Reviewerly/exHarmony huggingface dataset:
Android-based math learning to improve critical thinking

Abstract This research aims to describe and the test of effectiveness of android-based integer learning through Smart Apps Creator (SAC) to improve the critical thinking skills at elementary school students. The method research is research and development. The development procedure is adapted from Borg &amp; Gall using ten stages starting from research and information collecting, planning, develop preliminary form of product, preliminary field testing, main product revision, main field testing, operasional product revision, operational field testing, final product revision, dissemination and implementation. Product validation is performed by material, media, and practitioner validators. Research data is obtained from questionnaires, test, observations, and documentation of five elementary schools in Kudus. Analysis of research data uses T-test to determine effectiveness. The results were matched between classes treated using android-based media and SAC with comparison classes that were not treated using android-based media with SAC. The result of T-test in control and experiment class=4,457 trought the degrees significant 0,000&lt;α=0.05. The study results of the experiment group were higher than the control group. Therefore, android-based integer learning with smartapps creator is effective for improving the critical thinking skills of elementary school students.
A BERT-Based Two-Stage Model for Chinese Chengyu Recommendation

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this article, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the re-trained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on ChID and CCT datasets and find that it can achieve the state of the art on both datasets. Ablation studies show that both stages of training are critical for the performance gain.
A Design of Volunteer Computing System Based on Blockchain

In recent years, the application range of blockchain has become more and more extensive. This paper proposed a volunteer computing system design based on the blockchain. This system takes advantage of the decentralized, persistent and auditable characteristics of the blockchain to solve the scalability and single point of failure problem under the traditional centralized C/S framework and provides a certain traceability for volunteer calculations. Increased scalability implies that the system can accommodate more computing jobs and participants. Solving the single-point failure problem implies that the volunteer computing system can provide researchers with longer-term and more stable computing resources. The traceability implies that anyone can view the blockchain to understand which volunteers have participated in every computing work in history and the adoption of the results provided by each volunteer. At the same time, with the help of the persistence and auditability of the blockchain, the authenticity of history can be ensured. This paper studies the C/S framework volunteer computing as well as blockchain, and proposes a framework for volunteer computing based on the blockchain. Then discussed its pros and cons in terms of feasibility, scalability, security, authenticity, traceability etc.
Interactive Exploration of Large-Scale UI Datasets with Design Maps

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.
Active learning strategies for solving the cold user problem in model-based recommender systems

Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, model-based recommender systems are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users in a matrix factorization context. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopError, an active learning strategy that measures the variance score for each item.
Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Social scientists have shown that up to $$50\%$$ of the comments posted to a news article have no relation to its journalistic content. In this study we propose a classification algorithm to categorize user comments posted to a news article based on their alignment to its content. The alignment seeks to match user comments to an article based on similarity of content, entities in discussion, and topics. We propose a BERTAC, BERT-based approach that learns jointly article-comment embeddings and infers the relevance class of comments. We introduce an ordinal classification loss that penalizes the difference between the predicted and true labels. We conduct a thorough study to show influence of the proposed loss on the learning process. The results on five representative news outlets show that our approach can learn the comment class with up to $$36\%$$ average accuracy improvement comparing to the baselines, and up to $$25\%$$ comparing to the BA-BC. BA-BC is our approach that consists of two models aimed to capture dis-jointly the formal language of news articles and the informal language of comments. We also conduct a user study to evaluate human labeling performance to understand the difficulty of the classification task. The user agreement on comment-article alignment is "moderate" per Krippendorff's alpha score, which suggests that the classification task is difficult.
Ephemeral Social Media Visuals and Their Picturesque Design: Interaction and User Experience in Instagram Stories

Abstract This article examines the temporality of ephemeral visual posts to social media with an emphasis on Instagram stories. Drawing on theories of the spectacle, it is my contention that interaction and user-experience design, as it pertains to social media platforms, highlights the contemporary conditions and motivations in our society of abundant visual consumption. This article investigates what it means for a social media user to attend to such time-related visual experience. Throughout this piece, I critically survey the relationship between ephemeral Instagram stories’ popularity and the high speed temporality of the social media sphere with emphasis on the digital “picturesque.” Perishable daily sharing on social media reflects a contemplative glimpse into a personal lifespan presented as an object of mass appreciation. I examine how ephemerality as a component of design impacts online sociality through the picturesque visual mode. Contemporary boredom and competitive engagement in high-technological communication networks inform how we might direct digital publics to find alternative pathways to sociality.
A methodology to automatically translate user requirements into visualizations: Experimental validation

