Reviewerly/exHarmony huggingface dataset:
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus

The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approaches, has been hindered by the lack of resources with a comparable size in other languages. We have therefore developed the ItaCoLA corpus, containing almost 10,000 sentences with acceptability judgments, which has been created following the same approach and the same steps as the English one. In this paper we describe the corpus creation, we detail its content, and we present the first experiments on this new resource. We compare in-domain and out-of-domain classification, and perform a specific evaluation of nine linguistic phenomena. We also present the first cross-lingual experiments, aimed at assessing whether multilingual transformerbased approaches can benefit from using sentences in two languages during fine-tuning.
A Research on Detection and Classification of Breast Cancer using k- means GMM &amp; CNN Algorithms

Breast cancer, is a type of cancer that affects women in larger number in the world. Medical advances on all fronts to improve the care of patients and defeat this disease of the century. Because of this, it is essential that several disciplines continue to make their contribution and particularly data mining or artificial Intelligence. The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets. The proposed method consists of three steps: The first step is to find region of interest (ROI). The second step is texture feature extraction of ROI and optimization of features using optimized feature selection algorithm.. The third step is classification of detected abnormality as benign or malignant using Convolutional Neural Networks (CNN). The proposed method was evaluated using Mammographic Image Analysis Society MIAS) dataset. The proposed method has achieved 95.8% accuracy
Your OTP Is Also Not Secure: Security Issues Of Mobile Apps in the E-Commerce

Now a days most of the persons doing their banking transactions, bill payments online through mobile app or web. Almost all the financial institutions are providing second layer of authentication by providing one time password while online transactions. But one the other hand in almost every mobile applications have the features to read your contacts, your locations, your messages not only read but they have the features to write the massages, write the contacts in your contacts list. Are these features really helps us? Are they really for our time saving? In this research article we will see the dark side of these features of our mobile applications.
The influence of organisational and individual factors on organisational innovation with moderating role of innovation orientation

This study examines the influence of innovation factors (process innovativeness, behavioural innovations, strategic innovativeness) and organisational factors (organisational rewards, top management support) on organisation innovation. The present study extends the organisation innovation model and outlined moderating effect of innovation orientation between organisation innovation and organisation performance. For data analysis, structural equation modelling (SEM) was used. Findings showed that altogether behavioural innovativeness, process innovativeness, strategic innovativeness, top management support, organisational rewards, enjoyment and knowledge self-efficacy explained R2 79.3% variance in organisational innovation. The predictive relevance Q2 of the model was checked. Results of the blindfolding procedure revealed that organisation innovation had Q2 0.579 which is substantial. Effect size analysis (f2) revealed that enjoyment in helping others and organisation innovation were the most important factors to determine organisation performance. Practically, this study suggested that managers need to focus on organisation innovation and strategic innovativeness factors in order to enhance the organisation performance.
GLEAM -- An Illumination Estimation Framework for Real-time Photorealistic Augmented Reality on Mobile Devices (demo)

Mixed reality mobile platforms attempt to co-locate virtual scenes with physical environments, towards creating immersive user experiences. However, to create visual harmony between virtual and physical spaces, the virtual scene must be accurately illuminated with realistic lighting that matches the physical environment. To this end, we design GLEAM, a framework that provides robust illumination estimation in real-time by integrating physical light-probe estimation with current mobile AR systems. We present a demo implementation of GLEAM by means of an AR application that estimates environmental illumination and renders the scene with real-time illumination updates. We demonstrate the efficacy of GLEAM's estimation against a current commercial status quo solution, Apple's ARKit, with the same application.
The evaluation method of English teaching efficiency based on language recognition technology

At present, there are some problems in the evaluation methods of English teaching efficiency, such as poor significance of indicators, low evaluation efficiency and low evaluation accuracy. This paper proposes an evaluation method of English teaching efficiency based on language recognition technology. Language recognition technology is used to identify English sentences through error detection, eigenvalue extraction, pronunciation evaluation, phoneme association and phoneme recognition. The overall structure of English teaching efficiency evaluation is constructed by B/S structure, and the weight of evaluation indicator is calculated by AHP combined with hierarchical relevance theory. The degree of relevance is obtained, and the evaluation of English teaching efficiency is completed by ranking the degree of relevance. The experimental results show that the proposed method has high significance, high evaluation efficiency and high evaluation accuracy.
Blockchain Technology

A blockchain is a growing list of records called blocks, which are linked using cryptography.This paper introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges.Moreover, this paper also points out the future directions in the blockchain technology.
Comparison of 2D vs. 3D Unet Organ Segmentation in abdominal 3D CT images

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. Firsteach relevant organ’s volume of interest is extracted as bounding box. The extracted volume acts as input for asecond stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmen-tation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial resultsindicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instancewere tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal keyorgans, there were no significant differences, but we observe highly significant advantages for the 2D U-Net interms of GPU computational efforts for all organs under study.
Evaluating the intended use of Decision Support System (DSS) via Academic Staff: An Applying Technology Acceptance Model (TAM)

Since the beginning of the expansion of information systems, people have been considering using them for making decisions. Decision Support System (DSS) is a computer technology solution that can be used to support complex decision making and problem solving. In order to produce a high quality education decisions, managers have to be equipped with wide range of relevant information which makes the process of decision making even more complex. In situations like this, use of DSS can be a logical solution. The aim of this research is to investigate the intended use of DSS within academic staff at Universiti Tun Hussein Onn Malaysia by applying Technology Acceptance Model (TAM).In this research were employed structural equations modelling (SEM) approach with SmartPLS software to investigate students’ adoption process. Findings indicates that the perceived ease of use, perceived usefulness have a positive impact and substantially associated with intend to use DSS among academic staff at universities. The study concludes that academic staffs at universities in Malaysia have positive perceptions towards DSS and intend to practice it for educational purposes. Also, the study indicates the importance of perceived usefulness and perceived ease of use as core factors which influence on the perception of using DSS to support management decision process.
News from the Field

The March digital exhibit at University of OregonNevada State Library launches virtual reality cataloging projectIDEAL ’19: Advancing Inclusion, Diversity, Equity, and Accessibility in Libraries and ArchivesACRL information literacy programs, special collections security guidelines revisedFive librarians selected as 2019 IFLA/OCLC FellowsOASIS adds Project MUSE ebooksRecent Researches in Music OnlineEBSCO acquires StacksPALNI, WRLC, PALCI collaborate on IR solutionsProQuest One Academic debuts
Heterogeneous Medical Image Retrieval using Multi-Trend Structure Descriptor and Fuzzy SVM Classifier

This research work contributes a system for heterogeneeous medical image retrieval usiing Multi-trend structure descriptor (MTSD) and fuzzy support vector machine (FSVM) classifier. The MTSD encodes the local level structure in the form of trends for color, shape and texture information of medical images. Experimental results demonstrate thatt the fusion of MTSD and FSVM significantly increases the retrieval precisiion for heterogeneeous medical image dataset. The simplest Manhattan diistance is incorporated for measuring the similarity. The feasibility of thee proposeed system is extensively experimented on benchmark daataset and the experimental study clearly demonstrated that proposed fusion of MTSD with Fuzzy SVM gives significantly superior average retrieval precision.
Scrumbanfall: An Agile Integration of Scrum and Kanban with Waterfall in Software Engineering

Software industries have been growing with the rise of automation technologies and looking for change in their software development practices to gain the benefits of automation technologies for achieving their business goals. Agile Business Process Reengineering (ABPR) is trending approach for Software Engineering Management (SEM) in software industries, which helps software development organization in transformation of software development practices. Scrum and Kanban are fascinating Agile methodologies adopted by Software Engineering Management practitioners. Scrumban, a mixture of both Scrum and Kanban, has derived strengths of both frameworks, in formation of a robust framework against challenges of Agile Software Engineering (ASE) methodologies like work flow control, lead time management, product delivery that could not be solved by Scrum or Kanban as a standalone framework. But still some of challenges, which are out of scope of Scrumban like project requirement documentation, planning, scheduling, estimation and clear product vision at initial stage of the project. Such issues have raised a request of research for Software Process Reengineering (SPR) in Scrumban by customizing its structure for to form the next level of hybrid framework. The Traditional SDLC methodology ‘Waterfall’ has answers for such issues. An empirical analysis, for resolution of such issues, with the help of ‘Waterfall’ model and its life cycle protocols is the main goal this research study by integrating Scrum and Kanban with Waterfall to form a hybrid framework ‘Scrumbanfall’ aligning Kanban in center of integration under the boundary of Scrum rules. This research also highlights limitations of Scrumbanfall like team members and their roles and capabilities for the large scaled project having team members in distributed environment, which open the door for next level of research to overcome such challenges using further hybridization of Scrumbanfall.
Agriculture Talk BOT using AI

