Reviewerly/exHarmony huggingface dataset:
Multiregression spatially variant blur kernel estimation based on inter‐kernel consistency

Abstract Most single‐image super‐resolution (SR) models suffer from the degradation of image restoration performance when restoring a high‐resolution (HR) image from a low‐resolution (LR) image downscaled using an unknown blur kernel. The spatially invariant blur kernel estimators have been proposed to predict the blur kernel to address this issue. Nevertheless, the spatially variant blur exists in the real‐world; thus, these blur kernel estimators are unsuitable for real‐world applications. Although the spatially variant blur kernel estimators have been proposed, SR models still suffer from performance degradation because these estimators do neither consider the consistency between surrounding blur kernels nor refine non‐parametric blur kernels as parameters. To address this problem, the authors propose a multiregression spatially variant blur kernel estimation based on inter‐kernel consistency. The proposed estimator consists of three parts: non‐parametric regression, an inter‐kernel consistency block, and parametric regression. Specifically, it predicts spatially variant blur kernels while considering the inter‐kernel consistency between nearby blur kernels. Our source codes with pretrained models are available on https://github.com/alsgur0720/multiregression .
A Study on Chatbots in the Indian Banking Sector

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks' websites, and there is a need for enhancement in the capabilities of Indian chatbots.Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.Methodology: This chapter is based on secondary data collected from banks' websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.
Combined visual and spatial-temporal information for appearance change person re-identification

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R-PBFT: A secure and intelligent consensus algorithm for Internet of vehicles

Blockchain applications in the Internet of Vehicle (IoV) employ a secure and distributed consensus method, such as Practical Byzantine Fault Tolerance (PBFT) and Proof-of-Work (PoW), to reduce the load on the central server of IoV and the likelihood of single-node attacks. While traditional consensus algorithms based IoV improves consistency, efficiency, and eliminates single-node attacks, it has some limitations, including a delayed consensus process, restricted transaction frequency, and substantial block congestion. To address these limitations, we propose a fast and intelligent consensus mechanism called R-PBFT (Reputed PBFT). The R-PBFT improves the PBFT consensus process through the use of a reputation mechanism computed by logistic regression. We have built a testbed using the Raspberry Pi 4 and NVIDIA Jetson Nano platforms and experimented with the proposed scheme. The experiment result shows that the proposed R-PBFT outperforms state-of-the-art approaches by minimizing consensus delay by 50%, improving transaction frequency by 24% and throughput by 50%, reducing block congestion by 57%, and improves fairness by 15%. According to security analysis, R-PBFT is secure against Finney, Sybil, eclipse, routing, and impersonation attacks.
Attribute recognition for person re-identification using federated learning at all-in-edge

The advancement in person re-identification using attribute recognition is constrained by the increasingly strict data privacy standards since it necessitates the centralization of vast amounts of data containing sensitive personal data in the cloud. Cloud-based person re-identification requires the transfer of original video information to the servers, causing increased communication costs because of the need for significant bandwidth, resulting in unpredictable timing. This work presents an all-in-edge architecture for attribute-based person re-identification, which deploys training data in edge nodes that support distributed inference. Edge nodes independently learn but collaborate with specific neighboring nodes by sharing information to minimize communication and computational costs through the utilization of federated learning and transfer learning methods. Furthermore, this paper proposes a federated aggregation strategy-FedTransferLoss to obtain optimal global accuracy by using transfer learning to re-train the low-quality local models. Extensive experiments on two prominent pedestrian datasets-PETA and RAP show that FedTransferLoss achieves higher accuracy, recall and precision values compared to the traditional FedAvg algorithm.
Translating Braille into Chinese based on improved CBHG model

Transforming Mandarin Braille to Chinese text is a significant but less focused machine translation task. CBHG is a building block used in the Tacotron text-to-speech model. Since Mandarin Braille is constructed from the pronunciation of Chinese characters, CBHG can be used to perform Braille–Chinese translation. Unfortunately, only relying on the convolution blocks in CBHG cannot effectively extract the features of Braille sequences. Two ways are proposed to improve the CBHG model: CBHG-SE and CBHG-ECA. The two modules adaptively recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels in CBHG. The quality of representations produced by the network can also be improved. Meanwhile, the network can learn to use global information to emphasize informative features and suppress less useful ones selectively. CBHG-ECA has stronger feature recalibration capabilities than CBHG-SE due to its more direct correspondence between channels and their weights. These two models can achieve 92.23 BLEU and 91.48 BLEU on the Braille–Chinese dataset, outperforming CBHG and other neural machine translation models.
Sign Language to Text Classification using One-Shot Learning

Sign language is utilized by deaf individuals to communicate with each other. Sign languages, simply put are signs and gestures made by hand and are currently being used by approximately 80 million people around the world. The limited knowledge of sign languages in the hearing-abled community makes it difficult for them to interact with the deaf or mute community leading to the marginalization of the latter. Sign-to-text classification is a field of research that aims to convert sign language into written text, providing a communication bridge between the deaf-mute communities and the hearing population. However, there are some limitations in the current state of the field, such as the absence of datasets that are publicly available for sign language in many languages, and most of the existing models are trained in American Sign Language (ASL) which makes it difficult for researchers to train and evaluate their methods on other sign languages. Additionally, the ASL-trained models may generalize poorly to other sign languages, as they are heavily tuned to the specific dataset of ASL signs. The current models are also not best suited to incorporate any new additions to the sign language as it requires to train the model all over again. The proposed model makes use of one-shot learning, a new-age machine learning technique that allows the model to learn and recognize using limited training data, to identify and classify hand signs and convert them into corresponding text. The ability to learn from a very small dataset makes the model easily trainable and expandable in any sign language of choice. The proposed model has been trained on ASL and Indian Sign Language (ISL) datasets and achieved accuracy of 90.03% and 91.25% respectively.
Machine Learning based Credit Card Fraud Detection

Cashless transactions are increasing because electronic commerce technology is developing so quickly. Since everyone uses credit and ATM cards for transactions, fraud may also rise. The rise in fraud, which costs businesses money worldwide, the development of numerous procedures and strategies for spotting fraud, Analysis of user behaviors is necessary for fraud detection in order to identify users that act maliciously. Delinquent, fraudulent, intrusive, and account defaulting behaviors all fall under the umbrella of malicious behavior. This study analyzes credit card fraud detection by using machine learning algorithms, specifically the Random Forest and Decision Tree algorithm. The survey of current strategies utilized in credit card fraud detection were depicted. This study has employed the Principal Component Analysis (PCA) to perform feature selection and speed up the learning process. The comparison outcomes demonstrate that Random Forest (RF) outperforms Decision Tree (DT).
Modeling of Upper Limb and Prediction of Various Yoga Postures using Artificial Neural Networks

Regularly monitoring the movement and activity of a person is called activity recognition. Self-guided framework to practice yogasana makes individuals to learn correctly and practice yoga postures can be constructed using human posture recognition without taking help from others. We propose a method for effectively identifying and recognizing various yoga poses that makes use of “Artificial Neural Network” ANN algorithms. The selected dataset consists of 85 videos with 15 participants performing six yoga poses. The Media pipe library was used to extract the users’ key points. As a Artificial Neural Network (ANN) model, real-time-monitored videos have been used to identify yoga poses using an Artificial Neural Network (ANN) and Long-Short Term Memory (LSTM). In particular, the key point features are extracted by the ANN layer & subsequent LSTM layer recognizes sequence of frames in order to make predictions. The postures are categorized as wrong or right in the following: The system provides feedback in the form of text or speech if a correct pose is found. Data sets and machine learning are combined due to the synergy between the two fields as that machine learning methods, particularly ANN, can effectively recognize and make datasets interoperable.
Blockchain Technologies in Data Science: Challenges and Benefits

The rapid expansion of data in the modern era is primarily due to the emergence of social networks and information. Because of the number and diversity of data available, researchers are more likely to employ big data analysis to make business judgments. Additionally, this has contributed to the data science sector’s explosive growth. Many problems still need to be resolved, notably those involving data security and privacy. Concerns for data security and confidentiality arise at every stage of the data science sector, ranging from data extraction, gathering, preparation, and dissemination. The underlying reasons for risk are closely intertwined. Because of its distributed structure, privacy, protection, and other features, blockchain technology is highly regarded and acknowledged. This could alter the way the information is obtained and distributed. Even though experts think blockchain technology will assist data science in advancing and conquering some of its current obstacles, there may be some unintended consequences. Therefore, it is essential to investigate how blockchain technology and data science are related. Then examine blockchain technology challenges and its benefits that will be of great importance to data science. This discussion and its drawbacks will open up the future research direction in blockchain technology when it merges with data science.
Statemental algebra and truth calculus for unmanned systems

One crucial capability of unmanned systems is their ability to make decisions and inferences like humans. In this paper, we develop a novel logical system that imitates the way humans engage in reasoning with statements possessing varying degrees of ambiguity and unpredictability. Our proposed logical system is constructed using an axiomatic approach with self-evident rules, which allows us to define statemental operations and logical equivalence without the need for a concept of truth valuation. Our logical system includes both statemental algebra and truth calculus, which are designed to manipulate statements and assess their credibility. We believe that our proposed logical system has the potential to enhance the intelligence of unmanned systems.
An Improved Genetic-XGBoost Classifier for Customer Consumption Behavior Prediction

Abstract In an increasingly competitive market, predicting the customer’s consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (XGBoost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the XGBoost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-XGBoost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-XGBoost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.
On the granularity of linguistic reuse

Programming languages are complex software systems integrated across an ecosystem of different applications such as language compilers or interpreters but also an integrated development environment comprehensive of syntax highlighting, code completion, error recovery, and a debugger. The complexity of language ecosystems can be faced using language workbenches—i.e., tools that tackle the development of programming languages, domain specific languages and their ecosystems in a modular way. As with any other software system, one of the priorities that developers struggle to achieve when developing programming languages is reusability. After all, the capacity to easily reuse and adapt existing components to new scenarios can dramatically improve development times. Therefore, as programming languages offer features to reuse existing code, language workbenches should offer tools to reuse existing language assets. However, reusability can be achieved in many different ways. In this work, we identify six forms of linguistic reusability, ordered by level of granularity: (i) sub-languages composition, (ii) language features composition, (iii) syntax and semantics assets composition, (iv) semantic assets composition, (v) actions composition, and. (vi) action extension. We use these mechanisms to extend the taxonomy of language composition proposed by Erdweg et al. To show a concrete application of this taxonomy, we evaluate the capabilities provided by the Neverlang language workbench with regards to our taxonomy and extend it by adding explicit support for any granularity level that was originally not supported. This is done by instantiating two levels of reusability as actual operators—desugaring, and delegation. We evaluate these operators against the clone-and-own approach, which was the only form of reuse at that level of granularity prior to the introduction of explicit operators. We show that with the clone-and-own approach the design quality of the source code is negatively affected. We conclude that language workbenches can benefit from the introduction of mechanisms to explicitly support reuse at all granularity levels.
Automated facial expression recognition using novel textural transformation

Abstract Facial expressions demonstrate the important information about our emotions and show the real intentions. In this study, a novel texture transformation method using graph structures is presented for facial expression recognition. Our proposed method consists of five steps. First the face image is segmented and resized. Then the proposed graph-based texture transformation is used as feature extractor. The exemplar feature extraction is performed using the proposed deep graph texture transformation. The extracted features are concatenated to obtain one dimensional feature set. This feature set is subjected to maximum pooling and principle component analysis methods to reduce the number of features. These reduced features are fed to classifiers and we have obtained the highest classification accuracy of 97.09% and 99.25% for JAFFE and TFEID datasets respectively Moreover, we have used CK + dataset to obtain comparison results and our textural transformation based model yielded 100% classification accuracy on the CK + dataset. The proposed method has the potential to be employed for security applications like counter terrorism, day care, residential security, ATM machine and voter verification.
Quantum error correction with gauge symmetries

Abstract Quantum simulations of lattice gauge theories (LGTs) are often formulated on an enlarged Hilbert space containing both physical and unphysical sectors in order to retain a local Hamiltonian. We provide simple fault-tolerant procedures that exploit such redundancy by combining a phase flip error correction code with the Gauss’ law constraint to correct one-qubit errors for a $${{\mathbb{Z}}}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> or truncated U(1) LGT in 1+1 and 2+1 dimensions with a link flux cutoff of 1. Unlike existing work on detecting violations of Gauss’ law, our circuits are fault tolerant and the overall error correction scheme outperforms a naïve application of the [5, 1, 3] code. The constructions outlined can be extended to LGT systems with larger cutoffs and may be of use in understanding how to hybridize error correction and quantum simulation for LGTs in higher space-time dimensions and with different symmetry groups.
Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.
A multi-view contrastive learning for heterogeneous network embedding

Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.
Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.