Information visualization is paramount for the analysis of Big Data. The volume of data requiring interpretation is continuously growing. However, users are usually not experts in information visualization. Thus, defining the visualization that best suits a determined context is a very challenging task for them. Moreover, it is often the case that users do not have a clear idea of what objectives they are building the visualizations for. Consequently, it is possible that graphics are misinterpreted, making wrong decisions that lead to missed opportunities. One of the underlying problems in this process is the lack of methodologies and tools that non-expert users in visualizations can use to define their objectives and visualizations. The main objectives of this paper are to (i) enable non-expert users in data visualization to communicate their analytical needs with little effort, (ii) generate the visualizations that best fit their requirements, and (iii) evaluate the impact of our proposal with reference to a case study, describing an experiment with 97 non-expert users in data visualization. We propose a methodology that collects user requirements and semi-automatically creates suitable visualizations. Our proposal covers the whole process, from the definition of requirements to the implementation of visualizations. The methodology has been tested with several groups to measure its effectiveness and perceived usefulness. The experiments increase our confidence about the utility of our methodology. It significantly improves over the case when users face the same problem manually. Specifically: (i) users are allowed to cover more analytical questions, (ii) the visualizations produced are more effective, and (iii) the overall satisfaction of the users is larger. By following our proposal, non-expert users will be able to more effectively express their analytical needs and obtain the set of visualizations that best suits their goals.
Concise security bounds for sending-or-not-sending twin-field quantum key distribution with finite pulses

Since its key rate can overcome the PLOB bound, sending-or-not-sending twin-field quantum key distribution (SNS-TF QKD) schemes attract more and more attention in the past three years. However, the inflection of statistical fluctuations on key rate was not considered quite comprehensively, which blocks the practical application of SNS-TF QKD. We take into account all the statistical fluctuations on probabilities, propose the finite-key analysis for SNS-TF QKD without any assumption on the type of attacks, and obtain the lower bound of key rates by applying an optimizing model. Then the finite-key rates are simulated under the reasonable values of some observed parameters, which shows that the key rates overcome the PLOB bound when the transmission distance is far from 350 km, if the number of pulses is fixed as $N={10}^{14}$. Compared with other SNS-TF QKD schemes, we provide concise and tight finite-key bounds since the statistical fluctuations of all parameters are considered against general attacks.
Bayesian Data Analysis in Empirical Software Engineering: The Case of Missing Data

Bayesian data analysis (BDA) is today used by a multitude of research disciplines. These disciplines use BDA as a way to embrace uncertainty by using multilevel models and making use of all available information at hand. In this chapter, we first introduce the reader to BDA and then provide an example from empirical software engineering, where we also deal with a common issue in our field, i.e., missing data. The example we make use of presents the steps done when conducting state-of-the-art statistical analysis. First, we need to understand the problem we want to solve. Second, we conduct causal analysis. Third, we analyze non-identifiability. Fourth, we conduct missing data analysis. Finally, we do a sensitivity analysis of priors. All this before we design our statistical model. Once we have a model, we present several diagnostics one can use to conduct sanity checks. We hope that through these examples, the reader will see the advantages of using BDA. This way, we hope Bayesian statistics will become more prevalent in our field, thus partly avoiding the reproducibility crisis we have seen in other disciplines.
A More Fine-Grained Aspect–Sentiment–Opinion Triplet Extraction Task

Sentiment analysis aims to systematically study affective states and subjective information in digital text through computational methods. Aspect Sentiment Triplet Extraction (ASTE), a subtask of sentiment analysis, aims to extract aspect term, sentiment and opinion term triplets from sentences. However, some ASTE’s extracted triplets are not self-contained, as they reflect the sentence’s sentiment toward the aspect term, not the sentiment between the aspect and opinion terms. These triplets are not only unhelpful to people, but can also be detrimental to downstream tasks. In this paper, we introduce a more nuanced task, Aspect–Sentiment–Opinion Triplet Extraction (ASOTE), which also extracts aspect term, sentiment and opinion term triplets. However, the sentiment in a triplet extracted with ASOTE is the sentiment of the aspect term and opinion term pair. We build four datasets for ASOTE. A Position-aware BERT-based Framework (PBF) is proposed to address ASOTE. PBF first extracts aspect terms from sentences. For each extracted aspect term, PBF generates an aspect term-specific sentence representation, considering the aspect term’s position. It then extracts associated opinion terms and predicts the sentiments of the aspect–opinion term pairs based on the representation. In the experiments on the four datasets, PBF has set a benchmark performance on the novel ASOTE task.
Optimization algorithm for feedback and feedforward policies towards robot control robust to sensing failures

Model-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed. In this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the optimization problem considers minimization/maximization of divergences between trajectory, predicted by the composed policy and a stochastic dynamics model, and optimal/non-optimal trajectories. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion. Attached video is also uploaded on youtube: https://youtu.be/zLL4uXIRmrE
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space

