Reviewerly/exHarmony huggingface dataset:
Blockchain-based applications and energy effective electric vehicle charging – A systematic literature review, challenges, comparative analysis and opportunities

Blockchain is a secure distributed system having a shared resource that enables participants to read and update a common shared ledger in a decentralized fashion. It provides an immutable and tamper-resistant distributed ledger with no central authority and no single point of failure. Consensus protocols are the backbone of any blockchain system as these protocols enable participant nodes to reach an agreement in a trustless environment. The performance of a typical blockchain system largely depends upon selecting the best-fit consensus protocol. Blockchain has influenced many application areas apart from crypto currencies such as document verification, copyrighting of art and craft, verification of counterfeit products and electric vehicle (EV) charging. This review paper discusses various consensus mechanisms in public, private, and consortium-based blockchains. The review study also covers the impact of consensus protocols on the performance scalability of the blockchain solution. The blockchain application area focused in this study is related to electric vehicle management and charging. The paper elaborates on how blockchain is enabling energy-efficient and secure EV charging. It also covers energy trading in a typical EV environment.
Supply Chain Collaboration and Intention to Use Blockchain: Mediating Role of Perceived Usefulness

This study aims to examine the mediating role perceived usefulness between supply chain collaboration and the intention to use blockchain in the textile industry of Pakistan. A cross-sectional method is conducted, and data is gathered with the help of the adopted questionnaire from previous studies. The HR department of the concerned firm is requested to circulate the printed questionnaire among middle-level employees of the firm. A total of 240 cases are considered in this study. Five Likert scales, from strongly disagree to agree strongly, are considered to test the suggested hypotheses based on previous studies. The findings of this study confirmed the direct effect of supply chain collaboration on the intention to use the blockchain and the partial mediation effect of perceived usefulness is revealed. There are few studies on supply chain collaboration, blockchain use, and perceived usefulness in developing countries like Pakistan's textile industry, especially among middle-level employees. Existing scholarship ignores emerging countries' textile sectors and extends results to other industries. The insights also help managers create blockchain-integrated supply chain strategies. Blockchain technology may be maximized via technology providers and industry-wide cooperation. These practical consequences may improve developing nations' textile supply chains' efficiency, transparency, and competitiveness. The insights also help managers create blockchain-integrated supply chain strategies. Blockchain technology may be maximized via technology providers and industry-wide cooperation.
The Use Of Homonyms Words In Top 5 Tongue Twister English- Learn English With English Class

The purpose of the study is to determine the types of homonymous words and the meaning of each type of homonymous word in the top 5 Tongue twister English- learn English with English class on the YouTube channel Learning English. The data was analyzed using Parera's (2004) theory. The research design is qualitative, and the data sources for this investigation are the homonyms that appear within the tongue twister. According to the data, there are two types of homonyms: homophones and homographs. There are ten homophones on the video, two in each tongue twister, while there is only one homograph in the "if two witches were to watch two watches, which one would they watch?" tongue twister. It can be concluded that homophones shape the majority of homonyms. Understanding homonym and homograph usage and distinctions in tongue twisters and also to improve the use of pronunciation is the objective of this research
The Functions Of Derivational Suffixes Found In The “Moby-Dick” Novel By Herman Melville

This research collaborates with the examination of derivational suffixes, which play a crucial role in language development. These suffixes bring about changes in word meanings and grammatical structures, ultimately improving vocabulary and communication skills, a significant advantage for students. The data were gathered from a novel named “Moby-Dick,” consist of 786 pages, and 135 Chapters. The study employed a descriptive qualitative approach for its analysis. The theories are from Katamba (1993) and Carstairs-McCarthy (2002). The data collection techniques encompassed reading the novel; identifying and underlining words with potential suffixes; and marked words are categorized in the last step of data collection. The study's outcome revolved around the examination of the functional aspects of derivational suffixes, specifically delving into both class-maintaining and class-changing derivational suffixes. The total dataset comprised were 170. The class changing of derivational suffixes in The Moby-Dick novel was adverb derived from adjective (75), adjective derived from noun (52), verb derived from noun (1), noun derived from verb (40). It could be concluded that the highest frequency of data was adjectives that get the highest percentage, namely 75 words. The class maintaining of derivational suffixes in The Moby-Dick novel was nouns derived from nouns (2) there was no data found in the novel about adjective derived from adjective. This research’s implications lie in deepening literary analysis by revealing how derivational suffixes enrich the interpretation of "Moby-Dick," enhancing character development, symbolism, and linguistic nuances within the text.
Multi-modal Stream Fusion for Skeleton-Based Action Recognition

