Reviewerly/exHarmony huggingface dataset:
Implementation of robotics and its impact on sustainable banking: A futuristic study

Abstract Nowadays we are standing on the edge of technological rebellion which would vitally modify the way we live and work. This paper focuses on understanding the changing economic and financial situations with advent of modern technology. Banking sector was analyzed with the help of secondary data collected through several reports, journals, websites and past studies. The study helped me attain insight into probable impacts of implementation of robotics in banking sector and the way it would help to ease the bank’s transactions, maintain sustainability throughout and enhance profitability. While in the field of manufacturing, repetitive procedure have been subjected to automation involving robotics and other technologies, since industrial revolution, robotics has now opened doors to other sectors of the world like banking and finance. This study has been undertaken to understand the impact of RPA (Robotics process automation) technology on banks and its future in a developing nation like India. The research has emphasized on alterations made by changing technology specially robotics in the banking industry.
NeRF-VAE: A Geometry Aware 3D Scene Generative Model

We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into account shared structure across scenes, and is able to infer the structure of a novel scene -- without the need to re-train -- using amortized inference. NeRF-VAE's explicit 3D rendering process further contrasts previous generative models with convolution-based rendering which lacks geometric structure. Our model is a VAE that learns a distribution over radiance fields by conditioning them on a latent scene representation. We show that, once trained, NeRF-VAE is able to infer and render geometrically-consistent scenes from previously unseen 3D environments using very few input images. We further demonstrate that NeRF-VAE generalizes well to out-of-distribution cameras, while convolutional models do not. Finally, we introduce and study an attention-based conditioning mechanism of NeRF-VAE's decoder, which improves model performance.
An Efficient Implementation of the Multiple-Model Generalized Labeled Multi-Bernoulli Filter for Track-Before-Detect of Point Targets Using an Image Sensor

In this article, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter based on track-before-detect (TBD) measurement model, called as MM-GLMB-TBD filter, is presented for tracking maneuvering targets with low signal-to-noise rate (SNR) by integrating the prediction and update into a single step. Based on Gibbs sampling solution to truncating the GLMB densities, the convergence behavior is taken into consideration to reduce computational burden of MM-GLMB-TBD filter. In this article, the lattice-reduction Gibbs sampling with flexible proposal is presented to effectively truncate the filtering densities in the MM-GLMB-TBD filter with geometric ergodicity and better exponential convergence rate. The simulation results demonstrate that the proposed method is particularly suitable for multiple weak targets tracking solution based TBD measurement model due to faster convergence rate. Finally, it is verified from the results that the proposed method is highly robust to variance in different low SNRs.
Classification Under Human Assistance

Most supervised learning models are trained for full automation. However, their predictions are sometimes worse than those by human experts on some specific instances. Motivated by this empirical observation, our goal is to design classifiers that are optimized to operate under different automation levels. More specifically, we focus on convex margin-based classifiers and first show that the problem is NP-hard. Then, we further show that, for support vector machines, the corresponding objective function can be expressed as the difference of two functions f = g - c, where g is monotone, non-negative and gamma-weakly submodular, and c is non-negative and modular. This representation allows a recently introduced deterministic greedy algorithm, as well as a more efficient randomized variant of the algorithm, to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.
Recent Advances in Large Margin Learning

This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade. We generalize the formulation of classification margins from classical research to latest DNNs, summarize theoretical connections between the margin, network generalization, and robustness, and introduce recent efforts in enlarging the margins for DNNs comprehensively. Since the viewpoint of different methods is discrepant, we categorize them into groups for ease of comparison and discussion in the paper. Hopefully, our discussions and overview inspire new research work in the community that aim to improve the performance of DNNs, and we also point to directions where the large margin principle can be verified to provide theoretical evidence why certain regularizations for DNNs function well in practice. We managed to shorten the paper such that the crucial spirit of large margin learning and related methods are better emphasized.
A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

