Reviewerly/exHarmony huggingface dataset:
Accelerating subgraph matching by anchored relationship on labeled graph

Subgraph matching is one fundamental issue in the research area of graph analysis, which has a wild range of applications, including question answering, semantic search and community detection. Recent studies have designed some near-optimal matching orders and data indexes to reduce the unpromising redundant calculations. However, the influences of recalculation on some positive candidate vertices were ignored in the iterative process of subgraph matching. In this paper, the novel concepts of an anchored node pair and anchored relationship are proposed as the theoretical basis to solve recalculation on subgraph matching. Thus, two key aspects are considered to reduce the redundant and recalculated node pairs. The first aspect involves using the dominating relationship of the anchored node and its follower to prune the negative node pairs, and an index of matching-driven flow graph is built to minimize the positive candidate vertices by using a heuristic algorithm. The second aspect involves exploring the anchored relationship to analyze the recalculated region of a matching stream, and two novel strategies are designed to manipulate the intermediate results of the partial subgraph isomorphism to avoid revalidation in the subgraph matching process. Extensive empirical studies on real and synthetic datasets demonstrate that our techniques outperform the state-of-the-art algorithms.
A Novel Method of Transforming Real Images &amp; Videos into Cartoon Images &amp; Videos

The primary objective of this research work is to develop a novel solution for transforming real images and videos into animated photos and videos. The existing transformation method requires complicated computer graphics & skills. The main approach of this article is based on designated images & videos, which is then converted into an art form such as painting. Among all the technique used, the proposed model accepts the input image and video, which is then followed by the extraction of edges, grey image and application of median blur with Bilateral Filter technique to convert the input image into cartoon image.
Comparative Analysis: Role of Meta-Heuristic Algorithms in Image Watermarking Optimization

The aim of watermarking is to provide copyright protection and authentication to the digital contents available over the network. A good quality watermarking scheme should provide a balance between various characteristics of watermarking. In this paper, a DCT and DWT based hybrid watermarking system is implemented to provide copyright protection to the content and is secured by using Arnold transform. The paper's objective is to study and analyze the performance of various meta-heuristics algorithms in the watermarking field. This paper shows the performance of particle swarm, artificial bee colony, and firefly algorithms against digital image watermarking. These algorithms are used to find the optimal value of the watermark strength factor, which is responsible for providing the balance between robustness and imperceptibility. The proposed scheme shows good performance in terms of PSNR, NC, and BER values. For providing security to the watermark image, the image is scrambled by using the Arnold transform. The robustness of the scheme is evaluated against different types of image processing attacks.
A Real-time Tracking System Used for Surface Detection

In industrial production, the high speed of object on conveyor always causes motion blur when computer vision is used to achieve appearance inspection, results in difficulties in image processing. In this study, a real-time tracking system based on cam transmission was applied to eliminate the relative movement between camera and objects. The outline of the cam was specifically designed to meet the requirement of real-time tracking and simplified the motor control. To test the system, the velocities of both camera and object were collected using rotary encoders. It was found that the camera velocity would be almost equal to that of object in a certain period when cam rotated uniformly. Also, the images were captured by triggering the camera at the suitable moment, and it was shown that the object in image was very clear. Compared with conventional restoration algorithms, the proposed method resulted in clearer images without any distortion, and could save computation time, which was especially suitable for on-line inspection in industrial production such as boards.
Research on Valuation System of Listed Companies Based on Neural Network Model

In order to improve the accuracy of the value evaluation of listed companies, this paper puts forward a method to construct the value evaluation system of listed companies based on neural network model. This construction method gives full play to the unique advantages of BP neural network, that is, strong nonlinear mapping ability and strong self-learning ability. at the same time, the construction method also makes full use of the powerful functions of BP neural network scientifically, such as the ability of arbitrary complex pattern classification and powerful multi-dimensional function mapping. In addition, the construction method scientifically introduces BP neural network into the value evaluation system of listed companies, and skillfully constructs the value evaluation system of listed companies based on neural network model. The research results show that the construction method can break through the limitations of the traditional value evaluation system of listed companies, solve the problems that can not be solved by simple perceptrons, and have the function of simple operation. it can fundamentally improve the accuracy and efficiency of the value evaluation of listed companies.
Intelligent System for Preventing Rubber Ducky Attacks Using Deep Learning Neural Networks

At the present stage of development of hardware and software in the field of information technology an important place is occupied by information security, which implies the use of additional means to ensure secure processing of confidential data of end-users of target systems. The main problem, allocated in existing electronic computers, is insufficient protection of hardware ports from external influences. An example of such vulnerabilities is the USB Rubber Ducky hardware and software security solution from hak5 that exploits vulnerabilities by emulating peripherals to perform unauthorised actions on the target machine. Offensive Security's advanced, similar solutions allow complex target machine interception and remote control of the target machine, underscoring the urgency of the problem at hand. To solve this problem, an intelligent system was developed to evaluate and calculate the states of connected peripheral devices, in particular, to evaluate the comparability of input groups of peripheral device parameters. To improve security, a deep learning artificial neural network process was integrated into the implemented system. Based on user actions – software processing call speed, user errors and existing exploitation patterns of variant vulnerabilities – it can identify a device as potentially dangerous and then hardware disconnect the USB port. The artificial neural network's learning functionality based on user behaviour patterns also allowed for personal identification without an active account, which has a positive impact on system security. An important feature of the system is also the ability to interact with the system remotely using the Telegram API.
Generating Diverse Extended Summaries of Scientific Articles

