Reviewerly/exHarmony huggingface dataset:
Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set

Abstract Background Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the following insertion and before the commencement of tube feeds. Convolutional neural networks (CNNs) have shown encouraging results in assisting the tube positioning assessment. However, robust CNNs are often trained using large amounts of manually annotated data, which challenges applying CNNs on enteral feeding tube positioning assessment. Method We build a CNN model for feeding tube positioning assessment by pre-training the model under a weakly supervised fashion on large quantities of radiographs. Since most of the model was pre-trained, a small amount of labeled data is needed when fine-tuning the model for tube positioning assessment. We demonstrate the proposed method using a small dataset with 175 radiographs. Result The experimental result shows that the proposed model improves the area under the receiver operating characteristic curve (AUC) by up to 35.71% , from 0.56 to 0.76, and 14.49% on the accuracy, from 0.69 to 0.79 when compared with the no pre-trained method. The proposed method also has up to 40% less error when estimating its prediction confidence. Conclusion Our evaluation results show that the proposed model has a high prediction accuracy and a more accurate estimated prediction confidence when compared to the no pre-trained model and other baseline models. The proposed method can be potentially used for assessing the enteral tube positioning. It also provides a strong baseline for future studies.
A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced Data

Despite the availability of a large amount of free unlabeled data, collecting sufficient training data for supervised learning models is challenging due to the time and cost involved in the labeling process. The active learning technique we present here provides a solution by querying a small but highly informative set of unlabeled data. It ensures high generalizability across space, improving classification performance with test data that we have never seen before. Most active learners query either the most informative or the most representative data to annotate them. These two criteria are combined in the proposed algorithm by using two phases: exploration and exploitation phases. The former aims to explore the instance space by visiting new regions at each iteration. The second phase attempts to select highly informative points in uncertain regions. Without any predefined knowledge, such as initial training data, these two phases improve the search strategy of the proposed algorithm so that it can explore the minority class space with imbalanced data using a small query budget. Further, some pseudo-labeled points geometrically located in trusted explored regions around the new labeled points are added to the training data, but with lower weights than the original labeled points. These pseudo-labeled points play several roles in our model, such as (i) increasing the size of the training data and (ii) decreasing the size of the version space by reducing the number of hypotheses that are consistent with the training data. Experiments on synthetic and real datasets with different imbalance ratios and dimensions show that the proposed algorithm has significant advantages over various well-known active learners.
Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

An emerging computational paradigm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated edge learning</i> (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">whether to participate in FEL or not</i> given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mechanism design</i> to enable truthful information solicitation. With the help of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correlated equilibrium</i> , we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages.
Composing Music Inspired by Sculpture: A Cross-Domain Mapping and Genetic Algorithm Approach

In this article, a system that takes a 3D model of a sculpture as starting point to compose music is presented. We raised the hypothesis that cross-domain mapping can be an approach to model inspiration. The semantic meaning of the sculpture is not used directly but rather a more abstract approach was used. A Genetic Algorithm was used to obtain results with more musical interest. The results were promising: the majority of the participants gave a classification of 4 out of 5 to the preferred interpretations of the compositions and related them to the respective sculpture. This is a step toward a possible model for inspiration.
A Detection Method for Social Network Images with Spam, Based on Deep Neural Network and Frequency Domain Pre-Processing

