Reviewerly/exHarmony huggingface dataset:
Optimal network defense strategy selection based on Bayesian game

Existing passive defence methods cannot effectively guarantee network security; to solve this problem, a novel method is proposed that selects the optimal defence strategy. The network attack-defence process is modelled based on the Bayesian game. The payoff is quantified from the impact value of the attack-defence actions. The optimal defence strategy is selected that takes defence effectiveness as the criterion. The rationality and feasibility of the method are verified through a representative example, and the general rules of network defence are summarised. Compared to the classic strategy selection methods based on game theory, the proposed method can select the optimal strategy in the form of pure strategy by quantifying defence effectiveness, which was proven to perform better.
A Modified Method for Scene Text Detection by ResNet

In recent years, images have played a more and more important role in our daily life and social communication. To some extent, the textual information contained in the pictures is an important factor in understanding the content of the scenes themselves. The more accurate the text detection of the natural scenes is, the more accurate our semantic understanding of the images will be. Thus, scene text detection has also become the hot spot in the domain of computer vision. In this paper, we have presented a modified text detection network which is based on further research and improvement of Connectionist Text Proposal Network (CTPN) proposed by previous researchers. To extract deeper features that are less affected by different images, we use Residual Network (ResNet) to replace Visual Geometry Group Network (VGGNet) which is used in the original network. Meanwhile, to enhance the robustness of the models to multiple languages, we use the datasets for training from multi-lingual scene text detection and script identification datasets (MLT) of 2017 International Conference on Document Analysis and Recognition (ICDAR2017). And apart from that, the attention mechanism is used to get more reasonable weight distribution. We found the proposed models achieve 0.91 F1-score on ICDAR2011 test, better than CTPN trained on the same datasets by about 5%.
Interview with Christian M. I. M. Matthiessen

As a sequel to the first part of the interview on translation, this transcript further explores issues related to Systemic Functional Linguistics (SFL) and translation studies. Christian Matthiessen and Bo Wang first discuss the acceptance of the term ‘systemic functional translation studies’ in academia. Then, Christian Matthiessen comments on the contributions made by various scholars, including J. R. Firth, Erich Steiner and his group, Mona Baker, Juliane House, and J. R. Martin. Finally, Christian Matthiessen explains the relationships between SFL and some translation theories, i.e. skopos theory, polysystem theory and descriptive translation studies. Our discussions are expected to contribute to the debates on translation studies and SFL as well as the applications of SFL to translation.
Advances in artificial neural networks, machine learning and computational intelligence

Feature selection is of great importance for two possible scenarios: (1) prediction, i.e., improving (or minimally degrading) the predictions of a target variable while discarding redundant or uninformative features and (2) discovery, i.e., identifying features that are truly dependent on the target and may be genuine causes to be determined in experimental verifications (for example for the task of drug target discovery in genomics). In both cases, if variables have a large number of missing values, imputing them may lead to false positives; features that are not associated with the target become dependent as a result of imputation. In the first scenario, this may not harm prediction, but in the second one, it will erroneously select irrelevant features. In this paper, we study the risk/benefit trade-off of missing value imputation in the context of feature selection, using causal graphs to characterize when structural bias arises. Our aim is also to investigate situations in which imputing missing values may be beneficial to reduce false negatives, a situation that might arise when there is a dependency between feature and target, but the dependency is below the significance level when only complete cases are considered. However, the benefits of reducing false negatives must be balanced against the increased number of false positives. In the case of binary target variable and continuous features, the t-test is often used for univariate feature selection. In this paper, we also introduce a de-biased version of the t-test allowing us to reap the benefits of imputation, while not incurring the penalty of increasing the number of false positives.
E-voting system: the pathway to free and fair election in Nigeria

Voting is a critical component of any election. In most developing countries, this process is usually marred with multiple irregularities such as falsification of results, identity theft, stolen of ballot boxes, multiple voting and electoral fraud among others. This research aimed at solving these multifaceted problems associated with manual-based voting processes by using agile development and components-based software engineering (CBSE) principles to evolve a usable electronic voting system that can be used to conduct national election and any other election in the country. This will enhance and guarantee the credibility of the electoral processes and show a true reflection of the wishes of the people. The system was implemented using Visual Studio .Net and Microsoft SQL Server with attendant benefits of increased level of participation by the electorate, drastic cost reduction in organising election, voter's convenience, and faster release of election results.
Modelling a Plain N-Hypercube Topology for Migration in Fog Computing

Fog Computing deployments need consolidated Data Center infrastructures in order to get optimal performances in those special environments. One of the key points in attaining such achievements may be the implementation of Data Center topologies with enhanced features for a relatively small number of users, although ready for dealing with occasional traffic peaks. In this paper, a plain N-Hypercube switching infrastructure is modelled in different ways, such as using arithmetic, logical and algebraic ways, focusing on its capabilities to manage VM migrations among hosts within such a topology.
Information Security Risk and Management in Organizational Network

