Reviewerly/exHarmony huggingface dataset:
HITS-based Propagation Paradigm for Graph Neural Networks

Authors:
raw_author_name: Mehak Khan
raw_affiliation_string: Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway
raw_author_name: Gustavo B. M. Mello
raw_affiliation_string: Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway
raw_author_name: Laurence Habib
raw_affiliation_string: Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway
raw_author_name: Paal Engelstad
raw_affiliation_string: Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway
raw_author_name: Anis Yazidi
raw_affiliation_string: Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway

Abstract: In this article, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a “self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods, which rely solely on the idea of authorities (in-links), HITS considers the relevance of both authorities (in-links) and hubs (out-links), thereby allowing for a more informative graph learning process. To segregate node prediction and propagation, we use a Multilayer Perceptron in combination with a HITS-based propagation approach and propose two models: HITS-GNN and HITS-GNN+. We provided additional validation of our models’ efficacy by performing an ablation study to assess the performance of authority-hub in independent models. Moreover, the effect of the main hyper-parameters and normalization is also analyzed to uncover how these techniques influence the performance of our models. Extensive experimental results indicate that the proposed approach significantly improves baseline methods on the graph (citation network) benchmark datasets by a decent margin for semi-supervised node classification, which can aid in predicting the categories (labels) of scientific articles not exclusively based on their content but also based on the type of articles they cite.
Illumination variation robust circular target based on digital image correlation method

Authors:
raw_author_name: Shuai Dong
raw_affiliation_string: National-Local Joint Laboratory of Engineering Technology for Long-term Performance Enhancement of Bridges in Southern District, Changsha University of Science and Technology, Changsha 410114, China; School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
raw_author_name: Jianyang Li
raw_affiliation_string: School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
raw_author_name: Jia Ma
raw_affiliation_string: School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
raw_author_name: Zhenhao Zhang
raw_affiliation_string: School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
raw_author_name: Xin Kang
raw_affiliation_string: School of Electromechanical and Information Engineering, Putian University, Putian 351100, China; School of Science, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: Circular targets, as one of typical image features, are widely used in image-based measurement, such as photogrammetry, displacement sensing, and vibration monitoring. However, its positioning accuracy is usually sensitive to intensity changes in ambient lighting, particularly in outdoor applications and long-term monitoring scenarios. In this paper, a speckle-based circular target (SbCT) is introduced as a solution to the sensitivity of localization accuracy of traditional circular targets in illumination variation environment. By incorporating a speckle pattern on the circular target and utilizing digital image correlation method, the SbCT achieves insensitivity to illumination variations when an appropriate correlation function is chosen. This enables accurate and reliable measurements even in lighting varying environment. This paper initially outlines the design strategy of the SbCT and demonstrates its extraction procedure. Subsequently, simulations are conducted to analyze the positioning accuracy of SbCT under linearly varying illumination situations. Additionally, experiments are performed to study the accuracy and robustness of SbCT with the comparison to concentric circular targets under different lighting variation cases. The results from both simulation and experiment demonstrate the illumination robustness of SbCT, of which the stability is better than 0.01 pixel. According to the displacement tracking experiment, the relative error obtained by SbCT is smaller than 0.2%. Therefore, it can be concluded that SbCT is an illumination-robust feature, offering automatic recognition and accurate localization performance even in illumination varying situations.
Dual Sparse Structured Subspaces and Graph Regularisation for Particle Swarm Optimisation-Based Multi-Label Feature Selection

Authors:
raw_author_name: Kaan Demir
raw_affiliation_string: Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Bach Hoai Nguyen
raw_affiliation_string: Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Bing Xue
raw_affiliation_string: Victoria University of Wellington, Wellington, New Zealand
raw_author_name: Mengjie Zhang
raw_affiliation_string: Victoria University of Wellington, Wellington, New Zealand

Abstract: Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes.
Quantitative bounds to propagation of quantum correlations in many-body systems

Authors:
raw_author_name: Davide Girolami
raw_affiliation_string: DISAT, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy

Abstract: We investigate how much information about a quantum system can be simultaneously communicated to independent observers, by establishing quantitative limits to bipartite quantum correlations in many-body systems. As recently reported in Phys. Rev. Lett. 129, 010401 (2022), bounds on quantum discord and entanglement of formation between a single quantum system and its environment, e.g., a large number of photons, dictate that independent observers which monitor environment fragments inevitably acquire only classical information about the system. Here, we corroborate and generalize those findings. First, we calculate continuity bounds of quantum discord, which establish how much states with a small amount of quantum correlations deviate from being embeddings of classical probability distributions. Also, we demonstrate a universally valid upper bound to the bipartite entanglement of formation between an arbitrary pair of components of a many-body quantum system. The results confirm that proliferation of classical information in the Universe suppresses quantum correlations.
Enhancing sign language recognition using CNN and SIFT: A case study on Pakistan sign language

Authors:
raw_author_name: Saud Altaf
raw_affiliation_string: Department of Information Engineering Technology, National Skills University, Islamabad 44000, Pakistan
raw_author_name: Shafiq Ahmad
raw_affiliation_string: Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
raw_author_name: Haitham Mahmoud
raw_affiliation_string: Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Abstract: Millions of people who have trouble hearing rely heavily on sign language as their primary means of communication. As a form of visual language, sign language is primarily used as features by the deaf and hard of hearing to communicate with one another. These features make it challenging for the average person to grasp what is being said. As a result, it becomes more difficult for deaf people to interact with the hearing community. In order to solve this communication barrier, scientists have been working on sign language recognition systems. The purpose of this research is to propose a hand gesture recognition framework for Pakistan Sign Language (PSL) by training a Convolutional Neural Network (CNN) on PSL gesture images obtained with the aid of a testbed built in the laboratory using the Kinect motion sensor for Urdu alphabets. Kinect images of hands were taken in this study under a variety of lighting conditions. A feature vector set was created from hand margin, size, shape, palm center points, and finger position using Scale Invariant Feature Transform (SIFT). Important features of PSL-based sign images were extracted using SIFT and then converted to vector points. The proposed improved CNN model was able to achieve an impressive accuracy of 98.74% on the PSL dataset, demonstrating an impressively low error rate of only 1.26%. A case study is demonstrated in the assessment of the system that has been provided. Finally, a comparison is performed between others and our work to show the efficiency of the proposed framework and legitimize the concept.
Lexicon-grammar tables standardization and implementation

Authors:
raw_author_name: Asmaa Kourtin
raw_affiliation_string: Faculty of science, Ibn Tofail University
raw_author_name: Asmaa Amzali
raw_affiliation_string: Faculty of science, Ibn Tofail University
raw_author_name: Mohammed Mourchid
raw_affiliation_string: Faculty of science, Ibn Tofail University
raw_author_name: Abdelaziz Mouloudi
raw_affiliation_string: Faculty of Science, Ibn Tofail University
raw_author_name: Samir Mbarki
raw_affiliation_string: Faculty of Science, Ibn Tofail University

Abstract: &lt;span&gt;The lexicon-grammar approach is a very important linguistic approach in automatic natural language processing (NLP). It allows for the description of the lexicon of the language through readable and intuitive tables for human manual editing. However, the automatic use of the lexicon-grammar tables in the automatic NLP platforms remains difficult, given the incompatibility between the codes used to represent the properties in the lexicon-grammar tables and those used to represent the properties in the automatic NLP platforms. In this work, we present our method of standardizing the lexicon-grammar tables for the French language, since they constitute very rich lexical, syntactic, and semantic linguistic resources. First, we standardize their properties so that they can be compatible with those used in the NLP platforms. Then, to implement the standardized tables, we used a linguistic platform such as NooJ. For that, we describe the process of integrating these tables into this platform through the automatic generation of the dictionaries from these tables. Finally, to test the efficiency of the generated dictionaries, we create for some of them syntactic grammars that take into account all the grammatical, syntactic, and semantic information contained in the dictionaries.&lt;/span&gt;
Segmentary group-sparsity self-representation learning and spectral clustering via double <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e7481"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>21</mml:mn></mml:mrow></mml:msub></mml:math> norm

Authors:
raw_author_name: Deyu Zeng
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China; College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China
raw_author_name: Chris Ding
raw_affiliation_string: School of Data Science, Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China
raw_author_name: Zongze Wu
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, Guangdong, China
raw_author_name: Xiaopin Zhong
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China
raw_author_name: Weixiang Liu
raw_affiliation_string: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong, China

Abstract: With the rapid expansion of data dimensions, subspace representation learning, a method for mapping high-dimensional data samples to their corresponding underlying low-dimensional subspaces, has become an essential process for high-dimensional data clustering. Although the existing methods have achieved reliable data representation learning and precise clustering, few of them realized that the corrupted data points in the dataset will influence the linear representation of the others. When there are multiple heavily corrupted data in a dataset, the matrix of the self-representation coefficient would be influenced by these data. Therefore, this paper proposes the segmentary group-sparsity self-representation learning (SGSSL) and segmentary group-sparsity-based spectral clustering (SGSSC) models to eliminate their influence on representation learning and clustering results. We proposed that imposing varying degrees of row sparsity and column sparsity constraints on the representation coefficient matrix can prevent corrupted data from contaminating other data during the self-representation process, thus obtaining better spectral clustering results. Extensive experiments on several real datasets demonstrate that our proposed method can perform better than several related methods in recent years.
IoT Systems for Extreme Environments

Authors:
raw_author_name: Krishna Kant
raw_affiliation_string: Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
raw_author_name: Alireza Jolfaei
raw_affiliation_string: College of Science and Engineering, Flinders University, Tonsley, SA, Australia
raw_author_name: Klaus Moessner
raw_affiliation_string: Communications Engineering Department, University of Technology Chemnitz, Chemnitz, Germany; Institute for Communication Systems University of Surrey, Guildford, U.K.

Abstract: The deployment of Internet of Things (IoT) systems spans a large variety of applications, each with unique requirements. Many of these applications relate to the management of various cyber–physical systems, including road and rail traffic, electricity, water, food/goods transportation and storage, smart building management, crime/safety management, underwater systems, etc. Within this context, a growing concern is the deployment of IoT systems in extreme environments which may occur either because of the nature of the application or due to external factors. Some prominent examples of the former are 1) the IoT deployments in hazardous environments such as management of chemical or nuclear plants, management of underwater oil/gas infrastructure, mining operations, etc.; 2) IoT systems deployed specifically to manage accidents and disasters; and 3) IoT systems deployed in arctic/antarctic regions where they routinely experience extreme levels of changes in terms of temperature, wind conditions, sunlight availability, compression, etc. Such IoT systems generally are designed to specifically operate in the challenging environment they must operate in, and thus may be expected to be rather robust. However, the IoT systems designed to manage the physical infrastructures such as those in urban settings may also need to handle unprecedented and unexpected stresses due to the worldwide phenomena of aging physical infrastructure, demand that far exceeds the designed capacity, and increasingly extreme operating conditions due to climate change. This special issue covers all such scenarios and thus represents a vast and rich area for innovations.
On the Hotelling Game with Close Players

Authors:
raw_author_name: Luis Garcia-Perez
raw_affiliation_string: Complex Systems Group, Universidad Politécnica de Madrid, C. Universitaria, Madrid 28040, Spain
raw_author_name: Juan Grau-Climent
raw_affiliation_string: Complex Systems Group, Universidad Politécnica de Madrid, C. Universitaria, Madrid 28040, Spain
raw_author_name: Juan C. Losada
raw_affiliation_string: Complex Systems Group, Universidad Politécnica de Madrid, C. Universitaria, Madrid 28040, Spain
raw_author_name: Ramon Alonso-Sanz
raw_affiliation_string: Complex Systems Group, Universidad Politécnica de Madrid, C. Universitaria, Madrid 28040, Spain

Abstract: The principle of minimum differentiation stated by Hotelling in his pioneer paper was proved to be invalid 50 years later, since there is a region where the players are so close that Nash equilibrium does not exist. Some authors have contributed with amendments to the original game in the aim of ensuring the existence of Nash equilibrium independently of the location of the players. However, this work analyzes the less frequently studied case of the Hotelling game with close players under different implementations of the game, considering elastic and inelastic demand (conventional Hotelling game, the Hotelling game with reservation cost and Hotelling-Smithies game). The study is supported by means of numerical simulation of the game.
Cui Bono? Software Professionals Should Always Ask “Who Benefits?”

Authors:
raw_author_name: Keith W. Miller
raw_affiliation_string: Sciences, University of Missouri&#x2013;St. Louis, St. Louis, MO, USA

Abstract: We suggest a straightforward approach to recognizing and managing ethical challenges that arise as software is designed, developed, and deployed. The approach features two “Cui bono?” questions: “Who benefits?” and “Who is at risk?” These questions can help computing professionals deal effectively and honorably with their responsibilities.
Using Machine Learning Algorithms in Determining the Stage of Breast Cancer from Pathology Reports

Authors:
raw_author_name: Parinaz Eskandarian
raw_affiliation_string: Computer Engineering Department, Azad University, Zanjan Branch, Zanjan
raw_author_name: Zahra Niazkhani
raw_affiliation_string: Nephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran
Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam

Abstract: Introduction: After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports.Material and Methods: A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score.Results: The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time.Conclusion: We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.
The Effect of Flashcards in Improving Vocabulary at the Eighth Grade Students of SMP Negeri 6 Palu

Authors:
raw_author_name: Aminah Suriaman
raw_affiliation_string: Universitas Tadulako
raw_author_name: Ferry Rita
raw_affiliation_string: Universitas Tadulako

Abstract: Students face challenges in English communication primarily due to insufficient vocabulary. Consequently, teachers should actively implement strategic measures to enhance students' mastery of vocabulary, such as incorporating activities like the use of flashcard. The objective of this study is to investigate whether the utilization of flashcard media has a positive impact on enhancing vocabulary. This research is an experimental study with a pre post test design using 2 sample groups, namely the intervention group and the control group consisting of 32 people per group. In choosing the participants, the researcher used purposive sampling. This study was conducted in SMPN 6 Palu since the participants are the students of the eighth grade. The results were analyzed using t table and t calculations to determine the effect. The result showed that the mean score of pre-test of control group is 45.13 and that the mean score post test of control group was 53, whereas the mean score of pre-test of experimental group is 45.38, and the mean score of experimental group was 81.38. t-counted (14.57) is higher that t-table (1.9988). This implies that the research hypothesis has been validated. In summary, the utilization of flashcards proves to be effective in enhancing vocabulary among eighth-grade students at SMP Negeri 6 Palu. flashcard media could be used as one of media in the classroom, because it easy and interesting in applying for the students.