As a result of the rapid development of internet technology, images are widely used on various social networks, such as WeChat, Twitter or Facebook. It follows that images with spam can also be freely transmitted on social networks. Most of the traditional methods can only detect spam in the form of links and texts; there are few studies on detecting images with spam. To this end, a novel detection method for identifying social images with spam, based on deep neural network and frequency domain pre-processing, is proposed in this paper. Firstly, we collected several images with embedded spam and combined the DIV2K2017 dataset to build an image dataset for training the proposed detection model. Then, the specific components of the spam in the images were determined through experiments and the pre-processing module was specially designed. Low-frequency domain regions with less spam are discarded through Haar wavelet transform analysis. In addition, a feature extraction module with special convolutional layers was designed, and an appropriate number of modules was selected to maximize the extraction of three different high-frequency feature regions. Finally, the different high-frequency features are spliced along the channel dimension to obtain the final classification result. Our extensive experimental results indicate that the spam element mainly exists in the images as high-frequency information components; they also prove that the proposed model is superior to the state-of-the-art detection models in terms of detection accuracy and detection efficiency.
A Comparative Analysis of Various Approaches for Incorporating Contextual Information into Recommender Systems

Recommender systems are being widely applied in many fields, such as e-commerce, e-documents, places and travel, multimedia, news and advertising and transportation.These systems are similar to an information filtering system that helps to identify a set of items that best satisfy the users' demands based on their preference profiles.The integration of contextual information (e.g., location, weather conditions and user mood) into recommender systems to improve their performance has recently received considerable attention in the research literature.Studies in the relevant literature have focused on incorporating contextual information into conventional recommender systems by employing three approaches: Pre-filtering, post-filtering and modeling.In this paper, we conduct a systematic comparison of various approaches and show how to integrate contextual information into recommender systems.Additionally, we provide an in-depth analysis of the most notable studies to date and point out the strengths, weaknesses and application scenarios for each of the approaches.We also empirically evaluate the real-world datasets, analyzing distinct recommendation quality metrics and characteristics of the datasets.An important result is that accuracy-based comparisons show no clear winner among the approaches.
Using Movie Genres in Neural Network Based Collaborative Filtering Movie Recommendation System to Reduce Cold Start Problem

In a recommendation system, a cold start problem occurs when the system is unable to make any inferences for users or items for which it has not yet acquired adequate data. This research proposes a modified version of the Neural Collaborative Filtering model that has revolutionized collaborative filtering recommendation system with its implementation of neural network in the model. The proposed model is called Neural Collaborative Filtering Modified (NCFM). NCFM addresses the cold start problem by reducing the model dependency on users and items interactions with the integration of item feature (movie genres in this case) before the concatenation step in the NCF model. Hit ratio@10 and normalized discounted cumulative gain@10 were used as the evaluation method because they closely represents real-world recommendation scenario. Performance of both models were tested and compared using two movie rating dataset MovieLens1M provided by GroupLens and a modified version of MovieLens1M with significantly less amount of data to simulate the cold start problem called MovieLensCold. The results shows that NCFM can outperforms NCF by more than two times over or around 133% performance increase on MovieLensCold and around 15% increase on MovieLens1M dataset. Keywords— movie recommendation system, cold start problem, collaborative filtering, NCFM, neural network based recommendation system
Spatio-temporal analysis and comparison of 3D videos

Abstract Depth sensors of low-cost acquisition devices (e.g. Microsoft Kinect, Asus Xtion) are coming into widespread use; however, 3D acquired data are generally large, heterogeneous, and complex to analyse and interpret. In this context, our overall goal is the analysis of the action of a subject in a 3D video, e.g. the action of a human or the movement of its subparts. To this end, the action classification is achieved through the analysis of the temporal variation of geometric (e.g. centroid path, volume variation, activated voxels) and kinematic (e.g. speed) properties in consecutive frames. Then, these descriptors and the corresponding histograms are used to search a frame in a 3D video and to compare 3D videos. Our approach is applied to 3D videos represented as triangle meshes or point sets, and eventually to an underlying skeleton or to markers (if available). Our tests on the MIT, Berkley, i3DPost, NTU, and DUTH data sets confirm the usefulness of the proposed approach for the analysis and comparison of 3D videos, as well as for action classification.
Multi Time Horizon Ahead Solar Irradiation Prediction Using GRU, PCA, and GRID SEARCH Based on Multivariate Datasets

