Reviewerly/exHarmony huggingface dataset:
A Novel Ant Colony-inspired Coverage Path Planning for Internet of Drones

Coverage Path Planning (CPP) is a fundamental problem widely used in various applications such as monitoring, patrol, mapping, and sweeping. Several types of robots can perform CPP, which has become very popular with the use of multiple drones. The Internet of Drones (IoD) is an emerging technology that aims to facilitate a multi-drone environment for various applications. In this context, coordination and organization of drones are crucial to avoid collisions and congestion in the airspace. One way to achieve this is by using well-defined airways. CPP tasks are often performed using multiple drones to cover a given area, which can be integrated into the IoD system. However, existing CPP solutions for drones do not fully consider collaboration capabilities between them, specific energy constraints, and the dynamic IoD scenario. To address these challenges, this work proposes an Ant Colony Optimization Algorithm for CPP in IoD (AntIoD), considering energy consumption. This scenario is particularly challenging due to the unique characteristics of being a mix of CPP for terrestrial vehicles with road constraints and CPP for drone networks. We compared our approach, AntIoD, with three recent solutions from the literature. The results reveal that AntIoD outperformed them in all simulated scenarios by at least 10% in terms of energy efficiency when compared to the best scenarios of the other algorithms.
An attempt to apply the homotopy method to the domain of machine learning

The most essential purpose of the machine learning field is to minimize the difference between the true value and the predicted value, so that the predicted value can be as close to the true value as possible. For this reason, researchers have proposed a loss function as a criterion for learning, which can generally be connected with optimization learning through the loss function. In the field of machine learning, our commonly used optimization method is gradient descent, because it only needs to solve the first-order derivative, and the calculation complexity is small and the accuracy is within an acceptable range. But gradient descent has a problem that cannot be ignored-divergence. To solve this problem, we propose a homotopy analysis method. The homotopy analysis method can effectively control the convergence by introducing the convergence control parameters without divergence. At the same time, our experiments also show that our algorithm can not only guarantee convergence, but also reduce the number of iterations.
Bisimulations and bisimulation games between Verbrugge models

Abstract Interpretability logic is a modal formalization of relative interpretability between first‐order arithmetical theories. Verbrugge semantics is a generalization of Veltman semantics, the basic semantics for interpretability logic. Bisimulation is the basic equivalence between models for modal logic. We study various notions of bisimulation between Verbrugge models and develop a new one, which we call w‐bisimulation. We show that the new notion, while keeping the basic property that bisimilarity implies modal equivalence, is weak enough to allow the converse to hold in the finitary case. To do this, we develop and use an appropriate notion of bisimulation games between Verbrugge models.
Generative Adversarial Networks the Future of Consumer Deep Learning?

In recent years, deep learning and its subtopics have found a near gold-rush stature in the industry. This booming response has not been restricted to niche applications, but rather to titanic domains such as healthcare, self-driving cars, cybersecurity, and more. This “rise” has consequently led to a large influx of practitioners and users to this domain. One such subdomain is generative adversarial networks (GANs), an application of deep learning centered on image segmentation. The researchers aim to study the trajectory of and attempt to extrapolate the future of this subdomain in an attempt to discern if the meteoric rise of this technique is based on concrete positive results or a trend deemed to ebb. This study aims to first gather the most salient aspects and recent advancements of GANs. Specifically, the study emphasizes the importance of GANs and presents differing types utilized in various domains. Finally, the researchers present the current research gaps and the difficulties that could potentially be faced in the attainment of the aforementioned trajectory of this field.
Novel Union-Find-based Decoders for Scalable Quantum Error Correction on Systolic Arrays

Quantum Error Correction is an essential key ingredient for large-scale Quantum Computing devices. Indeed, a fast, scalable, and accurate decoding and correction process of errors appearing in qubits is critical to guarantee fault tolerance and usability. In this context, the Union-Find decoder incarnates a promising approach for such an essential task, as proved by various literature studies. Nonetheless, further advancements in this area are still feasible. For this reason, we propose a Systolic Array-based architecture for the Union-Find decoder that exploits a novel communication and decoding scheme to speed up the overall process. Furthermore, we design three solutions based on such a scheme and evaluate them according to different metrics, such as latency and accuracy.
Implementation and integration of Keccak accelerator on RISC-V for CRYSTALS-Kyber

