Reviewerly/exHarmony huggingface dataset:
Enabling Data Discovery with the Astrobiology Resource Metadata Standard

Authors:
raw_author_name: Shawn R. Wolfe
raw_affiliation_string: NASA Ames Research Center, Moffett Field, California, USA.
raw_author_name: Barbara Lafuente
raw_affiliation_string: The SETI Institute, Mountain View, California, USA.
raw_author_name: Richard M. Keller
raw_affiliation_string: NASA Ames Research Center, Moffett Field, California, USA.
raw_author_name: Angela M. Detweiler
raw_affiliation_string: Bay Area Environmental Research Institute, Moffett Field, California, USA.
raw_author_name: Thomas F. Bristow
raw_affiliation_string: NASA Ames Research Center, Moffett Field, California, USA.
raw_author_name: Linda L. Jahnke
raw_affiliation_string: NASA Ames Research Center, Moffett Field, California, USA.

Abstract: As scientific investigations increasingly adopt Open Science practices, reuse of data becomes paramount. However, despite decades of progress in internet search tools, finding relevant astrobiology datasets for an envisioned investigation remains challenging due to the precise and atypical needs of the astrobiology researcher. In response, we have developed the Astrobiology Resource Metadata Standard (ARMS), a metadata standard designed to uniformly describe astrobiology "resources," that is, virtually any product of astrobiology research. Those resources include datasets, physical samples, software (modeling codes and scripts), publications, websites, images, videos, presentations, and so on. ARMS has been formulated to describe astrobiology resources generated by individual scientists or smaller scientific teams, rather than larger mission teams who may be required to use more complex archival metadata schemes. In the following, we discuss the participatory development process, give an overview of the metadata standard, describe its current use in practice, and close with a discussion of additional possible uses and extensions.
Implementing and Testing a U-Space System: Lessons Learnt

Authors:
raw_author_name: Miguel-Ángel Fas-Millán
raw_affiliation_string: Institute of Flight Guidance, German Aerospace Center (DLR), 38108 Braunschweig, Lower Saxony, Germany

Abstract: Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles.
How to create and use a national cross-domain ontology and data infrastructure on the Semantic Web

Authors:
raw_author_name: Eero Hyvönen
raw_affiliation_string: Department of Computer Science, Aalto University, Finland; Helsinki Centre for Digital Humanities (HELDIG), University of Helsinki, Finland; Semantic Computing Research Group (SeCo), Finland

Abstract: This paper presents a model and lessons learned for creating a cross-domain national ontology and Linked (Open) Data (LOD) infrastructure. The idea is to extend the global, domain agnostic “layer cake model” underlying the Semantic Web with domain specific and local features needed in applications. To test and demonstrate the infrastructure, a series of LOD services and portals in use have been created in 2002–2023 that cover a wide range of application domains. They have attracted millions of users in total suggesting feasibility of the proposed model. This line of research and development is unique due to its systematic national level nature and long time span of over twenty years.
VSDHS-CIEA: Color image encryption algorithm based on novel variable-structure discrete hyperchaotic system and cross-plane confusion strategy

Authors:
raw_author_name: Hangming Zhang
raw_affiliation_string: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
raw_author_name: Hanping Hu
raw_affiliation_string: Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, Wuhan 430074, China; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information Science and Technology, Nantong University, Nantong 226019, China

Abstract: Recently, the color image encryption algorithm based on chaos theory has become the focus of current research. When encrypting color images, the common practice is to treat color images as different gray components and process them severally, which results in more redundancy and low efficiency. The security of chaotic cryptosystems also depends on the performance of the chaotic systems adopted. The structures of many current chaotic systems are fixed, making their behaviors highly predictable. Additionally, the range of chaotic region parameters is limited and discontinuous. To solve the above-mentioned problems, a new color image encryption algorithm (CIEA) using fractal and chaos theory is presented, which fully considers the inherent connection among the RGB components of color images. First, we propose a variable-structure discrete hyperchaotic system (VSDHS) to solve the dilemma encountered by existing chaotic systems. The excellent dynamic properties of VSDHS are verified by rigorous mathematical proof and simulation performance analyses. Then, VSDHS-CIEA is designed using fractal theory and VSDHS. The algorithm makes full use of the inherent connection among the different components of color images and performs cross-plane confusion. Simulations and performance analyses prove that VSDHS-CIEA has higher security and better performance than some representative image encryption algorithms.
Data-driven multinomial random forest: a new random forest variant with strong consistency

Authors:
raw_author_name: JunHao Chen
raw_affiliation_string: School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China
raw_author_name: XueLi Wang
raw_affiliation_string: School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China
raw_author_name: Fei Lei
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing, China

