Reviewerly/exHarmony huggingface dataset:
Assessing the Dynamics of AI Driven Technologies in Indian Banking and Financial Sector

Providing quality customer service is one of the crucial matters of concern for any organization as it ensures the best customer satisfaction levels. Organizations all around the globe are widely using newly evolving technologies like artificial intelligence (AI) and machine learning applications to provide the best customer experience. The banking and financial services industry is on the front line to adopt AI-driven technologies in various departments worldwide. However, the adoption rate of these technologies is low in India in comparison with developed economies. In this article, we discussed the significance of various factors (customer satisfaction, AI knowledge among employees, and third-party tie-ups) on implementing the AI-driven technologies from employees’ perspectives at various levels of the organization. We analysed the usefulness of these newly evolving technologies to increase customer satisfaction and studied the role of crucial factors like data security issues and third-party tie-ups on AI technology implementation. The study is based on the primary data collected from 97 employees working in the Indian banking and financial sector across various metro cities of India (Hyderabad, Mumbai, Delhi) in various departments. We identified and analysed various factors that could impact the implementation of the AI-driven technologies at various departments, and we used logistic regression to investigate their significance. Chi-square test was used to check the associations between different factors. The study identifies and examines three key factors—customer satisfaction, AI knowledge among employees, and third-party tie-ups—that could impact AI-driven technologies implementation. According to this study, AI technology considerably decreases the cost and improves the customer experience of services. However, despite of many advantages the adoption rate of AI technology in the Indian banking and financial sector found extremely low. The implications of this study can be extended to the organizations for 360 degrees comprehensive understanding about the AI implementation and repercussions as there is a limited study about this topic from the bottom of the organizational verticals. This analysis is purely based on survey responses received from banking and financial sector employees across various metro cities of India (Hyderabad, Mumbai and Delhi).
Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Abstract This paper proposes new algorithms of adaptive Gaussian filters for nonlinear state estimation with maximum one‐step randomly delayed measurements. The unknown random delay is modeled as a Bernoulli random variable with the latency probability known a priori. However, a contingent situation has been considered in this work when the measurement noise statistics remain partially unknown. Due to unavailability of the complete knowledge of measurement noise statistics, the unknown measurement noise covariance matrix is estimated along with states following: (i) variational Bayesian approach, (ii) maximum likelihood estimation. The adaptation algorithms are mathematically derived following both of the above approaches. Subsequently, a general framework for adaptive Gaussian filter is presented with which variants of adaptive nonlinear filters can be formulated using different rules of numerical approximation for Gaussian integrals. This paper presents a few of such filters, viz., adaptive cubature Kalman filter, adaptive cubature quadrature Kalman filter with their higher degree variants, adaptive unscented Kalman filter, and adaptive Gauss–Hermite filter, and demonstrates the comparative performance analysis with the help of a nontrivial Bearing only tracking problem in simulation. Additionally, the paper carries out relative performance comparison between maximum likelihood estimation and variational Bayesian approaches for adaptation using Monte Carlo simulation. The proposed algorithms are also validated with the help of an off‐line harmonics estimation problem with real data.
Graph Fine-Grained Contrastive Representation Learning

Existing graph contrastive methods have benefited from ingenious data augmantations and mutual information estimation operations that are carefully designated to augment graph views and maximize the agreement between representations produced at the aftermost layer of two view networks. However, the design of graph CL schemes is coarse-grained and difficult to capture the universal and intrinsic properties across intermediate layers. To address this problem, we propose a novel fine-grained graph contrastive learning model (FGCL), which decomposes graph CL into global-to-local levels and disentangles the two graph views into hierarchical graphs by pooling operation to capture both global and local dependencies across views and across layers. To prevent layers mismatch and automatically assign proper hierarchical representations of the augmented graph (Key view) for each pooling layer of the original graph (Query view), we propose a sematic-aware layer allocation strategy to integrate positive guidance from diverse representations rather than a fixed layer manually. Experimental results demonstrate the advantages of our model on graph classification task. This suggests that the proposed fine-grained graph CL presents great potential for graph representation learning.
Computational Application on English Translation System Based on Intelligent Image Text Recognition

