Reviewerly/exHarmony huggingface dataset:
An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System

Abstract With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.
Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.
Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine

Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.
Condition assessment of distribution automation remote terminal units based on double-layer improved cloud model

Distribution automation remote terminal units (DRTUs) are the most important devices to sample voltage and current signals, identify the fault position, isolate the fault, and restore the power supply along distribution lines. We cannot adopt time-based maintenance because of the contradiction between the limited manpower and the plenty of DRTUs distributed in broad regions. In this paper, a double-layer improved cloud model (ICM) is proposed for the first time to realize the condition assessment of DRTUs for condition-based maintenance. The first-layer ICM is applied in the main station of whole distribution automation system (DAS) to evaluate the condition of all DRTUs based on the upload limited condition parameters by DRTUs in real time. And the second-layer ICM is applied in field to evaluate one DRTU which belongs to the abnormal DRTUs obtained after the first-layer evaluation. The DRTU test device are used to obtain detailed condition parameters, and these parameters are used in second-layer ICM to obtain accurate condition assessment score. The results in second-layer ICM would be used to optimize the evaluation algorithm in the first-layer evaluation. In the meantime, our proposed ICM combines the normal cloud model and the trapezoidal cloud model to deal with the optimal, negative, and intermediate indicators more effectively. The comparison with traditional evaluation methods shows that our proposed method is more suitable to realize the condition assessment of DRTUs in the distribution power networks.
Research Study on the Use of CI/CD Among Slovak Students

Nowadays a great emphasis is placed on using secure and reliable software that’s freed from bugs. One of the ways that were proven effective in achieving this goal is to implement continuous integration and delivery principles. CI/CD in general helps automate steps required for deploying code. Continuous integration allows developers to push changes frequently, which helps developers get rapid feedback on possible problems. The next step after integration is the continuous deployment of the code to various development environments. To better understand the advantages and also possible problems with using CI/CD, a study was conducted among students at FIIT STU, including questions regarding students’ experiences.
Particle Swarm Optimization with a Simplex Strategy to Avoid Getting Stuck on Local Optimum

Heuristic methods, for global optimization, have been receiving much interest in the last years, among which Particle Swarm Optimization (PSO) algorithm can be highlighted. However, the application of heuristic methods can lead to premature convergence. In this work, the addition of a step on the PSO algorithm is proposed. This new step, based in Nelder–Mead simplex search method (NM), consists of repositioning the current particle with global best solution, not for a better position, but away from the current nearest local optimum, to avoid getting stuck on this local optimum. There are other PSO-NM algorithms, but the one we are proposing, has a different strategy. The proposed algorithm was also tested with the repositioning strategy in other particles beyond the current global best particle, depending on the repositioning probability. To evaluate the effectiveness of the proposed methods, and study its better parameters, were used various test functions, and for each test function, various number of particles were used in combination with various probabilities of particles repositioning. A thousand runs were performed for each case, resulting in more than two millions runs. The computational studies showed that the repositioning of of global best particle increases the percentage of success on reaching the global best solution, but better results can be obtained applying the repositioning strategy to other particles with repositioning probabilities between 1–5%.
AsyLink: user identity linkage from text to geo-location via sparse labeled data

User Identity Linkage (UIL) aims to reveal the correspondence among account pairs across different social platforms. It has been a popular but challenging task in recent years as complex application scenarios have emerged. Existing UIL methods mainly formalize a classification problem based on symmetric information, but these techniques are hard to apply to asymmetric, sparsely labeled, and imbalanced data. To combat the challenges, we propose a novel UIL framework (AsyLink) with asymmetric information in text and geographic forms. AsyLink first uses topic modeling technologies to associate words and locations, where external text-location pairs can be conveniently introduced to reduce bias caused by sparse linkage labels. Then the user-user interactive tensors are constructed as the basis for linking. Using 3D convolutional neural networks, matching patterns in user-user interactive tensors are captured, and final predictions are based on the extracted features. Meanwhile, instead of regular classification loss, the ranking loss is introduced to predict the best answer among candidates, which is conducive to imbalanced classification. Experiments performed on four real-world datasets indicate that AsyLink achieves state-of-the-art performances and has great potential for real-world applications.
Explaining quality attribute tradeoffs in automated planning for self-adaptive systems

