Reviewerly/exHarmony huggingface dataset:
A3Graph

Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph.
Unsupervised feature selection via multi-step markov probability relationship

Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step Markov Probability Relationship for Feature Selection). The idea is using multi-step Markov transition probability to describe the relation between any data pair. Two ways from the positive and negative viewpoints are employed respectively to keep the data structure after feature selection. From the positive viewpoint, the maximum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. Then, the features which can keep the compact data structure are selected. From the viewpoint of negative, the minimum transition probability that can be reached in a certain number of steps is used to describe the relation between two points. On the contrary, the features that least maintain the loose data structure are selected. The two ways can also be combined. Thus three algorithms are proposed. Our main contributions are a novel feature section approach which uses multi-step transition probability to characterize the data structure, and three algorithms proposed from the positive and negative aspects for keeping data structure and select the features to preserve such structure. The performance of our approach is compared with the state-of-the-art methods on eight real-world data sets, and the experimental results show that the proposed MMFS is effective in unsupervised feature selection.
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization

Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework. Even though Bayesian neural networks have been found more robust to adversarial attacks compared with vanilla neural networks, their ability to deal with adversarial noises in practice is still limited. In this paper, we propose Spectral Expectation Bound Regularization (SEBR) to enhance the robustness of Bayesian neural networks. Our theoretical analysis reveals that training with SEBR improves the robustness to adversarial noises. We also prove that training with SEBR can reduce the epistemic uncertainty of the model and hence it can make the model more confident with the predictions, which verifies the robustness of the model from another point of view. Experiments on multiple Bayesian neural network structures and different adversarial attacks validate the correctness of the theoretical findings and the effectiveness of the proposed approach.
Duty Cycle Correction Circuit and Its Application for High Speed Random Number Generation

This paper presents a wake-up & shut-down ring oscillator based Random Number Generator (RNG) together with a new method of duty cycle correction circuit for cryptographic applications more specifically irregular sampling of regular waveform method based True Random Number Generators (TRNGs). The proposed circuit is composed of fully digital elements and contains only one D type flip-flop, one inverter, one buffer, and one XOR gate. The bias at the one-zero balance that occurs in irregular sampling of regular waveform based RNGs due to the regular clock's duty cycle is avoided by discarding the sampled data at the problematic duty cycle regions. The improvement provided with the proposed circuit is shown by applying the method to a Random Number Generator based on irregular sampling of regular waveform method. The irregular clock is generated by employing wake-up and shut-down ring oscillators. The speed of the sampling is reduced by downsampling the irregular signal. All of the circuits mentioned in this paper are implemented in Xilinx Zynq7000 FPGA. Furthermore, NIST 800-22 test suite is used to test all of the designs. Data sets of one million bits are collected with a UART module. All the NIST 800-22 test suite requirements are satisfied with the duty cycle correction applied RNG. The proposed circuit improves the throughput of the wake-up&shut-down RNG 12.3 times. Furthermore, the proposed circuit can be used with any RNG based on irregular sampling of regular waveform method.
Comparison of Background Subtraction and Frame Differencing Methods for Indoor Moving Object Detection

This study analyzes the method of moving object detection using video images by comparing two methods, namely background subtraction and frame differencing. The purpose of this research is to design a moving object detection system, then analyze the test results to determine a more reliable method. The video image used in the test is the result of recording using a camera type Logitech webcam C922 pro Hd 1080 P in avi format. This study uses two types of testing, namely qualitative testing in the form of motion detection methods and quantitative testing in term of frame per second values. The results of the study show in terms of the success of the frame differencing method that is more accurate in detecting objects. While in terms of computing the background subtraction method is faster in detecting objects.
A Generalized Semantic Filter for Glossary Term Extraction from Large-Sized Software Requirements

