Reviewerly/exHarmony huggingface dataset:
Learning Knowledge Graph Embedding with Entity Descriptions based on LSTM Networks

Knowledge inference and knowledge prediction is widely used in the intelligent fault diagnosis that is very important to the product safety. Most learning knowledge graph embedding methods represent entities and relations only with fact triples of knowledge graphs (KGs) through translating embedding models without integrating the rich semantic information in entity descriptions. However, entity description-based method DKRL only takes high frequency words of entity descriptions as input data in training using CNN encoder model, which loss the word order feature in entity descriptions and the relevance in context. In this paper, we propose a novel learning knowledge graph embedding method with entity descriptions named as Learning Knowledge Graph Embedding with Entity Descriptions based on LSTM Networks(KGDL), which can integrate word order features of each sentence in entity descriptions and the semantic information in fact triples of KGs, to enrich the semantic representations of KGs for promoting knowledge acquisition and inference. More specifically, we explore LSTM encoder model to encode all semantic information of entity descriptions based on each sentence without losing the independent feature of each sentence and the semantic associations between sentences of entities descriptions, then encode these sentence embeddings into the entity descriptions embeddings, and further learn knowledge graph embeddings from fact triples with entity descriptions embeddings. The experiment results show that KGDL gets better performance than state-of-the-art method DKRL, in terms of mean rank value and HITS@K with highly accurate, fast and robust. Moreover, KGDL based on two-steps relational path of KGs with entity descriptions has promising abilities for relation prediction and entity prediction, which gets better performance than state-of-the-art method Path-based TransE in knowledge inference.
Research on Rail Profile Measurement Method Based on Machine Vision

As an important part of the railway system, the qualified quality of steel rail is an important guarantee for the safety of railway running, and efficient and accurate rail parameter measurement and detection methods are widely studied. Based on the analysis of the existing methods of rail profile detection in China, the laser triangulation method is used to realize the dynamic measurement of the rail profile size; A method based on the minimum distance method is proposed to extract the coordinate points of the contour coincidence area to realize the accurate splicing of the rail contours. The measurement method of the important characteristic parameters of the rail profile is proposed and the measurement principle diagram is designed; The static test and dynamic test of the rail sample are carried out respectively by using the rail profile on-line detection system, and the obtained static repeat measurement accuracy and dynamic measurement error are consistent Industry norms and standards have verified the accuracy and repeatability of the system.
Improvement of Enhanced Opposite Direction Searching Algorithm

Type-reduction is an important part of the interval type-2 fuzzy logic system (IT2 FLS), which is also the main factor that influences the effectiveness of IT2 FLS. The enhanced opposite direction searching (EODS) algorithm is currently the fastest form of the Karnik–Mendel algorithm, but it requires a large number of iterations. In this article, an improvement of the enhanced opposite direction searching (IEODS) algorithm is presented to obtain the type reduced set more efficiently. The IEODS algorithm improves the iterative process of the original EODS algorithm through the width of the upper and lower member functions of the IT2 FS. In this way, the IEODS algorithm uses a single-sided iteration process. It has a better initial point to greatly reduce the number of iterations. Quantitative analysis and simulation based experiments show that the IEODS algorithm can save more than 90% of the iterations required in the EODS algorithm. It makes the IEODS algorithm more suitable for practical applications, especially to various development platforms with an explanatory programming language as a tool, greatly expanding the application range of the IT2 FLS.
Robustness of Neuromorphic Computing with RRAM-based Crossbars and Optical Neural Networks

RRAM-based crossbars and optical neural networks are attractive platforms to accelerate neuromorphic computing. However, both accelerators suffer from hardware uncertainties such as process variations. These uncertainty issues left unaddressed, the inference accuracy of these computing platforms can degrade significantly. In this paper, a statistical training method where weights under process variations and noise are modeled as statistical random variables is presented. To incorporate these statistical weights into training, the computations in neural networks are modified accordingly. For optical neural networks, we modify the cost function during software training to reduce the effects of process variations and thermal imbalance. In addition, the residual effects of process variations are extracted and calibrated in hardware test, and thermal variations on devices are also compensated in advance. Simulation results demonstrate that the inference accuracy can be improved significantly under hardware uncertainties for both platforms.
Face Detection based on Extraction by K-NNC and NMC Framework

