Reviewerly/exHarmony huggingface dataset:
Discontinuum physics leads to a table of realities for making predictions

By following up on three papers that (1) were previously published, and (2) following Einstein's 1954 speculations, it is possible physics is based on a discontinuum theory, and the continuum concept along with space and time must be discarded, a table of realities is developed and used for making predictions. These predictions include (1) discontinuum gravity is required to detail a gravitational collapse, which means (2) general relativity is falsified, and (3) there are discontinuum gravity blackholes, not general relativity blackholes, and (4) there are no general relativity singularities.
A framework proposal for machine learning-driven agent-based models through a case study analysis

Agent-based modeling (ABM) has been widely employed by researchers in various domains. Developing valid and useful agent-based models (ABMs) imposes challenges on the modelers. Using machine learning (ML) techniques in ABMs may facilitate the development of these models and improve their performance. This paper provides a detailed overview of the relationship between ML and ABM approaches. The benefits and drawbacks of data-driven ABMs are evaluated. A main scheme for utilizing ML techniques in ABMs is provided and explored through references to the relevant studies. As part of the primary scheme, a framework for modeling agent behaviors in ABMs utilizing ML approaches is proposed. In the framework, theoretical support is also combined with ML approaches in order to increase the accuracy of agent behavior generated by ML approaches. Using the suggested framework, a real-world case study is performed to investigate the application of ML techniques to improve the accuracy of ABMs and facilitate their creation. The findings indicate that ML approaches may facilitate the construction of ABMs.
Generation of Greenberger-Horne-Zeilinger States on Two-Dimensional Superconducting-Qubit Lattices via Parallel Multiqubit-Gate Operations

A recent major technological breakthrough in superconducting circuits is the realization of more than 50 qubits arranged on two-dimensional (2D) lattices with tunable nearest-neighbor couplings. We propose a protocol to generate Greenberger-Horne-Zeilinger (GHZ) states on 2D superconducting-qubit lattices by applying multiqubit controlled-$i$swap gates in parallel. The multiqubit gate can be naturally implemented based on an effective three-body interaction, which can be synthesized with appropriate detunings and coupling strengths between qubits. We simulate the preparation process of GHZ states with realistic parameters, and show a $37$-qubit GHZ state can be generated with a controlled-$i$swap depth of $3$. Our proposal provides a more promising method of generating GHZ states on the latest 2D superconducting-qubit architectures and will stimulate the preparation of multiqubit entangled states based on multiqubit gates.
MI-RRT-Connect Algorithm for Quadruped Robotics Navigation with Efficiently Path Planning

Abstract Autonomous navigation is playing an increasingly important role in quadruped robotic systems. However, providing safe and reliable path planning for robots is still an open problem. In this paper, we propose a sampling-based path planning algorithm fused with a dual-tree structure, here called Multiple Informed RRT-Connect (MI-RRT-Connect). The proposed MI-RRT-Connect can overcome the disadvantage that the Informed RRT* algorithm takes a long time in the initial search path, by using the RRT-Connect algorithm and the target deviation method, and the initial search path can be obtained quickly. Then, for the optimization of the initial search path, the multi-level parent node selection strategy and the method of calculating the path cost for the parent node are used. The simulation and quadruped robot experimental results show that our method can find an optimal path in a shorter time, and it has been well applied in a real quadruped robot.
New Approaches to 3D Vision

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images

Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and expensive. One way to reduce the annotation cost approach is to migrate models trained on existing annotated data (source domain) to unannotated data (target domain). However, model generalization capability is often unsatisfactory due to the limit of the domain gap. In this work, we use an unsupervised adversarial domain adaptation method to train a neural network to close the gap between the source and target domains for unsupervised agricultural land extraction. The overall approach consists of two phases: inter-domain and intra-domain adaptation. In the inter-domain adaptation, we use a generative adversarial network (GAN) to reduce the inter-domain gap between the source domain (labeled dataset) and the target domain (unlabeled dataset). The transformer with robust long-range dependency modeling acts as the backbone of the generator. In addition, the multi-scale feature fusion (MSFF) module is designed in the generator to accommodate remote sensing datasets with different spatial resolutions. Further, we use an entropy-based approach to divide the target domain. The target domain is divided into two subdomains, easy split images and hard split images. By training against each other between the two subdomains, we reduce the intra-domain gap. Experiments results on the “DeepGlobe → LoveDA”, “GID → LoveDA” and “DeepGlobe → GID” unsupervised agricultural land extraction tasks demonstrate the effectiveness of our method and its superiority to other unsupervised domain adaptation techniques.
The Effect of Artificial Intelligence on Service Quality and Customer Satisfaction in Jordanian Banking Sector

The study emphasizes the importance of Artificial Intelligence (AI) and its applications on the service quality provided by Jordanian banks for their customer satisfaction. This research paper thoroughly reviews the literature on the numerous emergent applications of artificial intelligence and its impact on the banking sector. A rigorous study of the available literature is conducted to examine AI's uses in banking. Artificial intelligence improves the banking experience for millions of clients and employees by providing credit score checking, system failure prediction, emergency alarm systems, fraud detection, phishing website detection, liquidity risk assessment, customer loyalty evaluation and intelligence systems by reducing the employee workload. A questionnaire gathered data from 270 consumers in Jordan's banking sector. The SPSS program used exploratory factor analysis to statistically evaluate the sample data to determine service quality and customer satisfaction. The results show that the updated SERVQUAL Model extracted five subscales instead of the eight in the original model. The extracted subscales were tangibility, assurance, reliability, responsiveness, and empathy. According to this study, artificial intelligence is statistically relevant to service quality and customer satisfaction. The updated SERVQUAL model, according to the authors, helps address customer satisfaction in the banking sector. The research findings suggest that the demand for artificial intelligence in the Jordanian banking sector is equally essential for the customers; thus, there should be an optimal balance between virtual and human agents based on the customers' requirements and preferences. Further, this study found practical implications of using AI in banking, particularly those related to Jordanian customer perception.
Recommendation Model Based on Probabilistic Matrix Factorization and Rated Item Relevance

Personalized recommendation has become indispensable in today’s information society. Personalized recommendations play a significant role for both information producers and consumers. Studies have shown that probability matrix factorization can improve personalized recommendation performance. However, most probability matrix factorization models ignore the effect of item-implicit association and user-implicit similarity on recommendation performance. To overcome this lack, we propose a recommendation model based on probability matrix factorization that considers the correlation of user rating items. Our model uses the resource allocation of the bipartite graphs and the random walk of meta-paths in heterogeneous networks to determine the implicit association of items and the implicit similarity of users, respectively. Thus, the final item association and user similarity are obtained. The final item and user similarity relationships are integrated into the probability matrix factorization model to obtain the user’s prediction score for a specific project. Finally, we validated the model on the Delicious-2k, Movielens-2k and last.fm-2k datasets. The results show that our proposed algorithm model has higher recommendation accuracy than other recommendation algorithms.
A DEXiRE for Extracting Propositional Rules from Neural Networks via Binarization

Background: Despite the advancement in eXplainable Artificial Intelligence, the explanations provided by model-agnostic predictors still call for improvements (i.e., lack of accurate descriptions of predictors’ behaviors). Contribution: We present a tool for Deep Explanations and Rule Extraction (DEXiRE) to approximate rules for Deep Learning models with any number of hidden layers. Methodology: DEXiRE proposes the binarization of neural networks to induce Boolean functions in the hidden layers, generating as many intermediate rule sets. A rule set is inducted between the first hidden layer and the input layer. Finally, the complete rule set is obtained using inverse substitution on intermediate rule sets and first-layer rules. Statistical tests and satisfiability algorithms reduce the final rule set’s size and complexity (filtering redundant, inconsistent, and non-frequent rules). DEXiRE has been tested in binary and multiclass classifications with six datasets having different structures and models. Results: The performance is consistent (in terms of accuracy, fidelity, and rule length) with respect to the state-of-the-art rule extractors (i.e., ECLAIRE). Moreover, compared with ECLAIRE, DEXiRE has generated shorter rules (i.e., up to 74% fewer terms) and has shortened the execution time (improving up to 197% in the best-case scenario). Conclusions: DEXiRE can be applied for binary and multiclass classification of deep learning predictors with any number of hidden layers. Moreover, DEXiRE can identify the activation pattern per class and use it to reduce the search space for rule extractors (pruning irrelevant/redundant neurons)—shorter rules and execution times with respect to ECLAIRE.
Human-artificial intelligence approaches for secure analysis in CAPTCHA codes

Abstract CAPTCHA ( Completely Automated Public Turing test to tell Computers and Humans Apart ) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging bots and hackers performing malicious operations. However, automated attacks have effectively cracked all common conventional schemes, and the majority of present CAPTCHA methods are also vulnerable to human-assisted relay attacks. Invisible reCAPTCHA and some approaches have not yet been cracked. However, with the introduction of fourth-generation bots accurately mimicking human behavior, a secure CAPTCHA would be hardly designed without additional special devices. Almost all cognitive-based CAPTCHAs with sensor support have not yet been compromised by automated attacks. However, they are still compromised to human-assisted relay attacks due to having a limited number of challenges and can be only solved using trusted devices. Obviously, cognitive-based CAPTCHA schemes have an advantage over other schemes in the race against security attacks. In this study, as a strong starting point for creating future secure and usable CAPTCHA schemes, we have offered an overview analysis of HAI between computer users and computers under the security aspects of open problems, difficulties, and opportunities of current CAPTCHA schemes.
An efficient Bayesian network structure learning algorithm based on structural information

Bayesian networks (BNs) are probabilistic graphical models regarded as some of the most compelling theoretical models in the field of representation and reasoning under uncertainty. The search space of the model structure grows super-exponentially as the number of variables increases, which makes BN structure learning an NP-hard problem. Evolutionary algorithm-based BN structure learning algorithms perform better than traditional methods. This paper proposes a structural information-based genetic algorithm for BN structure learning (SIGA-BN) by employing the concepts of Markov blankets (MBs) and v-structures in BNs. In SIGA-BN, an elite learning strategy based on an MB is designed, allowing elite individuals' structural information to be learned more effectively and improving the convergence speed with high accuracy. Then, a v-structure-based adaptive preference mutation operator is introduced in SIGA-BN to reduce the redundancy of the search process by identifying changes in the v-structure. Furthermore, an adaptive mutation probability mechanism based on stagnation iterations is adopted and used to balance exploration and exploitation. Experimental results on eight widely used benchmark networks show that the proposed algorithm outperforms other GA-based and traditional BN structure learning algorithms regarding structural accuracy, convergence speed, and computational time.
A modular metamodel and refactoring rules to achieve software product line interoperability

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.
A New Application of Discrete Morse Theory to Optimizing Safe Motion Planning Paths

We present a novel method for extracting geometric and topological features from a robot’s configuration space. To accomplish this, we define a discrete Morse function on the Vietoris-Rips simplicial complex to identify critical points on the surface of obstacles present. These critical points serve as waypoints for determining feasible bounds near an identified obstacle. This work builds on previous work that provides a method to approximate the number of samples required to generate pathways. Our results achieve near-optimal paths with a low computation time and reduced path distance in this work. We conduct experiments in different environments and with various robots, including the Kuka YouBot and PR2 robots in simulation, and demonstrate the performance gains compared to state-of-the-art methods.
Simulated bifurcation for higher-order cost functions

High-performance Ising machines for solving combinatorial optimization problems have been developed with digital processors implementing heuristic algorithms such as simulated bifurcation (SB). Although Ising machines have been designed for second-order cost functions, there are practical problems expressed naturally by higher-order cost functions. In this work, we extend SB to such higher-order cost functions. By solving a problem having third-order cost functions, we show that the higher-order SB can outperform not only the second-order SB with additional spin variables, but also simulated annealing applied directly to the third-order cost functions. This result suggests that the higher-order SB can be practically useful.
Toward fast 3D human activity recognition: A refined feature based on minimum joint freedom model (Mint)

Human activity recognition (HAR) is crucial for human-centric smart manufacturing, especially the management of human involved production activities. Recently, 3D HAR based on skeleton data has emerged as an outstanding approach for good performance on both speed and accuracy. However, there are still flaws like insufficiency or redundancy in design of features. To address this gap, a refined skeleton-based feature for fast and accurate 3D HAR is proposed in this work, Specifically, a minimum and lossless skeleton feature named Mint is proposed based on minimum joint freedom model, with a reduction of around 52% skeleton data dimensions to classic coordinate-based feature. For demonstration, several comparison experiments are implemented based on the presented feature and classic coordinate-based feature. Results show that the utilization of proposed feature can improve the efficiency of 3D HAR around 29% with equivalent accuracy. It is expected this work can contribute a lot to the application of HAR in human-centric smart manufacturing systems and real-time human activity digital twin (HADT) modelling and interaction.
Performance of quantum kernel on initial learning process

Abstract For many manufacturing companies, the production line is very important. In recent years, the number of small-quantity, high-mix products have been increasing, and the identification of good and defective products must be carried out efficiently. At that time, machine learning is a very important issue on shipping inspection using small amounts of data. Quantum machine learning is one of most exciting prospective applications of quantum technologies. SVM using kernel estimation is one of most popular methods for classifiers. Our purpose is to search quantum advantage on classifier to enable us to classifier in inspection test for small size datasets. In this study, we made clear the difference between classical and quantum kernel learning in initial state and propose analysis of learning process by plotting ROC space. To meet the purpose, we investigated the effect of each feature map compared to classical one, using evaluation index. The simulation results show that the learning model construction process between quantum and classical kernel learning is different in initial state. Moreover, the result indicates that the learning model of quantum kernel is the method to decrease the false positive rate (FPR) from high FPR, keeping high true positive rates on several datasets. We demonstrate that learning process on quantum kernel is different from classical one in initial state and plotting to ROC space graph is effective when we analyse the learning model process.
Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

Abstract Workload execution is composed by mapping larger tasks onto heterogeneous environments such as cloud platforms for enhancing the efficiency of workload resource management techniques. Execution of scientific workflow on a cloud platform is time‐consuming, expensive, and requires a fault‐tolerance guarantee. The existing methodology has emphasized on minimizing processing time to reduce costs. However, the processing cost can be reduced by minimizing energy consumption. In providing fault‐tolerance while meeting the workload quality of service requirement the task is offloaded to a new physical machine; Hence, this increases energy consumption and thereby increases the cost of workload execution. In addressing the research challenges this paper presents the fault‐tolerant aware (FTA) workload resource management (WRM) technique. First, the FTA‐WRM optimizes processing and communication costs as an energy constraint leveraging the dynamic voltage frequency scaling technique. Then, a task offloading mechanism is modeled as an energy constraint with an application delay requirement for providing fault tolerance into the FTA‐WRM. The experiment outcome shows the FTA‐WRM significantly improves processing and energy efficiency in comparison with the existing workload resource management technique.