Reviewerly/exHarmony huggingface dataset:
Abnormality Detection in Financial Transactions Using Graph Representation Learning

Financial data analysis has become a crucial aspect of data analysis, primarily due to the increasing reliance on digital financial transactions. This has led to a massive generation of financial data that can be used to understand customer behavior and support data-driven business decisions. However, with the rise of digital financial transactions, there has also been a corresponding increase in fraudulent activities. A robust system is needed to detect such transactions to mitigate these fraudulent activities. This paper aims to build such a system using graph representation techniques. The primary steps in this work include preprocessing the tabular dataset, generating a graph dataset, analyzing the graph dataset to extract properties and communities, and implementing machine learning algorithms to classify transactions as fraudulent and non-fraudulent. This paper also includes a comparative study between the performances of supervised and unsupervised models trained using the original tabular dataset and the generated graph dataset.
Paramount—A Hidden Markov Model Based Intelligent Voice Assistant

Speech is a basic form of communication between human beings. Every human wants to work in an efficient and convenient environment. This voice assistant aids in accomplishing this task. The voice assistant is a software program that has the ability to convert voice input into commands. The main objective of our paper is to convert speech input into text efficiently, which is understandable by the machine. The output is the execution of the command. This is a custom software program that performs general tasks and also some exclusive tasks. It makes a preliminary exploration of the application of speech recognition in various fields. It is an intelligent program that reacts to voice instructions and can be used with any device, which includes desktop/laptop computers, speakers, wearable technology, TV consoles, gaming consoles, tablets, smartphones, virtual reality (VR) headsets, automobiles, and Internet of Things (IoT) gadgets. In our voice assistant, we are using Mel-frequency cestrum coefficient (MFCC) (i.e., feature vector) and vector quantization (VQ-CODE BOOK), hidden Markov model (HMM) (feature matching) to recognize the commands.
Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Inpainting

Image inpainting is a crucial task in computer vision that aims to restore missing and occluded parts of damaged images. Deep-learning-based image inpainting methods have gained popularity in recent research. One such method is the deep image prior, which is unsupervised and does not require a large number of training samples. However, the deep image prior method often encounters overfitting problems, resulting in blurred image edges. In contrast, the second-order total generalized variation can effectively protect the image edge information. In this paper, we propose a novel image restoration model that combines the strengths of both the deep image prior and the second-order total generalized variation. Our model aims to better preserve the edges of the image structure. To effectively solve the optimization problem, we employ the augmented Lagrangian method and the alternating direction method of the multiplier. Numerical experiments show that the proposed method can repair images more effectively, retain more image details, and achieve higher performance than some recent methods in terms of peak signal-to-noise ratio and structural similarity.
Prediction of Optimal Crop to Grow Based on Geographical Information

A country’s GDP is affected by the price fluctuations of agricultural products. In particular, India’s economy is largely dependent on agriculture, with farmers being the backbone of the economy. Predicting the price of grain and drawing conclusions to recommend crops help farmers and those in the agriculture industry make better decisions, which will help in minimizing losses, maximizing production, and reducing the risk of price fluctuations. Trends in prices can help farmers make an intelligent decision before farming a specific type of crop or grain. In our paper, we attempt to construct a reliable predictor of grain prices using data mining and machine learning algorithms. The classifier models used in this study are KNN, random forest, decision tree classifier, and gradient boosting of which gradient boosting provides maximum accuracy.
Stroke-Sense: A Neural Network for Identifying Four Types of Cricketing Shots

Machine learning (ML) and deep learning (DL) have become the most omnipotent branches of artificial intelligence (AI) aiming at the progress of human intelligence simulation in machines. Application of neural networks and computer vision algorithms in sport data analytics is not so idiosyncratic. However, the impact of deep neural networks in multifarious sports data analytics and their corresponding performance is yet to be explored. Cricket is an intricate game which involves bowlers, batsmen and fielders, where a batsman plays numerous types of shots in order to score runs. Our objective is to identify these cricket shots by implementing a neural network. Hence, this paper proposes a convolutional neural network (CNN) model referred to as the “Stroke-Sense” in order to classify four different cricketing shots from respective images. Our proposed model has achieved a decent accuracy of 83.2%, thereby outperforming other similar works.
Performance evaluation of keyword detection for the chatbot model

