Reviewerly/exHarmony huggingface dataset:
Extract, Compress and Encode: LitNet an Efficient Autoencoder for Noisy Time-Series Data

The Industrial Internet of Things has emerged to enhance massive sensory data collection. Characterized by their large volume and hard interpretability, these data require highly skilled human expertise to compress and extract meaningful information from the raw data using computationally intensive signal processing methods. Deep learning, and autoencoders, in particular, have shown promising results in computer vision and natural language processing. In this paper, different state-of-the-art deep learning blocks are investigated and a lightweight Inception Network, called LitNet, is presented. The experiments are performed with real-world vibration data collected from different CNC machines during production. The models are evaluated based on their compression and feature extraction capabilities. The results show that LitNet outperforms all models and provides a good trade-off between compression and feature extraction. The latent vector analysis shows promising results and that the LitNet encoder can be used as a pre-trained feature extractor for vibration data.
A path planning method for robot-aided aero-engine fleet inspection considering resource reuse strategy

For the robot-aided aero-engine fleet maintenance scenario, a multi-robot formation reuse strategy for inspection resource delivery path planning method is proposed, considering the reusability of inspection robots. The method considers a two-level reuse strategy, including multi-robot formation directly reuse and multi-robot formations reuse after recombination. Finally, experiments are carried out based on a simulated airport map. Results show that the proposed method could significantly reduce path length and improve robot reuse rate at the cost of a negligible increase in average task tardiness. The proposed method achieves a reduction in path transport costs and an increase in inspection efficiency compared to methods that do not consider robot reuse.
HCD-Mask: a multi-task model for small object detection and instance segmentation in high-resolution UAV images

Object detection and instance segmentation in images captured by unmanned aerial vehicles (UAVs) are becoming increasingly important. Although considerable progress has been made, there still exist challenges for the detection of small objects in high-resolution UAV images. Detecting objects and segmenting instances in aerial images are challengeable for extant methods due to the following two aspects: (1) original high-resolution images are usually resized before being fed into models. As a result, the features of small objects are mostly lost; (2) target objects like cars and trucks are very small in pixels, making them difficult to be distinguished from surrounding backgrounds. In order to address these issues, this paper presents a new multitask model for small object detection in high-resolution UAV images, namely HCD-Mask. Specifically, a high-resolution feature extractor is devised to distribute the features at different layers. Meanwhile, a coarse-to-fine region proposal network raises the intersection over union (IoU) threshold stage by stage, and maintains sufficient positive anchors in each stage. For more accurate mask estimation, the classification score and the position score are jointly scored for mask head. We demonstrate the effectiveness of our proposed model, by showing state-of-the-art performance on a UAV-based image dataset constructed by ourselves.
Remote sensing image segmentation of ground objects based on improved Deeplabv3+

Aiming at the problem of unsatisfactory extraction effect caused by unbalanced category distribution and large differences in scene styles in different domains in the process of extracting key feature information from remote sensing images, an improved Deeplabv3+ semantic segmentation network is adopted. The instance-batch normalization (IBN) module is used in the backbone network ResNetl01 to enhance the model’s generalization ability for remote sensing images with large differences in styles. At the same time, in order to further improve the segmentation accuracy of the model, The squeeze-and-excitation (SE) module is added to the network to strengthen important channel information and alleviate the problem of information loss. The loss function uses the joint loss function of Dice+Focal, dice loss can alleviate the impact of imbalanced category distribution on the extraction of small targets. Focal loss can not only make the model pay more attention to objects that are difficult to classify, but it can also improve the instability of network training caused by dice loss. Experimental results show that compared with the original Deeplabv3+model, the improved Deeplabv3+ improves Fl-Score by 7.78% and Intersection over Union(IoU) by 5.78%. Compared with other mainstream semantic segmentation models (including FCN, UNet, and SegNet), the improved Deeplabv3+ also achieves better segmentation accuracy in ground feature extraction.
Financial information security behavior in online banking

