Reviewerly/exHarmony huggingface dataset:
Impact of Foreign Direct Investment on Nigeria’s Agricultural Sector (1981 to 2019)

The need to augment the financial policy interventions of the Central Bank of Nigeria in the agricultural sector is sine qua non. Since agriculture is still the mainstay of Nigeria’s economy, its reliance on foreign direct investment (FDI) ought to be ascertained. Consequently, this study investigated the impact of foreign direct investment on Nigeria’s agricultural sector. Time series data between 1981 and 2019 were obtained from the databases of the Central Bank and Food and Agriculture Organisation. The Augmented Dickey-Fuller test shows that the variables were I(1). Johansen’s co-integration test suggested long-run relationship among the variables. Findings revealed slower acceleration of agricultural productivity (6.28) than FDI (17.99). Also, FDI and exchange rate had statistically significant (p &lt; 0.05) and negative impact on the agricultural productivity, while implicit price deflator for the agricultural sector had statistically significant (p &lt; 0.001) and positive impact on agricultural productivity in the long-run. The error correction term indicates that the speed of adjustment to the short-run equilibrium was high (79.71%). Hence, reliance on foreign direct investment would have adverse effect on agricultural gross domestic product in the long-run. The Federal Ministry of Agriculture and Rural Development should evolve policies that would guarantee steady inflow of foreign direct investment to agriculture in a manner to reverse the negative impact or explore alternatives. In addition, the Central Bank of Nigeria should adopt a sound monetary policy to attain stability in the exchange rate as well as supply of FOREX to ease purchase of agricultural inputs.
Use of artificial intelligence system to predict consumers’ behaviors

In online shopping enterprises, AI technology has been widely used to provide accurate and fast personalized consumer services. This research demonstrates the use of AI technology in the e-commerce business, specifically online enterprises, to determine different effects. The study was conducted in Jordan and involved about 230 participants. The study evaluated different impacts of AI, such as e-payment and stimulating consumers' sentiments. The study used the Stimulus–Organism–Response model (SOR) empirical model, which states that the examination of human processes differs from that of the machine assessment. The model classified the AI technology experienced by the customers' when they visit online to do purchasing. Online purchasing behaviors can be influenced by insight, accuracy, and interaction experience. Also, the perceived value was used as a mediating variable from the prospects of perceived hedonic and utility value. The research integrated empirical research models such as SEM and SPSS to analyze the data on the effects of three-dimension. The results indicated that the AI technology accuracy, interactive experience, and insight significantly affected customers' perceived hedonic and utilitarian values.
Maximizing WhatsApp Group for Students Group Presentation in Learning English Online

WhatsApp Group (WAG) is one favorite choice for an online learning platform in this Covid-19 pandemic era. It is accessible, quite cheap, and user-friendly. Meanwhile, one of the activities in online English learning via WAG is a class group division named Students' Group Presentation (SGP). The aim is to train the students to manage online learning in their classes confidently. Therefore, this study is to describe the lecturer's experience in handling SGP through WhatsApp Group. It provides the learning steps and strategies to maximize the SGP performance. It is not merely presenting the topics, but they also should be creative to live up to the online learning atmosphere. This study is qualitative research and the data were collected from SGP activities in WAG. Firstly, the lecturer gives the example to give the presentations online through WAG such as warming up, brainstorming, main-course, cooling down, giving quote of the day (QOTD), and then closing. Furthermore, the team made presentations alternately at each meeting according to the lecturer's presentation model in several meetings. It is hoped that the results of this study will give contributions and ideas for other lecturers and researchers in teaching English online via WhatsApp.
A Probability-Based Models Ranking Approach: An Alternative Method of Machine-Learning Model Performance Assessment

Performance measures are crucial in selecting the best machine learning model for a given problem. Estimating classical model performance measures by subsampling methods like bagging or cross-validation has several weaknesses. The most important ones are the inability to test the significance of the difference, and the lack of interpretability. Recently proposed Elo-based Predictive Power (EPP)-a meta-measure of machine learning model performance, is an attempt to address these weaknesses. However, the EPP is based on wrong assumptions, so its estimates may not be correct. This paper introduces the Probability-based Ranking Model Approach (PMRA), which is a modified EPP approach with a correction that makes its estimates more reliable. PMRA is based on the calculation of the probability that one model achieves a better result than another one, using the Mixed Effects Logistic Regression model. The empirical analysis was carried out on a real mortgage credits dataset. The analysis included a comparison of how the PMRA and state-of-the-art k-fold cross-validation ranked the 49 machine learning models, an example application of a novel method in hyperparameters tuning problem, and a comparison of PMRA and EPP indications. PMRA gives the opportunity to compare a newly developed algorithm to state-of-the-art algorithms based on statistical criteria. It is the solution to select the best hyperparameters configuration and to formulate criteria for the continuation of the hyperparameters space search.
Bidirectional long-short term memory and conditional random field for tourism named entity recognition

&lt;p&gt;&lt;span&gt;The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.&lt;/span&gt;&lt;/p&gt;
Blocking Of Spam Mail Using K-Means Clustering Algorithm

