Reviewerly/exHarmony huggingface dataset:
Detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization

Fraudulent activities associated with the credit card is a pertinent problem often occurring in a global level. The customers are losing their trust with the financial institutions and the financial institutions are in a difficult state to win the goodwill of customers. A substantial number of researchers show interest to work on fraud detection in order to develop an optimized method or model to identify the fraudulent activities that are happening in a regular and continuous form with the credit card in our everyday life. Genetic algorithm (GA) and the potential solution-based particle swarm optimization (PSO) are two optimization algorithms, which can be considered along with the neural network to analyze the possible fraudulent transactions. The optimization algorithms help to make the learning process faster and optimized with a superior and better predictive accuracy value. The PSO-based neural network has been trained thoroughly and performance values are compared with GA-based neural network, by increasing the number of iterations and the population or number of swarms. It has been observed that algorithm based on PSO gives an optimized result for fraudulent transaction detection.
Modeling Concept Drift Detection as Machine Learning Model Using Overlapping Window and Kolmogorov–Smirnov Test

Nowadays the large volume of data from different sources especially as streaming data opens us various opportunities for streaming analytics. Concept drift is one of the challenging streaming analytic problems which observes the changes in the distribution of the data over time, and detecting and adapting these attracted many researchers. In this work, we modeled concept drift detection as a machine learning problem. We have followed a semi-supervised learning approach by utilizing a statistical test, the Kolmogorov–Smirnov test which determines the variation of two time series distributions. The core work is to build a classifier that is capable of predicting the given window of data stream holds drift or not. As we have no labels representing drift or not drift for the stream windows, we have explicitly labeled some beginning parts of the stream using the Kolmogorov–Smirnov test and utilized these for building the classifier. Using this classifier, further, we can detect the drift. Also, we have used overlapping windows to avoid information loss. For building the classifier we have applied various classification models like Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. Among them KNN model has a low false-positive rate and outperformed others with an accuracy 96%.
A new two-stream network based on feature separation and complementation for ultrasound image segmentation

Automatic ultrasound (US) image segmentation is highly desired for improving clinical workflow and diagnostic accuracy. However, the task always has been challenging as the US image has the characteristics of speckle noise, blurred boundary, and inhomogeneous distributions. To solve these problems, this paper proposes a new two-stream network based on feature separation and complementation (FSC-Net) for ultrasound image segmentation. For the feature separation, FSC-Net uses two branches, namely Top-To-Bottom (T2B) and Bottom-To-Top (B2T) streams, to extract global semantic information and local detailed information respectively, and each branch can extract the concerned feature information more effectively. For the feature complementation, FSC-Net performs the interaction between the global semantic information and the local detailed information at each stage gradually, so it can complement the boundary feature of regions of interest (ROIs) in the T2B stream and suppress the noise in the B2T stream timely. We evaluate the proposed method on three publicly available datasets, i.e., UDIAT, BUSIS, and LUSI. The Dice of our FSC-Net in the three datasets are 0.8698, 0.9350, and 0.8972, respectively, which are at least 1.59%, 0.96%, and 3.74% higher than other state-of-the-art ultrasound image segmentation methods.
Research on Chinese News Text Classification Based on ERNIE Model

In the field of text classification, news text classification has always been the focus of research, and it is quite difficult. With the gradual maturity of deep learning technology and the advent of the information explosion era, the traditional text classification method has been unable to meet people's needs for rapid, efficient, and accurate news classification. The neural network based on bidirectional encoder representations from transformers (BERT) in deep learning is very suitable for news text classification. However, the random mask masking method adopted by BERT model does not incorporate the knowledge information of Chinese language, resulting in the problem of low classification accuracy. Based on this problem, this paper proposes a news topic classification method based on the ERNIE model of multi-stage fusion knowledge masking strategy. When the Chinese language knowledge information fusion model deals with the Chinese news text classification problem, the BERT model, BERT-CNN model, and ConvBERT model are compared. The results show that ERNIE model is superior to other models in the task of news text classification.
Method Level Refactoring Prediction by Weighted-SVM Machine Learning Classifier