To enable enriched free-text human-computer conversations, keyword detection is an important component in chatbot models as it helps to identify specific keywords in user inputs that can trigger the chatbot to respond in a certain way.The performance of keyword detection thus depends on several factors such as the quality and quantity of training data, the selection of apt learning algorithms, and the tuning of various parameters.The performance evaluation of keyword detection for a chatbot involves taking into consideration, the specific requirements of the chatbot and the expected usage patterns of its users.To that end, this work investigates the keyword detection performance with limited vocabulary in the closed-domain chatbot model.A keyword reduction methodology is presented and experimental results on one public and one custom closed-domain datasets indicate about 4.6% improvement in F1-score and comparable performance respectively.
Exploring the Effectiveness of Sentiment Analysis Techniques and Aspect-Based Sentiment Analysis on Twitter Data for Analyzing Public Opinion on National Education Policy in India

The National Education Policy 2020 aims to bring reforms and transform the Indian Education system in a big way. Since the time the policy was approved, it has generated significant public interest, with people expressing their opinions and sentiments toward different aspects of NEP on social media platforms such as Twitter. Analyzing the sentiment of the public and the change in the sentiment over a period of time can provide valuable insights to policymakers and education experts. Aspect-based sentiment analysis provides a deeper understanding of sentiments related to various aspects of the policy and will help in identifying the strengths and weaknesses of the policy. This study explores the effectiveness of various sentiment analysis tools and performs an aspect-based sentiment analysis of NEP tweets. The analysis successfully identifies some relevant and accurate sentiments about NEP2020, the opinion trend since the policy’s inception and positive as well as negative aspects related to the policy.
Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning

Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. To solve these problems, we proposed a new method for identifying large yellow croaker based on the CycleGAN (cycle generative adversarial network) and transfer learning. This method constructs source sample sets and target sample sets by acquiring large yellow croaker images in controllable scenes and actual farming conditions, respectively. The CycleGAN was used as the basic framework for image transformation from the source domain to the target domain to realize data amplification in the target domain. In particular, IDF (identity foreground loss) was used to optimize identity loss judgment criteria, and MMD (maximum mean discrepancy) was used to narrow the distribution between the source domain and target domain. Finally, transfer learning was carried out with the expanded samples to realize the identification of large yellow croaker under varying conditions. The experimental results showed that the proposed method achieved good identification results in both the controlled scene and the actual culture scene, with an average recognition accuracy of 96.9% and 94%, respectively. These provide effective technical support for the next steps in fish behavior tracking and phenotype measurement.
A Study on the Research Progress of Multimodal Sentiment Analysis in Indian Languages

Sentiment analysis reveals people’s views, attitudes, and emotions by analyzing primarily textual content. However, with the proliferation of social media platforms, people are also leaving sentiments through multiple modalities of information, like images, video, and audio. Analysis of sentiment based on different modes of information, or “multimodal sentiment analysis”, offers promising performance to textual content by leveraging contextual interpretation to reveal the underlying polarity of anyone’s sentiment. In this paper, we present a comprehensive study of various promising multimodal sentiment analysis approaches in Indian languages through a thorough investigation of the existing literature, describing the existing benchmark datasets, and different multimodal fusion techniques used, and highlighting the research challenges and opportunities in the domain.
Improving Motivation and Learning Experience with a Virtual Tour of an Assembly Line to Learn about Productivity

We propose the use of a Virtual Tour to substitute in-person visits to a manufacturing plant for a lecture on Enterprise Productivity at the School of Business Administration at our University. Traditionally, during this lecture, students are required to visit a production site to observe its process and apply their knowledge in a real-life scenario. However, finding businesses that are willing to participate and offer the right learning conditions has become a challenge. This situation is now worse due to the COVID-19 pandemic. In this paper, we present a prototype of a virtual tour of an assembly line in a simulated environment, where students can explore and learn about the manufacturing process of car seats. We performed a mixed method user study, with quantitative and qualitative data, to determine whether the application can help learn the intended concepts and improve the learning experience and motivation of students. Results show that the use of the virtual tour application increased motivation in learning.
Privacy Preservation of Maritime Vessel Data Using SHA-256

Remote Sensing imagery security plays an important role in different fields like navy, coastal areas, marine. Among all these, vessel detection and tracking in these fields is sensitive. Thus, privacy preservation and identification of vessel data using remote sensing is important. But locating it manually can be difficult, with the advent of cryptographic hash function, deep learning technology and image processing. The existing technique for the proposed system is YOLOv2, but the limitation is that the model struggles when there are groups of small objects and lack of privacy preservation. There are better detecting and tracking techniques such as YOLOv3, U-Net, and privacy preservation using secure hashing algorithm (SHA)-256 which is most widely used and best hashing algorithm. The proposed system can be used to monitor vessel traffic in a designated area, such as a port or harbour, and can also be used to detect vessels in real time for security and safety purposes.
Intelligent Activity Detection System by LF SVM and Tri-axial Accelerometer

A developing area of computer vision is the identification of human activity from IoT sensor. The size of the feature vectors in such an identification system is a concern. In this paper an intelligent system has been proposed by local spatio-temporal properties which is adjusted to the volume, frequency and speed of changing sequences and can record local occurrences in a video. An intelligent system is created by Tri-axial accelerometers and local space–time feature-based video depictions followed by incorporation into traditional classification algorithms for recognition. The proposed model provides a newly created video database with 3221 combinations of six human activities carried out by 35 participants in four different contexts for analysis purpose. A comparative study is made with available classification algorithms where the Local feature SVM gives highest accuracy and clearly judges the difference between arm and leg activities.
Lossless Watermarking Algorithm for Geographic Point Cloud Data Based on Vertical Stability

With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data.
PRIviLY: Private Remote Inference over fulLY connected deep networks for pervasive health monitoring with constrained client-side

Remote deep learning paradigm enables to better leverage the power of deep neural networks in pervasive health monitoring (PHM) applications, especially by addressing the constrained client-side environment. However, remote deep learning in the context of PHM requires to ensure three properties: (1) meet the high accuracy requirement of the healthcare domain, (2) satisfy the client-side constraints, and (3) cope with the privacy requirements related to the high sensitivity of health data. Different privacy-preserving solutions for remote deep learning exit in the literature but many of them fail to fully address the PHM requirements especially with respect to constrained client-side environments. To that end, we propose PRIviLY, a novel privacy-preserving remote inference solution, designed specifically for the popular Fully Connected Deep Networks (FCDNs). PRIviLY avoids the use of encryption for privacy preservation of sensitive information, in order to fully prevent accuracy loss, and to alleviate the server-side hardware requirements. Besides, PRIviLY adopts a non-colluding two-server architecture, and leverages the linear computations of FCDNs along with reversible random perturbation and permutation techniques in order to preserve privacy of sensitive information, while meeting low overhead requirement of constrained client-sides. At the cloud server, efficiency evaluation shows that PRIviLY achieves an improvement ratio of 4 to more than 15 times for communication, and a minimum improvement ratio of 135 times for computation overhead. At the intermediate server, the minimum improvement ratio is at least more than 10,900 for computation, while for communication, the improvement ratio varies from 5 to more than 21 times. As for the client-side, PRIviLY incurs an additional overhead of about 27% in terms of communication, and between 16% and at most 27% in terms of computation.
Web Privacy By Design: Evaluating Cross-layer Interactions of QUIC, DNS and H/3

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies

Text-to-image generation has achieved impressive results, featuring a variety of models trained on extensive datasets comprising text-image pairs. However, some methods depend on pre-trained models, using gradient-based approaches to update latent vectors in the latent space. In this work, we propose the use of Covariance Matrix Adaptation Evolution Strategy to explore the latent space of a Generative Adversarial Network. Our experimental study compares our approach with gradient-based and hybrid strategies, using diverse text inputs for image generation. We adapt an evaluation method that projects the generated samples into a two-dimensional grid to assess the diversity of the distribution. Results evidence that the evolutionary method produces more diverse samples across different grid regions, while the hybrid method combines gradient-based and evolutionary approaches, enhancing result quality.
Data-Driven Feedback Linearization Control of Distributed Energy Resources using Sparse Regression

A complex physics-based modeling procedure and the uncertainty and confidentiality of internal parameters of distributed energy resources (DERs) motivate system identification tools for control purposes in smart grids. This paper develops a framework for data-driven nonlinear modeling and control of DERs using sparse identification of nonlinear dynamics (SINDy). Using the proposed data-driven model for closed-loop control, we demonstrate the effectiveness of a model-free design in stability analysis of DERs in smart grids. Feedback linearization control of DERs was chosen over conventional vector control in this research due to its superior capability of accounting for DER nonlinearities and weak AC grid integration. Compared with existing physics-based designs that heavily rely on knowing the detailed system dynamics or uninterpretable data-driven designs that rely on large historical data, the proposed model-free DER identification and control framework can accurately capture the dynamics of the DERs based on available measurements and provide guaranteed performance for black-start, weak AC grid integration, microgrid integration, and stability analysis. Real-time and offline simulations in addition to a detailed eigenvalue analysis are conducted to compare the effectiveness of the proposed data-driven approach with physics-based controllers.
Machine Learning as A Predictive Technology and its Impact on Digital Markets

Machine Learning (ML) Approach is a prospective methodology that is frequently employed within global production, branding, economics, transit, and aviation today. The global banking markets of today have had a significant influence on several facets of digitized pricing. Large firms employ a variety of digitized pricing strategies to produce the highest revenue margins possible despite undertaking international commerce. On the opposite side, successful machine learning applications provide important benefits in the global digital markets. It has been determined that companies may trade revenue over digital channels more easily and at a lower cost with the usage of ML methods as they previously could. Additionally, machine learning may quickly enhance overall trade approaches in digital pricing marketplaces to increase profits and provide flexible company knowledge for upcoming experimentation. Using a supplementary approach and subject-related questionnaires, investigators will explore the research design issue with an efficient quantifiable way. Utilizing a variety of cutting-edge ML technologies to improve the effectiveness of digital pricing has gotten simpler in the current sharing marketplaces. In order to expand the area of future study, the research article provides certain significant insight into the effects of ML techniques and associated applications to digital pricing. This feature has a significant influence on and simplifies an organisation's fundamental judgement processes. However little available about managers' attitudes towards it and awareness of machine learning (ML) technologies, as well as their uptake and deployment to enhance both strategic and operational management, according to the information gaps study. This study that is being provided herein focused on how marketing departments, news businesses, & advertising can use employ ML-driven tools. A sampling of all these organisations that operate in Czechoslovakia was the subject of a qualitative and quantitative study. The results highlight: 1) the significance of intelligent analytical tools in the development and application of marketing techniques; 2) the ignorance of technological innovations, such as machine learning (ML) and intelligent systems (AI); 3) The minimal degree of acceptance & usage of ML-driven analysis tools in marketing, as well as 4) the prospective applicability of ML technologies in business. Thus, the aim of this paper involves discussing the concept of machine learning as a predictive technology and evaluating its impact on the digital markets. In order to achieve the research, aim and objectives, a thorough research methodology has been included in this study. At the end a brief conclusion has been drawn by considering all findings and facts together. Furthermore, to assist businesses in locating prospects & carrying out initiatives focused on the application and acceptance of critical ML tools in digital advertising, a framework made up of accelerators and a flow diagram was established.