Reviewerly/exHarmony huggingface dataset:
Detection of Hate Speech Text in Afan Oromo Social Media using Machine Learning Approach

Objectives: This study aims to develop a hate speech detection model for Afan Oromo's texts on social networks like Facebook and Twitter using a machine learning algorithm.Methods: we collected comments and posts from social media like Facebook and Twitter pages of BBC Afan Oromo, OBN Afan Oromo, Fana Afan Oromo Program, Politicians, Activists, Religious Men, and Oromia Communication Bureau using Face pager tool.The collected data was labelled using Afan Oromo hate speech evaluation system we developed.Text preprocessing tasks applied on data to remove special characters, stop-words, HTML Tags, extra whitespaces, numbers, lemmatization.The n-gram and TF-IDF was applied for feature extraction task to obtain benchmark Afan Oromo hate speech detection dataset.Researchers split dataset into train and test set.Finally, we applied Support Vector Classifier, Multinomial NB, Linear Support Vector Classifier, Logistic Regression decision tree and Random Forest Classifier on 67% of trained data.The performance of proposed model also evaluated using F-score.We also test the performance of developed model by loading test set into it.Findings: Hate speech on social media violates the welfare of Ethnic groups and citizens for living together.Many researches have been doing for English, Amharic, and other Languages to detect hate content from social media.This study has focused on developing a prototype for Afan Oromo hate speech detection model using machine learning algorithms and evaluate its performance in which we found Linear Support Vector Classifier scored highest f1-score value is 64%.Novelty: Afan Oromo hate speech detection framework proposed and successfully implemented to develop Afan Oromo hate speech detection model.We wrote python script that overcome problems typos in Afan Oromo in addition to designing python scripts that recognized apostrophe " ' " as important letter for Afan Oromo word formation.Yet, no researchers have used combination of n-gram and TF-IDF for feature extraction.In this study, the n-gram and TF-IDF used for feature extraction approach to build model https://www.indjst.org/
A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks

It is a well known fact that the weakest link in a cyber secure system is the people who configure, manage or use it. Security breaches are persistently being attributed to human error. Social engineered based attacks are becoming more sophisticated to such an extent where they are becoming increasingly more difficult to detect. Companies implement strong security policies as well as provide specific training for employees to minimise phishing attacks, however these practices rely on the individual adhering to them. This paper explores fuzzy logic and in particular a Mamdani type fuzzy inference system to determine an employees susceptibility to phishing attacks. To negate and identify the susceptibility levels of employees to social engineering attacks a Fuzzy Inference System FIS was created through the use of fuzzy logic. The utilisation of fuzzy logic is a novel way in determining susceptibility due to its ability to resemble human reasoning in order to solve complex inputs, or its Interpretability and simplicity to be able to compute with words. This proposed fuzzy inference system is based on a number of criteria which focuses on attributes relating to the individual employee as well as a companies practices and procedures and through this an extensive rule base was designed. The proposed scoring mechanism is a first attempt towards a holistic solution. To accurately predict an employees susceptibility to phishing attacks will in any future system require a more robust and relatable set of human characteristics in relation to the employee and the employer.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering semantically meaningful latent manipulations typically involves painstaking human examination of the many degrees of freedom, or an annotated collection of images for each desired manipulation. In this work, we explore leveraging the power of recently introduced Contrastive Language-Image Pre-training (CLIP) models in order to develop a text-based interface for StyleGAN image manipulation that does not require such manual effort. We first introduce an optimization scheme that utilizes a CLIP-based loss to modify an input latent vector in response to a user-provided text prompt. Next, we describe a latent mapper that infers a text-guided latent manipulation step for a given input image, allowing faster and more stable text-based manipulation. Finally, we present a method for mapping a text prompts to input-agnostic directions in StyleGAN's style space, enabling interactive text-driven image manipulation. Extensive results and comparisons demonstrate the effectiveness of our approaches.
The Research Output of Bibliometrics using Bibliometrix R Package and VOS Viewer

