Reviewerly/exHarmony huggingface dataset:
An examination of distributed and decentralized systems for trustworthy control of supply chains

Establishing a well-functioning Supply Chain Management (SCM) system is paramount during challenging times such as pandemics, natural disasters, and international conflicts. The complexity of global supply chains necessitates efficient systems, procedures, and personnel to ensure optimal results. Poor coordination among entities can lead to increased counterfeit products, increased ocean transportation costs, more expensive freight brokerage, bottlenecks in cargo flow, congestion, and complications in product accountability. To ensure a smooth and hassle-free operation, it’s essential to maintain unambiguity and accuracy throughout every process. Therefore, it is vital to have effective systems, procedures, and personnel in place for SCM. The challenges encountered in SCM can be effectively tackled by utilizing blockchain technology. The architecture of blockchain technology is characterized by its distributed, decentralized and robust safety measures, which guarantee the integrity of data storage and its distribution across a meticulously organized ledger. Users can confidently rely on this innovative design’s transparency, reliability, and safety. Implementing blockchain technology carries immense potential in bolstering safety and privacy measures in diverse sectors, including agriculture, healthcare, Goods and Services Tax (GST), academics, e-voting and automobile. This investigation delves into the practical applications of blockchain technology for SCM. It thoroughly analyses existing research and literature to uncover the latest advancements and potential future breakthroughs in this area.
Research on Motion Control Strategy of Flexible Manipulator Based on swarm intelligence Optimization

The requirement for motion control of robotic arms in industrial settings is a dynamic field. This study examines the principles and derivation of the kinematics of the robotic arm based on the D-H parameter model. Additionally, the introduction of the seventh joint is proposed as a faster solution for solving the inverse kinematics of the robotic arm. Swarm intelligence optimization for path planning is currently advancing, and our proposed improved algorithm, the Golden Eagle search algorithm, enhances the traditional Golden Eagle search algorithm Jining by integrating a stochastic gradient descent strategy and Cauchy mutation strategy. We compare our IGEO algorithm with various other algorithms, and the findings demonstrate that the robotic arm can adeptly circumnavigate obstacles while walking seamlessly through environments with multiple obstacles. The IGEO algorithm is adept at navigating paths obstructed by multiple obstacles. It improves the accuracy by 15.38% as compared to the conventional algorithm and also improves it a lot as compared to other optimisation algorithms by up to 29.88%. It provides a solution to the path planning problem of robotic arms with excellent robustness and accuracy in finding the shortest collision-free path.
Advances and Challenges in Intrinsic Plagiarism Detection: A Comprehensive Study

Intrinsic plagiarism detection is a critical domain in the field of text analysis that aims to identify plagiarized content within a document to check whether parts of the document are from the same author. As the development of Large Language Models (LLMs) based content generation tools such as, ChatGPT is publicly available, the challenge of intrinsic plagiarism has become increasingly significant in various domains. Consequently, there is a growing demand for robust and accurate detection methods to address this evolving landscape. In this context, the present study conducts a comprehensive Systematic Literature Review (SLR) to explore the landscape of intrinsic plagiarism detection. We systematically collected and rigorously analyzed 44 research papers that delve into various aspects of the domain, including common datasets, feature extraction techniques and intrinsic plagiarism detection methods. This SLR also highlights the evolution of detection approaches over time and the challenges faced in this context especially challenges associated with low-resource languages. To the best of our knowledge, there is no SLR exclusively based on the intrinsic plagiarism detection that bridge the gap in existing literature and offering valuable insights to researchers and practitioners. By consolidating the state-of-the-art findings, this SLR serves as a foundation for future research, enabling the development of more effective and efficient plagiarism detection solutions to combat the ever-evolving challenges posed by plagiarism in today’s digital age.
A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving

In this paper, we propose a new RGB-thermal image segmentation method based on parameter sharing and attention fusion for safe autonomous driving. An encoder-decoder network structure is adopted. The encoder, which has shared convolution layer parameters and private batch normalization layer parameters (parameter sharing scheme), is used to extract features from RGB and thermal images. The extracted features are then fused by spatial and channel attention. The output of each residual block is fused, and the self-learning weight is used to integrate the fusion information of all residual blocks of the same levels. Subsequently, the fused features are integrated through a feature integration (FI) module in the decoder. Cross-entropy supervision of segmentation and edge is performed on the outputs of the decoders. Our proposed method is evaluated and compared with 17 state-of-the-art image segmentation methods, both qualitatively and quantitatively on the MFNet dataset which includes various objects in urban scenes. The results show that the proposed method outperforms previous methods by at least 0.3% and 1.8% in MRecall and MIoU, respectively, providing foundations for the development of autonomous driving technologies for safety enhancement.
Bridge Gap in Pixel and Feature Level for Cross-Modality Person Re-Identification

Visible thermal person re-identification (VT-ReID) plays a vital role in intelligent surveillance systems, particularly in weak lighting environments. VT-ReID faces substantial challenges, including the cross-modality gap and intra-class variations. Existing methods address these challenges through either pixel-level image translation techniques or feature-level metric learning techniques. However, the former approaches require additional computational costs and often generate noisy images, making model training challenging. The latter methods focus on constraining the relations between individual instances or class centers, while often ignoring joint consideration of the relationship between the two aspects. In addition, these works do not fully investigate the mutual benefits at both pixel-level and feature-level. To address these limitations, we propose a unified Dual-level Smooth Gap (DSG) learning framework that simultaneously smooths the cross-modality gap at the pixel and feature levels. Specifically, on the one hand, we develop a parameter-free Class-aware Modality Mix (CMM) to smooth the cross-modality gap at the pixel level. CMM can capture and explore internal information between the two modalities by mixing images from different modalities belonging to the same class. On the other hand, we devise an efficient Center-guided Metric Learning (CML) to reduce the inter-modality discrepancy and intra-class variations at the feature level. CML enhances model discrimination and generalization by enforcing constraints on both class centers and instances. Experiments on two benchmark datasets demonstrate the mutual benefits of our proposed and show the superior performance of our method over state-of-the-art methods.
StoryLens: Personalizing News Recommendations for Older Adults with Their Life Stories

Personalizing news recommendations for older adults using their life stories has the potential to enhance their social connectedness, cognitive well-being, and overall quality of life. This approach has the potential to benefit not only older adults but also their family members and professional and informal caregivers who seek to provide human-centric care services. This paper explores this possibility by creating StoryLens—a personalized news recommender system that enables older adults to stay informed about current happenings around them while bringing back cherished memories. The StoryLens comprises four distinct modules: the Life Story Processing module, the News Crawling module, the News Recommendation module, and the News Display module. The Life Story Processing module creates a personalized profile of their news preferences by extracting relevant elements from individuals’ life stories. While the News Crawling module continuously crawls news from major news sites, the News Recommendation module deploys a unique algorithm calculating the similarity between the life story elements and the extracted features from the crawled news to personalize news recommendations. Finally, the News Display module displays recommended news with associated life stories to actively engage older adults. A survey shows that the StoryLens recommendations achieved average scores of 4.16 and 4.05 on diversity and relevance based on a 5-point Likert scale questionnaire.
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization

The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localizing manipulated areas. Nonetheless, with limited fine-grained pixel-wise supervision labels, deepfake detection models perform unsatisfactorily on precise forgery detection and localization. To address this challenge, we introduce the well-trained vision segmentation foundation model, i.e., Segment Anything Model (SAM) in face forgery detection and localization. Based on SAM, we propose the Detect Any Deepfakes (DADF) framework with the Multiscale Adapter, which can capture short- and long-range forgery contexts for efficient fine-tuning. Moreover, to better identify forged traces and augment the model’s sensitivity towards forgery regions, Reconstruction Guided Attention (RGA) module is proposed. The proposed framework seamlessly integrates end-to-end forgery localization and detection optimization. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach for both forgery detection and localization. The codes are available at Link .
Text-Based Face Retrieval: Methods and Challenges

Previous researches on face retrieval have concentrated on using image-based queries. In this paper, we focus on the task of retrieving faces from a database based on queries given as texts, which holds significant potential for practical applications in public security and multimedia. Our approach employs a vision-language pre-training model as the backbone, effectively incorporating contrastive learning, image-text matching learning, and masked language modeling tasks. Furthermore, it employs a coarse-to-fine retrieval strategy to enhance the accuracy of text-based face retrieval. We present CelebA-Text-Identity dataset, comprising of 202,599 facial images of 10,178 unique identities, each paired with an accompanying textual description. The experimental results we obtained on CelebA-Text-Identity demonstrate the inherent challenges of text-based face retrieval. We expect that our proposed benchmark will encourage the advancement of biometric retrieval techniques and expand the range of applications for text-image retrieval technology.
Boosting Medical Image Segmentation with Partial Class Supervision