Humans are truly adept at perceiving countenances and complex examples. The investigation on perceiving human appearances with a framework is done through image processing. Image processing is any type of sign handling for which the information is a image., For example, a photo or video outline and the yield of image handling might be either a image or a lot of attributes or boundaries are identified with the image. Numerous explorations are done in the field of image handling yet at the same time there are objectives to be reached. Most of the image processing strategies, where the image is considered as a two-dimensional sign and standard sign handling procedures are applied to it. Face, an essential characteristic that recognizes one subject from another, should be perceived commonly and regularly in current PC vision and mixed media utilizations. This includes, some notable identification of face models should be resent upon mobiles or else shrewd surveillance cameras to address this present reality necessities that intend to recognize low-goal faces along with incredibly minimum processing expense together with storage impression. Toward this end, it is important to investigate a possible arrangement can directly address a crucial test in recognize faces. These face identification frameworks are one such fields, where the processing of image is utilized. A powerful Face recognition strategy can be accomplished by making a framework that could perceive faces from an image or a video scene and analyzes it over the images in the information base.
Abnormal behavior detection based on the motion-changed rules

Abnormal activity will lead to the uncommon changes in the crowd behavior. In other words, the crowd motion changes conforms to certain rules for valid behaviors, while for abnormal events the motion changes are uncontrolled. For this, this paper discovers the motion-changed rules to detect and localize abnormal behavior in crowd videos. Specifically, we first generate the motion patterns based on the descriptor of collectiveness. Then each frame pair is represented as a transfer matrix whose elements are the difference of a set of motion patterns. Thereafter, the motion-changed rules are constructed in the transformation space using bag-of-words approach. Finally, the proposed approach measures the similarity between motion-changed rules and the incoming video data in order to examine whether the actions are anomalous. The approach is tested on the UMN dataset and a challenging dataset of crowd videos taken from the railway station. The experimental results demonstrate the effectiveness of the proposed method for detection the abnormal behavior.
Increasing the Efficiency Factor of Solar Power Plants Due to Solar Energy Localizing

The article presents an analysis of the state of development of solar energy in Europe and the Republic of Belarus for 2020. An algorithm for increasing the efficiency factor of solar power plants by localizing the solar trajectory depending on the latitude and longitude of the area has been proposed. In particular, taking into account the angle of the Sun position above the horizon and the azimuth angle of the Sun, the increase in the efficiency factor of solar power plants for the Republic of Belarus is calculated. Based on this algorithm, a program has been written that makes it possible to draw a diagram of the solar trajectory. An analysis has been made of the degree of localization of solar energy for solstice days in 6 oblast (regional) centers of the Republic of Belarus; it is found that the highest intensity of solar radiation is observed in Brest and Gomel, the average in Grodno, Minsk and Mogilev, while the lowest one – in the city of Vitebsk. A comparative analysis of the solar trajectory of the city of Berlin (Germany) with the city of Gomel and the city of Brest is carried out. Recommendations have been developed for the effective operation of solar power plants in the oblast (regional) cities of the Republic of Belarus during the year in an autonomous and combined mode of operation. The obtained numerical calculations of the solar trajectory make it possible to optimize the orientation of solar panels for permanently installed panels and for automated solar tracking systems, as well as to select the optimal configuration of the power plant equipment for any geographic area.
Binary Classification of Modulation Formats in the Presence of Noise through Convolutional Neural Networks

Automatic classification of modulation formats in the presence of noise is a challenging problem which has wide applications in intelligent communication systems. It is a major step towards accurate signal detection on the receiver side given stringent requirements such as spectral efficiency, reliability, latency etc. Deep learning techniques have found wide success in image recognition, detection, speech recognition as well as reinforcement learning domains. In this study, we compared and contrasted 2D and 3D Convolutional Neural Networks (CNNs) for the task of automatic recognition of modulation formats. We used random images of cat, that are modulated using 16 and 64 Quadrature Amplitude Modulation (QAM) formats, passed through Rayleigh and Rician channel formats and classified using both 2D and 3D CNN architectures. We tried 5 and 10 fold cross-validation procedures that are widely used in the literature. We have observed the best performance results in terms of reported metrics of the 3D CNN architecture trained using 10-fold cross-validation and the performance of 2D CNN architecture trained using 10-fold cross-validation procedure to be the worst. We obtained a perfect value of 100% in terms of performance metrics like sensitivity, precision, F-measure, balanced accuracy and specificity, when using 3D architecture trained using 10-fold cross-validation procedure for binary classification among 16 and 64 QAM modulation formats corrupted by Rayleigh and Rician channel noises.
A Meta-Model for Real-Time Fraud Detection in ERP Systems

Fraud is a worldwide issue affecting almost every organization once in a time.Recent studies have shown that fraudulent behavior impacts up to 5 % of a companies annual revenue.Information systems (IS) have become an integral part of every modern organization.They contain the data foundation of the entire company and thereby supporting business processes and day-to-day transactions.Although an IS usually contains control mechanisms to prevent different kinds of fraud, these mechanisms look insufficient, considering the role of IS in many fraud cases.Since many cases from different companies have shown the need for an appropriate countermeasure, we want to develop an application that efficiently detects fraud and fraudulent behavior.Therefore, we conducted a structured literature review and a qualitative survey to apply the design science research (DSR) methodology and derive requirements for a fraud detection system (FDS).As a result, we present a meta-model for a FDS for enterprise resource planning (ERP) systems.We also provide application requirements, principles, and features that define areas for further research.
Median-Pooling Grad-CAM: An Efficient Inference Level Visual Explanation for CNN Networks in Remote Sensing Image Classification

Gradient-based visual explanation techniques, such as Grad-CAM and Grad-CAM++ have been used to interpret how convolutional neural networks make decisions. But not all techniques can work properly in the task of remote sensing (RS) image classification. In this paper, after analyzing why Grad-CAM performs worse than Grad-CAM++ for RS images classification from the perspective of weight matrix of gradients, we propose an efficient visual explanation approach dubbed median-pooling Grad-CAM. It uses median pooling to capture the main trend of gradients and approximates the contributions of feature maps with respect to a specific class. We further propose a new evaluation index, confidence drop %, to express the degree of drop of classification accuracy when occluding the important regions that are captured by the visual saliency. Experiments on two RS image datasets and for two CNN models of VGG and ResNet, show our proposed method offers a good tradeoff between interpretability and efficiency of visual explanation for CNN-based models in RS image classification. The low time-complexity median-pooling Grad-CAM could provide a good complement to the gradient-based visual explanation techniques in practice.
Stochastic Hard Thresholding Algorithms for AUC Maximization

In this paper, we aim to develop stochastic hard thresholding algorithms for the important problem of AUC maximization in imbalanced classification. The main challenge is the pairwise loss involved in AUC maximization. We overcome this obstacle by reformulating the U-statistics objective function as an empirical risk minimization (ERM), from which a stochastic hard thresholding algorithm (SHT-AUC) is developed. To our best knowledge, this is the first attempt to provide stochastic hard thresholding algorithms for AUC maximization with a per-iteration cost O(bd) where d and b are the dimension of the data and the minibatch size, respectively. We show that the proposed algorithm enjoys the linear convergence rate up to a tolerance error. In particular, we show, if the data is generated from the Gaussian distribution, then its convergence becomes slower as the data gets more imbalanced. We conduct extensive experiments to show the efficiency and effectiveness of the proposed algorithms.
Super Resolution Deduction: Inferring Fine-Grained Capacity for Urban Signal Station Deployment

With the boosting of mobile devices, wireless sensor networks, and the internet of things, abundant multi-modal data, such as GPS signal, sensor data, are produced intentionally or unintentionally, which can represent the people's active patterns, vehicle's routes, and city's flows to develop a smart city.These multi-modal data are usually transmitted and received by signal stations deployed in the city.However, reasonably choosing the signal stations' locations is still an open issue for enhancing people's life quality in the smart city.To this end, we propose the Super Resolution Deduction (SRD) model for solving the signal station selection problem.SRD first initializes the city map as a coarse-grained heat map representing the capacity of the signal stations.Then an image-based super-resolution deduction model is proposed to obtain a fine-grained signal station capacity for deploying.To be specific, we employ Dense Block to capture the spatio-temporal correlations, C-Attention to selectively enhance useful feature maps, and S-Distribution to impose structural constraints.By sharing the GPS data load with the new deployment of signal stations, we ensure the smart city's efficiency and effectiveness.Extensive experimental results on real-world dataset Changchun City demonstrate that our proposed model achieves the superior performance among the stateof-the-art baselines.
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization

Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks. SZO methods only require the ability to evaluate the objective function at random input points, however, their weakness is the dependency of their convergence speed on the dimensionality of the function to be evaluated. We present a sparse SZO optimization method that reduces this factor to the expected dimensionality of the random perturbation during learning. We give a proof that justifies this reduction for sparse SZO optimization for non-convex functions. Furthermore, we present experimental results for neural networks on MNIST and CIFAR that show empirical sparsity of true gradients, and faster convergence in training loss and test accuracy and a smaller distance of the gradient approximation to the true gradient in sparse SZO compared to dense SZO.
RECol: Reconstruction Error Columns for Outlier Detection

