Reviewerly/exHarmony huggingface dataset:
Deep-Auto Encoders for Detecting Credit Card Fraud

Internet-based payment methods in recent times are rapidly growing both in developing and developed economies. Credit card-based payment systems are among the prominent cashless payment methods in all economies. Credit card frauds by cyber-criminals are increasing in spite of several precautionary measures. Thus, fraud detection in real-time is a challenging task. Several machine learning tasks have attempted to solve the problem. This chapter proposes a two-step method to detect credit card fraud by coupling the deep learning-machine learning approaches. In the first stage, the dimensionality of the data set is reduced to 50% by a deep auto-encoder. A machine learning classifier classifies the instances in the second stage. Among the machine learning algorithms, the CatBoost and Random Forest achieved better performance. Their performance aligned with the state-of-the-art approaches. The proposed method is robust against the labor-intensive feature selection and imbalanced class problems.
Abbreviating Labelling Cost for Sentinel-2 Image Scene Classification Through Active Learning

Over the years, due to the enrichment of paired-label datasets, supervised machine learning has become an important part of any problem-solving process. Active Learning gains importance when, given a large amount of freely available data, there's a lack of expert's manual labels. This paper proposes an active learning algorithm for selective choice of training samples in remote sensing image scene classification. Here, the classifier ranks the unlabeled pixels based on predefined heuristics and automatically selects those that are considered the most valuable for improvement; the expert then manually labels the selected pixels and the process is repeated. The system builds the optimal set of samples from a small and non-optimal training set, achieving a predefined classification accuracy. The experimental findings demonstrate that by adopting the proposed methodology, 0.02% of total training samples are required for Sentinel-2 Image Scene Classification while still reaching the same level of accuracy reached by complete training data sets. The advantages of the proposed method is highlighted by a comparison with the state-of-the-art active learning method named entropy sampling.
OpenApi Workflows for Model and Format Conversion of Machine Learning Frameworks

Various machine learning frameworks have their format in which they accept the model. ONNX is an open format. These models are the representation of the neural network with weights after training. A model created for a particular framework has to be converted into a different format as required for use with another framework So, this paper deals with the design of such conversion tools. Our paper mainly deals with the design of conversion tools of 4 popular frameworks of machine learning that is Keras, ONNX, Tensorflow Lite, and Pytorch. Moreover, we have also created the Open API-based workflow model. So, the users can be able to pipeline our project with another language also since the API became language agonistic. We have also created a private API using Flask.
Research on Enterprise Financial Accounting Based on Modern Information Technology

In the process of the continuous development of market economy, the market has brought development opportunities for enterprises. If an enterprise wants to maximize its own economic benefits, it requires the enterprise to continuously optimize and reform its financial accounting work, so as to promote its rapid and steady development. For modern enterprises, information technology has become the main factor of competition among enterprises, which promotes the wide application of modern information technology by enterprises to a great extent. Modern information technology is highly integrated with enterprise financial management, which improves the efficiency and quality of financial accounting. As an indispensable and important department in the process of enterprise development, enterprise financial accounting must keep up with the trend of big data era and apply information technology to enterprise financial accounting to improve enterprise management. This paper analyzes the impact of modern information technology on enterprise financial accounting, and puts forward corresponding actions to promote the positive impact of modern information technology on enterprise financial accounting.
On the Convergence of Federated Learning with Stochastic Quantization

This paper studies the distributed federated learning problem when the exchanged information between the server and the workers is quantized. A novel quantized federated averaging algorithm is developed by applying stochastic quantization scheme to the local and global model parameters. Specifically, the server broadcasts the quantized global model parameter to the workers; the workers update local model parameters using their own datasets and upload the quantized version to the server; then the server updates the global model parameter by aggregating all the quantized local model parameters and its previous global model parameter. This algorithm can be interpreted as a quantized variant of the federated averaging algorithm. Extensive experiments using realistic data are provided to show the effectiveness of the proposed algorithm.
Prioritized Semi-supervised Deep Embedded Clustering

