Reviewerly/exHarmony huggingface dataset:
Hedging in Academic Writing: Cross-disciplinary Comparisons in the Michigan Corpus of Upper-Level Student Papers (MICUSP).

Hedging has been a long-standing challenge for English learners. Emerging from the research on hedging in academic writing is the natural/social science dichotomy that hedging is more common in social sciences than in natural sciences. Yet, this line of research has been primarily based on a limited number of disciplines. To bridge this gap, this study compares sixteen disciplines to uncover the cross-disciplinary variation in hedging based on successful student writing captured by the Michigan Corpus of Upper-level Student Papers (MICUSP). Five types of hedging devices were investigated. The results suggest that hedging is more common in argumentation-driven disciplines than in the data-driven ones. Cross-disciplinary differences were also found between disciplines under the same division. The findings challenge assumptions and raise questions about the natural/social science dichotomy in academic writing, calling for discipline-specific instruction on hedging in teaching English for academic purposes. The study also demonstrates the affordances of corpus tools for data-driven teaching and computer-assisted language learning in remote learning during the COVID-19 pandemic.
IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many advantages, including complete results which include data display, data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used.
Research on the Practice of Cross Integration of “Artificial Intelligence + X” New Engineering Disciplines

Under the background of the rapid development of new economy characterized by new technology, new business form, new industry and new model, China's engineering education urgently needs to cultivate diversified and innovative scientific and technological talents. New engineering research has increasingly become the mainstream of the innovation and reform of engineering education. This paper focuses on the practical innovation of “artificial intelligence + X” new engineering cross integration, focuses on cultivating engineering and technical talents to meet social needs, carries out interdisciplinary cross integration engineering practice research, and investigates the cross integration practice of “artificial intelligence + X” new engineering from five aspects: multi-dimensional curriculum system, teaching mode, training platform, training environment and diversified teaching evaluation mechanism, Understanding the employment needs of enterprises, highlighting the characteristic teaching of interdisciplinary cross integration, giving full play to the role of interdisciplinary cross integration in discipline research and practical innovation, and helping the deep integration and efficient iterative development of new engineering education and artificial intelligence technology are of great significance for the construction of new engineering and improving the training quality of engineering and technical talents.
Corrigendum: MultiCapsNet: A General Framework for Data Integration and Interpretable Classification

[This corrects the article DOI: 10.3389/fgene.2021.767602.].
THE EFFECT OF PSYCHOTYPOLOGY AND LINGUISTIC ATTITUDES TOWARDS TARGET LANGUAGE ON THE OCCURRENCE OF TRANSFER ON THE RECEPTIVE LEVEL UPON THE FIRST CONTACT OF CROATIAN SPEAKERS WITH ITALIAN AS L2

Psychotypology is one of the conditions for the occurrence of crosslinguistic influences. Following empirical findings, we conducted a study to establish the presence and the effect of psychotypology on the language transfer between Italian, as a foreign language, and the local speech of Split, as a variant of learners’ L1. Two target groups with no prior knowledge of Italian were questioned: one hundred students regularly in contact with the speech of Split and another hundred with no competence and no contact with it. The two groups took the Yes/No Vocabulary test. The aim was to reveal the initial comprehension of Italian vocabulary. Through the questionnaire, we gained insight into the intuitive behaviour of both groups of participants regarding the meaning of given Italian words, distance perceived, and subjective perception of the relationship between the Italian language and the speech of Split. The results of the two groups were compared using the quantitative test data. In addition, the relationship between psychotypology and a successful test outcome was thoroughly examined, according to the qualitative data. The results demonstrated an extremely high percentage of informants in both groups being aware of similarities between the two idioms at the lexical level. The findings indicated that participants who perceived the similarities as significant performed better on the test. The analysis of results showed that the experimental group found the Italian language easily comprehensible, whereas the control group defined it as somewhat demanding. Significantly better vocabulary test results of the experimental group confirmed that participant’s attitude is essential to achieve language transfer.
Academic leadership: agility in the digital revolution

