Reviewerly/exHarmony huggingface dataset:
Unpacking the contribution of linguistic features to graph writing quality: An analytic scoring approach

Previous research on L2 writing quality has mostly focused on independent writing, and there is little empirical work on integrated writing quality. Furthermore, the majority of studies on L2 writing quality have adopted holistic scoring for writing assessment. Therefore, the purpose of this exploratory research was to investigate the contribution of linguistic features, i.e. measures of syntactic complexity, lexical complexity, cohesion, and linguistic accuracy, to scores of content, organization, and language use in graph writing, which is far less researched. Two hundred and forty-three undergraduate Iranian students described a graph with few visual chunks and a familiar topic. The findings of stepwise regression revealed that content nominals per clause and lexical diversity contributed to content and language use scores, linguistic accuracy could predict the scores of organization and language use, and contrastive and temporal connectives and temporal cohesion were predictive of the scores in all the three subscales. Moreover, some important discrepancies were observed between the raters’ scores to a subscale and its linguistic predictors. Implications for writing research, pedagogy, and assessment are offered.
Mutual Information-Based Variable Selection on Latent Class Cluster Analysis

Machine learning techniques are becoming indispensable tools for extracting useful information. Among many machine learning techniques, variable selection is a solution used for converting high-dimensional data into simpler data while still preserving the characteristics of the original data. Variable selection aims to find the best subset of variables that produce the smallest generalization error; it can also reduce computational complexity, storage, and costs. The variable selection method developed in this paper was part of a latent class cluster (LCC) analysis—i.e., it was not a pre-processing step but, instead, formed part of LCC analysis. Many studies have shown that variable selection in LCC analysis suffers from computational problems and has difficulty meeting local dependency assumptions—therefore, in this study, we developed a method for selecting variables using mutual information (MI) in LCC analysis. Mutual information (MI) is a symmetrical measure of information that is carried by two random variables. The proposed method was applied to MI-based variable selection in LCC analysis, and, as a result, four variables were selected for use in LCC-based village clustering.
Robojournalism – A Copyright Study on the Use of Artificial Intelligence in the European News Industry

Abstract The copyright protectability of outputs generated by, or with the help of Artificial Intelligence (AI) is a hotly debated question in academia and by many institutions. In practice, sophisticated AI algorithms have become a meaningful assistant in the European news industry for the reporting of sports (Retresco’s collaboration with the German Football Association), weather (textOmatic’s collaboration with FOCUS Online) and finance (the Guardian’s ‘Guarbot’). Furthermore, for the first time in copyright history a court in China assessed the validity of a company’s copyright claim over the articles produced by the corporation’s algorithm. The protection with copyright of this ‘robojournalism’ is no longer just a buzzwordy trend. From a technological perspective, robojournalism currently relies on assistive, generative and distributive technologies. The first two seem to be the most problematic from a copyright perspective as they challenge the well-rooted human authorship requirement. Experts have been able to agree so far that it does not look like AI technology is going to be a disruptive force in the media industry. However, researching the impact of AI in journalism matters a great deal. There are numerous benefits stemming from the use of AI in the newsroom – from expanding news coverage, through faster content production, all the way to leaving journalists more time for the more ‘creative’ and investigative tasks where the algorithm remains weak. This paper addresses, first, the protectability of the outputs of robojournalism under the existing European Union copyright laws. It then goes on to introduce findings related to the practical significance of robojournalism in the European news industry. Here, our focus is on the business, media, and communications studies’ perspectives of automated journalism. Our results demonstrate that the extent to which European journalism relies on assistive and generative technologies to produce written output does not justify, from a copyright perspective, the changing of the current anthropocentric copyright system. These findings have wider implications as AI-generated outputs have prompted many to talk about market failure if copyright (or related rights) protection was to be refused for such works.1
E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining

This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud detection model (FDM) based on IFT (namely, computer technology (CT), artificial intelligence (AI), and data mining (DM). Meanwhile, BDM technology, support vector machine (SVM), logistic regression model (LRM), and the proposed IFT-based FDM are comparatively employed to study e-commerce fraud risks deeply. Specifically, the LRM can effectively solve data classification problems. The proposed IFT-based FDM fuses different information sources. The experimental findings corroborate that the proposed Business-to-Business (B2B) e-commerce enterprises-oriented IFT-based FDM presents significantly higher fraud identification accuracy than SVM and LRM. Therefore, the IFT-based FDM is superior to SVM and LRM; it can process and calculate e-commerce enterprises' financial risk data from different sources and obtain higher accuracy. BDM technology provides an important research method for e-commerce fraud identification. The proposed e-commerce enterprise-oriented FDM based on IFT can correctly analyze enterprises' financial status and credit status, obtaining the probability of fraudulent behaviors. The results are of great significance to B2B e-commerce fraud identification and provide good technical support for promoting the healthy development of e-commerce.
Semantic Categorization of Social Knowledge for Commonsense Question Answering

Large pre-trained language models (PLMs) have led to great success on various commonsense question answering (QA) tasks in an end-to-end fashion.However, little attention has been paid to what commonsense knowledge is needed to deeply characterize these QA tasks.In this work, we proposed to categorize the semantics needed for these tasks using the SocialIQA as an example.Building upon our labeled social knowledge categories dataset on top of SocialIQA, we further train neural QA models to incorporate such social knowledge categories and relation information from a knowledge base.Unlike previous work, we observe our models with semantic categorizations of social knowledge can achieve comparable performance with a relatively simple model and smaller size compared to other complex approaches.
Think about it! Improving defeasible reasoning by first modeling the question scenario.

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence.Existing cognitive science literature on defeasible reasoning suggests that a person forms a mental model of the problem scenario before answering questions.Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query.Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question.Our system, CURIOUS, achieves a new stateof-the-art on three different defeasible reasoning datasets.This result is significant as it illustrates that performance can be improved by guiding a system to "think about" a question and explicitly model the scenario, rather than answering reflexively. 1
Rethinking the Objectives of Extractive Question Answering

This work demonstrates that using the objective with independence assumption for modelling the span probability P (a s , a e ) = P (a s )P (a e ) of span starting at position a s and ending at position a e has adverse effects.Therefore we propose multiple approaches to modelling joint probability P (a s , a e ) directly.Among those, we propose a compound objective, composed from the joint probability while still keeping the objective with independence assumption as an auxiliary objective.We find that the compound objective is consistently superior or equal to other assumptions in exact match.Additionally, we identified common errors caused by the assumption of independence and manually checked the counterpart predictions, demonstrating the impact of the compound objective on the real examples.Our findings are supported via experiments with three extractive QA models (BIDAF, BERT, ALBERT) over six datasets and our code, individual results and manual analysis are available online 1 .Question: What was the name of atom bomb dropped by USA on Hiroshima?Passage: ...The Allies issued orders for atomic
An Efficient Graph Database Model

Relational databases are holding the maximum amount of data underpinning the web. They show excellent record of convenience and efficiency in repository, optimized query execution, scalability, security and accuracy. Recently graph databases are seen as an good replacement for relational database. When compared to the relational data model, graph data model is more vivid, strong and data expressed in it models relationships among data properly. An important requirement is to increase the vast quantities of data stored in RDB into web. In this situation, migration from relational to graph format is very advantageous. Both databases have advantages and limitations depending on the form of queries. Thus, this paper converts relational to graph database by utilizing the schema in order to develop a dual database system through migration, which merges the capability of both relational db and graph db. The experimental results are provided to demonstrate the practicability of the method and query response time over the target database. The proposed concept is proved by implementing it on MySQL and Neo4j.
Classification of Historical Documents Based on LBP and LPQ Techniques

Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.
Training and evaluation of TreeTagger on Amazigh corpus

Part-of-speech (POS) tagging has high importance in the domain of natural language processing (NLP). POS tagging determines grammatical category to any token, such as noun, verb, adjective, person, gender, etc. Some of the words are ambiguous in their categories and what tagging does is to clear of ambiguous word according to their context. Many taggers are designed with different approaches to reach high accuracy. In this paper we present a Machine Learning algorithm, which combines decision trees model and HMM model to tag Amazigh unknown words. In case of statistical methods such as TreeTagger, this will have added practical advantages also. This paper presents creation of a POS tagged corpus and evaluation of TreeTagger on Amazigh text. The results of experiments on Amazigh text show that TreeTagger provides overall tagging accuracy of 93.19%, specifically, 94.10% on known words and 70.29% on unknown words.
ASR System for Isolated Words using ANN with Back Propagation and Fuzzy based DWT

Speech is the primary means through which human beings interact. Speech has become a way for Man Machine Interaction (MMI). The Speech Recognition (SR) systems have been widely used in smart phones to initiate searches or to type certain text messages, and in control devices to perform switch on or off functions etc. This system comprises three blocks: Pre-processing, Feature Extraction and Classification. The input speech signal is pre-processed to remove the noise and to convert it into a digital form for feature extraction. The feature extraction is a significant process during SR systems design because the features extracted form the basis for accurate recognition of the speech. Only a few features of this signal may be selected for classification purposes. For final recognition of the spoken word or the input signal, various optimization algorithms as classifiers are used. This paper presents an extensive literature review on SR Systems. The authors have attempted to do a brief survey to identify the progress in this field. The survey provides the reader with well-known methods used by previous researchers. It also compares the performance metrics for two ASR techniques developed by the authors. The first technique uses Artificial Neural Network with Back Propagation while the second uses Fuzzy based Discrete Wavelet Transform. It was found that the fuzzy based DWT system provided better results in terms of the performance metrics like accuracy, sensitivity, specificity and word error rate. The paper concludes by providing the reader with a direction of future scope in this research area.
Performance Evaluation of Lightweight Advanced Encryption Standard Hardware Implementation

Advanced Encryption Standard (AES) is one of the most secured encryption algorithm because of its robustness and complexity. Because of its complexity, AES has slow computation. This paper presents a Lightweight Advanced Encryption Standard (LAES) design by replacing the MixColumn transformation of the traditional AES with a 128-bit permutation to lessen its computational complexity. Implementation of hardware cryptographic encryption aims to find the best trade-off between throughput and resource utilization. The proposed design is synthesized on various Field Programmable Gate Array (FPGA) devices and achieves the maximum clock frequency of 480.50 MHz with the highest throughput of 6.15 Gbps when synthesized on Virtex 7 XC7VX690T. The results on other devices show a higher throughput, better performance efficiency, and lesser area utilization when compared to the existing AES hardware implementation.
Secure Data Transfer in Manet with Key Calculator and Key Distributer Using Cryptography Methods

A Mobile Ad Hoc Network (MANET) is combined with number of versatile nodes that can communicate with one another without having any predefined foundation.These versatile nodes in the MANET go about as routers to transfer the information from source to destination.Since there is an expansion in number of portable clients and its applications, the versatile nodes security assumes a significant job in it.Even there are many methods for providing security to MANET, there are still several attacks causing in MANET.Secure data transfer in MANET can be achieved by introducing strong cryptographic methods and key exchange techniques.The reason for key generation and key maintenance is to give secure techniques for avoiding malicious activities in the MANET and to increase system performance.In this paper a strong cryptographic method is proposed, which generates and maintains keys and distribute keys safely to trusted nodes avoiding malicious nodes.The proposed method detects the malicious nodes and avoids them to participate in communication to improve packet delivery rate and to reduce delay in the network.The proposed method considers a node as a MANET Key Calculator (MKC) which generates keys and selects another node as MANET Key Distributer (MKD) for providing secure data transfer in MANET by applying cryptography methods.The proposed method is compared with traditional methods and the results show that the proposed method is exhibiting better performance.
Uncanny Feedback

Combining dance with digital and sound art, the performances of Simon Biggs, Sue Hawksley, and Garth Paine produce uncanny effects at the interface of the movement of the performer’s body with the software and technological apparatus that track, map, and interact with it. In <italic>Bodytext, Crosstalk, Blowup and Dark Matter</italic> the uncanny is neither presented as a questioning of subjectivity nor does it emerge from the viewer’s experience of the artwork, but is rather created within the artwork itself, from the feedback among its different interacting elements. “Uncanny Feedback” examines how interactivity between humans and digital media generates uncanny events. Interactivity is not simply a play of surface effects but a complex interactive performance that explores the inter-relationships between kinesthetic experiences and memory, muscle memory and intentional movement, and dance as an imagined movement, a form of interaction, gesture and response to voice recognition, sonification and audio programming.
Indirectness and effectiveness of requests in professional emails

This chapter deals with the question: which strategies are used when formulating a request in a particular context – workplace – and with a specific means of communication (electronic mails). This research is based on a case study: the analysis of 60 emails sent over two days by three employees of a large French professional training company. Our research questions are: how are requests formulated in organizational context? What relationship can we observe between indirectness and effectiveness of email requests.
Education for sustainable development: new map topics in school atlas on economic and social geography of the world

Sustainable development of territories is one of the most fundamental topics of modern natural and social sciences. The 2030 Agenda for Sustainable Development, adopted by the UN General Assembly (2015), contains 17 Sustainable Development Goals (SDGs) on a wide range of human, socio-economic and environmental issues. The national education systems of all countries have been given the task of integrating the SDGs into the content of their school education. Education for Sustainable Development (ESD) is multidisciplinary. An exceptional role in the implementation of ESD ideas is played by geography, which comprehensively studies the system “nature – population – economy”. School maps and atlases have a special place in the arsenal of teaching aids. Maps are not only a source of geographic information, but also a tool for research and design work, the basis for discussion in the classroom. The inclusion of new subjects and themes in the content of school atlases is one of the most optimal and promising approaches to integrating the ideas of sustainable development into school education. The main result of this research are new cartographic plots reflecting the goals of sustainable development, which were developed and included in the content of the school atlas “Economic and social geography of the world. Natural resources. Population. Economics”. A list of the SDGs and atlas maps corresponding to their topics is provided. An integrated approach and consistency are the distinctive conditions for studying the problems of sustainable development. World maps of the school atlas are grouped into three thematic blocks: “Global resource problems”, “Global socio-demographic problems”, “Global environmental problems” and supplemented by charts, graphs and explanatory texts. The topic of sustainable development is also covered in other sections of the school atlas. As an example, we provide a list of atlas maps that can be used when studying the topic “The Problem of Water Shortage and Deterioration of Water Quality”. The world maps in the atlas are presented at scales of 1 : 80 000 000, 1 : 120 000 000, 1 : 160 000 000 and 1 : 250 000 000.
Secure Access Control to IoT Devices using Blockchain

Internet of Things (IoT) is growing at an exponential rate but the area of privacy and security in IoT still remains unexplored. The existing algorithms or methods are mainly centralized and hence they are vulnerable due to their single point authentication topology. As it has been estimated that by 2020 there will be more ‘things’ than people on this earth the problem of security becomes a major concern in IoT networks, as a person having control to an IoT network will be able to control a large portion of an organization. Blockchain has recently been used to provide security to peer-to-peer networks. Blockchains are computationally expensive, heavyweight and are considered unsuitable for IoT architecture. In this paper a new lightweight and secure architecture for IoT by using Ethereum Blockchain retaining most of its security providing powers is proposed. Since Blockchain is decentralized it solves the single point authentication problem existing in IoT networks. A Smart Home System as a representative case study has been implemented for broader IoT applications. The two parameters measured are temperature and intrusion detection. The proposed model tackles some more challenges that exist in IoT networks. The Qualitative evaluation of the proposed architecture highlights how it tackles various attacks.
Secured Aadhar Based E-Voting Application using RSA

The invasion of wireless, mobile phones and various internet technologies has resulted in the new implementation, making the e-voting method very quiet and effective. The E-voting ensures the option of a suitable, simple and secure manner of incarceration and the complete number of votes in an election. This study work offers the e-voting requirements and necessities using a platform based on Android or the internet. E-voting implies voting through electronic devices or websites in the election. An e-voting application is developed using the android or web platform. This request enables the user cast the ballot without going to the polling booth. To prevent fraud respondents using the scheme, the application utilizes adequate authentication steps. Once the polling process is completed in the electronic device, the outcomes can be obtained in a fraction of seconds instantly. All voting counts of applicants are encrypted using RSA and stored in the database to prevent third-party attack and disclosure of outcomes. Upon completion of the meeting, the admin can decrypt the count of votes and publish the outcome and finish the voting process.