Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems

Authors:
raw_author_name: Vinod Kumar
raw_affiliation_string: Department of Mathematics, Shyam Lal College, University of Delhi, New Delhi 110032, India
raw_author_name: Mukesh Kumar Sharma
raw_affiliation_string: Department Mathematics, Chaudhary Charan Singh University, Meerut, Uttar Pradesh 250004, India; Department of Mathematics, Baba Mastnath University, Rohtak 124021, India
raw_author_name: Akber Ali Khan
raw_affiliation_string: Department of Applied Sciences and Humanities, IIMT College of Engineering, Greater Noida, Uttar Pradesh, India
raw_author_name: M. Javed Idrisi
raw_affiliation_string: Department of Mathematics, College of Natural and Computational Science, Mizan-Tepi University, Tepi, Ethiopia

Abstract: Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.
Development of an Automated Wooden Handle Packaging System with Integrated Counting Technology

Authors:
raw_author_name: Filipe Pereira
raw_affiliation_string: MEtRICs Research Centre, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
raw_author_name: Luís Magalhães
raw_affiliation_string: MEtRICs Research Centre, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
raw_author_name: Adriano A. Santos
raw_affiliation_string: CIDEM—School of Engineering (ISEP), Polytechnic of Porto (P.Porto), 4249-015 Porto, Portugal; INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
raw_author_name: José Machado
raw_affiliation_string: MEtRICs Research Centre, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal

Abstract: Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept.
Understanding users' voice assistant exploration intention: unraveling the differential mechanisms of the multiple dimensions of perceived intelligence

Authors:
raw_author_name: Yiming Zhao
raw_affiliation_string: Center for Studies of Information Resources, Wuhan University, Wuhan, China
raw_author_name: Yongqiang Sun
raw_affiliation_string: Center for Studies of Information Resources, Wuhan University, Wuhan, China
raw_author_name: Xiao-Liang Shen
raw_affiliation_string: School of Information Management, Wuhan University, Wuhan, China

Abstract: Purpose The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI. Design/methodology/approach An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users ( N = 244). Findings According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI. Originality/value This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.
Enhancing Cultural Sustainability: Making Rescue Excavations Accessible through Educational Applications and Virtual Reality

Authors:
raw_author_name: Stavros Vlizos
raw_affiliation_string: Department of Archives, Library Science and Museology, Ionian University, 49100 Corfu, Greece
raw_author_name: Konstantinos Kotsopoulos
raw_affiliation_string: Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece

Abstract: The key questions of this article concern the extent to which digital documentation, 3D scanning technologies, and VR/AR technologies contribute to the preservation, enhancement, and sustainability of cultural heritage. The purpose of this research is to present an innovative methodology through which a platform for the diagnosis, management, and documentation of findings, progress, and reports and projections of rescue excavations in the area of Euboea, Greece was implemented. A holistic approach is proposed with a comprehensive system of archaeological documentation and visualization, which integrates the use of digital technologies in all steps of the archaeological process, enhances the tools of the scientific community, and provides immersive experiences for both researchers and the public. The results of the method include (1) a complex toolbox of innovative technologies that allow archaeologists to plan and execute a rescue excavation in an efficient and concise manner and (2) a VR installation at the Diachronic Museum of Chalkida “Arethousa”, which allows the public to participate in virtual rescue excavations and explore archaeological sites that are no longer accessible. Through this project, the impact of new technologies on cultural heritage and, in particular, on issues of sustainability, accessibility, education, and visitor participation is highlighted.
Filling the Gaps: Using Synthetic Low-Altitude Aerial Images to Increase Operational Design Domain Coverage

Authors:
raw_author_name: Joachim Rüter
raw_affiliation_string: German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany;
raw_author_name: Theresa Maienschein
raw_affiliation_string: German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany;
raw_author_name: Sebastian Schirmer
raw_affiliation_string: German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany;
raw_author_name: Simon Schopferer
raw_affiliation_string: German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany;
raw_author_name: Christoph Torens
raw_affiliation_string: German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany;

Abstract: A key necessity for the safe and autonomous flight of Unmanned Aircraft Systems (UAS) is their reliable perception of the environment, for example, to assess the safety of a landing site. For visual perception, Machine Learning (ML) provides state-of-the-art results in terms of performance, but the path to aviation certification has yet to be determined as current regulation and standard documents are not applicable to ML-based components due to their data-defined properties. However, the European Union Aviation Safety Agency (EASA) published the first usable guidance documents that take ML-specific challenges, such as data management and learning assurance, into account. In this paper, an important concept in this context is addressed, namely the Operational Design Domain (ODD) that defines the limitations under which a given ML-based system is designed to operate and function correctly. We investigated whether synthetic data can be used to complement a real-world training dataset which does not cover the whole ODD of an ML-based system component for visual object detection. The use-case in focus is the detection of humans on the ground to assess the safety of landing sites. Synthetic data are generated using the methods proposed in the EASA documents, namely augmentations, stitching and simulation environments. These data are used to augment a real-world dataset to increase ODD coverage during the training of Faster R-CNN object detection models. Our results give insights into the generation techniques and usefulness of synthetic data in the context of increasing ODD coverage. They indicate that the different types of synthetic images vary in their suitability but that augmentations seem to be particularly promising when there is not enough real-world data to cover the whole ODD. By doing so, our results contribute towards the adoption of ML technology in aviation and the reduction of data requirements for ML perception systems.
LangTest: A comprehensive evaluation library for custom LLM and NLP models

Authors:
raw_author_name: Thadaka Kalyan Chakravarthy
raw_affiliation_string: John Snow Labs, Coastal Highway, Lewes, 19958, DE, USA
raw_author_name: Rakshit Khajuria
raw_affiliation_string: John Snow Labs, Coastal Highway, Lewes, 19958, DE, USA
raw_author_name: Veysel Kocaman
raw_affiliation_string: John Snow Labs, Coastal Highway, Lewes, 19958, DE, USA
raw_author_name: David Talby
raw_affiliation_string: John Snow Labs, Coastal Highway, Lewes, 19958, DE, USA

Abstract: The use of natural language processing (NLP) models, including the more recent large language models (LLM) in real-world applications obtained relevant success in the past years. To measure the performance of these systems, traditional performance metrics such as accuracy, precision, recall, and f1-score are used. Although it is important to measure the performance of the models in those terms, natural language often requires an holistic evaluation that consider other important aspects such as robustness, bias, accuracy, toxicity, fairness, safety, efficiency, clinical relevance, security, representation, disinformation, political orientation, sensitivity, factuality, legal concerns, and vulnerabilities. To address the gap, we introduce LangTest, an open source Python toolkit, aimed at reshaping the evaluation of LLMs and NLP models in real-world applications. The project aims to empower data scientists, enabling them to meet high standards in the ever-evolving landscape of AI model development. Specifically, it provides a comprehensive suite of more than 60 test types, ensuring a more comprehensive understanding of a model’s behavior and responsible AI use. In this experiment, a Named Entity Recognition (NER) clinical model showed significant improvement in its capabilities to identify clinical entities in text after applying data augmentation for robustness.
Online hate speech victimization: consequences for victims’ feelings of insecurity

Authors:
raw_author_name: Arne Dreißigacker
raw_affiliation_string: Criminological Research Institute of Lower Saxony (KFN e.V.), Lützerodestr. 9, 30161, Hannover, Germany

Abstract: Abstract This paper addresses the question whether and to what extent the experience of online hate speech affects victims’ sense of security. Studies on hate crime in general show that such crimes are associated with a significantly higher feeling of insecurity, but there is little evidence concerning feeling of insecurity due to online hate speech. Based on a secondary data analysis of a representative population survey in Lower Saxony, Germany, on the topic of cybercrime in 2020 (N = 4,102), we tested three hypotheses regarding the effect of offline and online hate speech on feelings of insecurity. As a result, compared to non-victims, victims of online hate speech exhibit a more pronounced feeling of insecurity outside the Internet, while victims of other forms of cybercrime do not differ in this regard from non-victims. We found no effect for offline hate speech when relevant control variables were included in the statistical model. Possible reasons for this finding are assumed to lie in the characteristics of the phenomenon of online hate speech, for example, because the hateful content spreads uncontrollably on the Internet and reaches its victims even in protected private spheres.
Aspect-based sentiment classification with aspect-specific hypergraph attention networks

Authors:
raw_author_name: Jihong Ouyang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
raw_author_name: Bing Wang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
raw_author_name: Zhiyao Yang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China

Abstract: Aspect-based sentiment classification aims to infer the sentiment expression towards a specific aspect in a sentence. The key to this task is to utilize the relationship between sentiment words and aspect words. The mainstream methods use Recurrent Neural Networks (RNN), Attention mechanisms, or Graph Neural Networks (GNN) to explore the syntactic information. Though these methods are undoubtedly effective, they still encounter several challenges: 1. Since most of the studies used only syntactic dependency graphs, they lacked a more optimal representation of inter-word relationships. 2. Some studies have explored multiple relationship graphs, but they fail to effectively integrate syntactic dependencies with semantic or other information, thereby impeding the exchange of multiple information elements. Moreover, the inclusion of more information graphs increases the computational burden on the model. In this paper, we construct a word-level relational hypergraph containing various syntactic and semantic relationships between aspect words and other context words. We propose an aspect-specific hypergraph attention network (ASHGAT) to thoroughly investigate the hypergraph’s information. Furthermore, we design an aspect-oriented syntactic distance-based weight distribution mechanism to optimize hypergraph attention. We conducted extensive experiments on four benchmark datasets from SemEval 14, 15, and 16. The results show that ASHGAT demonstrates the other SOTA baselines.
All-optical neural network nonlinear activation function based on the optical bistability within a micro-ring resonator

Authors:
raw_author_name: Hui Zhang
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China
raw_author_name: Jin Wen
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China; State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, 710119, China
raw_author_name: Zhengwei Wu
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China
raw_author_name: Huimin Yu
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China
raw_author_name: Yu Pan
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China
raw_author_name: Lan Yin
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China
raw_author_name: Chenglong Wang
raw_affiliation_string: School of Science, Xi'an Shiyou University, Xi'an, 710065, China

Abstract: Training all-optical neural networks in itself remains an unresolved problem, and the challenges compound when the problem is turned into the hardware implementations. In this paper, we propose a nonlinear activation function based on optical bistability within a micro-ring resonator (MRR), achieving threshold control without external modulation. Furthermore, a convolutional neural network similar to the Le-Net-5 architecture is designed, in which all nonlinear activation functions are composed of optical bistable hysteresis loop. The numerical simulation results demonstrate that the recognition rate on the Fashion-MNIST dataset can achieve 91.3%, which means that the optical neuromorphic computation can be implemented by utilizing the nonlinear optical effects themselves in the all-optical hardware. Such a scheme promises access to the all-optical neural network training in the optical hardware environment compared to numerical activation functions.
Exploring better image captioning with grid features

Authors:
raw_author_name: Jie Yan
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Yuxiang Xie
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Yanming Guo
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Yingmei Wei
raw_affiliation_string: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Xidao Luan
raw_affiliation_string: College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410073, China

Abstract: Abstract Nowadays, Artificial Intelligence Generated Content (AIGC) has shown promising prospects in both computer vision and natural language processing communities. Meanwhile, as an essential aspect of AIGC, image to captions has received much more attention. Recent vision-language research is developing from the bulky region visual representations based on object detectors toward more convenient and flexible grid ones. However, this kind of research typically concentrates on image understanding tasks like image classification, with less attention paid to content generation tasks. In this paper, we explore how to capitalize on the expressive features embedded in the grid visual representations for better image captioning. To this end, we present a Transformer-based image captioning model, dubbed FeiM, with two straightforward yet effective designs. We first design the feature queries that consist of a limited set of learnable vectors, which act as the local signals to capture specific visual information from global grid features. Then, taking augmented global grid features and the local feature queries as inputs, we develop a feature interaction module to query relevant visual concepts from grid features, and to enable interaction between the local signal and overall context. Finally, the refined grid visual representations and the linguistic features pass through a Transformer architecture for multi-modal fusion. With the two novel and simple designs, FeiM can fully leverage meaningful visual knowledge to improve image captioning performance. Extensive experiments are performed on the competitive MSCOCO benchmark to confirm the effectiveness of the proposed approach, and the results show that FeiM yields more eminent results than existing advanced captioning models.
HousePrice_ML: An Efficient Framework for House Price Prediction Using Soft Computing

Authors:
raw_author_name: Maged Farouk
raw_affiliation_string: Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt
raw_author_name: Diaa AbdElminaam
raw_affiliation_string: Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt

Abstract: Predicting housing prices is important to many people, such as home buyers, real estate agents, and investors. By harnessing the power of machine learning models, this paper aims to develop a highly efficient system to calculate reliable housing price forecasts. The results of this research can facilitate decision-making processes, enable more informed investments, and improve the overall buying and selling experience in the real estate market. The relationship between house prices and the economy is an important motivating factor for predicting house prices. This paper focuses on how to predict housing prices using machine learning techniques. This paper proposes an efficient framework For prediction houses using six machine learning algorithms ( SVM, Tree, Neural Network, KNN, Linear Regression, Gradient Boosting). In best model 1, the number of fields equals Gradient Boosting; in best model 2, the number of fields equals Linear Regression; in Best model 3 number of fields equals Gradient Boosting. The best all model equal model 2 equal Linear Regression.
Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Authors:
raw_author_name: Zahra Nouri
raw_affiliation_string: Computer Engineering Department, University of Bojnord, Bojnord, Iran
raw_author_name: Vahid Kiani
raw_affiliation_string: Computer Engineering Department, University of Bojnord, Bojnord, Iran
raw_author_name: Hamid Fadishei
raw_affiliation_string: Computer Engineering Department, University of Bojnord, Bojnord, Iran

Abstract: Abstract Today's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO .