Reviewerly/exHarmony huggingface dataset:
LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures

This paper describes the system developed by the Laboratoire d'analyse statistique des textes (LAST) for the Lexical Complexity Prediction shared task at SemEval-2021.The proposed system is made up of a LightGBM model fed with features obtained from many word frequency lists, published lexical norms and psychometric data.For tackling the specificity of the multi-word task, it uses bigram association measures.Despite that the only contextual feature used was sentence length, the system achieved an honorable performance in the multi-word task, but poorer in the single word task.The bigram association measures were found useful, but to a limited extent.
Face Mask Detection Using SSDNET and Lightweight Custom CNN

One of the best ways to prevent the spread of the Covid-19 virus is the adoption of the face mask. Just by wearing a face mask can reduce the risk of contracting the virus by almost 70%. Several government agencies are trying to make wearing a face mask mandatory, this process can be aided by using a face mask detecting software based on the concepts of Artificial Intelligence and Image processing. The proposed model is one that has been built using several deep learning concepts and has been trained on a dataset consisting of more than 11,000 images of faces with and without masks. A SSDNET model is being deployed for face detection, the output of which is passed to a custom made Lightweight CNN for mask detection. The model achieves a high accuracy of ~96% and ~99% on two separate testing datasets. The model will help government agencies and health officials fight the global pandemic.
Using Generative Adversarial Networks for Handling Class Imbalance Problem

Having more samples belonging to one class than the samples of the other class in data used in a classification task is known as class imbalance problem. Handling class imbalance is crucial since the classifier's performance is highly affected. One of the solution approaches of this problem is to make the data balanced by generating synthetic data. Employing resampling methods is a common way of generating synthetic data. Although adversarial generative networks (GANs) are mainly designed to generate image data, they can also be an alternative to solve the class imbalance problem by generating tabular data. This work presents a comparative study of resampling methods with GANs based methods. The performance of machine learning methods improved by 27% if the data is balanced with resampling methods. However, similar performance results were observed with working on imbalanced data if the GANs based methods are employed for synthetic data generation.
Cloud-computing approach for an environmental, social, and corporate governance focus in universities and businesses

There is an increasing demand for high-performance computing on geophysical exploration applications, which implies more carbon emissions due to higher energy consumption. Furthermore, there is increasing concern about the environmental and social impact that this can generate. We show how cloud computing can handle these challenges simultaneously and thereby assist business leaders in their decision-making. Cloud computing is a paradigm in which users rent computing capacity from providers on a pay-as-you-go basis, thereby reducing the carbon footprint by up to 88%. It can run software for years uninterrupted using the same capital required to acquire and run on-premises infrastructure, even if such infrastructure has over a thousand graphics processing units. However, the managers must consider that challenges arise from using the cloud, such as trusting their data in a third-party server and expenses throughout the years, especially with storage.
TrajecNets: Online Failure Evolution Analysis in 2D Space

We propose a novel Recurrent Neural Network (RNN) based autoencoder for embedding the run-to-failure time series sensor data in a 2D feature space. The embedding, extracted from the network, is in the form of a smooth trajectory, which represents the temporal evolution of data from healthy to failure states, hence the name TrajecNets. The visualizable 2D trajectory can be used directly for highly intuitive and interpretable health monitoring, which can in turn be used for Remaining Useful Life (RUL) estimation task, without compromising the performance. We also propose a novel unsupervised failure prediction methodology which uses the 2D trajectories and health curve of the time series to compute evolving failure mode probabilities. Together, the visualizable 2D trajectories and the interpretable failure mode probabilities, health curve and RUL are envisaged to provide system and maintenance engineers, insight into failure dynamics. Experiments on NASA CMAPSS Turbofan benchmark dataset showpromising results on degradation tracking, health monitoring, failure prediction and RUL estimation tasks.
Restatement and Question Generation for Counsellor Chatbot

Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling.In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee.It is thus important for the counsellor chatbot to encourage the user to open up and talk.One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions.This paper applies models from two closely related NLP tasks -summarization and question generation -to restatement and question generation in the counselling context.We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety.We obtained the best performance in both restatement and question generation by finetuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.
HTML Content and Cascading Tree Sheets: Overview of Improving Web Content Visualization

The database system has contributed immensely to the present state of web pages and caching. Also, there are so many layers in broad-spectrum for catching numerous web content with culmination with extension .html, XML, .json, .txt, .jpg, .pdf, .png, .gif among others. Examples include web servers, content delivery networks, etc. But, these commonalities of presentational HTML cannot separate HTML content, which enables HTML documents harder to create, reuse, maintain, and tailor. Hence the objective of this study was aimed at cascading cache layer in a content management system using cascading tree sheets (CTS), an insubstantial language for unfolding the presentational aspects of page strategy that the commonalities of presentational HTML cannot characterize. To address the purpose of this study, we chose to address the implementation of cascading tree sheets and selection in the cascading cache layers in content management. The result showed the essential uncomplicatedness of cascading tree sheets by extracting its directions downcast to 2 tree processes that are related to the device and have helpful derivation preserving features.
A fast LED calibration method under near field lighting based on photometric stereo

We proposed and experimentally demonstrate a calibration method to estimate the position and the orientation of the anisotropic near point LED light source from a single view. In this work, a Lambertian sphere is used as the calibration target, where the reflection model of the Lambertian sphere under the illumination of LED with given radiant intensity distribution is analyzed theoretically to establish the relationship between the recorded image intensity and the observed surface. Based on this relationship, the geometrical principle of the calibration is researched and an iteration method is presented to calculate the LED position and orientation with a cost function through minimizing the estimation error. The accuracy of the proposed method is verified by simulations with the mean estimation error lower than 5.8mm and 1.7deg for near field lighting. The experiments are performed by measuring the plaster models using the presented system, which proves the effectiveness of the proposed calibration approach.
Photonic Reservoir Computer with Output Expansion for Unsupervized Parameter Drift Compensation

We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance. We can partially recover the lost performance by using the output layer expansion. The proposed scheme allows for a trade-off between performance gains and system complexity.
Diversity as a By-Product: Goal-oriented Language Generation Leads to Linguistic Variation

The ability for variation in language use is necessary for speakers to achieve their conversational goals, for instance when referring to objects in visual environments. We argue that diversity should not be modelled as an independent objective in dialogue, but should rather be a result or by-product of goal-oriented language generation. Different lines of work in neural language generation investigated decoding methods for generating more diverse utterances, or increasing the informativity through pragmatic reasoning. We connect those lines of work and analyze how pragmatic reasoning during decoding affects the diversity of generated image captions. We find that boosting diversity itself does not result in more pragmatically informative captions, but pragmatic reasoning does increase lexical diversity. Finally, we discuss whether the gain in informativity is achieved in linguistically plausible ways.
International Capital Inflow and Sub-Saharan African Economy: Does Capital Inflow Lead Growth?

Sub-Sahara African (SSA) countries have become more integrated with the world economy since the early 1990s leading to a surge in international capital inflows, but have also experienced sporadic downfalls in their growth rates leading to instabilities in some macroeconomic variables. This paper examined the dynamic and causal relationship between economic growth and international capital inflow in SSA using data covering the period of 1990 to 2018. Through Structural Vector Autoregression (SVAR) impulse response and variance decomposition, the study found a positive relationship between growth and international capital inflow in SSA. It also found negative relationships between macroeconomic instability and economic growth; and macroeconomic instability and international capital inflow in SSA. In addition, through VAR Granger Causality or Block Exogeneity Wald Tests, the study found a unidirectional causal relationship running from growth to international capital inflow in SSA. The study also found that international capital inflow did not cause economic growth but economic growth attracts international capital inflows. Therefore, the study concluded that a buoyant and viable economy provides conducive business environment for international investors better than less viable economy. Thus, the study recommended that governments and policy makers in SSA should put in place policies that will promote economic growth to attract more international capital inflows which would augment the scarcely available domestic capital resources most especially in terms of knowledge, skills and other technological advancement, that would later translate into further economic growth in SSA region.
Incremental Learning Based Radio Frequency Fingerprint Identification Using Intelligent Representation

