Reviewerly/exHarmony huggingface dataset:
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy

Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on robustness and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from two key aspects: robustness and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.
Predicting cybersickness using individual and task characteristics

The experience of cybersickness in virtual reality (VR) drastically differs between users, likely due to variability in individual and task characteristics, leaving cybersickness as a substantial barrier to the widespread adoption of VR technology. While these characteristics have been connected to cybersickness, analyses do not commonly consider the simultaneous effects of multiple factors on cybersickness. As such, the current research aims to evaluate how multiple individual and task characteristics impact cybersickness, as well as how much variance in cybersickness these characteristics account for. In this study, 150 participants were exposed to the 3D Cybersickness Corn Maze that was designed with cybersickness-inducing stimuli and could choose to exit at any time. Participants completed one of three tasks that varied in mental workload during their exposure. Hierarchical multiple regression models were used to examine how individual characteristics (i.e., motion sickness history, previous VR use, gender, age, and personality) and task characteristics (i.e., workload, presence) impacted cybersickness. Analyses revealed that both individual characteristics (particularly motion sickness history) and task characteristics (particularly workload) were important for predicting cybersickness, accounting for between 43.6% and 47.7% of the variance in cybersickness experiences. This study's results suggest that models of cybersickness that do not include task and individual characteristics can be shown to be lacking by not considering these important factors. Designers of virtual environments may also benefit from evaluating the impact of their tasks and their users' variable characteristics during design.
Resource-efficient quantum simulation of lattice gauge theories in arbitrary dimensions: Solving for Gauss's law and fermion elimination

Quantum simulation of lattice gauge theories has been proposed and used as a method to overcome theoretical difficulties in dealing with the nonperturbative nature of such models. In this work we focus on two important bottlenecks that make developing such simulators hard: one is the difficulty of simulating fermionic degrees of freedom, and the other is the redundancy of the Hilbert space, which leads to a waste of experimental resources and the need to impose and monitor the local symmetry constraints of gauge theories. This has previously been tackled in one-dimensional settings, using nonlocal methods. Here we show an alternative procedure for dealing with these problems, which removes the matter and the Hilbert space redundancy, and is valid for higher space dimensions. We demonstrate it for a ${\mathbb{Z}}_{2}$ lattice gauge theory and implement it experimentally via the IBMQ cloud quantum computing platform.
An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer

Lung and colon cancers are among the leading causes of human mortality and morbidity. Early diagnostic work up of these diseases include radiography, ultrasound, magnetic resonance imaging, and computed tomography. Certain blood tumor markers for carcinoma lung and colon also aid in the diagnosis. Despite the lab and diagnostic imaging, histopathology remains the gold standard, which provides cell-level images of tissue under examination. To read these images, a histopathologist spends a large amount of time. Furthermore, using conventional diagnostic methods involve high-end equipment as well. This leads to limited number of patients getting final diagnosis and early treatment. In addition, there are chances of inter-observer errors. In recent years, deep learning has shown promising results in the medical field. This has helped in early diagnosis and treatment according to severity of disease. With the help of EffcientNetV2 models that have been cross-validated and tested fivefold, we propose an automated method for detecting lung (lung adenocarcinoma, lung benign, and lung squamous cell carcinoma) and colon (colon adenocarcinoma and colon benign) cancer subtypes from LC25000 histopathology images. A state-of-the-art deep learning architecture based on the principles of compound scaling and progressive learning, EffcientNetV2 large, medium, and small models. An accuracy of 99.97%, AUC of 99.99%, F1-score of 99.97%, balanced accuracy of 99.97%, and Matthew's correlation coefficient of 99.96% were obtained on the test set using the EffcientNetV2-L model for the 5-class classification of lung and colon cancers, outperforming the existing methods. Using gradCAM, we created visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. Therefore, it is possible to use the proposed pipeline in clinical settings for fully automated lung and colon cancer detection from histopathology images with explainability.
Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review

Machine-learning-based text classification is one of the leading research areas and has a wide range of applications, which include spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modelling. Widely used machine-learning-based research differs in terms of the datasets, training methods, performance evaluation, and comparison methods used. In this paper, we surveyed 224 papers published between 2003 and 2022 that employed machine learning for text classification. The Preferred Reporting Items for Systematic Reviews (PRISMA) statement is used as the guidelines for the systematic review process. The comprehensive differences in the literature are analyzed in terms of six aspects: datasets, machine learning models, best accuracy, performance evaluation metrics, training and testing splitting methods, and comparisons among machine learning models. Furthermore, we highlight the limitations and research gaps in the literature. Although the research works included in the survey perform well in terms of text classification, improvement is required in many areas. We believe that this survey paper will be useful for researchers in the field of text classification.
SiamMDM: An Adaptive Fusion Network With Dynamic Template for Real-Time Satellite Video Single Object Tracking

Tracking moving targets in satellite videos has attracted wide attention recently. However, the development of target tracking in satellite videos is much slower than that in general videos for the following key reasons. First, typical moving objects in satellite videos consist of few pixels and lose most of their appearance features, making it difficult for the tracker to distinguish the target from the background. Second, the appearance of satellite video objects often changes due to occlusion, illumination variation, or other factors. Classic Siamese tracking networks only use the first frame as the target template, leading to poor tracking results. Third, when the target is fully occluded, it is difficult for the tracker to recapture the target. To address the above problems, we propose a Siamese tracking network based on multiple(M) response map fusion and spatiotemporal constraints in this paper. By generating response maps at different layers of the tracking network and fusing them adaptively, small objects in the satellite videos can be tracked more accurately. Furthermore, a dynamic(D) template update strategy is proposed to cope with possible changes in the appearance of objects in satellite videos, preventing the high dependence on the initial frame. To recapture the target, a score-guided target motion(M) trajectory prediction model is proposed. We call the proposed Siamese tracking network SiamMDM for short. We conducted complete experiments on SatSOT and SV248S, two large satellite video target tracking datasets. The results show that our method achieves state-of-the-art tracking performance while running at over 110 FPS.
Generic Models for Group Actions

We define the Generic Group Action Model (GGAM), an adaptation of the Generic Group Model to the setting of group actions (such as CSIDH). Compared to a previously proposed definition by Montgomery and Zhandry (ASIACRYPT '22), our GGAM more accurately abstracts the security properties of group actions. We are able to prove information-theoretic lower bounds in the GGAM for the discrete logarithm assumption, as well as for non-standard assumptions recently introduced in the setting of threshold and identification schemes on group actions. Unfortunately, in a natural quantum version of the GGAM, the discrete logarithm assumption does not hold. To this end we also introduce the weaker Quantum Algebraic Group Action Model (QAGAM), where every set element (in superposition) output by an adversary is required to have an explicit representation relative to known elements. In contrast to the Quantum Generic Group Action Model, in the QAGAM we are able to analyze the hardness of group action assumptions: We prove (among other things) the equivalence between the discrete logarithm assumption and non-standard assumptions recently introduced in the setting of QROM security for Password-Authenticated Key Exchange, Non-Interactive Key Exchange, and Public-Key Encryption.
Toward AI-supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting

Background Most low- and middle-income countries lack access to organized breast cancer screening, and women with lumps may wait months for diagnostic assessment. Purpose To demonstrate that artificial intelligence (AI) software applied to breast US images obtained with low-cost portable equipment and by minimally trained observers could accurately classify palpable breast masses for triage in a low-resource setting. Materials and Methods This prospective multicenter study evaluated participants with at least one palpable mass who were enrolled in a hospital in Jalisco, Mexico, from December 2017 through May 2021. Orthogonal US images were obtained first with portable US with and without calipers of any findings at the site of lump and adjacent tissue. Then women were imaged with standard-of-care (SOC) US with Breast Imaging Reporting and Data System assessments by a radiologist. After exclusions, 758 masses in 300 women were analyzable by AI, with outputs of benign, probably benign, suspicious, and malignant. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. Results The mean patient age ± SD was 50.0 years ± 12.5 (range, 18-92 years) and mean largest lesion diameter was 13 mm ± 8 (range, 2-54 mm). Of 758 masses, 360 (47.5%) were palpable and 56 (7.4%) malignant, including six ductal carcinoma in situ. AI correctly identified 47 or 48 of 49 women (96%-98%) with cancer with either portable US or SOC US images, with AUCs of 0.91 and 0.95, respectively. One circumscribed invasive ductal carcinoma was classified as probably benign with SOC US, ipsilateral to a spiculated invasive ductal carcinoma. Of 251 women with benign masses, 168 (67%) imaged with SOC US were classified as benign or probably benign by AI, as were 96 of 251 masses (38%, P < .001) with portable US. AI performance with images obtained by a radiologist was significantly better than with images obtained by a minimally trained observer. Conclusion AI applied to portable US images of breast masses can accurately identify malignancies. Moderate specificity, which could triage 38%-67% of women with benign masses without tertiary referral, should further improve with AI and observer training with portable US. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.
Local quantum Fisher information and local quantum uncertainty for general X states

A two-spin-1/2 Heisenberg XYZ model with Dzyaloshinsky--Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions in the presence of an inhomogeneous external magnetic field is considered at thermal equilibrium. Its density matrix has the general X form for which we derive explicit formulas for the local quantum Fisher information (LQFI) and local quantum uncertainty (LQU) directly in terms of model parameters. This allows us to perform a comparative study for the discord-type quantum correlations LQFI and LQU and to reveal a number of new features in their behavior. In particular, the sudden transitions of quantum correlations with a smooth change in temperature are found. Moreover, it is shown that there may be a sequence of such transitions for certain choices of interaction constants.
Multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification

Feature selection (FS) is an important technique in data preprocessing that aims to reduce the number of features for training while maintaining a high accuracy for classification. In recent studies, FS has been extended to optimize multiple objectives simultaneously in classification. To better solve this problem, this paper proposes a new multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification, called FS-DOS. First, two complementary search operators with different characteristics are designed, where the first operator is a quick search (QS) operator aiming to accelerate the convergence speed, and the other operator is a modified binary differential evolution (BDE) operator that can prevent the algorithm from falling into a local optimum. In addition, a dynamic selection strategy based on the idea of resource allocation is also designed to dynamically select the most suitable operator for each solution according to its corresponding performance improvement on aggregated objective values. The simulation results on fifteen different real-world high-dimensional FS datasets show that FS-DOS can obtain a feature subset with higher quality than several state-of-the-art FS algorithms. Importantly, in terms of error rate, FS-DOS wins 55 out of 75 comparisons. In terms of dimensionality reduction, the number of features selected by FS-DOS is between one hundredth and one thousandth of the original dataset.
Machine learning in cardiology: Clinical application and basic research

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increasingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.
Design of a novel mixed interval type-2 fuzzy logic controller for 2-DOF robot manipulator with payload

This paper investigates a novel coupling-based mixed interval type-2 fuzzy logic controller (MIT2FLC) for trajectory tracking problems of highly nonlinear and complex robot manipulator plants. The major impediment for these types of plants is coupling between the robotic links at the time of operation. In addition, the performances of these plants are adversely affected by parameter uncertainties, random noise and external disturbances. Therefore, the MIT2FLC approach with an additional degree of freedom in the membership functions is proposed to efficiently deal with the problem of uncertainty and provide robust performance. The Grey wolf optimization (GWO) is implemented to calculate the optimal parameters of the proposed controller. For checking the performance, the MIT2FLC approach is compared with its type-1 fuzzy counterparts, namely mixed type-1 fuzzy logic controller (MT1FLC), type-1 fuzzy logic controller (T1FLC) and PID controllers. Robustness analysis of the proposed controller is investigated for external disturbances, varying system parameters, and random noise.
Construction cost management using blockchain and encryption