Precise forecasting of solar irradiation helps to minimize photovoltaic power plant energy wastage, avoid system damage due to the high variability of solar irradiation, and focus the integration of power output among the various power grids.To forecast solar irradiation, it is crucial to consider the multiple dimensions of historical weather data as temperature, wind speed, and different type of irradiation, in addition to the categorical time data.The high dimensionality of these data can perturb the performance and can introduce very low calculation in a forecasting model.Hybrid combination between PCA and GRU with Grid Search hyperparameters optimization, proposed in this work to predict solar irradiation in different time horizons, using multiple variable data.Firstly, PCA changes the multiple variables to a few variables named components.Secondly, the prediction model GRU trained in optimized by using Grid search method.Finally, the optimized model predicts the solar irradiation.The proposed model compared in this study with simple GRU, LSTM, MLP and RNN models.The result of experience indicates that the PCA-GRU have a good forecasting accuracy in different time horizon, and has the better performance, and faster training compared with other models.
Review of Deep Transfer Learning Models for Image Classification

With the rapid rise in urbanization, solid waste generation has increased exceedingly. This study aims to develop a Convolutional Neural Network (CNN) to classify trash into biodegradable and non-biodegradable wastes. The TrashNet dataset was utilized in this study, and image augmentation was employed to make the model more robust against translation invariance. Transfer Learning methods based on CNN have shown promising outcomes on diverse image classification problems. This paper reviews the deep learning models available with pre-trained weights in the Keras library. The performance of the models was compared, and the model based on NASNetMobile had the highest accuracy of 97%. Further, the model’s hyper-parameters were tuned, and the significance of a hyper-parameter on the model’s accuracy was studied.
OntoEnricher

Information Security in the cyber world is a major concern, with significant increase in attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded security related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001 based information security ontology. The model trained and tested on high performance computing (HPC) environment to handle Wiki text dimensionality yielded a test accuracy of over 80\% when tested with knocked out concepts from ontology and web page instances to validate the robustness.
Comparison of Ground-Based Global Horizontal Irradiance and Direct Normal Irradiance with Satellite-Based SUNY Model

Since the fossil reserves are depleting day by day, the trend of modern energy sector is going towards renewable energy. The demand of solar power plants is therefore at the peak nowadays across the globe. However, the construction of these plants is extremely dependent on feasibility study to estimate the real solar potential before installing it in any region. To evaluate the solar energy potential of Peshawar region in Pakistan, Ground-based global horizontal irradiance (GHI) and direct normal irradiance (DNI) were compared with satellite-based model SUNY. Ground measurements were done at the University of Engineering and Technology Peshawar (UET Peshawar) with the help of pyranometer and shadowband irradiometer. Comparison of the data showed that there was a maximum difference of 42.90% in ground and satellite-based GHI in the month of December. Minimum difference in GHI was found for the month of March that was −3.83%. Moreover, ground-based GHI was overestimated in the month of February, March, and April, while in rest of the months, satellite values of GHI exceeded the ground measurements. Similarly, maximum difference of 55.86% was found in the month of November between ground and satellite-based DNI while minimum difference of −3.34% was seen in DNI in the month of March between the two data. Furthermore, satellite-based DNI was underestimated in the months of February, March, and April while in rest of the months it was overestimated compared to ground measurements. In addition to this, correlation of ground and satellite-based GHI and DNI showed R2 value of 0.8852 and 0.4139, respectively. The results of this study revealed that the difference between ground measurements and satellite values was considerable and hence real time measurements are necessary to properly estimate solar energy resource in the country.
NEURAL NETWORK WITH AGNOSTIC META-LEARNING MODEL FOR FACE-AGING RECOGNITION