The ubiquitous use of information technology in modern life leads to many problems of financial information security. Such security issues could be due to users’ insecure behaviors during their financial transactions. The purpose of this study was to investigate the university teacher's financial information security behavior (FISB) and to examine the factors that influence their FISB. By using a survey method, the study collected primary data quantitatively. A closed-ended questionnaire based on IMB Model (Information Motivation Behavioral Skills Model) was developed. The population of the study comprised teachers from six faculties of University of the Punjab, Lahore, Pakistan. A simple random sampling technique was used to collect the data from the teachers via a print-format based questionnaire. The structural equation model (SEM) was used to test the proposed model by using ADANCO (2.1). ADANCO is a software that is used for SEM analysis. The results exhibited that a majority of the teachers are frequent users of online banking. The findings confirmed that measures familiarity and self-efficacy have a direct impact on the financial information security behavior of the respondents and self-efficacy mediates the relation between threat awareness and financial information security behavior. The study concludes that online banking users can perform secure behavior during online transactions if they are familiar with security measures and are confident to perform different online banking-related tasks. It is, therefore, suggested that individuals may be familiarized with the financial information security measures so that they may believe in their selves and develop secure financial information behavior. This can be done through awareness seminars and hands-on training by the Federal Investigation Agency in liaison with banks and educational institutions. The present study has contributed to the domain of financial information security behavior in the local context.
Learning with few samples in deep learning for image classification, a mini-review

Deep learning has achieved enormous success in various computer tasks. The excellent performance depends heavily on adequate training datasets, however, it is difficult to obtain abundant samples in practical applications. Few-shot learning is proposed to address the data limitation problem in the training process, which can perform rapid learning with few samples by utilizing prior knowledge. In this paper, we focus on few-shot classification to conduct a survey about the recent methods. First, we elaborate on the definition of the few-shot classification problem. Then we propose a newly organized taxonomy, discuss the application scenarios in which each method is effective, and compare the pros and cons of different methods. We classify few-shot image classification methods from four perspectives: (i) Data augmentation, which contains sample-level and task-level data augmentation. (ii) Metric-based method, which analyzes both feature embedding and metric function. (iii) Optimization method, which is compared from the aspects of self-learning and mutual learning. (iv) Model-based method, which is discussed from the perspectives of memory-based, rapid adaptation and multi-task learning. Finally, we conduct the conclusion and prospect of this paper.
Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag

Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve the accuracy of image stitching by matching feature points based on the position of the image edge pixel so as to solve the accurate stitching of broken edge contour. In the first step of this method, the image containing the broken edge is preprocessed by edge detection, and the location of the broken edge pixel is proposed. Secondly, the feature descriptors were calculated to extract the shape and texture information of the feature points on the fracture edge. Finally, the feature points are optimized by minimum correction and image mosaic is carried out. In terms of image stitching, pre-registration is performed by finding the feature descriptors that are most similar to the edge of the optimum fracture surface profile. The matching operator is added to the overlapping region to obtain the corrected image, and the panoramic image mosaic of the image fracture surface is performed. The experimental results show that feature descriptor matching can ensure the integrity of the fracture, improve the matching accuracy, optimize the uneven deformation of the fracture, ensure the quality of image stitching, and reduce the degree of image distortion.
Stability Analysis for Autonomous Vehicle Navigation Trained over Deep Deterministic Policy Gradient

The Deep Deterministic Policy Gradient (DDPG) algorithm is a reinforcement learning algorithm that combines Q-learning with a policy. Nevertheless, this algorithm generates failures that are not well understood. Rather than looking for those errors, this study presents a way to evaluate the suitability of the results obtained. Using the purpose of autonomous vehicle navigation, the DDPG algorithm is applied, obtaining an agent capable of generating trajectories. This agent is evaluated in terms of stability through the Lyapunov function, verifying if the proposed navigation objectives are achieved. The reward function of the DDPG is used because it is unknown if the neural networks of the actor and the critic are correctly trained. Two agents are obtained, and a comparison is performed between them in terms of stability, demonstrating that the Lyapunov function can be used as an evaluation method for agents obtained by the DDPG algorithm. Verifying the stability at a fixed future horizon, it is possible to determine whether the obtained agent is valid and can be used as a vehicle controller, so a task-satisfaction assessment can be performed. Furthermore, the proposed analysis is an indication of which parts of the navigation area are insufficient in training terms.
Dual-process system based on mixed semantic fusion for Chinese medical knowledge-based question answering

Chinese medical knowledge-based question answering (cMed-KBQA) is a vital component of the intelligence question-answering assignment. Its purpose is to enable the model to comprehend questions and then deduce the proper answer from the knowledge base. Previous methods solely considered how questions and knowledge base paths were represented, disregarding their significance. Due to entity and path sparsity, the performance of question and answer cannot be effectively enhanced. To address this challenge, this paper presents a structured methodology for the cMed-KBQA based on the cognitive science dual systems theory by synchronizing an observation stage (System 1) and an expressive reasoning stage (System 2). System 1 learns the question's representation and queries the associated simple path. Then System 2 retrieves complicated paths for the question from the knowledge base by using the simple path provided by System 1. Specifically, System 1 is implemented by the entity extraction module, entity linking module, simple path retrieval module, and simple path-matching model. Meanwhile, System 2 is performed by using the complex path retrieval module and complex path-matching model. The public CKBQA2019 and CKBQA2020 datasets were extensively studied to evaluate the suggested technique. Using the metric average F1-score, our model achieved 78.12% on CKBQA2019 and 86.60% on CKBQA2020.
ML-k’sNN: Label Dependent k Values for Multi-Label k-Nearest Neighbor Rule

Multi-label classification as a data mining task has recently attracted increasing interest from researchers. Many current data mining applications address problems with instances that belong to more than one category. These problems require the development of new, efficient methods. Multi-label k-nearest neighbors rule, ML-kNN, is among the best-performing methods for multi-label problems. Current methods use a unique k value for all labels, as in the single-label method. However, the distributions of the labels are frequently very different. In such scenarios, a unique k value for the labels might be suboptimal. In this paper, we propose a novel approach in which each label is predicted with a different value of k. Obtaining the best k for each label is stated as an optimization problem. Three different algorithms are proposed for this task, depending on which multi-label metric is the target of our optimization process. In a large set of 40 real-world multi-label problems, our approach improves the results of two different tested ML-kNN implementations.
Acceleration of Particle Swarm Optimization with AVX Instructions

Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The availability of parallel code running on CPUs made them much easier and more accessible than GPUs. This article compares the performances of parallel implementations of the particle swarm optimization algorithm. The code was written in C++, and we used various techniques to obtain parallel execution through Advanced Vector Extensions. We present the performance on various benchmark functions and different problem configurations. The article describes and compares the performance boost gained from parallel execution on CPU, along with advantages and disadvantages of parallelization techniques.
A novel steganographic technique for medical image using SVM and IWT

This study presents an efficient authentication scheme for digital image steganography on medical images benefiting from the combination of both techniques: Support Vector Machine (SVM) and Integer Wavelet Transform (IWT). We use two different strategies in this paper, where SVM is used first to separate the Region of Interest (ROI) from Non-Region of Interest (NROI) in the medical image. Then IWT is applied to embed secret information within the NROI part of the medical image (Cover Image). Moreover, we have applied a circular array and a shared secret key to enhance the robustness of the proposed scheme. The research looked into the various experimental analyses to establish the acceptability of the existing scheme. The simulation is performed to measure the imperceptibility using Peak Signal to Noise Ratio (PSNR) and to test the robustness using the Structural Similarity Index Measure (SSIM). The experimental result shows good imperceptibility with a PSNR of 64 dB and better robustness with a SSIM of 0.96 for the proposed steganographic scheme.
Context-sensitive graph representation learning