One of the key metrics used for defying the security of the Internet of Things (IoT) is data integrity, which mostly relies on the use of cryptographic hash functions. In the last years, the National Institute of Standards and Technology (NIST) announced SHA-3 as the new standard for better security. SHA-3 is also exploited in most of the current post-quantum cryptographic (PQC) protocols. Nevertheless, the used algorithm, i.e. Keccak, is computationally heavy and consequently limits its utilization in RISC-V-based Systems on Chip (SoC). In this work, a Keccak accelerator is proposed to speed up SHA3 computations for the CRYSTALS-Kyber algorithm on the RISCV-based advanced microcontroller PULPissimo. Compared to the plain SW implementation on RISC-V, our results show a speedup factor of up to 2.79 at the expense of a 12.4% resources overhead.
Efficient Quantum Circuit Cutting by Neglecting Basis Elements

Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through scales exponentially. We propose a simple reduction scheme to lower the classical and quantum resources required to perform a cut. Particularly, we recognize that for some cuts, certain basis element might pass “no information” through the qubit wire and can effectively be neglected. We empirically demonstrate our method on circuit simulators as well as IBM quantum hardware, and we observed up to 33 percent reduction in wall time without loss of accuracy
Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection

The online review fraud process is an event in which a reviewer posts fraudulent reviews on an e-commerce platform with respect to a review target, such as a commodity or service. Extant studies on detecting fraudulent reviewers often rely on reviewer behavioral patterns and the textual content of reviews while ignoring the targets being reviewed. Based on the Goals-Plans-Action theory, we examine the relative importance of target features for fraudulent reviewer detection in comparison to that of non-target features. Target features refer to the features derived from the innate information related to the reviewed products or services while non-target features refer to the features derived from the acquired information related to the reviewed products or services. In this study, we analyze a sample from the Yelp.com dataset of restaurant reviews to help better understand the importance of target features and non-target features. The results suggest that using the combination of target features with non-target features can improve the performance of online fraudulent reviewer detection in comparison with using non-target features alone. Moreover, using the combination of target features with non-target features will further improve the performance of online fraudulent reviewer detection when we consider the non-target features as conditioned on the reviewed target features rather than treating them as independent of each other.
Efficiency comparison between tracking and optimally fixed flat solar collectors

We investigate the optimal orientation for a fixed flat plate solar collector using the clear sky model. The ground reflection component of irradiation that hits the collector's surface is ignored due to its relatively small magnitude when compared to the direct beam and sky diffusive components. Analytical calculations demonstrate that regardless of the collector's latitude, the most effective azimuthal angle, [Formula: see text], is 0, which generally corresponds to a North-South direction. However, the optimal tilt angle, [Formula: see text], is dependent on both the Day of Year (DoY) and the collector's local latitude. For latitudes typical of mid-altitude climate zones, we can calculate the optimal tilt angle and the maximum energy that the collector can harvest during each DoY. We compare the maximum daily received energy-which is the sum of the direct beam and sky diffusive energies-associated with this optimal orientation to their corresponding values when the flat plate tracks the Sun. The relative increase in total energy due to Sun tracking depends critically on the DoY, with a minimum value of about [Formula: see text] in early winter and a maximum value of [Formula: see text] over a large interval.
LUAD: A lightweight unsupervised anomaly detection scheme for multivariate time series data

Anomaly detection of multivariate time series data has drawn extensive research attention recently, as it can be widely applied into various different domains, such as Prognostics Health Management, community behaviour monitoring, financial Anti-fraud and so on. Anomalies typically refer to unexpected observations or sequences within the captured data. The prevailing solutions of current anomaly detection methods are not only highly related to the individual use, but also rely on the domain-specific prior knowledge. Existing methods of anomaly detection by detecting aberrations encounter fundamental engineering challenges in terms of steam data online nature and the lack of expert knowledge for the training data set. Also, to meet the practical requirements, the anomaly detection model is often required to be used in edge architectures where the computing resources are limited, which leads to the demand for developing light-weight anomaly detection methods. To address these challenges, we propose a lightweight, unsupervised anomaly detection scheme, called LUAD. LUAD is consists of a detection model and a diagnosis model. The detection model learns the normal patterns of input data via an encoder–decoder scheme that combines Temporal Convolutional Network (TCN) and Variational Auto-Encoder (VAE) to deconstruct and reconstruct multivariate time series data. The diagnosis model improves LUAD's overall detection accuracy and provides a reasonable explanation for an anomaly. Experiments on three very different public datasets indicate that LUAD is both highly generalizable and more accurate than the two current state-of-the-arts. Overall, the LUAD model outperforms the baselines both in effectiveness (0.71%∼1.45% higher) and efficiency (31X smaller in model size, 1.9X faster in training time).
SAR and Optical Image Registration Based on Deep Learning with Co-Attention Matching Module

Image registration is the basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, the significant nonlinear radiation difference (NRD) and the geometric imaging model difference render the registration quite challenging. To solve this problem, both traditional and deep learning methods are used to extract structural information with dense descriptions of the images, but they ignore that structural information of the image pair is coupled and often process images separately. In this paper, a deep learning-based registration method with a co-attention matching module (CAMM) for SAR and optical images is proposed, which integrates structural feature maps of the image pair to extract keypoints of a single image. First, joint feature detection and description are carried out densely in both images, for which the features are robust to radiation and geometric variation. Then, a CAMM is used to integrate both images’ structural features and generate the final keypoint feature maps so that the extracted keypoints are more distinctive and repeatable, which is beneficial to global registration. Finally, considering the difference in the imaging mechanism between SAR and optical images, this paper proposes a new sampling strategy that selects positive samples from the ground-truth position’s neighborhood and augments negative samples by randomly sampling distractors in the corresponding image, which makes positive samples more accurate and negative samples more abundant. The experimental results show that the proposed method can significantly improve the accuracy of SAR–optical image registration. Compared to the existing conventional and deep learning methods, the proposed method yields a detector with better repeatability and a descriptor with stronger modality-invariant feature representation.
Newton-based approach to solving K-SVCR and Twin-KSVC multi-class classification in the primal space

Multi-class classification is an important problem in machine learning, which often occurs in the real world and is an ongoing research issue. Support vector classification-regression machine for k-class classification (K-SVCR) and twin k-class support vector classification (Twin-KSVC) are two novel machine learning methods for multi-class classification problems. This paper presents novel methods to solve the primal problems of K-SVCR and Twin-KSVC, known as NK-SVCR and NTW-KSVC, respectively. The proposed methods evaluate all training data into a "1-versus-1-versus-rest" structure, so it generates ternary outputs {−1,0,+1}. The primal problems are reformulated as unconstrained optimization problems so that the objective functions are only once differentiable, not twice, therefore an extension of the Newton-Armijo algorithm is adopted for finding their solution. To test the efficiency and validity of the proposed methods, we compare the classification accuracy and learning time of these methods with K-SVCR and Twin-KSVC on the United States Postal Service (USPS) handwriting digital data sets and several University of California Irvine (UCI) benchmark data sets. To analyze more in aspect of training time, we also compared all methods on the multi-class version of the Normally Distributed Clustered (NDC) database. To further analyze classification accuracy and learning time differences between the classifiers, the statistical Friedman's test is used.
The effectiveness of T5, GPT-2, and BERT on text-to-image generation task

Text-to-image (T2I) generation, which involves synthesizing an image from a textual description, has emerged as a popular research topic in computer vision. Meanwhile, transformer-based models, such as BERT, GPT-2, and T5, have demonstrated promising results in various natural language processing tasks, including text generation and translation. However, their application to T2I generation remains largely unexplored. Therefore, a comparative study investigating the performance of BERT, GPT-2, and T5 in T2I generation is of significant importance. Such a study would shed light on the strengths and weaknesses of each model, facilitating the identification of the most suitable approach for this task. In this paper, we propose three architectures to conduct a comparative study of T5, GPT-2, and BERT in T2I generation tasks. We fine-tune these models to generate text vectors and then transform the textual information into images using the affine transformation into the DF-GAN generator. Subsequently, we evaluate the quality, diversity, and the models’ ability to recognize the words. Our experiments on the challenging CUB and Oxford-102 flower datasets demonstrate that T5 exhibits promising potential for T2I generation. It has the capability to generate visually appealing and semantically coherent images from textual descriptions.
Comparison of Modified Nazief&amp;Adriani and Modified Enhanced Confix Stripping algorithms for Madurese Language Stemming

The Madurese language has a unique morphology. The morphological uniqueness can be used to find basic words. The basic word process is called stemming. Stemming can be developed into an application for translating Madurese into Indonesian and even other languages. It can support the development of a Madurese language text plagiarism system. Stemming research on the Madurese language is still rare. Therefore, this study aims to find the basic words of the Madurese language using modifications to the Nazief &amp; Adriani algorithm and Enhanced Confix Stripping (ECS) modifications. The study used 1000 Madurese words, consisting of 630 prefix words, 74 ending words, and 296 confix words. The results showed that the modification of the Nazief &amp; Adriani algorithm was better, shown by the accuracy obtained of 88.8% with overstemming of 0.7% and understemming of 10.5%. As for ECS, an accuracy of 74.0% was obtained, 0.4% overstemming, and 25.6% understemming. In the same process, Nazief&amp;Adriani's modification is faster than the ECS modification. For the Nazief&amp;Adriani modification, it takes 13.31 seconds while for the ECS modification, it takes 210.88.
An Empirical Study of Adoption of ChatGPT for Bug Fixing among Professional Developers

ChatGPT is a powerful tool that assists software engineers in identifying and rectifying errors in code. One of its primary advantages is its ability to engage in natural language conversation with humans, which allows it to collaborate more closely with engineers in improving and optimizing the code. However, despite its potential advantages, software developers do not always utilize ChatGPT as a tool for bug fixing. In this study, we aim to examine the factors that influence the adoption of ChatGPT for bug fixing among professional software developers, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory. To accomplish this, we conducted 50 semi-structured interviews with professional software developers and other stakeholders. Our findings indicate that the performance expectancy and effort expectancy of professional software developers, as well as social influence, facilitating conditions, data security, and trust are the key factors of adoption. These findings suggest that understanding these factors can be critical in promoting the adoption and use of ChatGPT in the software development industry.
Quantum Computing in Telecommunication—A Survey

Quantum computing, an emerging paradigm based on the principles of quantum mechanics, has the potential to revolutionise various industries, including Telecommunications. This paper explores the transformative impact of quantum computing on the telecommunication market, focusing on its applications in solving computationally intensive problems. By leveraging the inherent properties of quantum systems, such as superposition and entanglement, quantum computers offer the promise of exponential computational speedup and enhanced problem-solving capabilities. This paper provides an in-depth analysis of the current state of quantum computing in telecommunication, examining key algorithms and approaches, discussing potential use cases, and highlighting the challenges and future prospects of this disruptive technology.
Enhancing Performance and Security in the Metaverse: Latency Reduction Using Trust and Reputation Management

In the rapidly evolving landscape of distributed systems, security stands as a significant challenge, especially in the face of network node attacks. Such threats introduce profound complexities into the dynamics of security protocols, trust management, and resource allocation, issues further amplified by the metaverse’s exponential growth. This paper proposes an innovative solution, offering unique technical contributions to address these multi-faceted challenges. We unveil a trust-based resource allocation framework designed to facilitate the secure and efficient sharing of computational resources within the metaverse. This system has the potential to markedly diminish latency, thereby enhancing overall performance. In parallel, we introduce a reputation system that systematically monitors latency across a spectrum of metaverse entities, providing valuable insights for making informed resource allocation decisions. Moreover, we advocate for a decentralized trust management system, specifically designed to withstand potential security breaches without reliance on a centralized authority. This significantly fortifies both system security and user trust. Alongside this, we unveil an inventive proof-of-trust consensus mechanism that fosters trust and collaboration among metaverse entities during resource allocation, thereby cultivating a more secure ecosystem. Our proposed model poses a robust challenge to malicious entities, and it substantially bolsters the security architecture. The simulation results lend substantial credence to the effectiveness of our approach, demonstrating significant improvements in latency reduction, scalability, and the detection of malicious nodes, thereby outperforming existing methodologies.
A two-step estimator for multilevel latent class analysis with covariates

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.
Developing a virtual reality healthcare product based on data-driven concepts: A case study

The rapid development of artificial intelligence (AI) and big data technologies has profoundly changed the way of human life and work, and in the healthcare field, the related product design is moving towards the trend of digitalization, intelligence, and personalization. The growing demands for personalised healthcare have contributed to a boom in product design theories in related fields. However, it is difficult to measure product users' preferences in precise numerical terms to make strategic decisions in product design. To fill this gap, this study proposes a framework for virtual reality (VR) healthcare product design based on a data-driven concept and develops a VR virtual roaming system for system users guided by this theory. The data-driven concept incorporates AI and big data technologies into product design, helping to shift the design of medical products towards a data-centred approach. Ultimately, an ergonomic experiment was conducted and a case study was carried out to assess the user's experience using brain function data to enable data-driven VR value-added services. This study provides a new perspective on rehabilitation product design, opening the way for better personalised healthcare delivery.