Reviewerly/exHarmony huggingface dataset:
International trade privacy data management system combining Internet of Things blockchain

Authors:
raw_author_name: Hongzhen Zhang

Abstract: Currently, international trade (Int’l Trade) often faces risks such as privacy data leakage and transaction security during transactions, which seriously affect the protection of privacy data in trade. Therefore, this article used the Internet of Things and blockchain to attempt to address these risk issues and improve the security of privacy data. By analyzing the security threats in Int’l Trade transactions, this article used additive aggregation functions to analyze the protection effect of privacy data and studied the core issues of Int’l Trade privacy data. This article analyzed the privacy protection model using the Internet of Things blockchain and proposed corresponding Int’l Trade privacy data protection strategies. Among the five surveyed enterprises, data audit and data backup were all above the standard value, while data query only exceeded the standard value for enterprises 2, 3, 4, and 5. The average abnormal value of the enterprise in data anomalies decreased by 0.06 compared to the standard value. Through experimental testing, it has been found that Internet of Things blockchain can effectively improve the privacy data protection effect of Int’l Trade and promote the convenience of Int’l Trade transactions.
Optimization of artificial intelligence cloud computing in information management design

Authors:
raw_author_name: Sasa Zhao
raw_affiliation_string: Jeonbuk National University, Jeonju, Jeollabuk-do, Korea; Office of Quality Control and Performance Appraisal, Zibo Vocational Institute, Zibo, Shandong, China

Abstract: In order to promote the construction of enterprise informatization, the author studied the adaptive optimization method of Efficient Management Information System (EMIS). The author proposes to combine the fuzzy C-means algorithm to form a server clustering algorithm, and adds an improved Drosophila optimization algorithm to overcome the problems of slow Rate of convergence of GRNN and easy to fall into the minimum, and the cloud platform collects 23 performance indicators, the output results of the coordinated evolutionary algorithm are analyzed by the neighborhood rough set analysis of algorithms to select features to avoid the curse of dimensionality problem. The experimental results indicate that, compared with existing research results, the algorithm proposed by the author has increased its speed by 1.43, 3.22, and 3.72 times, respectively; In terms of convergence steps, they have also been reduced by 1.61, 5, and 6 times respectively, and when running the algorithm, the computer’s memory and CPU usage are controlled at around 50%, without affecting normal functionality. This proves that the task scheduling of the cloud platform is more balanced, and indirectly proves the accuracy of the algorithm’s clustering effect.
A Survey on AutoML Methods and Systems for Clustering

Authors:
raw_author_name: Yannis Poulakis
raw_affiliation_string: Department of Digital Systems, University of Piraeus, Piraeus, Greece
raw_author_name: Christos Doulkeridis
raw_affiliation_string: Department of Digital Systems, University of Piraeus, Piraeus, Greece
raw_author_name: Dimosthenis Kyriazis
raw_affiliation_string: Department of Digital Systems, University of Piraeus, Piraeus, Greece

Abstract: 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.
FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams

Authors:
raw_author_name: Samira Khodabandehlou
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
raw_author_name: Alireza Hashemi Golpayegani
raw_affiliation_string: Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Abstract: 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.
Service Robots Roll Forward

Authors:
raw_author_name: Samuel Greengard
raw_affiliation_string: West Linn, OR, USA

Abstract: 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

Authors:
raw_author_name: Honghao Zhu
raw_affiliation_string: School of Computer and Information Engineering, Bengbu University,Bengbu,China,233030
raw_author_name: MengChu Zhou
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang Gongshang University,Hangzhou,China,310018
raw_author_name: Yu Xie
raw_affiliation_string: College of Information Engineering, Shanghai Maritime University,Shanghai,China,201306
raw_author_name: Aiiad Albeshri
raw_affiliation_string: King Abdulaziz University,Department of Computer Science,Jeddah,Saudi Arabia,21481

Abstract: 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.
Multi-UAVs Collaborative Path Planning in the Cramped Environment

Authors:
raw_author_name: Siyuan Feng
raw_affiliation_string: School of Automation, Beijing Institute of Technology,Beijing,China,100081
raw_author_name: Yi Yang
raw_affiliation_string: School of Automation, Beijing Institute of Technology,Beijing,China,100081
raw_author_name: Wenjie Song
raw_affiliation_string: School of Automation, Beijing Institute of Technology,Beijing,China,100081

Abstract: 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

Authors:
raw_author_name: Chaoyu Gong
raw_affiliation_string: School of Computing, National University of Singapore,Singapore
raw_author_name: Yang You
raw_affiliation_string: School of Computing, National University of Singapore,Singapore

Abstract: 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

Authors:
raw_author_name: Ahmed EL Tawargy
raw_affiliation_string: Department of Physics, Faculty of Science, Damietta Universityhttps://ror.org/035h3r191, 34517 Damietta, Egypt
raw_author_name: Wael Ramadan
raw_affiliation_string: Department of Physics, Faculty of Science, Damietta Universityhttps://ror.org/035h3r191, 34517 Damietta, Egypt
raw_author_name: Mohamed Nawareg
raw_affiliation_string: Department of Physics, Faculty of Science, Damietta Universityhttps://ror.org/035h3r191, 34517 Damietta, Egypt

Abstract: 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.
The Intersection of AI Technology and Intellectual Property Adjudication in Supply Chain Management

Authors:
raw_author_name: Sukhvinder Singh Dari
raw_affiliation_string: Symbiosis Law School, Symbiosis International University, Pune, India
raw_author_name: Dharmesh Dhabliya
raw_affiliation_string: Department of Information Technology, Vishwakarma Institute of Information Technology, India
raw_author_name: Pankaj Dadheech
raw_affiliation_string: Swami Keshvanand Institute of Technology, Management, and Gramothan, India

Abstract: 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

Authors:
raw_author_name: Mahmoud El Samad
raw_affiliation_string: Lebanese International University, Lebanon

Abstract: 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.
All-optical spin valve effect in nonlinear optics

Authors:
raw_author_name: Aviv Karnieli
raw_affiliation_string: Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel
raw_author_name: Ofir Yesharim
raw_affiliation_string: School of Electrical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv Universityhttps://ror.org/04mhzgx49, Ramat Aviv, Tel Aviv 69978, Israel
raw_author_name: Shai Tsesses
raw_affiliation_string: Technion—Israel Institute of Technology
raw_author_name: Ady Arie
raw_affiliation_string: School of Electrical Engineering, Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv Universityhttps://ror.org/04mhzgx49, Ramat Aviv, Tel Aviv 69978, Israel

Abstract: 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

Authors:
raw_author_name: Atse Louwen
raw_affiliation_string: Institute for Renewable Energy Eurac Research Viale Druso 1 39100 Bolzano Italy
raw_author_name: Sascha Lindig
raw_affiliation_string: Institute for Renewable Energy Eurac Research Viale Druso 1 39100 Bolzano Italy
raw_author_name: David Moser
raw_affiliation_string: Institute for Renewable Energy Eurac Research Viale Druso 1 39100 Bolzano Italy

Abstract: 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

Authors:
raw_author_name: Ohad Noy Feldheim
raw_affiliation_string: Einstein Institute of Mathematics, Hebrew University or Jerusalem
raw_author_name: Ori Gurel-Gurevich
raw_affiliation_string: Einstein Institute of Mathematics, Hebrew University or Jerusalem
raw_author_name: Jiange Li
raw_affiliation_string: Institute for Advanced Study in Mathematics, Harbin Institute of Technology

Abstract: 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

Authors:
raw_author_name: Kaustubh Laturkar
raw_affiliation_string: Michigan State University, USA
raw_author_name: Kasturi Laturkar
raw_affiliation_string: Validation Associates LLC, USA

Abstract: 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.