This paper proposes a fully decentralized federated learning (FL) scheme for Internet of Everything (IoE) devices that are connected via multi-hop networks. Because FL algorithms hardly converge the parameters of machine learning (ML) models, this paper focuses on the convergence of ML models in function spaces. Considering that the representative loss functions of ML tasks e.g, mean squared error (MSE) and Kullback-Leibler (KL) divergence, are convex functionals, algorithms that directly update functions in function spaces could converge to the optimal solution. The key concept of this paper is to tailor a consensus-based optimization algorithm to work in the function space and achieve the global optimum in a distributed manner. This paper first analyzes the convergence of the proposed algorithm in a function space, which is referred to as a meta-algorithm, and shows that the spectral graph theory can be applied to the function space in a manner similar to that of numerical vectors. Then, consensus-based multi-hop federated distillation (CMFD) is developed for a neural network (NN) to implement the meta-algorithm. CMFD leverages knowledge distillation to realize function aggregation among adjacent devices without parameter averaging. An advantage of CMFD is that it works even with different NN models among the distributed learners. Although CMFD does not perfectly reflect the behavior of the meta-algorithm, the discussion of the meta-algorithm's convergence property promotes an intuitive understanding of CMFD, and simulation evaluations show that NN models converge using CMFD for several tasks. The simulation results also show that CMFD achieves higher accuracy than parameter aggregation for weakly connected networks, and CMFD is more stable than parameter aggregation methods.
Bedtime Window

We present a system for connecting partners in long-distance relationships in bedrooms and at bedtime, a space and time that most couples share. Unlike communications explicitly initiated by users, our system is always-on, staying in the background and enabling remote presence without constant use. We present findings from a field study in which the system was deployed into participants' bedrooms. The system includes an automated photo-stream (rather than video), which was found to provide a balance between the feeling of presence and privacy, and to remove the pressure to communicate. The system also includes a real-time shared inking canvas with disappearing ink, which was found to provide a rich versatile medium allowing for new patterns of communication, live interventions, and collaborative drawing. Learnings from how our system balances privacy and remote connectedness may also have relevance for other domains such as remote healthcare and education.
Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition

Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.
Learning Unitary Transformation by Quantum Machine Learning Model

Quantum machine learning (QML) is a rapidly rising research field that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientific research and improving data processing. How to eff... | Find, read and cite all the research you need on Tech Science Press
Short Text Similarity Calculation Based on Jaccard and Semantic Mixture

For the sake of enhancing the accuracy of short text similarity calculation, a short text similarity calculation method on account of Jaccard and semantic mixture is proposed. Jaccard is a traditional similarity algorithm based on literal matching. It only considers word form, and its semantic calculation has certain limitations. The word vector can represent the semantic similarity by computing the cosine similarity of two terms in the vector space, and the semantic similarity is obtained by adding and averaging the word similarity of two sentences according to a certain method. The two methods are now weighted to compute the final text similarity. Experiments show that the algorithm improves the recall rate and F value of short text calculation to some extent.
Three-dimensional Epanechnikov mixture regression in image coding

Kernel methods have been studied extensively in recent years. We propose a three-dimensional (3-D) Epanechnikov Mixture Regression (EMR) based on our Epanechnikov Kernel (EK) and realize a complete framework for image coding. In our research, we deduce the covariance-matrix form of 3-D Epanechnikov kernels and their correlated statistics to obtain the Epanechnikov mixture models. To apply our theories to image coding, we propose the 3-D EMR which can better model an image in smaller blocks compared with the conventional Gaussian Mixture Regression (GMR). The regressions are all based on our improved Expectation-Maximization (EM) algorithm with mean square error optimization. Finally, we design an Adaptive Mode Selection (AMS) algorithm to realize the best model pattern combination for coding. Our recovered image has clear outlines and superior coding efficiency compared to JPEG below 0.25bpp. Our work realizes an unprecedented theory application by: (1) enriching the theory of Epanechnikov kernel,(2) improving the EM algorithm using MSE optimization, (3) exploiting the EMR and its application in image coding, and (4) AMS optimal modeling combined with Gaussian and Epanechnikov kernel.
Symbolic reach-avoid control of multi-agent systems

We consider the decentralized controller synthesis problem for multi-agent systems with global reach-avoid specifications. Each agent is modeled as a nonlinear dynamical system with disturbances. The objective is to synthesize local feedback controllers that guarantee that the overall multi-agent system meets the global specification despite the influence of disturbances. On the one hand, existing techniques based on planning or trajectory optimization usually ignore the effects of disturbances and produce open-loop nominal trajectories that are not generally sufficient in the presence of disturbances. On the other hand, techniques based on formal synthesis, which guarantee satisfaction of temporal specifications, do not scale as the number of agents increases.