Reviewerly/exHarmony huggingface dataset:
Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study

Authors:
raw_author_name: Mostafa Dianati-Nasab
raw_affiliation_string: School of Medical and Life Sciences, Sunway University, Malaysia
raw_author_name: Khodakaram Salimifard
raw_affiliation_string: Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Iran
raw_author_name: Reza Mohammadi
raw_affiliation_string: Department of Operation Management, Amsterdam Business School, University of Amsterdam, Netherlands
raw_author_name: Sara Saadatmand
raw_author_name: Kosar S. Hosseini
raw_author_name: Thierry Chaussalet
raw_affiliation_string: Computer Science and Engineering, University of Westminster, United Kingdom
raw_author_name: Samira Dehdar
raw_affiliation_string: Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Iran

Abstract: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors.The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data.The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6.This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.
Blockchain and public service delivery: a lifetime cross-referenced model for e-government

Authors:
raw_author_name: Maxat Kassen
raw_affiliation_string: Professor of Digital Governance, Astana IT University, Astana, Kazakhstan

Abstract: The article presents the results of field studies, analysing the perspectives of blockchain developers on decentralised service delivery and elaborating on unique algorithms for lifetime ledgers to reliably and safely record e-government transactions in an intrinsically cross-referenced manner. New interesting technological niches of service delivery and emerging models of related data management in the industry were proposed and further elaborated such as the generation of unique lifetime personal data profiles, blockchain-driven cross-referencing of e-government metadata, parallel maintenance of serviceable ledgers for data identifiers and phenomena of blockchain 'black holes' to ensure reliable protection of important public, corporate and civic information.
A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack

Authors:
raw_author_name: İ. Esra Büyüktahtakın
raw_affiliation_string: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, USA

Abstract: Abstract In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this DRL framework, we train different agents compatible with a discrete action space for sequential decision-making while still satisfying any resource constraint of the MKP. This novel framework incorporates the decision variable values in the 2D DRL where the agent is responsible for assigning a value of 1 or 0 to each of the variables. To the best of our knowledge, this is the first DRL model of its kind in which a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our framework is configured to solve MKP instances of different dimensions and distributions. We propose a K-means approach to obtain an initial feasible solution that is used to train the DRL agent. We train four different agents in our framework and present the results comparing each of them with the CPLEX commercial solver. The results show that our agents can learn and generalize over instances with different sizes and distributions. Our DRL framework shows that it can solve medium-sized instances at least 45 times faster in CPU solution time and at least 10 times faster for large instances, with a maximum solution gap of 0.28% compared to the performance of CPLEX. Furthermore, at least 95% of the items are predicted in line with the CPLEX solution. Computations with DRL also provide a better optimality gap with respect to state-of-the-art approaches.
LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction

Authors:
raw_author_name: Xinyu Hou
raw_affiliation_string: People’s Public Security University of China, School of Information and Cyber Security, Beijing 100038, China
raw_author_name: Peng Chen
raw_affiliation_string: People’s Public Security University of China, School of Information and Cyber Security, Beijing 100038, China

Abstract: Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.
Integrating foreground-background feature distillation and contrastive feature learning for ultra-fine-grained visual classification

Authors:
raw_author_name: Qiupu Chen
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China; Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
raw_author_name: Lin Jiao
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China; School of Internet, Anhui University, Hefei, Anhui, 230031, China
raw_author_name: Fenmei Wang
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China; Island Branch of Graduate School, University of Science and Technology of China, Hefei, China; PLA Army Academy of Artillery and Air Defense, Hefei 230031, China
raw_author_name: Jianming Du
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
raw_author_name: Haiyun Liu
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China; Island Branch of Graduate School, University of Science and Technology of China, Hefei, China
raw_author_name: Rujing Wang
raw_affiliation_string: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China; Island Branch of Graduate School, University of Science and Technology of China, Hefei, China

Abstract: In pattern recognition, ultra-fine-grained visual classification (ultra-FGVC) has emerged as a paramount challenge, focusing on sub-category distinction within fine-grained objects. The near-indistinguishable similarities among such objects, combined with the dearth of sample data, intensify this challenge. In response, our FDCL-DA method is introduced, which integrates Foreground-background feature Distillation (FD) and Contrastive feature Learning (CL) with Dual Augmentation (DA). This method uses two different data augmentation techniques, standard and auxiliary augmentation, to enhance model performance and generalization ability. The FD module reduces superfluous features and augments the contrast between the principal entity and its backdrop, while the CL focuses on creating unique data imprints by reducing intra-class resemblances and enhancing inter-class disparities. Integrating this method with different architectures, such as ResNet-50, Vision Transformer, and Swin-Transformer (Swin-T), significantly improves these backbone networks, especially when used with Swin-T, leading to promising results on eight popular datasets for ultra-FGVC tasks.1
When I use a word . . . Medical blue plaques in London

Authors:
raw_author_name: Jeffrey K Aronson
raw_affiliation_string: Centre for Evidence Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Abstract: In 1863 the MP William Ewart suggested that “it might be practicable... to have inscribed on those houses in London which have been inhabited by celebrated persons, the names of such persons.” Accordingly, in 1867 the first such inscriptions, which came to be known as blue plaques, were put up by the Society of Arts, commemorating Lord Byron and Napoleon III at places in London where they had lived. The society put up 35 such plaques over the next 35 years when the scheme was taken over by the London County Council, which gave way to the Greater London Council in 1965 and finally English Heritage, in 1986. Among London’s 1000 or so blue plaques several medical men and women, doctors, nurses, and other healthcare workers, are commemorated. They include Cecil Belfield-Clarke, Hannah Billig, Richard Bright, Edith Louisa Cavell, Henry Hallett Dale, Charles Darwin, Henry Havelock Ellis, Elizabeth Garrett Anderson, Thomas Hodgkin, William Hunter and his brother John, Joseph Lister, James Mackenzie, Rachel McMillan and her sister Margaret, William Marsden, Florence Nightingale, Ronald Ross, Mary Seacole, Hans Sloane, and George Frederick Still.
Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Authors:
raw_author_name: Amal S. Alali
raw_affiliation_string: Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
raw_author_name: Rashad Ali
raw_affiliation_string: Department of Mathematics, Riphah International University, Lahore 54000, Pakistan
raw_author_name: Muhammad Kamran Jamil
raw_affiliation_string: Department of Mathematics, Riphah International University, Lahore 54000, Pakistan

Abstract: Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.
Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Authors:
raw_author_name: Muhammad Waqas
raw_affiliation_string: FAST School of Computing, National University of Computer Emerging Science (FAST-NUCES), Karachi, Pakistan
raw_author_name: Muhammad Atif Tahir
raw_affiliation_string: FAST School of Computing, National University of Computer Emerging Science (FAST-NUCES), Karachi, Pakistan
raw_author_name: Sumaya Al-Maadeed
raw_affiliation_string: Department of Computer Science and Engineering, Qatar University, Doha, Qatar
raw_author_name: Ahmed Bouridane
raw_affiliation_string: Cybersecurity and Data Analytics Research Center, University of Sharjah, Sharjah, UAE
raw_author_name: Jia Wu
raw_affiliation_string: Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Abstract: Abstract In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.
Link prediction using low-dimensional node embeddings: The measurement problem

Authors:
raw_author_name: C. Seshadhri
raw_affiliation_string: Department of Computer Science, University of California, Santa Cruz, CA 95064

Abstract: Graph representation learning is a fundamental technique for machine learning (ML) on complex networks. Given an input network, these methods represent the vertices by low-dimensional real-valued vectors. These vectors can be used for a multitude of downstream ML tasks. We study one of the most important such task, link prediction. Much of the recent literature on graph representation learning has shown remarkable success in link prediction. On closer investigation, we observe that the performance is measured by the AUC (area under the curve), which suffers biases. Since the ground truth in link prediction is sparse, we design a vertex-centric measure of performance, called the VCMPR@k plots. Under this measure, we show that link predictors using graph representations show poor scores. Despite having extremely high AUC scores, the predictors miss much of the ground truth. We identify a mathematical connection between this performance, the sparsity of the ground truth, and the low-dimensional geometry of the node embeddings. Under a formal theoretical framework, we prove that low-dimensional vectors cannot capture sparse ground truth using dot product similarities (the standard practice in the literature). Our results call into question existing results on link prediction and pose a significant scientific challenge for graph representation learning. The VCMPR plots identify specific scientific challenges for link prediction using low-dimensional node embeddings.
Multiple objectives dynamic VM placement for application service availability in cloud networks

Authors:
raw_author_name: Yanal Alahmad
raw_affiliation_string: Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
raw_author_name: Anjali Agarwal
raw_affiliation_string: Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

Abstract: Abstract Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.
A Noval Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Authors:
raw_author_name: Upendra Kumar
raw_affiliation_string: Institute of Engineering and Technology, Dr. A.P.J. Abdul Kalam Technical University, India

Abstract: This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.