Self-adaptive systems commonly operate in heterogeneous contexts and need to consider multiple quality attributes. Human stakeholders often express their quality preferences by defining utility functions, which are used by self-adaptive systems to automatically generate adaptation plans. However, the adaptation space of realistic systems is large and it is obscure how utility functions impact the generated adaptation behavior, as well as structural, behavioral, and quality constraints. Moreover, human stakeholders are often not aware of the underlying tradeoffs between quality attributes. To address this issue, we present an approach that uses machine learning techniques (dimensionality reduction, clustering, and decision tree learning) to explain the reasoning behind automated planning. Our approach focuses on the tradeoffs between quality attributes and how the choice of weights in utility functions results in different plans being generated. We help humans understand quality attribute tradeoffs, identify key decisions in adaptation behavior, and explore how differences in utility functions result in different adaptation alternatives. We present two systems to demonstrate the approach's applicability and consider its potential application to 24 exemplar self-adaptive systems. Moreover, we describe our assessment of the tradeoff between the information reduction and the amount of explained variance retained by the results obtained with our approach.
Multi-network contrastive learning of visual representations

Contrastive learning, as an important way of self-supervised learning, has achieved great success in visual representations, which significantly reduces its gap with supervised learning. The essential strategy is to maximize the similarities between two augmented views of the same image (positive pairs) and to make such image easily distinguishable from other images of different types (negative pairs). Previous methods rely heavily on a large number of negative samples, such as SimCLR and MoCo. However, some recently proposed methods, such as BYOL and SimSiam, discard negative samples by introducing asymmetric structures. This paper proposes a multi-network contrastive learning methodology for visual representations (MNCLR), which integrates the end-to-end and the momentum encoder mechanisms to introduce more negative samples under a multi-network framework. The classification results on three benchmark image datasets demonstrate that the proposed MNCLR algorithm outperforms some classic contrastive learning methods.
The extent and specificity of visual exploration determines the formation of recollected memories in complex scenes

Our visual memories of complex scenes often appear as robust, detailed records of the past. Several studies have demonstrated that active exploration with eye movements improves recognition memory for scenes, but it is unclear whether this improvement is due to stronger feelings of familiarity or more detailed recollection. We related the extent and specificity of fixation patterns at encoding and retrieval to different recognition decisions in an incidental memory paradigm. After incidental encoding of 240 real-world scene photographs, participants (N = 44) answered a surprise memory test by reporting whether an image was new, remembered (indicating recollection), or just known to be old (indicating familiarity). To assess the specificity of their visual memories, we devised a novel report procedure in which participants selected the scene region that they specifically recollected, that appeared most familiar, or that was particularly new to them. At encoding, when considering the entire scene,subsequently recollected compared to familiar or forgotten scenes showed a larger number of fixations that were more broadly distributed, suggesting that more extensive visual exploration determines stronger and more detailed memories. However, when considering only the memory-relevant image areas, fixations were more dense and more clustered for subsequently recollected compared to subsequently familiar scenes. At retrieval, the extent of visual exploration was more restricted for recollected compared to new or forgotten scenes, with a smaller number of fixations. Importantly, fixation density and clustering was greater in memory-relevant areas for recollected versus familiar or falsely recognized images. Our findings suggest that more extensive visual exploration across the entire scene, with a subset of more focal and dense fixations in specific image areas, leads to increased potential for recollecting specific image aspects.
Pedestrian detection using a translation-invariant wavelet residual dense super-resolution

Pedestrian detection is an important research area and technology for car driving, gait recognition, and other applications. Although a lot of pedestrian detection techniques have been introduced, low-resolution imaging devices still exist in real life, so detection in low-resolution images remains a challenging problem. To address this issue, we propose a novel end-to-end Translation-invariant Wavelet Residual Dense Super-Resolution (TiWRD-SR) method to upscale LR images to SR images and then use Yolov4 for detection to address the low detection problem performance on low-resolution images. To make the enlarged SR image not only effectively distinguish the foreground and background of images but also highlight the characteristic structure of pedestrians, we decompose the image into low-frequency and high-frequency parts by stationary wavelet transform (SWT). The high- and low-frequency sub-images are trained through different network structures so that the network can reconstruct the high-frequency image edge information and the low-frequency image structure in a more detailed manner. In addition, a high-to-low branch information transmission (H2LBIT) is proposed to import high-frequency image edge information into the low-frequency network to make the reconstructed low-frequency structure more detailed. In addition, we also propose a novel loss function, which enables the SR network to focus on the reconstruction of image structure in the network by the characteristics of wavelet decomposition, thereby improving its detection performance. The experimental results indicate that the proposed TiWRD-SR can effectively improve detection performance.
An intelligent blockchain-based access control framework with federated learning for genome-wide association studies

