Reviewerly/exHarmony huggingface dataset:
Graph Neural Networks for Adapting Off-the-shelf General Domain Language Models to Low-Resource Specialised Domains

Language models encode linguistic proprieties and are used as input for more specific models.Using their word representations as-is for specialised and low-resource domains might be less efficient.Methods of adapting them exist, but these models often overlook global information about how words, terms, and concepts relate to each other in a corpus due to their strong reliance on attention.We consider that global information can influence the results of the downstream tasks, and combination with contextual information is performed using graph convolution networks or GCN built on vocabulary graphs.By outperforming baselines, we show that this architecture is profitable for domain-specific tasks.
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language

This paper narrates the work of the team Am-rita_CEN for the shared task on Patronizing and Condescending Language Detection at Se-mEval 2022.We implemented machine learning algorithms such as Support Vector Machine (SVM), Logistic regression, Naive Bayes, XG Boost and Random Forest for modelling the tasks.At the same time, we also applied a feature engineering method to solve the class imbalance problem with respect to training data.Among all the models, the logistic regression model outperformed all other models and we have submitted results based upon the same.
A Model of Discrete Random Walk with Varying Transition Probabilities and Logistic Regression

The contribution presents a model of discrete time Bernoulli-like random walk with transition probabilities depending on previous walk steps as well as on a set of covariates. These dependencies are expressed via the logistic regression model, such a choice enables an easy and reliable modelparameters estimation. Further, a model with time-depending parameters is considered and method of solution proposed. Application then deals with both artificial and real data cases, illustrating both the model evaluation and its use.
Effects of Competition and Reputation on Auditing Cloud Service Provider's Security Commitment

Auditing a cloud service provider's (CSP's) compliance with data backup requirements is of great value to data availability and redundancy. As the checking incurs extra costs, a lot of research has been done to find the optimal checking strategy. However, there remain two problems: 1) What kind of influence does the competition between CSPs for clients have on the checking strategies? 2) How to incorporate the influence of CSP's reputation in a reasonable and practicable way? To solve these problems, we propose a one-population evolutionary game model. The competition for clients is first captured via the game between CSPs. Then, we introduce a reputation term in the game model, which is defined as the function of checking probability. We analyze the model, and then, derive the thresholds of checking probability. A verifier can optimize the use of checking resources whatever checking methods are using according to the thresholds. Finally, we conduct various comparison experiments between our model and another game-based model. The comparison results show that the long-term effects of competition and reputation can help a verifier save checking resources or promote the checking effect.
Anomaly Behaviors Detection Method for Online Learning Based on Copula Function

How to achieve effective anomaly behaviors detection in online learning has become a hot topic in online learning user behavior research. Traditional machine learning methods have several problems, such as poor data quality, huge amount of data, and difficulty in obtaining labels. To solve these problems, an anomaly behaviors detection method based on copula function for online learning was proposed. It estimated the tail probability distribution by using empirical copula probability. The algorithm was a non-parametric model. It decomposed the probability distribution into several independent marginal distributions. The computational complexity of the high-dimensional probability distribution could be reduced, and the computational efficiency could be effectively improved. The AUC on the public data set reached 0.91, showing that the method proposed could realize anomaly behaviors detection effectively.
The Influence of Educational Mobile Apps Use on Teaching Effectiveness of Lecturers in Public Polytechnics in Ekiti and Ondo states, Nigeria

The quality of teaching being imparted to students is supposed to be of high quality and effective in this information superhighway age. Teaching effectiveness typified with teaching methodologies, classroom management, assessment procedures and content knowledge is of great unease to any tertiary education institution since it furthers the productivity of the institution. In tertiary institutions, like public polytechnics in Ekiti and Ondo states, teaching effectiveness seems to be of poor quality because of students’ low capacity to analytically think, poor lecturers proficiency. Educational mobile apps use can further the teaching effectiveness in any institution of higher learning. Therefore, this study investigated the influence of educational mobile apps use on teaching effectiveness of lecturers in public polytechnics in Ekiti and Ondo States, Nigeria. The study used survey research design. The population of the study consisted of 116 lecturers and 1,978 students in the three public polytechnics in Ekiti and Ondo states. The lecturers were all enumerated to participate, while Taro Yamane was used to select 333 students’ participants and multistage sampling was used to select the participants from the various faculties, departments and levels. A self-structured validated, and reliable questionnaire was used to gather data. The data collected were analyzed with the use of descriptive statistics, and linear regression. Findings showed that the most used educational mobile apps was Google Apps for education (GAFE) with a mean score of (x̅=1.64).The result also indicated that there was a weak positive but not significant influence of educational mobile apps use on the teaching effectiveness in public polytechnics in Ekiti and Ondo State, (β=0.027, t = 0.270, p-value&gt;0.05). The study concluded that educational mobile apps use contributes to teaching effectiveness of lecturers in public polytechnics in Ekiti and Ondo states, Nigeria. It was therefore recommended that the government of Nigeria, through the ministry in charge of education, and National Board for Technical Examinations (NBTE) should carry out continuous awareness program and training for lecturers in public polytechnics on the use of educational mobile apps.
Using Geostatistical Methods for Study of Groundwater Trace Elements in a Detrital Aquifer with High Agricultural Activity

The presence of trace elements (TEs) in water for domestic supply or irrigation could pose a significant toxic risk for health, due to direct consumption or bioaccumulation through the ingestion of fruit and vegetables irrigated with these waters. This paper explores the sources and concentration of the TEs in groundwater in the Vega de Granada aquifer (VGA), which is subject to intense anthropic activity especially, agricultural activity. The information gathered was analysed to find out more about spatial and temporal variations in the concentration of the TEs.In our analysis of the TEs in the groundwater, we applied a multivariate statistical tool based on Principal Component Analysis (PCA) and a geostatistical Kriging method. PCA revealed 11 significant PCs, which account for 82% and 81% of the total variance (information) for the two dates, respectively. Ordinary Kriging (OK) was applied to draw maps of the TEs and PC_scores, in order to facilitate spatial and temporal analysis.The results obtained indicate that PC1 represents the natural quality of the aquifer and that there is little change in the average PC1 value between the two dates studied (around maximum of recharge period and end of summer). Agriculture is the human activity that causes the greatest variations in the quality of groundwater due to the use of fertilizers and due to watering crops with wastewater (PC7 and PC5, June and November, respectively). Other elements of industrial origin which are dangerous for our health, such as Pb, Cu and Cd, are grouped together in other PCs.The results show that the decline, or even complete absence, of the natural recharge during the summer months leads to an increase in the TEs produced by human activity. This indicates that a temporary reduction in the natural recharge could worsen the quality of water resources.
JUST-DEEP at SemEval-2022 Task 4: Using Deep Learning Techniques to Reveal Patronizing and Condescending Language

Classification of language that favors or condones vulnerable communities (e.g., refugees, homeless, widows) has been considered a challenging task and a critical step in NLP applications.Moreover, the spread of this language among people and on social media harms society and harms the people concerned.Therefore, the classification of this language is considered a significant challenge for researchers in the world.In this paper, we propose JUST-DEEP architecture to classify a text and determine if it contains any form of patronizing and condescending language (Task 4-Subtask 1).The architecture uses state-of-art pre-trained models and empowers ensembling techniques that outperform the baseline (RoBERTa) in the SemEval-2022 task4 with a 0.502 F1 score.
Question Personalization in an Intelligent Tutoring System

This paper investigates personalization in the field of intelligent tutoring systems (ITS). We hypothesize that personalization in the way questions are asked improves student learning outcomes. Previous work on dialogue-based ITS personalization has yet to address question phrasing. We show that generating versions of the questions suitable for students at different levels of subject proficiency improves student learning gains, using variants written by a domain expert and an experimental A/B test. This insight demonstrates that the linguistic realization of questions in an ITS affects the learning outcomes for students.
Impact of Digitization on Bank’s Performance and Bank’s Competitiveness

The study aims to look into the relationship between digitization and financial performance of banks and its implication on competitiveness between the banks.In order to test the association between digitization and performance, regression using panel data is used in this paper. The Granger causality test is applied to bank-level data for Indian banks to assess the relationship between digitization and competitiveness from 2009 to 2019. The Lerner Index is a control over the market metric that is used to estimate competition.Panel data analysis technique is applied for measuring association between digitization and financial performance along with granger causality test to check relationship between digitization and competitiveness. For the purpose of examining competitiveness and digitization in the banking sector, this study examined more than 90% of banking assets. Policymakers/advisors can attempt to improve banking digital infrastructure in order to increase/decrease financial performance as well as competitive advantage in the banking sector, which will aid in the development of the investment-savings cycle.
Data-driven Behavioural and Affective Nudging of Online Learners: System Architecture and Design

In this work-in-progress paper, we describe the architecture of a system that can automatically sense an online learner's situation and context (affective-cognitive state, fatigue, cognitive load, and physical environment), analyse the needs for intervention, and react through an intelligent agent to shape the learner's self-regulated learning strategies. The paper describes the system concept and its software architecture and design: what sensory data are captured and how they are processed, analysed, and integrated; what intervention decision will follow and what behavioural and affective nudges will be given.
On Providing Natural Language Support for Intelligent Tutoring Systems

Conversational Intelligent Tutoring Systems (C-ITS) are capable of providing personalized instruction to students in different domains, using dialogues in natural language to interact with the user. In this paper we describe our recent experience in adapting an algebraic/arithmetic word problem-solving Intelligent Tutoring System by replacing the original button-based user interface by a conversational one primarily based on the use of natural language. The proposed method led to an average intent-recognition accuracy of 0.97, supporting the use of this kind of tools to seamlessly migrate existing interfaces to a more convenient form of interaction based on natural language.
Automatic Identification of Non-native English Speaker’s Phoneme Mispronunciation Tendencies

We explore the possibility of using word-level transcription to detect non-native English speaker (NNES)'s phoneme mispronunciation tendencies. We focus on word-level instead of phoneme-level transcription as the former is readily accessible and mature. We define phoneme mispronunciation tendency as the recurring imperfect pronunciation of a phoneme across different words. We use an Automatic Speech Recognition (ASR) service to generate alternative transcripts from speaker's reading aloud audio data. We build features based on the divergence of the audio transcriptions and the texts, as well as the confidence of the audio transcriptions. We found the features are informative for detecting phoneme mispronunciation tendencies.
Computer-Aided Forensic Authorship Identification in Criminology

The increasingly anonymous methods people use to communicate in the modern world allow for more freedom of speech. The safety of anonymity, however, can enable criminals to cause harm to others through various means, such as blackmail, verbal abuse, threat letters and numerous other ways. These culprits, often hiding behind computer screens, can be extremely difficult to identify and especially difficult to find definitive proof of their wrongdoings. They are not completely untraceable, however, as they are bound to leave clues in the text, linking it to them. The way they phrase sentences, the words they use, how often they use them and other parts of their idiolect can be used to identify them and even connect them to other texts. Through analyzing the text, it becomes possible to catch these individuals. This analysis is neither simple nor cheap, the aid of linguistic experts is critical, and even they are likely to encounter difficulties. This article explores the way in which the work of such experts can be assisted through computer analysis based on machine learning techniques and the role Artificial Intelligence plays in bringing these criminals to justice. Our current paper investigates how linguistic features can be automatically extracted to be used in the field. Through a total of 61 real text artefacts written in the Hungarian language by four different individuals, we extract various syntactic and semantic linguistic features which reflect the author’s idiolect and aid the expert’s work. We demonstrate how the technique can aid author identification in criminology.
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs

We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics.Contrary to previous work, this model does not need syntactic information such as constituency parses, or semantic information such as paraphrase pairs.Our model relies solely on the inductive bias found in attention-based architectures such as Transformers.In the attention of Transformers, keys handle information selection while values specify what information is conveyed.Our model, dubbed QKVAE, uses Attention in its decoder to read latent variables where one latent variable infers keys while another infers values.We run experiments on latent representations and experiments on syntax/semantics transfer which show that QKVAE displays clear signs of disentangled syntax and semantics.We also show that our model displays competitive syntax transfer capabilities when compared to supervised models and that comparable supervised models need a fairly large amount of data (more than 50K samples) to outperform it on both syntactic and semantic transfer.The code for our experiments is publicly available 1 .
Multidimensional Neural Network: Heterogeneous Activation Function on Same Neural Layer

All of the neural network architecture involves a single computational node called neuron, which is core computational unit of any architecture. Each neuron has an activation function which act as switch i.e., weather to fire the neuron or not and carry on the information to next layer. Furthermore, almost all neural network architectures comprise of different hidden layers are made up of it and having an individual activation function over any particular hidden layer. These layers are responsible for storing the pattern that is generated while training. There is a typical set of patterns developed for each layer is affected by the use of a particular activation function. For some time, these layers are comprising of same type of activation function over a single layer. Hence, inhibiting from using multiple activation function on same layer for different sets of neurons. Therefore, introduction to a new dense layer on same level but on different dimension will allow us to use multiple activation functions on same level, while receiving inputs from same previous level and calculating information and generating a different set of patterns with multiple activation functions before passing it to next level in architecture. The use of these multi-activation functions results in attaining higher accuracies in less amount of training and having quicker loss convergence. Which shows that the use of heterogenous activation function accompanies better development of patterns in any neural layer compared to that of conventionally used neural layers.
RangL: A Reinforcement Learning Competition Platform

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems. This article describes the reusable code repository developed by the RangL team and deployed for the 2022 Pathways to Net Zero Challenge, supported by the UK Net Zero Technology Centre. The winning solutions to this particular Challenge seek to optimize the UK's energy transition policy to net zero carbon emissions by 2050. The RangL repository includes an OpenAI Gym reinforcement learning environment and code that supports both submission to, and evaluation in, a remote instance of the open source EvalAI platform as well as all winning learning agent strategies. The repository is an illustrative example of RangL's capability to provide a reusable structure for future challenges.
CNN Based Severity Prediction of Bug Reports

Users can use bug tracking systems (BTS) such as Bugzilla, JIRA, and GitHub to report defects they detect while using or testing the software applications. Bugzilla has specified the bug&#x2019;s information such as bug ID, description, product, classification, component, platform, operating system, bug status, resolution, priority, and severity. The severity assigned to the bug report is conducted manually. So, it takes some considerable time to assign the severity manually, and also it is a tedious task. In this research, we used a deep learning-based technique to develop the prediction model for severity. Our proposed approach is based on the convolutional neural network (CNN) for severity prediction of the bug reports. First, we pre-process the textual content in bug reports using natural language processing (NLP) approaches. Then we extract the features from the textual context (short description) using the Bag-of-Words feature extraction method. Finally, we train a CNN-based classifier to severity prediction based on its input. Then our result is compared with the Support Vector Machine (SVM) and Temporal Convolutional Network (TCN) to find a better model for severity prediction. The final results show that the proposed approach based on the CNN classifier performs better than the other approaches, and it shows an 81% accuracy while others have low accuracy, like 61% and 48% for SVM and TCN, respectively. The proposed model&#x2019;s performance was evaluated using the Bugzilla dataset, which included over 25,000 bug reports. The accuracy, precision, recall, and F1-Score are used to measure the performance.
Non-Convex Optimization With Spherical Aberration Compensation for Efficient Excitation in Two-Photon Optogenetics

To stimulate neural ensemble selectively at high resolution, we propose non-convex optimization with spherical aberration compensation algorithm for two-photon optogenetics, which achieves high excitation efficiency and precise axial positioning.
The Impact of Good Governance on Human Development Iraq case study for the period (2005-2018)

Good governance has a major role in most economic, social and political variables, among them human development, as the success or failure of any country’s efforts to enhance the level of human development is closely linked to the nature and level of good governance. The latter constitutes the establishment of a sound environment for economic, social and human development. The study’s significance comes from the subject matter which explores good governance and its impact on human development. The study is based on the hypothesis that the relationship between good governance as an independent variable and human development as a dependent variable is a direct or positive one. In order to verify the research hypothesis, the level of both variables was studied as well as revealing the effect and the relationship between them, by using the descriptive method alongside the quantitative method represented by the econometric analysis. Finally, the study concluded with several findings which validate the hypothesis of the research, the most important of which is the existence of a positive relationship between the indicators of good governance and human development and that Iraq is experiencing a low level of good governance as it reached (7.9%) during the research period, leading to a poor level of human development which was (0.609) for the same period. In light of the findings, several proposals were put forward to raise the level of good governance in Iraq, the most important of which, as suggested by the researcher, is to elevate the level of political stability, absence of violence and combating corruption. Also the rule of law index should be increased due to its high level of influence among the indicators of good governance in Human Development.