Reviewerly/exHarmony huggingface dataset:
Digital citizenship trend in educational sphere: A systematic review

&lt;span&gt;The surge of information in the digital age has impacted social problems. Accordingly, digital citizenship had been discussed mostly within topics related to digital security, IT education. A systematic review is needed to provide an overview for educators and policymakers in Indonesia to address ethical and technical issues to improve the online environment safely. However, there are limited studies that systematically review the existing literature on implementing digital citizenship in Indonesia. The present study was guided by PRISMA review protocol using three databases, i.e., Scopus, Google Scholars, and ProQuest, concerned with digital citizenship, digital competence, digital literacy, and information and communications technology (ICT) skills. There were 205 articles screened; this study obtained 20 articles that fit the predetermined inclusion criteria. This study used content analysis to encode, organize categories, and develop the themes. Based on the thematic analysis, this study has three main themes namely digital readiness, digital citizenship competencies, and educational policies. This review contributes to the existing literature by providing direction to future study and embedded digital citizenship competencies in the educational setting as a catalyst for a new literacy.&lt;/span&gt;
A Guidance for Blockchain-Based Digital Transition in Supply Chains

Blockchains play a crucial role in the digitalization of future supply chains (SCs). In this study, we analyzed the major influences that blockchain-based digital business strategies may play in SC operations. We conducted 18 interviews with international experts from different sectors and analyzed the collected data using the grounded theory approach, identifying four major categories. A set of three matrices is presented to address those elements that may support digital transition procedures in SCs: (1) value of trust and automation, (2) transformative role for operations, and (3) digital business strategy identification. As an additional result, a reference framework was identified for the evaluation and detection of those aspects to be taken into consideration during blockchain adoption in SCs. For use as a guide, this result comprises 22 recommendations and was framed in four progressive steps as follows: (1) identify, (2) assess, (3) design, and (4) assure.
Development of a Predictive Model for Textual Data Using Support Vector Machine Based on Diverse Kernel Functions Upon Sentiment Score Analysis

This research work specifically focusses on the development of a predictive model for movie review data using support vector machine (SVM) classifier with its improvisations using different kernel functions upon sentiment score estimation. The predictive model development proceeds with user level data input with the data processing with the data stream for analysis. Then formal calculation of TF-IDF evaluation has been made upon data clustering using simple k-means algorithm. Once the labeled data has been sorted out, then the SVM with kernel functions corresponding to linear, sigmoid, rbf, and polynomial have been applied over the clustered data with specific parameter setting for each type of library functions. Performance of each of the kernels has been measured using precision, recall, and F-score values for each of the specified kernel, and from the analysis, it has been found that sentiment analysis using SVM linear kernel with sentiment score analysis has been found to provide an improved accuracy of about 91.18%.
Algorithm for Planning an Informative Route for Map-Aided Navigation

A brief overview of the route planning algorithms in robotics is carried out, and the approaches used in the map-aided navigation systems are highlighted. Common features and differences in route planning in these tasks are noted. An algorithm for choosing a piecewise-linear route is proposed, which allows planning the observation using several geophysical fields with a restriction on the length of linear sections. A brief analysis of the efficiency of the proposed algorithm is carried out.
Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction

In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives.This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approaches.In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation.We sidestep the lack of data through explicitation of implicit relations to reduce the task to two subproblems: language modeling and explicit discourse relation classification, a much easier problem.Our experimental results show that this method can even marginally outperform the state-of-the-art, in spite of being much simpler than alternative models of comparable performance.Moreover, we show that the achieved performance is robust across domains as suggested by the zero-shot experiments on a completely different domain.This indicates that recent advances in language modeling have made language models sufficiently good at capturing inter-sentence relations without the help of explicit discourse markers.
GHOST at SemEval-2021 Task 5: Is explanation all you need?