Bibliometrics is one of the statistical methods to analyze the research output of books, articles, and other scientific publications. This paper attempts to study the three types of Bibliometric indicators such as quantity, quality, and structural indicators. This study pertains to the information on the research growth of Bibliometric study, especially in the subject category of Library and Information Science published in Web of Science Database. This paper presents the findings of a Bibliometric study, targeting five year period (2014–2018), with the aim of identifying emerging research directions, the top-20 institutions, coupling, and collaboration by applying VOS viewer and Biblioshiny for bibliometric tools.
Reliability of COVID-19 symptom checkers as national triage tools: an international case comparison study

Objectives Triage is a critical component of the pandemic response. It affects morbidity, mortality and how effectively the available healthcare resources are used. In a number of nations the pandemic has sponsored the adoption of novel, online, patient-led triage systems—often referred to as COVID-19 symptom checkers. The current safety and reliability of these new automated triage systems remain unknown. Methods We tested six symptom checkers currently in use as triage tools at a national level against 52 cases simulating COVID-19 of various severities to determine if the symptom checkers appropriately triage time-critical cases onward to healthcare contact. We further analysed and compared each symptom checker to determine the discretionary aspects of triage decision-making that govern the automated advice generated. Results Of the 52 clinical presentations, the absolute rate of onward referral to any form of healthcare contact was: Singapore 100%, the USA 67%, Wales 65%, England 62%, Scotland 54% and Northern Ireland 46%. Triage decisions were broadly based on either estimates of ‘risk’ or ‘disease severity’. Risk-based symptom checkers were more reliable, with severity-based symptom checkers often triaging time-critical cases to stay home without clinical contact or follow-up. Conclusion The COVID-19 symptom checkers analysed here were unable to reliably discriminate between mild and severe COVID-19. Risk-based symptom checkers may hold some promise of contributing to pandemic case management, while severity-based symptom checkers—the CDC and NHS 111 versions—confer too much risk to both public and healthcare services to be deemed a viable option for COVID-19 triage.
Shadow Detection via Predicting the Confidence Maps of Shadow Detection Methods

Today's mainstream shadow detection methods are manually designed via a case-by-case approach. Accordingly, these methods may only be able to detect shadows for specific scenes. Given the complex and diverse shadow scenes in reality, none of the existing methods can provide a one-size-fits-all solution with satisfactory performance. To address this problem, this paper introduces a new concept, named shadow detection confidence, which can be used to evaluate the effect of any shadow detection method for any given scene. The best detection effect for a scene is achieved by combining prediction results by multiple methods. To measure the shadow detection confidence characteristics of an image, a novel relative confidence map prediction network (RCMPNet) is proposed. Experimental results show that the proposed method outperforms multiple state-of-the-art shadow detection methods on four shadow detection benchmark datasets.
Retrieval-guided Counterfactual Generation for QA

Deep NLP models have been shown to be brittle to input perturbations.Recent work has shown that data augmentation using counterfactuals -i.e.minimally perturbed inputscan help ameliorate this weakness.We focus on the task of creating counterfactuals for question answering, which presents unique challenges related to world knowledge, semantic diversity, and answerability.To address these challenges, we develop a Retrieve-Generate-Filter (RGF) technique to create counterfactual evaluation and training data with minimal human supervision.Using an open-domain QA framework and question generation model trained on original task data, we create counterfactuals that are fluent, semantically diverse, and automatically labeled.Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings.Moreover, we find that RGF data leads to significant improvements to robustness to local perturbations.
Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

We deal with a challenging scheduling problem on parallel-machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers.
Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image

The CT image is an important reference for clinical diagnosis. However, due to the external influence and equipment limitation in the imaging, the CT image often has problems such as blurring, a lack of detail and unclear edges, which affect the subsequent diagnosis. In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. In addition, in order to better capture information and focus on more important features, we use a multi-scale attention block with multiple branches, which can automatically generate weights to adjust the network. Through these improvements, our model effectively solves the problems of insufficient feature utilization and single attention source, improves the learning ability and expression ability, and thus can reconstruct the higher quality medical CT image. We conduct a series of experiments; the results show that our method outperforms the previous algorithms and has a better performance of medical CT image reconstruction in the objective evaluation and visual effect.
Multi-active contour cell segmentation method based on U-Net network