With the development of computer vision, high quality images with rich information have great research potential in both daily life and scientific research. However, due to different lighting conditions, surrounding noise and other reasons, the image quality is different, which seriously affects people's discrimination of the information in the image, thus causing unnecessary conflicts and results. Especially in the dark, the images captured by the camera are difficult to identify, and the smart system relies heavily on high-quality input images. The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low light image to normal light image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, in terms of PSNR, SSIM, NIQE, UQI, NQE and PIQE indexes, compared with the state-of-the-art enhancement algorithms, the values are ideal, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image.
In situ monitoring of elemental losses and gains during weathering using the spatial element patterns obtained by portable XRF

Weathering would produce damage to objects. This paper proposed a method to monitor geochemical variations on outcrop surfaces, through in situ measurement using portable X-ray fluorescence (pXRF) analyser at high spatial resolution, and qualitative and quantitative determination of elemental gains and losses based on spatial patterns of elemental concentrations and calculated enrichment factors. Taking into account the representativeness of sample matrices, a weathering diabase profile consisting of saprolite and the corresponding rock was selected. Comparisons were made between the laboratory results and the pXRF results, besides, the difference between the two test modes based on different calibration principles (mining mode: Fundamental Parameters calibration; soil mode: Compton normalization) in measuring multi-matrices was evaluated. The pXRF results were comparable to the laboratory results for most elements (excellent correlation: Ca, K, Mn, Mo, Zn, and V; good correlation: Ti, Rb, and Sr; moderate correlation: Fe, Si, and Zr; bad correlation: Ba and Al). Much lower readings for light elements would be obtained from the mining mode when loose samples were analysed, and the soil mode was more suitable for measuring a weathering profile, and if light elements such as Mg, Al, and Si are involved, the mining mode can be used, and the reading ranges of the light elements will require verification. Elemental enrichment and depletion were discussed based the spatial pattern of elemental concentrations, and four groups of elements of continuous emigration, slight emigration, local immigration caused by later veins, and continuous immigration with weathering were effectively discriminated based on the spatial pattern of elemental enrichment factors in the profile. Using this method we could rapidly ascertain the migration characteristics of multielements and locate the spatial positions of intense weathering, providing a new insight into the measuring and monitoring of chemical changes with weathering in rock outcrop, building stone, and so on.
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment

Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate heterogeneous data fusion and correlative analysis. The information-theoretic concept of mutual information (MI) is widely used as a similarity measure to guide multimodal alignment processes, where most works have focused on local maximization of MI, which typically works well only for small displacements. This points to a need for global maximization of MI, which has previously been computationally infeasible due to the high run-time complexity of existing algorithms. We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain. We show that the algorithm is equivalent to a direct method while superior in terms of run-time. Furthermore, we propose a method for multimodal image alignment for transformation models with few degrees of freedom (e.g., rigid) based on the proposed CMIF-algorithm. We evaluate the efficacy of the proposed method on three distinct benchmark datasets, containing remote sensing images, cytological images, and histological images, and we observe excellent success-rates (in recovering known rigid transformations), overall outperforming alternative methods, including local optimization of MI, as well as several recent deep learning-based approaches. We also evaluate the run-times of a GPU implementation of the proposed algorithm and observe speed-ups from 100 to more than 10,000 times for realistic image sizes compared to a GPU implementation of a direct method. Code is shared as open-source at github.com/MIDA-group/globalign.
Automatic Modulation Classification using DenseNet

Wireless Communication over long distances has revolutionised the way world works, functions and interacts.Modulation of a signal containing information has made all this possible.The identification of modulation type of a signal forms an integral part in several military and civilian applications.While the classical approaches of modulation classification provides optimal accuracy, they are less robust, requires human interference in the form of engineering expertise and involves computational complexity.The ability to automatically identify the modulation type using deep learning is termed as Automatic Modulation Classification (AMC).This mitigates the need for hand-crafted features and provides state of the art classification accuracy.AMC using neural network (NN) architectures such as Convolutional Neural Networks (CNN), Residual Neural Networks (ResNets) provide excellent modulation classification.While CNN suffers from vanishing gradient,ResNet architecture is used to facilitate smooth gradient flow over the layers so that learning happens more effectively.In this study the main focus is to reduce the training time taken by ResNets which is due to huge number of parameters to be trained.Towards this, Densely Connected Convolutional Neural Network (DenseNet) architecture is trained with few parameters which takes less time and is found to achieve optimal results.
The TESOL Encyclopedia of English Language Teaching

