Reviewerly/exHarmony huggingface dataset:
Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS

Abstract Background Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at risk of information overload and consequently missing information vital to patient care. Automatically identifying relevant information at the point of care has the potential to reduce these risks but represents a considerable research challenge. One software solution that has been proposed in industry is the IBM Watson analytics suite which includes rule-based analytics capable of processing large document collections at scale. Results In this paper we present an overview of IBM Watson Content Analytics and a feasibility study using Content Analytics with a large-scale corpus of clinical free-text reports within a UK National Health Service (NHS) context. We created dictionaries and rules for identifying positive incidence of hydronephrosis and brain metastasis from 5.6 m radiology reports and were able to achieve 94% precision, 95% recall and 89% precision, 94% recall respectively on a sample of manually annotated reports. With minor changes for US English we applied the same rule set to an open access corpus of 0.5 m radiology reports from a US hospital and achieved 93% precision, 94% recall and 84% precision, 88% recall respectively. Conclusions We were able to implement IBM Watson within a UK NHS context and demonstrate effective results that could provide clinicians with an automatic safety net which highlights clinically important information within free-text documents. Our results suggest that currently available technologies such as IBM Watson Content Analytics already have the potential to address information overload and improve clinical safety and that solutions developed in one hospital and country may be transportable to different hospitals and countries. Our study was limited to exploring technical aspects of the feasibility of one industry solution and we recognise that healthcare text analytics research is a fast-moving field. That said, we believe our study suggests that text analytics is sufficiently advanced to be implemented within industry solutions that can improve clinical safety.
Fall detection system based on BiLSTM neural network

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.
Heterogeneous cross-project defect prediction with multiple source projects based on transfer learning

Cross-project defect prediction (CPDP) aims to predict the defect proneness of target project with the defect data of source project. Existing CPDP methods are based on the assumption that source and target projects should have the same metrics. Heterogeneous cross-project defect prediction (HCPDP) builds a prediction model using heterogeneous source and target projects. Existing HCPDP methods just focus on one source project or multiple source projects with the same metrics. These methods limit the scope of getting the source project. In this paper, we propose Heterogeneous Defect Prediction with Multiple source projects (HDPM) which can use multiple heterogeneous source projects for defect prediction. HDPM based on transfer learning which can learn knowledge from one domain and use it to help with other domain. HDPM constructs a projective matrix between heterogeneous source and target projects to make the distributions of source and target projects similar. We conduct experiments on 14 projects from four public datasets and the results show that HDPM can achieve better performance compared with existing CPDP methods, and outperforms or is comparable to within-project defect prediction method. The use of multiple heterogeneous source projects for defect prediction can effectively extend the data acquisition range of defect prediction and make software defect prediction better applied to software engineering.
Blockchain and Cryptocurrencies Technology: a survey

Blockchain Â and Cryptocurrency has gotten wider considerations as of late. The decentralized digital Cryptocurrency Â and its underlying â€œBlockchain â€ technology has created much excitement in the technology community. The financial technology sector sees high potential value in Cryptocurrency Blockchain Â protocols, or distributed-ledger technology. The key advantage of this technology lies in the fact that it enables the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources. In this paper, we presented a systematic investigation of Blockchain Â and Cryptocurrencies with explained simply in a way that Cryptocurrency is a form of digital currency that is being used to make transactions using a ledger known as Blockchain Â which is a decentralized system of banking in which there is no centralized authority and all the control lies on an algorithm and its controlling users. Blockchain , a financial tool that can potentially play an important role in the sustainable development of the global economy. The new technology is expected to bring massive benefits to consumers, to current banking system and to the whole society in general.Â
A Multi-Task Learning Framework for Extracting Bacteria Biotope Information

This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019.To alleviate the data deficiency problem in domain-specific information extraction, we use BERT(Devlin et al., 2018) (Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction (Devlin et al., 2018) on both general corpus and medical corpus like PubMed.In fine-tuning stage, we fine-tune the relation extraction layer and mention recognition layer designed by us on the top of BERT to extract mentions and relations simultaneously.The evaluation results show that our method achieves the best performance on all metrics (including slot error rate, precision and recall) in the Bacteria Biotope rel+ner subtask.
Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages.We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia.We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise.A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities.Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute Fscore gain for unsupervised cross-lingual entity linking.Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all 302 2 language pairs in Wikipedia. 1 * ዓርብ የሳምንቱ ስድስተኛ ቀን ሲሆን ሐሙስ በኋላ ቅዳሜ በፊት ይገኛል ። * Friday is the day after Thursday and the day before Saturday . Amharic -EnglishYoruba -English * Glasgow ni ilu totobijulo ni orile-
Leveraging Multiple Implicit Feedback for Personalized Recommendation with Neural Network