Any association that needs to work deliberately with PC systems and data security should receive certain structure for system and data security the board. Suitable administration of PCs (host, servers and work areas), the system foundation interconnecting them and data security is a basic necessity for any association. Associations every day face dangers to their systems and data resources. Most administration procedures are not characteristically verify, and specialized arrangements are just one bit of an all-encompassing way to deal with oversee PC systems and data security. Late years have seen various devices made to robotize this methodology. There are moreover instruments that compass sorts out and discover possible attack circumstances including complex blend of different vulnerabilities. This Paper is worried about issues identifying with the administration of PC system and data security in associations.
SCORE framework: a layered approach for enterprise architecture

Continuous and rapid changing business environment is a driving force for agile enterprise system because several new services and solutions are emerging from the multiple exchanges over time. A suitable enterprise architecture framework (EAF) must be established for established for effective delivery of enterprise level technologies by adopting service-oriented architecture (SOA). However, existing non-SOA-based EAFs are lacking of adopting such agility. On the other hand, existing SOA-based EAFs though can resolve the issues of flexibility and scalability, but they are less efficient to handle context sensitivity and agility of enterprise system. To address these challenges, this paper proposes a novel SOA-based EAF, called SCORE framework that is comprised of five loosely coupled layers. Besides formal description, the proposed SCORE framework is illustrated with detailed case study on care management system. Moreover, a detailed comparative analysis is performed to express the strength of the proposed framework over the existing approaches.
Super-Resolution of Digital Images using CNN with Leaky ReLU

Image super-resolution (SR) has been used in many real world applications as a preprocessing phase. The improvement in image resolution increases the performance of image analysis process. The SR of digital images is achieved by taking the low resolution images as inputs. In this article, a novel deeplearning based super-resolution approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with test images taken from USC-SIPI dataset indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio.
Quasi-pixelwise motion compensation for 4-step phase-shifting profilometry based on a phase error estimation

Phase-shifting profilometry (PSP) is widely used in 3D shape measurement due to its high accuracy. However, in dynamic scenarios, the motion of objects will introduce phase-shifting errors and result in measurement errors. In this paper, a novel compensation method based on 4-step phase-shifting profilometry is proposed to reduce motion-induced errors when objects undergo uniform or uniformly accelerated motion. We utilize the periodic characteristic of fringe patterns to estimate the phase errors from only four phase-shifting patterns and realize a pixel-wise error compensation. This method can also be applied to non-rigid deforming objects and help restore high-quality texture. Both simulation and experiments demonstrate that the proposed method can effectively improve the measurement accuracy and reduce surface ripples introduced by motion for a standard monocular structured-light system.
MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment

Image aesthetics assessment (IAA) is a subjective and complex task. The aesthetics of different themes vary greatly in content and aesthetic results, whether they are in the same aesthetic community or not. In aesthetic evaluation tasks, the pretrained network with direct fine-tune may not be able to quickly adapt to tasks on various themes. This article introduces a metalearning-based multipatch (MetaMP) IAA method to adapt to various thematic tasks quickly. The network is trained based on metalearning to obtain content-oriented aesthetic expression. In addition, we design a complete-information patch selection scheme and a multipatch (MP) network to make the fine details fit the overall impression. Experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art models based on aesthetic visual analysis (AVA) benchmark datasets. In addition, the evaluation of the dataset shows the effectiveness of our metalearning training model, which not only improves MetaMP assessment accuracy but also provides valuable guidance for network initialization of IAA.
Artificial Intelligence-Based Family Health Education Public Service System

Family health education is a must for every family, so that children can be taught how to protect their own health. However, in this era of artificial intelligence, many technical operations based on artificial intelligence are born, so the purpose of this study is to apply artificial intelligence technology to family health education. This paper proposes a fusion of artificial intelligence and IoT technologies. Based on the characteristics of artificial intelligence technology, it combines ZigBee technology and RFID technology in the Internet of Things technology to design an artificial intelligence-based service system. Then it designs the theme of family health education by conducting a questionnaire on students' family education and analyzing the results of the questionnaire. And it designs database and performance analysis experiments to improve the artificial intelligence-based family health education public service system designed in this paper. Finally, a comparative experiment between the family health education public service system based on artificial intelligence and the traditional health education method will be carried out. The experimental results show that the family health education public service system based on artificial intelligence has improved by 21.74% compared with the traditional family health education method; compared with the traditional family health education method, the health education effect of the family health education public service system based on artificial intelligence has increased by 13.89%.
Decentralized Unsupervised Learning of Visual Representations

Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative learning methods require fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to the high labeling cost and the requirement of expertise. The lack of labels makes collaborative learning impractical in many realistic settings. Self-supervised learning can address this challenge by learning from unlabeled data. Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled image data. However, the distributed data collected on clients are usually not independent and identically distributed (non-IID) among clients, and each client may only have few classes of data, which degrades the performance of CL and learned representations. To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations. Feature fusion provides remote features as accurate contrastive information to each client for better local learning. Neighborhood matching further aligns each client’s local features to the remote features such that well-clustered features among clients can be learned. Extensive experiments show the effectiveness of the proposed framework. It outperforms other methods by 11% on IID data and matches the performance of centralized learning.
Background segmentation in difficult weather conditions

Background segmentation is a process in which an algorithm removes the static background from an image. This allows only a changing section of the image. This process is important for motion detection or object tracking. In this article, an approach is proposed to compare several existing algorithms for background segmentation under severe weather conditions. Three weather conditions were tested: falling snow, rain and a sunny windy day. The test algorithms were executed on a test video containing frames collected by a dedicated Raspberry Pi camera. The frames used in the tests included cars, bicycles, motorcycles, people, and trees. Preliminary results from these tests show interesting differences in detail detection and detection noise.
Hybrid Modeling of Deformable Linear Objects for Their Cooperative Transportation by Teams of Quadrotors

This paper deals with the control of a team of unmanned air vehicles (UAVs), specifically quadrotors, for which their mission is the transportation of a deformable linear object (DLO), i.e., a cable, hose or similar object in quasi-stationary state, while cruising towards destination. Such missions have strong industrial applications in the transportation of hoses or power cables to specific locations, such as the emergency power or water supply in hazard situations such as fires or earthquake damaged structures. This control must be robust to withstand strong and sudden wind disturbances and remain stable after aggressive maneuvers, i.e., sharp changes of direction or acceleration. To cope with these, we have previously developed the online adaptation of the proportional derivative (PD) controllers of the quadrotors thrusters, implemented by a fuzzy logic rule system that experienced adaptation by a stochastic gradient rule. However, sagging conditions appearing when the transporting drones are too close or too far away induce singularities in the DLO catenary models, breaking apart the control system. The paper’s main contribution is the formulation of the hybrid selective model of the DLO sections as either catenaries or parabolas, which allows us to overcome these sagging conditions. We provide the specific decision rule to shift between DLO models. Simulation results demonstrate the performance of the proposed approach under stringent conditions.
Identifying pathological slices of gastric cancer via deep learning

The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy. This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (H&E stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images. The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, speciﬁcity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice. In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue. For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.
Technical Consideration towards Robust 3D Reconstruction with Multi-View Active Stereo Sensors

It is possible to construct cost-efficient three-dimensional (3D) or four-dimensional (4D) scanning systems using multiple affordable off-the-shelf RGB-D sensors to produce high-quality reconstructions of 3D objects. However, the quality of these systems’ reconstructions is sensitive to a number of factors in reconstruction pipelines, such as multi-view calibration, depth estimation, 3D reconstruction, and color mapping accuracy, because the successive pipelines to reconstruct 3D meshes from multiple active stereo sensors are strongly correlated with each other. This paper categorizes the pipelines into sub-procedures and analyze various factors that can significantly affect reconstruction quality. Thus, this paper provides analytical and practical guidelines for high-quality 3D reconstructions with off-the-shelf sensors. For each sub-procedure, this paper shows comparisons and evaluations of several methods using data captured by 18 RGB-D sensors and provide analyses and discussions towards robust 3D reconstruction. Through various experiments, it has been demonstrated that significantly more accurate 3D scans can be obtained with the considerations along the pipelines. We believe our analyses, benchmarks, and guidelines will help anyone build their own studio and their further research for 3D reconstruction.
A Study of Data Security on E-Governance using Steganographic Optimization Algorithms

Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other methods
Privacy Leakage in Text Classification A Data Extraction Approach

Recent work has demonstrated the successful extraction of training data from generative language models.However, it is not evident whether such extraction is feasible in text classification models since the training objective is to predict the class label as opposed to next-word prediction.This poses an interesting challenge and raises an important question regarding the privacy of training data in text classification settings.Therefore, we study the potential privacy leakage in the text classification domain by investigating the problem of unintended memorization of training data that is not pertinent to the learning task.We propose an algorithm to extract missing tokens of a partial text by exploiting the likelihood of the class label provided by the model.We test the effectiveness of our algorithm by inserting canaries into the training set and attempting to extract tokens in these canaries post-training.In our experiments, we demonstrate that successful extraction is possible to some extent.This can also be used as an auditing strategy to assess any potential unauthorized use of personal data without consent.