Reviewerly/exHarmony huggingface dataset:
Utilization of Artificial Intelligence in Journalism in Nigeria

Artificial intelligence (AI) is changing present day journalism. Automated news writing and distribution, without human supervision, is already a reality. Infact, AI has changed the way journalists interact with the world outside the newsroom. It enables journalists to analyze data from multiple sources.  In sum, we can say that artificial intelligence will help journalists to survive and overcome the core problems faced by contemporary journalism. This article explored how artificial intelligence is impacting newsrooms and how it can be better adapted to the field of journalism. The study reveals that AI is already automating the newsroom, AI is augmenting journalists, AI is creating new forms of investigative reporting, AI is helping to verify and fact-check, AI is creating a personalized user experience, AI is creating ethical implications. However, the study reveals journalists in Nigeria are yet to embrace AI. Many challenges affacting the adoption of AI in Nwesrooms in Nigeria include lack of electricity to effectively power the AI applications; lack of adequate infrastructure; lack of finance to purchase and maintain these equipments. cost of Internet connection and  the  training  of AI handlers  among others. Although there are challenges facing journalists in the use of AI, these include that it undermines creativity, there is the absence of monitoring, there could be bias, and there could be lack of transparency, fact-checking and fairness among others. However, it is imparative journalists especially in the developing contries like Nigeria, adjust and enbrace these changes occasioned by Artificial intelligence. The study reveals artificial intelligence does not pose a serious threat to professional journalism. In other words, artificial intelligence technologies have added and still adding value to journalism in the digital age. Keywords: Artificial intelligence, journalism, newsroom, challenges
Where's my robot car?

Is the dream of self-driving vehicles going the way of the jetpack, asks Jeff Hecht
Teaching and Learning During The Covid-19 Pandemic

During the pandemic, distance education became a necessity with ICT tools becoming essential for teaching and learning at all levels of education. The present work is a bibliographic review that sought the advantages and disadvantages of distance education during covid-19 pandemic and occurred because the mandatory application of this methodology. The transition to educational technology and distance education first showed some advantages and disadvantages while teachers need more experience in online teaching.
Survey: using BERT model for Arabic Question Answering System.

In this paper, we deal with the community question answer problem. Using Burt's algorithm, the Question Answer Task (QA) is a Natural Domain Language Processing (NLP). We present a survey on language representation learning for the purpose of consolidating a set of common lessons learned across a variety of recent efforts. Which enables machine reading comprehension and natural language inference tasks. BERT controls its simplicity of use also is a light refinement method without substantial task-specific modifications. We highlight important considerations when interpreting recent contributions and choosing which model to use. We will address the strengths and weaknesses of the algorithm and what are the challenges that faced researchers.
Online Predictor Design for Conductor Galloping

Conductor galloping, like many other natural disasters, poses significant challenge to the reliable operation of power system. Such events happen in extreme weather conditions with complex physical triggering mechanisms. Hence, it is challenging to predict such events due to the limited (galloping) samples as well as complex physical dynamics. In this paper, we seek to employ a data-driven model to design galloping predictor. To mitigate the obstacles led by limited samples, we first design a greedy unit disk covering algorithm to extract the structural knowledge in the available dataset. Viewing such knowledge as states, we further propose a continuous time Markov chain to generate the pseudo online data. This data generator further allows us to design the data-driven online predictors for conductor galloping. Numerical studies highlight that k-nearest neighbor predictor works remarkably and more robust to the different degrees of data imbalance, compared with online logistic regression and online random forest.
Merged Biogeography-Based Optimization Algorithm for Color Image Segmentation

Image segmentation is an important step in image processing. Segmentation based on threshold is a common method. Searching the suitable threshold vector is essentially an optimization problem, especially for color image which have higher dimensions and complexity. This paper proposes a merged Biogeography-Based Optimization algorithm (MBBO) for color image segmentation based on threshold. We merge a mutation operation into the migration operator of BBO to enhance the global search ability. Then we merge a chemotaxis operation into the mutation operator of BBO to enhance the local search ability. A greedy selection method is also used to further improve the performance and reduce computation complexity. Experimental results show that MBBO obtains better optimization performance, stronger stability and faster running speed compared with other existing algorithms.
On the Connection between the GEP Performances and the Time Series Properties