Artificial Intelligence and Machine Learning are driving IT industry to new landscape. This system “The TalkBot” overcomes this problem and provides farmers the better opportunity to obtain the desired information and to scale up with upcoming market trends and technologies in a user friendly manner. TalkBot is actually a chatbot, which is a virtual conversational assistant, through which the users can communicate with the bot as if they are conversing with humans. The focus is on developing the bot in a more intellectual way, that it can even recognize not so well grammatically defined sentences, misspelled words, incomplete phrases, etc.,. This can help people to converse easily with the bot, since this system uses the Natural Language Processing technique to parse the user queries, identify the key words, match them with Knowledge Base and respond with the accurate results. To make the responses more understandable, the responses are generated using classification algorithms and produce non textual responses so that it can be easily perceived by the users. Bot also has an ability to provide voice oriented responses using text to speech techniques..
Easing Into Open Science: A Guide for Graduate Students and Their Advisors

This article provides a roadmap to assist graduate students and their advisors to engage in open science practices. We suggest eight open science practices that novice graduate students could begin adopting today. The topics we cover include journal clubs, project workflow, preprints, reproducible code, data sharing, transparent writing, preregistration, and registered reports.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis

Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations.As a result, RE still suffers from unintended entity bias, i.e., the spurious correlation between entity mentions (names) and relations.Entity bias can mislead the RE models to extract the relations that do not exist in the text.To combat this issue, some previous work masks the entity mentions to prevent the RE models from overfitting entity mentions.However, this strategy degrades the RE performance because it loses the semantic information of entities.In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.We first construct a causal graph for RE, which models the dependencies between variables in RE models.Then, we propose to conduct counterfactual analysis on our causal graph to distill and mitigate the entity bias, that captures the causal effects of specific entity mentions in each instance.Note that our CORE method is model-agnostic to debias existing RE systems during inference without changing their training processes.Extensive experimental results demonstrate that our CORE yields significant gains on both effectiveness and generalization for RE.
Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness

With recent advances in mobile and internet technologies, the digital payment market is an increasingly integral part of people’s lives, offering many useful and interesting services, e.g., m-banking and cryptocurrency. The m-banking system allows users to pay for goods, services, and earn money via cryptotrading using any device such as mobile phones from anywhere. With the recent trends in global digital markets, especially the cryptocurrency market, m-banking is projected to have a brighter future. However, information stored or conveyed via these channels is more vulnerable to different security threats. Thus, the aim of this study is to examine the influence of security and confidentiality on m-banking patronage using artificial intelligence ensemble methods (ANFIS, GPR, EANN, and BRT) for the prediction of safety and secrecy effects. AI models were trained and tested using 745 datasets obtained from the study areas. The results indicated that AI models predicted the influence of security with high precision (NSE &gt; 0.95), with the GPR model outperformed the other models. The results indicated that security and privacy were key influential parameters of m-payment system patronage (m-banking), followed by service and interface qualities. Unlike previous m-banking studies, the study results showed ease of use and culture to have no influence on m-banking patronage. These study results would assist m-payment system stakeholders, while the approach may serve as motivation for researchers to use AI techniques. The study also provides directions for future m-banking studies.
Virtual reality for educating Sign Language using signing avatar: The future of creative learning for deaf students

In the United States, the National Institute on Deafness and Other Communication Disorders estimates that 90&#x0025; or more deaf children have hearing parents. Communication is one of the first aspects of family life impacted by having a deaf child. The hearing parents of deaf children and teachers often have difficulty communicating with their deaf children and need to interact with them using sign language. Deaf children and hearing parents still face many challenges in learning sign languages despite technological advances such as mobile apps, desktop and web applications, and new instructional materials and methods. In Qatar, 13.7&#x0025; of the persons with disabilities have some difficulties, many challenges, or cannot hear totally, highlighting the need to include and foster ICT accessibility of deaf and hard of hearing persons in education. In this paper, we presented a new approach based on virtual reality (VR) to teach the basics of Qatari Sign Language (QSL) for teachers and parents, which can be extended to beginner interpreters for sign language. We experimented on 52 participants from specialized primary schools and higher institutes to teach them new signs in QSL (Arabic Sign Language). Virtual reality presented an innovative way for fast education through impact assessment conduct before and after the training sessions.
The Impact of Online Learning Strategies on Students’ Academic Performance

Higher education institutions have shifted from traditional face to face to online teaching due to Corona virus pandemic which has forced both teachers and students to be put in a compulsory lockdown. However the online teaching/learning constitutes a serious challenge that both university teachers and students have to face, as it necessarily requires the adoption of different new teaching/learning strategies to attain effective academic outcomes, imposing a virtual learning world which involves from the students’ part an online access to lectures and information, and on the teacher’s side the adoption of a new teaching approach to deliver the curriculum content, new means of evaluation of students’ personal skills and learning experience. This chapter explores and assesses the online teaching and learning impact on students’ academic achievement, encompassing the passing in review the adoption of students’ research strategies, the focus of the students’ main source of information viz. library online consultation and the collaboration with their peers. To reach this end, descriptive and parametric analyses are conducted in order to identify the impact of these new factors on students’ academic performance. The findings of the study shows that to what extent the students’ online learning has or has not led to any remarkable improvements in the students’ academic achievements and, whether or not, to any substantial changes in their e-learning competence. This study was carried out on a sample of University College (UAEU) students selected in Spring 2019 and Fall 2020.
Effectiveness of Employing the E-mind Mapping Strategy in Scientific Courses: Adopting the Blended Learning Approach at Emirati Private Preparatory Schools

This article explored the effectiveness of employing the e-mind mapping strategy in scientific courses in Emirati private preparatory schools in the light of adopting the blended learning approach. It explored that from the perspective of students. The researchers adopted the descriptive analytical and quantitative approaches. They used a survey that was developed by them based on several studies. Those studies were published in peer-reviewed journals. The forms of the survey were uploaded to the web through using the use of Google Form. The purposive sampling method was employed. For instance, the researchers sent the link of the survey to 400 students in five Emirati private preparatory schools. Two schools of those ones are located in Ajman, one school in Abu Dhabi, one school in Dubai and one school in Sharjah. However, 182 forms were filled. The response rate is 45.5%. The researchers used descriptive statistical methods. They found that the effectiveness of employing the e-mind mapping strategy in scientific courses in Emirati private preparatory schools in the light of adopting the blended learning approach is high. This strategy promotes innovation within students and improves their learning skills. It enables students to carry out self-learning practices. It improves students’ ability to do writing-related tasks. The researchers recommend holding courses for teachers for teaching them the way of creating e-mind maps through using various software.
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks. However, they still tend to perform poorly on multi-step logical reasoning problems. Here we carry out a comprehensive evaluation of LLMs on 50 tasks that probe different aspects of logical reasoning. We show that language models tend to perform fairly well at single step inference or entailment tasks, but struggle to chain together multiple reasoning steps to solve more complex problems. In light of this, we propose a Selection-Inference (SI) framework that exploits pre-trained LLMs as general processing modules, and alternates between selection and inference to generate a series of interpretable, casual reasoning steps leading to the final answer. We show that a 7B parameter LLM used within the SI framework in a 5-shot generalisation setting, with no fine-tuning, yields a performance improvement of over 100% compared to an equivalent vanilla baseline on a suite of 10 logical reasoning tasks. The same model in the same setting even outperforms a significantly larger 280B parameter baseline on the same suite of tasks. Moreover, answers produced by the SI framework are accompanied by a causal natural-language-based reasoning trace, which has important implications for the safety and trustworthiness of the system.