Reviewerly/exHarmony huggingface dataset:
Overview and Classification of Swarm Intelligence-Based Nature-Inspired Computing Algorithms and Their Applications in Cancer Detection and Diagnosis

With the emergence of nature-inspired computing (NIC) techniques, researchers have understood and modeled solutions for realistic and complex problems. NIC, a branch of artificial intelligence worked on the transferring of knowledge from natural phenomenon to engineered systems, applicable in various fields. Although there are many techniques to be used in disease diagnosis, NIC algorithms are very efficient and have gained more attention to problems of modern research. In recent years, these algorithms gained popularity in the detection and diagnosis of cancer, a life-threatening disease that led to a high rate of mortality in individuals. Swarm Intelligence (SI), one of the most used NIC-based algorithms motivated by the collection of social insects’ behavior such as termites, bees, wasps, etc. helps in solving various bioinformatics-related problems. Herein, a chapter has presented various nature-inspired computing intelligence algorithms, with more focus on different types of SI-based nature-inspired algorithms that focus on principles, developments, and application scopes. Further, the chapter has also described applications of SI-based algorithms in detecting and diagnosing different stages and types of cancers. Finally, it has focused on strengths and limitations followed by future directions of these techniques in cancer diagnosis.
WSI validation studies in breast and gynecological pathology

In the last two decades, there has been marked development in virtual slide technology as well as its application in various subspecialties of pathology. In particular, there have been several studies examining the utility of whole slide imaging (WSI) in breast and gynecological pathology. The aim of this systematic review is to analyse published evidence regarding validation studies of WSI applied specifically to the female genital tract and breast pathology.A systematic search was carried out in Pubmed and Embase databases and studies dealing with the validation of a WSI system for breast and gynaecological pathology. The topics evaluated concerned expertise of engaged pathologists, varied specimens, scanners, washout period, experience viewing WSI, and diagnostic concordance of WSI to traditional light microscopic diagnoses.Of 1467 publications retrieved, 23 studies were included. Most of these studies concerned breast pathology. Validation guidelines recommended by the College of American Pathologists pertaining to a dataset of at least 60 cases, washout period, and recording intra-observer variability were followed by most studies. Major challenges encountered with WSI included difficulty identifying high-grade nuclear atypia and mitotic count for borderline ovarian tumors, interpretation of squamous intraepithelial lesions in liquid-based cervical cytology, and grading breast cancer.Published data demonstrates the value of utilizing WSI in breast and gynecological pathology. Key issues reported with WSI systems were problems related to focus, resolution and the contrast and brightness of immunohistochemical staining patterns. Grading breast cancer and mitotic count remained challenging in WSI as in conventional microscopy.
An AFM-based methodology for planar size and local 3D parameters of large workpieces

The existing size measurements of large workpieces have drawbacks of slow speed, low-precision, and separation of planar size and 3D parameters measurements. An automatic flight measurement (AFM)-based methodology was proposed to measure large workpieces' planar and local 3D parameters. Firstly, the automatic flight path planning of the measured workpiece is carried out through shape decomposition before acquire the images with a bi-telecentric lens. Then, we measure the planar size of the workpiece through zero-crossing point (ZCP) feature contour extraction, and parameter fitting. Finally, we achieve local 3D parameter measurement through the seed point search algorithm to extract laser lines, system calibration, and the point cloud data (PCD) processing. We also measured the curvature and roughness of the curved workpiece. The experimental results obtained from multiple large-size workpieces show that the plane error of the method in this paper is within 0.05 mm within the measurement range of 216 mm × 236 mm, and the relative error of the local height measurement is within 1 %. Therefore, the proposed method has strong adaptability and robustness and can meet the measurement requirements of the large-sized plane and curved workpieces.
Synthesizing axiomatizations using logic learning

Axioms and inference rules form the foundation of deductive systems and are crucial in the study of reasoning with logics over structures. Historically, axiomatizations have been discovered manually with much expertise and effort. In this paper we show the feasibility of using synthesis techniques to discover axiomatizations for different classes of structures, and in some contexts, automatically prove their completeness. For evaluation, we apply our technique to find axioms for (1) classes of frames in modal logic characterized in first-order logic and (2) the class of language models with regular operations.
An Ontological Knowledge Base of Poisoning Attacks on Deep Neural Networks

Deep neural networks (DNNs) have successfully delivered cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs has become an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor attacks and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN poisoning attack ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from the IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework by which to create a knowledge base. Our findings open new lines of research within the field of AI security.
Lottery Ticket Structured Node Pruning for Tabular Datasets

This paper experiments with well known pruning approaches, iterative and one-shot, and presents a new approach to lottery ticket pruning applied to tabular neural networks based on iterative pruning. Our contribution is a standard model for comparison in terms of speed and performance for tabular datasets that often do not get optimized through research. We show leading results in several tabular datasets that can compete with ensemble approaches. We tested on a wide range of datasets with a general improvement over the original (already leading) model in 6 of 8 datasets tested in terms of F1/RMSE. This includes a total reduction of over 85% of nodes with the additional ability to prune over 98% of nodes with minimal affect to accuracy. The new iterative approach we present will first optimize for lottery ticket quality by selecting an optimal architecture size and weights, then apply the iterative pruning strategy. The new iterative approach shows minimal degradation in accuracy compared to the original iterative approach, but it is capable of pruning models much smaller due to optimal weight pre-selection. Training and inference time improved over 50% and 10%, respectively, and up to 90% and 35%, respectively, for large datasets.
The Opportunity in Difficulty: A Dynamic Privacy Budget Allocation Mechanism for Privacy-Preserving Multi-dimensional Data Collection

Data collection under local differential privacy (LDP) has been gradually on the stage. Compared with the implementation of LDP on the single attribute data collection, that on multi-dimensional data faces great challenges as follows: (1) Communication cost . Multivariate data collection needs to retain the correlations between attributes, which means that more complex privatization mechanisms will result in more communication costs. (2) Noise scale . More attributes have to share the privacy budget limited by data utility and privacy-preserving level, which means that less privacy budget can be allocated to each of them, resulting in more noise added to the data. In this work, we innovatively reverse the complex multi-dimensional attributes, i.e., the major negative factor that leads to the above difficulties, to act as a beneficial factor to improve the efficiency of privacy budget allocation, so as to realize a multi-dimensional data collection under LDP with high comprehensive performance. Specifically, we first present a Multivariate k-ary Randomized Response (kRR) mechanism, called Multi-kRR . It applies the RR directly to each attribute to reduce the communication cost. To deal with the impact of a large amount of noise, we propose a Markov-based dynamic privacy budget allocation mechanism Markov-kRR, which determines the present privacy budget (flipping probability) of an attribute related to the state of the previous attributes. Then, we fix the threshold of flipping times in Markov-kRR and propose an improved mechanism called MarkFixed-kRR, which can obtain more optimized utility by choosing the suitable threshold. Finally, extensive experiments demonstrate the efficiency and effectiveness of our proposed methods.
Evaluating the Robustness of Click Models to Policy Distributional Shift

Many click models have been proposed to interpret logs of natural interactions with search engines and extract unbiased information for evaluation or learning. The experimental set-up used to evaluate them typically involves measuring two metrics, namely the test perplexity for click prediction and nDCG for relevance estimation. In both cases, the data used for training and testing is assumed to be collected using the same ranking policy. We question this assumption. Important downstream tasks based on click models involve evaluating a different policy than the training policy, i.e., click models need to operate under policy distributional shift . We show that click models are sensitive to it. This can severely hinder their performance on the targeted task: conventional evaluation metrics cannot guarantee that a click model will perform equally well under distributional shift. In order to more reliably predict click model performance under policy distributional shift, we propose a new evaluation protocol. It allows us to compare the relative robustness of six types of click models under various shifts, training configurations and downstream tasks. We obtain insights into the factors that worsen the sensitivity to policy distributional shift, and formulate guidelines to mitigate the risks of deploying policies based on click models.
Cryptocurrency legitimation through rhetorical strategies: an institutional entrepreneurship approach

How can cryptocurrency gain legitimacy in the eyes of users? Drawing upon the theories of institutional uncertainty and legitimacy, we propose a process model in which legitimacy for cryptocurrencies acquired at the market level via rhetorical strategies (i.e. evasive action) will reduce uncertainty in the formal institutional environment. This reduction in institutional uncertainty will beget additional legitimacy, and thus higher performance for individual crypto firms, on average. This study (1) advances institutional entrepreneurship research by investigating the legitimation process of cryptocurrency; (2) extends our understanding of the evolution of an innovation and its diffusion under institutional uncertainty; (3) contributes to the development of institutional theory by elucidating how cryptocurrencies can change existing institutions, and even create new ones, through evasive entrepreneurship; and (4) provides an overall theoretical rationale for how cryptocurrency can become more widely accepted.
A self‐supervised monocular depth estimation model with scale recovery and transfer learning for construction scene analysis

Abstract Estimating the depth of a construction scene from a single red‐green‐blue image is a crucial prerequisite for various applications, including work zone safety, localization, productivity analysis, activity recognition, and scene understanding. Recently, self‐supervised representation learning methods have made significant progress and demonstrated state‐of‐the‐art performance on monocular depth estimation. However, the two leading open challenges are the ambiguity of estimated depth up to an unknown scale and representation transferability for a downstream task, which severely hinders the practical deployment of self‐supervised methods. We propose a prior information‐based method, not depending on additional sensors, to recover the unknown scale in monocular vision and predict per‐pixel absolute depth. Moreover, a new learning paradigm for a self‐supervised monocular depth estimation model is constructed to transfer the pre‐trained self‐supervised model to other downstream construction scene analysis tasks. Meanwhile, we also propose a novel depth loss to enforce depth consistency when transferring to a new downstream task and two new metrics to measure transfer performance. Finally, we verify the effectiveness of scale recovery and representation transferability in isolation. The new learning paradigm with our new metrics and depth loss is expected to estimate the monocular depth of a construction scene without depth ground truth like light detection and ranging. Our models will serve as a good foundation for further construction scene analysis tasks.
Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.
Personality modeling from image aesthetic attribute-aware graph representation learning

Recently, inferring users’ personality traits on social media has attracted extensive attention. Existing studies have shown that users’ personality traits can be inferred from their preferences for images. However, since users’ preferences on images are often affected by multiple factors, some liked images cannot effectively reflect their personality traits. To handle this issue, this paper proposes a personality modeling approach based on image aesthetic attribute-aware graph representation learning, which can leverage aesthetic attributes to refine the liked images that are consistent with users’ personality traits. Specifically, we first utilize a Convolutional Neural Network (CNN) to train an aesthetic attribute prediction module. Then, attribute-aware graph representation learning is introduced to refine the images with similar aesthetic attributes from users’ liked images. Finally, the aesthetic attributes of all refined images are combined to predict personality traits through a Multi-Layer Perceptron (MLP). Experimental results and visual analysis have shown that the proposed method is superior to state-of-the-art personality modeling methods.
Geometry, calibration, and robust centering procedures for refractive dome-port based imaging systems

Underwater imaging systems equipped with hemispherical ports can preserve the field of view and avert refraction when the camera and sphere centers coincide. Setting the camera at that center is a challenge, and deviation from this position introduces non-linear geometric distortions. These distortions have been treated by either computing the set of calibration parameters or by a more involved process of mechanically adjusting the camera center. Such strategies were approached empirically, and only recently have analytical system models begun to emerge. In this paper, we examine the geometry of dome-port-equipped imaging systems to develop a representation in which the non-linear ray trajectory is directly incorporated into the collinearity equations. The derivations apply for thin- and thick-layer interfaces and to the in-air setup. Our forms confirm the observations that when the camera and sphere center do not coincide, the system becomes axial. Yet, we improve on that observation by analytically demonstrating that with a slight modification to the camera parameters, the system model becomes central rather than the more involved form. Therefore, we not only link the axial realization with the commonly applied central form but also explain how to support this adaptation. Our representation and realizations translate to direct and optimal pose estimation and calibration forms that can be solved using standard protocols. To minimize distortions, we also propose a single-phase camera-centering solution. We demonstrate how our solution exhibits robustness to the initial camera placement and how the required parameters can be estimated linearly. Numerical and experimental evaluations support our calibration solution and demonstrate how our centering procedure captures the necessary displacement to a sub-tenth of a millimeter level.
Efficient Homomorphic Evaluation of Arbitrary Bivariate Integer Functions

We propose how to homomorphically evaluate arbitrary bivariate integer functions such as division. A prior work proposed by Okada et al.\ (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV, and is implemented with the input domain size \mathbbZ _257 . However, the scheme of Okada et al.\ requires the quadratic number of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., \mathbbZ _2^15 in a reasonably efficient way. In addition, we show how to extend the input domain size to \mathbbZ _2^16 with a relatively moderate overhead. We implement the prior work of Okada et al., our improved scheme of Okada et al., and our new scheme in PALISADE with the input domain size \mathbbZ _2^15 , and confirm that the estimated run-times of the prior work and our improved scheme of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.
FAS-UNet: A Novel FAS-Driven UNet to Learn Variational Image Segmentation

Solving variational image segmentation problems with hidden physics is often expensive and requires different algorithms and manually tunes model parameter. The deep learning methods based on the U-Net structure have obtained outstanding performances in many different medical image segmentation tasks, but designing such networks requires a lot of parameters and training data, not always available for practical problems. In this paper, inspired by traditional multi-phase convexity Mumford-Shah variational model and full approximation scheme (FAS) solving the nonlinear systems, we propose a novel variational-model-informed network (denoted as FAS-Unet) that exploits the model and algorithm priors to extract the multi-scale features. The proposed model-informed network integrates image data and mathematical models, and implements them through learning a few convolution kernels. Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features. Secondly, we further design a convolution block to fuse the extracted features from the previous stage, resulting in the final segmentation possibility. Experimental results on three different medical image segmentation tasks show that the proposed FAS-Unet is very competitive with other state-of-the-art methods in qualitative, quantitative and model complexity evaluations. Moreover, it may also be possible to train specialized network architectures that automatically satisfy some of the mathematical and physical laws in other image problems for better accuracy, faster training and improved generalization.The code is available at \url{https://github.com/zhuhui100/FASUNet}.
The Digitisation of a Firm Process and Its Impact on Corporate Governance

Digitisation can be considered as the continuing convergence of both real and virtual worlds by transforming data from analogue to electronic format. This has been the primary driver of innovation and change in various sectors of the economy. The exponentially growing amount of data has made it essential for firms to transform them into digital format for ensuring better flow and sharing of information across the organisation. Digitisation has been referred to as the process of converting various diverse forms of information, such as text, images, sound or voice, into an electronic format that leads to improved operations and reduced costs for firms. The research paper has discussed this digitisation process of a firm and analysed its consequent impact on the corporate governance. In this regard, various articles have been explored for the purposes of understanding how digitisation can improve or threaten corporate governance of firms. Digitisation has created some big disruptive storms some of which have resulted in a reimagined manner of doing business, affecting demand and supply principles in any company. As a result, in order to gain market confidence, businesses must establish credibility and integrity.