Reviewerly/exHarmony huggingface dataset:
Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

Authors:
raw_author_name: Alexander S. Behr
raw_affiliation_string: Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany
raw_author_name: Hendrik Borgelt
raw_affiliation_string: Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany
raw_author_name: Norbert Kockmann
raw_affiliation_string: Laboratory of Equipment Design, Faculty of Biochemical and Chemical Engineering, TU-Dortmund University, Emil-Figge-Strasse 68, 44139, Dortmund, NRW, Germany

Abstract: Abstract As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .
Exploring Automatic Readability Assessment for Science Documents within a Multilingual Educational Context

Authors:
raw_author_name: Itziar Aldabe
raw_affiliation_string: HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
raw_author_name: Nora Aranberri
raw_affiliation_string: HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain
raw_author_name: Ana Arruarte
raw_affiliation_string: Department of Computer Languages and Systems, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain

Abstract: Abstract Current student-centred, multilingual, active teaching methodologies require that teachers have continuous access to texts that are adequate in terms of topic and language competence. However, the task of finding appropriate materials is arduous and time consuming for teachers. To build on automatic readability assessment research that could help to assist teachers, we explore the performance of natural language processing approaches when dealing with educational science documents for secondary education. Currently, readability assessment is mainly explored in English. In this work we extend our research to Basque and Spanish together with English by compiling context-specific corpora and then testing the performance of feature-based machine-learning and deep learning models. Based on the evaluation of our results, we find that our models do not generalize well although deep learning models obtain better accuracy and F1 in all configurations. Further research in this area is still necessary to determine reliable characteristics of training corpora and model parameters to ensure generalizability.
The art of personalization of education: Artificial Intelligence on the stages of special education

Authors:
raw_author_name: Cláudio Gomes Da Silva

Abstract: This study investigated the use of artificial intelligence (AI) in special education, with the aim of personalizing teaching and promoting the inclusion of students with disabilities and special needs. The central problem addressed was the need to adapt teaching individually, taking into account the unique characteristics of each student. To achieve this objective, a literature review was conducted, analyzing studies, research and experiences related to AI in special education. The methodology included a literature review of reliable academic sources, such as scientific articles and research reports. The results of the analysis indicated that AI plays a key role in personalizing teaching, adapting content and resources according to the specific needs of students. Furthermore, AI contributes to promoting inclusion by creating accessible and equitable learning environments. However, ethical issues such as student data privacy and transparency in AI algorithms are identified that require careful consideration. Furthermore, practical limitations, such as the availability of technological resources and teacher training, represent challenges to be overcome in the successful implementation of AI in special education. Final considerations highlight the importance of AI as a promising tool for personalizing teaching in special education, as long as its application is conducted ethically and aware of practical limitations.
BIKAGCN: Knowledge-Aware Recommendations Under Bi-layer Graph Convolutional Networks

Authors:
raw_author_name: Guoshu Li
raw_affiliation_string: School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu Avenue, Chengdu, 610500, Sichuan, China
raw_author_name: Li Yang
raw_affiliation_string: School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu Avenue, Chengdu, 610500, Sichuan, China
raw_author_name: Sichang Bai
raw_affiliation_string: School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu Avenue, Chengdu, 610500, Sichuan, China
raw_author_name: Xinyu Song
raw_affiliation_string: School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu Avenue, Chengdu, 610500, Sichuan, China
raw_author_name: Yijun Ren
raw_affiliation_string: School of Computer Science and Software Engineering, Southwest Petroleum University, Xindu Avenue, Chengdu, 610500, Sichuan, China

Abstract: Abstract Recommender systems are a popular solution for the problem of information overload, offering personalized recommendations to users. Recent years, research has aimed to enhance recommender systems by employing knowledge graphs in conjunction with Graph convolutional network (GCN) to extract user and item features. Although GCN possess a great potential, they are still far from reaching their full capability in recommender systems. This paper introduces a novel approach—knowledge-aware recommendations under bi-layer graph convolutional networks (BIKAGCN) that combines attention and bi-layer GCNs to improve performance. The first layer of the BIKAGCN model trains embedding representations of users and items based on user-item interaction graphs. The second layer introduces a novel knowledge-aware layer of attention and graph convolutional network (KAGCN) layer that leverages both the first layer’s user-item embeddings and item knowledge graph embeddings. Experimental results on three publicly available datasets (MovieLens-20M, Last-FM, and Book-Crossing) demonstrate that BIKAGCN leads to significant performance improvements in recall@20 metric (14.41%, 8.86%, and 20.90%, respectively) compared to currently available state-of-the-art approaches. Moreover, the model maintains satisfactory performance in cold-start cases.The research provides some guidance for the direction of subsequent research on recommender systems.
Utilization of a blockchainized reputation management service for performance enhancement of Smart Grid 2.0 applications

Authors:
raw_author_name: Charithri Yapa
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka
raw_author_name: Chamitha de Alwis
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Sri Jayewardenepura, Gangodawila, Nugegoda, Sri Lanka
raw_author_name: Madhusanka Liyanage
raw_affiliation_string: Centre for Wireless Communications, University of Oulu, Finland; School of Computer Science, University College Dublin, Ireland
raw_author_name: Janaka Ekanayake
raw_affiliation_string: Department of Electrical and Electronic Engineering, University of Peradeniya, Sri Lanka; School of Engineering, Cardiff University, United Kingdom

Abstract: Blockchain has become the technology enabler in delivering modern Smart Grid 2.0 functionalities. Many services including Peer-to-Peer energy trading, distribution network management, financial settlements, and energy data management are catered through blockchain-enabled platforms. However, areas such as service quality-based pricing strategies, supply–demand balancing in distribution system to attain enhanced reliability and consumption-oriented rewarding mechanisms need improving in order to achieve the full benefits of the envisaged grid architecture. In response, this study proposes a novel Blockchain-as-a-Service for Energy Trading (BaaSET) platform, which offers reputation-based services, executed through smart contracts for smart grid applications. Reputation-based grid operations are automatically executed through smart contracts deployed onto a blockchain. The reputation is estimated using power quality and reliability indices, obtained through grid measurements. Further, tests have been conducted to evaluate the associated latency and the implementation cost of the proposed blockchainized service architecture. Test results signify the performance to be comparatively better considering the state-of-the-art. The results further suggest alternatives to improve the scalability of the architecture, to cater the increasing number of stakeholders in the SG 2.0 environment.
SPEC 2.0 Smart Pill Expert System

Authors:
raw_author_name: Vandana Rao Emaneni
raw_affiliation_string: JSS Academy of Technical Education, Bangalore, India
raw_author_name: P. Dayananda
raw_affiliation_string: JSS Academy of Technical Education, Bangalore, India
raw_author_name: Amrutha G. Upadhya
raw_affiliation_string: JSS Academy of Technical Education, Bangalore, India
raw_author_name: B.G. Nayana
raw_affiliation_string: JSS Academy of Technical Education, Bangalore, India
raw_author_name: Priyam Poddar
raw_affiliation_string: JSS Academy of Technical Education, Bangalore, India

Abstract: The article throws light on the implementation of an enhanced idea of the smart pill expert system. The proposed system is used to automate the capability of dispensing the right dosage of medicines at a given day-time interval. The spec 2.0 can be used at one's home, workplace, at hospitals by a user of any age group, and then possibly expanding the functionalities to a wider audience. It mainly focuses on providing access control and monitoring management functionality through a mobile app on a daily basis with no monthly subscriptions required. There will be alerts and SMSs sent across to the user based on the pill dispensing event's result. The spec 2.0 mainly focuses on providing an “over-dosage” functionality so that the user won't have to consume the medication if he hasn't taken them at the allocated time interval, to prevent over consumption. The system has been tested and the results have been formulated on a daily basis.
Point-level feature learning based on vision transformer for occluded person re-identification

Authors:
raw_author_name: Hua Gao
raw_affiliation_string: College of Computer Science, Zhejiang University of Technology, Hangzhou City 310014, China
raw_author_name: Chenchen Hu
raw_affiliation_string: College of Computer Science, Zhejiang University of Technology, Hangzhou City 310014, China
raw_author_name: Guang Han
raw_affiliation_string: School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing City 210023, China
raw_author_name: Jiafa Mao
raw_affiliation_string: College of Computer Science, Zhejiang University of Technology, Hangzhou City 310014, China
raw_author_name: Wei Huang
raw_affiliation_string: College of Computer Science, Zhejiang University of Technology, Hangzhou City 310014, China
raw_author_name: Qiu Guan
raw_affiliation_string: College of Computer Science, Zhejiang University of Technology, Hangzhou City 310014, China

Abstract: Person re-identification is challenging due to the presence of variations in pose and occlusion, which significantly impact the matching of visual features across different camera views and pose considerable difficulty for accurate person re-identification. This paper proposes a novel method for occluded person re-identification by introducing point-level feature learning based on vision transformers. Our approach utilizes a pose estimator to detect the keypoints of the human body and employs these points to locate intermediate features. These intermediate features of keypoints are input to a pose-based transformer branch to learn point-level features. Then, we design a part-based transformer branch to learn part-level features that capture visual features of different image parts, further enhancing the discriminative power of the learned features. Additionally, we employ a global branch to learn the global-level feature by treating the person's image as a single entity. Finally, we integrate point-level, part-level, and global-level features to represent a person's features. The experimental results on occluded and partial person re-identification datasets demonstrate the effectiveness of our proposed approach in improving re-identification. Our approach shows potential for improving person re-identification in scenarios with occlusion and pose variations.
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces

Authors:
raw_author_name: Michael Biehl
raw_affiliation_string: Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Nijenborgh 9, Groningen, 9747AG, Netherlands; SMQB, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B152TT, United Kingdom

Abstract: We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. The suggested Iterated Relevance Matrix Analysis (IRMA) identifies a linear subspace representing the classification specific information of the considered data sets using Generalized Matrix Learning Vector Quantization (GMLVQ). By iteratively determining a new discriminative subspace while projecting out all previously identified ones, a combined subspace carrying all class-specific information can be found. This facilitates a detailed analysis of feature relevances, and enables improved low-dimensional representations and visualizations of labeled data sets. Additionally, the IRMA-based class-discriminative subspace can be used for dimensionality reduction and the training of robust classifiers with potentially improved performance.
Contrastive completing learning for practical text-image person ReID: Robuster and cheaper

Authors:
raw_author_name: Guodong Du
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Tiantian Gong
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Liyan Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: Text-image person re-identification (TIReID) seeks to leverage textual descriptions for the retrieval of target pedestrians. Due to its versatility, TIReID has gained increasing attention. However, manual annotation of textual descriptions and identity labels can be time-consuming and costly, limiting its scalability in practical settings. Privacy concerns and poor data storage can lead to data loss or ineffectiveness, further exacerbating challenges in real-world scenarios. To address these limitations, we propose for the first time incomplete Text-image person re-identification (iTIReID), which comprises a small amount of complete pairwise data and a large amount of incomplete data, where all identity labels are unavailable. We introduce a novel Contrastive Completing Learning (CCL) framework for iTIReID, consisting of two stages: Pure Contrastive Learning (PCL) and Feature Completion Contrastive Learning (FCCL). In PCL, only complete pairwise data is utilized for training, which serves as a preliminary improvement of the model’s capacity and prepares for the upcoming feature completion stage. In FCCL, available features are used to complete missing modality features and facilitate effective training with incomplete data. During this process, Cross-modal Semantic Measure (CSM) is proposed to leverage intra-modality similarity to measure cross-modal similarity and filter out features with the highest semantic similarity, thereby circumventing modality discrepancy. Semantic-Weighted Generation (SWG) is proposed to generate approximate features based on the semantic similarity weight of the similar features. To fully leverage pairwise data for label-free training, we introduce the contrastive CMPM (CCMPM) loss for contrastive learning to achieve weakly supervised training. Experimental results verify the effectiveness of our proposed methods and demonstrate competitive performance compared to fully supervised methods using complete data.
On Size-Independent Sample Complexity of ReLU Networks

Authors:
raw_author_name: Mark Sellke
raw_affiliation_string: Harvard Statistics

Abstract: We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously [GRS20] obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.
TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision

Authors:
raw_author_name: Yukun Zhai
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Xiaoqiang Zhang
raw_affiliation_string: Department of Computer Vision Technology, Baidu Inc., Beijing, 100193, China
raw_author_name: Xiameng Qin
raw_affiliation_string: Department of Computer Vision Technology, Baidu Inc., Beijing, 100193, China
raw_author_name: Sanyuan Zhao
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Xingping Dong
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Jianbing Shen
raw_affiliation_string: School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

Abstract: End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on region-of-interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with a transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multitask modeling. It allows for mutual training and optimization of classification, segmentation and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an adaptive global aggregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the suboptimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on the TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.
QoS‐enhanced load balancing strategies for metaverse‐infused VR/AR in engineering education 5.0

Authors:
raw_author_name: Kiran Deep Singh
raw_affiliation_string: Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India
raw_author_name: Prabh Deep Singh
raw_affiliation_string: Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India

Abstract: Abstract The evolution of augmented reality (AR) and virtual reality (VR) technologies has ushered in a new era of immersive experiences, with applications ranging from entertainment to education. The proposed framework introduces a fog layer with an innovative, improved geographic load‐balancing algorithm. It optimizes load distribution and provides quality of service (QoS) parameters that are important for enhancing user experiences for AR/VR applications. The iFogSim toolkit experimentally validates the framework in electroencephalogram‐based VR/AR gaming applications. Also, the proposed framework is tested in a diverse range of scenarios. Results show that the proposed algorithm improves QoS for AR/VR applications with noticeable improvements on average latency, jitter, and packet loss. Future research should aim to address these limitations for a more comprehensive understanding of the proposed framework's practicality and effectiveness. The development of immersive technologies will continue to expand in multiple sectors, and future research will focus on energy efficiency, security, and real‐world applications.