Reviewerly/exHarmony huggingface dataset:
Recurrent Neural Networks with Fractional Order Gradient Method

In view of the possibility that Recurrent Neural Network(RNN)’s stochastic gradient descent method will converge to the local optimum problem, two fractional stochastic gradient descent methods are proposed in this paper. The methods respectively use the fractional order substitution derivative part defined by Caputo and the fractional order substitution difference form defined by Riemann Liouville to improve the updating method of network parameters. Combining with the gradient descent characteristics, the influence of fractional order on the training results is discussed, and two adaptive order adjustment methods are proposed. Experiments on MNIST and FashionMNIST datasets show that the fractional stochastic gradient optimization algorithm can improve the classification accuracy and training speed of recurrent neural network.
Cost Reduce: Credit Card Fraud Identification Using Machine Learning

Nowadays, one of the most serious ethical challenges in the financial arena is payment card skimming. Payment card fraud comes in many forms. It is a major and long-term menace to society, with a significant economic effect. With the advancement of contemporary technology, including worldwide interaction, fraud is on the rise. As a result, combating fraud has become a critical topic to investigate. There are a variety of machine learning methods that can assist us in classifying unusual or fraudulent transactions. The only requirements are historical data and an appropriate algorithm that can better match our data. The main agenda of the papers is to improve costs by reducing baseline with a new sample process, discuss the different choices of the optimization metric, and try to show the best performing models for fraud detection. For more details, this research study has used Baseline logistic regression, Gradient boosted model (GbM), Logistic regression, Logistic Regression Baseline Model (LgBm), Extreme Gradient (XGBoost) classifier with Synthetic Minority Oversampling Technique (SmOtE) and Adaptive Synthetic Sampling Method (AdAsYn), showed the accuracy. In the end, this research study reminds you that fraud, which represents 0.1731% of the studied dataset. The findings and conclusions are based on real-world transactional data given by a big European card processing business.
Anomaly Detection in Credit Card Transaction using Deep Learning Techniques

One of the most convenient ways to pay is by using a credit card. For both online and offline transactions, it is a handy tool. Credit card numbers are used extensively in online purchases, and there is a danger associated with this practice. There are different systems to identify fraudulent transactions, but they only catch on when there are many of them. The regulations and most of the difficulties are included in the green area’s layout. To detect credit card fraud, this article recommends the use of an algorithm called autoencoder, which uses deep learning to identify transactions that relate to certain coverage groups. In this neural net study, fraud on credit cards is detected using neural nets. Random forest and long short-term memory (LSTM) are that may be used to solve the VAE problem, and they are effective for learning order dependency in sequence prediction tasks. To compress data and preserve its original structure when decoding it, the LSTM autoencoder utilizes LSTM encoder-decoder architecture. This research study demonstrate the efficiency and effectiveness of the proposed method via user testing and extensive experimentation on real datasets by using LS TM to solve Machine Learning [ML] techniques in time sequence. The proposed algorithms outperform existing algorithms, such as the LS TM formulation.
Unsupervised Matching of Data and Text

Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as question answering and search. While recent methods achieve promising results for these two tasks, there is no clear solution for the more general problem of matching textual content and structured data. We introduce a framework that supports this new task in an unsupervised setting for any pair of corpora, being relational tables or text documents. Our method builds a fine-grained graph over the content of the corpora and derives word embeddings to represent the objects to match in a low dimensional space. The learned representation enables effective and efficient matching at different granularity, from relational tuples to text sentences and paragraphs. Our flexible framework can exploit pre-trained resources, but, differently from other solutions, it does not depends on their existence and achieves better quality performance in matching content when the vocab-ulary is domain specific. We also introduce optimizations in the graph creation process with an “expand and compress” approach that first identifies new valid relationships across elements, to improve matching, and then prunes nodes and edges, to reduce the graph size. Experiments on real use cases and public datasets show that our framework produces embeddings that outperform word embeddings and fine-tuned language models both in results' quality and in execution times.
MARS: A Hybrid Deep CNN-based Multi-Accent Recognition System for English Language

Classifying the speech of non-native English speakers is challenging due to various features that distinguish accents. Accents vary by sex, age, formality, social status, geographical area, mother tongue, quality of the voice, phoneme, and prosody. This paper proposes a novel, well-structured database of non-native Indian English speaker accents, referred to as IndicAccentDB. IndicAccentDB contains speech samples from 6 different states to address the unbalanced dataset (gender-bias) and speaker mismatch problems observed in the past. The proposed work also discusses the requirements for creating the IndicAccentDB database and pre-processing tasks performed on the dataset. Furthermore, we experimented with accent classification models, namely 1D-CNN, Support Vector Machines, Random forest, Decision tree, ResNet18, ResNet50, and xResNet18, using MFCC and Mel-Spectrogram features to build the robust Multi-Accent Recognition System (MARS). At last, we evaluated the performance of proposed models on the novel database and compared the results using evaluation metrics like precision, accuracy, F1-score, and recall. Based on our findings, xResNet18 was able to identify the accent classes with significant accuracy.
Research Data Preservation Practices of Library and Information Science Faculties

&#x0D; Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.
Quantum Algorithm of the Divide-and-Conquer Unitary Coupled Cluster Method with a Variational Quantum Eigensolver

The variational quantum eigensolver (VQE) with shallow or constant-depth quantum circuits is one of the most pursued approaches in the noisy intermediate-scale quantum (NISQ) devices with incoherent errors. In this study, the divide-and-conquer (DC) linear scaling technique, which divides the entire system into several fragments, is applied to the VQE algorithm based on the unitary coupled cluster (UCC) method, denoted as DC-qUCC/VQE, to reduce the number of required qubits. The unitarity of the UCC ansatz that enables the evaluation of the total energy as well as various molecular properties as expectation values can be easily implemented on quantum devices because the quantum gates are unitary operators themselves. Based on this feature, the present DC-qUCC/VQE algorithm is designed to conserve the total number of electrons in the entire system using the density matrix evaluated on a quantum computer. Numerical assessments clarified that the energy errors of the DC-qUCC/VQE calculations decrease by using the constraint of the total number of electrons. Furthermore, the DC-qUCC/VQE algorithm could reduce the number of quantum gates and shows the possibility of decreasing incoherent errors.
Combined Long Short-Term Memory Network-Based Short-Term Prediction of Solar Irradiance

Achieving the highly accurate and generic prediction of solar irradiance is arduous because solar irradiance possesses intermittent randomness and is influenced by meteorological parameters. Therefore, this paper endeavors a new combined long short-term memory network (CLSTMN) with various influence meteorological parameters as inputs. We investigated the proposed predictive model applicability for short-term solar irradiance prediction application and validated it in the real-time metrological dataset. The proposed prediction model is combined and accumulated by various inputs, incurring six individual long short-term memory models to improve solar irradiance prediction accuracy and generalization. Thus, the CLSTMN-based solar irradiance prediction can be generic and overcome the metrological parameters concerning variability. The experimental results ensure good prediction accuracy with minimal evaluation metrics of the proposed CLSTMN for solar irradiance prediction. The RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>7.7729</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>04</mn> </mrow> </msup> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>8.2479</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>05</mn> </mrow> </msup> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>6.0419</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>07</mn> </mrow> </msup> </math> and for six-hour-ahead prediction are 0.0157, 0.0017, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>2.4627</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>04</mn> </mrow> </msup> </math> for sunny days, and for cloudy days, the RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mn>1.2969</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>04</mn> </mrow> </msup> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mn>1.6882</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>04</mn> </mrow> </msup> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mn>1.6819</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>08</mn> </mrow> </msup> </math> and for six-hour-ahead prediction are 0.0176, 0.0043, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mn>3.0863</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>04</mn> </mrow> </msup> </math> , respectively. Finally, we investigate the CLSTMN performance effectiveness by comparative analysis with well-known baseline models. The investigative study shows the surpassing prediction performance of the proposed CLSTMN for short-term solar irradiance prediction.
Adaptive distribution calibration for few-shot learning via optimal transport

Few-shot learning (FSL) is a challenging task in the community of data mining but frequently appears in real-world applications. A popular strategy for FSL is transferring information from the domain with extensive amounts of data to the domain with few data. However, two main issues are still open for transfer learning: what kind of information and how much should be transferred. In this work, an Adaptive Distribution Calibration (ADC) is designed to adaptively transfer distribution informase classes for calibrating the biased distributions of novel classes. More specifically, ADC automatically determines the correlations between base classes and novel classes by considering the optimal transport among them. Then, ADC adaptively calibrates the distribution of each novel class according to its correlated base classes. More novel class data can be sampled from the calibrated distribution to train a robust classifier. Furthermore, we theoretically analyze the generalization error bound of the proposed ADC, which shows that the best hypothesis that considers both support and generated data performs at least as good as the best hypothesis learned on support data alone. This generalization error bound guarantees theoretically the effectiveness of the proposed method. Extensive experiments, including comparing with baselines(0.17% ∼ 2.6% improvement on different datasets compared to the next best), ablation studies and hyper-parameter analysis, have been conducted on three widely used FSL datasets (miniImageNet, tieredImageNet and CUB) to demonstrate the effectiveness of ADC.
Multi‐criteria approach for handling sophisticated data transmission over gateways in blockchain and Internet of Things ( <scp>IoT</scp> ) federated networks

Abstract In the real‐world scenario, the Internet of Things (IoT) and blockchain federated networks implicate smart devices for gathering the data and then directing the data to the destined nodes through gateway devices. A gateway usually supports a number of wireless sensor networks (WSNs) irrespective of their underlying communication technologies. Furthermore, a gateway usually supports a number of WSNs with blockchain master nodes, which employ diverse communication technologies. As a result, gateways are meant to play a versatile role without worrying about the underlying architecture, hardware, and software details of the connecting nodes in blockchain federated networks. This paper proposes a two‐phased methodology for blockchain and IoT federated networks where efficient link selection based on a multi‐criteria‐based approach is performed. The dynamic gateway scheduling strategy is capable enough to support blockchain‐based transactions as well as communication in IoT devices. Furthermore, the proposed methodology enhances data transfer fairness for each gateway, resulting in efficient data transmission. Machine learning (ML) methods are also devised to analyze the status of the communication channels before employing the link selection mechanism. Then the selection of links is performed based on multi‐criteria statistical techniques. Finally, the scheduling is performed for selecting the appropriate gateway for channelizing the blockchain data speedily. The selection of links is made based on the dynamic status of the links. The usage of time series ML methods such as LSTM is made to predict the upcoming traffic on the links. Multiple criteria‐based statistical approaches are utilized for the selection of the link optimally. Finally, the scheduling is performed to fulfil the criteria of 6G networks, where network users are rewarded with seamless connectivity and uninterrupted services. The efficacy of the proposed two‐phased mechanism is demonstrated through simulation results. The results are demonstrated with respect to the total data conveyed, packet delivery ratio (PDR), energy consumed by the links, and throughput of the network. The PDR obtained by the proposed work for the high volume of data is 93% and for the low volume of data is 97%. The packet delay is reduced by 21% for low volume data and 19% for high volume data by the proposed methods. The data transmission is also optimized remarkably by the proposed method. The proposed fairness‐driven mechanism (FDM) is compared with the state‐of‐the‐art methods to prove the viability and robustness of the two‐phased proposed mechanism of link selection for data transmission over blockchain and IoT federated networks.
Perception-Aware Receding Horizon Trajectory Planning for Multicopters With Visual-Inertial Odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights.In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm.Our approach is able to fly the vehicle to a goal position at high speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy.The proposed planner samples a group of minimum jerk trajectories and finds collisionfree trajectories among them, which are then evaluated based on their speed to the goal and perception quality.Both the motion blur of features and their locations are considered for the perception quality.Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory's aggressiveness under environments with different light levels.The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera.Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems.The proposed method can run in real time on a small embedded computer on board.We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments.Compared to a perception-agnostic planner, the proposed planner kept more features in the camera's view and made the flight less aggressive, making the VIO more accurate.It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner.The ability of the proposed planner to fly through dense obstacles was also validated.The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.
Crime data anlysis using machine learning

&#x0D; &#x0D; &#x0D; Crime is one of the biggest and dominating problems in our society and its prevention is an important task. Daily there are huge numbers of crimes committed frequently. This requires keeping track of all the crimes and maintaining a database for same which may be used for future reference. The current problem faced are maintaining of proper dataset of crime and analyzing this data to help in predicting and solving crimes in future. The objective of this project is to analyze dataset which consist of numerous crimes and predicting the type of crime which may happen in future depending upon various conditions. In this project, Machine Learning and data science techniques are used for crime prediction of Chicago crime data set. For this supervised classification Random Forest algorithm is used. This approach involves predicting crimes classifying, pattern detection and visualization with effective tools and technologies. Use of past crime data trends helps us to correlate factors which might help understanding the future scope of crimes. In this work, various visualizing techniques and machine learning algorithms are adopted for predicting the crime distribution over an area. In the first step, the raw datasets are processed and visualized based on the need.&#x0D; &#x0D; &#x0D;
Imperative Methodology to Detect the Palm Gestures (American Sign Language) using Y010v5 and MediaPipe

Humans place a high importance on the ability to interact. People with hearing or speaking difficulties had trouble expressing themselves. Despite the fact that sign language solved the problem, they were still unable to engage with the general populace., necessitating the development of sign language detectors. A variety of sign language detection algorithms are effectively open. This research investigates two well-known models for recognizing American Sign Language Gestures for alphabets: MediaPipe-LSTM and YOLO v5-PyTorch. They were given custom datasets., and the outcomes were inferred and compared to see how accurate and effective the models were.
PID parameter tuning algorithm of rotor UAV Based on Improved Particle Swarm Optimization

Aiming at the shortcomings of cascade proportional integral derivative (PID) in the flight control system of small rotary-wing UAV flight control system, it is difficult to manually adjust the parameters and the parameters obtained after the optimization is poor, an improved particle swarm algorithm with nonlinear dynamic weights is proposed., And use it in the tuning process of UAV PID parameters. Taking the quad-rotor UAV as the specific research object, firstly, the mathematical model of the UAV is established, and an improved particle swarm optimization method is adopted to process the inertial weights for nonlinear dynamic drop processing, which greatly improves its search performance and optimization efficiency. To make up for the shortcomings of the traditional particle swarm optimization method, finally through Matlab/Simulink simulation comparison and analysis. The experimental results show that the parameters adjusted by the improved particle swarm optimization method can enable the system to achieve better control performance indicators and shorter adjustment time, so that the UAV has a better dynamic performance, and it also eliminates manual tuning. It reduces the time of parameter setting and has certain guiding significance for practical engineering applications.
Optimization of Quantum Boolean Circuits by Relative-Phase Toffoli Gates

To realize quantum Boolean circuits, Toffoli gates are often used as logic primitives. Then Toffoli gates are decomposed to physically realizable gates, i.e., CNOT, H and T gates when we consider fault-tolerant implementation. The realization cost of a T gate is huge compared to the other gates, and thus we often consider the number of T gates. We need seven T gates to decompose a Toffoli gate. However, if we allow to add some relative phases to some output quantum states, we can implement a Toffoli gate by only four T gates. Such an approximate Toffoli gate is called a relative-phase Toffoli gate (RTOF). This paper proposes an optimization method of quantum circuit by using RTOFs. When we optimize a circuit by replacing a Toffoli gate with a RTOF, some relative phase errors are added. Our method tries to correct such relative phases by using S gates.