Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Machine Learning

From the day when payment systems emerged, there have been people willing to find novel ways to access someone&#x2019;s finances illegally. This menacing hazards has grown in the current period, as the majority of transactions are now completed entirely online using credit card information. Frauds due to Credit Cards is a broad phrase that refers to any type of fraud involving a payment card, specifically a credit cards.The solitary purpose of such transgressions is usually to gain goods and services, or to make a huge payment to another account without the owner&#x2019;s consent. According to the Nilson Report, By 2025, due to credit card fraud the United States has been projected to suffer losses up to 12.5 billion dollars. Using Machine learning algorithms to detect Credit card fraud is a process in which the data is investigated through various techniques to achieve the best possible outcomes in detecting and impeding fraudulent transactions. In order to evaluate different algorithms which accurately detect credit card fraud we have used techniques such as Random Forest, XGBoost, ANN (Artificial Neural Network). The results of these models can be used to effectively detect any credit card transaction happening whether a genuine one or fraudulent.
CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

In today's real world, an important research part in image processing is scene text detection and recognition.Scene text can be in different languages, fonts, sizes, colours, orientations and structures.Moreover, the aspect ratios and layouts of a scene text may differ significantly.All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes.In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN) has been proposed.Moreover, we have recommended a new text detection method for detecting the exact text from the input natural scene images.For enhancing the presentation of the edge detection process, image pre-processing activities such as edge detection and color modeling have beenapplied in this work.In addition, we have generated new fuzzy rules for making effective decisions on the processes of text detection and recognition.The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR 2005 and the SVT and have achieved better detection accuracy intext detection and recognition.By using these three datasets, five different experiments have been conducted for evaluating the proposed model.And also, we have compared the proposed system with the other classifiers such as the SVM, the MLP and the CNN.In these comparisons, the proposed model has achieved better classification accuracywhen compared with the other existing works.
A Service Pattern-Oriented Computing Architecture for Service Ecosystems

A service ecosystem is an increasingly popular service organization form, where participants, services, data, resources, and capital of different domains are integrated. It can empower business systems to consider the exchanged values and creations in a certain context. How to manage the participants’ cooperation and arrange the elements in the service ecosystem, namely service pattern, have become an important factor to influence the competitiveness of enterprises. However, at present, most of the relevant researches stay in the description of business model and qualitative analysis stage. The design and application of service pattern is still blind and risky. In this article, a service pattern-oriented computing architecture is proposed. It can systematically design and innovate new service patterns and comprehensively assess and simulate them.
Revealing Task-Relevant Model Memorization for Source-Protected Unsupervised Domain Adaptation

Source-data-free unsupervised domain adaptation (SF-UDA) is an approach to improve model performance in the target domain without accessing the source data. Some SF-UDA methods have been proposed and achieved promising results using the information from source-model parameters. However, current research on information security confirms the ability of a well-trained model to memorize its training data. Therefore, SF-UDA methods that access model parameters remain at risk of privacy disclosure. This paper introduces a new topic of source-protected UDA (SP-UDA) that adapts the source model to the target domain while protecting the source-domain data and model privacy. In SP-UDA, only a black-box source model and a set of unlabeled target data are available for domain adaptation. We consider SP-UDA from a new perspective of model memorization revelation. A Source-Protected Generative Model (SPGM) is developed to reveal task-relevant memorization from the source model. SPGM directly distills the inverse process of the source model without access to source-model parameters to meet the privacy protection objective in SP-UDA. The SPGM is learned under the supervision of a newly designed metric named privacy-protected transfer (PPT). The PPT metric measures the transferability and desensitization of the generated data to encourage the SPGM to extract task-relevant information rather than the unintended memorization. A set of desensitized pseudo data is then generated as substitutes for the real source data in UDA. The performance of the proposed method has been validated in four cross-dataset recognition applications with encouraging results.
Meaning maps detect the removal of local semantic scene content but deep saliency models do not

Meaning mapping uses human raters to estimate different semantic features in scenes, and has been a useful tool in demonstrating the important role semantics play in guiding attention. However, recent work has argued that meaning maps do not capture semantic content, but like deep learning models of scene attention, represent only semantically-neutral image features. In the present study, we directly tested this hypothesis using a diffeomorphic image transformation that is designed to remove the meaning of an image region while preserving its image features. Specifically, we tested whether meaning maps and three state-of-the-art deep learning models were sensitive to the loss of semantic content in this critical diffeomorphed scene region. The results were clear: meaning maps generated by human raters showed a large decrease in the diffeomorphed scene regions, while all three deep saliency models showed a moderate increase in the diffeomorphed scene regions. These results demonstrate that meaning maps reflect local semantic content in scenes while deep saliency models do something else. We conclude the meaning mapping approach is an effective tool for estimating semantic content in scenes.
Towards liveness detection in keystroke dynamics: Revealing synthetic forgeries

While the accuracy of keystroke dynamics verification systems has traditionally been evaluated using a zero-effort attack model, the current trend is to recognize that such an approach is too optimistic. Attacks using statistical models and synthetic forgeries have been shown to achieve significant rates of success, motivating the study of methods for improving the imitation of legitimate user’s keystroke timings as well as the detection of such counterfeits. For these purposes, we introduce two methods using higher-order contexts and empirical distributions to generate artificial samples of keystroke timings, together with a liveness detection system for keystroke dynamics that leverages them as adversaries. To aid with this objective, we present a family of distances based on the smoothed empirical cumulative distributions of keystroke timings. One of the proposed spoofing methods outperforms other spoofing methods previously evaluated in the literature by a large margin, doubling and sometimes tripling their false acceptance rates, to around 15%, when data of the targeted user is available. If only general population data is available to an attacker, the liveness detection system achieves false acceptance and false rejection rates between 1% and 2%, consistently, over three publicly available datasets previously used in other keystroke dynamics studies.
Evolutionary multi-objective trajectory optimization for a redundant robot in Cartesian space considering obstacle avoidance

Abstract. A method of end-effector trajectory planning in Cartesian space based on multi-objective optimization is proposed in this paper to solve the collision problem during the motion of the redundant manipulator. First, a cosine polynomial function is used to interpolate the trajectory of the end effector, enabling it to reach the desired pose at a specific time. Then, the joint trajectory of the manipulator is solved by inverse kinematics, and the null space term is introduced as the joint limit constraint in the inverse kinematics equation. During the operation of the manipulator, the collision detection algorithm is employed to calculate the distance between the obstacle and each arm in real time. Finally, a multi-objective, multi-optimization model of trajectory that considers the obstacle avoidance, joint velocity, joint jerk and energy consumption is established and optimized with a multi-objective particle swarm optimization algorithm. The simulation results demonstrate that the proposed method can effectively accomplish the trajectory planning task and avoid obstacles; the joint trajectories obtained are smooth and meet the limit constraints; the joint jerk and energy consumption are well suppressed.
From FAIR to FAIRS: Data security by design for the global burden of animal diseases

Solving complex global problems involving data and data analysis can require data from both the public and private sectors. The sharing of data has traditionally been restricted to open data. To facilitate the use of both open and private data, a new data-sharing framework has been constructed as an extension to the popular Findable-Accessible-Interoperable-Reusable (FAIR) framework. The "Secure by Design" approach has been taken to define the FAIRS data-sharing framework where S stands for Secure. A Cloud infrastructure architecture is proposed that would allow data brokers to implement FAIRS. This architecture is being constructed for the Global Burden of Animal Diseases (GBADs) to facilitate the sharing of livestock data.
Attention-Driven Appearance-Motion Fusion Network for Action Recognition

Recent years have witnessed the popularity of using a two-stream architecture and attention mechanism for action recognition with videos. However, it is time-consuming to train two separate convolutional neural networks (ConvNets), especially with the complex attention mechanism. In this paper, we present a novel architecture, termed as Appearance-Motion Fusion Network (AMFNet), to learn efficient and robust action representation from RGB and optical flow data in an end-to-end manner. AMFNet is constructed by connecting a convolutional neural network with an appearance-motion fusion block (AMFB), whose goal is to incorporate appearance and motion streams into a unified framework driven by a cross-modality attention (CMA) mechanism. More specifically, the CMA only relies on optical flow data, which consists of a Key-Frame Adaptive Selection Module (KFASM) and an Optical-Flow-Driven Spatial Attention Module (OFDSAM). The former aims to adaptively identify the discriminative key frames from a sequence, while the latter is able to guide our networks to focus on the action-relevant regions of each frame. We explore two schemes for appearance and motion streams fusion in AMFB from hierarchical and comprehensive levels. The proposed AMFNet is extensively evaluated on five action recognition data sets, including HMDB-51, UCF-101, JHMDB, Penn and Kinetics-400. Compared to the state-of-the-art methods operated at RGB and optical flow, the experimental results validate that our AMFNet achieves a comparable performance with a pure 2D-Single-ConvNet design.
Sequence Embeddings Help Detect Insurance Fraud

Roughly 10 percent of the insurance industry’s incurred losses are estimated to stem from fraudulent claims. One solution is to use tabular data to construct models that can distinguish between claims that are legitimate and those that are fraudulent. However, while canonical tabular data models enable robust fraud detection, complex sequential data have been out of the insurance industry’s scope. For health insurance, we propose deep learning architectures that process insurance data consisting of sequential records of patient visits and characteristics. Both the sequential and tabular components improve the quality of the model, generating new insights into the detection of health insurance fraud. Empirical results derived using relevant data from a health insurance company show that our approach outperforms state-of-the-art models and can substantially improve the claims management process. We obtain a ROC AUC metric of 0.873, while the best competitor based on state-of-the-art models achieves 0.815. Moreover, we demonstrate that our architectures are more robust to data corruption. As more and more semi-structured event sequence data become available to insurers, our methods will be valuable for many similar applications, particularly when variables have a large number of categories, such as those from the International Classification of Disease (ICD) codes or other classification codes.
Kalman-Like Filter Under Binary Sensors

This article is concerned with the Kalman-like filtering (KLF) problem for linear and nonlinear dynamic systems observed by binary sensors. Binary sensors are a special class of sensors that output only one bit of data and have considerable advantages in terms of energy consumption and economic costs. However, it is difficult to directly use binary sensors to estimate system states since the available information is compressed to the extreme and is difficult to be extracted. To address this problem, this article proposes an uncertainty measurement model to capture the innovations generated from binary sensors by analyzing their characteristics. Based on the proposed model, the KLFs are constructed for linear and nonlinear dynamical systems. Then, to deal with the uncertainties induced by binary sensors, conservative error covariances with adjustable parameters are derived for the KLFs via matrix inequalities and unscented transform. The optimal filter gains and some adjustable parameters are obtained by minimizing the traces and the upper bounds of the conservative covariances, respectively. Finally, arterial O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> system and damped mass-spring system are employed to show the effectiveness and advantages of the proposed methods.
Comparative Analysis of Deep Learning Techniques For Credit Card Fraud Detection

Today, many types of frauds are carried out in the credit and debit card transactions. These types of transactions are usually carried out due to the increasing popularity of online transactions. Therefore, there's need to detect the online frauds while using credit card. There is a danger of stealing credit card information for malpractices. This is sometimes done by making fake calls or sending fake SMS or by sending phishing mails. There are many algorithms that can detect frauds, such as machine learning. In this paper, we study the various aspects of machine learning to detect frauds. This paper deals with Local Outlier Factor, Isolation Forest and Convolution Neural Network techniques. We determined accuracy, precision, recall, F1 score and loss for these techniques. We achieved an accuracy of 99% for both deep learning and supervised machine learning techniques.
A Novel Image Encryption Technique Based on Cyclic Codes over Galois Field

In the modern world, the security of the digital image is vital due to the frequent communication of digital products over the open network. Accelerated advancement of digital data exchange, the importance of information security in the transmission of data, and its storage has emerged. Multiple uses of the images in the security agencies and the industries and the security of the confidential image data from unauthorized access are emergent and vital. In this paper, Bose Chaudhary Hocquenghem (BCH) codes over the Galois field are used for image encryption. The BCH codes over the Galois field construct MDS (maximum distance separable) matrices and secret keys for image encryption techniques. The encrypted image is calculated, by contrast, correlation, energy, homogeneity, and entropy. Histogram analysis of the encrypted image is also assured in this paper. The proposed image encryption scheme's security analysis results are improved compared to the original AES algorithm. Further, security agencies can utilize this work for their confidential image data.
Uniqueness of the inversive plane of order sixty-four

Abstract The uniqueness of the inversive plane of order sixty-four, up to isomorphism, is established. Equivalently, it is shown that every ovoid of $$\mathrm{PG}(3,64)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>PG</mml:mi> <mml:mo>(</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>64</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is an elliptic quadric.
Application for Counterfeit Detection in Supply Chain using Blockchain Technology

Counterfeiting is a global issue affecting a wide range of industries including luxury goods, clothing and pharmaceuticals among others. Proving or disproving the authenticity of an asset can be a challenge because traditional supply chains are long, complex and lack transparency. However, placing the supply chain on a decentralized technology like blockchain will ensure that goods will have provenance due to its immutable transaction history, which in turn makes it difficult to masquerade counterfeit products as real and take their place in the supply chain. Blockchain can bring all entities like the manufacturers, suppliers and distributors together in a close-knit and transparent manner. It keeps records of all transactions and other necessary information visible to all concerned parties while minimizing the possibility of records being tampered with. Therefore we intend to provide a lightweight, cost effective implementation that makes a supply chain secure, decentralized and verifiable besides omitting inadequacies in the supply of genuine products and cutting back on costs involved in detecting faulty areas.
Performance measurements of machine learning and different neural network designs for prediction of geochemical properties based on hyperspectral core scans

Performance results of predictive models for estimating chemistry grades for gold, copper and iron in drill cores, based on the mineralogy data derived from the hyperspectral observations and using automated tools are presented. The models were built using data from more than 700 km of drill core. Ten commonly used machine learning and neural network algorithms, including convolutional neural networks (CNNs), were assessed for classifying ore grades, with accuracy and errors reported through confusion matrixes. The CNN algorithm was the outstanding performer, with an averaged classifier accuracy of 80%, outperforming the other machine-learning methods and the DenseNet deep learning method. Also discussed is the outcome of using fewer ore-grade classes that led to better predictive accuracy. This work provides insight into the potential for predicting geochemistry from hyperspectral data to support exploration geologists in target detection.
A deep survey in the Applications of demosaicking

Image demosaicking is a basic research topic in different imaging system with the purpose to interpolate full-resolution color images from so-called color filter array (CFA) raw samples. Recent years, many demosaicking methods were proposed in imaging system with various CFA patterns to produce diferent image. In this article, we provide a systematic survey of over thirty published works in this field. Our review attempts to illustrate important issues to demosaicing and identify fundamental differences among competing approaches. we also give a deep survey on the application of the demosaicking in different imaging system such as HDR imaging system, Multispectral imaging system and so on with different CFA patterns.
Autonomous Learning in a Pseudo-Episodic Physical Environment

Abstract Forpractical considerations reinforcement learning has proven to be a difficult task outside of simulation when applied to a physical experiment. Here we derive an optional approach to model free reinforcement learning, achieved entirely online, through careful experimental design and algorithmic decision making. We design a reinforcement learning scheme to implement traditionally episodic algorithms for an unstable 1-dimensional mechanical environment. The training scheme is completely autonomous, requiring no human to be present throughout the learning process. We show that the pseudo-episodic technique allows for additional learning updates with off-policy actor-critic and experience replay methods. We show that including these additional updates between periods of traditional training episodes can improve speed and consistency of learning. Furthermore, we validate the procedure in experimental hardware. In the physical environment, several algorithm variants learned rapidly, each surpassing baseline maximum reward. The algorithms in this research are model free and use only information obtained by an onboard sensor during training.
Trend locally stationary wavelet processes

Most time series observed in practice exhibit first‐ as well as second‐order non‐stationarity. In this article we propose a novel framework for modelling series with simultaneous time‐varying first‐ and second‐order structure, removing the restrictive zero‐mean assumption of locally stationary wavelet processes and extending the applicability of the locally stationary wavelet model to include trend components. We develop an associated estimation theory for both first‐ and second‐order time series quantities and show that our estimators achieve good properties in isolation of each other by making appropriate assumptions on the series trend. We demonstrate the utility of the method by analysing the global mean sea temperature time series, highlighting the impact of the changing climate.
The Introduction of Digital Identity Evolution and the Industry of Decentralized Identity

The concept of decentralized identity was come up on Internet Identity Workshop in 2015. This new concept describes a new internet identifier system, which is not controlled by central registration authorities, and it aims to build a user-orientated, decentralized, and trusted digital identity ecosystem. Start from 2016, actions on this new digital identity were extremely increasing from governments to companies. The entities from the United States, Europe, Asia were doing plenty of works on this new technology, for example, products display, standardization work, ecosystem development and policy making. Initially, this paper illustrates three discrete paths in the evolution of digital identity, and some key technical elements in decentralized identity. Moreover, it gives the overview of industrial development for decentralized identity and proposes some recommendation for domestic development.