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.
The Frequency Discrepancy Between Real and Generated Images

Despite the success of Generative Adversarial Networks (GANs), little work has focused on the discrepancy between real and generated images in frequency domain. In this work, we provide a systematic analysis on this topic. We first demonstrate the general existence of the frequency discrepancy and further perform extensive experiments both on datasets with various frequency distributions and models with different upsampling methods to reveal the sources of the discrepancy. Experimental results show that: resize-convolution is not a perfect alternative to deconvolution, and natural images and unnatural images should be treated separately during training. Based on these studies, we provide some novel solutions to reduce the discrepancy. Finally, we further show the effectiveness of our solutions on Variational Auto Encoders (VAEs). We hope that the community should pay equal attention to the performance of generative models both in spatial and frequency domain.
Network Performance Through Virtual Local Area Network (VLAN) Implementation &amp; Enforcement On Network Security For Enterprise

The aim of this paper was to enhance an enterprise network performance through VLAN implementation by simulating an existing LAN and rectify LAN using VLAN at, in two different scenarios. To rectify the drawbacks of existing enterprise network using virtual local area network. To simulate and compare the new (VLAN) and existing (NO_VLAN) enterprise network performance Virtual LAN technology can also be used to mitigate vulnerability surface for hackers by reducing the traffic request to servers, network visualization leads to ease of administration. There were more benefits of network virtualization through the implementation of VLAN. Thus, VLANs improved bandwidth utilization, power, speed and security. However, nowadays the high-speed network makes people to sense that network is a much more powerful tool to enhance in any business enterprise and make the company running more efficient. The main purpose of the network ability in the universities is to support learning, teaching, research and sharing of information. Unfortunately, even though the capacity of the bandwidth considered to be sufficient, the network can appear slow and inconsistent due to some factors like: the poor network design, the number of devices on the network, local network protocols (MAC Protocol) and misuse of the bandwidth by accessing to bandwidth hungry websites and applications (Dhurgham , 2018).
New Studies in Mathematics Trails

The paper is about new technological approaches to mathematics trails and current developments in the empirical research on mathematics trails. For a long time, maths trails have been used for the popularization of mathematics. Therefore previous research has focused on motivational aspects and the change of mathematical beliefs. With the successful shift from pencil-and-paper maths trails to mobile devices and new technology came new ways to collect data and perform research on maths trails. Besides the classical maths trail activity, students show more motivation towards mathematics because of mobile devices and tasks that illustrate the usefulness and possible mathematics applications. Furthermore, walking a maths trail leads to a better mathematical performance than having a regular lecture without going outdoors or such activity. It affects long-term memory and intrinsic motivation positively.
Adaptive Multi-Scale HyperNet with Bi-Direction Residual Attention Module for Scene Text Detection

Scene text detection is an important step in the scene text reading system.There are still two problems during the existing text detection methods:(1) The small receptive of the convolutional layer in text detection is not sufficiently sensitive to the target area in the image; (2) The deep receptive of the convolutional layer in text detection lose a lot of spatial feature information.Therefore, detecting scene text remains a challenging issue.In this work, we design an effective text detector named Adaptive Multi-Scale HyperNet (AMSHN) to improve texts detection performance.Specifically, AMSHN enhances the sensitivity of target semantics in shallow features with a new attention mechanism to strengthen the region of interest in the image and weaken the region of no interest.In addition, it reduces the loss of spatial feature by fusing features on multiple paths, which significantly improves the detection performance of text.Experimental results on the Robust Reading Challenge on Reading Chinese Text on Signboard (ReCTS) dataset show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications.
Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine

Sentiment analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become a vital field of study since based on the extracted expressions, individuals or the businesses can access or update their reviews and take significant decisions. Sentimental mining is typically used to classify these reviews depending on its assessment as whether these reviews come out to be neutral, positive or negative. In our study, we have boosted feature selection technique with strong feature normalization for classifying the sentiments into negative, positive or neutral. Afterwards, support vector machine (SVM) classifier powered with radial basis kernel with adjusted hyper plane parameters, was employed to categorize reviews. Grid search with cross validation as well as logarithmic scale were employed for optimal values of hyper parameters. The classification results of this proposed system provides optimal results when compared to other state of art classification methods.
Label Similarity Based Graph Network for Badminton Activity Recognition

