Reviewerly/exHarmony huggingface dataset:
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing

We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing.Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations.Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function.Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.
Machine Learning Approaches for Predicting the Severity Level of Software Bug Reports in Closed Source Projects

In Software Development Life Cycle, fixing defect bugs is one of the essential activities of the software maintenance phase.Bug severity indicates how major or minor the bug impacts on the execution of the system and how rapidly the developer should fix it.Triaging a vast amount of new bugs submitted to the software bug repositories is a cumbersome and time-consuming process.Manual triage might lead to a mistake in assigning the appropriate severity level for each bug.As a consequence, a delay for fixing severe software bugs will take place.However, the whole process of assigning the severity level for bug reports should be automated.In this paper, we aim to build prediction models that will be utilized to determine the class of the severity (severe or non-severe) of the reported bug.To validate our approach, we have constructed a dataset from historical bug reports stored in JIRA bug tracking system.These bug reports are related to different closed-source projects developed by INTIX Company located in Amman, Jordan.We compare eight popular machine learning algorithms, namely Naive Bayes, Naive Bayes Multinomial, Support Vector Machine, Decision Tree (J48), Random Forest, Logistic Model Trees, Decision Rules (JRip) and K-Nearest Neighbor in terms of accuracy, F-measure and Area Under the Curve (AUC).According to the experimental results, a Decision Tree algorithm called Logistic Model Trees achieved better performance compared to other machine learning algorithms in terms of Accuracy, AUC and F-measure with values of 86.31, 0.90 and 0.91, respectively.
Digital Assessment of Stained Breast Tissue Images for Comprehensive Tumor and Microenvironment Analysis

Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. Here we sought to examine whether digital analysis of commonly used hematoxylin and eosin-stained images could provide precise and quantitative metrics of disease from both epithelial and stromal cells. We developed a convolutional neural network approach to identify epithelial breast cells from their microenvironment. Second, we analyzed the microenvironment to further observe different constituent cells using unsupervised clustering. Finally, we categorized breast cancer by the combined effects of stromal and epithelial inertia. Together, the work provides insight and evidence of cancer association for interpretable features from deep learning methods that provide new opportunities for comprehensive analysis of standard pathology images.
Transcoding Compositionally: Using Attention to Find More Generalizable Solutions

While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization.In this paper, we present seq2attn, a new architecture that is specifically designed to exploit attention to find compositional patterns in the input.In seq2attn, the two standard components of an encoder-decoder model are connected via a transcoder, that modulates the information flow between them.We show that seq2attn can successfully generalize, without requiring any additional supervision, on two tasks which are specifically constructed to challenge the compositional skills of neural networks.The solutions found by the model are highly interpretable, allowing easy analysis of both the types of solutions that are found and potential causes for mistakes.We exploit this opportunity to introduce a new paradigm to test compositionality that studies the extent to which a model overgeneralizes when confronted with exceptions.We show that seq2attn exhibits such overgeneralization to a larger degree than a standard sequence-to-sequence model.
Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks

Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is considered as the basis. In addition, two group recommendation strategies are used. One is an aggregated prediction strategy, and the other is derived from extending the RWR to the group. After performing the RWR algorithm, the group profile and location popularity are used to improve the effectiveness of the recommendation. The performance of the proposed system is examined using the Gowalla dataset related to the city of London from March 2009 to July 2011. The results indicate that the RWR algorithm outperforms popularity-based, collaborative filtering and content-based filtering. In addition, using the group profile and location popularity significantly improves the accuracy of recommendation. On the user-location graph, the number of users with recommendations matching the test data increases by 1.18 times, while the precision of creating relevant recommendations is increased by 3.4 times.
DAG-Based Distributed Ledger for Low-Latency Smart Grid Network

In this paper, we propose a scheme that implements a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) to generate, validate, and confirm the electricity transaction in Smart Grid. The convergence of the Smart Grid and distributed ledger concept has recently been introduced. Since Smart Grids require a distributed network architecture for power distribution and trading, the Distributed Ledger-based Smart Grid design is a spotlighted research domain. However, only the Blockchain-based methods, which are a type of the distributed ledger scheme, are currently either being considered or adopted in the Smart Grid. Due to computation-intensive consensus schemes such as Proof-of-Work and discrete block generation, Blockchain-based distributed ledger systems suffer from efficiency and latency issues. We propose a DAG-based distributed ledger for Smart Grids, called PowerGraph, to resolve this problem. Since a DAG-based distributed ledger does not need to generate blocks for confirmation, each transaction of the PowerGraph undergoes the validation and confirmation process individually. In addition, transactions in PowerGraph are used to keep track of the energy trade and include various types of transactions so that they can fully encompass the events in the Smart Grid network. Finally, to ensure that PowerGraph maintains a high performance, we modeled the PowerGraph performance and proposed a novel consensus algorithm that would result in the rapid confirmation of transactions. We use numerical evaluations to show that PowerGraph can accelerate the transaction processing speed by over 5 times compared to existing DAG-based DLT system.
A Robust Zero-watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

Remote medical diagnosis can be realized by using the Internet, but when transmitting medical images of patients through the Internet, personal information of patients may be leaked. Aim at the security of medical information... | Find, read and cite all the research you need on Tech Science Press
Pengaruh Penggunaan Buku Cerita Bergambar Terhadap Kemampuan Berbicara Anak

The development of speaking ability for children is very important so that children could adapt and socialize with friends. This research aimed to determine the influence of picture book on children speaking ability aged of 4-5 years. The type of the research was quasi-experiment. The subjects of this study were the children of group A in Pringwulung kindergarten Yogyakarta, Indonesia. The data collection used an observation sheet, and then analyzed using the t-test through SPSS 16 program to see the differences in speaking ability between the two groups. The result of the research was that there is a significant influence on the improvement of children's speaking ability through picture book of preschool children in Pringwulung kindergarten Yogyakarta. The recommendations of this research can be used by teachers, parents, education practitioners in order to provide an optimal stimulation to the development of children's speaking ability.
A Survey on Vertical and Horizontal Scaling Platforms for Big Data Analytics

For many years, the big data phenomenon was surprising neglected by researchers, but today, with the increasing amount of data to the tone of up to 35 trillion GB by 2020, it is mandatory to focus on this phenomenon.Clearly, we are living in the big data era [1, 2]; big data is not just about size, it is also about the complexity, noisy, heterogeneity, streaming, and verity of data.The large scale of data opened the domain for many studies in social networks, commerce, science, engineering, and security.The roadmap of this article is presented in Fig. 1.Fig. 1 -Roadmap of this paper.
Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing

This study introduces GAMET, which was developed to help writing researchers examine the types and percentages of structural and mechanical errors in texts.GAMET is a desktop application that expands LanguageTool v3.2 through a user-friendly, graphic user interface that affords the automatic assessment of writing samples for structural and mechanical errors.GAMET is freely available, works on a variety of operating systems, affords document batch processing, and groups errors into a number of structural and mechanical error categories.This study also tests LanguageTool's validity using hand-coded assessment for accuracy and meaningfulness on first language (L1) and second language (L2) writing corpora.The study also examines how well LanguageTool replicates human coding of structural and mechanical errors in an L1 corpus as well as assesses associations between GAMET and human ratings of essay quality.Results indicate that LanguageTool can be used to successful locate errors within text.However, while the accuracy of LanguageTool is high, the recall of errors is low, especially in terms of punctuation errors.Nevertheless, the errors coded by LanguageTool show significant correlations with human ratings of writing and grammar and mechanics errors.Overall, the results indicate that while LanguageTool fails to flag a number of errors, the errors flagged provide an accurate profile of the structural and mechanical errors made by writers.
Corporate social responsibility and financial profile of Spanish private hospitals

In the context of the recent economic crisis, the financial situation of the Spanish health sector has become a major concern for responsible actors from the public and private spheres, because of a decline in public spending and increased demand due to population growth and ageing. The public health system seeks collaborative synergies with the private health system to achieve better health care results, cut waiting lists and cope with financial pressure. The private health sector currently owns 452 hospitals in Spain (57% of the country's total). This paper analyses the financial statements of hospital companies in the Spanish private healthcare system, using compositional data (CoDa) methodology. It identifies a significant relationship between financial statement structure and corporate social responsibility (CSR). Relevant CSR indicators include a sustainability report according to the Global Reporting Initiative (GRI), accreditation by the Joint Commission International (JCI) and ISO 14001, 50001 and 26000 accreditations. These indicators reduce asset turnover and increase debt quality: the proportion of long-term debt within total liability. From a methodological perspective, the paper proposes the first application CoDa in statistical models to predict financial statements from non-financial variables, and the first use of CoDa in partial-least-squares structural equation models (PLS-SEM). Our methodological approach reduces the asymmetry, redundancy and outliers encountered in standard statistical models that predict financial ratios.
Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis

Effective fusion of multi-modality neuroimaging data, such as structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (PET), has attracted increasing interest in computer-aided brain disease diagnosis, by providing complementary structural and functional information of the brain to improve diagnostic performance. Although considerable progress has been made, there remain several significant challenges in traditional methods for fusing multi-modality data. First, the fusion of multi-modality data is usually independent of the training of diagnostic models, leading to sub-optimal performance. Second, it is challenging to effectively exploit the complementary information among multiple modalities based on low-level imaging features (e.g., image intensity or tissue volume). To this end, in this paper, we propose a novel Deep Latent Multi-modality Dementia Diagnosis (DLMD $$^2$$ ) framework based on a deep non-negative matrix factorization (NMF) model. Specifically, we integrate the feature fusion/learning process into the classifier construction step for eliminating the gap between neuroimaging features and disease labels. To exploit the correlations among multi-modality data, we learn latent representations for multi-modality data by sharing the common high-level representations in the last layer of each modality in the deep NMF model. Extensive experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset validate that our proposed method outperforms several state-of-the-art methods.
Estimation of half-hourly diffuse solar radiation over a mixed plantation in north China