Detecting outliers or anomalies is a common data analysis task. As a sub-field of unsupervised machine learning, a large variety of approaches exist, but the vast majority treats the input features as independent and often fails to recognize even simple (linear) relationships in the input feature space. Hence, we introduce RECol, a generic data pre-processing approach to generate additional columns (features) in a leave-one-out fashion: For each column, we try to predict its values based on the other columns, generating reconstruction error columns. We run experiments across a large variety of common baseline approaches and benchmark datasets with and without our RECol pre-processing method. From the more than 88k experiments, we conclude that the generated reconstruction error feature space generally seems to support common outlier detection methods and often considerably improves their ROC-AUC and PR-AUC values. Further, we provide parameter recommendations, such as starting with a simple squared error based random forest regression to generate RECols for new practical use-cases.
Blockchain and Other Distributed Ledger Technologies, An Advanced Primer

Although there are many versions of blockchain technology today, it was first introduced in 2008 as the technology supporting Bitcoin, the first successful virtual currency system. Yet in and of itself, Blockchain technology is much more than the underpinning for Bitcoin (and other cryptocurrencies) and has found many applications beyond its initial purpose. The goal of this advanced primer is to review the current state of this technology and to discuss some of its advantages and drawbacks in settings beyond crypto-currencies.
Auditing Network Embedding: An Edge Influence Based Approach

Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedding models, e.g., <i>why</i> a given embedding algorithm outputs the specific node representations and <i>how</i> the resulting node representations relate to the structure of the input network. In particular, we propose to discern the edge influence for two widely-studied classes of network embedding models, i.e., skip-gram based models and graph neural networks. We provide algorithms to effectively and efficiently quantify the edge influence on node representations, and further identify high-influential edges by exploiting the linkage between edge influence and network structure. Experimental evaluations are conducted on real datasets showing that: 1) in terms of quantifying edge influence, the proposed method is significantly faster (up to <inline-formula><tex-math notation="LaTeX">$2,000\times$</tex-math></inline-formula> ) than straightforward methods with little quality loss, and 2) in terms of identifying high-influential edges, the identified edges by the proposed method have a significant impact in the context of downstream prediction task and adversarial attacking.
A Method with Pre-trained Word Vectors for Detecting Wordlist-based Malicious Domain Names

Abstract In recent years, botnets have used the domain generation algorithm to generate dynamic typified malicious domain names to bypass various detection methods. Given the depth detection model of such domain names, domain names are generally processed by filling and transforming them into a fixed-length one-dimensional vector and then classifying them with poor detection performance. Therefore, this study first divides the domain into a word array and converts it into a word vector using pre-trained word vector models, Embeddings from Language Models. The domain is inputted into the TextCNN model for training classification. From approximately 100,000 data sets, a 94.22% accuracy rate and 6.87% FPR value can be obtained from the training. Compared with previous detection models (i.e., LSTM and CNN), more training and testing are needed, but improvements are made in all indicators.
Directed adversarial sampling attacks on phishing detection

Phishing websites trick honest users into believing that they interact with a legitimate website and capture sensitive information, such as user names, passwords, credit card numbers, and other personal information. Machine learning is a promising technique to distinguish between phishing and legitimate websites. However, machine learning approaches are susceptible to adversarial learning attacks where a phishing sample can bypass classifiers. Our experiments on publicly available datasets reveal that the phishing detection mechanisms are vulnerable to adversarial learning attacks. We investigate the robustness of machine learning-based phishing detection in the face of adversarial learning attacks. We propose a practical approach to simulate such attacks by generating adversarial samples through direct feature manipulation. To enhance the sample’s success probability, we describe a clustering approach that guides an attacker to select the best possible phishing samples that can bypass the classifier by appearing as legitimate samples. We define the notion of vulnerability level for each dataset that measures the number of features that can be manipulated and the cost for such manipulation. Further, we clustered phishing samples and showed that some clusters of samples are more likely to exhibit higher vulnerability levels than others. This helps an adversary identify the best candidates of phishing samples to generate adversarial samples at a lower cost. Our finding can be used to refine the dataset and develop better learning models to compensate for the weak samples in the training dataset.