Reviewerly/exHarmony huggingface dataset:
A Survey on AutoML Methods and Systems for Clustering

Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given dataset and a specific machine learning task. This is a challenging problem, as the process of finding the best model and tuning it for a particular problem at hand is both time-consuming for a data scientist and computationally expensive. In this survey, we focus on unsupervised learning, and we turn our attention on AutoML methods for clustering. We present a systematic review that includes many recent research works for automated clustering. Furthermore, we provide a taxonomy for the classification of existing works, and we perform a qualitative comparison. As a result, this survey provides a comprehensive overview of the field of AutoML for clustering. Moreover, we identify open challenges for future research in this field.
Open-set domain adaptation for scene classification using multi-adversarial learning

Domain adaptation methods are able to transfer knowledge across different domains, tackling multi-sensor, multi-temporal or cross-regional remote sensing scenarios as they do not rely on labels or annotations in the target domain. However, most of the previous studies have focused on closed-set domain adaptation, based on the assumption that the source and target domains share identical class labels. Real-world scenarios are typically more complex, and the model could potentially encounter novel classes that are not previously included in the source domain, commonly referred to as "unknown" classes. Here we investigate the open-set domain adaptation scenario in the field of remote sensing scene classification, where there is a partial overlap between the label space of the target domain and that of the source domain. To deal with this problem, we propose a novel open-set domain adaptation method for scene classification using remote sensing images, which is named Multi-Adversarial Open-Set Domain Adaptation Network (MAOSDAN). Our proposed MAOSDAN consists of three main components. First, we employ an attention-aware Open Set BackPropagation (OSBP) to better distinguish the "unknown" and "known" samples for the target domain. Then, an auxiliary adversarial learning is designed for mitigating the negative transfer effect that arises from forcefully aligning the "unknown" target sample in network training. Finally, we adopt an adaptive entropy suppression to increase the probability of samples and prevent some samples from being mistakenly classified. Our proposed MAOSDAN achieves an average score of 75.07% in three publicly available remote sensing datasets, which significantly outperforms other open-set domain adaptation algorithms by attaining 4.52∼17.15%. In addition, MAOSDAN surpasses the baseline deep learning model with 18.12% improvement. A comprehensive experimental evaluation demonstrates that our MAOSDAN shows promising prospects in addressing practical and general domain adaptation scenarios, especially in scenarios where the label set of the source domain is a subset of the target domain.
FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams

Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to quickly and accurately detect suspected traders and behavior patterns in real-time transactions, and it is still considered an open problem. To solve this problem and alleviate the existing challenges, in this article, we propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream. In this approach, real-time transactions between traders are converted into a stream of graphs and, instead of using supervised and semi-supervised learning methods, fraudulent traders are detected precisely by exploiting density signals in graphs. Specifically, we reveal the traits of fraudulent traders in the market and propose a novel metric from this perspective, i.e., graph topology, time, and behavior. Then, we search for suspicious blocks by greedily optimizing the proposed metric. Theoretical analysis demonstrates upper bounds for FiFrauD's effectiveness in catching suspicious trades. Extensive experiments on five real-world datasets with both actual and synthetic labels demonstrate that FiFrauD achieves significant accuracy improvements compared with state-of-the-art fraud detection methods. Also, it can find various suspicious behavior patterns in a linear runtime and provide interpretable results. Furthermore, FiFrauD is resistant to the camouflage tactics used by fraudulent traders.
Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing

Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.
Service Robots Roll Forward

Transitioning from classic 'programmed automation' to modern 'AI-powered autonomy.'
A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm (DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA, which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained; while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection (CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.
Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multi-UAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue (SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles (UAVs) in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search (ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes. The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.
Sparse Reconstructive Evidential Clustering for Multi-View Data

Although many multi-view clustering (MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects, which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm (SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional human-readable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides, SRMVEC delivers effectiveness on benchmark datasets by out-performing some state-of-the-art methods.
Noise resistance: a key factor in the metrological applications of highly entangled multiqubit states

