Reviewerly/exHarmony huggingface dataset:
DEIFDC framework: Evaluation of digital education deployment in India in the midst of the Covid-19 pandemic

The Digital Education Index for Developing Countries (DEIFDC) is a compound index that considers nine different variables grouped in three main levers that have been researched relevant to assess the overall state of readiness of Digital Education deployment in a developing country. Digital Education has been approached from an instrumental point of view, focusing on the advantages that the introduction at an early stage of digital tools brings to the teaching and learning processes to ensure children can acquire the required 21st competencies of a future workforce. In the application for the Indian case, social, cultural, economic and educational data obtained through desk research during the first semester of 2021 has been taken into consideration. Despite significant Government efforts on scaling up Digital Education, primarily due to the Covid-19 pandemic school closure, the 0.596 DEIFDC score on a 0–1 possible range has shown Inadequate Digital Education Deployment, derived mainly from poor school infrastructure, limited pedagogical capabilities and modest students' skills. Furthermore, the socio-demographic differences observed among school children and the existent digital divide in rural and urban areas demonstrate that major effort needs to be undertaken to ensure vulnerable Indian population does not lag behind under the new rules of the Digital Economy.
Geometry-Aware Light Field Angular Super Resolution Using Multiple Receptive Field Network

Since a light field camera acquires a set of images with multiple views, it has a fundamental limitation of spatial and angular resolution due to the limited sensor resolution and a number of lenses. This paper presents a light field angular super resolution method using a multi-receptive field network. The proposed network consists of two main modules, depth estimation and light field blending modules. Specifically, the proposed depth estimation module with hierarchical multiple receptive fields can accurately estimates flow maps for image warping. Experimental results demonstrate that the proposed method generates more improved results than existing methods in the sense of quantitative evaluation. For that reason, the proposed network can be applied to various light field applications.
Progressive selection‐channel networks for image steganalysis

Steganalysis is a detection technology against steganography that embeds secret data into digital media carriers. The selection channel, which indicates the embedding details of steganography, is well recognized in boosting the detection performance of image steganalysis. However, nearly all the selection channels are constructed in a hand-crafted manner, even when they are incorporated into end-to-end deep steganalytic networks, for which the embedding rate and steganographic algorithms also need to be predetermined. Such prior knowledge is usually assumed completely known in existing literature, which is obviously unreasonable and impractical. To address this issue, we propose to automatically learn the selection channels for deep learning-based image steganalysis in a progressive way. Specifically, we divide the image steganalysis task into two phases: selection channel estimation and steganalytic detection. For the first phase, we design a multistage progressive network, which enables the learning of selection channels in a coarse-to-fine fashion. For the second phase, we integrate the learned selection channels into the multilayers of the steganalytic network, allowing full exploitation of selection channels for accurate detection. Extensive experiments demonstrate that the proposed method can learn the selection channels rapidly and precisely, and also significantly improve the detection accuracy of the existing state-of-the-art steganographic network without any prior knowledge.
Dimension decoupling attention mechanism for time series prediction

The spatial-temporal sequence prediction task is a challenge for neural networks, as it requires capturing temporal and spatial changes simultaneously. Recent researches focus on the modification of the ConvLSTM internal elements, which improves the prediction ability to some extent but introduces a large number of parameters. We observe that although the result generated by the row ConvLSTM is not very accurate in position and have a blurry appearance, it contains enough elements required to reconstruct the prediction. The motivation is to further refine these feature maps. Therefore, we propose a multi-attention LSTM (MA-LSTM) based on dimensional decoupling attention to alleviate the problems of the existing ConvLSTM-based methods, including inaccurate positions prediction and blurry generated results. The proposed model includes the Dimensionality Decoupling Module (D2M) and the Channel Attention Module (CAM). D2M compresses dimensions to transfer motion features over time that squeezes the features by dimensions and calculates the motion distribution of each dimension. CAM maintains the predicted texture information, which integrates latent information between channels and weights of the effective channels. Through the coordination of multiple modes, the model has long-term predictive capability and the accuracy of the prediction results has been improved. Experimental results show that using a smaller number of parameters, the proposed method can obtain competitive results but less inference time compared with the state-of-the-art (SOTA) methods.
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.
Image Outpainting using Wasserstein Generative Adversarial Network with Gradient Penalty