Accurate segmentation of cells and nuclei is the key technology in computer-assisted diagnosis of cervical cancer. In this paper, a multi-active contour method, which was based on semantic segmentation of the U-Net model, was proposed for overlapping cells segmentation. First, each sample image was labeled as three parts:background, cell and cell nucleus. Then, the U-Net model was trained to segment the cervical images to obtain the cell and cell nucleus; meanwhile, the cell clump information could be obtained. The active contour for each cell was initialized according to the distance from the pixel point to the cell nucleus, and the energy functional was established based on shape prior of cells, the edge prior of images and the mutual prior between different contours. Finally, every single cell was segmented by minimizing the proposed energy functional. Experiment comparisons show that the segmentation method proposed in this paper can segment cervical cells under complex conditions, including independent cells, overlapping cells and their nuclei. The experimental results also prove the effectiveness of proposed method.
Disentangled Sequence to Sequence Learning for Compositional Generalization

There is mounting evidence that existing neural network models, in particular the very popular sequence-to-sequence architecture, struggle to systematically generalize to unseen compositions of seen components.We demonstrate that one of the reasons hindering compositional generalization relates to representations being entangled.We propose an extension to sequenceto-sequence models which encourages disentanglement by adaptively re-encoding (at each time step) the source input.Specifically, we condition the source representations on the newly decoded target context which makes it easier for the encoder to exploit specialized information for each prediction rather than capturing it all in a single forward pass.Experimental results on semantic parsing and machine translation empirically show that our proposal delivers more disentangled representations and better generalization. 11 Our code is available at https://github.com/mswellhao/Dangle.Training Set A boy ate the cake on the table in a house.*cake(x4); *table(x7); boy(x1) AND eat.agent(x2, x1) AND eat.theme(x2, x4) AND cake.nmod.on(x4,x7) AND table.nmod.in(x7,x10) AND house(x10) Test Set (Lexical Generalization) A boy likes the cake on the table in a house.*cake(x4); *table(x7); boy(x1) AND like.agent(x2,x1) AND like.theme(x2,x4) AND cake.nmod.on(x4,x7) AND table.nmod.in(x7,x10) AND house(x10) Test Set (Structural Generalization) A boy ate the cake on the table in a house beside the tree.*cake(x4); *table(x7); *tree(x13); boy(x1) AND eat.agent(x2, x1) AND eat.theme(x2, x4) AND cake.nmod.on(x4,x7) AND table.nmod.in(x7,x10) AND house(x10) AND house.nmod.beside(x10,x13)
MSJEP Classifier: “Modified Strong Jumping Emerging Patterns” for Fast Efficient Mining and for handling attributes whose values are associated with taxonomies

Modified Strong Jumping Emerging Patterns (MSJEPs) are those itemsets whose support increases from zero in one data set to non-zero in the other dataset with support constraints greater than the minimum support threshold (ζ). The support constraint of MSJEP removes potentially less useful JEPs while retaining those with high discriminating power. Contrast Pattern (CP)-tree-based discovery algorithm used for SJEP mining is a main-memory-based method. When the data set is large, it is unrealistic to assume that the CP-tree can fit in the main memory. The main idea to handle this problem is to first partition the data set into a set of projected data sets and then for each projected data set, we construct and mine its corresponding CP-tree. Trees of the projected data sets are called Separated Contrast Pattern Tree “SCP-trees” and Patterns generated from it are Called MSJEPs” Modified Strong Jumping Emerging Patterns”. Our proposal also investigates the weakness of emerging patterns in handling attributes whose values are associated with taxonomies and proposes using an MSJEP classifier to achieve better accuracy, better speed, and also handling attributes in taxonomy.
Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences

The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person's common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person's day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets.
Research on Basketball Teaching Network Course Resource Recommendation Method Based on Deep Learning Algorithm

