Reviewerly/exHarmony huggingface dataset:
GALOIS LCD CODES OVER

Abstract Wu and Shi [‘A note on k -Galois LCD codes over the ring $\mathbb {F}_q + u\mathbb {F}_q$ ’, Bull. Aust. Math. Soc. 104 (1) (2021), 154–161] studied $ k $ -Galois LCD codes over the finite chain ring $\mathcal {R}=\mathbb {F}_q+u\mathbb {F}_q$ , where $u^2=0$ and $ q=p^e$ for some prime p and positive integer e . We extend the results to the finite nonchain ring $ \mathcal {R} =\mathbb {F}_q+u\mathbb {F}_q+v\mathbb {F}_q+uv\mathbb {F}_q$ , where $u^2=u,v^2=v $ and $ uv=vu $ . We define a correspondence between the $ l $ -Galois dual of linear codes over $ \mathcal {R} $ and the $ l $ -Galois dual of their component codes over $ \mathbb {F}_q $ . Further, we construct Euclidean LCD and $ l $ -Galois LCD codes from linear codes over $ \mathcal {R} $ . We prove that any linear code over $ \mathcal {R} $ is equivalent to a Euclidean code over $\mathbb {F}_q$ with $ q&gt;3 $ and an $ l $ -Galois LCD code over $ \mathcal {R}$ with $0&lt;l&lt;e$ and $p^{e-l}+1\mid p^e-1$ . Finally, we investigate MDS codes over $ \mathcal {R}$ .
Heterogeneous graphlets-guided network embedding via eulerian-trail-based representation

Heterogeneous graph neural networks (GNNs) have excelled at learning to represent heterogeneous information networks (HINs). However, there are the following limitations in heterogeneous information networks representations: (1) existing models rely on pre-defined substructure sets, such as metapaths, in which the number of nodes is primarily determined based on experience; (2) except for a few simple pairwise relations, most models fail to capture multifaceted graph structures and commonly result in semantic redundancy. To tackle the aforementioned problems, in this study, we propose a Heterogeneous Graphlets-guided Network Embedding framework via Eulerian-trail-based Representation (HeGEER) for capturing complex structural characteristics automatically and efficiently. HeGEER first explores frequent heterogeneous graphlets through a random-algorithm-based method without any artificial pre-definition. Then, HeGEER conducts a Jaccard-coefficients-based sorting to reduce the semantical redundancy of derived heterogeneous graphlets, and transforms these heterogeneous graphlets into corresponding Eulerian-trails to preserve the entire relation information. The node embeddings are then generated by aggregating representations of intro- and inter- heterogeneous graphlets using a heterogeneous graph dual-attention mechanism. On various benchmark datasets, comprehensive experiments are undertaken on tasks including node classification and link prediction. The experimental results are thoroughly analyzed to illustrate HeGEER's improved performance compared to several typical models.
RobustFlow: An unsupervised paradigm toward real-world wear detection and segmentation with normalizing flow

Three main challenges in industrial wear detection that limited data-availability and time-consuming annotations, small areas of initial wear, and sensitivity to variable light, have impeded the real-world applications of deep learning-based methods. To this end, we propose RobustFlow, an unsupervised method based on the normalizing flow and attention mechanism. In our work, only the wear-free images are required for training, and then the trained model can be employed to detect and segment wear. Extensive experiments have demonstrated that RobustFlow can achieve predominant robustness in real-world wear detection and segmentation, especially for wear with small regions and variable light. Overall, our work provides a promising paradigm for wear detection and segmentation in real-world industry.
Effect of inconsistency rate of granulated datasets on classification performance: An experimental approach

An experiment was conducted to investigate the effect of the inconsistency rate (IR) of granulated datasets on classification performance. Unsupervised (equal-width interval, EWI) and supervised (minimum description length, MDL) techniques were used to granulate 36 datasets. An algorithm was developed to divide the original granulated datasets into consistent and inconsistent subsets. Five classifiers including one simple tree-based and four ensemble-based on datasets before granulation (BG), after granulation but before removal of inconsistent granulated datasets (AGBR), and after removal of inconsistent granulated datasets (AR) were used, followed by testing and comparisons of predication accuracy (PA). The experimental results showed the following: (1) 24 out of 36 via EWI and 28 out of 36 via MDL datasets contain inconsistent datasets. (2) PA of AR is more likely higher than of BG and AGBR datasets with both EWI and MDL by all classifiers. (3) Mean PA improvement ranges from 5.74% to 10.01% with EWI and from 8.74% to 13.73% with MDL. (4) The correlation coefficient between IR and PA improvement ranges from 0.7413 to 0.7901 with EWI and 0.7870 to 0.9683 with MDL. These results demonstrate the value of uncovering the effect of IR on classification performance in the domain of machine learning.
Motion planning for an under-actuated autonomous underwater vehicle based on fast marching nonlinear model-predictive quantum particle swarm optimization

The motion planner determines the autonomy of the autonomous underwater vehicle. However, many studies often ignore the characteristics of autonomous underwater vehicles and the underwater environment, resulting in unreasonable trajectories. To solve this problem, this paper plans the motion force through prediction and optimization to integrate the characteristics of the autonomous underwater vehicle. The fast travel algorithm is modified to take advantage of favorable currents and output arrival times, taking into account the peculiarities of the underwater environment. A novel objective function is proposed, which incorporates the arrival times to imply the requirements of obstacle avoidance and distance optimization. A penalty item is included in the objective function to avoid strong side flow. At the same time, the force and its changing rate limited in appropriate ranges are also included to balance energy consumption. In order to meet the high running time requirements in practical applications, the warm-start improved quantum particle swarm optimization is introduced into force optimization. Simulation studies are performed on a self-developed autonomous underwater vehicle. The method has good application effects in multi-island areas, ocean currents, and dynamic three-dimensional environments. Comparative simulations show that the planner plans shorter paths with good robustness and adaptability.
Multi-order similarity learning for multi-view spectral clustering

This paper explores the problem of multi-view spectral clustering (MVSC) based on multi-order similarity learning. Unlike the existing methods that focus on direct similarity of pairwise data points without considering the hidden multi-order similarity among different data points, a novel multi-order similarity learning model for MVSC (MOSL) is proposed. Specifically, the first-order similarity (FOS) and second-order similarity (SOS) are learned to excavate the local structure relation and adjacent structure relation of pairwise data points. Afterwards, the third-order similarity (TOS) based on low-rank tensor is learned to excavate the view-specific information and consensus information from multiple views. Moreover, a trace constraint on each affinity graph from multiple views is learned to ensure the strict block diagonal structure of each affinity graph. Extensive experiments on six commonly benchmark datasets show that the proposed method outperforms state-of-the-art methods in most scenarios and is capable of revealing a reliable affinity graph structure concealed in different data points.
Sensor-based continuous user authentication on smartphone through machine learning

With the rapid development of hardware and software technology, many end-users have often stored their data on smartphones through several in-built sensors. Thus, the security of stored data on smartphones has become a significant concern. This has fueled the importance of entry-point authentication methods on smartphones. Many entry-point authentication methods have failed to offer security due to insider or side-channel attacks. This article introduces a sensor-based continuous user authentication approach on the smartphone through multi-modal behavioral biometrics and a machine learning model to tackle the aforementioned issues. The proposed approach captures the touch and motion-based behavioral biometrics through the touchscreen and inertial sensors of the device. Then, the proposed approach extracts several features from captured behavioral data and selects the best set of features through a filter-based feature selection technique. Further, we implement a nonlinear support vector machine with optimized hyperparameters for training and predicting the features to generate the scores. We apply score-level fusion on generated scores of several sensors to compute the final score for identification of the genuine user. In this article, we systematically evaluate the proposed approach with the most commonly used behavioral activities of the smartphone user in our daily life. The experiment results on all behavioral activities show that the proposed approach obtained the best authentication score compared to the other machine learning models, and state-of-the-art methods. Finally, we conclude our article by addressing the limitations of the proposed approach and practical research issues for future exploration.
A new image decomposition approach using pixel-wise analysis sparsity model

Decomposing an image into two 'simpler' layers has been widely used in low-level vision tasks, such as image recovery and enhancement. It is an ill-posed problem since the number of unknowns are larger than the input. In this paper, a two-step strategy is introduced, including task-aware priors estimate and a decomposition model. A pixel-wise analysis sparsity model is proposed to regularize the separation layers, which supposes the transformed image generated with analysis operator is sparse. Unlike regularizing all pixels with one penalty weight, we try to estimate each pixel's sparsity level with task-aware priors and to achieve pixel-wise sparse penalty. Additionally, one separation layer is regularized with both synthesis sparsity model and pixel-wise analysis sparsity model to exploit their complementary mechanisms. Unlike the analysis one utilizing image local features, the synthesis one exploits an over-complete dictionary and non-local similarity cues to provide flexible prior for regularizing the decomposition results. The proposed model is solved by an alternating optimization algorithm. We evaluate it with two applications, Retinex model and rain streaks removal. Extensive experiments on multiple enhancement datasets, many synthetic and real rainy images demonstrate that our method can remove imaging noise during Retinex decomposition, and can produce high fidelity deraining results. It achieves competing performance in terms of quantitative metrics and visual quality compared with the state-of-the-art methods.
Utilizing Blogs on ESL learners’ vocabulary learning through social constructivist theory: A descriptive study

During this period in history, the advancement of technology is developing at a fast pace, and it is becoming more important to every normal person. In particular, the smart phone, which is quickly becoming one of the most frequently used gadgets in ordinary life, is no longer considered a luxury commodity. Because of the COVID-19 epidemic, students are not required to utilise textbooks while studying. Beyond textbooks, there are numerous other media that may be used to advance an individual's education and development. The purpose of this research study is to investigate the effect of social media, namely Blogs, on the vocabulary acquisition of ESL students. A questionnaire was adopted for data collection from 60 people from different universities, and the results were analysed using a descriptive method. Following the responses given by the respondents, it was noticed that a lot of ESL learners believed that using Blogs to improve vocabulary development is beneficial since it raises the degree of involvement in studying. The current research investigates the use of social constructivist theory in the teaching of ESL learners. The purpose of this study report is to demonstrate the statistically significant impact of the use of Blogs on the growth of learners' vocabulary. Finally, the article concluded that, in light of the descriptive study, the social constructivist method is very significant in English language learning, particularly in vocabulary development. The following objectives were established for the study through Descriptive Method of Social Constructivist Theory of Learning Vocabulary:•To explore the relationship between the use of Blogs and an interest in ESL learners' vocabulary knowledge.•To identify the effect of Blogs on the development of English vocabulary among ESL learners by using social constructivist theory.•To examine the effect of using Blogs on students' capacity to improve their vocabulary usage.
SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery

Solar power is a clean and renewable energy source. Promoting solar technology can not only offer all people affordable, reliable, and modern energy, but also mitigate energy-related emissions and pollutants. This significantly contributes to sustainable development goals. Aerial imagery can provide a cost-effective way for large-scale rooftop solar potential analysis when compared to other data sources. Existing studies mainly utilize aerial imagery and convolutional neural networks to learn the roof segmentation mask or the rooftop geometry map, which are the preliminary input for rooftop solar potential estimation. However, these methods fail to achieve precise solar potential analysis results. To address this issue, we propose a framework, which is termed as SolarNet for rooftop solar potential estimation. A novel multi-task learning network is devised in SolarNet to learn our proposed novel representation for rooftop geometry that incorporates 6 roof segments and orientations. Specifically, this network first learns a roof segmentation map, and then together with the extracted multiscale and contextual features to learn a roof geometry map. Finally, the solar potential can be estimated from the learned roof geometry map. The effectiveness of SolarNet is validated on two datasets: DeepRoof and RID datasets. Experimental results demonstrate that SolarNet can improve not only rooftop geometry prediction accuracy but also solar potential estimation precision, which significantly outperforms other competitors.
D<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si67.svg" display="inline" id="d1e674"><mml:msubsup><mml:mrow /><mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msubsup></mml:math>: A risk aware platform agnostic heterogeneous path planner

This article establishes the novel D+∗, a risk-aware and platform-agnostic heterogeneous global path planner for robotic navigation in complex environments. The proposed planner addresses a fundamental bottleneck of occupancy-based path planners related to their dependency on accurate and dense maps. More specifically, their performance is highly affected by poorly reconstructed or sparse areas (e.g. holes in the walls or ceilings) leading to faulty generated paths outside the physical boundaries of the 3-dimensional space. As it will be presented, D+∗ addresses this challenge with three novel contributions, integrated into one solution, namely: (a) the proximity risk, (b) the modeling of the unknown space, and (c) the map updates. By adding a risk layer to spaces that are closer to the occupied ones, some holes are filled, and thus the problematic short-cutting through them to the final goal is prevented. The novel established D+∗ also provides safety marginals to the walls and other obstacles, a property that results in paths that do not cut the corners that could potentially disrupt the platform operation. D+∗ has also the capability to model the unknown space as risk-free areas that should keep the paths inside, e.g in a tunnel environment, and thus heavily reducing the risk of larger shortcuts through openings in the walls. D+∗ is also introducing a dynamic map handling capability that continuously updates with the latest information acquired during the map building process, allowing the planner to use constant map growth and resolve cases of planning over outdated sparser map reconstructions. The proposed path planner is also capable to plan 2D and 3D paths by only changing the input map to a 2D or 3D map and it is independent of the dynamics of the robotic platform. The efficiency of the proposed scheme is experimentally evaluated in multiple real-life experiments where D+∗ is producing successfully proper planned paths, either in 2D in the use case of the Boston dynamics Spot robot or 3D paths in the case of an unmanned areal vehicle in varying and challenging scenarios.
ExtendedSketch+: Super host identification and network host trust evaluation with memory efficiency and high accuracy

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host cardinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct performance evaluation based on real network traffic traces. Experimental results show that ExtendedSketch+ performs better than state-of-the-art sketches regarding super host identification and provides accurate host trust evaluation.
Mode-pairing quantum key distribution based on pulse-position modulation

In order to solve the problem of low-key rate due to empty pulses of weak coherent source in mode-paring quantum key distribution protocol (MP-QKD), a MP-QKD based on pulse-position modulation (PPM) protocol is proposed in this paper. The protocol uses empty pulses in weak coherent source to encode, among M successive pulses, there is only one single photon pulse and the rest are empty pulses, and the position of the single photon pulse is used to encode, therefore, the key rate of the protocol is improved. Meanwhile, security analysis shows that the security of our scheme can be simplified to the single-photon MDI-QKD protocol and the security of our scheme has been proved. Finally, simulation results also show that the key rate and transmission distance of PPM-MP-QKD protocol are improved compared with MP-QKD protocol, while no added difficulty in implementation because of no additional devices.
Optimizing an irrigation treatment using an evolutionary algorithm and a knowledge discovery framework based on Deep models

Nowadays, learning models have good accuracy and learn complex patterns which are hidden in the data. Occasionally, patterns which are discovered using these models cannot be discovered by a human. So representing the discovered patterns and knowledge as an explainable knowledge for human is important. In this paper, a framework is proposed for knowledge discovery from real agricultural datasets. In the proposed framework, Deep Learning (DL) models are used for learning the patterns and effective features from tabular datasets; The integrated gradient method is used as an indirect metric for representing the black-box models as an interpretable model; A fuzzy expert system is used for interpreting the discovered knowledge as an expert system; An especial evolutionary algorithm is used for extracting the knowledge as a fuzzy expert system. In order to evaluate the proposed framework, some experiments are performed on the real tabular dataset on the agricultural field for extracting knowledge about productivity. For validating the discovered knowledge, an optimization algorithm is proposed which uses the discovered expert system for designing optimal irrigation treatments on the selected dataset. DSSAT, which is a farm simulator, is used for validating discovered knowledge. The obtained results show that the discovered knowledge can improve the productivity by 30% and decrease the used water by 30%. In addition, yields of the designed treatment and discovered knowledge correlate more than 0.8.
Unforgeable Digital Signature Integrated into Lightweight Encryption Based on Effective ECDH for Cybersecurity Mechanism in Internet of Things

Cybersecurity protocols enable several levels of protection against cyberattacks (digital attacks) that spread across network devices, platform programs, and network applications. On the Internet of Things (IoT), cyberattacks are generally intended to access and change/destroy sensitive information, which may reduce IoT benefits. Moreover, recent IoT systems are experiencing a critical challenge in designing a lightweight and robust cybersecurity mechanism on resource-constrained IoT devices. The cybersecurity challenges facing the IoT that should be taken into consideration are identifying compromised devices, data/service protection, and identifying impacted IoT users. This paper proposes an unforgeable digital signature integrated into an effective lightweight encryption (ELCD) mechanism that utilizes the secure key distribution in an elliptic curve Diffie–Hellman (ECDH) and resolves the weak bit problem in the shared secret key due to the Diffie–Hellman exchange. The ELCD mechanism proposes a secure combination between the digital signature and encryption, and it uses fast hash functions to confidentially transfer a shared secret key among IoT devices over an insecure communication channel. Furthermore, the ELCD mechanism checks the true identity of the sender with certainty through the proposed digital signature, which works based on a hash function and three steps of curve-point inspection. Furthermore, the security of ELCD was mathematically proven using the random oracle and IoT adversary models. The findings of the emulation results show the effectiveness of ELCD in terms of CPU execution time, storage cost, and power consumption that are less by 53.8%, 33–17%, and 68.7%, respectively, compared to the baseline cryptographic algorithms.