With advancements in AI technology, machines can perform or even mimic tasks that humans can do. One of its achievements can be seen in image generation, one of it being Image Inpainting (completion). In Image Inpainting, AI is used to complete missing data in an image. This is an extensive field of research, but its contemporary field, i.e. image outpainting, is not a well-researched one. In Image Outpainting (extrapolation) the image is extended beyond its borders. This is similar to our brain picturing the whole image of an object that is partially seen through a gap. This task can be achieved by using Generative Adversarial Networks (GANs). Compared to Inpainting, the biggest challenge is to achieve spatial correlation between the generated image and the ground truth image. Also, the process of overcoming this challenge is also sometimes affected because of the training instability of GAN. With the help of Wasserstein GAN (WGAN), the above issue can be solved. So, a model is proposed based on the Wasserstein GAN with Gradient Penalty (WGAN-GP) algorithm and deep convolutional neural networks for image outpainting using a dataset on natural images. From this proposed model it is found that the results of WGAN-GP algorithm was better than GAN algorithm in various aspects.
Digital Visual Communication for Public Health: Design Proposal for a Vaccinated Emoji

In the 21st century, the internet and particularly social media have become essential platforms for the spread of health information (including misinformation and disinformation). One of the distinguishing features of communication on these platforms is the widespread use of emojis. Though seemingly trivial emojis are now used by many if not most public health figures and organizations alongside important health updates. Much of that information has had to do with vaccination. Vaccines are a critical public health tool but one surrounded by falsehoods, phobias, and misinformation fueling vaccine hesitancy. Part of that has to do with their lack of positive representation on social media (eg, the syringe emoji is a plain needle, which for many people is an uncomfortable image). We thus argue that vaccination deserves an entirely new emoji to communicate vaccine confidence and discuss a design proposal for a vaccinated emoji that has gained traction in the global public health community.
Study on Temperature Sensor Data Anomaly Diagnosis Method Based on Deep Neural Network

In order to solve the problems of low accuracy and low efficiency of traditional sensor data anomaly diagnosis methods, a new temperature sensor data anomaly diagnosis method based on deep neural network is proposed in this paper. Firstly, the temperature sensor data in a running cycle is collected, and the characteristics of the temperature sensor data are extracted by the sliding window technology. Secondly, based on the feature extraction results, a deep neural network model for anomaly diagnosis of temperature sensor data is constructed. The feature data are input into the model, and the result obtained is the diagnosis result. Finally, the simulation comparison experiment is carried out. The experimental results show that the error rate of feature extraction of temperature sensor data in this method changes between −2.1% and 5.9%, the diagnosis accuracy remains above 95%, the average diagnosis time is only 59 ms, and the diagnosis efficiency is high.
An Innovative Model for Extracting OLAP Cubes from NOSQL Database Based on Scalable Naïve Bayes Classifier

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classifier method was used for this purpose. The proposed solution is divided into three stages of preparation, Naïve Bayes, and NBMR. Our proposed algorithm, NBMR, is based on the Naïve Bayes classifier (NBC) and the MapReduce (MR) programming model. Each NOSQL database document with nearly the same attribute will belong to the same class, and as a result, OLAP cubes can be used to perform data analysis. Because the proposed model allows for distributed and parallel Naïve Bayes Classifier computing, it is appropriate and suitable for large-scale data sets. Our proposed model is a proper and efficient approach when considering the speed and reduced the number of required comparisons.
Dynamic Obstacle Avoidance of Mobile Robots Using Real-Time Q-learning

As the field of autonomous navigation has been actively researched, the importance of route search is increasing. In particular, the field of autonomous navigation using reinforcement learning is being intensively studied. However, this requires very complex algorithms and high cost. Previous studies have shown that path planning can also be performed with Q-learning, a lightweight reinforcement learning algorithm, with proper selection of the exploration strategy. In this paper, we show that real-time Q-learning can be used for path planning and dynamic obstacle avoidance of mobile robots in various environments. And in a follow-up study, we plan to apply real-time Q-learning to real mobile robots rather than simulations.
Future Motion Estimation for Video Frame Extrapolation

We propose a novel video frame extrapolation algorithm based on future motion estimation. First, we estimate optical flows between a pair of input frames and predict future motions using two schemes: motion doubling and motion mirroring. Then, we forward warp the input frames by employing the two kinds of predicted motion fields, respectively. Finally, we develop a synthesis network to generate a future frame from the warped frames. Experimental results show that the proposed algorithm outperforms recent video frame extrapolation algorithms on various datasets.
A coarse-to-fine full attention guided capsule network for medical image segmentation

As an important material in clinical diagnoses, medical images are widely used by the doctors to discover the diseases and make therapeutic schedules. Accurately locating the lesions and correctly delineating their severities based on the medical images can significantly help to improve the detection rate and diagnosis accuracy of the diseases. In this paper, we design an effective attention guided capsule network, named HR-CapsSegNet, for segmenting medical targets from medical images. First, by forming a capsule-based high-resolution network architecture assisted by cross-branch multiscale feature augmentation, the HR-CapsSegNet performs advantageously in providing multiscale feature representations with high-level strong semantics. Second, by designing a capsule-based full attention mechanism, the feature encoding quality at each scale can be significantly promoted by sticking up the informative feature semantics from a cross-channel global perspective. In addition, by employing a hierarchical coarse-to-fine segmentation strategy, the feature distractions causing false recognitions can be progressively removed to provide an accurate segmentation map. Intensive quantitative assessments, visual examinations, and comparative analyses on four challenging datasets prove the promising applicability and competitive superiority of the HR-CapsSegNet for medical image segmentation applications.
Sociotechnical imaginaries of algorithmic governance in EU policy on online disinformation and FinTech

Datafication and the use of algorithmic systems increasingly blur distinctions between policy fields. In the financial sector, for example, algorithms are used in credit scoring, money has become transactional data sought after by large data-driven companies, while financial technologies (FinTech) are emerging as a locus of information warfare. To grasp the context specificity of algorithmic governance and the assumptions on which its evaluation within different domains is based, we comparatively study the sociotechnical imaginaries of algorithmic governance in European Union (EU) policy on online disinformation and FinTech. We find that sociotechnical imaginaries prevalent in EU policy documents on disinformation and FinTech are highly divergent. While the first can be characterized as an algorithm-facilitated attempt to return to the presupposed status quo (absence of manipulation) without a defined future imaginary, the latter places technological innovation at the centre of realizing a globally competitive Digital Single Market.
A Hybrid Approach to Explore Public Sentiments on COVID-19

Text processing methods like lexicon-based unsupervised approaches play important roles to quantify public opinions in the textual domain. While these methods have benefit to directly generate sentiment scores from text data based on the word-intensity scores, they perform poorly with shorter unstructured texts like tweets. Besides, these lexicon models often produce poor accuracy with the human annotated datasets. To overcome these limitations of lexicon models, a new hybrid approach has been proposed. This new approach capitalizes the prediction capabilities of two supervised machine learning models to revise the lexicon scores using Bipolar sigmoid function that confirm better accuracy in the sentiment analysis. Three pre-annotated datasets have been used to verify lexicon-based models and the proposed hybrid method. Finally, the proposed method has been applied to the corona-induced tweets, which were collected from Japan, USA, UK, and Australia during January–June 2020. Several sentiment and emotion timeseries have been constructed and evaluated using statistical analysis against three events namely the first declaration of lockdown (FLD), the first declaration of the economic support package (FEP), and the first death-severity (FDS) event. Results showed the significant reduction of the mean negative polarity (meanNeg) in the USA and the significant increase of the ratio between positive and negative tweets (pnRatio) in the UK after the FLD event. The UK people also showed significant reduction of the mean polarity (meanPol) after the FLD and FDS events, respectively. On the other hand, the sadness emotion in the UK after the FEP, the anger and sadness emotions in Australia after the FDS event, and the surprise emotion in the UK after the FDS event have shown significant changes. However, no emotional variables after the FLD event and no sentiment variables after the FEP event have shown any impact among the people in any of the four countries. Surpringly, no events including government responses (FLD, FEP) to COVID-19 showed significant changes to the emotions of Japanese people. Our results can help leader’s policy decisions and can also perform more accurate prediction of the disaster driven public sentiments.
Facial Expression Analysis and Estimation Based on Facial Salient Points and Action Unit (AUs)

Humans use their facial expressions as one of the most effective, quick, and natural ways to convey their feelings and intentions to others. In this research, presents the analyses of human facial structure along with its components using Facial Action Units (AUs) and Geometric structures for identifying human facial expressions. The approach considers facial components such as Nose, Mouth, eyes and eye brows for FER. Nostril contours such as left lower tip, right lower tip, and centre tip are considered as salient points of Nose. Various salient points for Mouth are extracted from the left and right end point, upper and lower lip mid points along with curve. These salient points are extracted for all facial expression of the same subject considering neutral face as reference. The Geometric structure for neutral face is mapped along with other facial expression faces. The deformation is estimated using the Euclidean distance. The classification algorithms such as LibSVM, MLP, RF has achieved classification accuracy of 86.56% on an average. The findings of the experiments show that the extraction of picture characteristics is more efficient in terms of computing and gives promising outcomes.
Linguistic Analysis of Hindi-English Mixed Tweets for Depression Detection

According to recent studies, young adults in India faced mental health issues due to closures of universities and loss of income, low self-esteem, distress, and reported symptoms of anxiety and/or depressive disorder (43%). This makes it a high time to come up with a solution. A new classifier proposed to find those individuals who might be having depression based on their tweets from the social media platform Twitter. The proposed model is based on linguistic analysis and text classification by calculating probability using the TF <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>∗</mi> </math> IDF (term frequency-inverse document frequency). Indians tend to tweet predominantly using English, Hindi, or a mix of these two languages (colloquially known as Hinglish). In this proposed approach, data has been collected from Twitter and screened via passing them through a classifier built using the multinomial Naive Bayes algorithm and grid search, the latter being used for hyperparameter optimization. Each tweet is classified as depressed or not depressed. The entire architecture works over English and Hindi languages, which shall help in implementation globally and across multiple platforms and help in putting a stop to the ever-increasing depression rates in a methodical and automated manner. In the proposed model pipeline, composed techniques are used to get the better results, as 96.15% accuracy and 0.914 as the F1 score have been attained.
Application of bioinspired global optimization algorithms to the improvement of the prediction accuracy of compact extreme learning machines

Objectives. Recent research in machine learning and artificial intelligence aimed at improving prediction accuracy and reducing computational complexity resulted in a novel neural network architecture referred to as an extreme learning machine (ELM). An ELM comprises a single-hidden-layer feedforward neural network in which the weights of connections among input-layer neurons and hidden-layer neurons are initialized randomly, while the weights of connections among hidden-layer neurons and output-layer neurons are computed using a generalized Moore– Penrose pseudoinverse operation. The replacement of the iterative learning process currently used in many neural network architectures with the random initialization of input weights and the explicit computation of output weights significantly increases the performance of this novel machine learning algorithm while preserving good generalization performance. However, since the random initialization of input weights does not necessarily guarantee optimal prediction accuracy, the purpose of the present work was to develop and study approaches to intelligent adjustment of input weights in ELMs using bioinspired algorithms in order to improve the prediction accuracy of this data analysis tool in regression problems. Methods. Methods of optimization theory, theory of evolutionary computation and swarm intelligence, probability theory, mathematical statistics and systems analysis were used. Results. Approaches to the intelligent adjustment of input weights in ELMs were developed and studied. These approaches are based on the genetic algorithm, the particle swarm algorithm, the fish school search algorithm, as well as the chaotic fish school search algorithm with exponential step decay proposed by the authors. By adjusting input weights with bioinspired optimization algorithms, it was shown that the prediction accuracy of ELMs in regression problems can be improved to reduce the number of hidden-layer neurons to reach a high prediction accuracy on learning and test datasets. In the considered problems, the best ELM configurations can be obtained using the chaotic fish school search algorithm with exponential step decay. Conclusions. The obtained results showed that the prediction accuracy of ELMs can be improved by using bioinspired algorithms for the intelligent adjustment of input weights. Additional calculations are required to adjust the weights; therefore, the use of ELMs in combination with bioinspired algorithms may be advisable where it is necessary to obtain the most accurate and most compact ELM configuration.
Deep Conversational Recommender Systems: Challenges and Opportunities

Unlike traditional recommender systems, the conversational recommender system (CRS) models a user’s preferences through interactive dialogue conversations. Recently, deep learning approaches have been applied to CRSs, producing fruitful results. We discuss the development of deep CRSs and future research directions.