This paper discusses different approaches to the Toxic Spans Detection task.The problem posed by the task was to determine which words contribute mostly to recognising a document as toxic.As opposed to binary classification of entire texts, word-level assessment could be of great use during comment moderation, also allowing for a more in-depth comprehension of the model's predictions.As the main goal was to ensure transparency and understanding, this paper focuses on the current state-of-the-art approaches based on the explainable AI concepts and compares them to a supervised learning solution with wordlevel labels.The work consists of two xAI approaches that automatically provide the explanation for models trained for binary classification of toxic documents: an LSTM model with attention as a model-specific approach and the Shapley values for interpreting BERT predictions as a model-agnostic method.The competing approach considers this problem as supervised token classification, where models like BERT and its modifications were tested.The paper aims to explore, compare and assess the quality of predictions for different methods on the task.The advantages of each approach and further research direction are also discussed.
Efficient Hierarchical Multi-Object Segmentation in Layered Graphs

Abstract We propose a novel efficient seed-based method for the multi-object segmentation of images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). It uses a tree of the relations between the image objects, with each node in the tree representing an object. Each tree node may contain different individual high-level priors of its corresponding object and defines a weighted digraph, named as layer. The layer graphs are then integrated into a hierarchical graph, considering the hierarchical relations of inclusion and exclusion. A single energy optimization is performed in the hierarchical layered weighted digraph leading to globally optimal results satisfying all the high-level priors. The experimental evaluations of HLOIFT, on medical, natural, and synthetic images, indicate promising results comparable to the related baseline methods that include structural information, but with lower computational complexity. Compared to the hierarchical segmentation by the min-cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios, and has a better running time.
Actitudes hacia las TIC y adaptación al aprendizaje virtual en contexto COVID-19, alumnos en Chile que ingresan a la educación superior

This The COVID19 pandemic forced higher education institutions to implement remote emergency education, testing their preparedness to deliver education based on virtual technologies.Prior to the crisis, these institutions made only limited and marginal use of Information and Communication Technologies (ICT) for learning, a situation that strongly contrasted with the penetration of these technologies in students.Young people have shown a favourable attitude to the use of technologies in the educational field, although their adoption and use seems to be marked by several factors.This study applied to a sample of 323 first-year students from a Chilean university explores the relationships between attitudes towards ICT and adaptation to remote emergency classes in virtual format.The results indicate that gender, access conditions and attitudes towards ICTs influence this adaptation.
News Image-Text Matching With News Knowledge Graph

Image-text matching using the image caption method has made a great progress. However, there are many named entities in news text, and existing approaches are unable to directly generate named entities in the news image caption. It leads to a semantic gap between text and news image caption. Moreover, the existing methods lack the analysis of indirect relations between named entities. Therefore those approaches easily leads to relations error when generating news image caption. To generate the news image caption with named entities by analyzing the indirect relations between named entities. We propose a novel model. In details, we propose the TopNews dataset with related news, which aims to construct the relations between named entities as widely as possible. Then we develop the news knowledge graph by extracting named entities from TopNews dataset. Furthermore, we propose News Knowledge Driven Graph Neural Network (NKD-GNN). We utilize NKD-GNN to analyzing the whole relations of entities in news knowledge graph. In this way, we generate the news image caption with named entities. The results of extensive experiments based on TopNews dataset and common dataset demonstrate that our approach is effective in detecting the consistency of news images and text.
Abductive Learning with Ground Knowledge Base

Abductive Learning is a framework that combines machine learning with first-order logical reasoning. It allows machine learning models to exploit complex symbolic domain knowledge represented by first-order logic rules. However, it is challenging to obtain or express the ground-truth domain knowledge explicitly as first-order logic rules in many applications. The only accessible knowledge base is implicitly represented by groundings, i.e., propositions or atomic formulas without variables. This paper proposes Grounded Abductive Learning (GABL) to enhance machine learning models with abductive reasoning in a ground domain knowledge base, which offers inexact supervision through a set of logic propositions. We apply GABL on two weakly supervised learning problems and found that the model's initial accuracy plays a crucial role in learning. The results on a real-world OCR task show that GABL can significantly reduce the effort of data labeling than the compared methods.
Hyperspectral imagery denoising using minimum noise fraction and VBM3D