Lousy design and development methods in software systems are well-known concepts in the field of object-oriented software engineering known as "code smells." New kinds of code smells identified as specific to mobile code smells due to the rise of mobile apps. Although numerous empirical research on these innovative code smells, their spread and evolution over change histories remain a mystery. When refactoring a program or piece of software, the goal is to make it more efficient without changing its usefulness. A large-scale experiential study that examines the evolution, introduction, and refactoring operations for the enhancement of code quality is presented in this chapter. Five open-source projects have been considered to apply weighted-SVM and SVM with SMOTE as machine learning classifier for refactoring prediction. This chapter has a conclusion that SVM with SMOTE gives better result for refactoring prediction as comparison to weighted-SVM. In comparison to all (linear, polynomial and RBF) kernels of SVM classification technique, RBF achieves better result.
A Novel Approach for Sentiment Analysis and Opinion Mining on Social Media Tweets

Social media is a powerful tool for people to share their thoughts and feelings. People post their personal feelings and thoughts on any topic, person, policy or product for marketing or social attraction. This text information can be broken down into facts and opinions. Reflect people's feelings about government policies, products, national and international personalities, the constitution, incidents and events. Sentiment analysis is an area of study that analyzes people's opinions, ratings, attitudes and emotions in general from the written language. The texts of the reviews are edited to give an accurate description of the author's opinion on the subject. This paper proposes an analysis of the feelings of the writers about the subject and comments of the users of social networks in the context of Ethiopia with Facebook as a platform. In this paper, natural language processing (NLP) and machine learning (ML) based on a novel sentiment analysis and opinion mining framework used to conduct sentiment analysis and opinion mining of posts and user comments on social media via a Facebook application.
The Use of Gendered Chatbots in Nigeria: Critical Perspectives

Abstract In a bid to improve service delivery and efficiency in Nigeria, the use of conversational agents such as chatbots capable of providing round the clock responses is growing. This is gaining more traction in the financial sector, particularly in fintechs where there have been significant improvements in the last year. However, not enough research has been conducted on the impact of the gendering of these conversational agents. This is concerning, especially as findings from our research reveal that the majority of chatbots currently deployed in the country are deliberately gendered to appear female, reinforcing already existing gender stereotypes within African societies. With an increase in the deployment of these bots in many other sectors, it is crucial to evaluate the significance of the gendering of these bots to ascertain the effect on gender inequality in Nigeria.
Application of Blockchain Technology in Healthcare Supply Chains

BlockchainBlockchain TechnologyTechnology (BCT) has been applied across many fields including healthcareHealthcare. It has gained popularity due to its inherent advantages like real timeReal-time, traceabilityTraceability, decentralisation, and high levels of securitySecurity. BCT in healthcareHealthcare has been applied in different areas like hospitalHospital servicesService, pharmaceutical industry, clinical diagnosticsDiagnostic, billing and claims, end user servicesService etc. In this chapterChapter we present BlockchainBlockchain technologyTechnology frameworkFramework and its applications in healthcareHealthcare supply chainsSupply chain along with challengesChallenge and opportunitiesOpportunity. We also highlight important insights and conclusions.
Cyber Threats and the Security Risks They Pose to National Security: An Assessment of Cybersecurity Policy in the United Kingdom

The COVID-19 Pandemic has accelerated the digital transformation of organisations and services across the United Kingdom (UK) providing numerous opportunities for economic and social development in the UK. However, these opportunities also bring about unprecedented challenges for law enforcement agencies (LEAs), and has led to the progression of serious and advanced cyber threats. This chapter aims to analyse different types of cyber threats, identify the risk they pose to national security, and provide a critical evaluation of cybersecurity policy in the UK. The chapter will examine how current UK Government policies and practices effectively mitigate the cyber threats to national security, and will explore how these responses can be further developed, with reference to the National Cyber Security Centre, the Active Cyber Defence programme, and the National Cyber Security Strategy 2022–2030. The methodological approach for this chapter utilises a literature-based review to further develop research on the criminological issue of cyber threats, cybersecurity and national security in the UK.
Working with Robots as Colleagues: Kenyan Perspectives of Ethical Concerns on Possible Integration of Co-bots in Workplaces

Abstract Robots are moving closer to human beings especially in work environments. The entry of co-bots into workspaces raises many questions. One of the key questions surrounds the likely relationship between the co-bots and their co-workers. Are co-bots to be considered as colleagues or are they tools? What ethical issues emerge from this consideration in the context of Kenyan workspaces? This chapter discusses these questions in the Kenyan context. Data for the chapter was collected using qualitative interviews with 20 data scientists selected through information-oriented purposive sampling. The chapter concludes that there are six ethical issues which can influence the perceptions of co-bots by data scientists in Kenya. These include the concept of work as a divine gift to humanity which cannot be shared with machines; the notion that treating co-bots as legal persons equates them to human beings which is viewed as demeaning to humanity; the fear that co-bots will dominate and eventually replace humans in ordinary workspaces thereby denying the latter not just an opportunity to work but to livelihood too; fear of unintended social consequences of “anthropomorphisation”; lack of trust for machines created by limited humans to offer unlimited services and companionship; and discomfort with exotic robots entering professional but also indigenous spaces. Until these ethical issues are addressed comprehensively, it is unlikely that information scientists would unreservedly welcome co-bots into their workspaces as colleagues.
Modeling and predicting user preferences with multiple item attributes for sequential recommendations

Sequential recommendations have become a focus of attention across the deep learning community owing to their fitness to the actual application scenario. Although recently we have witnessed a surge of work on sequential recommender systems, they are still insufficient in exploring and exploiting item-attribute relations to enhance prediction accuracy. In this work, we propose a novel technological framework, MIA-SR, for sequential recommendation (SR) by modeling and predicting user preferences with multiple item attributes (MIA). When modeling the dynamic behavior of a user, not only the item sequence but also the attribute sequence is used to generate the fused representation of users. Further, we propose using a graph convolution network on the item-attribute bipartite graph to enhance the representations of items and attribute entities. Moreover, MIA-SR is naturally empowered with a multi-tasking strategy to exploit inductive bias among different preference signals and enhance item recommendation. Extensive experiments on public benchmark datasets have verified the merits of MIA-SR. The source code and data are available at: https://github.com/619496775/MIA-SR.
Automatic Decentralized Behavior Tree Synthesis and Execution for Coordination of Intelligent Vehicles

This paper proposes a systematic approach for automatic tasking and coordination of a heterogeneous team of cooperative autonomous vehicles forming an intelligent vehicle. Each vehicle is equipped with different resources, operating in a shared dynamic environment, and capable of executing a set of specific tasks. To coordinate such a heterogeneous team of vehicles, we develop a hierarchical modular coordination algorithm for generating local Behavior Trees (BTs) for tasking individual vehicles so that they, as a team, can collectively achieve a global mission. A hierarchical auctioning algorithm is embedded in the proposed framework to effectively assign tasks among the vehicles, so that they can complement each other and complete a mission that may not be achieved individually. Furthermore, by introducing operational and duration cost weight terms, the proposed approach provides the possibility to adjust the total operation cost and duration of the tasks. The details of the developed algorithms are illustrated through different case studies.
HIGSA: Human image generation with self-attention

The goal of human image generation (HIG) is to synthesize a human image in a novel pose. HIG can potentially benefit various computer vision applications and engineering tasks. The recently-developed CNN-based approach applies the attention architecture to vision tasks. However, owing to the locality in CNNs, extracting and maintaining the long-range pixel interactions input images is difficult. Thus, existing human image generation methods face limited content representation. In this paper, we propose a novel human image generation framework called HIGSA that can utilize the position information from the input source image. The proposed HIGSA contains two complementary self-attention blocks to generate photo-realistic human images, named as stripe self-attention block (SSAB) and content attention block (CAB), respectively. In SSAB, this paper establishes global dependencies of human images and computes the attention map for each pixel based on its relative spatial positions concerning other pixels. In CAB, this paper introduces an effective feature extraction module to interactively enhance both person’s appearance and shape feature representations. Therefore, the HIGSA framework inherently preserves the better appearance consistency and shape consistency with sharper details. Extensive experiments on mainstream datasets demonstrate that HIGSA achieves the state-of-the-art (SOTA) results.
Machine Learning based Discrimination for Excited State Promoted Readout

A limiting factor for readout fidelity for superconducting qubits is the relaxation of the qubit to the ground state before the time needed for the resonator to reach its final target state. A technique known as excited state promoted (ESP) readout was proposed to reduce this effect and further improve the readout contrast on superconducting hardware. In this work, we use readout data from five-qubit IBMQ devices to measure the effectiveness of using deep neural networks, like feedforward neural networks, and various classification algorithms, like k-nearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These methods were compared to standardly used linear and quadratic discriminant analysis algorithms based on their qubit-state-assignment fidelity performance, robustness to readout crosstalk, and training time.
Energy-aware Edge Federated Learning for Enhanced Reliability and Sustainability

Federated Learning (FL) has emerged as a value added proposition for use in edge-based infrastructures, distributing the training process among collaborative workers without disclosing raw (user) data. In this context, we argue that, differently from what already present in most current literature, energy consumption of nodes (either workers or the ensembler node) is a central element to consider in FL, e.g., to have a more sustainable FL node selection strategy. To this end, a complete and detailed report about energy consumption at each FL round is required to allow for innovative and greener resource management approaches, taking into account residual energy and learning completion time of participating FL nodes. Filling this gap, we present the design of a novel distributed framework capable of collecting accurate (worker) energy expenditure and learning-centric metrics at each FL round. The frame-work comprises state-of-the-art technological building blocks, purposely integrated to enable advanced and energy-aware FL process orchestration capabilities. To validate the approach, we rely on a heterogeneous experimental testbed, and conduct a distributed learning process employing a realistic dataset. The preliminary evaluation results reported in this paper highlight the potential advantage in terms of overall energy consumption reduction and the suitability of an adaptive learning framework capable of autonomous evaluations of the most proper trade-off to apply between accuracy and energy expenditure.
Personalized Itinerary Recommendation via Expectation-Maximization

The personalized itinerary recommendation problem in selecting a subset of locations to visit from among a larger set while maximizing the benefit for the tourist. In this work, we propose an efficient deterministic method for the recommendation of personalized itineraries consisting of a sequence of Points of Interest (POIs) that maximizes the expected user satisfaction and adheres to user time constraints. Experimental results on a large number of synthetic and real-world datasets demonstrate the high performance of our framework.
Sublanguage Characteristics of Clinical Documents

Understanding the common or different characteristics of sublanguages in clinical documents through corpus analysis is essential for downstream applications of clinical natural language processing (NLP). Here, we conducted a sublanguage analysis of a corpus consisting of 500,000 clinical documents concerning clinical sections. We analyzed sublanguage characteristics per practice setting or document type for the top ten most frequent clinical sections. The named entity (NE) for the problem, test, and treatment concepts was extracted using fine-tuned bio-clinical Bidirectional Encoder Representations from Transformers (BERT). Fast-clustering using sentence-BERT was applied, and clustering results, a case study of terms containing “pain,” were visualized using SandDance. Our results confirmed that document types with a narrow scope (i.e., limited evaluation) presented high term frequencies in diverse disjoint clusters than document types with a broad scope (i.e., Discharge Summary). Family Medicine and Primary Care practice settings presented similar cluster distributions (i.e., the frequent use of similar co-occurring words with “pain”), implying the similar sublanguage. In contrast, Emergency Medicine showed a distinct sublanguage with high term frequencies in disjoint clusters than other practices. Those findings suggest that analyzing term distribution with respect to different combinations of the section, practicing setting, and document type provide important information when developing or implementing NLP systems.
Automatic Image Segmentation of Monocytes and Index Computation Using Deep Learning

The classification of white cells plays an important part in medical diagnosis. The counts may suggest the presence of infection, inflammation, anemia, bleeding, and other blood-associated issues. More specifically, the counting in our study is the calculation of the Monocyte Index (MI). The purpose of MI is to determine whether the patient can receive units of blood by analyzing the assay. In case of incompatible blood transfusion, monocytes may ingest or adhere red cells. The index is the percentage of red cells adhered, ingested, or both, versus free monocytes. Manual methods for blood cell counting may take several hours and are highly prone to different sources of errors. Automatic methods, such as Linear Discriminant Analysis, Quadratic Discriminant Analysis, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Convolutional Neural Network (CNN), Fast Region-based CNN, Faster Region-based CNN, Spatial Pyramidal Pooling network, Single Shot Detector and Mask Region-based CNN, exist for classification. However, these methods currently do not perform automatic counting and calculation of MI. The dataset is our own collection of images using ZEISS Axiocam 20Scolor/202 mono microscope camera. For the labels in our own collection, we performed polygonal annotation using the VGG Annotator tool. We trained the Mask R-CNN deep neural network model for automatic segmentation at the pixel-level, using COCO pre-trained weights. Our results look promising, as the Mask R-CNN can perform automatic segmentation with 72% accuracy. Compared to a medical laboratory scientist, the model can process large amount of data simultaneously, quickly and efficiently, with approximately the same judgment accuracy as a human eye. This may significantly reduce the burden of the laboratory scientist and provide a useful reference for doctors to identify a potential blood candidate to be transfused.