Reviewerly/exHarmony huggingface dataset:
The Utility Impact of Differential Privacy on Credit Card Data in Machine Learning Algorithms

With the development of networks and financial technologies, credit card data is increasingly being used in various fields of data analysis such as user behavior, financial transactions, and market analysis. These fields often use multiple machine learning algorithms for data mining on credit card dataset. It is worth noting that credit card data contains more diverse and comprehensive information compared to traditional data, and the dataset may contain multiple data types. At the same time, credit card data may also expose users’ privacy information. Differential privacy algorithms can add random noise to the data set, protecting sensitive information while ensuring certain data utility. However, there has been little research on the use of differential privacy algorithms on credit card data in multiple machine learning algorithms, and there has been insufficient exploration of the utility impact of differential privacy on complex credit card data. These research gaps exist in both the financial technology and privacy protection industries. Therefore, this paper applies differential privacy to credit card data in multiple classic machine learning algorithms, discusses the utility impact of various differential privacy algorithms on credit card data, and compares the performance of credit card data sets protected by differential privacy in different algorithms.
SAR Ship Detection Based on Deep Domain Adaptation with Limited Samples

Ship detection in synthetic aperture radar (SAR) images have important applications in military and civil fields. Nonetheless, SAR images usually suffer from the disadvantage of having limited labeled samples. This is undoubtedly a challenge for the deep learning based methods, which require a large number of samples for training. To solve this problem, many researchers have applied the domain adaptation (DA) methods for the SAR ship detection tasks, which use optical images as the source domain and transform optical images to SAR domain directly. However, there is large difference between SAR and optical images. Hence, the deep learning detector may not be able to learn the target features accurately when the transformed images are used for training. In order to solve above problem, a novel ship detection method with limited labeled samples is proposed, whose training and test process is mainly implemented in the optical domain. In the training stage, optical images are used to pre-train the model. Here, the transformed optical images from SAR images are used to further fine-tune the pre-trained model. Accordingly, in the test stage, SAR images are transformed to optical domain and input in the model to obtain the detection results. Experimental results based on the real dataset demonstrate the effectiveness of the proposed method for SAR ship detection.
Research on the tracing system of technological achievements transformation based on blockchain technology

Low transformation rate of Sci. & Tech achievements and high transaction costs are key issues that urgently need to be solved in the field of Sci. & Tech management in our country. Based on this, this article designs the information storage chain for the full life cycle of Sci. & Tech achievements based on blockchain technology, and realizes the open and transparent, tamper-free, data-sharing and traceability of the research and development, evaluation and transformed information, and reduces the cost of information searching, transaction decision-making and price negotiation, as well as the supervision cost of both parties during the transformation trading, secondary research and development and transformation benefits.
Zero-Shot Learning with Noisy Labels

Zero-shot learning (ZSL) is an attractive technique that can recognize novel object classes without any visual examples, but most existing methods assume that the class labels of the training instances from seen classes are accurate and reliable. However, in some real-world scenarios, the label quality may be compromised by various factors, and ZSL may suffer from performance degradation under label noise. In this paper, we propose an effective approach that utilizes a robust loss function to handle label noise in seen classes. Specifically, we construct a new denoising framework to reduce the impact of outliers and mislabeled samples. To mitigate overfitting to noisy labelled samples, a robust loss function called ramp-style loss is employed to filter out the negative influence of data with anomalous loss values, using only samples within a normal loss range for training, thereby enhancing the overall robust performance. Additionally, we introduce the concave-convex procedure (CCCP) method to address the non-convexity issue of ramp-style loss and devise an efficient update scheme based on ADMM. Extensive experiments conducted on several benchmark datasets (AWA2, SUN, CUB) demonstrate the superior performance of our framework over state-of-the-art ZSL methods in various noisy-label environments.
Shortest path graphs for semi-directed InDoor navigation

In this article, we explore methods for finding shortest paths for indoor navigation. We focus on the construction of the roadmap and thus, the graph representation of the public transport station's open space. Likewise, we propose a method which takes into account the possible directions of corridors. The resulting graph is generally smaller that the graph obtained using only bidirectional entry points, which helps to diminish memory consumption in big networks as well as to speed-up the calculation of the shortest path in the resulting network. Reducing the graph of connections will thus speed up the work of navigation algorithms.
Continuous transformation of nonmonotonic factors of the scoring model increasing its discriminating power

When developing scoring models, Weight of Evidence (WOE) transformation is actively used, which is in demand to improve the discriminating power of the factors included in the model. For WOE transformation, various binning methods are used, the dimension of which is limited by the available statistics. As an alternative, the method of continuous transformation of model factors is proposed, which is applied when the target binary variable depends nonmonotonically on these factors. Two types of one-parameter families of transformations and strict conditions are proposed that determine the need for "healing" of factors, as well as determining the appropriate type of transformation (Φ or Ψ). The method for calculating transformation parameters is substantiated, and practical examples are presented. The use of the proposed transformation in the practice of developing scoring models in the banking sector confirms that the use of ΦΨ - transformations to correct the nonmonotonicity of the model factors gives a significant increase in the discriminating power at the level up to 10% of the Gini index of applied models for binary classification.
Moving target detection based on background modeling and frame difference

The integration of Unmanned Aerial Vehicle (UAV) technology with moving target detection has diverse applications in military reconnaissance, space remote sensing, and smart cities. Traditional motion-based target detection algorithms offer fast processing speeds but lack accuracy. Deep learning-based algorithms, while accurate for specific targets only, are complex and not suitable for resource-limited UAV platforms and lack real-time performance. Therefore, this study proposes a real-time moving target detection algorithm for UAV platforms based on traditional frame difference algorithm. The purpose of this algorithm is to improve detection accuracy, which has been hindered by the limitations of traditional algorithms caused by camera shake, background changes, and fast-moving targets. The algorithm involves rough background modeling, background updating during subsequent video image sequences, image morphology processing, and background compensation. Experimental results from multiple sets of UAV-borne video data demonstrate the algorithm's high target detection rate, low false alarm rate, and ability to detect moving targets stably in complex environments. The proposed algorithm achieves a speed of 25 FPS and a detection accuracy of 91.8%, meeting real-time and accurate detection requirements.
Simulation of conflict transmission in technology standard alliance

Conflicts between participants in the technical standards alliance are inevitable. Mastering the laws of conflict transmission is crucial for managing conflict effectively. On the basis of examining the conceptual framework of conflict transmission, this paper deduces a SIR model for conflict transmission and uses a system dynamics model to simulate and analyze the transmission process. The results show that the transmission of conflicts tends to expand once conflicts are triggered. Reducing the infection rate and increasing the removal rate can effectively curb conflict transmission.
A Data-driven Causality Modeling Framework: An Empirical Study of Modeling the Effect of Indoor Air Quality Perception on Students' Cognitive Performance

In the information age, a paradigm revolution in applied data mining methods has emerged in response to the data explosion in management science research, but this artificial intelligence-based automated data-driven modeling process creates a new "big relationship" problem, where a large number of correlations obfuscate the identification of true causal relationships. In this paper, we use a causality modeling framework that combines correlation modeling and causality pruning processes. First, symbolic regression is used to model white-box correlations of human intelligence, and then spurious correlations that do not conform to causal graph theory are pruned so that ultimately causal relationships and explicit candidate models describing these relationships can be found automatically. In an empirical research problem, the framework is compared with a traditional hypothesis construction-validation process, and the results are consistent between the two. The proposed framework implements a data-driven "correlation + causation" automatic modeling capability, which will greatly improve modeling efficiency and reliability.
HY-RISE: Towards Risk Identification Learning from Massive Scientific Economic Activities

Scientific economic activities document the utilization of research funds, which form a critical component of scientific research. Detecting potential risk behaviors from scientific economic activities is crucial to risk management for research institutions. Most of the existing attempts, however, tackle the problem with traditional machine learning algorithms, which rely on the manual feature extraction. Undoubtedly, these methods cannot extract complex semantic features or fuse information from hybrid data for effective risk identification. To overcome these challenges, in this paper, we propose a novel Risk Identification model for Scientific Economic activities from HYbrid data (HY-RISE), which incorporates both textual and structured data. Firstly, we use a pretrained BERT module to capture the semantic information from textual data. After that, we introduce a BiGRU module to augment the contextual information in semantic embeddings. Finally, we use a shallow neural network to fuse the augmented semantic representation with other discrete features to obtain the final representation. Experimental results on the real reimbursement dataset demonstrate that HY-RISE apparently outperforms existing models in terms of effectiveness and robustness for risk identification.
Identifying key elements for evidence-base medicine using pretrained model and graph convolution network

Objective: Evidence-based medicine (EBM) provides a framework to support clinicians' decision-making processes using the best evidence currently available in the field. The key elements of clinical research can be defined by a framework called PICO, which identifies the sentences in a medical literature text that belong to the four key elements reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). This study aims to establish an effective detection method for key elements of evidence-based medical research. Methods: Based on the text features of key elements framework, we propose a deep learning model BERTGCN which combined large-scale pretraining model and graph convolution network (GCN) for detecting key elements of evidence-based medicine. In this model, the sentences which are initialized with pre-trained BERT representations and the words in the EBM evidence were recognized as the sentence nodes and word of the graph which were used to train GCN model. At the same time, the sentences were used to fine tune the pretraining model. Results: We tested our proposed approach over PubMed-PICO dataset is a data set containing tens of thousands of EBM key elements extracted from PubMed. The F1-score of P/I/O in the model we proposed reached 91.3%, 85.8% and 90.0% respectively. Experimental results show that our model outperforms the current optimal model. Conclusions: BERTGCN is able to leverage the advantages of both worlds: large-scale pretraining and transudative learning to improve the efficiency of detecting evidence-based medical evidence from research publications.
Study on behavioral risk for aging based on smart mattress monitoring data

With an aging population and a gap in demand for professional caregivers, China's elderly care system is facing severe challenges. Using data mining to expand the smart senior care scenario can effectively improve elderly care services. Based on smart mattress datasets, this paper uses machine learning classification and unsupervised anomaly detection models to analyze the possible risks in the daily behavior of older people from the perspectives of both sleep apnea problems and abnormal physiological information. The model results show that the Stacking algorithm based on data fusion can effectively identify the risk of sleep apnea. In contrast, the Prophet and DBSCAN models can carry out anomaly mining of physiological information of single and combined variables, respectively. Ultimately, based on the research, this paper provides targeted recommendations regarding data collection and integration applications.
Sparse regularization based feature selection: A survey

Feature selection, as an essential preprocessing tool, aims to identify a subset of crucial features by eliminating redundant and noisy features according to a predefined criterion. In recent years, sparse learning has received considerable attention. As an important component of sparse learning, sparse regularization has been widely used in feature selection to enforce irrelevant feature coefficients to be small or exactly zero, which is especially suitable for selecting the discriminative and relevant features. In this paper, we attempt to provide a survey on various sparse regularization based methods. We group the existing sparse regularization based methods into two categories, i.e., convex sparse regularization based feature selection and non-convex sparse regularization based feature selection. This survey not only compares the common and different characters of sparse regularizations based feature selection methods, but also offers useful guidance for practitioners of feature selection.
Optimization Ways in Neural Network Compression

Deep learning is a powerful tool that uses simple representations to express complex ideas and allows computers to mine hidden information and value from experience. It has achieved great success in a variety of fields. However, with the development of deep learning models, the computing resources consumed by training the models increase rapidly, making it difficult to directly deploy the models to portable devices or embedded systems. Therefore, compressing and accelerating DNN models without sacrificing performance has become a crucial area of research in the field of deep learning. In many existing compression techniques, optimization theory and approaches play an important role in their research and implementation. In this paper, we focus on neural network compression from an optimization perspective and review related optimization strategies. Specifically, we summarize optimization techniques emerging from four general categories of commonly used network compression approaches, including network pruning, low-bit quantization, low-rank factorization, and knowledge distillation. Finally, we provide a summary and discuss some possible research directions.
Introduction

