Reviewerly/exHarmony huggingface dataset:
High-Performing Training Large-Scale Binary Nonlinear SVMs Using Mean Decision Rule Method with Smart Sampling

In spite of multiple investigations in the field of large-scale SVM tasks, the problem of their solution is still actual. Previously, we proposed the kernel-based mean decision rules method with smart sampling (SS-KMDR). According to experiments, it provides the possibility to obtain results close in quality to the reference ones, but for much less time compared to traditional methods. In this paper, we present a new high-performance implementation (HP-SS-KMDR), which allows us to further speed-up computations through the development of efficient parallel algorithms and modern technologies for parallel and distributed computing. Besides, experiments show that in a number of cases the proposed HP-SS-KMDR method allows us to increase the recognition quality compared to the sequential version due to the possibility to process more data in less time.
A Registration Method for Dual-Frequency, High-Spatial-Resolution SAR Images

With the continuous development of synthetic-aperture-radar (SAR) technology, SAR-image data are becoming increasingly abundant. For the same scene, dual-frequency (high-frequency and low-frequency) SAR images can present different details and feature information. Image fusion of the two frequencies can combine the advantages of both, thus describing targets more comprehensively. Image registration is the key step of image fusion and determines the quality of fusion. Due to the complex geometric distortion and gray variance between dual-frequency SAR images with high resolution, it is difficult to realize accurate registration between the two. In order to solve this problem, this paper proposes a method to achieve accurate registration by combining edge features and gray information. Firstly, this paper applies the edge features of images and a registration algorithm based on fast Fourier transform (FFT) to realize rapid coarse registration. Then, combining a registration algorithm based on the enhanced correlation coefficient (ECC) with the concept of segmentation, the coarse-registration result is registered to achieve accurate registration. Finally, by processing the airborne L-band and Ku-band SAR data, the correctness, effectiveness, and practicability of the proposed method are verified, with a root mean square error (RMSE) of less than 2.
Automatic semantic knowledge extraction from electronic forms

Electronic tabular forms are an intuitive way for organisations to collect, present and store structured information for human readers. Forms use features such as fonts, colours and cell positioning to help readers navigate and find information. Millions of forms, typically in Portable Document Format (PDF), are generated by businesses as part of routine operations. Unlike human readers, machines are not able to directly ‘understand’ the implicit cues contained in the fonts, colours and use of boxes without explicit processing. In this paper, a supervised computer vision model is proposed to decompose the PDF form document into nested microtables. The cells within these microtables are then processed using a customisable rule bank for meaningful table content and semantic relationship extraction. The process is demonstrated on an industry dataset of 37 maintenance procedure documents containing 373 pages and 1016 unique microtables. A web application EMU (Extracting Machine Understandable Semantics from Forms) demonstrates how data captured in tables with different dimensions in procedural forms can be automatically extracted and stored in JavaScript Object Notation (JSON). Identifying and extracting nested tables is a critical fundamental step for future applications to support machine-automated search and extraction of data at scale for both maintenance and other procedural documentation.
Method for automated detection of outliers in crash simulations

AbstractAbstractStricter legal requirements in crash safety lead to more complex development processes in computer-aided engineering and result in an increasing number of simulations. Both, the construction of the simulation models as well as their evaluation are costly and time-consuming. Therefore, an automated workflow is required that significantly facilitates the analysis of the results by the engineer and increases the quality of the evaluation.In this study an automated evaluation process is proposed that detects anomalous crash behaviour in a bundle of crash simulations. The individual states from the simulation are analysed separately from each other and an outlier score is calculated using a kth-nearest-neighbour approach. Subsequently, these results are averaged into one score for each simulation. With the help of different statistical methods, a threshold value is calculated, from which a simulation can be identified as an outlier. The evaluation is carried out on 5 datasets. On average, the precision and recall of the presented method are 1.0 and 0.91, respectively.Keywords: Crash simulation analysisoutlier detectionmachine learningprocess automation Disclosure statementNo potential conflict of interest was reported by the authors.
Outlier robust extreme learning machine to simulate discharge coefficient of side slots

Abstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the flow depth to the side slot length ( Y m / L ), the side slot crest elevation to the side slot length ( W/L ), the main channel width to the side slot length ( B/L ), as well as the Froude number ( F r ) are determined and subsequently five ORELM models (ORELM 1 to ORELM 5) are created utilizing these variables. From that point forward, laboratory measurements are arranged into two datasets comprising training (70%) and testing (30%). At the subsequent stage, the best model alongside the most affecting input variables is presented by executing a sensitivity examination. The most impressive model (i.e., ORELM 3) reproduces DC values as far as B/L , W/L and F r . It is worth focusing on that ORELM 3 forecasts DC values with worthy precision. For instance, the correlation coefficient ( R ), the scatter index (SI) and the Nash–Sutcliffe effectiveness (NSC) for ORELM 3 are acquired in the examination state to be 0.936, 0.049 and 0.852, independently. Examining the outcomes yielded from the simulation demonstrates that W/L and F r are the most impacting factors to reproduce the DC. Besides, the findings of the sensitivity examination uncover that ORELM 3 acts in an underestimated way. Finally, a computer code is put forward to compute the DC of side slots.
Reversible Data Hiding in Encrypted Images Based on Adaptive Gradient Prediction

The existing reversible data hiding (RDH) technology solves the problem that the ciphertext data and carrier are easily damaged in the traditional scheme, which has become a research hotspot in information hiding. However, because of the small coverage area of the predictor and poor prediction ability of the existing reversible data hiding in encrypted images (RDH-EI) technology based on pixel predictor, the embedding performance of the algorithm is limited. Therefore, this paper proposes an adaptive gradient prediction (AGP) scheme. The AGP employs a comprehensive and efficient local complexity measurement strategy to make predictions based on pixel changes around the predicted pixel, including horizontal, vertical, and diagonal directions. The experimental results show that the AGP-RDHEI scheme has apparent advantages in embedding rate.
Improved storage capacity for health care center using ant lion- ramp secret sharing scheme for data deduplication in cloud computing

The Cloud-based storage is able to store more information in gigabyte size in all formats such as text, image or video and it can access at any time with their login credentials. In such a system, reducing the duplication of data and increasing security is an important factor for efficient storage. In this work, the file level de-duplication process is applied on the Magnetic Resonance Imaging (MRI) brain image by reducing the shares of the image to retrieve an original image from the cloud. To reduce the storage problem in this an optimization-based RSSS is used. The objective of this investigation is to decrease the storage blow-up problem in Cloud storage and reduce the duplicate files in the Cloud storage of the health care centre. The proposed model comprises of two subsets: In the first set, the input image is divided into a number of shares using RSSS scheme. In the second set, the minimum share is determined by using the optimization process and it is encrypted and it is stored in the Cloud. Initially, the image is divided into number of shares for reconstructing using the ramp secret sharing scheme.Without these shares, the original image cannot be recovered. But storing all the shares result in high storage capacity. It is overcome with the help of Ant Lion optimization (ALO) to determine the minimum number of shares required for recovering the image. The ALO works to minimizing the Mean Square Error (MSE) of the image reconstruction to find the minimum shares. Then, the minimum shares are encrypted and converted into hash keys. Those hash keys are stored in the Cloud storage. The proposed ALO-RSSS is achieved its objective by reducing the shares to 2 as compared to the traditional method as well as the PSNR is 27% improved.
Roles and Challenges of AI-Based Cybersecurity: A Case Study

Nowadays, organizations face complex cyber-attacks that are difficult to counter with traditional protections. Therefore, many modern organizations tend to use AI-based cybersecurity systems to secure their assets and infrastructure and to reduce the level of potential risks.&#x0D; Because of the lack of clarity of the AI roles in cybersecurity and the ambiguity of the related challenges, this study aims to explore the most prominent roles of AI-based cybersecurity related to information security in the prevention phase, the detection phase and the response phase. The study also aims to determine the most prominent challenges facing AI-based cybersecurity.&#x0D; To achieve the goals of this study, we adopt a case-study methodology that origins from the general framework which is information security and ends with the specific framework which is AI-based cybersecurity. After designing a matrix to analyze the case study, the study concludes nine important roles for AI-based cybersecurity distributed over the three phases. Three roles are in the prevention phase, which are automated assessment of security vulnerabilities, awareness and training and authentication. Two roles are in the detection phase; namely, detection of intrusion and security breaches and detection of spams and phishing and four rules are in the response stage, which are analyzing malware, automating routine tasks, deploying traps and topple attackers and isolating important assets.&#x0D; The study also identifies eight elements that represent the most prominent challenges facing AI-based cybersecurity: regulations and principles, trust, accountability, privacy, bias, training datasets, human resources and financial costs. In conclusion, the study presents a set of recommendations drawn from the results.
Micdir: Multi-Scale Inverse-Consistent Deformable Image Registration Using Unetmss with Self-Constructing Graph Latent

Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and most commonly in medical imaging. Deep Learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and therefore can not track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions have been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet - which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013±0.0243 for intramodal and 0.6211±0.0309 for intermodal, while VoxelMorph achieved 0.7747±0.0260 and 0.6071±0.0510, respectively.
A CTR prediction model based on session interest

Click-through rate prediction has become a hot research direction in the field of advertising. It is important to build an effective CTR prediction model. However, most existing models ignore the factor that the sequence is composed of sessions, and the user behaviors are highly correlated in each session and are not relevant across sessions. In this paper, we focus on user multiple session interest and propose a hierarchical model based on session interest (SIHM) for CTR prediction. First, we divide the user sequential behavior into session layer. Then, we employ a self-attention network obtain an accurate expression of interest for each session. Since different session interest may be related to each other or follow a sequential pattern, next, we utilize bidirectional long short-term memory network (BLSTM) to capture the interaction of different session interests. Finally, the attention mechanism based LSTM (A-LSTM) is used to aggregate their target ad to find the influences of different session interests. Experimental results show that the model performs better than other models.
Face Image Animation with Adversarial Learning and Motion Transfer

Significant advances have been made in facial image animation from a single image. Nonetheless, generating convincing facial feature movements remains a complex challenge in computer graphics. The purpose of this study is to develop an efficient and effective approach for transferring motion from a source video to a single facial image by governing the position and expression of the face in the video to generate a new video imitating the source image. Compared to prior methods that focus solely on manipulating facial expressions, this model has been trained to distinguish the moving foreground from the background image and to create motions such as facial rotation and translation as well as small local motions such as gaze shift. The pro-posed technique uses generative adversarial networks GANs with a motion transfer model. The network forecasts photo-realistic video frames for a given target image using synthetic input in renderings from a parametric face model. The authenticity in this postprocessing conversion is attained by precise image manipulation. Thorough adversarial training is used to produce greater accuracy in this postprocessing conver-sion. Although more improvements to face landmark identification on videos and face super-resolution techniques have been made to improve the results, the pro-posed technique can provide more coherent videos with improved visual quality, resulting in more aligned landmark sequences for training. In addition, experiments indicate that we obtain superior results compared to those obtained by the state-of-the-art image-driven technique with PSNR 30.74 and SSIM 0.90
Automated Identification of Uniqueness in JUnit Tests

In the context of testing, descriptive test names are desirable because they document the purpose of tests and facilitate comprehension tasks during maintenance. Unfortunately, prior work has shown that tests often do not have descriptive names. To address this limitation, techniques have been developed to automatically generate descriptive names. However, they often generated names that are invalid or do not meet developer approval. To help address these limitations, we present a novel approach to extract the attributes of a given test that make it unique among its siblings. Because such attributes often serve as the basis for descriptive names, identifying them is an important first step towards improving test name generation approaches. To evaluate the approach, we created a prototype implementation for JUnit tests and compared its output with human judgment. The results of the evaluation demonstrate that the attributes identified by the approach are consistent with human judgment and are likely to be useful for future name generation techniques.
Generative Adversarial Networks for Secure Data Transmission in Wireless Network

In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an increased detection rate in the generative adversarial network (GAN). The GDNN with game theory is designed to validate the channel condition with the cross entropy loss function and back-propagation algorithm, which improves the communication reliability in the network. The simulation is conducted in NS2.34 tool against several performance metrics to reduce the misdetection rate and false alarm rates. The results show that the GDNN obtains an increased rate of successful transmission by taking optimal actions to act as a defense mechanism to mislead the jammer, where the jammer makes high misclassification errors on transmission dynamics.
A personalized exercise recommendation method for teaching objectives

It can improve the learning effect and reduce the learning burden to recommend exercises which are in accordance with the teaching objectives to students. How to accurately judge the needs of students and recommend exercises that are consistent with the teaching objectives is an urgent problem to be solved in the field of personalized education. This paper proposes a personalized exercise recommendation method for teaching objectives. This method can recommend exercises that are highly compatible with the syllabus for students according to their selected knowledge points and expected score range. According to the experimental test, the average prediction success rate of this method is 72.5%, 57.2% higher than the exercise recommendation method based on collaborative filtering and KNN, and 10.6% higher than the exercise recommendation method based on cognitive diagnosis and probability matrix decomposition.
Research on social common sense knowledge reasoning method based on pre-training

Common sense knowledge is a part of knowledge reasoning. At present, common sense knowledge reasoning is also a hot and difficult point in the field of knowledge reasoning. In order to enable machines to use human language, and according to some content reasoning or inference rules, to get the results of common sense reasoning. This paper mainly explains how to obtain the common knowledge base, improve the reasoning ability of the machine to the social common knowledge, and test the common knowledge reasoning dataset by using the epoch-making pre-training model through the deep reinforcement learning method. The experiment shows that by adjusting the parameters of the data, the accuracy can be improved, which is also conducive to the realization of the downstream social common sense reasoning question and answer task.
Part-aware network: a simple but efficient method for occluded person re-identification

Person re-identification is to query person across cameras and occlusion is one of the difficulties. Previous works have proved that local feature extraction and alignment are critical for occluded person re-identification. However, directly horizontal partition causes mis-alignment and extra-cue methods highly depend on the quality of extra-cues. In this work, we propose a novel architecture including a weakly supervised mask generator without introducing extra-cues to create fine-grained semantic masks for local feature extraction and alignment, and a weight-shared fully connection to control the balance of local and global features. We also propose a general form of weighted pooling to improve gradient transfer, which gets rid of the probability explanation with softmax. Moreover, we unravel that there is a conflict between local branches and global branch, and a buffer convolution layer helps to fix this conflict. Extensive experiments show the effectiveness of our proposed method on occluded and holistic ReID tasks. Specially, we achieve 62.5% Rank-1 and 52.6% mAP (mean average precision) scores on the occluded-duke dataset.
Robust Long-Term Tracking via Localizing Occluders

Occlusion is known as one of the most challenging factors in long-term tracking because of its unpredictable shape. Existing works devoted into the design of loss functions, training strategies or model architectures, which are considered to have not directly touched the key point. Alternatively, we came up with a direct and natural idea that is discarding things that covers the target. We propose a novel occluder-aware representation learning framework to develop this idea. First, we design a local occluders detection module (LODM) to localize the occluders, which works on the principle that discriminates the non-noumenal part from a target based on the general knowledge of this category. An extra dataset and a clustering strategy is proposed to support this general knowledge. Second, we devise a feature reconstruction module to guide the occluder-aware representation learning. With the help of above methods, our localizing occluders tracker, called LOTracker, can learn an occluder-free representation and promote the performance that tracks with occlusion scenarios. Extensive experimental results show that our LOTracker achieves a state-of-the-art performance in multiple benchmarks such as LaSOT, VOTLT2018, VOTLT2019, and OxUvALT.