Clustering, to group similar objects, is an important problem. Recently deep learning-based methods like Deep Embedded Clustering (DEC) [6] and its semi-supervised version called Semi-supervised Deep Embedded Clustering (SDEC) [12], where partially labeled data or data with constraints is available, are shown to give promising results. Both DEC and SDEC learn a latent space where similar objects are closer and dissimilar are away. While promising results are shown, the information present in constraints or a labeled subset of the data is not fully utilized. This paper proposes to use priorities for constraints so that important constraints are given more weightage than unimportant ones. Those constraints with points that are far away, but should be clustered into a group, gets more weight than other labeled points. Similarly, those in different groups which are very close get more weightage. The appropriate loss function is used in the learning process. The proposed method is called Prioritized Semi-supervised Deep Embedded Clustering (PSDEC). The results are compared using a few standard data sets against recent and classical similar methods. PSDEC is found to achieve a better result than un-prioritized constraints.
A Preliminary Study on Using Text- and Image-Based Machine Learning to Predict Software Maintainability

Machine learning has emerged as a useful tool to aid software quality control. It can support identifying problematic code snippets or predicting maintenance efforts. The majority of these frameworks rely on code metrics as input. However, evidence suggests great potential for text- and image-based approaches to predict code quality as well. Using a manually labeled dataset, this preliminary study examines the use of five text- and two image-based algorithms to predict the readability, understandability, and complexity of source code. While the overall performance can still be improved, we find Support Vector Machines (SVM) outperform sophisticated text transformer models and image-based neural networks. Furthermore, text-based SVMs tend to perform well on predicting readability and understandability of code, while image-based SVMs can predict code complexity more accurately. Our study both shows the potential of text- and image-based algorithms for software quality prediction and outlines their weaknesses as a starting point for further research.
Quantum Fisher information at finite temperatures and the critical properties in Ising-Heisenberg diamond chain

In this work we evaluate quantum Fisher information (QFI) of a two-qubit reduced state in the Ising-XXZ diamond structure at finite temperatures, which is applied to demonstrate quantum criticality and quantum phase transition in this model. Here we adopt the average QFI with respect to the local orthonormal observable bases [Phys. Rev. A. 88, 014,301 (2013)]. The results show that QFI around quantum critical points decreases or increases steeper when the system temperature is relatively lower. Meanwhile the singularity of QFI’ first-derivatives is becoming more and more obvious as the temperature is approaching zero. By the finite-temperature QFI and its scaling relation with temperature, quantum phase transitions can be well detected from the entangled state in ferrimagnetic phase to an unentangled state in ferrimagnetic phase or to an unentangled state in ferromagnetic phase. Moreover it is further found that the two kinds of phase transitions can be distinguished by the different scaling behaviors. It has been proved that QFI has enough capability to characterize quantum phase transition at finite temperature.
Recognize Human Head Shot Top Down Based on Hough Transform

Hough transform has the advantage of incomplete edge recognition. A large number of practices show that: for the actual circular object detection, the detected points are usually concentrated in a relatively continuous arc. If the detected points are scattered in various positions of the circle, it is usually the interference of noise that causes the wrong recognition of the target. On this basis, this paper proposes an algorithm, that is, to calculate the standard deviation of the angle of the detected edge points, and compare the standard deviation value with the detected edge points, so as to judge whether the recognition of the circular object is correct. In this paper, the algorithm is applied to human head recognition and good results are obtained.
Superpixels-Guided Background Modeling Approach for Foreground Detection

Foreground detection is one of the active research topics in computer vision and a precondition for intelligent video analytics. Background subtraction is often considered as a reliable method for extracting foreground from the video sequences. The real-world challenges such as intermittent object motion, dynamic backgrounds, bad weather conditions, shadows, and illumination variations affect the performance of foreground detection. Most of the conventional background subtraction methods result in faulty detection and fragmented foreground in case of complex scenes. In recent years, deep neural networks make revolutionary advances in the area of foreground detection. The convolutional neural networks based background subtraction methods are notable for their performance but their performance drop in case of unseen videos and incur high-computational cost. In this paper, we investigate the fusion of superpixel with an adaptive background modeling for foreground detection in several challenging videos. A non-iterative clustering method is adopted for superpixel clustering because of its computational efficiency. Integration of effective superpixel algorithm with background subtraction has paved the way for improvements in unsupervised background subtraction methods. The proposed method is evaluated on different videos of changedetection.net 2014 dataset in order to validate its effectiveness on critical challenges of background subtraction.
Multiplication Algorithm for Multivariate Trigonometric Series

Multivariate trigonometric series are used for analytical and numerical solutions of a wide class of applied problems. This mathematical tool is in demand in celestial mechanics, quantum mechanics, electrical engineering, acoustics, optics, and the theory of signal and image processing. Multiplying such series when implementing numerical methods is a complex task that requires a large amount of machine time and memory. The formulas for the coefficients of the product of multivariate series derived in this article, can significantly reduce memory consumption and calculation time. Therefore, the use of these formulas in the software development to solve the mentioned tasks can improve its quality and competitiveness. All the results obtained are presented in the conclusion as final formulas for software implementation.
An Additive Graphical Model for Discrete Data

We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. We develop an estimator of the new graphical model via the penalized estimation of the discrete version of the additive precision operator and establish the consistency of the estimator under the ultrahigh-dimensional setting. Along with these methodological developments, we also exploit the properties of discrete random variables to uncover a deeper relation between additive conditional independence and conditional independence than previously known. The new graphical model reduces to a conditional independence graphical model under certain sparsity conditions. We conduct simulation experiments and analysis of an HIV antiretroviral therapy dataset to compare the new method with existing ones. Supplementary materials for this article are available online.
Media Awareness Preferences for Information on Sickle Cell Disorder Among University Students in Southwestern Nigeria

Sickle cell disorder (SCD) is a defective blood disorder that causes pain associated with blood genotypes of victims who risk dying before their 30th birthday. SCD is best controlled when victims avoid getting married to one another to prevent the spread of susceptible genotype. Many youths lack basic facts of SCD. For long, its awareness was limited to broadcast and print media. But today, social media platforms—Facebook, Instagram, Twitter, and WhatsApp—are also used to create awareness on the disease. This study attempts to determine the preferred communication channels for creating awareness on SCD among university students in South-Western Nigeria. Survey research design was adopted. Respondents were 259 students of Obafemi Awolowo University, Ile-Ife, and University of Ibadan, both in South-West Nigeria. Descriptive and inferential statistics were used for data analysis. Social media awareness was 51.6%, and conventional media was 48.4%. Thus, more of the respondents preferred awareness on SCD through social media platforms.
Hand Gesture Recognition Through Depth Sensors

A hand gesture recognition system allows for nonverbal communication that is natural, inventive, and modern. It can be used in a variety of applications involving human-computer interaction. Sign language is a type of communication in which people communicate using their gestures. The identification of hand gestures has become one of the most important aspects of human-computer interaction (HCI). The goal of HCI is to get to a point where human-computer interactions are as natural as human-human interactions, and adding gestures into HCI is an essential research topic in that direction. The history of hand gesture recognition is initially presented in this chapter, followed by a list of technical challenges. Researchers from all over the world are working on developing a reliable and efficient gesture recognition system, particularly a hand gesture recognition system, for a variety of applications. In this chapter, the core and advanced application areas and various applications that use hand gestures for efficient interaction have been addressed.
A Robot for Facade Cleaning Based on a Cartesian Configuration: Kinematic Analysis and Prototype Construction

Despite advances in service robotics and automation, building facade cleaning continues to be performed manually by specialized technicians operating from complex scaffoldings. This form of cleaning, in addition to be inefficient, is expensive and dangerous for cleaning personnel who must work under the risk of falls from considerable height and exposed to toxic substances that are often required for cleaning. This work presents a robotic device with 5 degrees of freedom based on a Cartesian configuration and a pan-tilt type system; the latter allows directing a high-pressure jet of some specific substance to clean the facade. The robotic device is placed in front of the facade and an operator directs the cleaning from the ground in a teleoperated way. In a first stage, the cleaning substance can be water supplied by a high-pressure washer. The kinematic analysis of the robot is presented, as well as a prototype construction.
Design and Implementation of Teaching System Based on RGB-D Human-Computer Interaction