A glossary is an essential component of every software requirements document. Extracting glossary terms manually from a large requirements document is expensive in terms of both time and cost required to do so. Additionally, this is also an error-prone task. To overcome these issues, we propose a generalized semantic filter which can automatically extract key technical terms present in a large body of software requirements. Our semantic filter is based on a word embeddings model which can identify domain-specific terms. To achieve this goal, a domain-neutral reference corpus is created containing data of news headlines published over a period of 17 years by Australian Broadcasting Corp news website. We use this domain-neutral corpus to calculate the similarity scores of potential glossary terms extracted using text chunking and coverage filtering on the requirements document. The key idea is that if the context of a candidate term in the requirements document is different from the context in which it was used in the domain-neutral corpus, then the term is labeled as domain-specific. Since our semantic filter is domain-neutral, it can potentially be applied to requirements documents of any application domain. Our proposed technique has been applied to the CrowdRE document which is a large-sized document with roughly 3000 user stories for smart home application domain. Results show that our approach is very effective for glossary extraction from enormous documents containing software requirements.
The KF-SVM-based fusion method for multi sensor uncertain system with correlated noise

For multi-sensor target tracking system, the accurate state estimation is obtained under the condition that the system model is unbiased and the noise disturbance satisfies the characteristics of independent Gaussian white noise. However, in engineering practice, it is almost impossible to get the accurate system model and also the system noise is non-independent Gaussian white noise. So the traditional state estimation methods are not suitable for uncertainty system with non Gaussian white noise. In this paper, the Kalman Filter-Support Vector Machine (KF-SVM) based data fusion algorithm is proposed for system with model uncertainty and correlated noise. Firstly, the state pre-estimates are calculated by the proposed improved Kalman Filter for single sensor system. Then, the state estimation is obtained via proposed KF-SVM data fusion algorithm for multi-sensor system. Finally, compared with the traditional algorithms, the simulation results show that the proposed fusion algorithm based on KF-SVM does not need to calculate the sensor cross-covariance matrix and has better estimation accuracy.
Fast blending of planar shapes based on invariant invertible and stable descriptors

In this paper, a novel method for blending planar shapes is introduced. This approach is based on the Fined-Fourier-based Invariant Descriptor (Fined-FID) that is invertible, invariant under Euclidean transformations and stable. Our approach extracts the Fined-FID from the two shapes of interest (the source and the target ones). Then, the extracted descriptors are averaged enabling the calculation of intermediate descriptors. Finally, thanks to the inversion criterion, the intermediate shapes are easily recovered by applying the inverse analytical expression to these intermediate descriptors. Compared to previous works, the Fined-FID-based morphing avoid the usual registration step, generates naturally closed intermediate contours and ensure invariance under Euclidean transformations and invariance to the starting point, while being computationally efficient (almost-linear complexity). The performed experiments show the performance of the proposed blending approach with respect to curvature-based methods.
ROC Structure Analysis of Lean Software Development in SME’s Using Mathematical CHAID Model

These days, numerous software associations are utilizing agile philosophies to improve the execution of their procedures. In any case, some of them are discovering benefits in the better approaches for improving these officially settled procedures. Lean software development has been utilized to upgrade these procedures significantly more, for the most part because of the decrease of waste. So as to have the capacity to push forward the impact of this marvel, giving progressively empiric proof on this theme is required. This Paper attempts to present a questionnaire survey summarized results of SME’s in Bengaluru regarding Lean software development , Results are analyzed using IBM SPSS package , The questionnaire used was verified using Cronbach alpha test reading a high reliable and valid status of the conduction of collection process.
Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events

While several techniques for detecting trace-level anomalies in event logs in offline settings have appeared recently in the literature, such techniques are currently lacking for online settings. Event log anomaly detection in online settings can be crucial for discovering anomalies in process execution as soon as they occur and, consequently, allowing to promptly take early corrective actions. This paper describes a novel approach to event log anomaly detection on event streams that uses statistical leverage. Leverage has been used extensively in statistics to develop measures to identify outliers and it has been adapted in this paper to the specific scenario of event stream data. The proposed approach has been evaluated on both artificial and real event streams.
Automatic Construction of Knowledge Graph and Its Application in Electric Power System

The purpose of introducing the knowledge graph (KG) for electric power system is to make full use of the data information carried by the electric power Internet of Things, to describe the concepts, entities, events, and relationships between them in a structured way, so as to provide a more effective organization, management and cognitive ability of the cross-media big data for electrical power industrial chains. This paper firstly proposed an automatic construction of knowledge graph. And then, the detail information in power equipment inspection application was revealed. Finally, the application directions and future challenges of KG technology was discussed.
Mining Customer Requirement from Online Reviews Based on Multi-aspected Sentiment Analysis and KANO Model

Customer requirement plays a more and more important role in the enterprise innovation. With the development of the internet, online reviews have become an important information source of mining customer requirement. In this paper, a novel method is proposed to mine customer requirement form online reviews based on multi-aspected sentiment analysis and KANO model. In the method, product features are first extracted and clustered in the form of feature-sentiment word pairs from online reviews. Then, a special domain sentiment lexicon is constructed by modified SO-PMI method to quantify the customer satisfaction. The KANO model is innovatively applied to identify the category of customer requirement from online reviews. Finally, an experiment is given to illustrate the accuracy of the sentiment analysis and the effectiveness of the proposed method.
Single Image Deblurring Using Bi-Attention Network

Recently, deep convolutional neural networks have been extensively applied into image deblurring and have achieved remarkable performance. However, most CNN-based image de-blurring methods focus on simply increasing network depth, neglecting the contextual information of the blurred image and the reconstructed image. Meanwhile, most encoder-decoder based methods rarely exploit encoder's multi-layer features. To address these issues, we propose a bi-attention neural network for single image deblurring, which mainly consists of a bi-attention network and a feature fusion network. Specifically, two crisscross attention modules are plugged before and after the encoder-decoder to capture long-range spatial contextual information in the blurred image and the reconstructed image simultaneously, and the feature fusion network combines multi-layer features from encoder to enable the decoder reconstruct the image with multi-scale features. The whole network is end-to-end trainable. Quantitative and qualitative experiment results validate that the proposed network outperforms state-of-the-art methods in terms of PSNR and SSIM on benchmark datasets.
Analysis of breast cancer event logs using various regression techniques

The breast cancer is a chronic disorder that causes serious illness to the patients despite their age groups. Breast cancer has more number of research to identify the root causes. But in recent research finding also concentrated more on factors affecting the breast cancer with different type of event logs, such as healthcare centers generated data and trail data taken from various webpages. The machine learning techniques are mostly applied on complex type of event logs such as cancer data set, brain tumor dataset and heart related diseases. Among various diseases breast cancer is the one has more complex event logs, which is very complex to analyze and to find the root causes. This research article discuss about the breast cancer data set using logistic regression technique applied with python programming language. This paper also deals about the root causes about the breast cancer and related issues.
Memetic Evolution of Training Sets with Adaptive Radial Basis Kernels for Support Vector Machines

Support vector machines (SVMs) are a supervised learning technique that can be applied in both binary and multi-class classification and regression tasks. SVMs seamlessly handle continuous and categorical variables. Their training is, however, both time- and memory-costly for large training data, and selecting an incorrect kernel function or its hyperparameters leads to suboptimal decision hyperplanes. In this paper, we introduce a memetic algorithm for evolving SVM training sets with adaptive radial basis function kernels to not only make the deployment of SVMs easier for emerging big data applications, but also to improve their generalization abilities over the unseen data. We build upon two observations: first, only a small subset of all training vectors, called the support vectors, contribute to the position of the decision boundary, hence the other vectors can be removed from the training set without deteriorating the performance of the model. Second, selecting different kernel hyperparameters for different training vectors may help better reflect the subtle characteristics of the space while determining the hyperplane. The experiments over almost 100 benchmark and synthetic sets showed that our algorithm delivers models outperforming both SVMs optimized using state-of-the-art evolutionary techniques, and other supervised learners.
Adaptive Boosting Graph Convolution Network for Recommendation

Graph Convolution Networks (GCNs) have been widely applied to recommender systems during the last few years. Existing GCNs propagate the collaborative signals within the user-item interacted graph to find the similar nodes. However, this kind of network only uses implicit features that limited by the sparsity of the graph, and many nodes cannot be well represented. Here implicit feature refers to the interaction of user-item. In this paper, we propose an adaptive boosting graph convolution network algorithm (BoostGCN) for recommender, which combines multiple homogeneous component graph-based models linearly. In the framework, we choose the Normalized Discounted Cumulative Gain (NDCG) as the parameter of re-weight function for each component models. At last, we conduct experiments on two public datasets and our proposed method outperforms the state-of-the-art approaches on common metrics and convergence time.
Computational quantum key distribution (CQKD) on decentralized ledger and blockchain

In this work, we propose a new protocol for data transmission, using both the potential of quantum encryption expressed by the BB84 protocol and the possibilities offered by distributed ledgers and blockchain. As we shall see, this approach allows the transmission of keys in maximum security whether it is owned in quantum communication channel, or by using traditional channel and emulating on it a quantum functionalization thanks to the use of network nodes as new virtual quantum components, called i) Quantum Spin Generator (QSG), ii) Base Generator (BG), iii) Quantum Photon Polarizer (QPP), iv) Quantum Photon Meter (QPM), v) Quantum Photon Collider (QPC). The end result will be the security of one time pad encryption and quantum encryption, an intrinsic crypto agility linked to the dynamic allocation and functionalization of nodes, a growing security proportional to the growth of the number of network nodes used to encrypt and transmit the information.
Named Entity Recognition Using a Semi-supervised Model Based on BERT and Bootstrapping

Named Entity Recognition (NER) aims to recognize entities from texts, which is important for lots of natural language processing tasks, including knowledge graph and so on. Supervised NER method requires expensive datasets. In order to reduce expensive labor costs, we propose a semi-supervised model based on BERT and Bootstrapping to recognize more entities with a small amount of labeled data and less human labor. The method works in the following steps. Firstly, we build a small entity dictionary, and tag the collected texts using this dictionary. Secondly, we learn a model based on the small dataset. Thirdly, recognize entities that are not in dictionary and add the high-quality entities to the dictionary. Then, we repeat these steps until no new high-quality entities are found. Experiments show that the proposed model performs better than baselines on the Financial Entity Recognition task in 2019 CCF BDCI.
IoT-based security service for the documentary chain of custody

Document and process digitalization is considered an essential factor for sustainable cities as it facilitates socio-economic interaction and contributes to climate-friendly urban environments and societies. Since digital documents are vulnerable to illegitimate modifications and forgery, there is a risk of these documents being rejected as untrustful. Therefore, to really benefit from digitalized documents and processes in sustainable urban environments and society, it is imperative to put in place means to generate trust toward these documents and processes. As computational trust and information security mutually contribute to building each other, this paper is devoted to building trust in digital documents by ensuring the chain of custody (CoC) of the produced and preserved documents. The CoC comprises a set of resources and means to ensure the properties of security required for digitalized documents and is a comprehensive concept allowing to integrate the security measures in the whole producer-to-consumer information flow, being explicitly informed to the user so that she/he can trust that available protections were put in place for their digital documents. As the CoC needs to be operative in diverse document production chains, our design considers the Industry Internet of Things (IIoT) paradigm to conceive an IIoT-Based Security Service for the documentary chain of custody – IoTSec2CoC, with a service model to identify and authorize all of its components by providing microservices for the integration of the fully distributed proactive and reactive security measures, to ensure the confidentiality, availability, and integrity of the flowing document units. This design constitutes a production chain that presents the plasticity adequate for smart sustainable cities. The proposal validation was carried out with an implemented prototype whose resilience tests show that the proposed new model increases security at all stages of the digital document processing CoC.