Reviewerly/exHarmony huggingface dataset:
A Study on the Measuring of Combat Effectiveness for Naval Frigates Using Analytic Hierarchy Process

Modern combat has been extended to the concept of real-time response to a variety of threats simultaneously occurring in vast areas. In order to quick command determination and accurate engagement in these threats, the combat system has emerged in frigate. Frigates conduct anti-surface, anti-submarine, and anti-aircraft as the core forces of the fleet. In this study, the combat effectiveness measures naval frigates using AHP (analytic hierarchy process) method. A hierarchical structure for measuring the combat effectiveness was developed, and weights of criteria were calculated by expert surveys and pair-wise comparisons. In addition, the combat effectiveness of frigates was synthesized and compared. The weights for each attribute were calculated, and the weights for the three main attributes were in the order of act (0.594), evaluate (0.277), and see (0.129). As a result of calculating the weight, anti surface warfare (0.203) was the highest. The combat effectiveness of FFG Batch-III, which has advanced hardware and software and improved combat system capabilities, see (1.73 to 2.56 times), evaluate (1.68 to 2.08 times), and act (1.31 to 3.80 times) better than the comparative frigate. In summarizing the combat effects of the frigate, FFG Batch-III was 1.41~2.95 times superior to the comparative frigate. In particular, a group of experts evaluated the act importantly, resulting in better combat effectiveness.
Context-aware Neural Machine Translation with Mini-batch Embedding

It is crucial to provide an inter-sentence context in Neural Machine Translation (NMT) models for higher-quality translation.With the aim of using a simple approach to incorporate inter-sentence information, we propose minibatch embedding (MBE) as a way to represent the features of sentences in a mini-batch.We construct a mini-batch by choosing sentences from the same document, and thus the MBE is expected to have contextual information across sentences.Here, we incorporate MBE in an NMT model, and our experiments show that the proposed method consistently outperforms the translation capabilities of strong baselines and improves writing style or terminology to fit the document's context. 1
ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN

The development of online media platforms has given users more opportunities to post and comment freely, but the negative impact of offensive language has become increasingly apparent. It is very necessary for the automatic identification system of offensive language. This paper describes our work on the task of Offensive Language Identification in Dravidian language-EACL 2021. To complete this task, we propose a system based on the multilingual model XLM-Roberta and DPCNN. The test results on the official test data set confirm the effectiveness of our system. The weighted average F1-score of Kannada, Malayalam, and Tami language are 0.69, 0.92, and 0.76 respectively, ranked 6th, 6th, and 3rd
Determinants of Mobile Learning Systems Adoption in Zimbabwe Rural Secondary Schools

In todays technologically-driven educational landscape, mobile learning systems (MBL) have progressively risen to become critical tools for effective education delivery in many rural schools. Like in most developing countries, the education sector in Zimbabwe plays a critical role in facilitating human capital development and economic growth and development. However, the country’s rural secondary schools are still lagging behind urban secondary schools in embracing MBLs. The adoption of MBLs could represent a significant breakthrough not only in terms of reducing educational costs but also for providing effective and efficient learning and teaching processes. The adoption and acceptance of MBLs is likely to transform rural secondary school students’ lives by enabling them to access cheap education and also to meaningfully contribute to the eradication of poverty and hunger in their communities. The study employed structural equation modelling where data was collected with the aid of a structured questionnaire from 100 randomly selected respondents attending rural schools in Guruve and Chiota Rural Districts. The findings show that the likelihood of adopting or deferring the adoption of MBLs in rural secondary schools is influenced by factors such as perceived usefulness, compatibility, perceived ease of use, social influence, learner and teacher’s autonomy, relative advantages and learner awareness. Policies that increase usage of mobile banking learning systems in rural secondary schools especially the reduction in transaction cost, perceived risk, complexity of user interfaces are recommended.
Embedding dynamic knowledge graphs based on observational ontologies in semantic vector spaces

Knowledge graphs (KGs) provide a useful representation format for capturing complex knowledge about an information domain, with rich logical descriptions available for defining the relationships between entities. Separately, semantic vector spaces (SVSs) capture the relative meanings of terms based on their actual usage within a dataset and allow useful operations for exploring the relationships between these terms. Combining KGs and SVSs via knowledge graph embedding (KGE) enables further analysis tasks to leverage learned semantic vectors to gain additional insights. Therefore, KGE represents an interesting and potentially powerful tool for identifying emergent or unexpected behavior, or for seeking previously unaccounted for relationships, event, and groups. In this work, we report on the state-of-the-art in KGE. We describe the operational benefits that can be gained from this approach and the considerations that apply for observational ontologies that describe a complex, untrusted, time-sensitive, and rapidly-evolving environment. We suggest several promising avenues for future research in this context.
Machine Learning-Based Model for Sentiment and Sarcasm Detection

Within the last few years, the number of Arabic internet users and Arabic online content is in exponential growth. Dealing with Arabic datasets and the usage of non-explicit sentences to express an opinion are considered to be the major challenges in the field of natural language processing. Hence, sarcasm and sentiment analysis has gained a major interest from the research community, especially in this language. Automatic sarcasm detection and sentiment analysis can be applied using three approaches, namely supervised, unsupervised and hybrid approach. In this paper, a model based on a supervised machine learning algorithm called Support Vector Machine (SVM) has been used for this process. The proposed model has been evaluated using ArSarcasm-v2 dataset. The performance of the proposed model has been compared with other models submitted to sentiment analysis and sarcasm detection shared task.
Seg-CapNet: A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.
Hybrid Filtering Techniques For Medical Image Denoising

Acquiring images with high resolution and noise free image is a fundamental prerequisite in the handling of therapeutic picture. Nearness of commotion can prompt different misdirecting brings about the method for appropriate visual examination to separate data. Even though various noises are corrupting medical image but speckle noise is the major predominating noise in all type of medical image. Hence, it is essential to reduce the speckle noise up to acceptable level as it helps in the process of segmentation and machine learning. Many filters have been proposed for removing speckle clatter with their own points of interest and detriments for different capacities and purposes.The proposed strategies are explicitly in context of diminishing the speckle clamour in ultrasound therapeutic pictures by utilizing hybrid purifying procedures. The projected hybrid filtering techniques combines the best part of high performing filters to obtain an image with less density speckle noise while also preserving fine details, edges and textures. Hybrid filters makes use of individual filter properties and gives the best possible results. The results justify the claim and gives the cost-effective solution. The outline of the paper is to make sustainable model that increases the awareness in medical domain. It will help to reduce the frequent check-ups that indirectly gives the monetary benefit to the society.
Spatial Graph Regularized Correlation Filters for Visual Object Tracking

In Visual object tracking (VOT), Discriminative correlation filters trackers have achieved promising results for VOT in many complex scenarios. However, because of the unwanted boundary effects and lack of structural constraints, these methods suffer from performance degradation. In the current work, we propose a spatial graph-regularized correlation filter for robust VOT. In this method, we transform the circulant shifted target samples to a particular subspace such that the target and the background become linearly separable. For this purpose, we encode pairwise similarities among the circulant shifted target samples as a spatial graph via a learnt correlation filter constrained to act as an eigenvector of the Laplacian of this spatial graph. We propose an objective function which incorporates this spatial constraint into the DCFs learning framework, which we solve using ADMM with a closed-form solution. Evaluated on a set of four datasets, our framework showed superior performance when compared to competitive state-of-the-art (SOTA) methods.
Multi‐factor joint normalisation for face recognition in the wild

Face recognition has become very challenging in unconstrained conditions due to strong intra-personal variations, such as large pose changes. Face normalisation can help to resolve these problems and effectively improve the face recognition performance in unconstrained conditions by converting non-frontal faces to frontal ones. However, there are other complex facial variations in addition to pose, such as illumination and expression, which will also influence face recognition performance. The authors propose a well-designed generative adversarial network-based multi-factor joint normalisation network (MFJNN) to normalise multiple factors simultaneously. First, a multi-encoder generator and a feature fusion strategy are designed and implemented in the MFJNN to realise the joint normalisation of multiple factors in addition to pose. Second, a convolutional neural network-based (CNN-based) network is applied in the MFJNN, which allows the MFJNN to simultaneously realise image synthesis and facial representation learning. Moreover, an identity perceptive loss is introduced based on the CNN-based network to produce reliable identity-preserving features of the input face images. The experimental results demonstrate that the proposed method can synthesise multi-factor normalisation results with identity preservation and effectively improve the face recognition performance.
Face Mask Detection Using Deep Learning

COVID-19 pandemic is proving to be one of the biggest challenges faced by the world. Different safety measures are being taken by the governments such as lockdowns and mandatory application of face mask. Wearing a face mask is one of most efficacious methods of prevention according to the World Health Organisation (WHO). In this paper, a convolutional neural network model has been proposed which identifies whether a person is wearing a face mask or not. The model uses the robust TensorFlow library to work constructively. The model has been trained on an image dataset consisting of 3835 images where 1916 images are with face masks and 1919 images are of people without face mask. The images in the dataset has been collected from Bing Search API, Kaggle Datasets and RMFD Datasets. The proposed Deep Learning model gave an accuracy of 97.98% when trained on TensorFlow cpu 2.3.0
Analisis Perbandingan Kinerja Cryptocurrency Bitcoin, Saham, dan Emas sebagai Alternatif Investasi

Abstract Purpose: This research aimed to find out and analyze the comparison of Bitcoin, stocks, and gold performances. Research methodology: The analytical method used in this research is a comparative method by using secondary data. Microsoft Excel program was used to calculate the formula of each variable. Then, the data were processed statistically by using the SPSS application, the Kurskall-Wallis Test. Results: This study indicates that there is no significant difference between Bitcoin, LQ45 stocks, and gold when measured from return and Jensen’s performance measure. Then, there are significant differences between Bitcoin, LQ45 stocks, and gold when measured from risk, Sharpe performance’s measure, and Treynor performance’s measure Limitations: The data used in this research were only from April 2013 – October 2019. Contribution: This research contributes to investors and anyone who wants to invest.
Demystifying the Better Performance of Position Encoding Variants for Transformer

Transformers are state of the art models in NLP that map a given input sequence of vectors to an output sequence of vectors. However these models are permutation equivariant, and additive position embeddings to the input are used to supply the information about the order of the input tokens. Further, for some tasks, additional additive segment embeddings are used to denote different types of input sentences. Recent works proposed variations of positional encodings with relative position encodings achieving better performance. In this work, we do a systematic study comparing different position encodings and understanding the reasons for differences in their performance. We demonstrate a simple yet effective way to encode position and segment into the Transformer models. The proposed method performs on par with SOTA on GLUE, XTREME and WMT benchmarks while saving computation costs.
Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.
Risks Involved With CBDCs: On Cash, Privacy, and Information Centralization

A central bank digital currency (CBDC) is planned to be a direct deposit a citizen has in the central bank. CBDC is supposed to be backed by central bank assets, to be purely digital, it can be private or non-private, it may or may not replace cash and can compete with bank deposits. CBDC is the central banks' digital answer to cryptocurrencies. I discuss risks involved with CBDC such as the role of cash, information centralization and regulation.
Comment: Settle the Unsettling: An Inferential Models Perspective

Here, we demonstrate that the inferential model (IM) framework, unlike the updating rules that Gong and Meng show to be unreliable, provides valid and efficient inferences/prediction while not being susceptible to sure loss. In this sense, the IM framework settles what Gong and Meng characterized as “unsettling.”
RETRACTED: The human-like trajectory planning for autonomous vehicles based on optimal control in a test track environment

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver’s trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human’s trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people’s driving intuition and improve the occupants’ comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides.
Despeckling Algorithms For Removing Noise In Medical Images

The medical and satellite images are mostly corrupted by a multiplicative granular noise called speckle noise which degrades the quality of the images captured by using medical imaging techniques and also Synthetic Aperture Radar images. It causes difficulties in image interpretation and this is mainly due to back scattered signals from the multiple targets. In medical field, the diagnosis of the tissues, bones and organs takes place by using imaging techniques. By using different imaging techniques, the medical images are captured and used for diagnosis. Different types of filtering techniques are proposed in the literature to remove the speckle noise in medical and satellite images. In this research paper different types of adaptive filters and its modifications are proposed and compared. The filters like modified lee filter, modified Edge Enhanced lee filter, modified fast bilateral filter and Modified Particle Swarm Optimization based despeckling algorithm. The results are verified for both simulated images and real medical images and also for Synthetic Aperture Radar images. The results are compared in terms of both objective and subjective analysis for simulated and real medical images. The simulation is done using MATLAB R2013 and the visual qualities of the images are analyzed for varying noise densities.
New approaches to radiocarbon calibration arising from statistical developments in IntCal20

&amp;lt;p&amp;gt;Calibration is a key element of the radiocarbon dating methodology and the underlying Bayesian statistical approach taken, and algorithms used, are well established and used in calibration software and associated analysis packages.&amp;amp;#160; These calibration methods are based on a calibration curve which provides a mean estimate for the radiocarbon isotope ratio (fractionation corrected) expected in samples, and the associated standard uncertainty, both as a function of time (or calendar age).&amp;amp;#160; The measured samples also have their radiocarbon isotope ratio reported in the same form and so the calibration process involves comparison of the sample radiocarbon measurements with the calibration curve at different points on the calendar age scale.&amp;amp;#160; This then yields a probability distribution function, with associated highest probability density ranges, for the sample calendar age.&amp;amp;#160; We discuss here how improvements in the construction of the IntCal20 curve offer new opportunities, enabling users to obtain more from the calibration curve than previously possible and address some of the limitations of previous calibration approaches.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Previous approaches to calibration assumed that the values of the calibration curves at any time were normally distributed around their estimated mean. However, there are time periods where the distribution of these curves are not well represented by such a normal distribution. This is potentially significant even for calibrations of single samples. The new IntCal20 curve generates multiple possible calibration curves, providing us with the opportunity to identify and adapt to such non-normality.&amp;amp;#160; A second limitation of previous approaches to calibration arises when multiple determinations are used within a broader chronological model. In such cases the usual assumption is that the calibrated uncertainties are independent.&amp;amp;#160; This is certainly not the case if all the samples are the same age (which is currently addressed by combination before calibration) but also is potentially wrong if the samples are close enough in age for there to be correlated uncertainty in the calibration curve.&amp;amp;#160; Again, using the collection of possible curves provided in the construction of IntCal20, rather than just the summary curve, we look at possible solutions to this challenge.&amp;amp;#160; The implications for high-precision chronologies are also discussed.&amp;lt;/p&amp;gt;