Medical image data are often limited due to expensive acquisition and annotation processes. Directly using such limited annotated samples can easily lead to the deep learning models overfitting on the training dataset. An alternative way is to leverage the unlabeled dataset which is free to obtain in most cases. Semi-supervised methods using a small set of labeled data and large amounts of unlabeled data have received much attention. In this paper, we propose a novel semi-supervised method for medical image segmentation that uses partial class supervision. Specifically, for a given multi-class label, we extend it to generate several labeled images with partial classes annotated while others remain unannotated. The unlabeled part in the partially annotated label is supervised by a pseudo-labels approach. In addition, we project the labeled pixel values into pseudo-labels to achieve rectified pixel-level pseudo-labels. In this way, our method can effectively increase the number of training samples. The experimental results on two public medical datasets of heart and prostate anatomy demonstrate that our method outperforms the state-of-the-art semi-supervised methods. Additional experiments also show that the proposed method gives better results compared to fully supervised segmentation methods.
Facial Adversarial Sample Augmentation for Robust Low-Quality 3D Face Recognition

Compared with traditional 3D face recognition tasks using high precision 3D face scans, 3D face recognition based on low-quality data captured by consumer depth cameras is more practicable for real-world applications. However, it is also more challenging to deal with the variations of facial expressions, poses, occlusions, data noises, scanning distance, and so on. In this paper, we propose a novel robust low-quality 3D face recognition method based on Facial Adversarial Sample Augmentation, namely FASA-3DFR. It consists of two modules, namely facial adversarial sample generation, and facial adversarial sample training. For the first module, to enlarge the diversity of facial adversarial samples and boost the robustness of 3DFR, we propose to utilize the Kullback-Leibler divergence to maximize the distribution distance between the original and adversarial facial samples. For the second module, a distribution alignment loss is designed to make the distribution of facial adversarial samples gradually close to the one of the original facial samples, and the common and valuable information from both distributions can be effectively extracted. Extensive experiments conducted on the CAS-AIR-3D Face database show the effectiveness of the proposed method.
VERSIFICATION OF LANGUAGE UNITS FROM THE LENS OF MORPHOLOGICAL STATISTICS (MODERN BRITISH AND GERMAN POETRY)

The article presents a statistical linguistic analysis of the morphological structure represented in British poetry. The part-of-speech belonging of words as a subject of stylometry is analyzed. The use of statistical text analysis to determine the set of keywords in British poetry helps to quantify the parameters of its linguistic style, and this complements the qualitative analysis of linguistic phenomena. The statistics of the researched language of British and German poetry includes the analysis of various linguistic aspects that are characteristic of poetic speech in the contemporary literature of Great Britain. The article focuses on the scientific interest in the morphological statistics of the language of British poetry for the analysis of the structural features of the poetic text. Nouns in poetry are used to name objects, ideas, abstract concepts, as well as to create images and symbols. They can play a significant role in creating images and concepts in a poetic text. Verbs define actions, events, and the state of objects in poetry; other grammatical classes of words help create rhythm, pace, movement, and expressiveness in a poetic text. Service words are used to build syntactic structures and provide coherence between words and sentences in poetry. Determining the number of linguistic units of a certain grammatical class in poetry serves as a reliable tool for studying the reflection of the most essential features and statistical parameters of poetic discourse. The material presented in the article for the study and further analysis of their morphological potential was selected from contemporary original texts written by British poets contained in literary anthologies, libraries and web resources specializing in poetry. The examples for linguistic morphological analysis are the works of Carol Ann Duffy, Seamus Justin Heaney, Simon Armitage, T. C. Eliot Doris Kareckendey, Ulrike Almut Sandig, Jan Wagner, Durs Grünbein and Kai Wieland. It has been observed that there are no strict rules as to which parts of speech should prevail in poetry in general, and this always remains in the sphere of the author's creativity. The analyzed aspects of morphological analysis can be applied to the poetry of different historical periods and literary trends in British and German poetry in order to study their features and the evolution of linguistic expression, which determines the prospects for further research in this area.
Blockchain and machine learning for data analytics, privacy preserving, and security in fraud detection

Blockchain technology has emerged as a revolutionary distributed ledger system with the potential to transform various industries, including finance, supply chain, healthcare, and more. However, the decentralized nature of blockchain introduces unique challenges in terms of fraud detection and prevention. This abstract provides an overview of the current state of research and technologies related to fraud detection in blockchain technology-based systems. The paper begins by discussing the fundamental characteristics of blockchain, highlighting its immutability, transparency, and decentralization. These characteristics provide a promising foundation for ensuring data integrity and security but also pose significant challenges in detecting and mitigating fraudulent activities. Next, the paper explores various types of fraud that can occur in blockchain systems, such as double-spending, Sybil attacks, 51% attacks, smart contract vulnerabilities, and identity theft. Each type of fraud is explained along with its potential impact on the integrity and reliability of blockchain systems. To address these challenges, the paper presents an overview of existing fraud detection techniques in blockchain systems. These techniques encompass a range of approaches, including anomaly detection, machine learning algorithms, consensus mechanisms, cryptographic techniques, and forensic analysis. The strengths and limitations of each technique are discussed to provide a comprehensive understanding of their applicability in different scenarios. Furthermore, the paper highlights emerging trends in fraud detection research within the blockchain domain. These trends include the integration of artificial intelligence and blockchain technology, the use of decentralized and federated machine learning approaches, the development of privacy-preserving fraud detection mechanisms, and the utilization of data analytics and visualization techniques for improved detection and investigation. The paper concludes by emphasizing the importance of continuous research and development in fraud detection for blockchain technology-based systems. As blockchain adoption expands across industries, it is crucial to enhance the security and trustworthiness of these systems by effectively detecting and preventing fraud. Future directions for research and potential challenges are also discussed, encouraging further exploration in this vital area of study.
“Objection, Your Honor”: Use of Social Media by Civilians to Challenge the Criminal Justice System

Social media constitute useful and effective platforms for miscarriage of justice campaigners tochallenge state authorities and decisions taken by the criminal justice system. To characterizesuch endeavors, this study analyzes the activity in such a major group dedicated to the murdercase of Tair Rada and the trial of Roman Zadorov, one of the most controversial legal cases inIsrael’s history. Using digital data extraction and linguistic analysis tools, we focus on fivethemes: (1) the central role of group administrators in directing the discourse and setting thegroup agenda; (2) correspondence of group activity with off-line events and mainstream mediacoverage; (3) skewed distribution of post publications per user and engagement measures perpost; (4) prominent topics in group discussions, revolving around key figures, institutions andofficials, making justice, considering alternative theories and examining investigative and forensicmaterials; and (5) the framing of key figures, institutions, and values in portraying a somewhatdichotomous image of a corrupted justice system, an innocent man wrongly convicted and aFacebook group in the search for the truth.
Data sharing or algorithm sharing?

Data combination and analytics can generate valuable insights for firms and society as a whole. Multiple firms can do so by means of new technologies that bring the algorithm to the data (“algorithm sharing”) or, more conventionally, by sharing the data (“data sharing”). Algorithm-sharing technologies are gaining traction because of their advantages in terms of privacy, security, and environmental impact. We present a model that allows us to study the economic incentives generated by these technologies for both firms and a platform facilitating data combination. We find that, first, the platform chooses data sharing unless algorithm sharing’s analytics are sufficiently superior to those associated to data sharing. Second, we identify the properties of the analytics benefit function that ensure that algorithm sharing results in a higher total data contribution. Third, we highlight scenarios in which, in presence of data externalities, there can be a mismatch between the choice of the platform and the preference of a social planner.
Metaverse as a cutting-edge platform for attaining Sustainable Development Goals (SDGs)

The Metaverse, an immersive virtual reality realm where users engage with computer-generated environments, is swiftly emerging as a transformative platform poised to address and propel Sustainable Development Goals (SDGs). This research delves into the intersections between the Metaverse and SDGs, shedding light on how this cutting-edge technology can be harnessed to propel sustainable development across diverse sectors. Commencing with a comprehensive overview, the paper details the evolution, key components, and immersive capabilities of the Metaverse. Underscoring its dynamic and interactive essence, the research emphasizes the potential of the Metaverse to revolutionize conventional approaches to education, healthcare, environmental conservation, and economic empowerment. A thorough analysis unveils the Metaverse's role in democratizing education by transcending geographical boundaries and providing inclusive learning environments. The immersive nature of the Metaverse facilitates experiential learning, enhancing education accessibility, and fostering global collaboration to fulfill SDG 4 (Quality Education). Additionally, the paper explores how the Metaverse can contribute to healthcare solutions through virtual medical consultations, training healthcare professionals, and simulating medical scenarios. This innovative approach holds the potential to address healthcare disparities, advance SDG 3 (Good Health and Well-being), and improve overall public health. Environmental sustainability takes center stage as the Metaverse serves as a platform for raising awareness about climate change, promoting sustainable practices, and simulating eco-friendly solutions. This aligns with SDG 13 (Climate Action) and SDG 15 (Life on Land), emphasizing the Metaverse's capacity to inspire real-world environmental stewardship. Economic empowerment is addressed through discussions on virtual economies within the Metaverse, highlighting opportunities for entrepreneurship, job creation, and financial inclusion. This exploration corresponds to SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequality).