Reviewerly/exHarmony huggingface dataset:
Preserving Potential Neighbors for Low-Degree Nodes via Reweighting in Link Prediction

Link prediction is an important task for graph data. Methods based on graph neural networks achieve high accuracy by simultaneously modeling the node attributes and structure of the observed graph. However, these methods often get worse performance for low-degree nodes. After theoretical analysis, we find that current link prediction methods focus more on negative samples for low-degree nodes, which makes it hard to find potential neighbors for these nodes during inference. In order to improve the performance on low-degree nodes, we first design a node-wise score to quantify how seriously the training is biased to negative samples. Based on the score, we develop a reweighting method called harmonic weighting(HAW) to help the model preserve potential neighbors for low-degree nodes. Experimental results show that the model combined with HAW can achieve better performance on most datasets. By detailedly analyzing the performance on nodes with different degrees, we find that HAW can preserve more potential neighbors for low-degree nodes without reducing the performance of other nodes.
PatchFinger: A Model Fingerprinting Scheme Based on Adversarial Patch

As deep neural networks (DNNs) gain great popularity and importance, protecting their intellectual property is always the topic. Previous model watermarking schemes based on backdoors require explicit embedding of the backdoor, which changes the structure and parameters. Model fingerprinting based on adversarial examples does not require any modification of the model, but is limited by the characteristics of the original task and not versatile enough. We find that adversarial patch can be regarded as an inherent backdoor and can achieve the output of specific categories injected. Inspired by this, we propose PatchFinger, a model fingerprinting scheme based on adversarial patch which is applied to the original samples as a model fingerprinting through a specific fusion method. As a model fingerprinting scheme, PatchFinger does not sacrifice the accuracy of the source model, and the characteristics of the adversarial patch make it more flexible and highly robust. Experimental results show that PatchFinger achieves an ARUC value of 0.936 in a series of tests on the Tiny-ImageNet dataset, which exceeds the baseline by 19%. When considering average query accuracy, PatchFinger gets 97.04% outperforming the method tested.
FE-YOLOv5: Improved YOLOv5 Network for Multi-scale Drone-Captured Scene Detection

Due to the different angles and heights of UAV shooting, the shooting environment is complex, and the shooting targets are mostly small, so the target detection task in the drone-captured scene is still challenging. In this study, we present a highly precise technique for identifying objects in scenes captured by drones, which we refer to as FE-YOLOv5. First, to optimize cross-scale feature fusion and maximize the utilization of shallow feature information, we propose a novel feature pyramid model called MSF-BiFPN as our primary approach. Furthermore, to improve the fusion of features at different scales and boost their representational power, our innovative approach proposes an adaptive attention module. Moreover, we propose a novel feature enhancement module that effectively strengthens high-level features before feature fusion. This module effectively minimized feature loss during the fusion process, ultimately resulting in enhanced detection accuracy. Finally, the utilization of the normalized Wasserstein distance serves as a novel metric for enhancing the model's sensitivity and accuracy in detecting small targets. The experimental results of FE-YOLOv5 on the VisDrone data set show that mAP 0.5 has increased by 7.8 $$\%$$ , and mAP 0.5:0.95 increased by 5.7 $$\%$$ . At the same time, the training results of the model at $$960 \times 960$$ image resolution are better than the current YOLO series models, among which mAP 0.5 can reach 56.3 $$\%$$ . Based on the experiments conducted, it has been demonstrated that the FE-YOLOv5 model effectively enhances the accuracy of object detection in UAV capture scenes.
A survey of the development of image captioning techniques

Image description technology is an important research direction in the field of deep learning. It is a task that uses computer vision techniques and natural language processing techniques to generate textual descriptions of the image features extracted from the corresponding images into high-level semantic information, i.e. to enable computers to learn the ability to "read pictures and talk". This paper collates several representative research methods that have emerged successively in the continuous development of image description. The popular template- and retrieval-based image description methods at the beginning of the research, and later, as deep learning flourishes, deep learning-based image description techniques have become mainstream, starting from end-to-end encoder-decoder, subsequently, the model began to be refined using the attention mechanism, and nowadays, new techniques based on Transformer technology and generative adversarial networks have greatly improved the accuracy of description.
Adaptive Hard Parameter Sharing Method Based on Multi-Task Deep Learning

