Reviewerly/exHarmony huggingface dataset:
BunCho: AI Supported Story Co-Creation via Unsupervised Multitask Learning to Increase Writers’ Creativity in Japanese

Co-creation with artificial intelligence (AI) is an upcoming trend. However, less attention has been given to the construction of systems for Japanese novelists. In this study, we built "BunCho", an AI supported story co-creation system in Japanese. BunCho's AI is GPT-2 (an unsupervised multitask language model) trained using a large-scale dataset of Japanese web texts and novels. With BunCho, users can generate titles and synopses from keywords. Furthermore, we propose an interactive story co-creation AI system as a tabletop role-playing game. According to summative studies of writers (N=16) and readers (N=32), 69% writers enjoyed writing synopses with BunCho more than by themselves, and at least one of five common metrics were improved at objective evaluation, including creativity. In addition, 63% writers indicated that BunCho broadened their stories. BunCho showed paths to assist Japanese novelists in creating high-level and creative writing.
Augmented Reality of 3D Content Application in Common Operational Picture Training System for Army

The purpose of this article is to discuss how to improve the ways the Taiwanese Ground Forces use the common operational picture (COP) for training to improve the efficacy of tactical training and understanding of the combat field. Augmented Reality (AR) has made huge progress in the past decade and is widely applied in education, industrial sector, medical industry, military industry. More attention is focused on training simulations for soldiers and improving battlefield perception systems. The application of AR in tactical training is still an emerging realm of research but has a huge potential. Related studies have shown the application and technical limitations of AR technology in the military field. Then, using a user-oriented research method, a tactical training system based on 3D augmented reality is proposed and constructs a system prototype to combine virtual information with the real environment and provide the advantages of intuition, interaction, and information visualization. The experiment had an experimental group and control group, and an independent sample "t" was tested for evaluation and comparison. The experimental results show that the understanding of battlefield situations and tactical actions in the AR-based tactical teaching system has changed significantly. The system prototype and experimental content provide useful contributions to the tactical teaching of the Army Academy, and it can be used as a reference for the development of AR in future military applications.
Distributed Representation Computation Using CBOW Model and Skip–gram Model

Word2Vec is one of methods for generating a distribution representation. Two methods of calculation, CBOW model and skip-gram model, are proposed. However, the skip-gram model has high accuracy of the distribution representation of words, but it takes a long time to learn it. On the other hand, the CBOW model has been shown to be faster but less accurate than the skip-gram model in the distribution representation of words. In this study, we propose a method to combine the CBOW model and the skip-gram model in a way that shares the weight matrix of the CBOW model with the skip-gram model in order to achieve both learning speed and accuracy of the distribution representation of words.
Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities

Requirements specification includes technical concerns of an information system and is used throughout its life cycle. It allows for sharing the vision of the system among stakeholders and facilitates its development and operation processes. Natural languages are the most common form of requirements representation, however, they also exhibit characteristics that often introduce quality problems, such as inconsistency, incompleteness, and ambiguousness. This paper adopts the notions of linguistic pattern and linguistic style and discusses their relevance to produce better technical documentation. It focuses on the textual specification of data entities, which are elements commonly referred to throughout different types of requirements, like use cases, user stories, or functional requirements. This paper discusses how to textually represent the following elements: data entity, attribute, data type, data entity constraint, attribute constraint, and even cluster of data entities. This paper shows concrete examples and supports the discussion with three linguistic styles, represented by a rigorous requirements specification language and two informal controlled natural languages, one with a more compact and another with a more verbose, expressive, and complete representation. We analyzed how other languages cope with the representation of these data entity elements and complemented that analysis and comparison based on the PENS classification scheme. We conducted a pilot evaluation session with nineteen professional subjects who participated and provided encouraging feedback, with positive scores in all the analyzed dimensions. From this feedback, we preliminarily conclude that the adoption of these linguistic patterns would help to produce better requirements specifications written more systematically and consistently.
Blockchain for agricultural sector: The case of South Africa

New technologies are playing a fundamental role in the postmodern era of globalization where interpersonal interactions at the international level and the exchange of goods, services, information and capital are the basis of all activities. The agriculture sector is constantly facing numerous challenges including the steady growth of the population, climate change, the increasing number of catastrophes, the loss of biodiversity and the spread of parasites. This paper analyzes the impacts of Blockchain applications in agriculture and the food supply chain, through a survey literature review, providing the various players in the agriculture value chain with new tools and key technologies to improve production and distribution processes. To demonstrate the importance of applying the Blockchain in the agriculture sector, especially for emerging countries, the case of South Africa is examined. This focus is one of the unique aspects of this paper, which is the first to deal with this kind of solution applied to the South African context. Our findings indicate that Blockchain, in the e-agricultural context, has the potential for reshaping the entire sector, contributing also to the resolution of the food crisis. This paper discusses the overall implications, limits, challenges and potentials of these applications, from a critical point of view.
Probing Acoustic Representations for Phonetic Properties

Pre-trained acoustic representations such as wav2vec and DeCoAR have attained impressive word error rates (WER) for speech recognition benchmarks, particularly when labeled data is limited. But little is known about what phonetic properties these various representations acquire, and how well they encode transferable features of speech. We compare features from two conventional and four pre-trained systems in some simple frame-level phonetic classification tasks, with classifiers trained on features from one version of the TIMIT dataset and tested on features from another. All contextualized representations offered some level of transferability across domains, and models pre-trained on more audio data give better results; but overall, DeCoAR, the system with the simplest architecture, performs best. This type of benchmarking analysis can thus uncover relative strengths of various proposed acoustic representations.
Deep Universal Blind Image Denoising

Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. Traditionally, many researchers have investigated image priors for the denoising, within the Bayesian perspective based on image properties and statistics. Recently, deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets. However, they both have pros and cons. While the deep CNNs are powerful for removing the noise with known statistics, they tend to lack flexibility and practicality for the blind and real-world noise. Moreover, they cannot easily employ explicit priors. On the other hand, traditional non-learning methods can involve explicit image priors, but they require considerable computation time and cannot exploit large-scale external datasets. In this paper, we present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective. Concretely, we divide the blind image denoising problem into sub-problems and conquer each inference problem separately. As the CNN is a powerful tool for inference, our method is rooted in CNNs and propose a novel design of network for efficient inference. With our proposed method, we can successfully remove blind and real-world noise, with a moderate number of parameters of universal CNN.
GML: Efficiently Auto-Tuning Flink's Configurations Via Guided Machine Learning

The increasingly popular fused batch-streaming big data framework, Apache Flink, has many performance-critical as well as untamed configuration parameters. However, how to tune them for optimal performance has not yet been explored. Machine learning (ML) has been chosen to tune the configurations for other big data frameworks (e.g., Apache Spark), showing significant performance improvements. However, it needs a long time to collect a large amount of training data by nature. In this article, we propose a guided machine learning (GML) approach to tune the configurations of Flink with significantly shorter time for collecting training data compared to traditional ML approaches. GML innovates two techniques. First, it leverages generative adversarial networks (GANs) to generate a part of training data, reducing the time needed for training data collection. Second, GML guides a ML algorithm to select configurations that the corresponding performance is higher than the average performance of random configurations. We evaluate GML on a lab cluster with 4 servers and a real production cluster in an internet company. The results show that GML significantly outperforms the state-of-the-art, DAC (Datasize-Aware-Configuration) (Z. Yu et al. 2018) for tuning the configurations of Spark, with 2.4× of reduced data collection time but with 30 percent reduced 99th percentile latency. When GML is used in the internet company, it reduces the latency by up to 57.8× compared to the configurations made by the company.
Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children’s videos to be toxic, highlighting the importance of monitoring comments, particularly on children’s platforms.
Semantic text-pairing for relevant provision identification in construction specification reviews

Field engineers should manually analyze the site appropriateness of every provision in a construction specification by comparing the requirements against the national standards. To support the manual review involving multiple documents, the authors proposed a semantic text-pairing method that identified relevant provisions from different specifications considering the textual properties. First, 2527 provisions were prepared from two construction specifications of highway projects undertaken in Qatar and five national standards from Australia, the United Kingdom, and the United States. Second, the Doc2Vec model trained the provisions and learned the textual features based on Paragraph Vector with Distributed Memory. Third, the provision relevance was estimated by normalizing cosine similarities between provision vectors generated by the Doc2Vec model. The experiments returned promising results, with an average matching accuracy of 84.40%. The results contribute to the specification review by automatically identifying the most relevant provisions and making the process objective and robust to human errors.
Quantum speedup of Monte Carlo integration with respect to the number of dimensions and its application to finance

Monte Carlo integration using quantum computers has been widely investigated, including applications to concrete problems. It is known that quantum algorithms based on quantum amplitude estimation (QAE) can compute an integral with a smaller number of iterative calls of the quantum circuit which calculates the integrand, than classical methods call the integrand subroutine. However, the issues about the iterative operations in the integrand circuit have not been discussed so much. That is, in the high-dimensional integration, many random numbers are used for calculation of the integrand and in some cases similar calculations are repeated to obtain one sample value of the integrand. In this paper, we point out that we can reduce the number of such repeated operations by a combination of the nested QAE and the use of pseudorandom numbers (PRNs), if the integrand has the separable form with respect to contributions from distinct random numbers. The use of PRNs, which the authors originally proposed in the context of the quantum algorithm for Monte Carlo, is the key factor also in this paper, since it enables parallel computation of the separable terms in the integrand. Furthermore, we pick up one use case of this method in finance, the credit portfolio risk measurement and estimate to what extent the complexity is reduced.
Addressing some of bill of lading issues using the Internet of Things and blockchain technologies: a digitalized conceptual framework

This paper proposes a multi-layer integrated IoT-blockchain conceptual architecture to solve the issues of the traditional bill of ladings, namely double-spending, duplicated documents, masqueraded information, and fraud and hacking threads related to the complexity of traditional format and fragmented supply chain activities. We formulate the tasks and functions of each IoT-blockchain layer and illustrates how the proposed conceptual architecture can overcome several inefficiency and security issues in the status quo. The proposed conceptual architecture can link IoT and blockchain technologies with port community systems' practices to reduce potential risks behind issuing the bill of lading.
Creating a Sustainable Education Environment with Augmented Reality Technology

As the COVID-19 epidemic caused new requirements in education, the use of various technologies and materials in science education has gained more importance for sustainability. Among other objectives, the subject of science aims to help students gain skills such as identifying problems, doing research, forming hypotheses, completing experiments, conducting analyses, and reporting the findings. Some of the problems experienced in science education are caused by the lack of tools and equipment. Through augmented reality (AR), a developing technology that is also used in the field of education, a digital layer is superimposed over authentic world images. The main aim of this study is to determine the views of students and teachers regarding augmented reality content developed for science education. The study group consists of 80 seventh-grade students and 4 science teachers. The study adopted a qualitative data collection method so the researchers developed and used semi-structured interview forms for the students and the teachers during the interviews. Both the students and the teachers reported the positive effects of AR practices on improving the understanding of science topics, offering a visual topic introduction, and contributing to the in-class interaction during class hours.
DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment

Task scheduling has always been one of the crucial problem in cloud computing. With the transition of task types from static batch processing to dynamic stream processing, the dynamic online task scheduling problem has attracted widespread attention. At this stage, explore an effective task scheduling method to implement high quality of service (QoS) requests with limited resources is a considerable challenge. This paper proposes a novel scheduling algorithm called double deep Q-network task scheduling (DDQN-TS), which uses the adaptive learning ability of double deep Q-network (DDQN) to explore the optimal task scheduling strategy. Experiments conducted using the Random, Google, and Alibaba benchmarks to compare several classic algorithms show that the proposed DDQN-TS can guarantee a high task completion rate and efficiently reduce the task average response time.
Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies

Rationally justifying Bitcoin's immense price fluctuations has remained a persistent challenge for both investors and researchers in this field. A primary reason is our potential weakness toward robustly quantifying unquantifiable risks or ambiguity in Bitcoin returns. This paper introduces a behavioral channel to argue that the degree of ambiguity aversion is a prominent source of abnormal returns from investment in Bitcoin markets. Using data over a ten-year period, we show that Bitcoin investors exhibit, on average, an increasing aversion to ambiguity. Furthermore, investors are found to earn abnormal returns only when ambiguity is low. Robustness exercises reassure on the validity of our results.
CovidChain: An Anonymity Preserving Blockchain Based Framework for Protection Against Covid-19

In the past year the entire world has faced incredible health and economic challenges due to the life-threatening novel Coronavirus Disease – 2019 (COVID-19) and is an ongoing crisis. In this situation, the way forward seems to be a balanced approach – where, infected/exposed people are strictly quarantined and recovered/unexposed people are allowed to carry on with their day-to-day business activities. However, appropriate social/physical distancing norms will have to be strictly followed till the pandemic is completely over. Therefore, mechanisms are required that will assist people in following the social and physical distancing norms in public places. In this paper, we propose a privacy-preserving blockchain-based framework that allows people, through use of their smartphones and other communication devices, to protect themselves from infections as they go about their day-to-day activities.
NeighborLoss: A Loss Function Considering Spatial Correlation for Semantic Segmentation of Remote Sensing Image

House segmentation of remote sensing image based on deep learning has become the main segmentation method because it can automatically extract features. However, the accuracy of image segmentation is affected not only by the network model, but also by the loss function, but the existing loss functions, except Binary Cross Entropy, are designed to deal with imbalanced dataset, no new research on improving Binary Cross Entropy for balanced dataset, and all loss function treat each pixel in isolation, without considering the spatial correlation between pixel and its neighbor pixels. To solve this problem, a new loss function, named NeighborLoss function, is proposed. Firstly, the deep learning network is used to get the prediction results of each pixel. According to whether the prediction results of the eight neighboring pixels of each pixel are consistent with the each pixel prediction, different weights are given to each pixel. Finally, the weighted average value of cross entropy of all pixels in the batch is taken as the final loss function value. We use the main deep learning semantic segmentation networks SegNet, PSPNet, UNET ++, MUNet with both NeighborLoss and cross entropy loss respectively to extract houses on the open data set named WHU dataset for remote sensing. The results show that compared with cross entropy loss functions, the MIoU, Precision, Recall, and Accuracy of NeighborLoss function are improved. From the predicted graph, the NeighborLoss function is more accurate to extract the edge of the house, especially in the corner of the house. NeighborLoss function is a more effective loss function for remote sensing image segmentation.
Improving Video Temporal Consistency via Broad Learning System

Applying image-based processing methods to original videos on a framewise level breaks the temporal consistency between consecutive frames. Traditional video temporal consistency methods reconstruct an original frame containing flickers from corresponding nonflickering frames, but the inaccurate correspondence realized by optical flow restricts their practical use. In this article, we propose a temporally broad learning system (TBLS), an approach that enforces temporal consistency between frames. We establish the TBLS as a flat network comprising the input data, consisting of an original frame in an original video, a corresponding frame in the temporally inconsistent video on which the image-based technique was applied, and an output frame of the last original frame, as mapped features in feature nodes. Then, we refine extracted features by enhancing the mapped features as enhancement nodes with randomly generated weights. We then connect all extracted features to the output layer with a target weight vector. With the target weight vector, we can minimize the temporal information loss between consecutive frames and the video fidelity loss in the output videos. Finally, we remove the temporal inconsistency in the processed video and output a temporally consistent video. Besides, we propose an alternative incremental learning algorithm based on the increment of the mapped feature nodes, enhancement nodes, or input data to improve learning accuracy by a broad expansion. We demonstrate the superiority of our proposed TBLS by conducting extensive experiments.