Reviewerly/exHarmony huggingface dataset:
KolamNetV2: efficient attention-based deep learning network for tamil heritage art-kolam classification

Authors:
raw_author_name: A. Sasithradevi
raw_author_name: None Sabarinathan
raw_author_name: S. Shoba
raw_author_name: S. Mohamed Mansoor Roomi
raw_author_name: P. Prakash

Abstract: Abstract In India, kolam, commonly referred to as rangoli, is a traditional style of art. It involves using rice flour, chalk, or coloured powders to create elaborate patterns and motifs on the ground. Kolam is a common daily ritual in many regions of India, especially in South India, where it is seen as a significant cultural tradition and a means to greet visitors. Unfortunately, as a result of people’s hectic lives nowadays, the habit of drawing kolam on a regular basis is dwindling. The art of making kolam patterns is in danger of disappearing as so many individuals no longer have the time or space to do it on a regular basis. Therefore, it is imperative that ancient art be conserved and digitally documented in order to enlighten our next generation about kolam and its classifications. Deep learning has become a powerful technique because of its ability to learn from raw image data without the aid of a feature engineering process. In this article, we attempted to understand the types of Kolam images using the proposed deep architecture called KolamNetV2. KolamNetV2 comprises EfficientNet and attention layers, ensuring high accuracy with minimal training data and parameters. We evaluated KolamNetV2 to reveal its ability to learn the various types in our challenging Kolam dataset. The experimental findings show that the proposed network achieves fine enhancement in performance metrics viz, precision-0.7954, recall-0.7846, F1score-0.7854 and accuracy-81%. We compared our results with state-of-the-art deep learning methodologies, proving the astounding capability. Graphical Abstract
Information Tools for Organizing Distance Education Within the Framework of Digitalization of the Educational Process

Authors:
raw_author_name: M. Choguldurov

Abstract: The article is devoted to the study of information means of an educational institution for the organization of distance education within the framework of digitalization of the educational process, which sets out the principles and means of effective functioning of distance education. Specialized automated information systems developed with the active use of computer and telecommunications tools make it possible to globally increase the efficiency of distance learning. The purpose of this work is to draw conclusions based on the results of a study of information means for organizing distance education using automated information systems. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist. The results of the study allow us to conclude that the effective functioning of the mechanism of continuous education using information systems contributes to the implementation of educational activities of students, ensuring an improvement in the quality of professional competencies applicable in the professional practice of a future specialist.
Transductive meta-learning with enhanced feature ensemble for few-shot semantic segmentation

Authors:
raw_author_name: Charalambos Poullis
raw_affiliation_string: Immersive and Creative Technologies Lab, Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada. charalambos@poullis.org.

Abstract: This paper addresses few-shot semantic segmentation and proposes a novel transductive end-to-end method that overcomes three key problems affecting performance. First, we present a novel ensemble of visual features learned from pretrained classification and semantic segmentation networks with the same architecture. Our approach leverages the varying discriminative power of these networks, resulting in rich and diverse visual features that are more informative than a pretrained classification backbone that is not optimized for dense pixel-wise classification tasks used in most state-of-the-art methods. Secondly, the pretrained semantic segmentation network serves as a base class extractor, which effectively mitigates false positives that occur during inference time and are caused by base objects other than the object of interest. Thirdly, a two-step segmentation approach using transductive meta-learning is presented to address the episodes with poor similarity between the support and query images. The proposed transductive meta-learning method addresses the prediction by first learning the relationship between labeled and unlabeled data points with matching support foreground to query features (intra-class similarity) and then applying this knowledge to predict on the unlabeled query image (intra-object similarity), which simultaneously learns propagation and false positive suppression. To evaluate our method, we performed experiments on benchmark datasets, and the results demonstrate significant improvement with minimal trainable parameters of 2.98M. Specifically, using Resnet-101, we achieve state-of-the-art performance for both 1-shot and 5-shot Pascal-[Formula: see text], as well as for 1-shot and 5-shot COCO-[Formula: see text].
Parallel GEMM-based convolution for deep learning on multicore RISC-V processors

Authors:
raw_author_name: Adrián Castelló
raw_affiliation_string: Universitat Politècnica de València, 
València, Spain
raw_author_name: Héctor Martínez
raw_affiliation_string: Universidad de Córdoba, Córdoba
, Spain
raw_author_name: Enrique S. Quintana-Ortí
raw_affiliation_string: Universitat Politècnica de València, 
València, Spain

