Reviewerly/exHarmony huggingface dataset:
Guidelines for Empowering Children to Make and Shape Digital Technology—Case Fab Lab Oulu

Digital technology design and making skills are seen as important 'twenty-first century skills' that children need to learn to become future changemakers, i.e., to manage and master in the current and future technology-rich everyday life. Fab labs (digital fabrication laboratories) are one example of non-formal learning environments where schoolteachers bring children to work with projects on digital technology design and making. Even though the value of fab labs in such endeavors has been acknowledged, the potential of fab labs in empowering children to make and shape digital technology remains poorly explored. This study scrutinizes the current theoretical understanding of empowerment related to design and making and relates that on empirical data of practical work done with children in the fab lab of the University of Oulu. Based on that, we offer theory- and practice-based guidelines for practitioners who wish to empower children to make and shape digital technology in the context of non-formal learning and fab labs. These guidelines should be useful for teachers when planning and implementing children's work in fab labs as well as for fab lab personnel who help children to conduct their projects, with special emphasis on school visits to fab lab premises.
Proposal: An Efficient Security and Privacy Scheme based on Elliptic Curve Cryptography (ECC) for Vehicular Ad hoc Network (VANET)

Vehicles in a vehicular ad-hoc network (VANET) broadcast information about the driving environment in the road. Due to the open-access environment, this means that the VANET is susceptible to security and privacy issues. However, none of the related works satisfies all security and privacy requirements. Besides, their proposed has huge overhead in terms of computation and communication. To address the above issues, we will propose the security and privacy scheme based on Elliptic Curve Cryptography (ECC) and one secure hash function. Hence the reliability of each message being signed and checked has been improved. The main aim of this work is to satisfy all aspect requirements of security and privacy and reduce the computational complexity of the system.
MLMLM: Link Prediction with Mean Likelihood Masked Language Model

Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. They however scale with man-hours and high-quality data. Masked Language Models (MLMs), such as BERT, scale with computing power as well as unstructured raw text data. The knowledge contained within those models is however not directly interpretable. We propose to perform link prediction with MLMs to address both the KBs scalability issues and the MLMs interpretability issues. To do that we introduce MLMLM, Mean Likelihood Masked Language Model, an approach comparing the mean likelihood of generating the different entities to perform link prediction in a tractable manner. We obtain State of the Art (SotA) results on the WN18RR dataset and the best non-entity-embedding based results on the FB15k-237 dataset. We also obtain convincing results on link prediction on previously unseen entities, making MLMLM a suitable approach to introducing new entities to a KB.
How good is good enough for COVID19 apps

A growing number of contact tracing apps are being developed to complement manual contact tracing. A key question is whether users will be willing to adopt these contact tracing apps. In this work, we survey over 4,500 Americans to evaluate (1) the effect of both accuracy and privacy concerns on reported willingness to install COVID19 contact tracing apps and (2) how different groups of users weight accuracy vs. privacy. Drawing on our findings from these first two research questions, we (3) empirically model how the amount of public health benefit (reduction in infection rate), amount of individual benefit (true-positive detection of exposures to COVID), and degree of privacy risk in a hypothetical contact tracing app may influence Americans’ willingness to install. Our work takes a descriptive ethics approach toward offering implications for the development of policy and app designs related to COVID19.
Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors

From disinfection and remote triage, to logistics and delivery, countries around the world are making use of robots to address the unique challenges presented by the COVID-19 pandemic. Robots are being used to manage the pandemic in Canada too, but relative to other regions, we have been more cautious in our adoption -- this despite the important role that robots of Canadian origin are now playing on the global stage. This white paper discusses why this is the case, and argues that strategic investment and support for the Canadian robotics industry are urgently needed to bring the benefits of robotics home, where we have more control in shaping the future of this game-changing technology. Such investments will not only serve to support Canada's current pandemic response and post pandemic recovery, but will also prepare this country to weather future crises. Without such support, Canada risks falling behind other developed nations that are investing heavily in hardware automation at this time.
Length-controllable Abstractive Summarization by Guiding with Summary Prototype

We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one summary per source text. However, controllable summarization, especially of the length, is an important aspect for practical applications. Previous studies on length-controllable abstractive summarization incorporate length embeddings in the decoder module for controlling the summary length. Although the length embeddings can control where to stop decoding, they do not decide which information should be included in the summary within the length constraint. Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings. Our model generates a summary in two steps. First, our word-level extractor extracts a sequence of important words (we call it the text) from the source text according to the word-level importance scores and the length constraint. Second, the prototype text is used as additional input to the encoder-decoder model, which generates a summary by jointly encoding and copying words from both the prototype text and source text. Since the prototype text is a guide to both the content and length of the summary, our model can generate an informative and length-controlled summary. Experiments with the CNN/Daily Mail dataset and the NEWSROOM dataset show that our model outperformed previous models in length-controlled settings.
Scale-Localized Abstract Reasoning

We consider the abstract relational reasoning task, which is commonly used as an intelligence test. Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions. We show that indeed different rules are solved by different resolutions and a combined multi-scale approach outperforms the existing state of the art in this task on all benchmarks by 5-54%. The success of our method is shown to arise from multiple novelties. First, it searches for relational patterns in multiple resolutions, which allows it to readily detect visual relations, such as location, in higher resolution, while allowing the lower resolution module to focus on semantic relations, such as shape type. Second, we optimize the reasoning network of each resolution proportionally to its performance, hereby we motivate each resolution to specialize on the rules for which it performs better than the others and ignore cases that are already solved by the other resolutions. Third, we propose a new way to pool information along the rows and the columns of the illustration-grid of the query. Our work also analyses the existing benchmarks, demonstrating that the RAVEN dataset selects the negative examples in a way that is easily exploited. We, therefore, propose a modified version of the RAVEN dataset, named RAVEN-FAIR. Our code and pretrained models are available at this https URL.
Urban Road Object Detection and Tracking Applications Based on Acoustic Localization

The detection and tracking of urban road traffic videos based on acoustic positioning is used to detect and track target vehicles in urban road environments of this paper. In order to speed up the overall detection and tracking operation rate, the innovation of this paper is the combination of acoustics and images, which can achieve the effect of real-time detection on ordinary hardware. This paper is generally divided into three modules: acoustic positioning, target detection, and target tracking. This article can realize the positioning, classification, detection and tracking of the target vehicle, and provide an effective basis for the traffic management department to timely and effectively grasp the abnormal situation of urban roads.
Extended Abstract: What to Expect When You Are Learning Data Visualization: A Textual Analysis of Course Syllabi

This article discusses the content coverage of introductory data visualization courses in U.S. higher education. I conducted a corpus-assisted textual analysis of 20 publicly available course syllabi. The findings suggest that existing courses predominantly focus on the practical experience of producing visualization design (e.g., tools, techniques, methods), but rarely devote time to broader sociocultural factors and ethical issues involved in the process. I concluded by suggesting that technical and professional communication can illuminate the pedagogical approaches to data visualization through its ethical, humanistic, and contextual concerns.
OSACT4 Shared Tasks: Ensembled Stacked Classification for Offensive and Hate Speech in Arabic Tweets

In this paper, we describe our submission for the OCAST4 2020 shared tasks on offensive language and hate speech detection in the Arabic language. Our solution builds upon combining a number of deep learning models using pre-trained word vectors. To improve the word representation and increase word coverage, we compare a number of existing pre-trained word embeddings and finally concatenate the two empirically best among them. To avoid under- as well as over-fitting, we train each deep model multiple times, and we include the optimization of the decision threshold into the training process. The predictions of the resulting models are then combined into a tuned ensemble by stacking a classifier on top of the predictions by these base models. We name our approach “ESOTP” (Ensembled Stacking classifier over Optimized Thresholded Predictions of multiple deep models). The resulting ESOTP-based system ranked 6th out of 35 on the shared task of Offensive Language detection (sub-task A) and 5th out of 30 on Hate Speech Detection (sub-task B).
Multivariate Distributions with Gaussian Conditional Structure

Multivariate distributions exhibiting some features of the conditional structure associated with the classical normal model are investigated. Features considered include conditional distributions of subvectors and conditional moments. Our understanding of the classical normal model is enhanced by the study of such quasi-Gaussian distributions together with investigation of additional assumptions required to characterize the classical normal model. Special attention is paid to the class of distributions exhibiting Gaussian conditional structure of the second order, i.e. those in which the conditional moments of orders one and two match the Gaussian model.
Research on the Design of Comprehensive Storm Flood Prevention and Treatment Project of Sponge Airport

Abstract In view of the current situation that the airport is often flooded in recent years, based on the analysis of comprehensive storm flood prevention and treatment measures in different regions, this paper puts forward the overall framework scheme for the spatial layout design of comprehensive storm flood prevention and treatment project of sponge airport, and focuses on the composition of purification function, workflow process and large-scale release function of new storage tank, so as to promote the new-type “assembled” and “modular” regulation innovative application of storage tank.
A Novel Gradient Neural Network for Tackling the Complex-valued System of Linear Equations Online

To increase the convergence speed for tackling the complex-valued system of linear equation (CVSLE) problem online, a novel fully complex-valued gradient neural network (FCGNN) model is proposed. Firstly, to tackle the CVSLE problem in the complex number field instead of the real number field, the negative gradient descent information and a new nonlinear activation function are used to design a FCGNN model. Secondly, the proof for the global convergence property of the FCGNN model is deduced from the strict derivation. Finally, the simulation results show that this FCGNN model has a higher convergence rate than the original gradient neural network (GNN) model and the improved Zhang neural network (IZNN) model.
Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing

NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. Human language processing data can be leveraged to increase the performance of models and to pursue explanatory research for a better understanding of the differences between human and machine language processing. We review recent studies leveraging different types of cognitive processing signals, namely eye-tracking, M/EEG and fMRI data recorded during language understanding. We discuss the role of cognitive data for machine learning-based NLP methods and identify fundamental challenges for processing pipelines. Finally, we propose practical strategies for using these types of cognitive signals to enhance NLP models.
Word Embedding-Based Reformulation for Long Queries in Information Search

It has been found that very often long queries are more challenging than short queries for information search engines to obtain good results. In this paper, we present a word embedding-based approach. First short queries or concepts are extracted from the original query. Then with the help of a trained word embedding model, all of the query elements go through a series of reformulation operations including deletion, substitution, and addition of terms so as to obtain more profitable query representations. Finally all the reformulated elements are linearly combined with the original query. Experiments are conducted on three TREC collections, and the experimental results show that the proposed method is able to improve retrieval performance on average and especially effective for long queries. Compared with several state-of-the-art baseline methods, the proposed method is very good.
Comparison-Based Study to Predict Breast Cancer: A Survey

Cancer is utmost dangerous disease that leads to death stage if not cured on time. Breast cancer is the second most common disease after lung cancer in women. Therefore, its early detection is of utmost importance. Machine learning plays an important role to predict breast cancer in the early stages. In this paper, the authors present a comparison study to predict breast cancer on the Breast Cancer Wisconsin Diagnostic dataset by applying six different machine learning algorithms such as CART, logistic regression, support vector classifier, hard voting classifier, Extreme Gradient Boosting, and artificial neural network. Authors have used various metrics for model evaluation keeping accuracy as one of the most important factors since higher accuracy models can help doctors to better detect the presence of breast cancer.
Enriching Historic Photography with Structured Data using Image Region Segmentation

Cultural institutions such as galleries, libraries, archives and museums continue to make commitments to large scale digitization of collections. An ongoing challenge is how to increase discovery and access through structured data and the semantic web. In this paper we describe a method for using computer vision algorithms that automatically detect regions of “stuff” — such as the sky, water, and roads — to produce rich and accurate structured data triples for describing the content of historic photography. We apply our method to a collection of 1610 documentary photographs produced in the 1930s and 1940 by the FSA-OWI division of the U.S. federal government. Manual verification of the extracted annotations yields an accuracy rate of 97.5%, compared to 70.7% for relations extracted from object detection and 31.5% for automatically generated captions. Our method also produces a rich set of features, providing more unique labels (1170) than either the captions (1040) or object detection (178) methods. We conclude by describing directions for a linguistically-focused ontology of region categories that can better enrich historical image data. Open source code and the extracted metadata from our corpus are made available as external resources.
Correction to: Bound smoothing based time series anomaly detection using multiple similarity measures

After publication, the authors realized that they failed to z-normalize the subsequences for BFDD, making it perform much worse than a correct implementation, such as detection on xmitdb_1108_2.
Hardware Performance Analysis of Mobile-Based Augmented Reality Systems

Mobile-based augmented reality (AR) systems are occupying consumer markets at a fast rate. Mobile-based AR applications are successful owing to the ease of access and good hardware that our smartphones have. However, with the increasing demand for applications, the AR systems need to be accurate and energy-efficient. State of the art hardware specifications of our smartphones allows us to perform a variety of tasks, including video screening, navigation, and browsing. Firstly, applications of augmented reality and the hardware challenges associated with them are discussed. Next, an analysis of two important hardware components - global positioning system (GPS) and battery performance is presented, both in the capacity of AR applications. It is observed that current hardware specifications are good enough for a wide range of applications except for applications that require high accuracy.
Mengenal Pembelajaran Baca Tulis Alqur’an

This article aims to discuss the Basically Reading the Qur'an. This study uses the literature review method by critically tracing various sources of references to obtain reliable, intact data. The results of this study state that the Read the Qur’an is a lesson that learns how to read and write the Qur’an in accordance with the rules that are good and right. In studying the Qur’an it is necessary to understand the reading and writing of the Qur’an, because understanding reading and writing the Qur’an becomes an important requirement that must be mastered in studying and understanding the meaning of the verses of the Qur’an.
DWIE: an entity-centric dataset for multi-task document-level information extraction.

This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start from the detection and classification of named entity mentions in individual sentences. Further, DWIE presented two main challenges when building and evaluating IE models for it. First, the use of traditional mention-level evaluation metrics for NER and RE tasks on entity-centric DWIE dataset can result in measurements dominated by predictions on more frequently mentioned entities. We tackle this issue by proposing a new entity-driven metric that takes into account the number of mentions that compose each of the predicted and ground truth entities. Second, the document-level multi-task annotations require the models to transfer information between entity mentions located in different parts of the document, as well as between different tasks, in a joint learning setting. To realize this, we propose to use graph-based neural message passing techniques between document-level mention spans. Our experiments show an improvement of up to 5.5 F1 percentage points when incorporating neural graph propagation into our joint model. This demonstrates DWIE's potential to stimulate further research in graph neural networks for representation learning in multi-task IE. We make DWIE publicly available at this https URL.
Change Detection in Drilling Process Based on Temperature Nearby Cutting Edge by LSTM Neural Network

Abstract This study considers to efficiently collect “highly value-added” data for in-process anomaly detection of cutting tools in machining processes, and focuses on collection of time-series data of temperature nearby the tool cutting-edge by using a wireless tool holder system composed of an internal temperature measuring device and a wireless transmitter, which is connected with a thermocouple built-in the cutting tool. We then propose a method to detect a change of tool performance based on a recurrent neural network (RNN) with a long short-term memory (LSTM) structure. The capability of the proposed RNN system with LSTM is demonstrated through computational experiments, and demonstrate the time-series data of temperature nearby cutting tool tip is applicable for change detection of cutting tools status.
An Early Development Process of an Augmented Reality-Based Healthy Diet Tool Prototype

This paper presents an early development process of an augmented reality-based healthy diet tool prototype i.e. getting the initial requirements for the prototype. This tool is being developed using the evolutionary prototype model. Thus, the tool development will follow several complete 4-phase iterations of the prototype model. To get the initial requirements, we have analysed several existing applications that are related to diet, food and cooking. We found out that the augmented reality is not a common feature. We had also conducted an interview with one of 2018 diet challenge participants. The interviewee welcomed the idea of having augmented reality in the diet tool with a condition that the feature should be designed to accommodate the users. Therefore, several iterations will be conducted to get the best specifications for a diet tool prototype and finding the best ways to embed the augmented reality features into the tool. We will also validate with experts sets of menu, advice and instructions to be used in the tool. It is hoped that our effort will help in developing a reliable, healthy diet tool with suitable augmented reality feature.