Abstract The COVID-19 pandemic has brought tremendous challenges for the educational field, especially in terms of leadership strategies to be taken on a short notice in order to ensure both health security of the personnel and learners, but also adapt to an online teaching environment and other challenges. While several higher education institutions had partially or completely adapted to an online educational model, the COVID-19 period offered new challenges in terms of internationalization, investment in technology and financing, but also in terms of implementing new educational models, fully online or hybrid models. Based on a sample of 23 Universities from the European Union, the current research aimed to propose new directions of the academic leadership strategies and focus points during the COVID-19 pandemic, as well as expected trends after the end of the pandemic. Based on the study of the academic leadership strategies and models in the European Union, this paper offers an additional practical overview to existing studies in the field, as it inquires the implemented university leadership strategies in order to adapt and overcome the current pandemic crisis, as well as ensuring the continuity of activities and development of higher educational institutions.
The Influence of Parents' Social Status and Economic Conditions on Social Studies Learning Achievement of Elementary School Students 25 Madello Soppeng regency

The economic role of parents, in general, can be said to have a positive relationship to increasing student learning achievement because students' teaching and learning process requires tools or a set of teaching or learning. The aims of this study are 1) to find out whether social status affects the social studies learning achievement of students at SD 25 Madello, Kabupaten Soppeng., 2) To find out whether the economic conditions of parents affect the social studies learning achievement of students at SD 25 Madello, Soppeng Regency., 3) To find out whether the social status and economic conditions of parents affect the social studies learning achievement of students at SD 25 Madello, Soppeng Regency. This research method uses quantitative expost facto research through a correlational approach. The sampling technique of this study is a probability technique with 43 students from grades III, IV, V. The results of this study and alternative hypothesis testing (H3) there is a significant influence between social status and parents' economic condition on social studies learning achievements of elementary school students. 25 Madello, Soppeng Regency.Peranan ekonomi orang tua secara umum dapat dikatakan mempunyai hubungan yang positif terhadap peningkatan prestasi belajar peserta didik ini, disebabkan proses belajar mengajar peserta didik membutuhkan alat-alat atau seperangkat pengajaran atau pembelajaran. Tujuan dari penelitian ini yakni: 1) untuk menganalisis apakah status sosial berpengaruh terhadap prestasi belajar IPS peserta didik SD 25 Madello Kabupaten Soppeng., 2) Untuk mengetahui apakah kondisi ekonomi orang tua berpengaruh terhadap prestasi belajar IPS peserta didik SD 25 Madello Kabupaten Soppeng., 3) Untuk menganalisis apakah status sosial dan kondisi ekonomi orang tua berpengaruh terhadap prestasi belajar IPS peserta didik SD 25 Madello Kabupaten Soppeng. Metode penelitian ini menggunakan kuantitatif dengan jenis penelitian expost facto melalui pendekatan korelasional. Teknik sampling penelitian ini adalah teknik probality dengan subjek penelitian sejumlah 43 siswa dari kelas III, IV, V. Hasil dari penelitian ini dan pengujian hipotesis alternatif (H3) ada pengaruh yang signifikan antara status sosial dan kondisi ekonomi orangtua terhadap prestasi belajar IPS peserta didik SD 25 Madello Kabupaten Soppeng.
Digital library initiatives: An overview of national and international scenario

The emergence of digital technology and computer networking have provided means where by Information can be stored, retrieved, disseminated, and duplicated in a very fast manner. Digital libraries have made considerable advances, both in technology and its applications. The digital library initiatives at international level are many, but in developing counties like India they are still in a nascent stage. But with initiative like Google Books Projects, Million Book Project, initiated by Carnegie Mellon University, the culture of digital library has also made a beginning in India. This paper discusses the various national and international digital library initiatives projects like Million Book Project, Google Books Projects, NDL, NDLTD.WDL, ICDL, Kalasampada, Shodhganga, KhudaBaksh OrientalPublic Library,Vidyanidhi etc.
Distributed Training and Optimization of Neural Networks

Deep learning models are yielding increasingly better performances thanks to multiple factors.To be successful, model may have large number of parameters or complex architectures and be trained on large dataset.This leads to large requirements on computing resource and turn around time, even more so when hyper-parameter optimization is done (e.g search over model architectures).While this is a challenge that goes beyond particle physics, we review the various ways to do the necessary computations in parallel, and put it in the context of high energy physics.
Image Tampering Forgery Detection Using Convolutional Neural Network with Blockchain