Data accountability (i.e., data integrity and traceability) concerns existing construction cost management platforms due to the risk of data manipulation in a centralized paradigm. Blockchain is a promising technology that provides immutable and traceable data storage with a decentralized architecture. However, the transparency of blockchain conflicts with the confidentiality nature of cost information. Therefore, this paper presents a novel framework based on blockchain and encryption to preserve both data accountability and confidentiality in construction cost management. The proposed new framework involves the development of a cost data model containing the required confidential cost information to facilitate partially transparent recordings on the blockchain. An access control model based on symmetric and asymmetric encryption as well as proxy re-encryption mechanisms is developed to prevent unauthorized access to sensitive cost data on the blockchain and transfer data access in dynamic construction projects (i.e., construction projects involving new members continuously). Subsequently, encryption-integrated smart contracts are developed for automatic and secure cost activities on the blockchain. The framework is validated with a desirable latency (at the millisecond level), throughput (at the hundred level), and storage cost (at the MB level) in three illustrative cost management scenarios. The results indicate that project members can have accountable and confidential cost data for dispute resolution and cost analysis, respectively, as well as efficient cost data access transfer.
VIGA: A variational graph autoencoder model to infer user interest representations for recommendation

Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based methods have significant advantages, there are still two inherent limitations: (i) users or items are often modeled as single embedding points in the vector space, and (ii) these methods usually focus more on the graph structure of user–item interactions, rather than the attribute-related information and useful attribute interactions. These limitations make it difficult to model complex user interests and learn high-quality representations, thus fail to obtain high recommendation accuracy. To overcome these limitations, we propose a variational inference-based graph autoencoder (VIGA) model to explore a multivariate distribution over latent representations for recommendation. In brief, to identify both of the complex users' interests and their interactive behaviors, VIGA first encodes users and items into latent variables, and subjects them to a multivariate Gaussian distribution via a graph autoencoder framework based on Bayesian variational inference. Then a feature fusion layer is established for the exchange and integration of both the topological structure and nodes attributes in the user–item interaction graph. Experiments on three real-world datasets show that the proposed VIGA method significantly outperforms state-of-the-art recommendation methods on recommendation accuracy.
Shot Boundary Detection Using Color Clustering and Attention Mechanism

Shot boundary detection (SBD) is widely used in scene segmentation, semantic analysis, and video retrieval. However, existing SBD algorithms have certain applications in video processing, but they have the following three problems. First, these algorithms cannot effectively handle shot boundaries caused by sudden lighting changes. Second, when there are dimly lighting frames in the video, these algorithms cannot perform boundary detection well. Third, when there is object or camera motion in the video, these algorithms also fail to work. To resolve these issues, we propose an SBD algorithm with color clustering changes in small regions (CCSR) to detect the shot transitions, which are abrupt changes and gradual transitions (dissolve and fade). The main idea behind the CCSR algorithm is to compute the distance of color features and to preserve the spatio-temporal information as much as possible. This model has relatively less dependence on the threshold parameters and sliding windows. Unlike other SBD algorithms, the clustering results of CCSR weaken factors such as object motion and illumination changes between adjacent frames in the video, which is helpful for reducing false detections. Furthermore, we utilize an attention mechanism in the gradual transitions to improve detection efficiency and accuracy. Finally, we evaluated the SBD algorithm, which was tested on a standard TRECVID dataset. The experimental results suggest that our algorithm yields significant improvements in precision and recall compared to the current techniques, with an average improvement of 10.35% and 8.85%, respectively. Moreover, compared with state-of-the-art algorithms, the results prove that the proposed method improves the F-score by more than 2.64% and the computation time efficiency by over 10%.