Reviewerly/exHarmony huggingface dataset:
Desain E-Smart Inclusive Sebagai Sistem Informasi Manajemen Pendidikan Inklusif

This study aims to design E-Smart Inclusive as a basis for developing a website-based inclusive education management information system so that inclusive schools can be created that are friendly to all students. The E-Smart Inclusive design was developed from the Kemendikbud RI Dapodik, foundation websites, inclusive education guidebooks, and previous research. This research refers to the Borg &amp; Gall development model in stages one to three. Starting from the data collection stage through interviews and library studies, the planning stages of E-Smart Inclusive, and the initial product development stages of E-Smart Inclusive. The results of the research that has been carried out can be drawn a conclusion that researchers have designed E-Smart Inclusive as a basis for developing a website-based inclusive education management information system through the stages of development from Borg &amp; Gall. At the data collection stage, the researcher conducted a literature study and interviews. The literature study was carried out through previous research, guidebooks for the implementation of inclusive education, the Kemendikbud RI dapodik website, foundation websites. Interviews were conducted with Special Education Teachers at SMPN 259 Jakarta and the Segar Amanah inclusive school. At the planning stage of E-Smart Inclusive, researchers made a list of E-Smart Inclusive features, namely the homepage feature, about us, inclusive education data, inclusive education information, our contacts. In the initial product development stage of E-Smart Inclusive, researchers developed it according to the list of features at the planning stage with materials in the form of laptops, servers, domains, and frameworks.
CLASS IMBALANCE PROBLEM IN DATA SCIENCE: REVIEW

In last few years there are many changes and evolution has been done on the classification of data. Application area of technology increases then the size of data also increases. Classification of data becomes difficult because of imbalance nature and unbounded size of data. Class imbalance problem becomes the greatest issue in the data mining. Imbalance problem occurs where one of the two classes having more sample than the other classes. The most of the algorithms are focusing on the classification of major sample while ignoring minority sample. The minority samples are those samples that rarely occur but are very important. There are different methods available for the classification of imbalance data set which is divided into three categories i.e. the algorithmic approach, feature selection approach and the data processing approach. These approaches have their own advantages and disadvantages. In this paper a systematic study of each process is defined which gives the right direction for research in class imbalance problem.
Effect rainfall season on coastal flood in Semarang City, Central Java, Indonesia

Floods of rain always occur on the coast of Semarang City every time there is a change of season. The coastal city of Semarang has a flat topography with 13 rivers passing through, causing flood insecurity. The purpose of the study is reviewing rainfall patterns in Semarang City, and reviewing the relationship of rainfall with flood events in coastal parts of Semarang City. The materials used are secondary data (rainfall from BMKG, flood information from online media and newspapers) and field surveys (flood measurement and observation). Rainfall data processed with Excel, flood events (place, time, and height of puddles) are associated with the magnitude of rainfall. Analysis of the interrelationship of rainfall and flooding is associated with aspects of environmental conditions (topography, hydrology, geology). The results showed that during the period June 2018-June 2019 daily rainfall above 100 mm has caused flooding. Monthly rainfall above 100 mm occurs in December, January, February. The conclusion that the rainfall pattern in Semarang City is unimodal with the November-March rainy period and the peak occurs in January.
Sensitivity Analysis of a BERT-based scholarly recommendation system

With the exponential growth of publicly available datasets, a scholarly recommendation system of datasets would be an essential tool in the field of information filtering. Recommending datasets to users can be formulated as a classification problem where deep learning models can be carefully trained. In such a case, when preparing training data for the learning models, one needs to consider different ratios of false and true pairs. Therefore, a sensitivity analysis is necessary. In this work, we conduct a sensitivity analysis using different class ratios on a deep learning model (BERT) for recommending datasets. We found out that our BERT-based recommender model is relatively robust using recommender metrics such as Mean Reciprocal Rank (MRR)@k, Recall@k, etc., except for the extreme class imbalance case (1:5000). Therefore, we conclude that a moderate ratio of the random negative sampling scheme, (in our case 1:10) is reasonable, sufficient and time-efficient in the recommendation system training
Hate Speech on Campus: How Student Leaders of Color Respond

The spike of hate speech incidents on college campuses since the 2016 U.S. presidential election has compounded the racial hostility students of color face on historically White campuses. These ongoing incidents require institutions to respond purposively to address the harm students of color experience in their aftermath. Using an "inclusive freedom" framework that connects freedom of expression with goals of inclusion, we employ an embedded single-case study of 28 student leaders of color to examine how they responded to hate speech in light of insufficient institutional action. Findings illustrate how student leaders of color bolster inclusion by leveraging their freedom of expression following hate speech incidents. Through efforts that were taxing and left them feeling drained, students generated affinity spaces and fostered dialogue about the negative impacts of hate speech on campus. Their experiences illuminate how institutions can more deliberately promote principles of inclusion and freedom of expression in their responses to hate speech within legal boundaries. These intentional efforts require an antiracist approach to inclusion that attends to the systemic factors that foster hate speech in the first place and to the deleterious consequences for students of color when they encounter hate speech on campus.
Autonomous Landing of a Rotor Unmanned Aerial Vehicle on a Boat Using Image-Based Visual Servoing

This paper proposes an image-based visual servoing (IBVS) method for a rotor unmanned aerial vehicle (UAV) to land on a boat with a downward-looking camera. The controller is designed in the image plane directly with the aid of image Jacobian matrix. In order to decouple the horizontal translation from the rotation of pitch angle and roll angle of the UAV, a virtual camera method is adopted to compensate the effect of UAV’s attitude changes. For the purpose of enabling the UAV to land from a high altitude, we design a nested 2D tag to ensure the detection within a large altitude range. However, due to the shake of the boat, the landing tag is no longer horizontal, which increases the difficulty of obtaining the desired position of the feature point. To solve the problem, we solely use the tag center in the image as the input, then the desired position of it will coincide with the image center of the virtual camera, irrespective of whether the tag is horizontal. Furthermore, Kalman filter is introduced to optimally estimate the position of the feature point with observation noise. Finally, simulation experiments based on Gazebo and the field experiments based on the deck simulation platform are conducted. The results show that our method can achieve a precise landing within errors of 0.2m from altitude around 50m.
A 97-mW Bitwise-Early-Terminating Multiuser Detector for IDMA Systems

This brief presents a low-power multiuser detector that can terminate its processing earlier than usual for interleave division multiple access (IDMA) systems. As a baseline detection algorithm, a recently proposed bitwise early termination (BET) is adopted. Despite algorithmically feasible, the BET has never been realized in circuitry yet, pending the demonstration of physical practicality. For the first time ever, we efficiently implement the BET in a 65-nm CMOS technology, and demonstrate that it is indeed effective in realizing a fast and low-power detector. In doing so, we organize an essential set of information bits to examine the feasibility of the BET, and define a systematic procedure of manipulating the set of bits. In addition, we build an efficient hardware architecture that enables the proposed manipulation procedure. Through an extensive comparison with the state-of-the-art detectors in the literature, we validate that the prototype implementation completes the detection at the earliest, and draws the lowest power. Specifically, the proposed 16-user detector takes 3.98 iterations per bit, and dissipates 97 mW to process one 8192-chip frame. In contrast, even the latest low-power counterpart iterates 6 times, and consumes 24% more power.
A Secured BlockChain Based Facial Recognition System for Two Factor Authentication Process

Verification of a human being on the other side of the screen has always been a problem and to encounter these issues captcha and OTP verification had been introduced. We have seen various methodologies being deployed to actual businesses and government initiatives for identity verification. Some include physical verification while some stick to video conferencing methods. Video conferencing methods have been a huge advantage especially during the pandemic. With such technologies in place we firmly believe that this would lead us to the next phase in identity verification. Another challenge that needs to be taken care of is storage of such huge delicate and confidential data. Cloud computing has come so far today and the future predicts that it still has miles to go. Studies show that centralised systems will not be able to handle such huge piles of cluttered data. The answer to this problem was Blockchain. Working towards this problem of face recognition and data storage on blockchain, this research work pushes the technical limits and tries to lay the foundation for a new and better form of identity verification.
An Efficient and Real-Time Emergency Exit Detection Technology for the Visually Impaired People Based on YOLOv5

Obtaining information from the surroundings has always been a great difficulty problem for visually impaired people. One of the most important problem is when facing an emergency, their security cannot be guaranteed. Whereas, there are no efficient assistant tools to help them recognize the emergency exit, which bring them great sense of fear. To get out this predicament, we adopt mature IFLYTEK voice recognition API and self-trained YOLOv5 object detection technology to purpose a emergency exit detection mini-program based on WeChat platform which can recognize the emergency exit signs by just acquiring a picture from the mobile phone camera. With the assistance of this mini-program, the safety of the visually impaired can be greatly improved.
Deep Convolutional Generative Adversarial Network

Deep convolutional generative adversarial networks consist of two models that are trained simultaneously by an adversarial process. A generator network learns to produce images that look real, while a discriminator network learns to tell real images apart from fakes. This process trains the generator network to generate real images that may not be found in the original dataset.
Aspect Based Sentiment Analysis for Bangla Newspaper Headlines

Aspect-based sentiment analysis means recognizing an aspect in a given content and afterwards perform sentiment analysis of the content regarding that aspect. Sentiment analysis in the Bengali language advances and is also viewed as a significant examination interest. Because of shortage of assets like appropriately commented on the data-set, dictionary, for example, tagger and so on aspect-based sentiment analysis barely has been done in the Bengali language. This research paper aims to find the Bangla newspaper headlines sentiment based on the aspect. Everywhere in the world, people would like to read newspaper headlines first and set up their minds with a summary of the news content instead of reading the news article. This impact may be positive, negative, or neutral. There are many approaches to find the true sentiment among them. Here We use aspect-based sentiment analysis. However, using the small training classifiers data-set for this analysis in Multinomial and Bernoulli Naive Bayes, Logistic Regression (LR), SGD Classifier, SVM, Random Forest, MLP Classifier and Voting Classifier. In our experiment Bernoulli Naive Bayes performed better, resulting in the highest F1 score of 70.75.
The web ad-click fraud detection approach for supporting to the online advertising system

With the introduction of e-commercial activities, revenue is generated in several legal and illegal ways. Marketing of a product or service in an online environment is increasing day by day since most users are getting involved in an online environment for buying or selling items. As a result, attackers are getting more space for performing online attacks by different malicious activities. The advertisement (ad) fraud, which is increasing exponentially with each passing day, is one of them. Ad fraud causes defame for the online advertising system and low return on the advertiser's investment (ROI). The paper reveals a methodology for detecting web ad-click frauds so that the ROI of an advertiser can be increased. The developed algorithm to detect the ad-click frauds in the proposed methodology can detect the web ad-click frauds. The results of the ad-click fraud algorithm are verified with the help of popular machine learning (ML) algorithms - k-nearest neighbour (k-NN), random forest (RF), decision tree (DT), logistic regression (LR) and support vector machine (SVM), with accuracies achieved.
Specific Expert Learning: Enriching Ensemble Diversity via Knowledge Distillation

In recent years, ensemble methods have shown sterling performance and gained popularity in visual tasks. However, the performance of an ensemble is limited by the paucity of diversity among the models. Thus, to enrich the diversity of the ensemble, we present the distillation approach—learning from experts (LFEs). Such method involves a novel knowledge distillation (KD) method that we present, specific expert learning (SEL), which can reduce class selectivity and improve the performance on specific weaker classes and overall accuracy. Through SEL, models can acquire different knowledge from distinct networks with various areas of expertise, and a highly diverse ensemble can be obtained afterward. Our experimental results demonstrate that, on CIFAR-10, the accuracy of the ResNet-32 increases 0.91% with SEL, and that the ensemble trained by SEL increases accuracy by 1.13%. Compared to state-of-the-art approaches, for example, DML only improves accuracy by 0.3% and 1.02% on single ResNet-32 and the ensemble, respectively. Furthermore, our proposed architecture also can be applied to ensemble distillation (ED), which applies KD on the ensemble model. In conclusion, our experimental results show that our proposed SEL not only improves the accuracy of a single classifier but also boosts the diversity of the ensemble model.
Key success factors in the continuous use of MOOC education in South Korea

Society is facing a paradigm shift where general education is moving towards lifelong education. Massive open online course (MOOC) is the leading online education with large-scale open classes. South Korea has localised 1,340 MOOCs known as K-MOOCs. However, K-MOOCs are not being managed effectively, and are generally low-level lectures compared to MOOCs. The variance-based structural equation modelling (SEM) using the partial least squares (PLS) model was applied for data analysis. Online surveys and analysis of 224 respondents studied 14 hypotheses and analysed eight factors to improve K-MOOC operation in South Korea. Findings indicated 'authentic learning' positively affects 'learning transfer'; 'gamification', 'teaching presence' and 'reputation' positively affect 'satisfaction' and 'commitment'. 'Learning transfer' and 'satisfaction' impacts 'continuous use intention'. Furthermore, 'commitment' positively affects 'satisfaction', and 'continuous use intention'. This research suggests that these factors must be taken into account when constructing teaching methods and educational content to improve K-MOOCs continuous usage.
Meta-learning with Logistic Regression for Multi-classification

The current classifiers and basic learners for few-shot meta-learning is based on distance rules and a series of linear classifiers, such as ridge regression, and linear support vector machine. This study introduces a nonlinear basic learner-logistic regression to improve meta-learning through fast convergence in learning downstream tasks and obtaining the global optimal solution. The Woodbury identity is utilized to express our advantages in a small number of samples. This helps to reduce the consumption in the process of matrix operation. The prototype network and residual network are employed as embedding models. The performance on data sets CIFAR-FS, FC100 and MiniImagenet demonstrate the competitiveness of our method.
Construction method of knowledge graph under machine learning

With the increasing trade among China and Southeast Asian countries, cultural exchanges have become more and more intensified. Convenient language communication constitutes an important part of the cooperation channels among different countries. To explore the named entity recognition (NER) in the field of knowledge graph construction, the Vietnamese grammar and word formation are analysed deeply in this study, aiming to solve the low recognition precision and low network calculation efficiency in Vietnamese named entity recognition. Firstly, the Vietnamese person names, location names, and institution names in Vietnamese corpus are collected statistically to build a corresponding entity database to assist the Vietnamese named entity recognition. Then, a Vietnamese named entity recognition model is proposed based on residual dense block (RDB) convolutional neural network (CNN).
Convolutional Neural Networks

Convolutional Neural Networks are neural networks with convolution layers which perform operations similar to image processing filters. Convolutional Neural Networks are applied in a variety of tasks related to images such as image classification, object detection, and semantic segmentation. Popular Network architectures include ResNet, GoogleNet, and VGG. These networks are often trained on very large datasets, can be downloaded in Keras and Tensorflow, and can be later used for finetuning on other tasks.
A Cross-Lingual Sentence Similarity Calculation Method With Multifeature Fusion

Cross-language sentence similarity computation is among the focuses of research in natural language processing (NLP). At present, some researchers have introduced fine-grained word and character features to help models understand sentence meanings, but they do not consider coarse-grained prior knowledge at the sentence level. Even if two cross-linguistic sentence pairs have the same meaning, the sentence representations extracted by the baseline approach may have language-specific biases. Considering the above problems, in this paper, we construct a Chinese–Uyghur cross-lingual sentence similarity dataset and propose a method to compute cross-lingual sentence similarity by fusing multiple features. The method is based on the cross-lingual pretraining model XLM-RoBERTa and assists the model in similarity calculation by introducing two coarse-grained prior knowledge features, i.e., sentence sentiment and length features. At the same time, to eliminate possible language-specific biases in the vectors, we whitened the sentence vectors of different languages to ensure that they were all represented under the standard orthogonal basis. Considering that the combination of different vectors has different effects on the final performance of the model, we introduce different vector features for comparison experiments based on the basic feature splicing method. The results show that the absolute value feature of the difference between two vectors can reflect the similarity of two sentences well. The final F1 value of our method reaches 98.97%, which is 19.81% higher than that of the baseline.
<scp>PixJS</scp>: A novel chaos‐based approach for image encryption

Abstract Image encryption has variety of applications in the healthcare, military, e‐commerce, and education sectors. Along with processing images for various applications, secure storage of such images is essential. For many years, encryption of images is a fruitful method for the secure transmission of multimedia data that carries sensitive information across multiple mediums. There is umpteen number of image encryption methods available which exhibit improved performance either in time, space, or susceptance to attacks in a controlled environment. Few image encryption algorithms have higher computational complexity, whereas some are immune to a handful of attacks (either differential, statistical, brute force, or known‐plaintext and chosen‐plain text attacks). We have developed a novel chaos‐based image encryption algorithm—PixJS for 8‐bit grayscale images based on linear feedback shift register, logistic map, and jumbling process. The algorithm was initially proposed for the text‐based passwords called as jumbling‐salting algorithm in 2014. We conducted a comprehensive analysis of the proposed algorithm on sets of symmetric and asymmetric images (10 samples each) and addressed four research questions primarily based on speed and security parameters. After conducting thorough experimentation, we found that our algorithm outperformed existing algorithms in security and has proven to be resistant to statistical, differential, brute‐force, known plain text, and chosen plain text attacks.
If We Succeed

Abstract Since its inception, AI has operated within a standard model whereby systems are designed to optimize a fixed, known objective. This model has been increasingly successful. I briefly summarize the state of the art and its likely evolution over the next decade. Substantial breakthroughs leading to general-purpose AI are much harder to predict, but they will have an enormous impact on society. At the same time, the standard model will become progressively untenable in real-world applications because of the difficulty of specifying objectives completely and correctly. I propose a new model for AI development in which the machine's uncertainty about the true objective leads to qualitatively new modes of behavior that are more robust, controllable, and deferential.
Defining virtual reality enabled learning

Virtual reality (VR), as a set of human-computer interaction (HCI) technologies, allows the creation of computer-generated virtual environments, in which users are free to interact with the environments, virtual objects, agents, or even other users. Previous empirical studies have demonstrated the feasibility and effectiveness of using VR for education. However, the ambiguity in the definition has caused both theoretical and practical obstacles in this research area. This article aims to address this issue by considering the interdisciplinary nature of this research area. Specifically, the term VR-enabled learning is coined here, and the definition of VR-enabled learning is decomposed into five dimensions of concerns, namely immersion, presence, pedagogy, intended learning outcomes, and learner specifics. These five dimensions of concerns should not only help to address the ambiguity in the definition but also serve as a framework to guide the planning and practising of future research studies in VR-enabled learning.