The traditional teaching model has shortcomings such as single presentation method and difficult storage of writing on the blackboard, The emergence of multimedia teaching will change the form of chalk and blackboard in traditional teaching and improve teaching efficiency. However, there are also defects such as relying on input device control, which affects teaching quality. The control method based on human-computer interaction can solve this drawback and improve the interaction between teachers and students. Therefore, this paper designs a teaching system based on RGB-D human-computer interaction. This system uses Kinect to obtain depth images to identify the skeleton model of the human body. By analyzing the needs of the teaching system, designing corresponding control actions and acquiring three-dimensional data of key joint points for these actions, Analyze this data to get the main feature information and auxiliary judgment information of the movement, Design a control action recognition algorithm based on this information, Finally, comprehensive control requirements and control actions are designed to design a teaching control system to realize auxiliary control of common teaching functions. After testing, the correct rate of the system's various action recognition algorithms is about 95%, and the real-time, stability and other indicators meet the requirements of teaching control, and the system performance is good.
Reactive Hybrid Model for Fault Mitigation in Real-Time Cloud Computing

The Fourth Industrial Revolution is driven by cloud computing for increased productivity and efficiency. Despite the influence of cost-effectiveness, on-demand service and scalability, cloud computing is faced with many challenges such as security, performance, and fault tolerance. This research proposes a reactive mitigation model using checkpointing and replication techniques to overcome the challenges. The developed model has four layers: client/user, controller, fault tolerance (hybrid) and virtual machines layers. The functionalities of the developed model were compared to existing system using the same simulation conditions. The results showed a relative improvement over the existing system in terms of response time and computational cost and the developed system is able to react to faults by transferring requests to stable VMs and resume task execution at the last checkpoint. The developed model proved to work efficiently under various simulation conditions.
Mobile assisted language learning concepts, contexts and challenges

"Mobile assisted language learning concepts, contexts and challenges." Educational Review, 74(3), pp. 750–751
A Comparison of Streaming Models and Data Augmentation Methods for Robust Speech Recognition

In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neural Network-Transducer (RNN-T). We explore three recently proposed data augmentation techniques, namely, multi-conditioned training using an acoustic simulator, Vocal Tract Length Perturbation (VTLP) for speaker variability, and SpecAugment. Experimental results show that unidirectional models are in general more sensitive to noisy examples in the training set. It is observed that the final performance of the model depends on the proportion of training examples processed by data augmentation techniques. MoChA models generally perform better than RNN-T models. However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques. On the other hand, RNN-T models perform better than MoChA models in terms of latency, inference time, and the stability of training. Additionally, RNN-T models are generally more robust against noise and reverberation. All these advantages make RNN-T models a better choice for streaming on-device speech recognition compared to MoChA models.
NePTuNe: Neural Powered Tucker Networkfor Knowledge Graph Completion

Knowledge graphs link entities through relations to provide a structured representation of real world facts. However, they are often incomplete, because they are based on only a small fraction of all plausible facts. The task of knowledge graph completion via link prediction aims to overcome this challenge by inferring missing facts represented as links between entities. Current approaches to link prediction leverage tensor factorization and/or deep learning. Factorization methods train and deploy rapidly thanks to their small number of parameters but have limited expressiveness due to their underlying linear methodology. Deep learning methods are more expressive but also computationally expensive and prone to overfitting due to their large number of trainable parameters. We propose Neural Powered Tucker Network (NePTuNe), a new hybrid link prediction model that couples the expressiveness of deep models with the speed and size of linear models. We demonstrate that NePTuNe provides state-of-the-art performance on the FB15K-237 dataset and near state-of-the-art performance on the WN18RR dataset.
Embedding Knowledge Graphs into Multi-vector Space

Knowledge representation learning, also known as knowledge embedding, projects entities and relations into continuous vector space to quantify entities and relations. Among various embedding models, the translation-based models are one branch which interpret relations as translations operating on the embeddings of entities. However, these models cannot well separate entities or relations with similar facts. In this paper, we introduce TransMult, a novel approach, which projects entities and relations into a multi-vector space instead of a single vector space. TransMult does not increase the number of parameters and computational complexity, but can well distinguish similar entities or relations. We explained the rationality of TransMult and we further explored the influence of the multiplicity of the vector space on the model. As far as we know, TransMult is the first multi-vector space approach for knowledge graph embedding. To this end, in this paper, we extend the existing models TransE and TransH with TransMult. Experimental studies validate the effectiveness of the proposed approach against baselines.