Genome-Wide Association Studies (GWAS) aim to find various variations in human disorders and have become one of the most commonly-used methods to find the pathogenesis and genetic mechanisms of complex diseases. However, the GWAS process needs to frequently search the genome-wide data, especially in the calculation process of multi-party participation. The statistical value calculation and interactive search of Single Nucleotide Polymorphisms (SNPs) and model training processes might easily disclose personal information. Therefore, to solve these problems, we propose a Blockchain-based access control Framework for GWAS with Federated Learning-BFGF. Specifically, before training local models, this framework implements Automated Quality Control (AQC) to guarantee the quality of training data. Design the authentication mechanism in blockchain to filter out users who are malicious attackers to protect the security of other users' information initially. To accelerate the speed of cloud model training and resist multiple attacks in federated learning, propose a periodic aggregation method combining differential privacy mechanisms. Finally, simulated experiments have shown that the BFGF framework can protect the security of genetic data and balance availability and accuracy.
Subdomain adaptation network with category isolation strategy for tire defect detection

Data-driven tire defect detection has been greatly confronted with the scarcity of defective samples and the poor transferability of models. One of the best ways to alleviate these issues is domain adaptation. However, the tire defect features have large intra-class differences and small inter-class differences. Therefore, most of the existing domain adaptation methods that only concentrate on global distributions will fail for such a complex scenario. To overcome the weakness, this article first proposes the multi-representation-based subdomain adaptation network with category isolation strategy, and then describes the category isolation strategy and multi-representation-based alignment. With the consideration of fine-grained distributions of two subdomains, the designed category isolation strategy makes the features of different subdomains more discriminative. Besides, an Inception module is introduced to extract multi-representation of the complex tire X-ray images. Finally, extensive experiments conducted on the tire defect dataset and a benchmark dataset demonstrate the effectiveness of the proposed method.
Fraud detection and prevention in e-commerce: A systematic literature review

The high volume of money involved in e-commerce transactions draws the attention of fraudsters, which makes fraud prevention and detection techniques of high importance. Current surveys and reviews on fraud systems focuses mainly on financial-specific domains or general areas, leaving e-commerce aside. In this context, this article presents a systematic literature review on fraud detection and prevention for e-commerce systems. Our methodology involved searching for articles published in the last six years into four different literature databases. The search of articles employs a search string composed of the following keywords: purchase, buy, transactions, fraud prevention, fraud detection, e-commerce, web commerce, online store, real-time, and stream. We apply six filtering criteria to remove irrelevant articles. The methodology resulted in 64 articles, which we carefully analyzed to answer five research questions. Our contribution appears in the updated perception of fraud types, computational methods for fraud detection and prevention, as well as the employed domains. To the best of our knowledge, this is the first survey on combining prevention and detection of e-commerce frauds, linking also architectural insights, artificial intelligence methods, and open challenges and gaps in the research area. The study main findings demonstrate that from 64 articles, only five focus on the fraud prevention problem, and credit card fraud is the most explored fraud type. In addition, current literature lacks studies that propose strategies for detecting fraudsters and automated bots in real-time.
Comparison of classification techniques for the control of EOG-based HCIs

Electrooculogram (EOG) is the measurement of the biopotential generated by eye movement. These signals are crucial for people with severe motor disabilities because they rarely suffer alterations in eye movement. Therefore, the correct classification of these signals could find application in the design of simple user interfaces that allow independence and communication skills. This paper presents a comparison of the main classification techniques in the literature for the control of EOG-based human–computer interfaces (HCIs). Static threshold, K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) techniques, together with two new ensembles of classifiers. One is based on a voting scheme while the other employs two stages to encode the outcomes from the KNN, SVM, and ANN classifiers. All classifiers were compared based on four parameters – precision, specificity, sensitivity, and accuracy – to select the most appropriate approach in real-time. This work also provides a novel data set consisting of signals from nine healthy participants and compares the above methods also on another public data set. Machine learning-based models proved to be more robust for continuous use of an EOG-based HCI, while static thresholds are better for specific and repetitive actions. • To classify the Electrooculogram (EOG) features. • To compare the classification performance of different well-known machine learning and threshold-based techniques. • To propose two novel ensemble classifiers. • To show the advantages and disadvantages of the classification techniques under study. • To provide a novel dataset and four trained models.
Quantum Image Steganography Schemes for Data Hiding: A Survey

Quantum steganography plays a critical role in embedding confidential data into carrier messages using quantum computing schemes. The quantum variant of steganography outperforms its classical counterpart from security, embedding efficiency and capacity, imperceptibility, and time-complexity perspectives. Considerable work has been carried out in the literature focusing on quantum steganography. However, a holistic view of available schemes is missing. This paper provides an overview of latest advances in the field of quantum-steganography and image-steganography schemes. Moreover, the paper includes discussion of improvements made in the aforementioned fields, a brief explanation of the methodologies used for each presented algorithm, and a comparative study of existing schemes.