To solve the problem of invalid resource recommendation data and poor recommendation effect in basketball teaching network course resource recommendation, a basketball teaching network course resource recommendation method based on a deep learning algorithm is proposed. The objective function is applied to eliminate the noise in the basketball teaching network course resource data. The prominent characteristics of basketball teaching network curriculum resources are extracted using a kernel function and combined into a feature set. A convolution neural network (CNN) was employed to realize the basketball teaching network curriculum resources recommendation model. The model was assessed in terms of computation time and recognition error. To validate the performance, the proposed model was compared with two well-known recommendation models such as the learning resource recommendation method based on transfer learning and the personalized learning resource recommendation method based on three-dimensional feature collaborative domination. Experimental results show that the proposed model achieved the lowest computation time of 15 s and recommendation error less than 0.4% as compared with the existing model.
Digitisation or Digitalisation: Diverse Practices of the Distance Education Period in Finland

This case study explores how Finnish primary school teachers orchestrated school days and how teachers and headmasters organised virtual workplace collaboration and collaborated with parents during a period of distance education forced by the Covid-19 crisis in Spring 2020. The data was collected by interviewing primary and secondary school teachers (n = 15) from eight schools in various parts of Finland. Teachers’ experiences were analysed with qualitative content analysis. In this study, the school is seen as a Complex Adaptive System (CAS) and the Covid-19 crisis as a disorder forcing teachers to adapt to a rapidly changing environment. Teachers are viewed here as innovators who address both pedagogical and digital challenges under abnormal circumstances. We identify diverse practices at different stages of digitalisation during the distance education period within four domains: 1) structures of school days, 2) forms of teaching, 3) collaborative activities of teachers and headmaster, and 4) forms of home and school collaboration. We also identify three groups of enablers of distance education practices: 1) the use of digital technology, 2) digipedagogical competence of the teachers, and 3) the ability of teachers to act as adaptive innovators. We find that teachers’ ability to innovate and to adapt pedagogical and digipedagogical expertise become critical success factors when change is forced upon the educational field. We suggest that the results of this study, portrayed as the enablers and domains of distance education, be utilised in planning post-Covid education. All stakeholders influencing schools at different levels should be included in envisioning and implementing future classroom practices of innovative post-Covid schools.
Characteristics and Trends of English Testing Research in China: Visual Analysis Based on CiteSpace

This thesis aims to have a visualized analysis of English testing from 1995 to 2020 in China with the help of the Scientometric software CiteSpace by drawing a map of keyword co-occurrence, time zone, and author cooperation network as well as scientific research institution cooperation network. It found that the cooperation between the authors is not strong and so do the research institutions. Besides, research focus detected mainly include college English, formative assessment, assessment, college English teaching, summative assessment and language testing. And according to the map of time zone, this research field is constantly moving towards enrichment and refinement. Finally, based on the mapping knowledge domains drawn by CiteSpace, the future development trends and deficiencies of this research are summarized.
Beyond Single-Shot Fault-Tolerant Quantum Error Correction

Extensive quantum error correction is necessary in order to perform a useful computation on a noisy quantum computer. Moreover, quantum error correction must be implemented based on imperfect parity check measurements that may return incorrect outcomes or inject additional faults into the qubits. To achieve fault-tolerant error correction, Shor proposed to repeat the sequence of parity check measurements until the same outcome is observed sufficiently many times. Then, one can use this information to perform error correction. A basic implementation of this fault tolerance strategy requires $\Omega(r d^2)$ parity check measurements for a distance-d code defined by r parity checks. For some specific highly structured quantum codes, Bombin has shown that single-shot fault-tolerant quantum error correction is possible using only r measurements. In this work, we demonstrate that fault-tolerant quantum error correction can be achieved using $O(d \log(d))$ measurements for any code with distance $d \geq \Omega(n^\alpha)$ for some constant $\alpha > 0$. Moreover, we prove the existence of a sub-single-shot fault-tolerant quantum error correction scheme using fewer than r measurements. In some cases, the number of parity check measurements required for fault-tolerant quantum error correction is exponentially smaller than the number of parity checks defining the code.