In recent years, graph convolution networks (GCN) have been widely used in skeleton-based action recognition to pursue higher accuracy. In general, traditional approaches directly integrate different modalities together using uniform fusion weights, which results in inadequate information fusion across modalities, sacrificing flexibility and robustness. In this paper, we explore the potential of adaptively fusing different modalities, and deliver a new fusion algorithm, coined Multi-modal Stream Fusion GCN (MSF-GCN). In principle, our proposed algorithm consists of three branches: JS-GCN, BS-GCN, and MS-GCN, corresponding to joint, bone, and motion modeling, respectively. In our design, the motion patterns between the joint and bone modalities are dynamically fused using an MLP layer. After conducting typical motion modeling, the static joint and bone branches are accompanied to perform the final fusion for the category predictions. Our MSF-GCN emphasizes static and dynamic fusion simultaneously, which greatly increases the interaction degree between the information of each modality, with improved flexibility. The proposed fusion strategy is applicable to different backbones, exhibiting the power to boost performance with marginal computation increase. Extensive experiments on a widely-used NTU-RGB+D dataset demonstrate that our model can achieve better or comparable results to current solutions, reflecting the merit of our fusion strategy.
Adaptive Fine-Grained Region Matching for Image Harmonization

Image harmonization aims to generate composite images that are visually consistent by adjusting the foreground to be compatible with the background. However, previous image harmonization methods overlook the fact that in a real image, the appearance (e.g., illumination, color temperature, saturation, hue, and texture) of different regions can vary significantly depending on content and position. For each foreground region, the background regions related to it should be taken as major references to adjust its appearance. To address this, a fine-grained appearance translation strategy is designed in this work. When adjusting the appearance of each foreground region, our method pays more attention to the background regions that are more relevant to it based on content similarity and position information. Furthermore, a multi-scale feature calibration strategy is introduced to adaptively calibrate the fine-grained features. Finally, an adaptive reconstruction strategy is proposed to further improve the harmonization result. Extensive experiments show our method significantly reduces parameters and achieves state-of-the-art performance compared with previous methods.
End-To-End Phase Retrieval from Single-Shot Fringe Image for 3D Face Reconstruction

Phase retrieval is a critical aspect of structured light projection-based three-dimensional (3D) face imaging. The accurate extraction of desired phase information from a minimal number of fringe images without motion blur distortion remains a major challenge. Deep learning has gained attention in solving various optical measurement tasks, and in this study, we propose a deep learning-based dual-generator neural network (DGNET) that consists of two generators in series. The first generator filters interference caused by invalid regions, while the second converts valid regions in the fringe pattern into the final unwrapped phase. Moreover, the DGNET utilizes a single fringe image as input, which enhances the measurement speed and eliminates the impact of pose changes. Experimental results confirm the accuracy and feasibility of performing a phase-retrieval task from a single fringe pattern using the proposed DGNET.
CSDNet: Contrastive Similarity Distillation Network for Multi-lingual Image-Text Retrieval

Cross-modal image-text retrieval is a crucial task in the field of vision and language, aimed at retrieving the relevant samples from one modality as per the given user expressed in another modality. While most methods developed for this task have focused on English, recent advances expanded the scope of this task to the Multi-lingual domain. However, these methods face challenges due to the limited availability of annotated data in non-English languages. In this work, we propose a novel method that leverages an English pre-training model as a teacher to improve Multi-lingual image-text retrieval performance. Our method trains a student model that produces better Multi-lingual image-text similarity scores by learning from the English image-text similarity scores of the trained teacher. We introduce the contrastive loss to align the two different representations of the image and text, and the Contrastive Similarity Distillation loss to align the Multi-lingual image-text distribution of the student with that of the English teacher. We evaluate our method on two popular datasets, i.e., MS-COCO and Flickr-30K, and achieve state-of-the-art performance. Our approach shows significant improvement over existing methods and has potential for practical applications.
MGP-Net: Margin-Global Information Optimization-Prototype Network for Few-Shot Ancient Inscriptions Classification

This article focuses on the challenge of classifying bronze inscription rubbings, which have a limited number of samples and diverse characteristics. Traditional classification methods have failed to produce satisfactory results. With the emergence of meta-learning, few-shot image classification has become a popular research topic. This approach allows a classifier to recognize datasets outside the training set and complete classification with only a small number of samples. However, due to the existence of multiple categories in the ancient inscription dataset and the tendency for overfitting, existing prototype network structures have not achieved satisfactory prediction accuracy on ancient inscription datasets. To address this challenge, we propose two strategies. The first strategy is the Margin Prototype (MP), which expands the distribution of different class prototypes during the softmax operation. The second strategy is the global information optimization strategy (GioP), which reverses the prediction of support set samples to obtain more representative prototypes. Our proposed method achieves better accuracy without adding new parameters to the model. The end-to-end structural pattern remains unchanged.
Foreign Object Detection Based on Compositional Scene Modeling

We are dedicated to researching the problem of foreign object detection in industrial production. Due to dynamic interference such as occlusion, lighting, and color difference in scenes in industrial production, it is difficult for many anomaly detection methods to maintain high performance. We propose a foreign object detection method based on compositional scene modeling, which includes a background denoising module and a foreground reconstruction module. In the background denoising module, we model dynamic disturbances in the scene into the background representation via a deep generative model. In the foreground reconstruction module, we use an autoencoder to learn abnormal foreground representations to achieve foreign object detection. To verify the effectiveness of our method, we simulate an industrial production environment and construct a synthetic dataset in a circuit board assembly scenario. Experiments show that our proposed architecture has good performance on both synthetic dataset and real dataset. While maintaining a low false detection rate, all abnormal objects are detected, with a miss detection rate of 0% in real dataset.
Community preserving social recommendation with Cyclic Transfer Learning

Transfer learning-based recommendation mitigates the sparsity of user-item interactions by introducing auxiliary domains. Social influence extracted from direct connections between users typically serves as an auxiliary domain to improve prediction performance. However, direct social connections also face severe data sparsity problems that limit model performance. In contrast, users’ dependency on communities is another valuable social information that has not yet received sufficient attention. Although studies have incorporated community information into recommendation by aggregating users’ preferences within the same community, they seldom capture the structural discrepancies among communities and the influence of structural discrepancies on users’ preferences. To address these challenges, we propose a community-preserving recommendation framework with cyclic transfer learning, incorporating heterogeneous community influence into the rating domain. We analyze the characteristics of the community domain and its inter-influence on the rating domain, and construct link constraints and preference constraints in the community domain. The shared vectors that bridge the rating domain and the community domain are allowed to be more consistent with the characteristics of both domains. Extensive experiments are conducted on four real-world datasets. The results manifest the excellent performance of our approach in capturing real users’ preferences compared with other state-of-the-art methods.
On the hybridization of geometric semantic GP with gradient-based optimizers

Abstract Geometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.
A Multimodal Text Block Segmentation Framework for Photo Translation

Nowadays, with the vigorous development of OCR (Optical Character Recognition) and machine translation, photo translation technology brings great convenience to people’s life and study. However, when translating the content of an image line by line, the lack of contextual information in adjacent semantic-related text lines will seriously influence the actual effect of translation, making it difficult for people to understand. To tackle the above problem, we propose a novel multimodal text block segmentation encoder-decoder model. Specifically, we construct a convolutional encoder to extract the multimodal representation which combines visual, semantic, and positional features together for each text line. In the decoder stage, the LSTM (Long Short Term Memory) module is employed to output the predicted segmentation sequence inspired by the pointer network. Experimental results illustrate that our model outperforms the other baselines by a large margin.
ProMoTe: A Data Product Model Template for Data Meshes

As the shortcomings of monolithic data platforms such as data lakes are quickly becoming more grave and evident, many organisations are struggling to transition to data meshes, making data available for consumption in a decentralised manner. However, the emerging data mesh paradigm fails to provide sufficient (modelling) support to effectively create, manage, and describe data products, the architectural quanta of a data mesh. In this work, we introduce the data Product Model Template (ProMoTe): a formal meta-model of data products that is fully aligned with a data mesh. ProMoTe was devised, explored and partially validated based on industry requirements in tandem with academic literature and is currently being used by a major Dutch Telecom company to enable their data mesh transition.
Sanity-Checking Multiple Levels of Classification