Is the seemingly endless search through libraries and bookstores for references, guidance, and authentic resources over forever? Has the invention of the internet with its ever-growing collection of multimedia information made quality knowledge instantly accessible for everyone? Will the manifold forms of non-digital access and storage that were wrought well at birth endure or die? The digital superhighway may seem to endow limitless opportunities, but it has only added another layer of required knowledge to the search, digital literacy. Now, only those with experience in the advanced levels of research and are digitally savvy can discern if what is available is genuine quality or just quantity. In the last two decades alone, blogs, vlogs, and the self-publishing industry have changed the landscape of educational resources. They created a labyrinth of questionable sources and outlets often masked in the clothing of reputable works. As more peer-reviewed journals consider the way of free access versus controlled distribution, and more publications take the path of e-books and digital downloads, questions arise as to how reliable the information available really is.
The Wonderless Dataset for Serverless Computing

Function as a Service (FaaS) has grown in popularity in recent years, with an increasing number of applications following the Serverless computing model. Serverless computing supports out of the box autoscaling in a pay-as-you-go manner, letting developers focus on the application logic rather than worrying about resource management. With the increasing adoption of the this model, researchers have started studying a wide variety of aspects of Serverless computing, including communication, security, performance, and cost optimization. Yet, we still know very little of how Serverless computing is used in practice.In this paper, we introduce Wonderless, a novel dataset of open-source Serverless applications. Wonderless consists of 1,877 real-world Serverless applications extracted from GitHub, and it can be used as a data source for further research in the Serverless ecosystem, such as performance evaluation and software mining. To the best of our knowledge, Wonderless is currently the most diverse and largest dataset for research on Serverless computing.
ASSESS - Automated Subjective Answer Evaluation Using Semantic Learning

Drift in the digitization of education is a prime concern at present to enable quality education to every individual. Now, there are no geographical barriers to the availability of education and evaluation. Imparting education is easier through digitization but inconvenient to evaluate. In this paper, we propose ‘ASSESS’, a system where the evaluation of subjective answers for an examination becomes easier and convenient. We have even catered to the requirements of specially-abled students online. The diversity in educational courses encouraged us to research how we can efficiently auto-evaluate subjective answers and provide feedback for the purpose of self-analysis. During the pandemic of COVID-19, most of the colleges and organizations shifted to the online mode of examinations. These examinations only had MCQs or objective questions which can be easily assessed by the online system. Since such systems can only be used for the evaluation of objective questions, the subjective questions pose a great challenge. In this paper, we directed our research to propose a system that gives features like full-length subjective tests, automated subjective answer evaluation using natural language processing and semantic learning, auto-generated feedback for self-improvement of the students, visual statistics for both teacher and student after each test, text-to-speech & speech-to-text accessibility options and a fully functional hands-free mode for the specially-abled students with disabilities like sluggish typing, poor eyesight, and amputated hands. Since everything will be automated from the evaluation of the answers to providing feedback, there will be minimal stress on the assessors.
On Stein Variational Neural Network Ensembles.

Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification. Moreover, while they allow for averaging of predictions over several hypotheses, they do not provide any guarantees for their diversity, leading to redundant solutions in function space. In contrast, particle-based inference methods, such as Stein variational gradient descent (SVGD), offer a Bayesian framework, but rely on the choice of a kernel to measure the similarity between ensemble members. In this work, we study different SVGD methods operating in the weight space, function space, and in a hybrid setting. We compare the SVGD approaches to other ensembling-based methods in terms of their theoretical properties and assess their empirical performance on synthetic and real-world tasks. We find that SVGD using functional and hybrid kernels can overcome the limitations of deep ensembles. It improves on functional diversity and uncertainty estimation and approaches the true Bayesian posterior more closely. Moreover, we show that using stochastic SVGD updates, as opposed to the standard deterministic ones, can further improve the performance.
Cluster Medical Image Segmentation using Morphological Adaptive Bilateral Filter based BSA Algorithm

The main aim for this research work is to develop an Adaptive BSA (Backtracking Optimized Search Algorithm) method to solve the optimization problem in image segmentation. In adaptive optimization algorithms, the probability of intersection and mutation depends on the value of the appropriate solution to improve convergence performance. Because of its memory function and simple structure, BSA has powerful features to find a globally optimized solution. However, the algorithm is not yet sufficient to strike a balance between exploration and exploitation of a medical image. Therefore, an improved adaptive tracking and search algorithm has been proposed together with morphological operations, where adaptive bilateral filter will improve the sharpness of edges of a unique region for obtaining global digital optimization in order to reach the cluster image segmentation. The proposed work shows better color quality-based image segmentation for the detection of tumors in medical images. The proposed optimization algorithm results show better performance, when compared to the basic BSA optimization method.
Work-in-Progress–Design and Evaluation of 360 VR Immersive Interactions in Nursing Education

Immersive technologies for education have been investigated for some time; it is only recently that Virtual Reality has been widely adopted within nursing and healthcare professions. In this work in progress, we present the current status of our immersive 360 video Antepartum 360, which is targeted towards undergraduate nursing students. We discuss the creation process of Antepartum 360 including the design of the lesson structure, design of the user experience and interactions, and the filming and editing the 360 video itself. We then present the initial findings from a user study testing the effectiveness of Antepartum 360. Participant results show high levels of engagement with the 360 video, satisfaction with the effectiveness of the video, and confidence in using the application.
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings

Part-of-Speech (POS) tags routinely appear as features in morphological tasks.POS taggers are often one of the first NLP tools developed for low-resource languages.However, as NLP expands to new languages it cannot assume that POS tags will be available to train a POS tagger.This paper empirically examines the impact of POS tags on two morphological tasks with the Transformer architecture.Each task is run twice, once with and once without POS tags, on otherwise identical data from ten well-described languages and five underdocumented languages.We find that the presence or absence of POS tags does not have a significant bearing on the performance of either task.In joint segmentation and glossing, the largest average difference is an .09improvement in F 1 -scores by removing POS tags.In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished data.These results are indicators that NLP and documentary linguistics may benefit each other even when a POS tag set does not yet exist for a language.
Complexity Assessments for Decidable Fragments of Set Theory. I: A Taxonomy for the Boolean Case*

We report on an investigation aimed at identifying small fragments of set theory (typically, sublanguages of Multi-Level Syllogistic) endowed with polynomial-time satisfiability decision tests, potentially useful for automated proof verification. Leaving out of consideration the membership relator ∈ for the time being, in this paper we provide a complete taxonomy of the polynomial and the NP-complete fragments involving, besides variables intended to range over the von Neumann set-universe, the Boolean operators ∪ ∩ \, the Boolean relators ⊆, ⊈,=, ≠, and the predicates ‘• = Ø’ and ‘Disj(•, •)’, meaning ‘the argument set is empty’ and ‘the arguments are disjoint sets’, along with their opposites ‘• ≠ Ø and ‘¬Disj(•, •)’. We also examine in detail how to test for satisfiability the formulae of six sample fragments: three sample problems are shown to be NP-complete, two to admit quadratic-time decision algorithms, and one to be solvable in linear time.
Care and Feeding of Your Security Champion

In agile software development, adoption of security practices poses challenges, often because security activities are not prioritized, or because the practitioners are not able to see the relevance and importance of the activities to the improvement of the security in the project. In many teams, security activities can be seen as an innovation and as such, there is a need for a champion to realize these innovations in the teams. Security champions make software security possible. Even though all developers need to know a minimum of software security, every team needs someone to lean on when the ride gets rough - and that person is the security champion. In this paper we present the results of a case study with security champions and possible steps for establishing and maintaining this role in agile teams.