Email is a method of exchanging digital messages between people using digital devices such as computers, tablets and mobile phones. Email spam also known as junk email is unsolicited bulk messages send through email. The use of spam has been growing in popularity since 1990s and is a problem faced by most email users. Recipients of spam often have had their email addresses obtained by spambots which are automated programs that crawl the internet looking for email addresses. Origin blacklisting is used to detect and filter these kinds of emails. The sources of emails are provided by origin detection. In this origin identity, the spam blocking system finds the source and if any source matches with the user identity, the spammer is blocked to send email. But the system cannot check the contents of spam. The Propose system verifies the mail contents. If the spam content matches with the spam database cluster, then it blocked while sending to receiver. In this system blocks the mail and prevents the flow of data in a network. The porter stemmer and k-means clustering algorithms are using in this propose system.
SAR Image Segmentation by Merging Multiple Feature Regions

SAR image is widely used in many fields, such as military, agricultural, and environmental, of its distinct characteristics and advantages. As one of the most critical steps in SAR image processing, image segmentation has always been the research focus. However, SAR images often contain complex scenes and have a significant speckle noise resulting in an unsatisfactory segmentation effect. To address the impacts of intricate texture and noise, we used various features to segment the image, such as gray features, texture features, and morphological characteristics. Firstly, the image is initially segmented using superpixels, and the image is in a state of over-segmentation, but the influence of noise will be largely eliminated. Secondly, we use the normalized grayscale co-occurrence matrix to extract the texture features of each superpixel. Whether the two regions belong to the same category can be judged by calculating the difference between the grayscale histogram and the texture features. Moreover, we propose the concept of bracketing coefficient and choose the order of merging. Finally, the merged residual regions are classified using the K-means method. We conduct experiments on a simulated SAR image and a real SAR image. The experimental results show that the segmentation accuracy of the proposed method has reached more than 95, and it has a good segmentation effect.
Cohesive Devices used in Students’ Argumentative Essay; A Discourse Analysis

The cohesive devices are one of the important aspects of academic writing that has been recognized as a fundamental characteristic of good writing. The aims of this study are to find out the types of cohesive devices used by students and to identify the error of using cohesive devices when composing an argumentative text. This study recruited ten students from the fifth semester of the English language program in the 2021/2022 academic year. The research approach used in this study was descriptive qualitative. This entire data were analyzed by identifying the kind of cohesive devices used and also errors of using the cohesive devices. Based on the documentary results of the student's argumentative essay, it indicated that the students used only three types of cohesive devices as a reference, conjunctions, and substitutions. While there are two types of errors revealed in student essays of using cohesive devices, namely; references and conjunctions. The types of references included personal references, such as; they, them, and it, as well as demonstrative references, such as; that, those, and these. Furthermore, there are errors with additive conjunctions such as the using of and or. The Errors in causal conjunctions, such as; using the conjunctions so and because. The students often used the cohesive devices inefficiently, incorrectly, or excessively. In other words, the sentences are not linked correctly and properly. It seems that students cannot use cohesive devices in writing because they do not understand how to use them properly.
The Effect of Online Learning on Academic Writing Course During Covid-19 Pandemic

Since the outbreak of Covid 19 in Indonesia, all universities, including Universitas Muhammadiyah Makassar, had made a decision for lecturers and students to learn from home (LFH) since March 16, 2020. This decision certainly has an impact on the change of the learning process which is used to face-to-face in the classroom then drastically changed and become online learning. This study aims at investigating the effectiveness of online learning on the Academic Writing course in the English Education Study Program. Pre-experimental design is the method where the sample is purposively chosen. The data is collected by giving pre-test and post-test and questionnaire. The study reveals that students get better writing skills after learning online. It can be seen that the pretest is 50.6 becomes 75.8 in the posttest. Meanwhile, after calculating the t-test it is also found that the effectiveness of online learning on Academic Writing Course through Google Meet is 55%. It is then concluded that online learning using Google Meet on Academic Writing Course has a positive effect on students' writing skills.
A Two-Phase Method for Optimization of the SPARQL Query

With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.
An Image Captioning Algorithm Based on Combination Attention Mechanism

With the maturity of computer vision and natural language processing technology, we are becoming more ambitious in image captioning. In particular, we are more ambitious in generating longer, richer, and more accurate sentences as image descriptions. Most existing image caption models use an encoder—decoder structure, and most of the best-performing models incorporate attention mechanisms in the encoder—decoder structure. However, existing image captioning methods focus only on visual attention mechanism and not on keywords attention mechanism, thus leading to model-generated sentences that are not rich and accurate enough, and errors in visual feature extraction can directly lead to generated caption sentences that are incorrect. To fill this gap, we propose a combination attention module. This module comprises a visual attention module and a keyword attention module. The visual attention module helps in performing fast extractions of key local features, and the keyword attention module focuses on keywords that may appear in generated sentences. The results generated by the two modules can be corrected for each other. We embed the combination attention module into the framework of the Transformer, thus constructing a new image caption model CAT (Combination Attention Transformer) to generate more accurate and rich image caption sentences. Extensive experiments on the MSCOCO dataset demonstrate the effectiveness and superiority of our method over many state-of-the-art methods.
Knowledge sharing challenges in hybrid knowledge work: Lessons from Denmark

During the COVID-19 pandemic, knowledge workers worked from home (WFH) and had to share knowledge mainly online. Studies show that remote work influence knowledge sharing. Beyond the pandemic, several studies report that companies expect more people to work partly or fully from home or anywhere. Therefore, we investigate how knowledge workers experience working from home (WFH), full or part-time, and how it affects their work and knowledge sharing.&#x0D; We conducted an online survey at six different time points between May 2020 and November 2021, allowing us to analyse different working from home situations for the first time. Our survey included 23 questions covering positive and negative experiences from WFH and demographics. Data was collected from 3406 knowledge workers in Denmark working fully or partly from home.&#x0D; The answers were analysed by fitting proportional odds logistic regressions. During the lockdown around February 2021, when Danish restrictions were high, knowledge workers reported that they could focus less on their work at home than at other points of time when they were allowed back in the office. Furthermore, they missed seeing their colleagues more during the lockdown period than at times when the society was completely open again, as they felt a lack of discussion and creative problem-solving. Despite using software solutions for collaboration and communication, knowledge workers missed opportunities for knowledge sharing when WFH. In general, during the whole period, female respondents reported that they got more time to focus on their work when WFH than males did. Finally, older respondents experienced more time to focus on work than the young respondents did when WFH.&#x0D; The results show the differences in the situation of knowledge workers, whether it is enforced or flexible/voluntary to work from home. Thus, this study contributes to a better understanding of the challenges when knowledge workers WFH, which groups of knowledge workers can gain from WFH regarding efficiency and knowledge sharing needs. Beyond the pandemic, when companies want to offer more flexibility to WFH, this study provides conclusions on which conditions to be aware of to ensure efficient knowledge sharing.
Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation

Recent study shows that recommendation system not only relys on user's static preference, but also dynamic preference.Consequently, it leads to the emergence of session-based recommendation.With the development of recurrent neural network, this kind of method can capture representations of users' sequential behaviors from a large number of sessions.However it is prone to spurious dependency problem.Recently, convolutional neural network has also shown its potential in modelling session, especially in extracting complex local pattern of subsequence.Therefore, we propose a hybrid neural model, called SGPD, for learning sequential general pattern and dependency for session-based recommendation.In SGPD, we propose recurrent residual convolution network to extract general pattern of subsequence in a session.Furthermore, the SGPD scans sequence from forward and reverse direction by bidirectional recurrent neural network, and learns sequential dependency of a session.Finally, the objective function is constructed by cross entropy and the model parameters are learned.The experimental results show that the precision rate, recall rate and mean reciprocal ranking of SGPD are greatly improved compared with the state-ofart methods.It has good application prospect.
Scoping the Future of Visualization Literacy: A Review

Access to information is increasing rapidly, and data visualizations are becoming increasingly common as a way to communicate summaries of large datasets, especially in media designed for the general public. Accompanying this rise in visualization is a need for visualization literacy. In this work we survey the current body of research on visualization literacy to find current research themes and guide future work. We found three themes of visualization literacy: interpretation, construction, and believability. Construction and believability are not traditionally discussed in visualization literacy research. We analyzed participant pools for visualization literacy user studies and found them to be lacking in diversity in gender and sex, age, education level, and recruitment country.
Graph-Embedded Subspace Support Vector Data Description

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.
Upgrading the protection of children from manipulative and addictive strategies in online games

Despite the increasing awareness from academia, civil society and media to the issue of child manipulation online, the current EU regulatory system fails at providing sufficient levels of protection. Given the universality of the issue, there is a need to combine and further these scattered efforts into a unitary, multidisciplinary theory of digital manipulation that identifies causes and effects, systematizes the technical and legal knowledge on manipulative and addictive tactics, and to find effective regulatory mechanisms to fill the legislative gaps. In this paper we discuss manipulative and exploitative strategies in the context of online games for children, suggest a number of possible reasons for the failure of the applicable regulatory system, propose an "upgrade" for the regulatory approach to address these risks from the perspective of freedom of thought, and present and discuss technological approaches that allow for the development of games that verifiably protect the privacy and freedoms of players.
Sentiment analysis through twitter as a mechanism for assessing university satisfaction

Currently, the data generated in the university environment related to the perception of satisfaction is generated through surveys with categorical response questions defined on a Likert scale, with factors already defined to be evaluated, applied once per academic semester, which generates very biased information. This leads us to wonder why this survey is applied only once and why it only asks about some factors. The objective of the article is to demonstrate the feasibility of a proposal to determine the degree of perception of student satisfaction through the use of data science and natural language processing (NLP), supported by the social network twitter, as an element of data collection. As a result of the application of this proposal based on data science, it was possible to determine the level of student satisfaction, being 57.27%, through sentiment analysis using the Python library "NLTK"; Thus, it was also possible to extract texts linked to the relevant factors of teaching performance to achieve student satisfaction, through the term frequency and inverse document frequency (TF-IDF) approach, these being those linked to the use of tools of simulation in the virtual learning process.
Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used.