We begin this chapter with a preliminary discussion of the book, then describe six directions of research, review related work and the contents of the book, and finally discuss the use of the book.
The Rise and Rise of Illegal Ticket Touting. By MorettiAlessandro (Routledge, 2023, 294 pp. £96.00 hbk)

Journal Article The Rise and Rise of Illegal Ticket Touting. By MorettiAlessandro (Routledge, 2023, 294 pp. £96.00 hbk) Get access The Rise and Rise of Illegal Ticket Touting. By Alessandro Moretti ( Routledge, 2023, 294 pp. £96.00 hbk) Xavier D L'Hoiry Xavier D L'Hoiry University of Sheffield, UK x.lhoiry@sheffield.ac.uk Search for other works by this author on: Oxford Academic Google Scholar The British Journal of Criminology, azad041, https://doi.org/10.1093/bjc/azad041 Published: 11 August 2023
Intra‐nucleus mosaic pattern (<scp>InMop</scp>) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.
Technology: A metaparadigm concept of nursing

Undoubtedly, technology continues to permeate the world at an unprecedented pace. The discipline of nursing is not alien to this phenomenon as nurses continue to employ various technological objects and applications in clinical practice, education, administration and research. Despite the centrality of technology in nursing, it has not been recognised as a metaparadigm domain of interest in the discipline of nursing. Thus, this paper sought to examine if technology truly reflected a metaparadigm domain using the four requirements posited by Fawcett. Using these requirements, we examined the onto-epistemology of technology in relation to nursing and conclude that technology potentially represents a distinct domain that intersects with nursing (particularly, from the humanities perspective). Also, technology encompasses some phenomena of interest to the discipline of nursing, demonstrates perspective-neutrality, and is international in scope and substance albeit with some nuances which do not fit well with nursing onto-epistemology. Put together, it is highlighted that technology intersects with the existing metaparadigm domains (person, health, environment and nursing) which positions it as a potential phenomenon of interest to the discipline of nursing requiring further work to articulate its position and role.
Assessing the Impact of Vigilante Patrolling and Community Engagement on Crime Prevention in Dekina Local Government Area of Kogi State, Nigeria

It is worrisome that despite the existence of both the police and vigilantes in the various districts and communities within Dekina Local Government Area, there are still some elements of insecurity, lawlessness, intimidation, kidnapping, daylight assassinations, armed robbery and burglary etc. Hence, in assessing the impact of vigilantes in this regard, the study examined the prevalence of crime in Dekina LGA; examined crime prevention and control mechanisms adopted by the vigilante group; assessing the effectiveness of their roles while ascertaining the factors militating against the operational efficiency of the vigilantes and suggested measures to improve on their performances in crime prevention and control in Dekina Local Government Area of Kogi State. Structural strain theory and social contract theory were used for the study. A survey research design was employed for the study in which a systematic simple sampling technique was utilized to select the respondents from the sample of 297 out of the total population of the study, which was 1304. A pre-test was conducted on every question in the questionnaire to ensure that the research instrument was valid. Two hypotheses formulated were tested using Multiple Linear Regression and Chi-square. The findings of the study revealed that there is a significant relationship between effective vigilantism and the prevalence of crime in Dekina Local Government Area. The study also revealed that beat patrol, stop, detain and search, undercover work, personality escorts, and static guards, among others, were the mechanisms adopted for vigilantism in Dekina LGA. The study further revealed that inadequate operational equipment, improper training, inadequate funding, corruption and godfatherism were some of the factors militating against the operational efficiency of the vigilantes. Recommendations include providing more funding, proper training, and better welfare for vigilante group members to prevent similar situations that erode public confidence in the Nigeria Police Force. Vigilante members should receive proper orientation on handling sophisticated weapons, and exceptional individuals within the vigilante group should be acknowledged with awards to enhance their contributions to crime prevention and control in Dekina Local Government Area.