Reviewerly/exHarmony huggingface dataset:
Characterizing and Detecting WebAssembly Runtime Bugs

WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and desktop applications. These applications, named WASM applications, commonly run in WASM runtimes. Bugs in WASM runtimes are frequently reported by developers and cause the crash of WASM applications. However, these bugs have not been well studied. To fill in the knowledge gap, we present a systematic study to characterize and detect bugs in WASM runtimes. We first harvest a dataset of 311 real-world bugs from hundreds of related posts on GitHub. Based on the collected high-quality bug reports, we distill 31 bug categories of WASM runtimes and summarize their common fix strategies. Furthermore, we develop a pattern-based bug detection framework to automatically detect bugs in WASM runtimes. We apply the detection framework to seven popular WASM runtimes and successfully uncover 60 bugs that have never been reported previously, among which 13 have been confirmed and 9 have been fixed by runtime developers.
An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10−4, 9.82484851784979 × 10−4, 9.82484851784993 × 10−4, 2.42507486809489 × 10−3, 1.72981370994064 × 10−3, and 1.66006031250846 × 10−2. Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification.
Machine learning and blockchain technologies for cybersecurity in connected vehicles

Abstract Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi‐modal sensor fusion. A comprehensive review of cyberattacks on intra‐vehicle and inter‐vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining‐aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy Technologies &gt; Machine Learning Technologies &gt; Internet of Things
A tutorial-based survey on feature selection: Recent advancements on feature selection

Curse of dimensionality is known as big challenges in data mining, pattern recognition, computer vison and machine learning in recent years. Feature selection and feature extraction are two main approaches to circumvent this challenge. The main objective in feature selection is to remove the redundant features and preserve the relevant features in order to improve the learning algorithm performance. This survey provides a comprehensive overview of state-of-art feature selection techniques including mathematical formulas and fundamental algorithm to facilitate understanding. This survey encompasses different approaches of feature selection which can be categorized to five domains including: A) subspace learning which involves matrix factorization and matrix projection, B) sparse representation learning which includes compressed sensing and dictionary learning, C) information theory which covers multi-label neighborhood entropy, symmetrical uncertainty, Monte Carlo and Markov blanket, D) evolutionary computational algorithms including Genetic algorithm (GA), particle swarm optimization (PSO), Ant colony (AC) and Grey wolf optimization (GWO), and E) reinforcement learning techniques. This survey can be helpful for researchers to acquire deep understanding of feature selection techniques and choose a proper feature selection technique. Moreover, researcher can choose one of the A, B, C, D and E domains to become deep in this field for future study. A potential avenue for future research could involve exploring methods to reduce computational complexity while simultaneously maintaining performance efficiency. This would involve investigating ways to achieve a more efficient balance between computational resources and overall performance. For matrix-based techniques, the main limitation of these techniques lies in the need to tune the coefficients of the regularization terms, as this process can be challenging and time-consuming. For evolutionary computational techniques, getting stuck in local minimum and finding an appropriate objective function are two main limitations.
Tight finite-key analysis for mode-pairing quantum key distribution

Abstract Mode-pairing quantum key distribution (MP-QKD) is a potential protocol that is not only immune to all possible detector side channel attacks, but also breaks the repeaterless rate-transmittance bound without needing global phase locking. Here we analyze the finite-key effect for the MP-QKD protocol with rigorous security proof against general attacks. Moreover, we propose a six-state MP-QKD protocol and analyze its finite-key effect. The results show that the original protocol can break the repeaterless rate-transmittance bound with a typical finite number of pulses in practice. And our six-state protocol can improve the secret key rate significantly in long distance cases.
Evolutionary neural architecture search combining multi-branch ConvNet and improved transformer

Abstract Deep convolutional neural networks (CNNs) have achieved promising performance in the field of deep learning, but the manual design turns out to be very difficult due to the increasingly complex topologies of CNNs. Recently, neural architecture search (NAS) methods have been proposed to automatically design network architectures, which are superior to handcrafted counterparts. Unfortunately, most current NAS methods suffer from either highly computational complexity of generated architectures or limitations in the flexibility of architecture design. To address above issues, this article proposes an evolutionary neural architecture search (ENAS) method based on improved Transformer and multi-branch ConvNet. The multi-branch block enriches the feature space and enhances the representational capacity of a network by combining paths with different complexities. Since convolution is inherently a local operation, a simple yet powerful “batch-free normalization Transformer Block” (BFNTBlock) is proposed to leverage both local information and long-range feature dependencies. In particular, the design of batch-free normalization (BFN) and batch normalization (BN) mixed in the BFNTBlock blocks the accumulation of estimation shift ascribe to the stack of BN, which has favorable effects for performance improvement. The proposed method achieves remarkable accuracies, 97.24 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> and 80.06 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on CIFAR10 and CIFAR100, respectively, with high computational efficiency, i.e. only 1.46 and 1.53 GPU days. To validate the universality of our method in application scenarios, the proposed algorithm is verified on two real-world applications, including the GTSRB and NEU-CLS dataset, and achieves a better performance than common methods.
The Effect of Dust Deposition on the Performance of Photovoltaic Panels

Given the energy crisis and climate change due to pollution, and given that the largest emissions of greenhouse gases are produced by the energy industry, we must turn our attention to the efficient use of solar energy, which is the cleanest and most abundant of all renewable energies. In this paper, based on an analysis of the specialized literature, we studied the effect of dust accumulation on the surface of photovoltaic modules on some performance characteristics and on the efficiency of these panels and modules compared to the efficiency of clean modules. We analyzed the cause of dust accumulation and the influence of the tilt angles of the photovoltaic panels on the dust deposition rate. We highlighted the influence of atmospheric temperature, solar radiation, wind speed, and relative humidity depending on the density of the dust deposited on the surface of the photovoltaic panel, and we found a decrease in the efficiency of the panel based on the increase in dust density for slightly high values of solar radiation, wind speed, and relative humidity. We highlighted the reduction in CO2 emissions by replacing electricity from fossil fuels with solar energy. The efficient use of solar energy is a solution for the decarbonization of the energy sector.
Quantum synchronization of a single trapped-ion qubit

Synchronizing a few-level quantum system is of fundamental importance to the understanding of synchronization in the deep quantum regime. Whether a two-level system, the smallest quantum system, can be synchronized has been theoretically debated for the past several years. Here, for the first time, we demonstrate that a qubit can indeed be synchronized to an external driving signal by using a trapped-ion system. By engineering fully controllable gain and damping processes, an ion qubit is locked to the driving signal and oscillates in phase. Moreover, upon tuning the parameters of the driving signal, we observe characteristic features of the Arnold tongue as well. Our measurements agree remarkably well with numerical simulations based on recent theory on qubit synchronization. By synchronizing the basic unit of quantum information, our study opens up the possibility of exploring the application of quantum synchronization to quantum information processing in the near future.
Parallel Hybrid Networks: An Interplay between Quantum and Classical Neural Networks

The use of quantum neural networks for machine learning is a paradigm that has recently attracted considerable interest. Under certain conditions, these models approximate the distributions of their datasets using truncated Fourier series. Owing to the trigonometric nature of this fit, angle-embedded quantum neural networks may have difficulty fitting nonharmonic features in a given dataset. Moreover, the interpretability of hybrid neural networks remains a challenge. In this study, we introduce an interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to (a) a classical multi-layered perceptron and (b) a variational quantum circuit, after which the 2 outputs are linearly combined. The quantum neural network creates a smooth sinusoidal foundation based on the training set, and the classical perceptrons fill the nonharmonic gaps in the landscape. We demonstrate this claim using 2 synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that parallel hybrid network architecture can improve solution optimality on periodic datasets with additional noise.
A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions

5th Generation(5G) -enabled Internet of Things (IoT) connects billions of devices for high-speed data transfer. The data collected fuel many automation applications such as smart cities, industrial automation 5.0, autonomous vehicles and healthcare sectors. However, the critical challenges concerning such an environment are Open Web Security and Mutual Authentication. The recent security protocols of IoT are based on hard mathematical cryptographic structures. Nevertheless, dependency on the security of classical cryptographic primitives is at risk of being broken by Quantum Computing. Recent Quantum attacks on IoT security have motivated us to survey Quantum-resistant solutions comprehensively. Therefore in this paper, we analysed how the technology shifts from classical cryptographic techniques towards quantum-enabled solutions to achieve end-to-end security. The main objective of this survey is to provide a systematic roadmap in the area of Quantum secured 5G-enabled IoT communication by covering current research on 5G-enabled IoT with its key enabling technologies, threats imposed on 5G-enabled IoT applications, state-of-the-art quantum-based solutions and initiatives. A detailed overview of the quantum computing preliminaries, Post Quantum Cryptography (PQC) schemes, Quantum Cryptography (QC), and Quantum Key Distribution (QKD) with their significant advantage in securing IoT-enabled communication over classical scenarios are also presented. Particularly, we emphasize Post-Quantum resistant techniques that must be used soon to secure 5G-enabled IoT communications. The comparative analysis of quantum-resistant schemes with classical cryptographic schemes in terms of key size, data size, time complexity and impact of quantum computers on these schemes are discussed. This paper identifies the technical challenges in Quantum-based schemes and provides future research directions to enable Post-Quantum resistant cryptography for secure communication in 5G-enabled IoT.
AI Technology in the Field of Logistics

This academic paper explores the implications and future trends of Artificial Intelligence (AI) in the logistics industry. The study focuses on the impact of AI on job roles and employment, as well as addressing concerns related to data privacy, security, and ethical considerations. The research highlights that AI technology is reshaping job roles in logistics through automation and the emergence of new positions. Routine tasks such as data entry and inventory management are being automated, resulting in streamlined processes and increased operational efficiency. Simultaneously, the introduction of AI necessitates the development of new job roles such as AI system trainers, data analysts, and AI strategists. The paper also outlines future trends in AI adoption in logistics. These include the integration of AI with robotics, enabling cognitive robots to perform complex tasks such as autonomous picking and packing in warehouses. Additionally, the integration of AI with blockchain technology enhances transparency, traceability, and security in logistics operations. However, the paper acknowledges the challenges associated with AI implementation. Logistics companies must ensure compliance with privacy regulations and implement robust cybersecurity measures to safeguard critical infrastructure and data. Furthermore, ethical considerations regarding transparency, fairness, and accountability must be addressed to ensure responsible and unbiased use of AI technology. Overall, the research highlights the significance of AI in transforming the logistics industry. AI enables enhanced efficiency, improved decision-making, and cost savings. It provides opportunities for upskilling while creating new job roles. To fully harness the benefits of AI, logistics companies must navigate the challenges of data privacy, security, and ethical considerations.
A smoothing Group Lasso based interval type-2 fuzzy neural network for simultaneous feature selection and system identification

Inspired by the life philosophy, an ingenious gate (membership) function, which can mimic the open and close of the gate in the real world, is proposed to realize feature selection (FS) for interval type-2 fuzzy neural network. Based on the widths of gate functions, a smoothing integrated learning strategy is designed, and in the regularized FS error function the gate widths corresponding to one feature are grouped as one Group Lasso (GL) regularization term. During the training procedure of feature selection, all the group terms with respect to bad features will be punished, but for the good features the corresponding group terms converge to different nonzero values. When feature selection is finished, using the selected features, the interpretable clustering algorithm based initial interval type-2 rule generation method is detailedly introduced firstly, then a simple and efficient memory-based gradient method is presented for the tuning of rule structure parameters. Lastly, employing the simulation results of four regression problems and four classification problems, the effectiveness on simultaneous feature selection and system identification of our proposed model, i.e., the smoothing Group Lasso based interval type-2 fuzzy neural network (SGLIT2FNN), is proved, which illustrates that SGLIT2FNN not only simplifies the model structure by deleting the bad features, but also maintains the performance.
Underwater Image Restoration via Adaptive Color Correction and Contrast Enhancement Fusion

When light traverses through water, it undergoes influence from the absorption and scattering of particles, resulting in diminished contrast and color distortion within underwater imaging. These effects further constrain the observation of underwater environments and the extraction of features from submerged objects. To address these challenges, we introduce an underwater color image processing approach, which amalgamates the frequency and spatial domains, enhancing image contrast in the frequency domain, adaptively refining image color within the spatial domain, and ultimately merging the contrast-enhanced image with the color-corrected counterpart within the CIE L*a*b* color space. Experiments conducted on standard underwater image benchmark datasets highlight the significant improvements our proposed method achieves in terms of enhancing contrast and rendering more natural colors compared to several state-of-the-art methods. The results are further evaluated using four commonly used image metrics, consistently showing that our method yields the highest average value. The proposed method effectively addresses challenges related to low contrast, color distortion, and obscured details in underwater images, a fact especially evident in various scenarios involving color-affected underwater imagery.
Ensemble Multifeatured Deep Learning Models and Applications: A Survey

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models.
Machine learning job failure analysis and prediction model for the cloud environment

Reliable and accessible cloud applications are essential for the future of ubiquitous computing, smart appliances, and electronic health. Owing to the vastness and diversity of the cloud, a most cloud services, both physical and logical services have failed. Using currently accessible traces, we assessed and characterized the behaviors of successful and unsuccessful activities. We devised and implemented a method to forecast which jobs will fail. The proposed method optimizes cloud applications more efficiently in terms of resource usage. Using Google Cluster, Mustang, and Trinity traces, which are publicly available, an in-depth evaluation of the proposed model was conducted. The traces were also fed into several different machine learning models to select the most reliable model. Our efficiency analysis proves that the model performs well in terms of accuracy, F1-score, and recall. Several factors, such as failure of forecasting work, design of scheduling algorithms, modification of priority criteria, and restriction of task resubmission, may increase cloud service dependability and availability.
Bimodal Camera Pose Prediction for Endoscopy

Deducing the 3D structure of endoscopic scenes from images is exceedingly challenging. In addition to deformation and view-dependent lighting, tubular structures like the colon present problems stemming from their self-occluding and repetitive anatomical structure. In this paper, we propose SimCol, a synthetic dataset for camera pose estimation in colonoscopy, and a novel method that explicitly learns a bimodal distribution to predict the endoscope pose. Our dataset replicates real colonoscope motion and highlights the drawbacks of existing methods. We publish 18k RGB images from simulated colonoscopy with corresponding depth and camera poses and make our data generation environment in Unity publicly available. We evaluate different camera pose prediction methods and demonstrate that, when trained on our data, they generalize to real colonoscopy sequences, and our bimodal approach outperforms prior unimodal work. Our project and dataset can be found here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/anitarau/simcol</uri> .