Reviewerly/exHarmony huggingface dataset:
Role of the School Library in Supporting the Teaching and Learning Process and Student Literacy at SDN Sawojajar 1

Authors:
raw_author_name: Dela Resti Anggraeni
raw_affiliation_string: Universitas Terbuka
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka

Abstract: Background: A school library is a library established by each school where the library has an important role in the student teaching and learning process.&#x0D; Purpose: This research aims to determine what efforts the Library at SDN Sawojajar 1 made to support the teaching and learning process and student literacy.&#x0D; Design and methods: This research uses a descriptive qualitative approach. The data collection techniques used were observation, interviews, and documentation.&#x0D; Results: The results of the research show that the efforts made by the SDN Sawojajar 1 Library, which plays a role in supporting the teaching and learning process and literacy, are by providing various kinds of collections, implementing the Steel Corner program, implementing the literacy movement (GLS) program, and making library visits mandatory. The policy of providing a collection of textbooks, applicable books, and references helps meet students' needs in understanding the school curriculum. It stimulates interest in reading outside the context of formal learning. The Reading Corner Program provides opportunities for students to read books according to their interests. In contrast, the Literacy Movement (GLS) provides a holistic approach by integrating audio-visual media to improve student literacy. With the various efforts made by the library, it is hoped that it can become a dynamic and effective learning center in supporting the teaching and learning process and increasing student literacy at SDN Sawojajar 1.
Can an Ideal Library Guarantee High Elementary School Students' Interest in Reading?

Authors:
raw_author_name: Muhammad Abduh
raw_affiliation_string: Universitas Muhammadiyah Surakarta

Abstract: Reading is a very important activity because it is used to disseminate information for learning. This is due to access to information and knowledge related to reading. This research aims to explore reading interest and the factors that influence reading interest in private elementary schools in Surakarta. This research uses a qualitative approach with a case study method. The informant selection technique in this research used several data collection techniques, namely observation, interviews, and documentation. The results of this research indicate that interest in reading in Surakarta private elementary schools is relatively low. Due to students' lack of reading ability, reading must be ordered by the teacher. Students often play during recess. The influencing factors are internal and external factors. Internal factors are factors that come from oneself, while external factors are from outside, such as the family environment, peers, gadgets, and libraries that are far from the classroom. In conclusion, the low interest in reading among Surakarta Private Elementary School students is influenced by several factors.
Boosting HPC data analysis performance with the ParSoDA-Py library

Authors:
raw_author_name: Loris Belcastro
raw_affiliation_string: DIMES, University of Calabria, Rende, Italy
raw_author_name: Fabrizio Marozzo
raw_affiliation_string: DIMES, University of Calabria, Rende, Italy
raw_author_name: Domenico Talia
raw_affiliation_string: DIMES, University of Calabria, Rende, Italy
raw_author_name: Paolo Trunfio
raw_affiliation_string: DIMES, University of Calabria, Rende, Italy
raw_author_name: Rosa M. Badia
raw_affiliation_string: Barcelona Supercomputing Center, Barcelona, Spain
raw_author_name: Jorge Ejarque
raw_affiliation_string: Barcelona Supercomputing Center, Barcelona, Spain

Abstract: Abstract Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.
Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Authors:
raw_author_name: Derek R. Magee
raw_affiliation_string: School of Computing, University of Leeds, Leeds, UK
raw_author_name: Henry C. Woodruff
raw_affiliation_string: Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, The Netherlands
raw_author_name: Heike I. Grabsch
raw_affiliation_string: Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, The Netherlands; Pathology and Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK

Abstract: Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.
HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement

Authors:
raw_author_name: Jingchun Zhou
raw_affiliation_string: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
raw_author_name: Jiaming Sun
raw_affiliation_string: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
raw_author_name: Chongyi Li
raw_affiliation_string: College of Computer Science, Nankai University, Tianjin, 300350, China
raw_author_name: Qiuping Jiang
raw_affiliation_string: School of Information Science and Engineering, Ningbo University, Ningbo, 315211, Zhejiang, China
raw_author_name: Man Zhou
raw_affiliation_string: S-Lab, Nanyang Technological University, Singapore, 639798, Singapore
raw_author_name: Kin-Man Lam
raw_affiliation_string: Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong, 999077, China
raw_author_name: Weishi Zhang
raw_affiliation_string: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
raw_author_name: Xianping Fu
raw_affiliation_string: School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China

Abstract: Abstract Underwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model’s potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net .
Multi‐Hop anonymous payment channel network based on onion routing

Authors:
raw_author_name: Caimei Wang
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
raw_author_name: Yudong Ren
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei, China
raw_author_name: Zhize Wu
raw_affiliation_string: School of Artificial Intelligence and Big Data, Hefei University, Hefei, China

Abstract: Abstract Payment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.
MVIndEmo: a dataset for micro video public-induced emotion prediction on social media

Authors:
raw_author_name: Zhenhua Guo
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Qi Jia
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Baoyu Fan
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Di Wang
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Cong Xu
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Yanwei Wang
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Yaqian Zhao
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China
raw_author_name: Rengang Li
raw_affiliation_string: Inspur Electronic Information Industry Co.,Ltd., Beijing, 100084, China

Abstract: Abstract Distinct from the realm of perceived emotion research, induced emotion pertains to the emotional responses engendered within content consumers. This facet has garnered considerable attention and finds extensive application in the analysis of public social media. However, the advent of micro videos presents unique challenges when attempting to discern the induced emotional patterns exhibited by content consumers, owing to their free-style representation and other factors. Consequently, we have put forth two novel tasks concerning the recognition of public-induced emotion on micro videos: emotion polarity and emotion classification. Additionally, we have introduced a accessible dataset specifically tailored for the analysis of public-induced emotion on micro videos. The data corpus has been meticulously collected from Tiktok, a burgeoning social media platform renowned for its trendsetting content. To construct the dataset, we have selected eight captivating topics that elicit vibrant social discussions. In devising our label generation strategy, we have employed an automated approach characterized by the fusion of multiple expert models. This strategy incorporates a confidence measure method that relies on three distinct models for effectively aggregating user comments. To accommodate adaptable benchmark configurations, we provide both binary classification labels and probability distribution labels. The dataset encompasses a vast collection of 7,153 labeled micro videos. We have undertaken an extensive statistical analysis of the dataset to provide a comprehensive overview composition. It is our earnest aspiration that this dataset will serve as a catalyst for pioneering research avenues in the analysis of emotional patterns and the understanding of multi-modal information.
Geochemistry of the North Gondwana Paleozoic Araba and Naqus formation siliciclastics, Sinai: implications for provenance, paleoweathering, and tectonic setting

Authors:
raw_author_name: Mohammed Z. El-Bialy
raw_affiliation_string: Geology Department, Faculty of Science, Port Said University, Port Said, Egypt

