Reviewerly/exHarmony huggingface dataset:
Multi-swarm improved Grey Wolf Optimizer with double adaptive weights and dimension learning for global optimization problems

Grey Wolf Optimizer (GWO) is a swarm intelligent optimization algorithm that simulates the leadership and social behavior of grey wolves to prey. GWO is extensive used in various fields since it has the advantage of being simple and easy to implement. However, when solving complex optimization problems, GWO has insufficient population diversity and unbalanced exploration and exploitation. To overcome these shortcomings of GWO, a multi-swarm improved grey wolf optimizer (MIGWO) is proposed. In MIGWO, firstly, chaotic grouping mechanism is utilized to improve population diversity and dynamic regrouping mechanism to further improve population diversity and balance exploration and exploitation. Secondly, double adaptive weights and dimension learning are utilized to ameliorate the hunting behavior of grey wolves, which can improve search performance. The MIGWO is verified on 53 test problems. The test results show that it is better to other metaheuristic algorithms and GWO variants in solving the optimal solution and convergence accuracy. In addition, MIGWO is utilized to solve 4 engineering design problems.
Image feature extraction based on fuzzy restricted Boltzmann machine

In order to accelerate the speed of visual image feature extraction and solve the problem of training deceleration caused by the introduction of fuzzy numbers into the original Restricted Boltzmann Machine (RBM), a Mixed Accelerated learning method based on a Fuzzy Restricted Boltzmann Machine (MAFRBM) is studied. Firstly, the penalty factor is introduced to increase the sparsity of data and improve the effectiveness of feature information extraction. Secondly, the characteristic similarity of the hidden layer units of the network is compared. When the unit similarity is higher than the threshold, the unit is a redundant unit for repeated learning, which is eliminated to accelerate learning. Thirdly, the reconstruction error of each training is used to adaptively change the network learning rate to achieve further acceleration. Finally, compared with the change of network reconstruction error, the accuracy of network convergence is defined to accelerate learning convergence. And a multi-functional image recognition model is constructed, which is MAFRBM + SVM. The features extracted by MAFRBM unsupervised learning are imported into SVM to test the effectiveness and practicability of MAFRBM. The experimental results show that the learning speed and feature extraction ability of MAFRBM are better than other models.
A comprehensive survey and mathematical insights towards video summarization

• Video Summarization is a technique to reduce the original raw video into a short video summary. • Design of a generic framework for video summarization based on different criteria to identify suitable summarization model. • Inclusion of important mathematical formulations to provide meaningful insights for video summarization model. • A detailed review & comparative analysis of significant research works in the area of video summarization against the framework. Video Summarization is a technique to reduce the original raw video into a short video summary. Video summarization automates the task of acquiring key frames/segments from the video and combining them to generate a video summary. This paper provides a framework for summarization based on different criteria and also compares different literature work related to video summarization. The framework deals with formulating model for video summarization based on different criteria. Based on target audience/ viewership, number of videos, type of output intended, type of video summary and summarization factor; a model generating video summarization framework is proposed. The paper examines significant research works in the area of video summarization to present a comprehensive review against the framework. Different techniques, perspectives and modalities are considered to preserve the diversity of survey. This paper examines important mathematical formulations to provide meaningful insights for video summarization model creation.
Behavioral evidence of public aircraft with historical data: The case of Boeing 737 MAX 8 PK-LQP

This paper studies a significant amount of residual evidence characterized by the historical flight trajectory of PKLQP (B737 MAX 8), which underwent an accident. Subsequently, this method is employed to generate novel safety-relevant knowledge based on existing flight data. At the beginning of this study, the method is applied by developing the hypothesis with the support of all data collected from online and offline reports, ADS-B data from flightradar24, and a statistical approach. This preliminary study employs Python as an essential program for the purpose of data collation and analysis. The results show that in the data offered by KNKT (The Indonesian National Transportation Safety Committee-Indonesia), the aircraft (PK-LQP) demonstrated unusual behaviour in comparison with the typical climbing phase that is produced by the dataset valid B737 MAX 8 (the ground truth data). The results also confirmed the hypothesis proposed in this study.
Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning

Dialogue state tracking (DST), a crucial component of the task-oriented dialogue system (TOD), is designed to track the user’s goal. Existing DST models mainly focus on monolingual dialogue input, failing to meet the growing needs of a TOD to provide multilingual services. Therefore, this paper proposes a novel Zero-shot Language Extension scenario for DST, extending the monolingual DST to multilingual DST without extra high-cost dialogue data annotation. In this scenario, the multilingual DST only needs a single shared model to handle multilingual input and generate a unified dialogue state. This setting makes deploying a complete multilingual TOD easy since it could be reused by the downstream components from existing monolingual TOD. Specifically, we achieve the language extension by multi-auxiliary-tasks fine-tuning of multilingual pre-trained models, where five relevant auxiliary tasks are jointly designed, including monolingual DST, cross-lingual DST, forward word translation, utterance recovery, and semantic similarity. The extended multilingual DST model can be enhanced through joint optimization with all the auxiliary tasks by capturing multilingual context understanding and cross-lingual alignment characteristics. Comprehensive experiments on Multilingual WOZ dataset (English → German and English → Italian) and cross-lingual MultiWOZ dataset (English → Chinese and Chinese → English) demonstrate the effectiveness and superiority of the proposed method.
RiscyROP: Automated Return-Oriented Programming Attacks on RISC-V and ARM64

Return-oriented programming (ROP) is a powerful run-time exploitation technique to attack vulnerable software. Modern RISC architectures like RISC-V and ARM64 pose new challenges for ROP execution due to the lack of a stack-based return instruction and strict instruction alignment. Further, the large number of caller-saved argument registers significantly reduces the gadget space available to the attacker. Consequently, existing ROP gadget tools for other processor architectures cannot be applied to these RISC architectures. Previous work on RISC-V provides only manual construction of ROP attacks against specially crafted programs, and no analysis of ROP attacks has been conducted for ARM64 yet.
DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.
Deep-learning-based ciphertext-only attack on optical scanning cryptosystem

Optical scanning cryptography (OSC) has garnered considerable attention because of its ability to acquire an incoherent hologram from a physical object using a single-pixel sensor. Security analysis is crucial to improve the performance of cryptographic systems. In this study, we prove that the optical scanning cryptosystem is vulnerable to a deep-learning-based ciphertext-only attack (COA) strategy. The conventional algorithm denotes the decryption process as an inverse problem of the encryption process and directly trains the deep learning architecture with a large number of pairs of plaintext-ciphertext images. However, this study first presents a combined deep-learning-based framework dedicated to processing COA on OSC using three deep neural networks (DNNs). With these DNNs, the plaintext image can be retrieved from an unknown ciphertext image in real time. The proposed method is independent of plaintext-ciphertext image pairs acquired from the OSC system, and thus makes COA available. Further, the proposed deep-learning-based COA method can attack an optical scanning cryptosystem encrypted with either pure phase keys or complex amplitude keys. Simulations and experimental results proved the feasibility and effectiveness of the proposed deep-learning-based COA method.
Automating Feature Model maintainability evaluation using machine learning techniques

Software Product Lines (SPL) are generally specified using a Feature Model (FM), an artifact designed in the early stages of the SPL development life cycle. This artifact can quickly become too complex, which makes it challenging to maintain an SPL. Therefore, it is essential to evaluate the artifact’s maintainability continuously. The literature brings some approaches that evaluate FM maintainability through the aggregation of maintainability measures. Machine Learning (ML) models can be used to create these approaches. This work proposes white-box ML models intending to classify the FM maintainability based on 15 measures. To build the models, we performed the following steps: (i) we compared two approaches to evaluate the FM maintainability through a human-based oracle of FM maintainability classifications; (ii) we used the best approach to pre-classify the ML training dataset; (iii) we generated three ML models and compared them against classification accuracy, precision, recall, F1 and AUC-ROC; and, (iv) we used the best model to create a mechanism capable of providing improvement indicators to domain engineers. The best model used the decision tree algorithm that obtained accuracy, precision, and recall of 0.81, F1-Score of 0.79, and AUC-ROC of 0.91. Using this model, we could reduce the number of measures needed to evaluate the FM maintainability from 15 to 9 measures. Furthermore, we created a mechanism to suggest FM refactorings to improve the maintainability of this artifact. FM maintainability evaluation and the refactoring suggestion mechanism were automated in the DyMMer tool. We conclude this work by presenting a way to combine FM maintainability assessment with FM refactorings. The results of this work provide to domain engineers inputs that will allow them to carry out a continuous improvement of an SPL.
An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

Teaching-learning-based optimization (TLBO) is a powerful metaheuristic algorithm for solving complex optimization problems pertaining to the global optimum. Many TLBO variants have been presented to improve the local optima avoidance capability and to increase the convergence speed. In this study, a modified learner phase and a new gradient-based mutation strategy are proposed in an improved TLBO algorithm (TLBO-LM). A restarting strategy is adopted to help TLBO-LM escape from local optima and is combined with the gradient-based mutation strategy to build a mutation-restarting phase in an improved TLBO algorithm (LMRTLBO). The modified learner phase integrates a mutation operator to ensure the exploration capability and a dynamic boundary constraint strategy to increase the convergence speed. A greedy gradient information estimation method is developed to accelerate the convergence rate and then is combined with a mutation operator to establish the gradient-based mutation strategy with a good balance between exploration and exploitation. The proposed algorithms are examined by the CEC 2014 and 2020 benchmark functions. The results suggest that both the modified learner phase and gradient-based mutation strategy significantly enhance the exploration and exploitation capabilities of TLBO and that the restarting strategy effectively avoids local optima but sacrifices the exploitation capability to some extent. TLBO-LM and LMRTLBO obtain better results compared with ten algorithms, including TLBO variants, and are competitive compared with five CEC winners.
Lightweight MIMO-WNet for single image deblurring

Single image deblurring, aiming at recovering a latent sharp image from a blurry image, is a highly ill-posed task as there exist infinite feasible solutions. One successful practice of the existing popular approaches is to extensively stack deep networks to regress the complicated relationships between the sharp and blurry image pairs, which results in inevitably high computational costs. In this work, we present a synergistic framework named Lightweight MIMO-WNet that optimally balances the performance and the computational costs. Our main proposal consists of a MIMO-WNet architecture that attempts to learn the complicated blurry-sharp relationships via balancing the spatial details and the high-level contextualized information, and a multiple information refining block (MIRB) that reduces the parameters while deepening the network. At each layer, the original ResBlock is replaced with the MIRB that divides the input into multiple parts and refines the information hierarchically. The experimental results on three benchmarks demonstrate that Lightweight MIMO-WNet obtains a better trade-off between the performance and the model complexity.
Light field image super-resolution based on raw data with transformers

Light field (LF) image super-resolution (SR) can improve the limited spatial resolution of LF images by using complementary information from different perspectives. However, current LF image SR methods only use the RGB data to implicitly exploit the information among different perspectives, without paying attention to the information loss from raw data to RGB data and the explicit structure information utilization. To address the first issue, a data generation pipeline is developed to collect LF raw data for LF image SR. In addition, to make full use of the multiview information, an end-to-end convolutional neural network architecture (namely, LF-RawSR) is proposed for LF image SR. Specifically, an aggregated module is first used to fuse the angular information based on a volume transformer with plane sweep volume. Then the aggregated feature is warped to all LF views using a cross-view transformer for nonlocal dependencies utilization. The experimental results demonstrate that our method outperforms existing state-of-the-art methods with a comparative computational cost, and fine details and clear structures can be restored.
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity labels. However, manual collection of these explanations is expensive and time-consuming. Recent work has used extractive summarization to select a sufficient subset of the most important facts from the ruling comments (RCs) of a professional journalist to obtain fact-checking explanations. However, these explanations lack fluency and sentence coherence. In this work, we present an iterative edit-based algorithm that uses only phrase-level edits to perform unsupervised post-editing of disconnected RCs. To regulate our editing algorithm, we use a scoring function with components including fluency and semantic preservation. In addition, we show the applicability of our approach in a completely unsupervised setting. We experiment with two benchmark datasets, namely LIAR-PLUS and PubHealth. We show that our model generates explanations that are fluent, readable, non-redundant, and cover important information for the fact check.
An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss

Most unsupervised outlier detection methods from the global perspective cannot effectively detect local outliers when normal samples present clusters with different densities. Moreover, existing local outlier detectors are not sensitive to clustered outliers. This paper proposes an unsupervised outlier detection method with differential potential spread loss (DPSL) to simultaneously detect global, local, and clustered outliers. DPSL first subsamples the original dataset several times. For each subset, the samples are sequentially linked in reverse to construct the first-level potential chains according to their nearest neighbor relationship, whose starting points are called first-level potential peak points. Secondly, the lower-level potential peak points are extracted to build higher-level potential chains, and this process is performed iteratively until the global potential peak point is obtained. The potential spread loss of a sample, which measures its local anomaly degree, is defined based on the distance and the ratio of isolation radius between the sample and its potential peak point on the same potential chain. The potential circles are established according to the levels of potential peak points and determine the global anomaly degree of the samples. Finally, DPSL calculates the outlier scores of all samples by combining the potential spread losses and potential circle levels of the subset samples and integrates the results obtained by every subsampling. DPSL outperforms 11 typical unsupervised outlier detection methods on 12 artificial datasets with four types of clustered outlier distributions. Meanwhile, it performs well on other 12 artificial datasets with various local outliers and 35 real-world datasets. • An outlier detection method for clustered and local outliers is proposed. • A local outlierness metric named potential spread loss is proposed. • A hierarchical method to measure the samples’ global anomaly degree is proposed.
Radar signal recognition exploiting information geometry and support vector machine

Aiming at the recognition of low-probability-of-intercept (LPI) radar signals, a support vector machine (SVM)-based algorithm is proposed, where the information geometry theory is utilised to optimise the kernel function of the SVM. Since signals with different modulations have different characteristics in the time-frequency domain, the time-frequency transformation result of the LPI radar signal is considered as an image, referred to as the time-frequency image, and computer vision techniques are utilized to perform recognition. Specifically, the time-frequency images of different LPI radar signals are obtained via the Choi-Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time-frequency features. Then, an SVM is adopted to recognise LPI radar signals due to its superiority in addressing the dimension disaster and non-linear inseparability issue. The extracted time-frequency features are fed into the SVM for classification and recognition. Note that the classification performance of SVM depends on the kernel function. Therefore, in the proposed algorithm, information geometry theory is exploited to improve the Gaussian kernel function, and the maximum margin between different categories of samples is further enlarged. As a consequence, the recognition accuracy for LPI radar signals with similar time-frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning-based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. Simulation results verify the effectiveness of the proposed algorithm.
Fast quantum image encryption scheme based on multilayer short memory fractional order Lotka-Volterra system and dual-scale triangular map

The Caputo fractional order Lotka-Volterra system is time-consuming in practical applications, since its starting point is fixed. To tackle this problem, a short memory fractional order Lotka-Volterra system (SMFrLVS) is proposed, where the chaotic attractor of the short memory fractional order Lotka-Volterra system is achieved by the predictor-corrector method. Then, a multilayer fractional order Lotka-Volterra system with short memory (MSMFrLVS) is introduced, whose chaotic behaviors are explored via Poincare sections and frequency power spectra. A quantum image encryption algorithm is proposed by combining MSMFrLVS with quantum dual-scale triangular map. A quantum circuit of the dual-scale triangular map is designed with ADDER-MOD <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup></mml:mrow></mml:math> . At the permutation stage, the plaintext image is transformed into quantum form with the generalized quantum image representation model. The resulting quantum image is divided into sub-blocks and scrambled by the quantum dual-scale triangular map. Subsequently, the intra and the inter permutation operations on bit-planes are realized by sorting pseudo-random sequence and by quantum Gray code, respectively. At the diffusion stage, the initial values of the MSMFrLVS are generated with a plaintext correlation mechanism. The ciphertext image can be acquired by carrying out three-level diffusion operations. It is demonstrated that the proposed quantum image encryption algorithm performs better than some typical image encryption algorithm in terms of security, robustness, computational complexity and encryption speed.