Fake news contains misinformation communicated through various channels including social media. Fake news is spreading in large numbers in this internet era as social media provide an easy way to share it without any authentication. Fake news may have multiple forms like text and images, from which we can extract features to identify the patterns and operations applied on the images. Objective of the system is to build the model for “Detection of fake news presented as image” problem. Deep learning algorithms can be used to develop approaches which can detect tampered images retrieved from any source. However, it is challenging to determine the tampered images as it requires training of models to differentiate between the actual images and tampered images in order to classify it as fake. Automatic separating of fake news requires thorough investigation of images data to get explicit features from the images used in the fake news. With the use of CNN, the images features including the hidden patterns are extracted using the multiple convolutional layers. People upload and spread images over the internet and there is no control on the authenticity and ownership on their own photos. Images stored can be audited to trace the transactions applied on the images using the smart contracts deployed on blockchain. Image forgery detection using convolutional neural network with blockchain (CNNB) is proposed in this work where we combine blockchain technology with image processing techniques using deep learning to build a new hybrid model for mining the tampered images used in the news.
An Improved Algorithm and Implementation of Data Mining for Intelligent Manufacturing Association Rules Based on Pattern Recognition

With the deepening of the research of artificial intelligence theory, the research of pattern recognition has made further development, and the applicable fields have also been continuously expanded. Pattern recognition can be used in text and speech recognition, remote sensing nuclear medicine diagnosis. This article optimizes and analyzes intelligent manufacturing based on pattern recognition, and uses improved association rule data mining algorithms to improve it from many aspects, Association rules are not limited by the number of dependent variables and can discover associations between data in large databases. Data analysis is carried out based on factors in different production stages of intelligent manufacturing to demonstrate the feasibility of the improved algorithm of association rule data mining. The experimental simulation results show that the application of the improved pattern recognition model in this research has made the estimated value of the remanufactured product remanufactured to a maximum of 4.05, and the production effect has also been greatly improved, the floating range is between 6.13 and 7.90, and the overall evaluation can reach 9.06. The experimental results show that the effect of the improved pattern recognition model is greatly improved.
The Changing Role of Academics from the Perspective of Educational Transformation in Education 4.0

The fourth Industrial Revolution with all its elements of new technologies and futuristic innovations is bringing dramatic changes to human life. These include changes to business models across all sectors, the increasing pace of change in jobs (creation, destruction, new forms) with the realignment of the workforce in the sectors, and so on, while expected new knowledge, skills, and competencies from individuals are increasing. Consequently, these changes demand a response from educational institutions. Thus, the question arises: Are educational institutions equipped and prepared to meet the needs and lead the change toward the future? From this question, this chapter discusses the changing role of academics from the perspective of educational transformation in the era and beyond. The chapter begins with an introduction to the Education 4.0 concept and discusses the evolution from the traditional to the emerging concept. It considers the key skills and competencies of the new era including soft skills, and the challenges and solutions that the digital transformation in education has brought to higher education institutions (HEIs). Finally, future-ready education ecosystems together with their active participants are discussed.
Legal Origins and Intersectoral Regulation of AEO Institution

The main elements of AEO institution initially emerged in legislations of different countries, which legal systems belong both to common law and civil law families. In addition, there was an inevitable variability towards the tasks being solved, from general supply chain security issues to a focus on countering terrorist threats. At the same time, the modern AEO standards formed both at the level of international organizations, like WCO and WTO, and the regional level, such as the EU and associated countries. Therefore, we argue that there are several promising approaches to analyzing the legal status of AEO, including the concept of 'legal origins' and the institutions' legal theories. They have a significant potential for explaining national differences in the implementation of the AEO institution, the specifics of the interaction of the norms of different branches of law in regulating the activities of the AEO, the methods, and methods of state administration of the AEO programs. Besides, the paper addresses the issue of intersectoral regulation of AEO institution, which embraces norms of several branches of law, including both public (administrative, tax and customs) law and private. Furthermore, the idea of delegation of competence and responsibilities towards granting supply chains security from state authorities to private entities (i.e. 'trusted traders') creates a shift in legal regulation where different internal rules and practices of private companies become essential to achieving the tasks assigned to AEO institution.
Lattice-Based Self-Enhancement Authorized Accessible Privacy Authentication for Cyber-Physical Systems

