Reviewerly/exHarmony huggingface dataset:
INVESTIGATING MOOC PLATFORMS AS A PROSPECTIVE TOOL FOR MOBILE LEARNING

The Massive Open Online Courses (MOOCs) provides new m-learning solutions for "learning-on-the-go", packing in one educational content, pedagogical framework and community support.Reaching millions of users world-wide, the MOOCs have the potential to fully transform educational landscape.However, the spread of the MOOCs is still limited, the retention rate and the degree of completion is small and the MOOCs efficiency remain doubtful.Thus, the present research aims to make an overview of the recent developments in MOOCs and m-learning solutions to recommend strategies for prospective learning providers.Considering the opportunities, it investigates suitable approaches for learning institutions to design and deliver m-learning MOOCs that will empower learners' experience.The paper is structured as follows.On first place, it makes an introduction to the MOOCs main concepts and identifies common features and classifications.Then, a short overview is made on the most popular MOOCs platforms.After that, the paper analyzes the MOOCs providers' approaches, including learners, technologies, instructional design, factors of success and lessons' learned of the MOOCs implementation.Finally, the discussion makes an overview of the steps and recommendations for implementing a successful MOOC.
Learning Portable Representations for High-Level Planning

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representations can be learned in a task-independent egocentric space specific to the agent that, when grounded with problem-specific information, are provably sufficient for planning. We demonstrate transfer in two different domains, where an agent learns a portable, task-independent symbolic vocabulary, as well as rules expressed in that vocabulary, and then learns to instantiate those rules on a per-task basis. This reduces the number of samples required to learn a representation of a new task.
Metasemiotic Projects and Lifestyle Media: Formulating Commodities as Resources for Identity Enactment

There has recently been a notable increase in the amount and perceived significance of new lifestyle media. Besides the instructive and entertaining function, these media arguably play a more fundamental sociocultural role of constructing identities. In consumer societies, these identities are to a great extent enacted through the acquisition of commodities and engagement in commodified practices, which thereby become semiotic resources of identity stylization. The purpose of this article is to explore the discursive mechanisms underpinning the process of formulating commodities and practices as such semiotic resources. To this end, several discourses from new online men’s magazines have been analyzed drawing on a model of discourse analysis that sees discourse as one of the “moments” of the social practice it is embedded in. The results indicate that the mechanism behind the processes in question can be described as a metasemiotic project. As such a project unfolds in discourse, various commodities and practices are being typified by a metasemiotic term. One of the most frequent prototypical metasemiotic terms in these resources is stylish man . The term is instantiated in texts by several lexemes, including the lexeme style and its derivatives, as well as lexemes naming various “masculine personas” such as man , guy, kid, gentleman, bad ass. It has been shown that an increasing number of commodities and practices are being “theorized” by the discourse of new online men’s magazines and typified by this term. One important feature behind the workings of the metasemiotic project is intertextuality. Specific texts are always dialogically linked to other texts of lifestyle discourse, while object-signs are reformulated and imbued with different social values. These results contribute to the exploration of contemporary lifestyle media and discursive mechanisms of identity construction used by them, and, in a more general sense, to recent discussions of operationalizing wider sociocultural context in textually oriented discourse analysis.
Fuzzy Mapped Histogram Equalization Method for Contrast Enhancement of Remotely Sensed Images

Histogram equalisation (HE) is a widely used image contrast enhancement technique which is less computationally complex, but it fails to preserve the brightness and natural appearance of the remotely sensed images. To overcome these limitations several modifications have been reported in the literature. However, the images processed by most of the methods still suffer with the problems of saturation artifacts and un-even expansion of intensities. This paper proposes a novel fuzzy mapped HE method to overcome the aforementioned limitations by partitioning the histogram into multiple segments, expanding each segment to full dynamic range using fuzzy mapping function, then equalising each segment independently, and finally normalising the combination of equalised segments. The normalisation process is controlled by a non-negative control factor, which requires a training data for its estimation. Experimental results demonstrate that the application of proposed method in the area of remote sensing yields better results than the contemporary methods.
Parallel Machine Translation with Disentangled Context Transformer