Multiple levels of classification naturally occur in many domains. Several multi-level modeling approaches account for this and a subset of them attempt to provide their users with sanity-checking mechanisms in order to guard them against conceptually ill-formed models. Historically, the respective multi-level well-formedness schemes have either been overly restrictive or too lax. Orthogonal Ontological Classification has been proposed as a foundation that combines the selectivity of strict schemes with the flexibility afforded by laxer schemes. In this paper, we present a formalization of Orthogonal Ontological Classification, which we empirically validated to demonstrate some of its hitherto only postulated claims using an implementation in ConceptBase. We discuss both the formalization and the implementation, and report on the limitations we encountered.
An Ontology for Context Modeling in Smart Spaces

Smart environments are one of the hot topics in the recent scientific literature in computer science. An essential aspect of these applications is how to obtain data about their users and understand them. Context-aware approaches proved to be successful in understanding these data. Therefore, user context representation is one of the problems to address in this domain. Context information can contain multidimensional information. This paper presents an ontology for representing context in smart environments called SpaceCOn. This ontology contains a definition of several context-related concepts. This ontology can be a strategic tool to integrate context data from different sources in large smart environments, such as smart universities and cities. Tests carried out in a case study demonstrated the developed ontology’s potential.
Use of Competency Questions in Ontology Engineering: A Survey

The interest in the ontology subject has grown in recent decades. Ontologies can be used to assign semantics to information items and solve interoperability and knowledge-related problems. Many methods have been proposed to improve the ontology engineering process. The use of competence questions (CQs) is suggested by several of them as a means to define the ontology requirements and help identify the necessary concepts, properties, and relations. CQs are questions that the ontology should be able to answer. Thus, they provide a mechanism to verify if the ontology is in accordance with the established requirements and properly represents the desired knowledge. Despite the important role of CQs, there is a lack of deeper investigation to provide evidence about their use. Therefore, aiming to investigate how CQs have been used in ontology engineering practice, we performed a survey with 63 ontology engineers. The results indicate that CQs have helped mainly to define the ontology scope and evaluate the ontology conceptualization. However, ontology engineers still face difficulties when writing, using, and managing CQs. Although there is a range of methods and tools that support ontology development, guidance regarding CQs is still limited. This paper presents our study and discusses its main findings.
Building a Framework for Identifying Arabic Dialects Using Deep Learning Techniques

Statista statistics show that social media usage has rapidly increased over the past ten years, and that by 2021, there will be approximately 4 billion 871 million Internet users worldwide. This is because mobile phone platforms allow users to express their sentiments and opinions in a variety of languages, including Arabic. One of the most widely used social media sites, Twitter offers a conducive environment for users to voice their thoughts. It is used by magazines and government websites to publish official statements and decisions, and while they write in Modern Standard Arabic, consumers interact with it in their native languages. To tackle issues with natural language processing, such as sentiment analysis and translation, a huge volume of data in many languages must be interpreted and processed. We combine a dataset from Katherine that covers the dialects of 8 countries with a dataset from the NADI shared tasks that was gathered via Twitter and includes the dialects of 21 countries. Three deep learning models with various word embeddings were used. The best results were obtained by CNN/BiLSTM with FastText (51.4
A Conceptual Modeling Approach for Risk Assessment and Mitigation in Collision-Free UAV Routing Planning for Beyond-the-Visual-Line-of-Sight Flights

The domain of beyond-the-visual-line-of-sight (BVLOS) flights of unmanned aerial vehicles (UAVs) has unique navigational challenges such as the reliable estimation, evaluation and mitigation of the risk of the associated flight paths. To tackle these challenges domain data from heterogeneous sources is needed. Failing to integrate this data carefully could result in inaccurate navigation decisions, poor situational awareness, and in general unsafe flight operations. In this paper we present a conceptual model that can be used to design conceptual graph databases that integrate the information of several domains to BVLOS. Furthermore, we show that our proposed conceptual graph database schema scales well for increasing sizes of stored domain and application data and permits constant query execution times for important recurring queries.
On the characterization of some non-abelian simple groups using codegree set

Let G be a finite group and χ∈Irr(G). The codegree of χ is defined as cod(χ)=|G:ker(χ)|/χ(1) and cod(G)={cod(χ)|χ∈Irr(G)} is called the set of codegrees of G. In this paper, we show that the set of codegrees of U4(2),S4(q)(q=2f≥4),U4(3),2F4(2)′,J3,G2(3),A9,J2,PSL(4,3),McL,S4(5),G2(4),HS,O′N and M24 determines the group up to isomorphism.