Abstract: The Araba and Naqus formations in Ras Milan, Sinai, constitute part of the major continuous Paleozoic clastic belt stretching across northern Gondwana. They are sandstone-dominated successions, resting on Precambrian granitic basement, with lowermost thin basal polymictic conglomerate deposits (1–2 m), marking the unconformity. The Araba Formation section is composed mainly of varicoloured violet to reddish brown, fine to coarse-grained laminated and planner and overturned cross-bedded sandstones, while the overlying Naqus Formation is characterized by chaotically distributed poorly sorted quartz pebbles to cobbles within a moderately-sorted coarse- to medium-grained quartz arenite and by abundance and variety of large-scale tabular and trough cross bedding. Modal QFL classification of Araba and Naqus formation sandstones revealed that they are merely quartzose sandstones (quartz arenites). Geochemistry of the succession shows extremely wide compositional ranges for almost all major elements, but the Araba Formation is relatively enriched in SiO2, TiO2, Fe2O3*, Na2O, P2O5, and depleted in Al2O3, MgO, CaO and K2O relative to Naqus Formation. Only the Araba Formation samples are depleted in the transition elements Ni, Cr and Co and oppositely possess extreme enrichment in the HFSEs (i.e. Zr, Hf, Y, Nb, Ta, Th, U), whereas both formations show variable depletion in the LILE Rb, K and Ba, and excessive REE abundances relative to the PAAS and UCC, and LREE-enriched patterns with marked negative Eu anomalies. The provenance of the Naqus and Araba formations could be an earlier felsic magmatic source, which has undergone extensive sedimentary recycling. They possess high compositional maturity and were subjected to intensive to moderate weathering during humid climatic conditions. Passive margin tectonic setting is implied which denotes the derivation of Araba and Naqus formation siliciclastics from stable continental regions or plate interiors and deposition in intracratonic basins.
“TimeOntoImperfection”: An OWL 2 ontology to representing temporal data imperfection using imperfection theories

Authors:
raw_author_name: Nassira Achich
raw_affiliation_string: CEDRIC. Ingénierie des Systèmes d'Information et de Décision; Multimedia, InfoRmation systems and Advanced Computing Laboratory
raw_author_name: Fatma Ghorbel
raw_affiliation_string: CEDRIC. Ingénierie des Systèmes d'Information et de Décision; Multimedia, InfoRmation systems and Advanced Computing Laboratory
raw_author_name: Fayçal Hamdi
raw_affiliation_string: CEDRIC. Ingénierie des Systèmes d'Information et de Décision
raw_author_name: Elisabeth Metais
raw_affiliation_string: CEDRIC. Ingénierie des Systèmes d'Information et de Décision
raw_author_name: Faiez Gargouri
raw_affiliation_string: Multimedia, InfoRmation systems and Advanced Computing Laboratory

Abstract: Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step.
Literature Survey on College Information Chatbot

Authors:
raw_author_name: None Sri Varshini K
raw_affiliation_string: Global Academy of Technology, Bangalore, India
raw_author_name: None Shilpa Shri R
raw_affiliation_string: Global Academy of Technology, Bangalore, India

Abstract: A chatbot is software that allows users to communicate with computers in natural language, similar to human conversations. Chatbots interact with clients and provide answers based on human input. The user may believe they are conversing with a human, while in reality, they are interacting with a machine. The chat bot program enables students to inquire about college admissions from any location with an internet connection and receive prompt responses. The chatbot technology streamlines the admissions process by delivering necessary information to students and parents, reducing the manpower effort in addressing inquiries.
Goal exploration augmentation via pre-trained skills for sparse-reward long-horizon goal-conditioned reinforcement learning

Authors:
raw_author_name: Lisheng Wu
raw_affiliation_string: Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
raw_author_name: Ke Chen
raw_affiliation_string: Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK

Abstract: Abstract Reinforcement learning often struggles to accomplish a sparse-reward long-horizon task in a complex environment. Goal-conditioned reinforcement learning (GCRL) has been employed to tackle this difficult problem via a curriculum of easy-to-reach sub-goals. In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal. How to explore novel sub-goals efficiently is one of the most challenging issues in GCRL. Several goal exploration methods have been proposed to address this issue but still struggle to find the desired goals efficiently. In this paper, we propose a novel learning objective by optimizing the entropy of both achieved and new goals to be explored for more efficient goal exploration in sub-goal selection based GCRL. To optimize this objective, we first explore and exploit the frequently occurring goal-transition patterns mined in the environments similar to the current task to compose skills via skill learning. Then, the pre-trained skills are applied in goal exploration with theoretical justification. Evaluation on a variety of spare-reward long-horizon benchmark tasks suggests that incorporating our method into several state-of-the-art GCRL baselines significantly boosts their exploration efficiency while improving or maintaining their performance.