Multi-task learning (MTL) improves the performance achieved on each task by exploiting the relevant information between tasks. At present, most of the mainstream deep MTL models are based on hard parameter sharing mechanisms, which can reduce the risk of model overfitting. However, negative knowledge transfer may occur, which hinders the performance improvement achieved for each task. In this paper, for situations when multiple tasks are jointly trained, we propose the adaptive hard parameter sharing method. On the basis of the adaptive hard parameter sharing method, the number of nodes in the network is dynamically updated by setting a continuous gradient difference-based sign threshold and a warm-up training iteration threshold through the relationships between the parameters and the loss function. After each task fully utilizes the shared information, adaptive nodes are used to further optimize each task, reducing the impact of negative migration. By using simulation studies and instance analyses, we demonstrate theoretical proof that the performance of the proposed method is better than that of the competing method.
An Improved NSGA-II for UAV Path Planning

Palm oil is an edible vegetable oil that can be used in a wide range of products across different industries ranging from food and beverages, personal care and cosmetics, animal feed, industrial products, to biofuel. The palm oil industry contributes slightly less than 4% of Malaysia's overall GDP, making it the country's second-largest producer and exporter of palm oil worldwide. In Malaysia, it has been estimated that there are around 500,000 plantation workers in palm oil industries. In addition to getting a sufficient and steady supply of such usually low skilled workers, there are also issues related to the limits of the human body in performing tough physical work. As a result, UAVs may be utilized to support some of the processes in the palm oil businesses. However, the power of the batteries used in these UAVs is finite before they need to be recharged. Hence, the flight path for the UAV should be optimally computed for it to be able to cover the area it is assigned. In this paper, an improved Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was developed to compute the optimal flight path of UAVs which also includes the turning angle and elevation. Enhancements to the algorithm is done by improving the selection, crossover, and mutation operations of the genetic algorithm which helps to improve the convergence and diversity of the algorithm beside avoiding getting trapped in local optimal solutions. In the majority of the tests, the improved NSGA-II was able to generate paths that are better than those identified by the human expert. Moreover, the proposed improved NSGA-II algorithm was able to compute good paths in less than the threshold of 10 min.
Small-World Echo State Networks for Nonlinear Time-Series Prediction

Echo state network (ESN) is a reservoir computing approach for efficiently training recurrent neural networks. However, it sometimes suffers from poor performance and robustness due to the non-trainable reservoir. This paper proposes a novel computational framework for ESNs to improve prediction performance and robustness. A small-world network is applied as the reservoir topology, a biologically plausible unsupervised learning method named dual-threshold Bienenstock-Cooper-Munro learning rule is applied to adjust reservoir weights adaptively, and a recursive least-squares-based composite learning algorithm is introduced to update readout weights. The proposed method is compared with several kinds of ESNs on the Mackey-Glass system, a benchmark problem of nonlinear time-series prediction. Simulation results have shown that the proposed method not only achieves the best prediction performance but also exhibits remarkable robustness against noise.
SLAM: A Lightweight Spatial Location Attention Module for Object Detection

Aiming to address the shortcomings of current object detection models, including a large number of parameters, the lack of accurate localization of target bounding boxes, and ineffective detection, this paper proposes a lightweight spatial location attention module (SLAM) that achieves adaptive adjustment of the attention weights of the location information in the feature map while greatly improving the feature representation capability of the network by learning the spatial location information in the input feature map. First, the SLAM module obtains the spatial distribution of the input feature map in the horizontal, vertical, and channel directions through the average pooling and maximum pooling operations, then generates the corresponding location attention weights by computing convolution and activation functions, and finally achieves the weighted feature map by aggregating the features along the three spatial directions respectively. Extensive experiments show that the SLAM module improves the detection performance of the model on the MS COCO dataset and the PASCAL VOC 2012 dataset with almost no additional computational overhead.
Challenges of Blockchain Implementation for Patient Services

Today, technology dominates businesses across all scales and sectors, including healthcare. Blockchain (BC) is a pivotal technology in this transformation. It securely stores medical data, updates clinics seamlessly, enhances workflows, and thwarts hacking threats. However, research on BC's implementation challenges for patient services remains limited. This chapter investigates these challenges, categorizing BC into public, private, hybrid, and consortium forms. Major obstacles encompass interconnectivity, security, standardization absence, storage limits, hospital hesitancy, patient mistrust, cost, limited medical proficiency, and data governance. Overcoming these hurdles is crucial for widespread healthcare BC adoption, despite the abundant opportunities. In comparison to other technologies, BC's unique issues require deeper exploration. Researchers can scrutinize BC comprehensively for its business potential. This chapter stands as a prominent effort in pinpointing and analyzing BC's integration in healthcare.
Unveiling the Shadows