The sensor-based human activity recognition is a key technology for modern intelligent sports. However, the complexity of sport activities and lacking of large-scale dataset give rise to the challenges on training effective deep neural networks for it. On image/video-based computer vision tasks, deep learning models can be pretrained on large-scale datasets which are semantically similar with specific tasks. However, we cannot pretrain deep learning models for sensor-based human activity recognition due to lacking public large-scale datasets. To get rid of this problem, we propose a similarity-based graph network for the sensor-based human activity recognition. Specifically, it is a Convolutional Neural Network (CNN) being enhanced with an embedded Graph Neural Network (GNN) for learning the label relationship in terms of two proposed similarity measures. The experimental results on BSS-V2 dataset demonstrate that our proposed network outperforms prior state-of-the-art work by 10.3% in accuracy and 13.3% better than backbone CNN model.
Development of A Computer Aided Real-Time Interpretation System for Indigenous Sign Language in Nigeria Using Convolutional Neural Network

Sign language is the primary method of communication adopted by deaf and hearing-impaired individuals. The indigenous sign language in Nigeria is one area receiving growing interest, with the major challenge faced is communication between signers and non-signers. Recent advancements in computer vision and deep learning neural networks (DLNN) have led to the exploration of necessary technological concepts towards tackling existing challenges. One area with extensive impact from the use of DLNN is the interpretation of hand signs. This study presents an interpretation system for the indigenous sign language in Nigeria. The methodology comprises three key phases: dataset creation, computer vision techniques, and deep learning model development. A multi-class Convolutional Neural Network (CNN) is designed to train and interpret the indigenous signs in Nigeria. The model is evaluated using a custom-built dataset of some selected indigenous words comprising of 15000 image samples. The experimental outcome shows excellent performance from the interpretation system, with accuracy attaining 95.67%.
Challenges in Incorporating Exploratory Data Analysis into Statistical Workflow

It is a pleasure to take part in such a lively discussion about the relationship between exploratory and confirmatory data analysis (EDA and CDA), and what is and is not feasible and likely to be valuable in supporting visual analysis.Graphics and data exploration have long been the ugly duckling of statistics even while they have become important aspects of data science, so we are thrilled to see the Harvard Data Science Review give this topic a prominent place of discussion.Several discussants point out that we offer a framework, more of a placeholder for a theory than a theory itself, and no novel graphical or analytical methods.And, indeed, our immediate goal in writing this paper is to not to develop or present new methods so much as to point to the potential value for integration of existing practical and theoretical ideas.To put it another way, when we write, "Designing for interactive exploratory data analysis requires theories of graphical inference," we do not claim to offer any comprehensive theories ourselves, beyond the meta-theory that such a theory would be useful, and a discussion of alternatives and their
Yoga Pose Detection and Correction System

Activity recognition is useful in many domains. These include biometrics, video -surveillance, human-computer interaction, assisted living, sports arbitration, in-home health monitoring, etc. The health status of an individual can be evaluated and predicted by monitoring and recognizing their activities. Yoga is one such domain that can be used to bring harmony to both body and mind with the help of asana, meditation, and various other breathing techniques. Nowadays in a fast-paced lifestyle, people do not have time to go to yoga classes. Hence, they prefer practicing yoga at home. However, there is a need for a tutor to assess their yoga poses. Hence, the system is presented where the user needs to do the yoga pose which is recognized in real-time video. Then, PoseNet is used to generate key points for the body parts. The identified pose is then compared with the target pose. Based on the comparison status generated by the function, verbal instructions are provided for the user to correct the yoga pose.
Sentiment Analysis Using machines Learning Approaches of Twitter Data and Semantic Analysis

The widespread use of the World Wide Web has ushered in a new way for people to share their feelings. It is also a medium with a wealth of knowledge where users can see other users' opinions, which are divided into various sentiment groups and are gradually becoming a key factor in decision- making. This paper contributes to the sentiment analysis for consumer review classification, which is useful for analysing information in the form of a large number of tweets with highly unstructured views that are either positive or negative, or somewhere in between. To do so, we first pre-processed the dataset, then extracted the adjectives from it that have some context, which is known as feature extraction. vector, then added the function vector list classification algorithms that use machine learning, such as: The content function is extracted using Naive Bayes, Maximum Entropy, and SVM, as well as the Semantic Orientation based WordNet, which extracts synonyms and similarity. Finally, we evaluated the classifier's output in terms of recall, precision, and accuracy.
HyperJump: Accelerating HyperBand via Risk Modelling

In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e.g., training with sub-sampled datasets) to identify promising configurations to be tested via high-fidelity observations (e.g., using the full dataset). Among these, HyperBand is arguably one of the most popular solutions, due to its efficiency and theoretically provable robustness. In this work, we introduce HyperJump, a new approach that builds on HyperBand’s robust search strategy and complements it with novel model-based risk analysis techniques that accelerate the search by skipping the evaluation of low risk configurations, i.e., configurations that are likely to be eventually discarded by HyperBand. We evaluate HyperJump on a suite of hyper-parameter optimization problems and show that it provides over one-order of magnitude speed-ups, both in sequential and parallel deployments, on a variety of deep-learning, kernel-based learning and neural architectural search problems when compared to HyperBand and to several state-of-the-art optimizers.
Multi-hypothesis inspired super-resolution for compression distorted screen content image

Multi-hypothesis-based prediction has been repetitively proven to be effective in improving prediction accuracy and enhancing coding performance. In this paper, we introduce the principle of multi-hypothesis to the super-resolution (SR) of compressed screen content images, with the goal of improving the restoration quality of the compression contaminated screen content images. More specifically, the super-resolution is achieved by a deep neural network. The deep neural network learns the mapping relationship between the compressed low-resolution (LR) image and the original high-resolution (HR) image. During learning process, we feed multiple LR patches for training, including the current patch and five neighboring patches, providing more informative clues for the learning of the high-quality restoration. In the inference process, input LR image will be translated with random offsets, yielding five assistant LR items for the SR of the input LR image. The LR and assistant LR items employ separate modules for feature extraction and then the features are fused with concatenation. Subsequently, the deep residual feature extraction is applied, which is composed of multiple consecutive residual blocks. Finally, the deep features are reconstructed with pixel shuffle, producing the SR image. Experimental results verify the effectiveness of the proposed multi-hypothesis-based SR scheme.
The Online Intersection among Islamophobia, Populism, and Hate Speech: An Italian Perspective

Abstract Online radical Islam is a topic widely studied by scholars and notoriously discussed among non- experts as well (Awan, 2007; Von Behr et al. 2013; Gray &amp; Head 2009). Because of its intrinsic characteristics (i.e. accessibility, anonymity, or users’ identity dissimulation), the internet has always been a useful tool for propagandists of Islamic fundamentalism (Fighel, 2007; Stenersen, 2008; Koehler, 2014). However, in the last decade, studies have questioned the real importance and magnitude of Islamic radicalization online (Gill et al., 2017). In fact, while scholars were focused on observing digital Islamic radicalization, a galaxy of new forms of extremism was growing online (Silva et al., 2017; Roversi, 2008) that no longer made Islam an exceptional case study. Today, Muslim people are one of the groups most aggressively targeted by extremist, intolerant, violent, and radical discourses (Elahi &amp; Khan, 2017; Amnesty International, 2019). Anti-Muslim hate speech has spread online throughout Europe and the United States, reinforced by the propaganda and political discourse of populist right-wing parties (Hafez, 2014; Bakali 2016). This paper introduces some large-scale action-research projects developed in Europe and Italy in the last three years (2016–2019) and aims to reconstruct the most updated Islamophobia state of the art in terms of numbers, characteristics, and phenomenology from the offline to the online context.
SOCIO-PSYCHOLOGICAL ASPECTS OF CREATING AN ELECTRONIC TEXTBOOK

The article proves that the electronic textbook and the electronic version of the textbook are completely different concepts and cannot be equaled. It has been established that actions and information related to the use of the Internet cannot be described as "useful" or "harmful", as such conclusions depend on the result of the activity and not on the activity itself. It is substantiated that the equal entry of young people into the communication system of online communication leads to the formation of an appropriate structure of communication in the offline system. This affects the overall structure of social relations based on the system of interpersonal interactions. It is proved that at the present stage of education development the most rational is the creation of electronic textbooks, which in their content should be aimed at presenting information in audio and video formats of small volumes with a short presentation of the question content.