This work offers an enhanced intelligent picture text recognition algorithm based on the intelligent image text recognition method to increase the impact of English image text translation. Texture blocks of adaptable size are used to successfully increase the accuracy and efficiency of restoration due to the varying texture information present in various photos. Furthermore, the repair sequence is altered as a result of the improved priority calculation algorithm, and the weight of the structural information is enhanced at the same time. In addition, in order to reduce the overall structural complexity and calculation amount of the system, the gated loop unit is also selected as the RNN structure. Finally, this article constructs an English translation system based on intelligent image text recognition according to the requirements of intelligent image text recognition and designs experiments to evaluate the performance of the system constructed in this article. The experimental statistical results show that the English translation system constructed in this article can basically meet the needs of English image text recognition.
A Framework for Formal Analysis of Anonymous Communication Protocols

Anonymity is the property of keeping secret the identity of the user performing a certain action. The need for anonymity may arise in a wide range of situations, like electronic voting, Web browsing, and so on. In order to verify the anonymity of security protocols, a framework for formal analysis of anonymous communication protocols is proposed. In this framework, we define operational semantics for security protocols using a labelled transition system; the transition relation is defined by the transition rules, which include create rule, send rule, and receive rule. In addition, the formal description of intruder model in this framework is given. The proposed intruder model specifies the capabilities of the intruder and is weaker than the Dolev–Yao model. Moreover, the concepts of mapping and trace equivalence are proposed; the sender anonymity is formally defined. To illustrate the applicability of proposed framework, we explore the use of probabilistic model checking tool PRISM to analyze the sender anonymity of Crowds protocol. The experimental results show the relationship between sender anonymity and the number of nodes, path reformulations, and forwarding probability, which provides a good way about how to protect the sender anonymity of anonymous communication protocols.
Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning

With the increase in the performance of deep learning models, the model parameter has increased exponentially. An increase in model parameters leads to an increase in computation and training time, i.e., an increase in training cost. To reduce the training cost, we propose Compositional Intelligence (CI). This is a reuse method that combines pre-trained models for different tasks. Since the CI uses a well-trained model, good performance and small training cost can be expected in the target task. We applied the CI to the Image Captioning task. Compared to using a trained feature extractor, the caption generator is usually trained from scratch. On the other hand, we pre-trained the Transformer model as a caption generator and applied CI, i.e., we used a pre-trained feature extractor and a pre-trained caption generator. To compare the training cost of the From Scratch model and the CI model, early stopping was applied during fine-tuning of the image captioning task. On the MS-COCO dataset, the vanilla image captioning model reduced training cost by 13.8% and improved performance by up to 3.2%, and the Object Relation Transformer model reduced training cost by 21.3%.
Inscripts and Ownership Stamps in A. I. Klibanov’s Book Collection of SPSTL SB RAS

The article investigates insufficiently researched issues related to A. I. Klibanov’s book collection. The review of the detected autographs, exlibris and ownership stamps provides an opportunity to determine custodial history of books from both state libraries and personal collections, as well as to reveal unique information about relationships and cooperation between medieval scholars in the second half of the 20th century. The dedicatory inscriptions on the books differ in their length and originality; therefore, their significance as a source is directly proportional to how close a sender and a receiver were. Identification and description of bookplates and stamps in the study of the “Klibanovskaya” collection, still unknown from available reference books, will help in the study of other memorial book collections where such provenance marks can be found. The article also details the features of the formation of the foreign part of the A. I. Klibanov’s book collection. The foreign part of the memorial book collection, which is relatively small compared with the entire collection, can be divided into several thematic blocks: books with autographs donated by researchers dealing with similar scientific problems as the owner of the collection; multi-temporal books on historical and religious subjects, acquired by the owner; instances not related to the previous two blocks. The article identifies, systematizes and describes the inscriptions and ownership marks on the books of the collection of A. I. Klibanov.
EARTHQUAKE HAZARD ASSESSMENT STUDIES IN THE STATE OF KUWAIT