In recent years, deep neural networks have been widely applied on recommender systems. Most research efforts are put on modeling the side information such as textual information, contextual information and social network information, but the core part, i.e., interaction relationship between users and items are relatively less explored by neural networks, in particular, when multiple types of implicit feedbacks, e.g., click, browsing, add-to-cart, etc. are available in the system. In this paper, we propose an end-to-end learning framework, which systematically and comprehensively models multiple implicit feedback between users and items. Firstly, for each type of implicit feedback, we apply matrix factorization and Multi-Layer Perception (MLP) to capture both linearity and nonlinearity of user-item interactions. Then we fuse the effects of multiple implicit feedback through neural networks to boost the quality of recommendation. Experiments on Alibaba real production dataset with over two million interactions demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods.
Design and analysis of pseudo hadamard transformation and non-chaotic substitution based image encryption scheme

In this paper, Pseudo Hadamard Transformation (PHT) and non-chaotic substitution based image encryption scheme has been proposed. Images are characterized by intrinsic properties such as, strong redundancy and correlation between the adjacent pixels, hence more vulnerable to cyber-attacks. In the proposed technique, the redundancy and correlation have been effectively reduced by pixel position transformation using PHT and pixel value variation using non chaotic substitution, providing two stage security in encryption for images. Fifteen standard test images are considered for experimental analysis. Better average Number of Pixel Changing Rate (NPCR) and Unified Average pixel Changing Intensity (UACI) are obtained for a set of standard test images compared to more popular existing algorithms.
On the longest common prefix of suffixes in an inverse Lyndon factorization and other properties

The Lyndon factorization of a word has been largely studied and recently variants of it have been introduced and investigated with different motivations. In particular, the canonical inverse Lyndon factorization ICFL(w) of a word w, introduced in [1], maintains the main properties of the Lyndon factorization since it can be computed in linear time and it is uniquely determined. In this paper we investigate new properties of this factorization with the aim of exploring their use in some classical queries on w. The main property we prove is related to a classical query on words. We prove that there are relations between the length of the longest common prefix (or longest common extension) lcp(x,y) of two different suffixes x,y of a word w and the maximum length M of two consecutive factors of ICFL(w). More precisely, M is an upper bound on the length of lcp(x,y). A main tool used in the proof of the above result is a property that we state for factors mi with nonempty borders in ICFL(w): a nonempty border of mi cannot be a prefix of the next factor mi+1. Another interesting result relates sorting of global suffixes, i.e., suffixes of a word w, and sorting of local suffixes, i.e., suffixes of products of factors in ICFL(w). This is the counterpart for ICFL(w) of the compatibility property, proved in [2], [3] for the Lyndon factorization. Roughly, the compatibility property allows us to extend the mutual order between suffixes of products of the (inverse) Lyndon factors to the suffixes of the whole word. The last property we prove focuses on the Lyndon factorizations of a word and its factors. It suggests that the Lyndon factorizations of two words sharing a common overlap could be used to capture the common overlap of these two words.
Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach

In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents.The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents.We outline the shortcomings of conventional approaches based on heuristics and machine learningbased top-down approaches.In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells.We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets.To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents.Our model achieves an overall accuracy of 97.95% and an F1-Score of 92.62% on the test dataset.
Investigating the Factors Affecting Students’ Smartphone Purchasing Behaviors in the Context of Mobile Learning

Since we are living in the information era, it is highly important that people can reach the desired knowledge whenever required. Mobile devices have become in-dispensable in the everyday lives of humans. The recent developments in the field of technology have affected all sectors as well as the education sector, and have thus increased the importance of mobile learning. However, in order to gain the maximum benefit from mobile learning, it is important to know the specifications of the mobile devices used by the students so that the teaching materials can be prepared accordingly. Therefore, it is necessary to understand the factors that affect students’ choices when they are making the decision to purchase a mobile phone. Differences are observed between countries, as there are differences be-tween students. In this context, when the literature is studied, it is seen that insufficient research has been conducted in many countries, particularly in Northern Cyprus. This study focuses on investigating the factors affecting university students’ mobile phone purchasing behaviors in North Cyprus. Data was collected from 472 voluntary students by using a survey research model. The factors affecting students’ mobile phone purchasing behaviors, such as brand image, price, product/application features, and peer group influence were considered in this study. Consequently, four hypotheses were proposed and only one of these hypotheses was not supported. In other words, brand image, product/application features, and peer group influence were found to be effective factors in students’ choice, while the price seemed not to be an important factor. The most important result of the study was that the smartphones occupy a very important place in students’ everyday lives and they were not affected by price considerations. Therefore, it is thought that these high demands of students for smartphones will be very advantageous in the transition of educational institutions to mobile learning, particularly from the perspective of teachers. It can be said that students’ familiarity with using mobile technologies will affect their motivation positively and this may also positively affect their academic success rates in mobile learning. Therefore, it is hoped that the results obtained in this study will guide teachers in developing countries to prepare their course materials and consequently revise their curriculum in the context of mobile learning.
Term Based Semantic Clusters for Very Short Text Classification

Very short texts, such as tweets and invoices, present challenges in classification.Although term occurrences are strong indicators of content, in very short texts, the sparsity of these texts makes it difficult to capture important semantic relationships.A solution calls for a method that not only considers term occurrence, but also handles sparseness well.In this work, we introduce such an approach, the Term Based Semantic Clusters (TBSeC) that employs terms to create distinctive semantic concept clusters.These clusters are ranked using a semantic similarity function which in turn defines a semantic feature space that can be used for text classification.Our method is evaluated in an invoice classification task.Compared to well-known content representation methods the proposed method performs competitively.
Pre-service Language Teachers’ Use of Social Networking Sites for Language Learning: A Quantitative Investigation

This study examined pre-service language teachers' attitudes towards social networking site (SNS) use for language learning, their SNS practices for improving their target languages, and the relationship between them.279 English and German language teacher candidates participated in the surveys designed for the present study.The statistical analyses of the data revealed that pre-service language teachers had positive perceptions towards SNS use for the purpose of language development and they took advantage of SNSs to improve their target languages.It was also found that variables such as participants' departments, years of study, and self-reported SNS literacies significantly affected their attitudes towards SNS use and their SNS practices.Finally, the quantitative data obtained confirmed a significant correlation between teacher candidates' attitudes and their SNS practices for language learning in general and for particular skills.The results are discussed within the context of relevant scholarship, and the study is concluded with recommendations for future research.
Hierarchical Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has drawn much attention recently. Previous methods mainly focus on using RNNs or CNNs to process skeletons. But they ignore the topological structure of the skeleton which is very important for action recognition. Recently, Graph Convolutional Networks (GCNs) achieve remarkable performance in modeling non-Euclidean structures. However, current graph convolutional networks lack the capacity of modeling hierarchical information, which may be sub-optimal for classifying actions which are performed in a hierarchical way. In this work, a novel Hierarchical Graph Convolutional Network (HiGCN) is proposed to deal with these problems. The proposed model includes several Hierarchical Graph Convolutional Layers (HiGCLs). Each layer consists of an attention block and a hierarchical graph convolutional block, which are used for salient feature enhancement and hierarchical representation learning, respectively. To represent hierarchical information of human actions, we propose a graph pooling method, which is differentiable and can be plugged into GCN in an end-to-end manner. Extensive experiments on two benchmark datasets show the state-of-the-art performance of our method.
Challenges for Risk and Security Modelling in Enterprise Architecture

From our experience cooperating with the Norwegian Armed Forces, we outline two interconnected challenges for modelling risk and security in an enterprise architecture: (1) modelling what is protected and why it is protected with sufficient detail whilst being simple enough to facilitate analysis; and (2) establishing automated support for analysing and reasoning about the security models, something we deem crucial to exploit the full potential of an enterprise security architecture. In addition, we sketch out our approach to tackle these challenges and outline our future direction of work.
Psycholinguistic Aspects of the Internet Memes’ Visual Components

The aim of the article is to describe mechanism of the influence of the Internet memes’ visual components upon consciousness; to analyze functional and semantic relationships between visual and verbal components; to research role models (character types) of the internauts who are the creators of Internet memes with wolf as a visual component; emotional states created by Internet memes.&#x0D; Psycholinguistic research methods have helped to achieve this goal, namely, discourse analysis, the method of contextual and intuitive logical interpretation analysis, content analysis.&#x0D; Discussion &amp; Results. Internet meme creators achieve communicative purpose by means of the wolf image due to the use of ramified conscious and unconscious associative links. The correspondence of the meaning originally put by the author in the Internet meme with its comprehension by the addressee occurs through the use of anchoring techniques which mechanism of impact is based on linking human states to certain patterns of behavior. “Anchors” usually work automatically therefore the emotional state of a person changes positively or negatively without the possibility of its regulation by the addressee. With the help of visual images Internet memes create a model world of emotions. The ultimate goal of such polycode messages is mental effect on consciousness through the method of psychological infection. As soon as an addressee’s emotional mood to be in a ready state to apprehend the message the meanings of the visual image which may be expressed explicitly or implicitly are developed in the verbal corpus of an Internet meme. In this study the Internet memes’ verbal components were divided into thematic groups (TG) describing the internauts’ emotional states. Based on the analysis of the TG it is possible to describe the role model of the Internet meme creator manifested in the features of communicative behavior. The analysis of the TG has proved that in the verbal component the internauts intend to position the background of their mental world with the corresponding emotional tone. It should be noted that verbal and visual components do not compete but complement each other, enrich the Internet meme with new meanings accumulating cultural and emotional-evaluative meanings, social stereotypes of consciousness.&#x0D; Conclusion. The function of the visual component is to form a special emotional tone of the information, to provide a holistic and at the same time flexible understanding of a particular message due to placing semantic accents allowing the recipient to specify the perception and interpretation of the verbal component. The link between the visual and verbal parts of Internet memes is implicit. The information presented in the visual component is based on various associative links therefore; its interpretation requires careful correlation between both components, identification of internal structural and semantic links.