In an ever-evolving financial landscape, where FinTech is making commendable progress, one must be aware of the negative drawbacks that come with it as the opportunities for unscrupulous actors to exploit vulnerabilities and engage in fraudulent practices have expanded concurrently. This chapter discusses the negative influence of FinTech and explores various fraudulent services that have arisen due to technology's impact on finance. The aim is to examine the methods employed, associated risks, and outcomes of these deceptive activities while emphasizing effective strategies and recommended approaches for safeguarding ourselves against their harmful effects.
Prospects and Challenges of Blockchain Technology in Managing Healthcare Data

In terms of data transparency, traceability, immutability, audit, data provenance, flexible access, trust, privacy, and security, healthcare data management systems face significant hurdles. Additionally, a significant portion of the existing healthcare data management systems are centralized, which could be dangerous in the event of a single point of failure. The handling of data in the healthcare sector has the potential to undergo a big transformation. Blockchain is an emerging, disruptive, decentralized technology. In this chapter, the authors explore how utilizing blockchain for healthcare data management systems might spur innovation and result in significant advancements.
Security Social Networking and Cybersecurity Technology Challenges

Cyber-ethics, cyber-safety, and cyber-security problems must be incorporated into schooling beginning at a young age. Cyber ethics, safety, and security tendencies threaten security and necessitate increased government security and public awareness to protect individual information on the world wide web. This study focuses on social media and social network difficulties and risks due to increasing social network users. The chapter also includes a guideline for protecting individual personal information.
Understanding Blockchain's Role in Social Media Marketing

Blockchain technology's introduction to social media marketing is an influential force to transform security and build trust in a social media platform, thus encouraging greater consumer engagement. This chapter explores the economic benefits of combining blockchain and social media marketing, and how this creates a powerful synergy to address consumer behaviour, through a process of a systematic and thematic content analysis of 30 selected peer-reviewed literature as well as 11 pieces of grey literature (which included social media site posts and blogs) published between 2012 and 2021. The implementation of blockchain technology has the potential to bring about a strategic transformation by inculcating a change in the customer's behaviour towards the organisation. The information in this chapter will provide compelling support that organisations can increase their consumer as well as influencer numbers online and successfully entice high traffic to their home page by implementing blockchain technology in their social media marketing campaigns.
Conceptual Blockchain Analysis for Disaster Management

Blockchain is a transparent, encrypted, distributed database. It is utilised effectively by financial instruments, e-government, supply networks, and others. This paper examines emergency uses for the Blockchain. There needs to be a sound information system for managing and controlling disasters, making decisions, and creating a conceptual model from disaster and blockchain literature. This chapter discusses parties exchanging and storing data on one platform. This study proposes a system model for pre-, during-, and post-disaster user scenarios. The methodology asserts holistic disaster resolution.
Blockchain for Student Experience

The chapter aims to unveil blockchain's impact on student experience. As per the literature, blockchain benefits education through: smart contracts for tasks, curriculum, and documents; affordability and wider access; self-data analysis; safety; improved classroom efficacy; enhanced trust between recruiters and candidates; streamlined payments; clear new requirements; transparent credentials; new digital asset exchange; practical application of class knowledge; added credentials; training delivery; heightened visibility; accountability through smart contracts; lowered degree costs; research publication; innate student information processing; transparent transcripts; and cost-effectiveness. These advantages enhance student (customer) experience. For learners and educators, blockchain offers novel avenues in assessment, activity management, and progress tracking in education.
Parody against hate speech

Abstract Political crises often lead to the violent use of digital platforms. This text aims to analyze a particular response to that vitriolic discourse on Twitter: political parody. During the institutional crisis and political battle between the government of former Peruvian President Martín Vizcarra and the Peruvian Congress between 2017 and 2019, some political actors stood out, but not for good reasons. Karina Beteta, a right-wing Peruvian parliamentarian, used Twitter to interact with people, defend her decisions and attack anyone who did not share her viewpoints. She distinguished herself by tweeting confusing and, according to some, mocking texts. These tweets received an original counter-discourse with the creation of @smartbeteta, a parody account of politics. This paper analyzes the parodic performance by focusing on the pragmatic strategies embedded in @smartbeteta’s tweets and the role adopted by this account in the communication flows.
Challenges of Blockchain Usage in the Education Sector

The blockchain (BC) is the next technologically mediated socioeconomic megatrend in the age of technological revolution. In this chapter, the authors look at some of the obstacles that BC technology faces in the classroom. It is stated that the education sector cannot take for granted that the digital transformation. BC has many potential uses in education, including improving teacher and student agency, streamlining the rollout of educational projects, and establishing a transparent, decentralized system for accrediting and certifying students' proficiency. Educational BC with its distributed ledger offers innovative norms of crypto-learning and crypto-administration that are acceptable across companies and nations, strengthening thus the objectivity, authenticity, and control of information without being compromised by socio-economic instabilities. BC technology's delayed acceptance in the education sector mirrors that in the teaching and learning sectors, but it also presents several major hurdles, including a lack of funding, security, cost, scalability, and awareness.
Blockchain Application for Healthcare Data Management and Patient Experience

Blockchain technology is transforming the healthcare sector in light of data management and patient experience. It can be used in building a more secure data management platform that allows patients to retain ownership over their medical records. Permitted healthcare practitioners can also monitor patients' medical histories, view their most recent prescriptions, and provide prognoses with excellent patient experience. Ultimately, healthcare policymakers responsible for the regulation of innovation should create a favourable environment that will spur the building of blockchain digital solutions with the propensity of providing a single protocol of trust among healthcare stakeholders. This will standardise the implementation of blockchain in the global healthcare space. Green-oriented organisations should also join the conversation on how blockchains are designed, especially ensuring that blockchain is built with sustainability principles.
Blockchain and Customer Engagement

Business-to-consumer (B2C) commerce has flourished as a result of the widespread availability of advanced e-commerce platforms integrated with mobile applications, which have also reformed organizational structures and revitalized the value-generating processes. This chapter seeks to provide literature into the effect of blockchain and customer engagement. Blockchain helps firms to deal with customers by fostering security, transparency, and fast payment. It was concluded that blockchain leads to customer engagement. In the future, governments are likely to use it, and it will be globally recognised.
Rapid and Low-Cost Evaluation of Multi-fidelity Scheduling Algorithms for Hyperparameter Optimization

Hyperparameter optimization (HPO), the process of searching for optimal hyperparameter configurations for a model, is becoming increasingly important with the rise of automated machine learning. Using HPO can be incredibly expensive as models increase in size. Multi-fidelity HPO schedulers attempt to more efficiently utilize resources given to each model configuration by determining the order they are evaluated in and the number of epochs they are run for. Pre-tabulated benchmarks are often used to reduce the compute power required to evaluate state-of-the-art schedulers. However, over-reliance on evaluation using these benchmarks can lead to overfitting. To solve this problem, we introduce a Platform for Hyperparameter Optimization Search Simulation (PHOSS), that enables rapid HPO scheduler evaluation by dynamically generating surrogate benchmarks. PHOSS uses a computationally efficient and expressive exponential decay parametric modeling approach to accurately generate surrogate benchmarks from real-world dataset samples without having to train an expensive surrogate model. We demonstrate that PHOSS can simulate several state-of-the-art schedulers on real-world benchmarks 4.5 $$\times $$ faster while also dramatically reducing multi-GPU compute requirements by enabling full testing of HPO schedulers on a single commodity CPU.
The Applicability of Federated Learning to Official Statistics

This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods. FL is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data. By simulating three different use cases, important insights on the applicability of the technology are gained. The use cases are based on a medical insurance data set, a fine dust pollution data set and a mobile radio coverage data set–all of which are from domains close to official statistics. We provide a detailed analysis of the results, including a comparison of centralized and FL algorithm performances for each simulation. In all three use cases, we were able to train models via FL which reach a performance very close to the centralized model benchmarks. Our key observations and their implications for transferring the simulations into practice are summarized. We arrive at the conclusion that FL has the potential to emerge as a pivotal technology in future use cases of official statistics.
A Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation

Unsupervised domain adaptation focuses on reusing a model trained on a source domain in an unlabeled target domain. Two main approaches stand out in the literature: adversarial training for generating invariant features and minimizing the discrepancy between feature distributions. This paper presents a hybrid approach that combines these two methods with pseudo-labeling. The proposed loss function enhances the invariance between the features across domains and further defines the inter-class differences of the target set. Using a well-known dataset, Office31, we demonstrate that our proposal improves the overall performance, especially when the gap between domains is more significant.