State-of-the-art neural machine translation models generate a translation from left to right and every step is conditioned on the previously generated tokens. The sequential nature of this generation process causes fundamental latency in inference since we cannot generate multiple tokens in each sentence in parallel. We propose an attention-masking based model, called Disentangled Context (DisCo) transformer, that simultaneously generates all tokens given different contexts. The DisCo transformer is trained to predict every output token given an arbitrary subset of the other reference tokens. We also develop the parallel easy-first inference algorithm, which iteratively refines every token in parallel and reduces the number of required iterations. Our extensive experiments on 7 translation directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. Our code is available at this https URL.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion

Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neural models with vector representations of users. In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling. The proposed framework can be layered on top of all information diffusion techniques that leverage user representations, so as to boost the predictive power and learning efficiency of the original technique. Extensive experiments on three real-world datasets showcase the superiority of our method.
Adaptive carrier fringe pattern enhancement for wavelet transform profilometry through modifying intrinsic time-scale decomposition

The uneven background illumination and random noise will degrade the quality of the optical fringe pattern, resulting in reduced accuracy or errors in phase extraction of wavelet transform profilometry (WTP). An adaptive fringe pattern enhancement method is proposed in this paper, which can effectively solve the above problems and improve the robustness of WTP. First, a modified intrinsic time-scale decomposition (MITD) algorithm is used to decompose each row of the fringe pattern adaptively, which can obtain a set of reasonable and pure proper rotation components (PRCs) with a frequency ranging from high to low and a monotonic trend. The MITD algorithm can overcome the mode mixing problem while ensuring the completeness of decomposition. Then, based on the obtained pure PRCs, an innovative background-carrier signal-noise automatic grouping strategy is proposed. Specifically, weighted-permutation entropy (WPE) is adopted to handle noise removal, and fuzzy gray correlation analysis (FGCA) is used to separate the background and carrier signal. Finally, the desired phase information can be easily and accurately extracted from the enhanced carrier signal component by a direct wavelet ridge detection method. Both the simulation and experimental results demonstrate the effectiveness and functionality of the proposed method.
Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities

Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:”online harassment”, “vulgar language” and “valance” and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.
Color-Based Hybrid Modeling to Classify the Acute Lymphoblastic Leukemia

In this research, a new approach color based hybrid modeling to separate and classify the Acute Lymphoblastic Leukemia is proposed to resolve the classification tasks.A series of the process is offered to conduct the classification tasks, i.e., Pre-processing, Image Segmentation, Features Extraction, and Similarity Measurement.Furthermore, the Otsu thresholding-based contrast stretching model is proposed to enhance the image quality on the Pre-processing stage.Moreover, the largest object selection of the found objects indicates the right choice to capture the desired object.Furthermore, the feature extraction is performed by combining the color based densitometry and shape features.Lastly, the feature extraction results are measured by the Euclidian Distance and Manhattan method to classify the sample used, which is Acute Lymphoblastic Leukemia Image Database (ALL-IDB).The results of the proposed model have produced 95.38% accuracy.Therefore, it showed that the classification had produced higher accuracy than the others, i.e., Naïve Bayesian, Color Correlation, Fuzzy-based Leukemia Detection, Hausdrof SVMbased Leukemia Detection, and Automated Differential (Learning Vector Quantization, Multi-Layer Perceptron, and Support Vector Machine.
An Efficient Way of Finding Polarity of Roman Urdu Reviews by using Boolean Rules

Opinion mining is the technique of analyzing the sentiment, behavior, feelings, emotions, and attitudes of customers about a product, topic, comments on social media, etc. Online shopping has revolutionized the way customers do shopping. The customer likes to visit the online store to find their product of interest. It is becoming more difficult for customers to make purchasing decisions solely based on photos and product descriptions. Customer reviews provides a rich source of information to compare products and make purchasing decisions commonly on the basis of other customer reviews. Clients provide comments in the language of their choice, e.g. the people of Pakistan use Roman script based the Urdu language. Normally such comments are free from scripting rules. Hundreds of comments are given on a single product, which may contain noisy comments. Identifying noisy comments and finding the polarity of these comments is an active area of research. Limited research is being carried out on roman Urdu sentiment analysis. In this research paper, we propose a novel approach by using Boolean rules for the identification of the related and non-related comments. Related reviews are those which show the behavior of a customer about a particular product. Lexicons are built for the identification of noise, positive and negative reviews. The precision of the evaluation results is 68%, recall is also 68% and F-measure is 68%. Ṫhe accuracy of the whole evaluation is 60%.
A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING

Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision-making in forecasting.
BLOCKCHAIN PERSPECTIVES IN THE DEVELOPMENT OF GREEN ECONOMY FINANCE

The work describes the concept of blockchain technology, analyzes its advantages and disadvantages compared to centralized systems of accounting, storage and exchange of information. Key problems in the development of technology and its integration into everyday life are described and analyzed in this work. The relatively high cost, the presence of critical vulnerabilities in the software code, as well as the insufficient level of development of related technologies are significant obstacles to the development of blockchain technology. The directions of development and implementation of blockchain in the financial sector, as well as in the sphere of green finance are described. This direction can be the green bond market, for which is extremely important to track compliance with the requirement of targeted financing. Blockchain, technologically allows to effectively meet this requirement. An important conclusion obtained from the study was that despite a number of attempts to create analogues of private money (Libra, Gram, etc.) in the near future the replacement of traditional fiate money circulation by cryptocurrencies is unlikely for a number of objective reasons.
AdaptiveReID: Adaptive L2 Regularization in Person Re-Identification.

We introduce an adaptive L2 regularization mechanism termed AdaptiveReID, in the setting of person re-identification. In the literature, it is common practice to utilize hand-picked regularization factors which remain constant throughout the training procedure. Unlike existing approaches, the regularization factors in our proposed method are updated adaptively through backpropagation. This is achieved by incorporating trainable scalar variables as the regularization factors, which are further fed into a scaled hard sigmoid function. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 datasets validate the effectiveness of our framework. Most notably, we obtain state-of-the-art performance on MSMT17, which is the largest dataset for person re-identification. Source code will be published at this https URL.
Comparing recollection and nonrecollection memory states for recall of general knowledge: A nontrivial pursuit.

Based on the classic distinction between semantic and episodic memory, people answer general-knowledge questions by querying their semantic memory. And yet, an appeal of trivia games is the variety of memory experiences they arouse-including the recollection of episodic details. We report the first in-depth exploration of the memory states that arise for recalled answers to general-knowledge questions. In 2 experiments, participants classified their answers as learning memory or related memory forms of recollection, as feels familiar or just know forms of nonrecollection, or as a guess. A recollection state was reported for nearly half of the correct answers. Learning memory, related memory, and just know states showed similarly high accuracy and confidence-whereas the feels familiar state was much lower. The differences between familiarity and knowing highlight the importance of distinguishing these oft-conflated states. Our study establishes that episodic memory often contributes to retrieval of general-knowledge, and that the memory states arising during retrieval can be diagnostic of accuracy. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Overview of Azure Infrastructure as a Service (IaaS) Services

The National Institute of Standards and Technology (NIST), a division of the US Department of Commerce, defines cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that are rapidly provisioned and released with minimal management effort or service provider interaction.” Within the NIST definition of cloud computing, three service models exist: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS).
UNCERTAINTY ESTIMATION FOR END-TO-END LEARNED DENSE STEREO MATCHING VIA PROBABILISTIC DEEP LEARNING

Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets.
DEEP DOMAIN ADAPTATION BY WEIGHTED ENTROPY MINIMIZATION FOR THE CLASSIFICATION OF AERIAL IMAGES

Abstract. Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs.