed functional scheme of text classification and developed software is given. The TF-IDF algorithm and word-level and character-level n-gram models were used as the feature extraction methods. When defining hyperparameters for text classification, 5-fold cross-validation was used. Experiments were conducted and the highest accuracy was 86.88%. The models and methods that are proposed in this paper can be successfully used in the classification of texts written in the Uzbek language and further research in this area.
R-MStorm: A Resilient Mobile Stream Processing System for Dynamic Edge Networks

Mobile Stream Processing (MSP) provides a promising approach to run computation-intensive stream applications, e.g., video face recognition, on a cluster of mobile devices at the edge. However, the performance of MSP is severely restricted by the fluctuating bandwidth and intermittent connectivity of the wireless networks connecting those devices. Therefore, to achieve a good MSP performance, implementing a resilient MSP system that adapts to dynamic edge networks is essential. In this paper, we present R-MStorm, a resilient MSP system deployed at the edge. R-MStorm improves the system survivability by (1) assigning tasks to mobile devices with higher availability to improve the availability of whole system; (2) assigning tasks of the same application components to different devices to increase the diversity of physical stream paths. Besides, to efficiently divide the output of upstream tasks to downstream tasks, R-MStorm adopts adaptive stream grouping, which considers both the transmission rate to and processing rate at each downstream task. Moreover, to alleviate congestion caused by network disconnection and stream redirection, adaptive stream selection is applied to skip some data to achieve a short response time. We conduct extensive experiments on R-MStorm by executing a video face recognition App under different network conditions. The experimental results show that, compared with baseline approaches, R-MStorm achieves up to 1.5x higher throughput, 75% lower response time, at a cost of 3.3% accuracy loss.
Viewpoint constrained and unconstrained Cricket stroke localization from untrimmed videos

In this work, we create two new video datasets for the task of temporal Cricket stroke extraction. The two datasets, namely, the Highlights dataset (with approx. 117K frames) and the Generic dataset (with approx. 1.93M frames), comprise of Cricket telecast videos collected from available online sources and down-sampled to 360×640 at 25FPS. These untrimmed videos have been manually annotated with temporal Cricket strokes considering viewpoint invariance assumption. We construct two learning based localization pipelines which are dependent (Constrained) and independent (Unconstrained) of our viewpoint labeling assumption. The Unconstrained pipeline finetunes a pretrained C3D model with GRU training in disconnected and connected modes, while our Constrained pipeline uses boundary detection with first frame classification for generating the temporal localizations. Two post-processing steps, of filtering and boundary correction, are also discussed which help in improving the overall accuracy values. A modified evaluation metric, Weighted Mean TIoU, for single category temporal localization problem is also presented and compared with the evaluations of the standard mAP metric (threshold ≥ 0.5) on the created dataset. The best weighted mean TIoU of our method was 0.9376 and 0.7145 on the Highlights and Generic test partitions, respectively. Moreover, we compare our baseline method with 3D Segment CNNs and Temporal Recurrent Networks (TRNs) which have state of art results on THUMOS 2014 dataset.
Effects of School-Based Meditation Courses on Self-Reflection, Academic Attention, and Subjective Well-Being in South Korean Middle School Students

<h2>Abstract</h2><h3>Purpose</h3> The purpose of this study was to examine the effects of school-based meditation courses on middle school students' self-reflection, academic attention (ability to focus in classrooms), and subjective well-being. <h3>Design and methods</h3> The research design was a nonequivalent group comparison (n = 163) with pretest and post-test. The experimental group (n = 81) was given an eight-week meditation course and the control group (n = 82) was given other elective courses such as calligraphy and reading. <h3>Results</h3> The experimental group showed significant increases in self-reflection (<i>t</i> = 2.536, <i>p</i> = .012) and academic attention (<i>t</i> = 2.767, <i>p</i> = .006), but subjective well-being did not increase significantly (<i>t</i> = 0.906, <i>p</i> = .367). Life satisfaction was the only subcomponent of subjective well-being that increased significantly (<i>t</i> = 2.438, <i>p</i> = .016); the other subjective well-being subcomponents did not show any significant changes. <h3>Conclusions</h3> Self-reflection and academic attention significantly increased in middle school students after an eight-week meditation course. Even though changes in subjective well-being were not significant, one of its subcomponents (life satisfaction) did show significant improvement. Self-reflection and subjective well-being were shown to be influential factors for academic attention (48.5% of the variance explained). <h3>Practice implications</h3> This study is meaningful in that it examined positive benefits of a meditation course in middle school students and explored the feasibility of such a course in a school system.
MicroMentor: Peer-to-Peer Software Help Sessions in Three Minutes or Less

While synchronous one-on-one help for software learning is rich and valuable, it can be difficult to find and connect with someone who can provide assistance. Through a formative user study, we explore the idea of fixed-duration, one-on-one help sessions and find that 3 minutes is often enough time for novice users to explain their problem and receive meaningful help from an expert. To facilitate this type of interaction, we developed MicroMentor, an on-demand help system that connects users via video chat for 3-minute help sessions. MicroMentor automatically attaches relevant supplementary materials and uses contextual information, such as command history and expertise, to encourage the most qualified users to accept incoming requests. These help sessions are recorded and archived, building a bank of knowledge that can further help a broader audience. Through a user study, we find MicroMentor to be useful and successful in connecting users for short teaching moments.
Blockchain for Integrated Nuclear Power Plants Management System

In nuclear power plants, plant management systems are not only very important for operation and maintenance of the facilities, but also play a very important role in analyzing and reporting the events to the authorities when a failure or accident occurs in the facility. In addition, it is also important to ensure that event records are managed transparently so as not to cause any attempt to cover up events. Therefore, this paper proposes a tamper free plant operation system by applying blockchain technology to the integrated plant management system of Korea hydro and nuclear power (KHNP). As a result, this paper will contribute to improving public acceptance by eliminating distrust in safe operation of nuclear power plants.
Introducing Functional Biometrics: Using Body-Reflections as a Novel Class of Biometric Authentication Systems

Human-computer authentication is a continuously important topic where besides security also the aspects of usability must be taken into consideration. Biometric authentication methods promise to fulfill both aspects to a high degree, yet they come with severe drawbacks, such as the lack of changeability of the utilized trait, in case it is leaked or stolen. To compensate for these disadvantages, we introduce a novel class of biometric authentication systems in this work, named "Functional Biometrics". This approach regards the human body as a function that transforms a stimulus which is applied to the body by the authentication system. Both, the stimulus and the measured body reflection form a pair that can subsequently be used for authentication, yet the underlying function remains secret. Following this approach, we intend to disprove some of the drawbacks of traditional biometrics.
A smart utensil for detecting food pick-up gesture and amount while eating

Higher food intake rates (i.e., eating too fast) are linked to several health concerns such as an elevated risk of obesity or gastritis. Raising awareness of one's eating habits can regulate one's pace of food intake. In this paper, we propose a novel smart-eating utensil that can potentially increase the users' awareness of their eating rate by detecting their food pick-up gesture as well as the weight upon each bite. We design and implement a proof-of-concept prototype fork with multiple embedded sensors and processor to collect the eating data. After that, we propose a solution for food pick-up gesture detection and food amount estimation in each food pick-up. We assess the accuracy of our solution through ten successful data collection sessions with participants. We demonstrate that our method has strong potential to accurately detect the food pick-up gesture and estimate the amount of food on each pick-up.