Graph representation learning, which maps high-dimensional graphs or sparse graphs into a low-dimensional vector space, has shown its superiority in numerous learning tasks. Recently, researchers have identified some advantages of context-sensitive graph representation learning methods in functions such as link predictions and ranking recommendations. However, most existing methods depend on convolutional neural networks or recursive neural networks to obtain additional information outside a node, or require community algorithms to extract multiple contexts of a node, or focus only on the local neighboring nodes without their structural information. In this paper, we propose a novel context-sensitive representation method, Context-Sensitive Graph Representation Learning (CSGRL), which simultaneously combines attention networks and a variant of graph auto-encoder to learn weighty information about various aspects of participating neighboring nodes. The core of CSGRL is to utilize an asymmetric graph encoder to aggregate information about neighboring nodes and local structures to optimize the learning goal. The main benefit of CSGRL is that it does not need additional features and multiple contexts for the node. The message of neighboring nodes and their structures spread through the encoder. Experiments are conducted on three real datasets for both tasks of link prediction and node clustering, and the results demonstrate that CSGRL can significantly improve the effectiveness of all challenging learning tasks compared with 14 state-of-the-art baselines.
An Overview of Fraud Applications and Software on Social Media

Fraud involves intentional deception. While the victim's rights are violated, the criminal profits from the situation. Fraud can be committed by individuals, groups, or even entire companies. The importance of social media cannot be overstated. The management of natural disasters and medical difficulties are both handled through social media. The use of social media has resulted in the establishment of a marketplace for goods and services. Because social networking has evolved into a money-making opportunity, dishonest users now cheat in many ways. Fraud on social media platforms has been on the rise. Cybercriminals exploit unethical social engineering to steal data. Indicators of fraud include excessive pressure, a broad scope, honey traps, and justification. People who are unaware of something lose both money and information. In the current environment, information technology professionals are unable to protect social media users from being scammed or spammed.
Mid-Wave Infrared Snapshot Compressive Spectral Imager with Deep Infrared Denoising Prior

Although various infrared imaging spectrometers have been studied, most of them are developed under the Nyquist sampling theorem, which severely burdens 3D data acquisition, storage, transmission, and processing, in terms of both hardware and software. Recently, computational imaging, which avoids direct imaging, has been investigated for its potential in the visible field. However, it has been rarely studied in the infrared domain, as it suffers from inconsistency in spectral response and reconstruction. To address this, we propose a novel mid-wave infrared snapshot compressive spectral imager (MWIR-SCSI). This design scheme provides a high degree of randomness in the measurement projection, which is more conducive to the reconstruction of image information and makes spectral correction implementable. Furthermore, leveraging the explainability of model-based algorithms and the high efficiency of deep learning algorithms, we designed a deep infrared denoising prior plug-in for the optimization algorithm to perform in terms of both imaging quality and reconstruction speed. The system calibration obtains 111 real coded masks, filling the gap between theory and practice. Experimental results on simulation datasets and real infrared scenarios prove the efficacy of the designed deep infrared denoising prior plug-in and the proposed acquisition architecture that acquires mid-infrared spectral images of 640 pixels × 512 pixels × 111 spectral channels at an acquisition frame rate of 50 fps.
Continuous and Secure Integration Framework for Smart Contracts

As part of agile methodologies seen in the past few years, IT organizations have continuously adopted new practices in their software delivery life-cycle to improve both efficiency and effectiveness of development teams. Two of these practices are continuous integration and continuous deployment, which are part of the DevOps cycle which has helped organizations build software effectively and efficiently. These practices must be considered for new technologies such as smart contracts, where security concerns and bugs might cost more once deployed than traditional software. This paper states the importance of using a proper DevOps routine and how it is possible to apply this practice to a smart contract build. Specifically, this paper introduces a framework to implement DevOps for smart contracts development by describing multiple DevOps tools and their applicability to smart contract development.
Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering

Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.
Secure Multi-Party Computation of Graphs’ Intersection and Union under the Malicious Model

In recent years, with the development of information security, secure multi-party computation has gradually become a research hotspot in the field of privacy protection. The intersection and union computation of graphs is an important branch of secure computing geometry. At present, the intersection and union of graphs are almost designed under the semi-honest model, and few solutions are proposed under the malicious model. However, the solution under the malicious model is more secure and has important theoretical and practical significance. In this paper, the possible malicious behaviors of computing the intersection and union of graphs are analyzed. Using the Lifted-ElGamal threshold cryptosystem and zero-knowledge proof method, the secure multi-party computation algorithm of graphs’ intersection and union under the malicious model is designed. The real/ideal model paradigm is used to prove the security of the algorithm, the efficiency of the algorithm is analyzed in detail, and the feasibility is verified through experiment.