Abstract: Abstract We address the efficient implementation of the convolution operator on the GAP8 parallel ultra-low power platform (PULP), a heterogeneous multi-core processor equipped with a fabric controller (FC); a cluster of eight compute cores; and a four-level memory hierarchy with scratchpads instead of conventional, hardware-assisted cache memories. Our solution for this platform transforms the convolution into a general matrix–matrix multiplication ( gemm ) via the lowering approach, demonstrating that it is possible to attain reasonable performance on the GAP8 by carefully adapting techniques such as tiling and loop parallelism, which are mainstream in the multi-threaded, cache-aware realization of gemm .
@llegra: a chatbot for Vallader

Authors:
raw_author_name: Oliver Bendel
raw_affiliation_string: School of Business FHNW, 5210, Windisch, Switzerland

Abstract: Abstract Extinct and endangered languages have been preserved primarily through audio conservation and the collection and digitization of scripts and have been promoted through targeted language acquisition efforts. Another possibility would be to build conversational agents like chatbots or voice assistants that can master these languages. This would provide an artificial, active conversational partner which has knowledge of the vocabulary and grammar and allows one to learn with it in a different way. The chatbot, @llegra, with which one can communicate in the Rhaeto-Romanic idiom Vallader was developed in 2023 based on GPT-4. It can process and output text and has voice output. It was additionally equipped with a manually created knowledge base. After laying the conceptual groundwork, this paper presents the preparation and implementation of the project. In addition, it summarizes the tests that native speakers conducted with the chatbot. A critical discussion elaborates advantages and disadvantages. @llegra could be a new tool for teaching and learning Vallader in a memorable and entertaining way through dialog. It not only masters the idiom, but also has extensive knowledge about the Lower Engadine, that is, the area where Vallader is spoken. In conclusion, it is argued that conversational agents are an innovative approach to promoting and preserving languages.
APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Authors:
raw_author_name: Mingqiang Gao
raw_affiliation_string: School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
raw_author_name: Xu Yang
raw_affiliation_string: School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China

Abstract: Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.
Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification

Authors:
raw_author_name: Hamad Binsalleeh
raw_affiliation_string: Department of Computer Science, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia

Abstract: In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware executable adversarial examples of PE, we conduct two white-box attacks, Jacobian-based Saliency Map Attack (JSMA) and Carlini and Wagner attack (C&amp;W). An adversarial payload was injected into the DOS header, and a section was added to the file to preserve the PE functionality. The attacks successfully evaded the CNN model with a 91% evasion rate, whereas the RNN model evaded attacks at an 84.6% rate. Two defense mechanisms based on distillation and training techniques are examined in this study for overcoming adversarial example challenges. Distillation and training against JSMA resulted in the highest reductions in the evasion rates of 48.1% and 41.49%, respectively. Distillation and training against C&amp;W resulted in the highest decrease in evasion rates, at 48.1% and 49.9%, respectively.
Agent-Guided Non-Local Network for Underwater Image Enhancement and Super-Resolution Using Multi-Color Space

Authors:
raw_author_name: Yonghui Zhang
raw_affiliation_string: School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Abstract: The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater images, resulting in the loss of image details. Efficient modeling of long-range dependency is essential for understanding the global structure and local context of underwater images to enhance and restore details, which is a challenging task. In this paper, we propose an agent-guided non-local attention network using a multi-color space for underwater image enhancement and super-resolution. Specifically, local features with different receptive fields are first extracted simultaneously in the RGB, Lab, and HSI color spaces of underwater images. Then, the designed agent-guided non-local attention module with high expressiveness and lower computational complexity is utilized to model long-range dependency. Subsequently, the results from the multi-color space are adaptively fused with learned weights, and finally, the reconstruction block composed of deconvolution and the designed non-local attention module is used to output enhanced and super-resolution images. Experiments on multiple datasets demonstrated that our method significantly improves the visual perception of degraded underwater images and efficiently reconstructs missing details, and objective evaluations confirmed the superiority of our method over other state-of-the-art methods.
Pure Decoherence of the Jaynes–Cummings Model: Initial Entanglement with the Environment, Spin Oscillations and Detection of Non-Orthogonal States

Authors:
raw_author_name: Jerzy Dajka
raw_affiliation_string: Institute of Physics, University of Silesia in Katowice, 40-007 Katowice, Poland

Abstract: A model based on pure decoherence for the Jaynes–Cummings spin–boson system, coupled through its integral of motion to an infinite bosonic bath, is proposed and examined. The properties of the spin oscillation process suggest an initial entanglement between the environment and the spin–boson degrees of freedom. The study demonstrates that the potential applicability of the Jaynes–Cummings model in detecting non-orthogonal bosonic states is preserved in the presence of pure decoherence.
SEGCN: a subgraph encoding based graph convolutional network model for social bot detection

Authors:
raw_author_name: Feng Liu
raw_affiliation_string: School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450002, China
raw_author_name: Zhenyu Li
raw_affiliation_string: Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, 450001, China
raw_author_name: Chunfang Yang
raw_affiliation_string: Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, 450001, China
raw_author_name: Daofu Gong
raw_affiliation_string: Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, 450001, China
raw_author_name: Haoyu Lu
raw_affiliation_string: Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, 450001, China
raw_author_name: Fenlin Liu
raw_affiliation_string: Henan Provincial Key Laboratory of Cyberspace Situational Awareness, Zhenzhou, 450001, China

Abstract: Message passing neural networks such as graph convolutional networks (GCN) can jointly consider various types of features for social bot detection. However, the expressive power of GCN is upper-bounded by the 1st-order Weisfeiler-Leman isomorphism test, which limits the detection performance for the social bots. In this paper, we propose a subgraph encoding based GCN model, SEGCN, with stronger expressive power for social bot detection. Each node representation of this model is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only. Extensive experimental results on two publicly available datasets, Twibot-20 and Twibot-22, showed that the proposed model improves the accuracy of the state-of-the-art social bot detection models by around 2.4%, 3.1%, respectively.
AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection

Authors:
raw_author_name: Saba Aslam
raw_affiliation_string: Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
raw_author_name: Arslan Manzoor
raw_affiliation_string: Department of Mathematics and Informatics, University of Catania, 95123 Catania, Italy
raw_author_name: Hui Chen
raw_affiliation_string: School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518055, China
raw_author_name: Abdur Rasool
raw_affiliation_string: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Abstract: The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model’s exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.
Efficient astrodynamics-informed kinodynamic motion planning for relative spacecraft motion

Authors:
raw_author_name: Taralicin Deka
raw_affiliation_string: University of Colorado Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA
raw_author_name: Jay McMahon
raw_affiliation_string: University of Colorado Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA

Abstract: In this paper, we present a computationally efficient sampling-based spacecraft relative motion planning algorithm to reconfigure a spacecraft from a given initial state to a desired final state while avoiding collisions with obstacles and minimizing the total fuel required for the transfer. In our previous work, we presented an astrodynamics-informed kinodynamic motion planning (AIKMP) algorithm that can regularly find fuel-efficient and collision-free transfer solutions for relative spacecraft motion – without requiring an initial guess of the solution. In this work, we improve the computation efficiency of the AIKMP algorithm. We demonstrate that by using a linearized Lambert solution (LLS) instead of a full Lambert solution as the steering law for the developed motion planning algorithm, along with an additional tree-pruning module, the computation efficiency of the AIKMP algorithm can be improved by ≈96%. The new pruning module added to the AIKMP algorithm also significantly reduces the storage requirement by the algorithm. Using different example relative transfer scenarios comprising two spacecraft and multiple obstacles/keep-out zones, we demonstrate the performance of the proposed efficient kinodynamic orbital motion planner and show that the algorithm efficiently computes near-optimal, fuel-efficient, and collision-free trajectories for spacecraft relative transfer problems.
Analysis of Criminal Landscape by Utilizing Statistical Analysis and Deep Learning Techniques

Authors:
raw_author_name: Ram Krishn Mishra
raw_affiliation_string: Department of Computer Science, BITS Pilani, Dubai, United Arab Emirates
raw_author_name: Abdul Rahmaan Ansari
raw_affiliation_string: Department of Computer Science, BITS Pilani, Dubai, United Arab Emirates
raw_author_name: J. Angel Arul Jothi
raw_affiliation_string: Department of Computer Science, BITS Pilani, Dubai, United Arab Emirates
raw_author_name: Vinaytosh Mishra
raw_affiliation_string: College of Healthcare Management and Economics, Gulf Medical University Ajman, Ajman, United Arab Emirates

Abstract: This research intends to provide law enforcement organizations with a deep learning model that uses trends in past crimes to forecast future crimes. The study will enable them to dispatch security patrols to the most susceptible regions and take preventive actions. The experimental study demonstrates that the LSTM-based deep learning method beats the conventional ARIMA model. Multiple parameter tuning techniques were examined to create an optimized model, such as different LSTM layers, epochs, and batch sizes. The developed model has a training accuracy of around 90%, while on the test data, the minimum and highest accuracy levels were around 75%.
A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT

Authors:
raw_author_name: Tianyu Xing
raw_affiliation_string: Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
raw_author_name: Xiaohao Wang
raw_affiliation_string: Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
raw_author_name: Kai Ni
raw_affiliation_string: Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
raw_author_name: Qian Zhou
raw_affiliation_string: Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

Abstract: Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified.