Reviewerly/exHarmony huggingface dataset:
A randomized algorithm for clustering discrete sequences

Authors:
raw_author_name: Mudi Jiang
raw_affiliation_string: School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
raw_author_name: Lianyu Hu
raw_affiliation_string: School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
raw_author_name: Xin Han
raw_affiliation_string: School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
raw_author_name: Yong Zhou
raw_affiliation_string: School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
raw_author_name: Zengyou He
raw_affiliation_string: Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Tuqiang Road 321, 116620 Dalian, China; School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China

Abstract: Cluster analysis is one of the most important research issues in data mining and machine learning. To date, numerous clustering algorithms have been proposed to tackle the fixed-length vector data. In many real applications, we need to detect clusters from a set of discrete sequences in which each sequence is an ordered list of items. Due to the sequential and discrete nature, the discrete sequence clustering problem is more challenging and most of existing vector data clustering algorithms cannot be directly employed. In this paper, we present a stochastic algorithm for clustering discrete sequences. Our method first quickly generates a set of random partitions over the sequential data set and then merges these random clustering results via weighted graph construction and partition. We perform extensive empirical comparisons on real data sets to show that our method is comparable to those state-of-the-art clustering algorithms with respect to both accuracy and efficiency.
Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review

Authors:
raw_author_name: Ahmed M. Shamsan Saleh
raw_affiliation_string: Department of Information Technology, University of Tabuk, Tabuk, Saudi Arabia

Abstract: As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology (BT) has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of BT with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of BT and decentralized AI for cybersecurity serving as the starting point for the study. This paper begins with an overview of BT and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between BT and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.
An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph

Authors:
raw_author_name: Xin Mei
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Libin Yang
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Zuowei Jiang
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Xiaoyan Cai
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Dehong Gao
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Junwei Han
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Shirui Pan
raw_affiliation_string: Griffith University, Australia

Abstract: Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based or logical rule-based. Embedding-based methods depend on learned entity and relation embeddings for prediction, but they suffer from the lack of interpretability due to the opaque reasoning process. On the other hand, logical rule-based methods face scalability challenges as they heavily rely on predefined logical rules. To overcome these limitations, we propose a hybrid model that combines embedding-based and logical rule-based methods to capture deep causal logic. Our model, called the Inductive Reasoning Model based on Interpretable Logical Rule (ILR-IR), aims to provide interpretable insights while effectively predicting future events in TKGs. ILR-IR delves into historical information, extracting valuable insights from logical rules embedded within relations and interaction preferences between entities. By considering both logical rules and interaction preferences, ILR-IR offers a comprehensive perspective for predicting future events. In addition, we propose the incorporation of a one-class augmented matching loss during optimization, which serves to enhance performance of the model during training. We evaluate ILR-IR on multiple datasets, including ICEWS14, ICEWS0515, and ICEWS18. Experimental results demonstrate that ILR-IR outperforms state-of-the-art baselines, showcasing its superior performance in TKG extrapolation reasoning. Moreover, ILR-IR demonstrates remarkable generalization capabilities, even when applied to related datasets that share a common relation vocabulary. This suggests that our proposed model exhibits robust zero-shot reasoning abilities. For interested parties, we have made our code publicly available at https://github.com/mxadorable/ILR-IR.
Exploring explainable artificial intelligence techniques for evaluating cervical intraepithelial neoplasia (CIN) diagnosis using colposcopy images

Authors:
raw_author_name: Elima Hussain
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh 522502, India
raw_author_name: Lipi B. Mahanta
raw_affiliation_string: Mathematical & Computational Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati, Assam 781036, India
raw_author_name: Khurshid A. Borbora
raw_affiliation_string: Gauhati University, Guwahati, Assam, India
raw_author_name: Himakshi Borah
raw_affiliation_string: Rognidaan Technologies Pvt. Ltd, Guwahati, Assam, India

Abstract: Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning.
Introducing teacher students to digital fabrication to support children’s mathematical learning

Authors:
raw_author_name: Susanne Stigberg
raw_affiliation_string: Department of Computer Science and Communication, Østfold University College, 1757, Halden, Norway

Abstract: While digital fabrication has been closely linked to narratives of science, technology, engineering, and mathematics (STEM) education and design thinking, it can provide a gateway to reimagining mathematical teaching and deepening the understanding of both mathematical content and pedagogy. In this paper, we present a workshop series that engages teacher students in the creation of their own mathematical manipulatives using digital fabrication tools. Manipulatives are tangible objects that can be used to support children’s learning of mathematical concepts, such as numbers, fractions, or geometry. The workshops included activities for finding, adapting, creating, and sharing manipulatives (FACS) using 2D and 3D modeling and fabrication techniques. Our in-depth analysis of video recordings presents how teacher students successfully acquire digital fabrication skills and reflect on the use of customized manipulatives to support children’s mathematical learning. The findings suggest that introducing digital fabrication in teacher education programs can shift the focus from consuming ready-made manipulatives to creating customized materials that better suit the teaching context. The authors propose FACS as a specific approach for introducing digital fabrication to teacher students.
Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement

Authors:
raw_author_name: Anastasia Ushakova
raw_affiliation_string: Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom

Abstract: The focus of this study is the identification of reader profiles that differ in performance and progression in an educational literacy app. A total of 19,830 students in Grade 2 from 347 Elementary schools located in 30 different districts in the United States played the app from 2020 to 2021. Our aim was to identify unique groups of readers using an unsupervised statistical learning technique - cluster analysis. Six indicators generated from the students’ log files were included to provide insights into engagement and learning across four different reading-related skills: phonological awareness, early decoding, vocabulary, and comprehension processes. A key aim was to evaluate the implementation and performance of Gaussian mixture models, k-means, k-medoids, clustering large applications and hierarchical clustering, alongside provision of detailed guidance that can benefit researchers in the field. K-means algorithm performed the best and identified nine groups of readers. Children with low initial reading ability showed greater engagement with code-related games (phonological awareness, early decoding) and took longer to master these games, whereas children with higher initial ability showed more engagement with meaning-related games (vocabulary, comprehension processes). Our findings can inform further research that aims to understand individual differences in learning behaviour within digital environments both over time and across various cohorts of children.
Batch Active Learning of Reward Functions from Human Preferences

Authors:
raw_author_name: Erdem Bıyık
raw_affiliation_string: Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, USA
raw_author_name: Nima Anari
raw_affiliation_string: Department of Computer Science, Stanford University, Stanford, USA
raw_author_name: Dorsa Sadigh
raw_affiliation_string: Department of Computer Science, Stanford University, Stanford, USA

Abstract: Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly employed in preference-based learning to generate more informative data at the expense of parallelization and computation time. In this paper, we develop a set of novel algorithms, batch active preference-based learning methods, that enable efficient learning of reward functions using as few data samples as possible while still having short query generation times and also retaining parallelizability. We introduce a method based on determinantal point processes (DPP) for active batch generation and several heuristic-based alternatives. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We showcase one of our algorithms in a study to learn human users’ preferences.
MHGCN+: Multiplex Heterogeneous Graph Convolutional Network

Authors:
raw_author_name: Chaofan Fu
raw_affiliation_string: Ocean University of China, Qingdao, China
raw_author_name: Pengyang Yu
raw_affiliation_string: Ocean University of China, Qingdao, China
raw_author_name: Yanwei Yu
raw_affiliation_string: Ocean University of China, Qingdao, China
raw_author_name: Chao Huang
raw_affiliation_string: The University of Hong Kong, Hong Kong, China
raw_author_name: Zhongying Zhao
raw_affiliation_string: Shandong University of Science and Technology, Qingdao, China
raw_author_name: Junyu Dong
raw_affiliation_string: Ocean University of China, Qingdao, China

Abstract: Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a M ultiplex H eterogeneous G raph C onvolutional N etwork (MHGCN+) for multiplex heterogeneous network embedding. Our MHGCN+ can automatically learn the useful heterogeneous meta-path interactions of different lengths with different importance in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on seven real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN+ against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/FuChF/MHGCN-plus.
Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

Authors:
raw_author_name: Ye Xu
raw_affiliation_string: School of IoT Technology, Wuxi Institute of Technology, Wuxi, Jiangsu, China
raw_author_name: Xin Zhang
raw_affiliation_string: School of IoT Technology, Wuxi Institute of Technology, Wuxi, Jiangsu, China
raw_author_name: Xiaorong Qiu
raw_affiliation_string: School of IoT Technology, Wuxi Institute of Technology, Wuxi, Jiangsu, China

Abstract: This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method-we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV). We also employ two popular feature extraction methods: one (denoted as Ext-DFs(CP)) uses a convolutional or non-global pooling layer, and another (denoted as Ext-DFs(FC)) uses a fully-connected or global pooling layer. Three pre-trained models-VGGNet-16, ResNext-50(32×4d), and Swin-B-are utilized as feature extractors. Experimental results on six datasets (15-Scenes, TF-Flowers, MIT Indoor-67, COVID-19 CXR, NWPU-RESISC45, and Caltech-101) reveal that compared to using the pre-trained model with only the new classification layer re-trained for classification, employing it as the feature extractor in the BoDVW model improves the accuracy in 35 out of 36 experiments when using FV. With Ext-DFs(CP), the accuracy increases by 0.13% to 8.43% (averaged at 3.11%), and with Ext-DFs(FC), it increases by 1.06% to 14.63% (averaged at 5.66%). Furthermore, when all layers of the pre-trained model are fine-tuned and used as the feature extractor, the results vary depending on the methods used. If FV and Ext-DFs(FC) are used, the accuracy increases by 0.21% to 5.65% (averaged at 1.58%) in 14 out of 18 experiments. Our results suggest that while using a pre-trained deep learning model as the feature extractor does not always improve classification accuracy, it holds great potential as an accuracy improvement technique.
Base-extension semantics for modal logic

Authors:
raw_author_name: David J Pym
raw_affiliation_string: Institute of Philosophy , University of London, London WC1H 0AR, UK , d.pym@ucl.ac.uk

Abstract: Abstract In proof-theoretic semantics, meaning is based on inference. It may seen as the mathematical expression of the inferentialist interpretation of logic. Much recent work has focused on base-extension semantics, in which the validity of formulas is given by an inductive definition generated by provability in a ‘base’ of atomic rules. Base-extension semantics for classical and intuitionistic propositional logic have been explored by several authors. In this paper, we develop base-extension semantics for the classical propositional modal systems $K$, $KT$, $K4$ and $S4$, with $\square $ as the primary modal operator. We establish appropriate soundness and completeness theorems and establish the duality between $\square $ and a natural presentation of $\lozenge $. We also show that our semantics is in its current form not complete with respect to euclidean modal logics. Our formulation makes essential use of relational structures on bases.
An NLP Technique on Sentiment Analysis

Authors:
raw_author_name: Alok Rai
raw_author_name: Yash Malhotra

Abstract: We have to structure the data which was given to us from the Twitter social media for accurate analysis and make something outof it. We will be finding the sentiment behind the given comment by a user on twitter so that we can sort out the meaning of the text. To get the negative emotions of the text, we will be using different algorithms to find the intention behind it. Fathom this kind of issue, estimation investigation and profound learning methods are two combining methods. We are using Naive Bayes algorithms, SVM (Support Vector Machine) and otherclassification algorithms to get our required output.These are known deep learning /Machine Learning ways to extract the feelings in sentences. At the end of the result we will get the desired output and we will check the accuracy of our output accordingly.
The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing

Authors:
raw_author_name: Ping Chen
raw_affiliation_string: China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China
raw_author_name: Jingjing Hu
raw_affiliation_string: China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China
raw_author_name: Zhitao Wu
raw_affiliation_string: China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, China
raw_author_name: Ruoting Xiong
raw_affiliation_string: School of Computing Sciences, University of East Anglia, Norfolk, Norwich NR47TJ, UK
raw_author_name: Wei Ren
raw_affiliation_string: Key Laboratory of Data Protection and Intelligent Management (Sichuan University), Ministry of Education, Beijing, Sichuan, China; School of Computer Science, China University of Geosciences, Wuhan 430078, Hubei, China; State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM, Beijing, China

Abstract: Negative information selection is an approach to protect the privacy by using negative information to replace original information. In this paper, we prove some bounds for negative information selection. Those bounds reveal the privacy protection strength of quantitative probability analysis. We also analyzed the reconstruction probability of original information from available negative information. The formal analysis can specify the bound on the strength of security and utility for negative information selection. Besides, we simulate brute force attacks under different data leakage ratios. Specifically, we calculate the attacker’s guess times before and after the data leakage. Experimental results indicate that the data leakage of over 30% can put the original information in a dangerous situation. Furthermore, we found that the leakage possibility has little relevance to the number of elements in the full set, but it is influenced by the ratio of the leaked information.
Artificial intelligence as a tool to optimize the work of a higher school teacher

Authors:
raw_author_name: Anastasia Georgievna Shirokolobova
raw_affiliation_string: T. F. Gorbachev Kuzbass State Technical University

Abstract: The aim of the article is to justify the possibility of using artificial intelligence technologies – neural networks – to optimize the activities of a higher education teacher. The article discusses the educational capabilities of artificial intelligence for use in the higher education process. Procedural characteristics of artificial intelligence are presented in order to identify and describe its functions for use in higher education. The scientific novelty of the research is in identifying the potential of artificial intelligence technologies – neural networks – in relation to the educational process and optimizing the work of a higher education teacher. The results of the study include the following indicators: 1) artificial intelligence has a wide range of applications in the educational process; 2) neural networks have demonstrated the potential of their capabilities to optimize the work of a higher education teacher.