Reviewerly/exHarmony huggingface dataset:
Genetic Algorithm for Optimization of Heating Networks

An algorithm for redistribution of flows in an oriented network is proposed, the maximum flow of which is sought by the Push-Relabel method in the Maple system of computer mathematics. At each stage of optimization, the most effective options are identified, the combination of which gives the next generation of genes. It is shown that highly ramified networks are most sensitive to the distribution of the capacity of individual branches. For example, the maximum flows in networks with two and three parallel branches of the same bandwidth are compared. It is shown which parameters of the algorithm have the greatest influence on the result.
Feedback Multi-scale Residual Dense Network for image super-resolution

The image super-resolution algorithm based on deep learning has a good reconstruction effect, and the reconstruction can be further enhanced by using multi-scale features. There are different extraction methods for multi-scale features, and current deep learning-based super-resolution algorithms often use only one method when utilizing multi-scale features. We use an error feedback mechanism with a dense residual mechanism to fuse multi-scale features and propose Feedback Multi-scale Residual Dense Network (FMDN), which uses two different multi-scale features to enhance the reconstruction effect. On the other hand, in the previous multi-scale feature fusion often used the method of concatenating. We design a new error feedback-based feature fusion method, and the experimental results show that it has better results than the common method of concatenating. In addition, we further use the feedback mechanism of recurrent to improve the efficiency of the module, which can use fewer layers to achieve the effect of more layers of the basic model, and take up less space, faster, or make a network with a larger number of layers have better results. Compared with the state-of-the-art method, the proposed method shows promising performance according to qualitative and quantitative evaluation.
Development of a Hybrid Model using Deep Neural Network and XGBOOST for Mortality Prediction of ICU Patients

With the rapid development of various machine learning techniques, this paper proposes a hybrid model using Deep Neural Network (DNN) and XGBoost (XGB) to predict the ICU patient's mortality accurately. For prediction of mortality Physionet challenge 2012 dataset is used, which consists of 8000 records (set A and B) and 42 features. The outcome of this study reveals whether the ICU patient will survive or dies. Since, the dataset has large number of missing values and also highly imbalanced, makes the classification problem more complex. To circumvent these problems, k-Nearest Neighbor (k-NN) imputation is used to handle the missing value and (Synthetic Minority Oversampling Technique) SMOTE for balancing the dataset. Then, the proposed model is trained using 5-fold cross-validation in 80% of the data. The model is tested with rest 20% of the data and various performance measures such as accuracy, sensitivity, specificity, f1-score, score1 and AUC are obtained. The hybrid model (DNN+XGB) is compared with the Gaussian Support Vector Machine and Multinomial Naive Bayes models and reported to be better predictor with AUC of 75.05%.
Organising lexica into analogical grids: a study of a holistic approach for morphological generation under various sizes of data in various languages

Morphological generation is a task where given a lemma and a morphosyntactic description of the target form, we are asked to generate the target form. Knowing that the syntactic and semantic relations to other forms are reflected by the word form itself, we show how to exploit these relations between word forms, holistically, that is, as a whole, to derive the target form without even breaking them into morphemes. Experimental results show that by organising the lexica into analogical grids we are able to improve the accuracy of morphological generation by up to 8% in low data scenarios. Our holistic approach always performs better than a morpheme-based baseline. We also enquire possible improvements by using data augmentation for neural approaches, especially in low data scenarios. However, our system seems not to gain any advantage from having more data after some point in time.
Concurrent 21. Presentation for: Gold (hydrogen) rush: risks and uncertainties in exploring for naturally occurring hydrogen

Presented on Thursday 19 May: Session 21 Interest in hydrogen (H2) energy has exploded in the last few years. Much of the interest comes from transitioning to a decarbonised energy future, through the use of renewables, to convert hydrogen-rich materials (methane, water) to pure hydrogen gas streams. Each of these methods have their own challenges, such as the need for carbon capture and storage to manage carbon emissions or perspectives on the use of fresh water. At the same time as this engineered approach to generating hydrogen, there has been a quiet but exponential upsurge in research surrounding the origins and fate of naturally occurring hydrogen. Sometimes referred to as ‘gold’ or ‘white’ hydrogen, geological forms of hydrogen have been recognised for thousands of years. While already present as H2, hydrogen may exist with other gases, such as methane, helium, hydrogen sulfide and/or nitrogen. But is it real? Is it volumetrically significant, discoverable, predictable or exploitable? Early work suggests that we can begin to characterise potential sources of hydrogen, the depth ranges they may be generated from, the migration mechanisms that are relevant, and how they might be retained or lost in relation to the discovery and exploitation of this gas. However, existing publicly available data is constrained by a lack of analysis and limited evidence. While there are increased reports of seeps containing hydrogen, there is an absence of evidence of more significant finds and a lack of major analogues and case studies to date. We explore these risks and uncertainties and provide a roadmap for reducing the evidence gap. To access the presentation click the link on the right. To read the full paper click here
Prediction of the Impact of Land Usage Changes on Water Pollution in Public Space Planning with Machine Learning

In urban public space planning, changes in land use, structure, and construction impact the urban environment to a certain degree. Land usage changes the urban surface water environment by impacting it through numerous ways. This paper studies about prediction of land use changes on surface water pollution in public space planning. This paper analyzes the characteristics of land use changes in public space planning from the quantitative characteristics of land use types, land use structure characteristics, and land usage degree in different years. The protection of natural resources is important, and water is one of the most important natural resources consumed by human beings. The environmental changes impacting these natural resources are to be studied to preserve the natural resources. The prediction of over-consumption of natural resources using soft computing techniques can certainly provide a solution for appropriate decision making. The prediction of relationship between land use changes and surface water pollution is required. In order to achieve this, the regression analysis on land use changes of different spatial scales with four surface water pollution indicators in the dry and wet seasons is performed to obtain the regression of each water pollution indicator. According to the determination coefficient, the determination coefficient of the model uses the comprehensive pollution index method to predict the impact of land use changes on surface water pollution. The experimental results show that the prediction accuracy of the proposed method is high and it is helpful in studying the impact of land use change on surface water pollution. It can help in decision making on consumption of natural resources to preserve the natural resources for next generations.
Empowering Long‐Term ELs with Social Emotional Learning, Language, and Literacy By MargaritaCalderón and HectorMontenegro. Velázquez Press. ISBN 978–1–59495‐771‐0. Price USD 34.95 (paperback). 172 pages

TESOL JournalVolume 13, Issue 3 e661 MEDIA REVIEW Empowering Long-Term ELs with Social Emotional Learning, Language, and Literacy By Margarita Calderón and Hector Montenegro. Velázquez Press. ISBN 978–1–59495-771-0. Price USD 34.95 (paperback). 172 pages Huseyin Uysal, Corresponding Author Huseyin Uysal huysal9@gmail.com orcid.org/0000-0003-2499-3097 Knox College Correspondence Email: huysal9@gmail.comSearch for more papers by this author Huseyin Uysal, Corresponding Author Huseyin Uysal huysal9@gmail.com orcid.org/0000-0003-2499-3097 Knox College Correspondence Email: huysal9@gmail.comSearch for more papers by this author First published: 07 June 2022 https://doi.org/10.1002/tesj.661Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume13, Issue3September 2022e661 RelatedInformation
A grid-based approach to exploratory data analysis

A grid-based approach to exploratory data analysis is described wherein the size of a grid’s cells may be altered. The primary tool of exploratory data analysis described here is cluster analysis. Background information is provided regarding the different types of clustering algorithms and their effectiveness. A grid-based approach for exploratory and cluster analysis is then described. Both exploratory and cluster analyses are performed for a series of different sizes of grid cells. Two marketing data sets are utilized to illustrate research questions often faced by market researchers. The first is a cluster analysis of cities used to create strata for selecting a representative probability sample. The second example is a segmentation of customers into groups for the purpose of targeting advertising and product insight. These examples illustrate related issues such as the elimination of “background noise” observations, significance tests for departures from uniform density, and metrics for the shape of a cluster.
Assistance technique for Fiducial Localization in MRI images with Subslice Classification Neural Network

Image-guided neurosurgeries(IGNS) require patient-to-image registration for treatment planning. As the patient head-surface has very few recognizable features, adhesive markers called Fiducials are affixed on the patient head and are localized to establish the registration. While various approaches have been proposed in literature to automate the localization process, this research work has faced roadblocks while constructing a proof- of-concept over these approaches: These techniques assume access to a ground-truth annotated dataset which either requires availability of the same in the public domain or via an IGNS expert. To the best of our knowledge, there is no publicly available annotated dataset for evaluation of fiducial localization algorithms. Similarly, access to IGNS experts is also a strong assumption which greatly deters the entry of researchers willing to address the challenge of fiducial localization. The proposed study has attempted to address these challenges by reporting a simple ground-truth annotation procedure with which the study have applied over 50 preoperative MRI volumes, for annotation of fiducial centers. Further, this research work proposes an approach based on a sub-slice classification neural network to extract region-of-interest in a MRI slice over which clustering and edge-detection algorithms are applied to localize the fiducials. Our approach achieves mean 4mm Fiducial localization error(FLE) and around 70% precision and recall in predicting fiducial center coordinates, thereby achieving significant improvement over our earlier approach based on classification of detected corners in MRI slices. Also, this research study has observed a 30% reduction in the time required for fiducial localization in a randomly selected volume using the proposed approach with respect to manual localization. While the clinical requirements for fiducial localization error are in the order of 1mm, our technique can serve as a reasonable initial guess, thereby assisting neurosurgeons in the pre-operative workflow.
Low-Latency Modular Exponentiation for FPGAs

Modular exponentiation, especially for very large integers of hundreds or thousands of bits, is a commonly used function in popular cryptosystems such as RSA. The complexity of this algorithm is partly driven by the very large word sizes, which require many - often millions - of primitive operations in a CPU implementation, or a large amount of logic when accelerated by an ASIC. FPGAs, with their many embedded DSP resources have started to be used as well. In almost all cases, the calculations have required multiple - occasionally many - clock cycles to complete. Recently, blockchain algorithms have required very low-latency implementations of modular multiplications, motivating new implementations and approaches.In this paper we show nine different high performance modular exponentiation for 1024-bit operands, using a 1024-bit modular multiplication as it’s core. Rather than just showing a number of completed designs, our paper shows the evolution of architectures which lead to different resource mix options. This will allow the reader to apply the examples to different FPGA targets which may have differing ratios of logic, memory, and embedded DSP blocks. In one design, we show a 1024b modular multiplier requiring 83K ALMs and 2372 DSPs, with a delay of 21.21ns.
dGG, dRNG, DSC: New Degree-based Shape-based Faithfulness Metrics for Large and Complex Graph Visualization

Shape-based metrics measure how faithfully a drawing D of a large graph G shows the structure of graph, by comparing the similarity between G and a proximity graph S computed from D. Although these metrics can successfully evaluate drawings of large graphs, they are limited to relatively sparse graphs, since existing metrics use planar proximity graphs GG (Gabriel Graph) and RNG (Relative Neighbourhood Graph). This paper presents new shape-based faithfulness metrics for evaluating drawings of large and complex graphs, using high-order prox-imity graphs k-GG and k-RNG. Extensive experiments demonstrate that our new shape-based metrics using degree-based proximity graphs dGG and dRNG can more accurately measure the faithful-ness of drawings of large and complex graphs, with a significant improvement of over 100% better, on average, than the existing shape-based metrics using GG and RNG. Moreover, we present a new shape change faithfulness metric DSC for evaluating drawings of dynamic graphs, by measuring how proportional the geometric shape change in the drawings of dynamic graphs is to the ground truth change in dynamic graphs. Validation using deformation experiments support that DSC can accurately measure shape change faithfulness in dynamic graph drawing. Furthermore, we present extensive comparison experiments of ten popular graph layouts using our new shape-based metrics dGG, dRNG and DSC, to recommend which layouts can give a better shape-faithful graph drawing for large and complex graphs.
A Novel Hybrid (Po-Thk) Method For Ids and Privacy-Preserving In Distributed Data Environment Using Metaheuristic Techniques

Due to its unique benefits such as distributing IT infrastructure, limitless expansion, versatility, and a significant degree of automating, the Cloud-Computing (CC) was evolving as upcoming Digital-Technologies. Together with the fast development, the CC innovation often raises questions about the privacy and security of data and applications stored in the online context. The Privacy-Preservation (PP) is perhaps the action of safeguarding an individual's private information, including confidential information, before publication. As a result, in the CC context, an innovative strategy for information along with PP is needed. Data-publication utilizing PP demands the management of distribution when leveraging a person's sensitive details. Throughout this research, we develop and implement an "Intrusion Detection System" (IDS) for data access using a hybrid strategy. Data-Gathering and Data-Publishing are two stages of system implementation. The data is being collected from the owner first by cloud server in the initial phase, however, during the latter phase, the server transmits the gathered data to the general or data-miners, referred to as data-consumers. Thus this work aims to build up a method for PP in the CC environment utilizing the Pigeon-Optimization (PO) method for locating the secure storage space by feature selection and Tiger-Hash-key (THK) generation method for converting the original data into PP data, thus by combining Pigeon with Tiger (PO-THK) hybrid method was proposed to improve the security in a cloud environment. The proposed PO-THK method performs a better outcome in UAR, IDR, and Space complexity while comparing it with the existing method AUA-MHT.
Online Learning for DNN Training: A Stochastic Block Adaptive Gradient Algorithm