Abstract: Abstract In this paper, we modify the proof methods of some previously weakly consistent variants of random forest into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and experimental performance. In addition, we propose the Data-driven Multinomial Random Forest (DMRF) algorithm, which has the same complexity with BreimanRF (proposed by Breiman) while satisfying strong consistency with probability 1. It has better performance in classification and regression tasks than previous RF variants that only satisfy weak consistency, and in most cases even surpasses BreimanRF in classification tasks. To the best of our knowledge, DMRF is currently a low-complexity and high-performing variation of random forest that achieves strong consistency with probability 1.
What can machine vision do for lymphatic histopathology image analysis: a comprehensive review

Authors:
raw_author_name: Haoyuan Chen
raw_affiliation_string: Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
raw_author_name: Xiaoqi Li
raw_affiliation_string: Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
raw_author_name: Chen Li
raw_affiliation_string: Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
raw_author_name: Xintong Li
raw_affiliation_string: Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
raw_author_name: Jian Wu
raw_affiliation_string: Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
raw_author_name: Hongzan Sun
raw_affiliation_string: Shengjing Hospital of China Medical University, Shenyang, 110004, China
raw_author_name: Marcin Grzegorzek
raw_affiliation_string: Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
raw_author_name: Xiaoyan Li
raw_affiliation_string: Cancer Hospital of China Medical University, Shenyang, 110042, China

Abstract: Abstract Over the past 10 years, machine vision (MV) algorithms for image analysis have been developing rapidly with computing power. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide diagnostic references to doctors. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the application of image processing techniques based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some potential methods are proposed, and further prospects are made.
New improved model for joint segmentation and registration of multi-modality images: application to medical images

Authors:
raw_author_name: Noor Badshah
raw_affiliation_string: Department of Basic Sciences and Islamiat, University of Engineering and Technology, Peshawar, Pakistan
raw_author_name: Nasra Begum
raw_affiliation_string: Department of Basic Sciences and Islamiat, University of Engineering and Technology, Peshawar, Pakistan
raw_author_name: Lavdie Rada
raw_affiliation_string: Faculty of Engineering and Natural Science Bahcesehir University, Istanbul, Turkey
raw_author_name: Muniba Ashfaq
raw_affiliation_string: Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan
raw_author_name: Hadia Atta
raw_affiliation_string: Department of Mathematics, Islamia College Peshawar, Pakistan

Abstract: Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.
Personalized multi-attribute recommendation method based on partial order set

Authors:
raw_author_name: Yue Lizhu
raw_affiliation_string: Department of Business Administration, Liaoning Technical University, Huludao, China
raw_author_name: Wang Qian
raw_affiliation_string: Department of Business Administration, Liaoning Technical University, Huludao, China

Abstract: With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.
Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Authors:
raw_author_name: Sasha Petrenko
raw_affiliation_string: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
raw_author_name: Daniel B. Hier
raw_affiliation_string: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL 60607, USA
raw_author_name: Mary A. Bone
raw_affiliation_string: Department of Science and Industry Systems, University of Southeastern Norway, 3616 Kongsberg, Norway
raw_author_name: Tayo Obafemi-Ajayi
raw_affiliation_string: Engineering Program, Missouri State University, Springfield, MO 65897, USA
raw_author_name: Donald C. Wunsch0 Wunsch
raw_affiliation_string: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA

Abstract: Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.
Robustness test-time augmentation via learnable aggregation and anomaly detection

Authors:
raw_author_name: Haoyu Xiong
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kun’ming, Yunnan, PR China
raw_author_name: Leixin Yang
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kun’ming, Yunnan, PR China
raw_author_name: Gang Fang
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kun’ming, Yunnan, PR China
raw_author_name: Yu Xiang
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kun’ming, Yunnan, PR China
raw_author_name: Yaping Zhang
raw_affiliation_string: School of Information Science and Technology, Yunnan Normal University, Kun’ming, Yunnan, PR China

Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models.
Slender loris optimization algorithm for solving optimization problems

Authors:
raw_author_name: A. Vijaya Lakshmi
raw_affiliation_string: Department of Computer Science, School of Engineering and Technology, Pondicherry University, India
raw_author_name: K. Suresh Joseph
raw_affiliation_string: Department of Computer Science, School of Engineering and Technology, Pondicherry University, India

Abstract: Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms.
Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

Authors:
raw_author_name: Rimsha Gul
raw_affiliation_string: FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan
raw_author_name: Maryam Bashir
raw_affiliation_string: FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan

Abstract: As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.