Diffuse solar radiation plays important role in carbon exchange between forest ecosystems and the atmosphere. Based on a 2-year (2016–2017) dataset of global and diffuse solar radiation, the annual mean diffuse fraction (the proportion of diffuse solar radiation to global solar radiation, DF) was approximately 0.45 over the plantation canopy. A proposed model (BRL-1), including six predictors (half-hourly clearness index CI, solar elevation angle, apparent solar time, daily CI, a persistence of global solar radiation and relative humidity), was developed using the ground-based measurements data of 2016. The DF values estimated by the proposed model were in good agreement with the measured ones. The values of the coefficient of determination (R2), the root mean squared error (RMSE), the mean bias error (MBE) and the tstat for the proposed model were 0.82, 0.12, 0.002 and 1.16, respectively, lower than the results derived from other models. Therefore, the performance of the proposed model estimating half-hourly DF was better than the other models. Moreover, half-hourly diffuse solar radiation in cloudy skies estimated by the proposed model was more consistent with the measured one than that under clear skies. This study aims at helping us understand the change of diffuse solar radiation with clouds.
DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator

The ubiquitous adoption of systems specialized for AI requires bridging two seemingly conflicting challenges—the need to deliver extreme processing efficiencies while employing familiar programming interfaces, making them compelling even for non-expert users. We take a significant first step towards this goal and present an end-to-end software stack for the RaPiD AI accelerator developed by IBM Research. We present a set of software extensions, called Deeptools, that leverage and work within popular deep learning frameworks. DeepTools requires no additional user input and enables aggressive, accelerator-specific performance optimization akin to a full, custom framework. DeepTools has two key components: 1) a compiler runtime called DeepRT, which automatically identifies how best to execute a given DNN graph on RaPiD and constructs the requisite program binaries; and 2) an execution runtime called RaPiDLib, which triggers and manages the execution of compute and data-transfer operations on RaPiD. We integrate DeepTools with TensorFlow and map popular DNNs (AlexNet, VGG, ResNet, LSTM) to RaPiD. We demonstrate substantial improvement in performance over hand-tuned mappings.
Parameter tuning in KNN for software defect prediction: an empirical analysis

Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k&gt;1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.
Democratizing the Maker Movement: A Case Study of One Public Library System’s Makerspace Program

The maker movement has found a home in public libraries. Field leaders including public libraries in Chicago, Chattanooga, Houston, Louisville, and Toronto have built robust makerspaces, developed maker programming for a diverse range of patrons, connected community experts with library users for the purpose of sharing information, and fostered communities of practice. Characterized by open exploration, intrinsic interest, and creative ideation, the maker movement can be broadly defined as participation in the creative production of physical and digital artifacts in people’s day-to-day lives. The maker movement employs a do-it-yourself orientation toward a range of disciplines, including robotics, woodworking, textiles, and electronics. But the maker ethos also includes a do-it-with-others approach, valuing collaboration, distributed expertise, and open workspaces. To many in the library profession, the values ingrained in the maker movement seem to be shared with the aims and goals of public libraries. However, critiques of the maker movement raise questions about current iterations of makerspaces across settings. This article highlights critiques and responses regarding the “democratic” nature of the maker movement, and in particular, the article analyzes ways librarians involved in a prominent public library maker program discursively construct making and maker programming in relation to the maker movement more generally.
Turning Your Book into a Game

Augmented reality game-based learning has become an emerging trend in the field of education as it has the potential to increase children's learning motivation for subjects such as mathematics. However, to achieve the benefits for children effectively, AR serious games need to be appropriately designed, especially in respect to their novel interactions and representation paradigms. In this paper, we report on an exploratory experiment to investigate how different interaction techniques (digital screen-touch interaction vs real-world tangible interaction) and different feedback mechanisms (non-diegetic feedback vs diegetic feedback) affect 7-8-year-old children's motivation for mathematics learning. Our results show that diegetic feedback led to the game being considered significantly more enjoyable, as well as inducing greater feelings of competence and autonomy; screen-touch interaction versus tangible interaction did not change motivation directly, nor did we find interaction effects between the presentation and interaction modes. By analyzing the results and based on previous studies, we identify recommendations for designers to develop motivating serious AR games for children.
Schema Validation and Evolution for Graph Databases

Despite the maturity of commercial graph databases, little consensus has been reached so far on the standardization of data definition languages (DDLs) for property graphs (PG). Discussion on the characteristics of PG schemas is ongoing in many standardization and community groups. Although some basic aspects of a schema are already present in most commercial graph databases, full support is missing allowing to constraint property graphs with more or less flexibility. In this paper, we show how schema validation can be enforced through homomorphisms between PG schemas and PG instances by leveraging a concise schema DDL inspired by Cypher syntax. We also briefly discuss PG schema evolution that relies on graph rewriting operations allowing to consider both prescriptive and descriptive schemas.