Adaptive algorithms are widely used because of their fast convergence rate for training deep neural networks (DNNs). However, the training cost becomes prohibitively expensive due to the computation of the full gradient when training complicated DNN. To reduce the computational cost, we present a stochastic block adaptive gradient online training algorithm in this study, called SBAG. In this algorithm, stochastic block coordinate descent and the adaptive learning rate are utilized at each iteration. We also prove that the regret bound of OT can be achieved via SBAG, in which T is a time horizon. In addition, we use SBAG to train ResNet-34 and DenseNet-121 on CIFAR-10, respectively. The results demonstrate that SBAG has better training speed and generalized ability than other existing training methods.
The Application of Wireless Network-Based Artificial Intelligence Robots in Badminton Teaching and Training

Artificial intelligence technology has already set its foot in various industries, including sports, to train athletes. In this research article, people will study the application of wireless networks based on artificial intelligence robots in badminton teaching and training. People propose a system that deploys intelligent robots to teach badminton to athletes. The robots will train the players with various moves and techniques required for the game. The wireless networking system allows the robot to connect to the network. Various sets of plays and players' movements were preprogrammed for the robot. The trainer has to select essential factors such as training mode and set height required for a particular player in the robot-these are the complexities in badminton training. Moreover, in the case of effective and efficient training, people need a robot that will aid in different training modes. The changing variables, such as speed, frequency, angle, height, and change in coordinates, are utilised in the training and teaching of robots, which are more efficient than the traditional training methods given by people. The decision tree algorithm (DTA) is used in this research and is compared with the existing sports motion segmentation method (SMSM). From the results, it is observed that the proposed DTA has given improved accuracy of 93% compared with the SMSM.
An assistive interface protocol for communication between visually and hearing-speech impaired persons in internet platform

The article presents a design and development of a generic assistive system to establish an independent conversation-platform for hearing-speech impaired and visually impaired persons.The developed software system is accomplished through programming using python and html.Considering the constraints associated to the above mentioned impairments, the system implements both speech-to-text/gesture and text/gesture-to-speech conversion in its operation. In real-time hand-gesture to speech generation process is implemented using static image tracking, CNN based deep learning technique and MediaPipe hand-tracking solution. The software-prototype-terminals can be accessed through internet using MQTT protocol to accomplish the communicative conversation between visually impaired and hearing-speech impaired persons.The software system exhibits an average prediction time of less than approximately 1 s and 2 s for a four-letter based audio-word and a single hand-gesture, respectively, which are commensurate to the average time-complexity during human-to-human conversation. The average accuracy and loss for the hand-gestures through the CNN based deep learning are 0.9996 and 0.0008, respectively. The confusion matrix related to the prediction of alphabet-specific hand-gestures shows its satisfactory performance in gesture recognition.The software-prototype of the generic assistive device shows its potential to establish an exclusive communication between a visually impaired and a hearing-speech impaired person through the internet. The same software-interface can also be used to accomplish a communicative conversation between either only visually-impaired persons or only hearing-speech impaired persons. IMPLICATIONS FOR REHABILITATIONThe article presents a design and development of a generic assistive interface to establish an independent conversation-platform for hearing-speech impaired and visually impaired people via internet network.The same software-interface can also be used to accomplish a communicative conversation between either only visually-impaired persons or only hearing-speech impaired persons.The design can be further extended by incorporating multi-modal impairments to make a universal assistive device for all-in-one communication.
Towards a Construction Kit for Visual Recommender Systems

When designing the interface of a recommender system, interactive visualizations can be used to support challenges such as transparency and controllability, and thus increasing the user’s trust. In this paper, we propose a construction kit for visual recommender systems that can be used to construct new solutions or deconstruct existing approaches to identify and analyze key aspects.
Semi-Supervised Anomaly Detection Based on Deep Generative Models with Transformer

In this work, we propose a novel semi-supervised anomaly detection approach based on deep generative models with Transformers for identifying unusual (abnormal) images from normal ones. Our approach is based on the combination of autoencoder (AE) and generative adversarial networks (GAN). Similar to the vanilla GAN, our model is mainly composed of the generator and discriminator. The generator adopts an encoder-decoderencoder structure to extract meaningful latent representations, in which each encoder is constructed by a Transformer whereas the decoder is realized through the transposed convolution. The discriminator, which is built upon another Transformer, is used to distinguish whether the given image comes from the generator or the training set, while optimizing the encoder in the generator for better latent representations through adversarial training. The distribution of the normal data can be learned by minimizing the gap between the original image space and the latent image space during the training process. The abnormal images are detected if their distributions are different from the learned normal distributions. The merits of the proposed anomaly detection approach are demonstrated by comparing it with other generative anomaly detection approaches through experiments on three benchmark image data sets.