Reviewerly/exHarmony huggingface dataset:
Feature Selection Using Extra Trees Classifier for Research Productivity Framework in Indonesia

In this study, the authors succeeded in proving the Extra Trees Classifier reliable as a feature selection mechanism for the research productivity framework. In order to find features that significantly impact research output in higher education institutions, the selection was carried out. The eight selected features are Scientific Article (Gain: 0.36), Number of Citations (Gain: 0.22), Number of Conferences Attended (Gain: 0.17), Intellectual Property Right (Gain: 0.04), Education Qualification (Gain: 0.03), Grant Awardee (Gain: 0.27), Number of Grants (Gain: 0.05), and Lecturer Grade (Gain: 0.05). For the testing phase, the Naive Bayes (NB) classifier and K-Nearest Neighbors classifier are used. The selected features successfully passed it with a high accuracy score, for K-NN by 87%, and NB by 81%. These features are used as variables for the research productivity framework. Up to this point, the variables that construct the framework that the authors propose can go through a testing phase with results as expected, with an accuracy score of more than 70%, which is categorized as relevant or acceptable results. For future work, the univariate selection and correlation matrix will be added as a feature selection with the Extra Trees Classifier, so the selected features are more relevant and are used as a construct for the research productivity framework.
A Reconfigurable 2D-Convolution Accelerator for DNNs Quantized with Mixed-Precision

Mixed-precision uses in each layer of a Deep Neural Network the minimum bit-width that preserves accuracy. In this context, our new Reconfigurable 2D-Convolution Module (RCM) computes N = 1, 2 or 4 Multiply-and-Accumulate operations in parallel with configurable precision from 1 to 16/N bits. Our design-space exploration via high-level synthesis obtains the best points in the latency vs area space, varying the size of the tensor tile handled by our RCM and its parallelism. A comparison with a non-configurable module on a 28-nm technology shows many reconfigurable Pareto points for low bit-width configurations, making our RCM a promising mixed-precision accelerator for inference.
Study on Image-to-image Translation Based on Generative Adversarial Networks

Image-to-image translation has become an increasingly popular technology across multiple areas such as and computer vision, computer graphics, and image processing. The technology learns the features of existing pictures through neural network algorithms and apply them to the output pictures. For example, a picture can be transferred into an oil painting style, and a scene can be presented in the form of RGB images, gradient fields, edge maps, semantic label maps and so on. This paper provides an overview on current research progress on image-to-image translation based on generative adversarial networks (GANs) approaches, including supervised methods based on conditional GAN and unsupervised methods using Cycle GAN and StarGAN. Their performance is compared and their shortcomings are discussed, respectively. Finally, some possible directions for future research are provided.
Industry Relations and Innovative Teaching - Learning Approach Towards Education 4.0 in Malaysia

The industrial sector has been through an industrial revolution for a long time. Now, the higher education institutions in Malaysia are intensifying efforts towards an education system inspired by Industry 4.0. This revolution has attracted many scholars and practitioners with research investigations by developing strategies to enhance and exploit the capabilities of their educational system. The reason because an imbalance skills and qualifications in workforce needed for a future world shaped by the 4IR technologies. The educational system needs to facilitate students to be desirable job market employees, thus contribute to a higher employment rate. The purpose of this study to discover the indicators and elements of teaching towards Education 4.0 involved Engineering Technology. The participants involved in this study are seven (7) experts using purposive sampling strategy. The interviews were transcribed verbatim and analyzed thematically. The findings from this study suggest seven (7) elements to implement teaching towards Education 4.0; these include industrial involvement, industrial collaboration, work-based learning, student ownership, blended learning, distance education, and technology involvement. These elements may be viewed in two mains constructs: industrial relations and innovative teaching-learning approach. The findings provide valuable insights for Education 4.0 and further studies need to be taken with these variables.
QSLRS-CNN: Qur'anic sign language recognition system based on convolutional neural networks

Deaf and dumb Muslims face educational barriers. They can't read, recite, or comprehend the Holy Qur'an, hence they can't practise Islamic ceremonies. This study proposes a CNN-based Qur'anic sign language recognition methodology. First, photos are used to train for dynamic and static gesture recognition. Second, preparing images diversifies datasets. Finally, CNN-based deep learning models extract and classify features. To teach the deaf and dumb Islamic ceremonies, the programme recognises Arabic sign language hand motions referring to dashed Qur'anic letters. Only 24,137 photos of the Holy Qur'an's 14 dashed letters were used in the trials from ArSL2018, a huge Arabic sign language collection. SMOTE raises training and testing accuracy to 98.31% and 97.67%, respectively, whereas the proposed model reaches 98.05% and 97.13%. RMU obtains 98.66% and 97.52% training and testing accuracy, whereas RMO achieves 98.37% and 97.36%.
Blockchain and COVID-19 pandemic: applications and challenges

