Reviewerly/exHarmony huggingface dataset:
CATrack: Convolution and Attention Feature Fusion for Visual Object Tracking

In visual object tracking, information embedding and feature fusion between the target template and the search have been hot research spots in the past decades. Linear convolution is a common way to perform correlation operations. The convolution operation is good at processing local information, while ignoring global information. By contrast, the attention mechanism has the advantages of innate global information modeling. To model the local information of the target template and the global information of the search area, we propose a convolution and attention feature fusion module (CAM). Thus, the efficient information embedding and feature fusion can be achieved in parallel. Moreover, a bi-directional information flow bridge is constructed to realize information embedding and feature fusion between the target template and the search area. Specifically, it includes a convolution-to-attention bridge module (CABM) and an attention-to-convolutional bridge module(ACBM). Finally, we present a novel tracker based on convolution and attention (CATrack), which combines the advantages of convolution and attention operators, and has enhanced ability for accurate target positioning. Comprehensive experiments have been conducted on four tracking benchmarks: LaSOT, TrackingNet, GOT-10k and UAV123. Experiments show that the performance of our CATrack is more competitive than the state-of-the-art trackers.
Enhancing Adversarial Robustness via Stochastic Robust Framework

Despite deep neural networks (DNNs) have attained remarkable success in image classification, the vulnerability of DNNs to adversarial attacks poses significant security risks to their reliability. The design of robust modules in adversarial defense often focuses excessively on individual layers of the model architecture, overlooking the important inter-module facilitation. To this issue, this paper proposes a novel stochastic robust framework that employs the Random Local winner take all module and the random Normalization Aggregation module (RLNA). RLNA designs a random competitive selection mechanism to filter out outputs with high confidence in the classification. This filtering process improves the model’s robustness against adversarial attacks. Moreover, we employ a novel balance strategy in adversarial training (AT) to optimize the trade-off between robust accuracy and natural accuracy. Empirical evidence demonstrates that RLNA achieves state-of-the-art robustness accuracy against powerful adversarial attacks on two benchmarking datasets, CIFAR-10 and CIFAR-100. Compared to the method that focuses on individual network layers, RLNA achieves a remarkable 24.78% improvement in robust accuracy on CIFAR-10.
Explainable AI and its instantiation in Computational Pathology for a better understanding of Alzheimer’s disease

Abstract Background EXplainable Artificial Intelligence (XAI) has an essential role to accelerate the AI adoption by empowering biomedical experts and catalyzing modern research through the emergence of a new generation of virtual actors, enabling traceable knowledge‐based quantification, as a guided qualification support. XAI instantiation is shown through our study, hypothesizing that rapid Alzheimer’s Disease (rAD) brains display subtle histological changes that would be undercovered by high‐throughput automated microscopic analysis. We study the topography and morphology of tau lesions’ aggregates to better understand the morphological substratum of Alzheimer’s Disease (AD) heterogeneity. Method To address this question at a large scale, we designed, tested and implemented a software for automatic segmentation, annotation and quantitation of brain lesions in histopathological whole slide images (WSI). A dataset of 15 whole slide images of postmortem human brain tissue is used in this study. These are fully annotated by neuropathologists yielding a set of more than 30,000 annotated plaques and tangle objects. A deep learning pipeline consisting of an attention‐UNet model with explainability features is trained for segmentation of tau aggregates in WSIs. The attention maps corresponding to segmentation predictions from the model are used by pathologists as a comparable reference to assist their decision making process. Result The DL‐based WSI analysis framework allows detecting a majority of the tau aggregates and refining tau aggregate boundaries with higher accuracy compared to manual annotations. Besides, with such a DL model based tau identification, the pathologist needs to annotate only a few samples for the DL model training. Overall, DL assisted analysis of tau aggregates could ease the effort of pathologists and facilitate analysis of hundreds of WSIs which are vital in AD research areas such as identifying different AD forms and AD patient stratification. Conclusion XAI is not only providing traceability by opening the way to an effective adoption, but also allows optimizing the ML/DL design, by keeping high performances. The green footprint is, therefore, considered, as well as the GDPR (European General Data Protection Regulation), towards Responsible Artificial Intelligence.
Bitcoin price prediction based on sentiment analysis and LSTM

As cryptocurrencies become widely accepted due to technical improvements, reliable approaches to capture their future price movements of them become critical. This study mainly combines weighted sentiment analysis results from social media-related comments and financial news headlines with a stacked LSTM model to predict second-day Bitcoin price evolution. This study also compared our results and the results produced by MLP, RF, and SVM after feeding the sentiment analysis results.
Boundary Spanning Methodological Approaches for Collaborative Moose Governance in Eeyou Istchee

Natural resource governance challenges are often highly complex, particularly in Indigenous contexts. These challenges involve numerous landscape-level interactions, spanning jurisdictional, disciplinary, social, and ecological boundaries. In Eeyou Istchee, the James Bay Cree Territory of northern Quebec, Canada, traditional livelihoods depend on wild food species like moose. However, these species are increasingly being impacted by forestry and other resource development projects. The complex relationships between moose, resource development, and Cree livelihoods can limit shared understandings and the ability of diverse actors to respond to these pressures. Contributing to this complexity are the different knowledge systems held by governance actors who, while not always aligned, have broadly shared species conservation and sustainable development goals. This paper presents fuzzy cognitive mapping (FCM) as a methodological approach used to help elicit and interpret the knowledge of land-users concerning the impacts of forest management on moose habitat in Eeyou Istchee. We explore the difficulties of weaving this knowledge together with the results of moose GPS collar analysis and the knowledges of scientists and government agencies. The ways in which participatory, relational mapping approaches can be applied in practice, and what they offer to pluralistic natural resource governance research more widely, are then addressed.
AI‐based histological analysis in Alzheimer’s disease : the point of view of the biologist

Abstract Background Tauopathies in Alzheimer’s disease are characterized by occurrence of neurofibrillary tangles (NFTs) and neuritic plaques (NPs) that are closely associated to cognitive decline and are key elements for neuropathological diagnosis and staging. The automated detection and segmentation of NFTs and NPs has not been fully implemented but thanks to deep learning (DL) based approaches could be assessed today. Method Whole‐slide images of phosphotau‐immunostained brain sections (frontal cortex) from AD patients presenting discrete to florid lesion loads were generated. Using a commercial software (©Visiopharm) we developed an iterative “human‐in‐the‐loop” pipeline to improve quality of annotations of NFTs and NPs and create an exhaustive and accurate ground truth database that was used to train DL algorithms for the different objects morphologies to be detected and segmented in brain parenchyma. Result A U‐Net based CNN was trained, and the obtained algorithm was precise in NP detection (F1 score: 0.86) and segmentation (Dice score: 0.77). A second algorithm was precise in NFT detection (F1 score: 0.85) and segmentation (Dice score: 0.82). Conclusion Two main objectives were reached : 1) optimization of human annotations thanks to iterative AI‐expert feedbacks, 2) implementation of methods to automatically accelerate, refine and robustify histological analysis of discrete objects. Such approaches may help to uncover and quantify subtle morphological differences between AD variants that otherwise could not be neuropathologically segregated. The biological relevance of dichotomizing NFTs and NPs will be discussed in terms of clinico‐pathological correlations and stratification needs. Also, the scope and limits of user‐friendly and commercially available DL‐learning image analysis softwares, directly accessible to researchers and pathologists with limited IT skills, will be reviewed.
Using depth sensors for a fall risk prediction system for socially vulnerable older adults with cognitive impairment

Abstract Falls and fall‐related injuries are significant public health issues for adults 65 years of age and older. Over a third of older adults (OA) fall each year and 10‐20% of falls result in serious injuries such as fractures and head trauma. Cognitive impairment is a leading risk factor for falls in OA. Over 60% of OA with MCI fall annually – two to three times the rate of those without cognitive impairment. OA living in low‐resource neighborhoods with poor housing conditions have twice the risk of falling. We have developed a technology‐based intervention called Sense4Safety that facilitates fall risk management and fall risk reduction for older adults (OA) with cognitive impairment living in low‐resource communities. Specifically, the intervention is designed to to 1) identify escalating risk for falls real‐time through in‐home passive sensor monitoring using depth sensors; 2) employ machine learning to inform individualized alerts for fall risk; and 3) link OA with a nurse tele‐coach who guides them in implementing evidence‐based individualized plans to reduce fall‐risk. The nurse coach is accessible via video‐conferencing or phone to discuss fall prevention and risk reduction strategies with the OA and their trusted other depending on the patterns identified in sensor‐detected changes in functional mobility and gait. The measurable primary outcomes include number of fall and near‐fall events, number of fall‐related hospitalizations, functional mobility, self‐reports of fear of falling and perceived independence. We have conducted an initial feasibility study of the intervention with 10 older adults in low income housing settings for a period of 3 months. The Sense4Safety intervention has the potential to become a feasible and cost‐effective approach to increase safety and quality of life for community dwelling low income OA with cognitive impairment and prevent or reduce falls. Currently, in most cases fall risk is addressed only after an adverse event has occurred or cognitive decline has progressed significantly. This intervention changes that paradigm to maximize safety and promote aging in place.
A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems

&lt;abstract&gt;&lt;p&gt;Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.&lt;/p&gt;&lt;/abstract&gt;
A Survey on Progressive Visualization

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.
New classes of permutation quadrinomials over 𝔽&lt;sub&gt;&lt;i&gt;q&lt;/i&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/sub&gt;

Permutation polynomials have been studied for a long time and have important applications in cryptography, coding theory and combinatorial designs. In this paper, by means of the multivariate method and the resultant, we propose four new classes of permutation quadrinomials over 𝔽q3, where q is a prime power. We also show that they are not quasi-multiplicative equivalent to known ones. Moreover, we compare their differential uniformity with that of some known classes of permutation trinomials for some small q.
Improved Generalization of White Blood Cell Classification by Learnable Illumination Intensity Invariant Layer

Categorizing white blood cells (WBCs) in blood specimens is crucial for early disease detection in hematology and pathology. Numerous research endeavors have been undertaken to investigate the precise classification of WBCs in microscopic blood smear images, utilizing machine learning and deep learning methodologies. However, the notable accuracy achieved by most of these techniques relies on the assumption that the conditions under which the test microscope images are acquired closely resemble those of the training images, including the use of identical microscopes and consistent lighting conditions. The primary aim of this research is to design a novel network layer that can be used as a component in combination with other backone network, and improve the generalization ability of this backbone for classification tasks on WBC data. In this investigation, we predominantly employ the color invariance technique to fashion a trainable convolutional layer, which serves as the tool for extracting edge features from the original image. This layer is amenable to integration with various backbone architectures. Ultimately, the model was trained utilizing the Raabin-WBC dataset, and its efficacy was evaluated by appraising its performance on WBC images acquired through a variety of microscope instruments.
Review of High-Dimensional and Complex Data Visualization

In the age of big data, high-dimensional and complex data create significant challenges for data visualization. This research analyzes their impact on visualization techniques. Increasing data dimensions make traditional methods struggle to depict complex structures and patterns. This study comprehensively explores challenges arising from high dimensionality and data complexity in visualization. By examining various techniques and their suitability for such data, we aim to reveal the pros and cons of each approach in conveying essential information. Additionally, this research investigates using dimension reduction techniques to tackle the complexities of the data. The effectiveness of these techniques in preserving crucial information while enhancing visualization quality is evaluated. Some of the models examined include Multidimensional Scaling (MDS), which projects data into a lower-dimensional space to maintain distance relationships but does not consider class labels. MDS operates with a distance matrix but is sensitive to data that do not conform to distance metrics and can yield suboptimal projections in high-dimensional space. Principal Component Analysis (PCA) focuses on dimension reduction by maximizing data variance but does not consider class labels. PCA requires high-dimensional data and cannot handle non-linear structures. Linear Discriminant Analysis (LDA) is used for classification by maximizing inter-class distances, minimizing intra-class distances, and requiring class labels. However, LDA works well only under the assumptions of normal distribution and fulfilled class variance differences. Conversely, t-SNE and UMAP reveal data cluster structures. t-SNE is computationally complex and sensitive to parameters, while UMAP performs faster. t-SNE interprets data with topological and network context, suited for data with strong topological structures but requiring deep domain knowledge. Each method has its uses and limitations, depending on analysis goals and data nature. This research offers valuable insights into how complex, high-dimensional data impacts visualization. Understanding this aids practitioners and researchers in selecting appropriate visualization techniques, enhancing interpretation and insights. Ultimately, it contributes to effective visualization strategies for modern, high-dimensional, and complex datasets
Intelligent System Concept of Integrated Education History in Single Identity Number Using Grid-Based Model (GBM)

The growing number of educated individuals in Indonesia is the reason behind the significance of research on intelligent system development, which can yield a new model to assess the accuracy of data on educated individuals in Indonesia. This will allow all education data to be integrated with the Single Identity Number, facilitating the retrieval of information on educated individuals such as elementary graduates, junior high school graduates, high school graduates, young experts, associate experts, bachelors, masters, and doctors. Naturally, the Grid-Based Model (GBM) is the ideal role model to use in order to maximize accuracy in the construction of this intelligent system when measuring data accuracy for graduates of formal education. In order to make it simpler to determine research outcomes, while developing an intelligent system in research employing the Paradigm Model of Verification, Evaluation, Restructuring, Data Integration (VERDI Model). It is anticipated that this research will result in an intelligent system that achieves ideal data accuracy within the range of 65% to 100%. The achievement of highly optimal accuracy can have a beneficial effect on the data integration of Indonesian graduates in education. Unfied Modeling Language (UML), an object-oriented design tool, will be used for the design modeling of this intelligent system's development, whereas flowcharts are used for the design of the study's algorithm model.
Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of ‘nodes’ on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods.
Reinforcement Learning Model for Finding Optimal Path

Reinforcement learning (RL) has become a powerful method for addressing complex optimization challenges, such as determining optimal (shortest) paths across various fields. This study aims to design and examine RL algorithms to identify the shortest path in expansive and dynamic environments. Our research advances RL algorithms for optimal pathfinding, showcasing Q-Learning's scalability, path length, and cumulative rewards superiority, enriching optimization methodologies, and guiding future RL explorations. We utilized a blend of Q-learning and Double Q-learning methods to improve our model's performance in conjunction with the Reward Shaping technique and exploration-exploitation tactics like epsilon-greedy with decaying epsilon. We evaluated our approach on a 27x27 matrix representing the environment. In both 17x17 and 27x27 environments, Q-Learning consistently showed optimal paths with shorter distances, while having a slightly slower performance than Dijkstra's algorithm, with completion times of (0.00093 seconds versus 0.000023 seconds and 0.0097 seconds versus 0.000034 seconds); nevertheless, our assessment encompassed Q-Learning, Dijkstra, and Random-selection algorithms. Q-Learning consistently showcased excellence, while random selection yielded suboptimal routes due to its inherent randomness. Cumulative rewards highlighted Q-Learning's superiority (ranging from 2,197,570.15 to 205,823.20), reflecting its adept learning capacity. Dijkstra prioritized minimal path length (cumulative rewards ranging from 6 to 1), whereas Random-selection exhibited considerable variation (ranging from 132,449 to 11,901).