Hyperspectral imagery denoising is a classical problem in hyperspectral remote sensing applications. In our previous work, we combined principal component analysis with wavelet shrinkage, and good denoising results were obtained. We combine minimum noise fraction (MNF) with video block matching and 3D filtering (VBM3D), which is a powerful video denoising method. After MNF transform, we automatically select k0, the number of spectral band images as a threshold for denoising. We reduce the noise in spectral band images of MNF transformed data from k0 to the last band image and do not denoise the first k0 − 1 spectral band images. Finally, we perform an inverse MNF transform to obtain the denoised data cubes. We compare our MNF + VBM3D method with different denoising methods such as bivariate wavelet shrinkage (BivShrink), non-local means, SURELET, and block matching and 3D filtering (BM3D). Experimental results demonstrate that MNF + VBM3D achieves the best denoising results among almost all methods for three testing data cubes and with different noise levels.
Transfer Learning Strategies for Credit Card Fraud Detection

Credit card fraud jeopardizes the trust of customers in e-commerce transactions. This led in recent years to major advances in the design of automatic Fraud Detection Systems (FDS) able to detect fraudulent transactions with short reaction time and high precision. Nevertheless, the heterogeneous nature of the fraud behavior makes it difficult to tailor existing systems to different contexts (e.g. new payment systems, different countries and/or population segments). Given the high cost (research, prototype development, and implementation in production) of designing data-driven FDSs, it is crucial for transactional companies to define procedures able to adapt existing pipelines to new challenges. From an AI/machine learning perspective, this is known as the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transfer learning</i> . This paper discusses the design and implementation of transfer learning approaches for e-commerce credit card fraud detection and their assessment in a real setting. The case study, based on a six-month dataset (more than 200 million e-commerce transactions) provided by the industrial partner, relates to the transfer of detection models developed for a European country to another country. In particular, we present and discuss 15 transfer learning techniques (ranging from naive baselines to state-of-the-art and new approaches), making a critical and quantitative comparison in terms of precision for different transfer scenarios. Our contributions are twofold: (i) we show that the accuracy of many transfer methods is strongly dependent on the number of labeled samples in the target domain and (ii) we propose an ensemble solution to this problem based on self-supervised and semi-supervised domain adaptation classifiers. The thorough experimental assessment shows that this solution is both highly accurate and hardly sensitive to the number of labeled samples.
Double constrained bag of words for human action recognition

Various improved methods based on the strategy of bag of words (BoW) are widely used to solve the problem of human action recognition. However, the spatial relationship between features is measured and utilized by these methods in a relatively single way. It limits the recognition performance of these methods. To solve this problem, double constrained bag of words (DC-BoW) is proposed to utilize the spatial distribution information between features belonging to three levels, which include descriptor-level, presentation-level and hidden layer features. Aiming at the problem that most coding methods only rely on Euclidean distance to constrain the relationship between descriptor-level features, the constraints of the difference in length and cosine of angle between visual word and local feature are designed to construct the loss function to obtain the length and angle constrained linear coding (LACLC) method. In order to improve the recognizability of the representation-level features, the spatial distribution between the encoded features around each cluster center is considered. Hierarchical weighting and LACLC are jointly applied to the distribution to construct aggregated word group feature (AWGF). At the same time, the constraint form of correntropy is changed according to the principle of constructing constraints in LACLC. The hidden layer features are combined with new constraint forms to construct double constrained extreme learning machine (DC-ELM), which improves the classification performance of the network while avoiding iterative training of correntropy weight. In order to verify the feasibility of DC-BoW, experiments are conducted on KTH, Olympic Sports, UCF11, Hollywood2 and UCF101 datasets. Experimental results show that the proposed DC-BoW can further utilize the spatial distribution information between features to obtain excellent recognition accuracy compared with other improved methods based on BoW.
Automatic Question Generation for Repeated Testing to Improve Student Learning Outcome

In recent years, educational resources have gradually been digitized, and digital education platforms have gradually become popular. We use AI to accurately assist people in performing daily tasks through a machine learning process. In education, we can use AI in many situations, such as predicting student's learning outcome and discovering student's learning strategies. However, most solutions have not yet utilized modern AI capabilities, such as natural language processing. This research aims to help teachers use machines to automatically generate short answer questions to reduce the time for teachers to write exam questions. In addition, the main reason we focus on short answers is that many studies prove that short answer exercises can enhance student's long-term memory, thereby improving their learning performance. We propose an automatic question generation (AQG) system that combines syntax-base and semantics-base, in order to prove that the system is highly available and improve student's learning performance, we conducted experiments with 41 students. The experimental results show that student's learning performance has been significantly improved, which means that by repeatedly testing the machine question generation system, students can deepen their long-term memory of course knowledge.
Multiple Streams with Recurrence-Based, Counter-Based, and Splittable Random Number Generators

We give an overview of the state of the art on the design and implementation of random number generators for simulation and general Monte Carlo sampling in parallel computing environments. We emphasize the need for multiple independent streams and substreams of random numbers, as well as the advantages (and potential pitfalls) of the increasingly popular counter-based and dynamically splittable generators. We look at recently-proposed constructions and software. We also recall the basic quality criteria for good random number generators and their theoretical and empirical testing. The paper outlines solutions and also raises issues that would require further study.
A survey: Making “Smart Contracts” really smart

Abstract Blockchain technology is a distributed data recording system developed to monitor and secure all encrypted transactions with the shortest identification. Blockchain technology made a name for itself in 2008. However, the real development of this technology has been realized with use of smart contracts in Blockchain technology. In the cryptocurrency world, a smart contract can be defined as an application or program running on the Blockchain. They operate as digital deals that have to comply with certain rules. These rules are predetermined by computer codes and then copied and implemented by the servers in the entire network. The use of the word “smart” as a term in smart contracts comes from the fact that it is not made manually and is realized digitally. In this study, it is aimed to contribute to future studies in the transformation of smart contracts into real “smart” contract structures by applying artificial intelligence algorithms.
Reconstruction of moving object with single fringe pattern based on phase shifting profilometry

Phase shifting profilometry (PSP) shifts the phase by projecting multiple fringe patterns with different initial phase values. Errors will be introduced when the dynamic object moves among the multiple fringe patterns. This paper presents a new 3D reconstruction method of moving object based on PSP by projecting single fringe pattern. The phase shift is caused by the object movement. Multiple images of the object with movement are captured. The object movement is tracked first; then, the phase variation caused by the movement in the single projected fringe pattern is analyzed and the reconstruction model describing the fringe pattern with movement is given; at last, the phase value is retrieved by utilizing the phase variation caused by the movement. The effectiveness of the proposed method is verified by the simulations and the experiment.
Human-like Communication

The most famous grand challenge for machine intelligence is human-like communication. This chapter explores two problem that need to be solved in order for machines to meet this challenge. The first is the technical difficulties posed by ordinary conversation. Production and comprehension in conversation are: multimodal, multi-person, incremental, concurrent, and jointly managed. The fine-grained complexity of these aspects of human interaction are beyond the current state of the art but should, ultimately, be tractable. The second set of problems are foundational. Models that assume human communication is underwritten by a shared language are unable to account for the ubiuquitous and systematic role misunderstanding plays in everyday interaction. As a result they also fail to explain how people adapt their language use to each new person and new situation in real time. This capability is essential for any machine that aims to engage constructively with human diversity.
From Local to Global: Efficient Dual Attention Mechanism for Single Image Super-Resolution

Convolutional neural networks (CNNs) have become a powerful approach for single image super-resolution (SISR). Recently, attention mechanisms are incorporated to enhance the network performance further. However, most methods use them locally to gather and model information at a single layer, which is not sufficient to capture the hierarchical relationship among various channels and restore high-frequency features. Here, we propose an efficient dual attention mechanism, with a global cross-layer attention (GCA) mechanism to emphasize high-frequency information learning by modeling cross-layer feature dependencies, and a local enhanced attention (LEA) mechanism, complementing GCA by offering attention-aware features for accurate feature fusion and also facilitating structure preservation. Experiments demonstrate that our method adapts well to multiple image degradation models and performs favorably against state-of-the-art methods.
Deep YOLO-Based Detection of Breast Cancer Mitotic-Cells in Histopathological Images

Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.