5G physical security technology plays an important role in the integration of security and communication. In this paper, we propose to use incremental learning consider to use the radio frequency fingerprint identification technology to realize the physical layer security. Our method incremental learning to improve the neural network put forward the idea of incremental learning to improve the neural network. If we receive part of the data, we can train part of it. On the premise of ensuring certain recognition accuracy, the training time and storage space are reduced. In this paper, the received signals are extracted by traditional methods, such as Hilbert Huang transform, I/Q data input, Bi-spectral transform, etc., and then input into the neural network for training classification and incremental learning for training. Simulation results show that the recognition accuracy can reach 95% with 5dB SNR, and the training time can be reduced by nearly 50% with incremental learning.
Primary school teachers’ professional development through the learning ecologies lens: new ways for keeping up to date in uncertain times

Learning is evolving, just as the world and society are. In this context, teachers are also experiencing new ways for updating their professional development. The metamorphosis of learning is currently being pushed by four main catalysts: the connectivity of networks, the empowerment of the students, the overcoming of space and time barriers, and the assumption of the existence of silent, unnoticed learning. But these uncertain times have added a new, recent catalyst for change in learning and education: the COVID-19 pandemic.&#x0D; In the framework of the ECO4LEARN research project, a study was designed and carried out to check to what extent the learning ecologies approach could provide information on how primary school teachers organise their learning and how they make decisions about it. A survey was sent to the entire population of primary school teachers in Catalonia, getting 1,253 answers. The calculated margin of error was ±3.14%. Data analysis was conducted along three steps: a) Descriptive statistics; b) Principal Component Analysis (PCA); and c) Multiple Regression.&#x0D; Results show that the learning ecologies approach proves to be useful for analysing the actual means teachers use for their professional development and updating. Although some teachers are more advanced, practices do not respond to what could be expected regarding the use of less formal training for teachers and its mediation through the use of technologies. Regarding the sample analysed, the digital dimension of teacher professional development practices is still in its inception. Some recommendations are provided.
Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer Learning

Tables are widely used in various kinds of documents to present information concisely.Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning.In this paper, we present our systems to solve Task 9 of SemEval-2021: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACTS).The task consists of two subtasks: (A) Given a table and a statement, predicting whether the table supports the statement and (B) Predicting which cells in the table provide evidence for/against the statement.We fine-tune TAPAS (a model which extends BERT's architecture to capture tabular structure) for both the subtasks as it has shown state-of-the-art performance in various table understanding tasks.In subtask A, we evaluate how transfer learning and standardizing tables to have a single header row improves TAPAS' performance.In subtask B, we evaluate how different fine-tuning strategies can improve TAPAS' performance.Our systems achieve an F1 score of 67.34 in subtask A three-way classification, 72.89 in subtask A two-way classification, and 62.95 in subtask B.
Social Network Valuation Using the Discounting Cash Flow Method

Social networks are different from traditional companies, therefore, the article discusses the methodology of social networks valuation is considered, as well as the features arising by the specifics of the activities of such companies. The results are interpreted and the conclusions are made regarding the accuracy of social network valuation by discounting cash flows of the company.
SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block

In the era of big data, convolutional neural network (CNN) has been widely used in the field of image classification and has achieved excellent performance. More and more researchers are beginning to combine deep neural networks with steganalysis to improve performance in recent years. However, most of the steganalysis algorithm based on the convolutional neural network has only run test against the WOW and S-UNIWARD algorithms; meanwhile, their versatility is insufficient due to long training time and the limit of image size. This paper proposes a new network architecture, called SFRNet, to solve these problems. The feature extraction and fusion layer can extract more features from the digital image. The RepVgg block is used to accelerate the inference and increase memory utilization. The SE block improves the detection accuracy rate because it can learn feature weights to make effective feature maps with significant weights and invalid or ineffective feature maps with small weights. Experimental results show that the SFRNet has achieved excellent performance in the detection accuracy rate against four state-of-the-art steganography algorithms in the spatial domain, e.g., HUGO, WOW, S-UNIWARD, and MiPOD, under different payloads. The SFRNet detection accuracy rate achieves 89.6% against S-UNIWARD algorithm with the payload of 0.4bpp and 72.5% at 0.2bpp. As the same time, the training time of our network is greatly reduced by 35% compared with Yedroudj-Net.
Face Parsing from RGB and Depth Using Cross-Domain Mutual Learning

Existing methods of face parsing have proven effective at classifying each pixel of an RGB image into different facial components. However, there is a lack of face parsing research that utilizes depth domain. To the best of our knowledge, we present the first study to exploit 2.5D data for face parsing. We introduce a novel framework to jointly learn (1) RGB face parsing, (2) depth face parsing and (3) RGB-to-depth domain translation, which can be effective even when only a small amount of annotated depth data is available for training. To this end, we also create the first RGB-D face parsing benchmarks based on CelebAMask-HQ, LaPa and Helen by utilizing an off-the-shelf 3D head reconstruction model. Overall, our approach makes two main contributions. First, our method leverages mutual learning between RGB and depth face parsing, which enables bidirectional knowledge distillation between the two data domains. Second, our method utilizes end-to-end learning of RGB-to-depth domain translation and depth face parsing, which can help overcome the scarcity of annotated depth data. We perform extensive experiments to validate the effectiveness of our method, in which we achieve state-of-the-art results in RGB face parsing. As far as we know, we also report the first results on face parsing from depth data. All experiments are conducted on our new RGB-D face parsing datasets, which are publicly available at https://github.com/jyunlee/CelebAMask-HQ-D_LaPa-D_Helen-D.
Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.
Challenges of Online Language Teaching during the COVID-19 Pandemic: A Process Tracing Approach

Having witnessed the unprecedented prevalence of online education during the COVID-19 pandemic worldwide, the present research was motivated to explore the challenges facing an EFL teacher throughout an online English course of intermediate level. To this aim, a process-tracing approach (Checkel, 2006) was employed to unravel the causal mechanisms involved in the beginning, middle, and end of the course. The results showed that deficient technological resources caused the greatest challenges throughout the course, especially in the initial and mid-sessions. Also, the two other causal categories, human and content resources, were at their peak at the beginning of the course and were no longer noticeable at the end of the course. The most troublesome challenges the teacher faced were platform limitations, internet connection, and human resources’ unpreparedness for online education. Furthermore, the teacher’s and most students’ technological knowledge, as well as their media literacy, increased by the end of the course, but some students’ rather slow adaptation to the sudden online environment challenged the teacher during the course. Finally, suggestions were made to prevent these challenges or handle them effectively upon occurrence, especially in developing countries, where the required infrastructure for online education is lacking, and the majority of teachers, students, and institutions might not be yet adequately prepared for the online mode of teaching and learning languages.
Artificial Intelligence Based Early Warning System for Coastal Disasters

Disaster indicates a serious disruption or deviation from the norm that generally occurs for a short period of time and affects the community and society as a whole by way of widespread harm or damage to humans, wildlife, environment, infrastructure and economy. It requires a response that encompasses multidimensional processes in order to mitigate, respond to and recover from its consequences. Disaster that impact the coastal areas is the focus of this chapter. Coastal areas are one of the most sensitive and altered ecosystems world-wide, as they are subject to many disasters and risks including high winds resulting in cyclones and underwater earthquakes initiating strong tidal waves or tsunamis. These disasters are responsible for the loss of lives and/or infrastructural damages in coastal areas. To address this entails the designing of early warning systems that disseminate relevant information effectively and efficiently, as alarms or warnings, to communities at risk during or before such disasters so that timely and adequate steps can be taken to minimize the loss and damages associated with such disasters. In this chapter, an attempt has been made to design an early warning system that uses artificial intelligence to predict the horizontal in-city flooding caused by underwater earthquakes. For experimental purposes, the tsunami dataset would be considered by the proposed early warning system.