The advent of quantum entanglement has revolutionized metrology, enabling the development of ultra-precise measurement techniques that surpass the limitations of classical systems. However, the delicate nature of entangled states makes them vulnerable to various noise sources, significantly impeding their metrological utility. To address this challenge, we delve into the intricate relationship between noise and the metrological usefulness of highly entangled multiqubit systems. By studying the impact of various factors, we aim to unravel the fundamental limits of quantum metrology and devise strategies to enhance the resilience of quantum systems against noise. Our investigation reveals that increasing the number of qubits in an entangled state can significantly enhance its noise robustness, particularly for certain entangled states. Furthermore, we uncover the surprising advantage of utilizing different local operators, surpassing the robustness offered by the conventional identical-operator approach. Additionally, by employing a technique that utilizes multiple copies of the quantum state, we identified many states with latent metrological usefulness and demonstrated their hidden noise robustness. While these strategies significantly enhance the robustness of many studied states, it is crucial to note that generalizations may not apply universally. Therefore, validating these findings for each specific state is essential before practical implementation. Our findings proved that by rigorously assessing noise susceptibility and adopting appropriate strategies, we can achieve substantial gains in precision and noise robustness. This paves the way for the development of more robust and reliable quantum metrological techniques, offering valuable insights across a wide range of applications, from fundamental physics to cutting-edge technologies like quantum computing, sensing, and communication.
Blockchain-Based Smart Contracts

Blockchains are decentralized, distributed, and immutable ledgers that can execute contracts on the blockchain, called smart contracts. Blockchain-based smart contracts have different challenges, opportunities, and applications in various fields like cryptocurrency systems, real estate, agriculture, logistics, and fire industry. The codes that can be executed and which operate on top of a blockchain to facilitate, implement, and enforce agreements between numerous parties in the absence of a trusted third party are called smart contracts. This chapter uses a taxonomy to classify different processes, such as comparing and contrasting the strengths and weaknesses of various methods and identifying research gaps and unanswered questions. The chapter also outlines some promising research guidelines for blockchain-based smart contracts and discusses the benefits and challenges. Researchers and practitioners in the field of blockchain-based smart contracts may find this chapter useful because it offers an overview of current and emerging developments in the field.
The Intersection of AI Technology and Intellectual Property Adjudication in Supply Chain Management

In modern Nigerian supply chain management, the adjudication of intellectual property rights has gained paramount importance. With the rapid advancement of technology, the integration of AI-powered analytics has emerged as a promising resolution for settling disputes and safeguarding intellectual property rights. This chapter highlights the multifaceted role of AI-driven analytics in the adjudication process, exploring its impact on improving efficiency, precision, and impartiality in resolving disputes related to intellectual property within the Nigerian supply chain. The authors suggest that comprehensive ethical standards, data privacy regulations, and transparency protocols should be established by the government, stakeholders, and law enforcement agencies to mitigate potential biases, ensure data integrity, and ensure adherence to ethical and legal norms in AI technologies.
Blockchain Technology Through Bitcoin and Ethereum

With a focus on the two most important cryptocurrencies, Bitcoin and Ethereum, this chapter explains the significance of blockchain in today's environment and analyzes its implementation in both networks as well as investigates how they work, describing their transactions, unique characteristics, benefits, and necessities, noting how Ethereum addressed some of Bitcoin's shortcomings with the introduction of smart contracts. They both depended on the proof of work (PoW) consensus mechanism at first, which Ethereum has moved to proof of stake (PoS) in its new upgrade as Ethereum 2.0. They examine the benefits and differences of these consensus protocols and other ones that deserve consideration and shed light on how they both have dealt with their challenges and drawbacks. Moreover, they analyze the prospects and likely improvements in blockchain technology, as they hope to illustrate the potential of blockchain along with Bitcoin and Ethereum to spark future innovation and address contemporary difficulties through their capabilities and network applications.
Virtual Education Modality and Digital Competence in University Students

This chapter considers students from different Panamanian universities after the experience in the 2021-2022 academic year. There are important considerations about digital competence and professional progression, gender, age, and attitude influencing the virtual modality. Their behaviors and attitudes are also influenced by their competencies, going beyond a consideration referred solely to the rational approach of the study. 253 students participated in the study, most of whom were women (74.7 percent). The results evidently reveal the massive use and application of information and communication technologies in the university classroom. That technological tools put the focus on the curricula of universities and intervene in the degree of competence of their students. The digital skills present in university students are linked to daily activity, to systematic use in their subjects. This allows the assessment of competence, and an approach to the evaluation of digital competences that are not specific to a specialty, but are also generic competences.
All-optical spin valve effect in nonlinear optics

More than three decades after the inception of electron spin-based information encoding inspired by nonlinear electro-optic devices, we present a complementary approach: nonlinear optical devices directly inspired by spintronics. We theoretically propose an all-optical spin-valve device and a spin-dependent beam splitter, where the optical pseudospin is a superposition of signal and idler beams undergoing a sum-frequency generation process inside a 2D nonlinear photonic crystal. We delve into the operation of these devices, examining key properties such as the transmission angle and splitting ratio, optically controlled by the pump beam. Our findings open new avenues for both classical and quantum optical information processing in the frequency domain.
Climate‐ and Technology‐Dependent Performance Loss Rates in a Large Commercial Photovoltaic Monitoring Dataset

Detailed knowledge of the performance of photovoltaic (PV) systems over their full lifetime is invaluable for evaluating their profitability and sustainability. PV systems are known to have gradually declining performance; for long‐term yield assessments, typically a performance loss rate (PLR) of −0.5% yr −1 is assumed. Herein, monitoring data of thousands of commercial PV systems is evaluated to determine how performance loss trends and PLR values are affected by operational climate, PV module technology, and other parameters. Using performance ratio (PR) measurements from almost 6000 PV systems, the long‐term trends in PR and overall PLR value are evaluated. Subsequently, the relation between climate, performance loss, and PLR for three different climate classification schemes is evaluated. Finally, how long‐term performance loss trends and estimated PLR values differ for PV module technology and other parameters are evaluated. The results show that in general, PV systems that have operated now for at least 3 but up to 25 years show a PLR of roughly −1% yr −1 , indicating substantially higher performance loss compared to typical estimates used for new PV systems. Climate by itself does not seem to be the leading factor that determines overall performance loss trends or PLR values in the dataset analyzed.
Long-term balanced allocation via thinning

In the two-thinning balls-and-bins model, an overseer is provided with uniform random allocation of m balls into n bins in an on-line fashion. The overseer may reject the allocation of each ball, in which case it is placed into a new bin, drawn independently, uniformly at random. The purpose of the overseer is to reduce the maximum load, that is, the difference between the maximum number of balls in a single bin and the average number of balls among all bins. We provide tight estimates for three quantities: the lowest achievable maximum load at a given time m, the lowest achievable maximum load uniformly over the entire time interval [m]:={1,2,…,m} and the lowest achievable typical maximum load over the interval [m], that is, a load which upper-bounds 1−o(1) portion of the times in [m]. In particular, for m polynomial in n and sufficiently large, we provide an explicit strategy, which achieves a typical maximum load of (logn)1/2+o(1), asymptotically the same as that can be achieved at a single time m. In contrast, we show that no strategy can achieve better than Θ(logn loglogn) maximum load for all times up to time m.
Blockchain Integration in Upstream Oil and Gas

With its complex exploration and production processes, extensive information exchange and stringent safety standards, the upstream oil and gas industry is embracing cutting-edge technologies to maximize operational efficiency and improve data reliability. Blockchain technology has risen to prominence as an appealing solution offering decentralized, trustworthy, and immutable information management capabilities. An outline of the upstream oil and gas industry is provided to demonstrate its role and impact on societal development. The fundamental principles of blockchain are then discussed, emphasizing its potential to revolutionize data handling, transactions, and smart contracts. The chapter also provides examples of some of the current operational projects in the upstream oil and gas industry that are exploring its potential uses and the challenges associated with its implementation. Through the insights presented in this chapter, oil and gas companies and key stakeholders can utilize blockchain technology to boost business performance and enhance efficiency.