Face recognition is one of the most approachable and accessible authentication methods. It is also accepted by users, as it is non-invasive. However, aging results in changes in the texture and shape of a face. Hence, age is one of the factors that decreases the accuracy of face recognition. Face aging, or age progression, is thus a significant challenge in face recognition methods. This paper presents the use of artificial neural network with model-agnostic meta-learning (ANN-MAML) for face-aging recognition. Model-agnostic meta-learning (MAML) is a meta-learning method used to train a model using parameters obtained from identical tasks with certain updates. This study aims to design and model a framework to recognize face aging based on artificial neural network. In addition, the face-aging recognition framework is evaluated against previous frameworks. Furthermore, the performance and the accuracy of ANN-MAML was evaluated using the CALFW (Cross-Age LFW) dataset. A comparison with other methods showed superior performance by ANN-MAML.
Teaching Entrepreneurial Communications Using Educational Platforms: Russian and Foreign Practices

Entrepreneurial education today is the subject of close attention of the educational community. An important component of such training is the issue of entrepreneurial communication, which affects all aspects of the functioning of any business. External communications are aimed at ensuring the company’s business interaction with such stakeholders as customers, partners, competitors, government authorities, investors, while internal communications are aimed at improving the efficiency of business processes. Therefore teaching entrepreneurial communications is an urgent scientific and practical task. The relevance increases even more in the conditions of intensive development of ICT and especially in the context of the pandemic, when the educational process is moving to a large extent online. The purpose of this article is to provide an analytical review of Russian and foreign practices of teaching entrepreneurial communications on educational platforms. The article clarifies the concept of entrepreneurial communications and presents their consideration as a subject of study, it is shown that in the scientific literature there is no unity in the interpretation of this concept. In most sources it is replaced by the concept of “business communication”. A broad overview of business communications training courses on Russian and foreign educational platforms, such as Open Education, Skillbox, Netology, Product Live, Product Star, Laba, Yandex Practicum, Coursera, Udemy, EdX, MIT OCW, Alison, is presented. The largest number of business communications courses are presented on the Coursera, Udemy and Open Education platforms. The content of the identified courses is aimed at the formation of personal communicative competence in business interaction – the ability to negotiate, speak in public, etc., regardless of entrepreneurial specifics. The use of such methods of teaching entrepreneurial communications as hackathons, meetups, VR and AR technologies is substantiated. It is shown that the following applications can be used to develop entrepreneurial communication: VirtualSpeech, Glue, MeetinVR, Stage.
Eye Gaze Relevance Feedback Indicators for Information Retrieval

There is a growing interest in the research on interactive information retrieval, particularly in the study of eye gaze-enhanced interaction. Feedback generated from user gaze features is important for developing an interactive information retrieval system. Generating these gaze features have become less difficult with the advancement of the eye tracker system over the years. In this work, eye movement as a source of relevant feedback was examined. A controlled user experiment was carried out and a set of documents were given to users to read before an eye tracker and rate the documents according to how relevant they are to a given task. Gaze features such as fixation duration, fixation count and heat maps were captured. The result showed a medium linear relationship between fixation count and user explicit ratings. Further analysis was carried out and three classifiers were compared in terms of predicting document relevance based on gaze features. It was found that the J48 decision tree classifier produced the highest accuracy.
Emerging Pedagogies developed for higher education from the covid-19 curfew

Presenting the results had the goal was to identify the knowledge obtained by the experience of teachers during the confinement for the pandemic, in the months of January to June 2020. The methodological approach was a mixture of two stages: The first one was quantitative, in which a survey was carried out on a suitable non-random sample on six universities with in the country. The second one was qualitative, in which semi-structured interviews were carried out on teachers that have experience on both regular learning and distance learning modes caused by the lockdown. The results, from a systemic perspective on the emergent situation, allowed us to recognize the institutions and teachers as knowledge managers. We can observe how the actors managed to bring balance to their institutional structures, as well as to the teaching and learning processes. Notwithstanding the fact that this research was carried out on private universities and that only the learning experience acquired by the transitioning to distant learning was retrieved, the findings contribute pedagogical field create in the crisis, regarding the efficient learning-oriented measurements and organizational structure.