This study addresses the first and most important step in measuring the level of seismic hazard in Kuwait. As a result of development, there has been a rise in urban growth leading to the establishment of massive structures and skyscrapers in Kuwait. Therefore, there is demand for seismic risk assessment and the creation of a single seismic code for the country. Consequently, the historical and instrumental data were compiled into seismic catalogs of Kuwait and the active Zagros Seismic Belt. The magnitudes have been unified, extraneous earthquakes removed, and the catalogs' entirety has been taken into account. A seismotectonic model for the Kuwait region has been built to minimize the epistemic uncertainties. The model functions by combining seismicity patterns and structural geological conditions. The probability of experiencing the largest predicted earthquake emerging from every seismic source was calculated together with the parameters set for recurrence. Furthermore, the creation of hazard maps required a suitable ground motion attenuation relation within a logic tree design. Considering recurrence intervals of 100, 475, 975, and 2475 years (corresponding to 39.3 percent, 10%, 5%, and 2%, accordingly, the likelihood of exceedance in 50 years), a probability-based technique is utilized in the study to construct hazard maps at the following 0, 0.1, 0.2, 0.5, 1, 2, 3 and 4 seconds. The maps of scaling hazard areas were created using a 0.2 ° x 0.2 ° spacing grid throughout Kuwait. The five regions of Kuwait (Kuwait City, Salmiya Area, Sabriya Area, Managish Area, and Um Gudair Area) have all implemented a consistent spectrum of hazards. The findings of this study plus the vulnerability index serve the essential features required to calculate Kuwait's seismic risk.
Detectives and technological frames: integrating technology and social media into everyday work

Technology and social media have increasingly transformed contemporary policing. Yet, scarce research explores how detectives view and use technology and social media in their everyday work. This study of a mid-sized American police department fills this gap in research by exploring how technological frames inform how detectives use technology and social media. First, detectives view technology and social media as tools, yet acknowledge the barriers inherent to using them (i.e. time-consuming). Therefore, the task at hand will determine to what extent detectives use technology. Second, detectives embrace the use of technology and social media and use it more than just in their role as a crime fighter, as their job calls for them to handle a variety of tasks: (1) prioritising suicide threats; (2) acting as a school resource officer; and (3) conducting prostitution stings. I argue that technological frames can contribute to officers overcoming barriers inherent with using technology and social media and encourage proactive policing activities. The findings from this study bring about several practical and policy implications for police departments including how to adapt to technological barriers.
Finite mixture model of hidden Markov regression with covariate dependence

In recent days, a combination of finite mixture model (FMM) and hidden Markov model (HMM) is becoming popular for partitioning heterogeneous temporal data into homogeneous groups (clusters) with homogeneous time points (regimes). The regression mixtures commonly considered in this approach can also accommodate for covariates present in data. The classical fixed covariate approach, however, may not always serve as a reasonable assumption as it is incapable of accounting for the contribution of covariates in cluster formation. This paper introduces a novel approach for detecting clusters and regimes in time series data in the presence of random covariates. The computational challenges related to the proposed model has been discussed, and several simulation studies are performed. An application to United States COVID‐19 data yields meaningful clusters and regimes.
Accurate Multiscale Selective Fusion of CT and Video Images for Real-Time Endoscopic Camera 3D Tracking in Robotic Surgery

Robotic surgery requires endoscope 3D tracking to navigate the endoscope in the body. This paper proposes an accurate multiscale selective fusion framework to register 2D endoscopic video images to 3D pre-operative CT data for endoscope 3D tracking. Current video-based 3D tracking depends on the performance of the 2D-3D fusion procedure that suffers from inaccurate similarity and image uncertainties. To boost video-based 3D tracking, we develop multiscale selective similarity characterization to enhance the 2D-3D fusion procedure. Such fusion not only uses image pyramids in multiple scales to represent endoscopic images but also selects specific structure information from these multiscale images to compute the similarity. We validated our method on clinical data. Our method can reduce the current tracking error from 8.9 to 5.4 mm without using any external trackers, while it provides surgeons with robust real-time surgical 3D tracking.
SAR-to-optical image translation by a variational generative adversarial network

Due to all-weather and all-time work characteristics, synthetic aperture radar (SAR) images have been widely used in remote sensing. There is great difficulty in understanding SAR images because they are quite different from optical images in imaging mechanism, geometric characteristics, and radiation characteristics. It can greatly improve the readability of SAR images if we can translate them into optical image styles. In this paper, we propose a variational generative adversarial network for SAR images to optical images translation (S2O-VGAN). To demonstrate the validity of the proposed model, a new large-scale dataset called SARGB is proposed. Experimental results based on the proposed dataset with several evaluations show the superiority of the proposed model over the existing methods on SAR-optical image translation.
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR Prediction

The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting in an excessively latency and the limited performance in online recommenders. In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. This is the first attempt to integrate non-parametric multiresolution analysis technique into deep neural network to model user exposure sequence. Extensive experiments on large scale benchmark dataset and real production dataset confirm the effectiveness of Gama for exposure sequence modeling, especially in cold-start scenarios. Benefited from its low latency and high effecitveness, Gama has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.
Making It Simple? Training Deep Learning Models Toward Simplicity

Deep Learning aims to achieve high performances at the expense of explainability. Explainable Artificial Intelligence consists of all the methods addressing this problem. These methods do not provide interpretability right away, and their usage is limited to model debugging. Furthermore, it&#x2019;s unclear when an explanation qualifies as understandable. This paper aims at creating a double backpropagation technique restricting the model&#x2019;s feature effects. The approach ensures interpretable Deep Learning models&#x2019; explanations during the learning phase. The problem is framed as a Multicriteria one allowing the stakeholders to control the degree of regularization. As a result, the Deep Learning model embodies simple interpretability from the start and is compliant with recent regulations. A series of numerical examples show that our method produces performant yet flexible models that can generalize even when data is scarce.
Innovation of Teaching Method of Digital Media Art Based on Convolutional Neural Network

In order to improve the effect of digital media art teaching, this study combines the neural network algorithm to carry out the innovation of digital media art teaching resource management and teaching method innovation. The scheme proposed in this study divides the video frame into image blocks and then uses the BDCT transform to convert the video frame from the spatial domain to the frequency domain. Generally speaking, the DCT coefficient has the characteristic of energy concentration. In order to reduce the metadata needed to transmit the position of the discarded frequency-domain coefficients, the method proposed in this study divides the frequency-domain coefficients of different blocks into bands according to frequency and compresses the video in units of bands. Finally, this study constructs a digital media art teaching innovation system based on a convolutional neural network. The experimental research results show that the digital media art teaching system based on the convolutional neural network can effectively improve the teaching effect of digital media art.
Multi-data Image Steganography using Generative Adversarial Networks

The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.
Secondhand Smart IoT Devices Data Recovery and Digital Investigation

The probability of having sensitive information retrieved from secondhand IoT devices has increased, even though the previous user removed data and deleted apps in those IoT devices. Due to the increase in flash memory storage technology, the ability to store residual information from various applications has improved. There has been a lot of identity theft reported due to the data acquired from secondhand machines. Recovering the deleted or formatted files from a user's device is easy to get.This study investigates retrieving data from secondhand memory cards from such devices. This paper examines how residual data are left in used IoT devices memory chips via a combined approach. One can recover data from secondhand phones, and the best way to wipe out such devices is to utilize software tools to prevent data leakage. To experiment with the data retrieval, Samsung Galaxy devices and other IoT devices were considered for the various ways in which new users can retrieve the data. In our preliminary results showed that our combined approach recovered more deleted data from secondhand phones.