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.
A 3D‐Printed Computer

Vast amounts of data are generated by sensors that are used to monitor people, animals, plants, machines, structures, and the environment. Increasingly, this data is used to create relevant context based on sophisticated pattern recognition algorithms trained using past labeled data. However, most of these sensor systems are severely constrained regarding their communication and computation capabilities due to limitations on available energy, size, or location. New computational approaches are needed to overcome the limitations of existing digital processors in contextual processing. This article discusses the development of the first such computer that is entirely made based on common 3D‐printing materials and techniques. It is demonstrated that a simple structure printed with regular 3D printers can be driven and used with common measurement tools to perform sophisticated contextual computations, including standard benchmarks and a demonstration of user activity detection from sensor data. The correlation between memory capacity, nonlinearity, and sampling rates with this computer is examined. The 3D‐printed structure may be used as a stand‐alone computer to detect patterns in general data streams. Moreover, the computer can be integrated with the sensorized 3D‐printed structures, leading to the development of cognizant 3D‐printed systems comprising sensors and contextual processors.
ICON: An Ontology for Comprehensive Artistic Interpretations

In this work, we introduce ICON, an ontology that models artistic interpretations of artworks’ subject matter (i.e., iconographies) and meanings (i.e., symbols, iconological aspects). Developed by conceptualizing authoritative knowledge and notions taken from Panofsky’s levels of interpretation theory, ICON ontology focuses on the granularity of interpretations. It can be used to describe an interpretation of an artwork from the pre-iconographical, icongraphical, and iconological levels. Its main classes have been aligned to ontologies that come from the domains of cultural descriptions (ArCo, CIDOC-CRM, VIR), semiotics (DOLCE), bibliometrics (CITO), and symbolism (Simulation Ontology), to grant a robust schema that can be extendable using additional classes and properties coming from these ontologies. The ontology was evaluated through competency questions that range from simple recognition on a specific level of interpretation to complex scenarios. Data written using this model was compared to state-of-the-art ontologies and schemas to both highlight the current lack of a domain-specific ontology on art interpretation and show how our work fills some of the current gaps. The ontology is openly available and compliant with FAIR principles. With our ontology, we hope to encourage digital art historians working for cultural institutions in making more detailed linked open data about the content of their artifacts, to exploit the full potential of Semantic Web in linking artworks through not only subjects and common metadata but also specific symbolic interpretations, intrinsic meanings, and the motifs through which their subjects are represented. Additionally, by basing our work on theories made by different art history scholars in the last century, we make sure that their knowledge and studies will not be lost in the transition to the digital, linked open data era.
Benefits of using blended generative adversarial network images to augment classification model training data sets

Object detection algorithms have reached nearly superhuman levels within the last decade; however, these algorithms require large diverse training data sets to ensure their operational performance matches performance demonstrated during testing. The collection and human labeling of such data sets can be expensive and, in some cases, such as Intelligence, Surveillance and Reconnaissance of rare events it may not even be feasible. This research proposes a novel method for creating additional variability within the training data set by utilizing multiple models of generative adversarial networks producing both high- and low-quality synthetic images of vehicles and inserting those images alongside images of real vehicles into real backgrounds. This research demonstrates a 17.90% increase in mean absolute percentage error, on average, compared to the YOLOv4-Tiny Model trained on the original non-augmented training set as well as a 14.44% average improvement in the average intersection over union rate. In addition, our research adds to a small, but growing, body of literature indicating that the inclusion of low-quality images into training data sets is beneficial to the performance of computer vision models.
Intelligence Inequality and Income Inequality

Abstract This article evaluates the relationship between inequality in cognitive test scores across countries and income inequality. By meta-analyzing the standard deviations in PISA results from 2000 to 2018, the authors construct a measure of intelligence inequalities across countries. They then test this measure to investigate if it has any association with income inequality as measured by the gini index. Across all models and subsamples, the authors do not find a positive association between intelligence inequality and income inequality. In models where the coefficient is statistically significant, the direction is unexpectedly negative, implying greater intelligence inequality is associated with lower income inequality. This is contrary to theoretical predictions of a positive association between these variables. Finally, the authors also find some evidence that greater mean intelligence is associated with lower levels of income inequality.
Approaching Legal Multinomials from the Sociolinguistic Perspective – Insights into Authorship-Based Distinctions

Abstract The paper explores the hypothesis that multinomials can act as authorship-based style distinguishing markers in legal communication. Specifically, the analysis focuses on identifying the quantitative distribution patterns of structural categories of multinomials as typical for two authorship categories and on their communicative function. The two authorship categories that are contrasted here are legal professionals/experts and lay people. The analysis is conducted in the corpus-based methodology with a custom-designed corpus of English, authentic texts found in the legal trade, in the domain of company registration proceedings. The findings confirm that multinomials that are conventionally considered to be a feature of professional legal communication are also cognitively salient in lay communication. Further, the texts drafted by the two categories of authors are profiled by structurally distinct multinomials. Functionally, it has been demonstrated that the structurally distinct types of multinomials that are found quantitatively salient in the two authorship categories are used predominantly for specific stylistic and/or pragmatic functions. Stylistically, multinomials contribute to conventional and ritual patterns which are used to meet the formality standards that have evolved in specific legal professions where authority is of particular importance. Pragmatic factors which account for quantitative salience of specific, structurally profiled categories of multinomials involve mainly reduplication of multinomials that embody norm-related concepts, which is required on the ground of intertextuality and ensures the materialisation of legal effect.
Conditional feature importance for mixed data

Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analyzing a variable's importance before and after adjusting for covariates - i.e., between $\textit{marginal}$ and $\textit{conditional}$ measures. Our work draws attention to this rarely acknowledged, yet crucial distinction and showcases its implications. Further, we reveal that for testing conditional FI, only few methods are available and practitioners have hitherto been severely restricted in method application due to mismatching data requirements. Most real-world data exhibits complex feature dependencies and incorporates both continuous and categorical data (mixed data). Both properties are oftentimes neglected by conditional FI measures. To fill this gap, we propose to combine the conditional predictive impact (CPI) framework with sequential knockoff sampling. The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed. Sequential knockoffs were deliberately designed to handle mixed data and thus allow us to extend the CPI approach to such datasets. We demonstrate through numerous simulations and a real-world example that our proposed workflow controls type I error, achieves high power and is in line with results given by other conditional FI measures, whereas marginal FI metrics result in misleading interpretations. Our findings highlight the necessity of developing statistically adequate, specialized methods for mixed data.
Development of a Mobile Application to Study Sewing Techniques for Manufacturing Fur and Leather Clothes

Abstract The global COVID-19 crisis has led to the international isolation of educational institutions, which have had to adapt to changing ways of presenting the information. Mobile applications are the future of the education sector, leading toward its development. The study aims to develop a mobile application to support learning the peculiarities of leather and fur garment manufacturing sewing techniques. The mobile app “TechLab” was developed by using MIT App Inventor. The article proves the effectiveness of its use in the educational space based on social surveys among teachers and students at different levels of education.
A Lightweight, Secure Big Data-Based Authentication and Key-Agreement Scheme for IoT with Revocability

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.’s scheme which reduces the execution time by one third for security levels of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>λ</mi> <mo>=</mo> <mn>128</mn> </math> . Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>λ</mi> <mo>=</mo> <mn>128</mn> </math> bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool.
graphlayouts: Layout algorithms for network visualizations in R

Network analysis offers a powerful set of methods to understand the relationships among entities, such as people, or organizations, and the patterns that emerge from these connections.It is increasingly popular in various fields, including sociology, biology, economics, and computer science, and has been used to study diverse phenomena including the spread of diseases, flow of information, and the structure of political organizations.Network visualization is a powerful tool for exploring, analyzing, and communicating network structures and patterns therein.However, in contrast to tabular data, nodes can technically be placed arbitrarily on the plane and it is easy to draw wrong conclusion based on an inadequate layout.To circumvent arbitrary placement of nodes, many different layout algorithms have been developed which optimize different stylistic features and can serve purposes in communicating structural properties.The package graphlayouts implements several state-of-the-art algorithms which are so far not available for R.This includes algorithms for large graphs, to emphasize hidden group structures, and important nodes within a network.
Non-English academics face inequality via AI-generated essays and countermeasure tools

The recent introduction of ChatGPT by OpenAI has broadly introduced artificially intelligent (AI) tools as writing aids to the general public. We examined the limits of available detection tools in identifying writing that was fully aided by AI. Short essays (fewer than 400 words) were written by humans in two languages (English, Malay) or were procedurally generated with ChatGPT in four languages (English, Malay, Mandarin, Japanese). Malay, Mandarin, and Japanese essays were translated into English and paraphrased using a paraphrasing tool. All of the essays were then examined with three AI-detection tools available online. All of the English essays generated with the aid of AI were flagged as AI generated, including the AI-translated or paraphrased, non-English, human-written essays. This raises serious concerns for the academic world, especially non-English academics, in clarifying the differences between AI-generated and AI-aided essays in new policies. Blanket policies completely banning all AI tools may put nonnative English speakers at a disadvantage.
Selecting informative data for defect segmentation from imbalanced datasets via active learning

The storage and labeling of industrial data incur significant costs during the development of defect detection algorithms. Active learning solves these problems by selecting the most informative data among the given unlabeled data. The existing active learning methods for image segmentation focus on studying natural images and medical images, with less attention given to industrial images, and little research has been performed on imbalanced data. To solve these problems, we propose an active learning framework to selecting informative data for defect segmentation under imbalanced data. In the initialization stage, the framework uses self-supervised learning to initialize the data so that the initialization data contain more defect data, thereby solving the cold-start problem. During the iterative stage, we design the main body of the active learning framework, which is composed of a segmentation learner and a reconstruction learner. These learners use supervised learning to further improve the framework's ability to select informative data. The experimental results obtained on public and self-owned datasets show that the framework can save 70% of the required storage space and greatly reduce the cost of labeling. The intersection over union value proves that the designed framework can achieve the equivalent effect of labeling the whole dataset by labeling partial data.