Reviewerly/exHarmony huggingface dataset:
ON THE METHOD FOR ASSESSING THE QUALITY OF E-LEARNING RESOURCES

ABSTRACT&#x0D; Without the introduction of modern e-learning resources, it is hard to imagine a quality, understandable education that meets modern trends and standards. Under the current circumstances, one of the most important issues facing the world educational community is the problem of searching e-learning resources for quality assessment methods. Assessment is a multi-criteria issue that is very relevant in the current trends of ICT-oriented education and requires the use of multi-criteria decision-making methods.&#x0D; The purpose of this article is to introduce an interesting approach for evaluating the quality of e-learning resources, based on the method of analysis of hierarchy.&#x0D; The presented method is not hard to apply; it is oriented for pedagogical purposes and can also be used in the decision-making process for any multi-criteria problem within the field of education.
Multilingual Event Linking to Wikidata

We present a task of multilingual linking of events to a knowledge base.We automatically compile a large-scale dataset for this task, comprising of 1.8M mentions across 44 languages referring to over 10.9K events from Wikidata.We propose two variants of the event linking task: 1) multilingual, where event descriptions are from the same language as the mention, and 2) crosslingual, where all event descriptions are in English.On the two proposed tasks, we compare multiple event linking systems including BM25+ (Lv and Zhai, 2011a) and multilingual adaptations of the biencoder and crossencoder architectures from BLINK (Wu et al., 2020).In our experiments on the two task variants, we find both biencoder and crossencoder models significantly outperform the BM25+ baseline.Our results also indicate that the crosslingual task is in general more challenging than the multilingual task.To test the out-of-domain generalization of the proposed linking systems, we additionally create a Wikinews-based evaluation set.We present qualitative analysis highlighting various aspects captured by the proposed dataset, including the need for temporal reasoning over context and tackling diverse event descriptions across languages. 1
National Costume Art Design Optimization under the Background of Artificial Intelligence Decision Making and Internet of Things

As the most external symbolic representation of the nation, national costumes are an integral part of the wonderful culture of the Chinese nation. Our country has many ethnic minorities and has a unique national costume culture, which provides rich resources for the art design of ethnic costumes. This paper uses artificial intelligence technology and Internet of Things technology to design a national costume element library system. In this system, users can match national costume suits according to their own preferences, and they can also transmit national culture to people through this system. After the system is designed, the system performance is optimized by interactive algorithms, and the availability of the system is verified by testing system security, stress resistance, concurrency, etc. Through the verification of the system designed in this paper, the national costumes designed by innovative technology can be copied in batches, which enhances the innovation of national costume design in our country and has high production efficiency. It is finally proved that the design results of this paper meet the design requirements.
Strategi Pembelajaran Bahasa Inggris Untuk Meningkatkan Skor TOEIC Mahasiswa Politeknik Darussalam

The purpose of this research is to find out the students' strategies in learning English. This research is a descriptive study with a qualitative approach and was carried out at the Darussalam Polytechnic Travel Business Study Program for the academic year 2021/2022. The study involved 40 business travel students in the first and fourth semesters with a TOEIC prediction score of 400 as subjects in this study. To measure students' use of English learning strategies, researchers used the Strategy Inventory of Language Learning (SILL) version 7.0, which was designed by Oxford. The results of this study reveal that the language learning strategies used by students are memory strategies and cognitive strategies with an average of 3.84 and 3.81 which are included in the high category, which is often used. Compensation strategies, metacognitive strategies, affective strategies and social strategies are in the moderate category, which is sometimes used.
DCSN: Deformable Convolutional Semantic Segmentation Neural Network for Non-Rigid Scenes

This paper presents a novel semantic segmentation network for outdoor and unstructured scenarios for autonomous driving based on deformable convolution and geometric distortion pipelines. The semantic segmentation tasks for autonomous driving are generally designed for the urban scene, city-view, and highly structured scenarios, such as the CityScapes dataset, KITTI, and BDD, while rare study focuses on outskirts scenarios. Therefore, the performance of existing semantic segmentation networks on such datasets might be unreliable. To conquer this issue, a novel densely connected residual block (DCRB) with the deformable convolution is proposed to form our backbone for capturing the non-rigid feature representation. In this way, the gradient flow of our DCRB could be better back-propagated from the segmentation head, resulting in a stable training process. Second, geometric distortion augmentation is introduced in the data augmentation pipeline, simulating the possible deformation situations in real-world outdoor scenarios. The experiments are conducted that the proposed semantic segmentation network significantly outperforms the state-of-the-art methods for both Cityscapes and Outdoor scenarios.
Boundary-Aware Bias Loss for Transformer-Based Aerial Image Segmentation Model

Inspired by the tremendous success of the transformer-based model in natural language processing (NLP), many efforts introduce the transformer-based model into the image processing tasks. However, naive transformer models have to down-sample the image resolution to satisfy computational restrictions, thus discarding the local information, which is catastrophic for high-performance remote sensing image segmentation. Hence, this paper proposes a novel trainable boundary-aware bias loss function to enhance transformer-based models of extracting local information. On the Challenging ISPRS Potsdam dataset, two representative transformer-based models achieve remarkable performance improvements, proving the effectiveness of the proposed method.
A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism

&lt;p&gt;The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm&amp;rsquo;s overall optimization performance is improved. The teaching mechanism is added to wolf to improve the algorithm&amp;rsquo;s convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm&amp;rsquo;s accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
English Language Reading Strategies: Low Proficiency Adult Learners in a Malaysian University

In order to comprehend reading, readers need to apply various reading strategies.The same goes to adult learners in universities as higher education demands substantial readings in the English language, thus they need to be able to comprehend it via implementation of various reading strategies.This paper investigates the reading strategies used by low proficiency adult learners in a Malaysian university when reading English materials.The findings are that as these students are low proficiency second language learners of English, they do not always apply reading strategies when it comes to reading English materials.Also, the respondents do not have a specific preference on implementing any of the reading strategies.However, if English language reading strategies are given better emphasis in schools, it might be implemented more frequently by the adult learners.The findings are believed to help educators facilitate reading strategies in order to be employed by readers.
Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network

Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial brightness changes nor the temporal correlation between events at each time step, resulting in insufficient use of spatio-temporal information. To address this issue, we propose a new Spatio-Temporal Relation-Attention network (STRA), for the specific event-based video deblurring. Concretely, to utilize spatial consistency between the frame and event, we model the brightness changes as an extra prior to aware blurring contexts in each frame; to record temporal relationship among different events, we develop a temporal memory block to restore long-range dependencies of event sequences continuously. In this way, the complementary information contained in the events and frames, as well as the correlation of neighboring events, can be fully utilized to recover spatial texture from events constantly. Experiments show that our STRA significantly outperforms several competing methods, e.g., on the HQF dataset, our network achieves up to 1.3 dB in terms of PSNR over the most advanced method. The code is available at https://github.com/Chengzhi-Cao/STRA.
IMGTXT: Image to Text Encryption Based on Encoding Pixel Contrasts

Nowadays, when data is exchanged over the internet, the security of data is critical in every element of life. Unauthorized network access is possible due to information transmission. As image usage increased in most communications, image privacy became an issue. Image encryption is one of the methods used to protect images online. In this paper, we proposed a new approach called IMGTXT that converts the image to text by coding the pixel values depending on locations then encrypts them by any trust encryption text algorithm, so that this method provides resistance to a variety of attacks such as histogram attacks and brute force attack. The state of the art of this research is the image is represented as a text and there is no relationship between the cipher-image and the plain image. Although this results in a large data volume. The proposed technique builds and testes on various images with different sizes, the recorded results demonstrate the technique’s efficacy and robustness to resist the brute force attack and statistical cryptanalysis of original and encrypted images.
Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information.
Interaction between domestic investment foreign direct investment and economic growth in Nigeria

The most inclusive definitions of economic growth always include the best utilisation of native resources in addition to increasing GDP and integration into the global market. As a result, the impact of domestic investment in an economy is one of the most examined subjects in capital movement and economic development. Flowing this, the aim of this study was to evaluate the effects of domestic investment on the economic growth of Nigeria. The objectives were to assess the growth of the economy of Nigeria from 1981 to 2018, determine the relationship of domestic investment on the Nigerian economy, evaluate the relationship of foreign direct investment on the economy of Nigeria, and determine the association between capital inflow and the Nigerian economy. Using secondary time series data obtained for domestic investment, foreign direct investment, exchange rate, and interest rate – from Nigerian Central Bank Statistical Bulletin and World Development Indicators, Autoregressive Distributed Lags (ARDL) technique was employed in estimating the short term and long run dynamics. The results revealed that foreign direct investment (t = 2.2385, p = 0.033) and interest rate (t = -2.5141, p = 0.0177) are the only significant determinants of real GDP in the short term, while the significant long run exponents are domestic investment (t = 139.5577, p = 0.0000), foreign direct investment (t = -3.2445, p = 0.0026) and exchange rate (t = -2.8316, p = 0.0071). Furthermore, the Granger Causality test revealed that both domestic investment and foreign direct investment cause economic growth. Therefore, the study recommended, among others, that policy makers optimise local investment options and normalize exchange rate and trade operations.
Analysis of Network Modeling for Real-world Recommender Systems

Nowadays, recommendation systems are existing everywhere in the internet world, online people are presented with the required needs not just for actual physical products, but also for several other things such as songs, places, books, friends, movies, and many more requirements. Most of the systems are developed with the basic collaborative and hybrid filtering, where the people or users are recommended items that the choices are based on the right preferences of other people by applying the machine intelligence strategies. In this research, the importance of network modeling is analyzed in solving real-world problems.
I love to hate!: the racist hate speech in social media

It is undeniable that, nowadays, hate speeches have flourished, they have become every day, banal and available to everyone. The interaction allowed by the use of devices, the potential of ICT and Social Media, formed a new participatory culture and contributed to rethinking social dynamics. The supposed illusion of anonymity and the rapid dissemination of narratives and images opened space for the proliferation of p hateful discourses against minority groups, such as those of a racist nature. This research intends to study, from the Digital Social Networks (DSN), the communicational flows of racist hate speeches in Portugal. Specifically, we propose to diagnose racist hate speech in the DSN; understand and characterize the narratives that support the spread of racist hate speech in DSN; and analyze which content and hate narratives generate more engagement in DSN; In this study, a mixed convergent methodology will be adopted. The quantitative approach will combine the use of digital methods with the analysis of social networks and graph theories. There will be 2 panels of 2 social networks (Facebook and Instagram), totaling 24 months of data collection. The qualitative approach will resort to the content analysis of the comments. In terms of results, we intend to strengthen scientific production in the area and develop a barometer on racist hate speech in Portugal.
Transfer Learning Improving Predictive Mortality Models for Patients in End-Stage Renal Disease

Deep learning is becoming a fundamental piece in the paradigm shift from evidence-based to data-based medicine. However, its learning capacity is rarely exploited when working with small data sets. Through transfer learning (TL), information from a source domain is transferred to a target one to enhance a learning task in such domain. The proposed TL mechanisms are based on sample and feature space augmentation. Thus, deep autoencoders extract complex representations for the data in the TL approach. Their latent representations, the so-called codes, are handled to transfer information among domains. The transfer of samples is carried out by computing a latent space mapping matrix that links codes from both domains for later reconstruction. The feature space augmentation is based on the computation of the average of the most similar codes from one domain. Such an average augments the features in a target domain. The proposed framework is evaluated in the prediction of mortality in patients in end-stage renal disease, transferring information related to the mortality of patients with acute kidney injury from the massive database MIMIC-III. Compared to other TL mechanisms, the proposed approach improves 6–11% in previous mortality predictive models. The integration of TL approaches into learning tasks in pathologies with data volume issues could encourage the use of data-based medicine in a clinical setting.
GAZING AT ‘THE-YOU-KNOW-WHO/WHAT’ IN WHATSAPP: DEIXIS USED IN ONLINE LEARNING AMID COVID-19 PANDEMIC

WhatsApp, as one of the most powerful and ubiquitous social media platforms, has become one of the promising tools utilized for English online learning during the current crisis, COVID-19 pandemic. Nevertheless, interacting effectively via this platform might be challenging for messages communicated to this platform might have more than one meaning that is likely to result in misunderstanding among the users. Thus, understanding deixis, known as one of the interactional phenomena in which language and context can be figured out, is invaluable. Nevertheless, to the best of the writers’ knowledge, studies concerning the use of deixis with respect to WhatsApp for English online learning are still left under-researched. Therefore, the present study aimed to investigate the deixis employed during online learning via a WhatsApp group. The method utilized in this research was a descriptive study. Besides, the participants comprised 23 postgraduate students and an instructor from an English Education Study Program of one university in Bandung city. The data were taken from the artefact in the form of an online chat archive. Based on the data analyzed, the findings revealed that all the types of deixis encompassing personal, social, discourse, place, and time deixis were found in online learning through the WhatsApp application. In this regard, the person(al) deixis (46%) was the most frequently used deixis, followed by social (39%), discourse (8%), place (5%), and time deixis (2%), respectively. Moreover, the appropriate and effective use of deixis by the instructor was found to influence students’ engagement and critical thinking ability. Besides, the WhatsApp feature, emojis, was found to be utilized along with the deixis, as well as it assists in providing some emphasis, understanding of the messages conveyed, and showing respect or compliment among the participants.
Blockchain-Based Online Voting System

Digital voting is the use of electronic device, such as voting machines or an internet browser to cast vote. It has disadvantages like security of data and potential attacks such as DoS and DDoS attack. One way to solve these security credentials problems is through the blockchain technology. Blockchain technology embraces a decentralized system and the entire database is owned by many users that overcome the disadvantage of centralized system. The existing system has many security, confidentiality, and anonymity issues. These issues can be resolved by using blockchain technology. Here in the proposed system Ethereum blockchain is used, which enables smart contracts. Smart contracts can be written with solidity, which is object oriented programming language. The outcome of the project is it provides privacy by keeping individual’s vote secret by providing a unique transaction id to each voter. Eligibility is made once to vote and the transaction will be highly secure and encrypted.
Improving Robustness of Language Models from a Geometry-aware Perspective

Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness.However, we observe that a too large number of search steps can hurt accuracy.We aim to obtain strong robustness efficiently using fewer steps.Through a toy experiment, we find that perturbing the clean data to the decision boundary but not crossing it does not degrade the test accuracy.Inspired by this, we propose friendly adversarial data augmentation (FADA) to generate friendly adversarial data.On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps.Comprehensive experiments across two widely used datasets and three pretrained language models demonstrate that GAT can obtain stronger robustness via fewer steps.In addition, we provide extensive empirical results and in-depth analyses on robustness to facilitate future studies.