Healthcare cyber-physical system significantly facilitates healthcare services and patient treatment effectiveness by analyzing patients’ health information data conveniently. Nevertheless, it also develops the threats to the confidentiality of health information, patients’ privacy, and decidability of medical disputes. And, with the advances of quantum computing technology, most existing anonymous authentication schemes are becoming a growing threat to traditional cryptosystems. To address these problems, for healthcare cyber-physical systems, we propose a new lattice-based self-enhancement authorized accessible privacy authentication scheme by using a strong designated verifier double-authentication-preventing signature technique, called SEAPA. The SEAPA achieves three security and privacy requirements including unforgeability, anonymity for patients’ information, and self-enhancement for patients themselves. A detailed security proof shows our proposal achieves those required security goals. Finally, our construction is demonstrated by parameter analysis and performance evaluation to have reasonable efficiency.
Dataset of network simulator related-question posts in stack overflow

Although the use of network simulator (NS) in predicting the behavior of computer networks has increased, the users often face a variety of challenges and share them on Stack Overflow (SO). However, the challenges that users deal with have not been studied. This paper presents an NS discussion dataset extracted from SOTorrent, which consists of 2,322 NS-related question posts spanning 17 features. The process of data collection was conducted in five steps, including filtering initial post dataset using simulator tags, discovering NS-related tags, collecting the tagged posts, extracting the posts title and preprocessing for LDA (Latent Dirichlet Allocation), and finally applying the LDA topic modeling to obtain the NS posts clustered into eight different topic names. We believe that this dataset will help research community in highlighting issues faced by NS users.
Security Threats in Cloud Rooted from Machine Learning-Based Resource Provisioning Systems

Resources provisioning on the cloud is problematic due to heterogeneous resources and diverse applications. The complexity of such tasks can be reduced with the aid of Machine Learning. Researchers have found, however, that machine learning poses new threats such as adversarial attacks. Based on our investigation, we found that adversarial ML can target resource provisioning systems (RPS) to perform distributed attacks. Our work proposes a fake trace generator (FTG), which can be wrapped around an adversary kernel to avoid detection by the RPS and to enable the adversary to get co-located with the victim’s virtual machine.
Face Recognition Using Cascading of HOG and LBP Feature Extraction

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.
Nacelle: Knowledge Graph-Based Conversational AI for Skills Gap Analysis to Achieve Sustainable Learning at Workplace

Skills Gap Analysis (SGA) is critical to identify the training needs to develop competencies and improve the productivity of the workforce. The current methodology to conduct SGA is done through interviews, surveys and focus group discussions. This process is time-consuming; requires companies to commit manpower; and outcomes are often dependent on the interviewer, facilitator and scribe. To achieve consistent outcomes, conversational AI technologies can be used. The knowledge graphs are constructed using the SGA workflow and skills map to dynamically support the needs of the respective industry sector. Conversational AI—Nacelle—is developed, leveraging the knowledge graphs, to mimic an SGA consultant in the conduct of interviews through online chat. With Nacelle, consistent outcomes for SGA can be achieved with minimized dependency on the skills of the consultant. More importantly, SGA conducted through Nacelle can be applied to train the AI models to enable self-learning and improvement across sectors. Our validation with the Human Resource sector shows promising results with 99% accuracy.
The dark industry of data brokers: need for regulation?

Abstract Measuring the impact of harm when the perpetrator is a faceless company operating in the shadows is akin to walking in the dark. You never know what’s awaiting you next! The data brokering industry is one such instance, possessing against you, arguably the strongest weapon today—a secret digital twin or a digital doppelganger of your attributes. In the ever-growing digital economy, is there a need to regulate this industry? This article identifies the lacuna in the law that permits its operation and argues for fixing the loopholes in order to regulate the data brokering industry. Furthermore, it sheds light on India’s missed opportunity to regulate the industry given the inchoate nature of data protection laws in the country. Finally, it raises ethical concerns and argues for a distinction between reasonable and unreasonable inferences building on Prof. Sandra Wachter’s concept of an ex ante right to reasonable inference.
The Online Safety Bill and the Tech Agenda for 2022

Abstract The Online Safety Bill, which returns to parliament in spring, is likely to define the tech year. Thanks to BCS members and our partners, we’re already part of the debate, and calling for greater clarity on how this legislation will guarantee the accountability it is calling for.