In recent years, there has been an explosion in the number of research publications from various conferences and journals. This large volume of scholarly articles is an invaluable source of information and knowledge which need to be effectively summarized to be useful. In contrast to the base summary, an extended summary gives low-level and structured information about the scholarly articles. In this paper, we propose to develop an attention-based BiLSTM-CNN framework for the purpose of generating extended summaries of scientific articles. To validate our approach, we conduct experiments on the dataset published by LongSumm shared task 2020. The performances of all our systems were evaluated in terms of a well-known metric ROUGE. We also benchmark our model against baseline techniques and our results reveal that the proposed attention-based deep neural network outperforms other models with a significant margin.
Attention Guided Slit Lamp Image Quality Assessment

Learning human visual attention into a deep convolutional network contributes to classification performance improvement. In this paper, we propose a novel attention-guided architecture for image quality assessment (IQA) of slit lamp images. Its characteristics are threefold: First, we build a two-branch classification network, where the input of one branch uses masked images to learning regional prior. Second, we use a Forward Grad-CAM (FG-CAM) to represent the attention of each branch and generate the saliency maps. Third, we further design an Attention Decision Module (ADM) to decide which part of the gradient flow of both two branch saliency maps will be updated. The experiments on 23,197 slit lamp images show that the proposed method allows the network closer to human visual attention compared with other state-of-the-art methods. Our method achieves 97.41%, 84.79%, 92.71% on AUC, F1-score and accuracy, respectively. The code is open accessible: https://github.com/nhoddJ/CSRA-module.
Window Size Optimization for Gaussian Processes in Large Time Series Forecasting

Many current machine learning applications rely on performance rather than on model interpretability. Robust confidence projection is underrated as well. These qualities are of key importance in experimental sciences where benefits of IA applicability is intended to precisely bound sensible results. A Gaussian Process (GP) constitutes a non-parametric soft computing method which encompasses model interpretability and transparency. Additionally GPs feature rigorous uncertainty estimations by means of convenient kernel specification fitting data stochastic properties. Extensive temporal series of data may efficiently be characterised by GPs. GPs perform selecting the most suitable distribution of solutions conditioning over observations by means of maximizing the logarithm of the marginal-likelihood of data. Selecting an appropriate time-training interval stands out as a requirement and to successfully extract unambiguous structural information from kernel hyperparameters. Nevertheless demanding computational cost in the course of the training stage, scaling as \(\mathcal {O}(n^{3})\) being n the data set size, sets a trade-off on how much data to include. Our work aims at providing a general procedure to optimize input training selection to be minimum meanwhile retaining unambiguous information to the process modelled by the GP. To address this problem our ideas are evaluated performing forecasting on Madrid air-quality time series.
A Generalization of Secure Comparison Protocol with Encrypted Output and Its Efficiency Estimation

Secure comparison protocol outputs an unencrypted or encrypted comparison result. In this paper, we focus on a secure comparison protocol with an encrypted output. In recent works, since the computation of such protocols proceeds bitwise, the efficiency problem has not yet been solved. In this study, we propose a new secure comparison protocol with an encrypted output, which is a generalization of one proposed by Kobayashi and Hakuta (2018). As an interesting feature, a computation of our proposed protocol proceeds w bits-by-w bits for any positive integer w to compute an output. We discuss the security under semi-honest model. Furthermore, we estimate for the efficiency.
AdaptiveSystems: An Integrated Framework for Adaptive Systems Design and Development Using MPS JetBrains Domain-Specific Modeling Environment

This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language - DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc.
Enhanced Thesaurus Framework Using Service-Oriented Architecture

The conceptual and relational structure of a natural language is in multiform. The degree of complexity is also variable in terms of recall and precision. Generating and extracting thesaurus systems are being treated as building complex systems. A modest attempt in this study is an interaction between the Thesaurus Systems and Service Oriented Architecture has been carried out for designing and development of a Thesaurus framework for a natural language. The aim of this paper is to construct a thesaurus framework in which its services are explored. Towards this aim and objective, this paper also focuses on Service Oriented Architecture to implement the proposed thesaurus framework. SOA is a loosely coupled architecture in which the thesaurus services are being treated as web services. The Thesaurus framework consists of three layers namely, Presentation Layer, Business Layer and Data Layer. The Presentation Layer conveys the user interface in terms of various kinds of users and their roles along with presentation techniques. The Business layer deals with the fine-grained services of Thesaurus operations like addition of words, along with their approvals and conflict resolving techniques. The Data layer focuses on major entities of words and their synonymous semantic relations.
A Structural Representation Method for Information of Things in Internet of Things

IoT (Internet of Things) has already gained more and more attentions globally. But the IoT is still young. With development of IoT and more and more requirements of integration of things information based on different connection technologies, a connection-technology-independent representation method for information of things is needed to give a unified definition and specification for information of things. In this paper, the necessary the unified definition and specification for information of things are discussed and a structural representation method for information of things in IoT is introduced, including the selection of representation tool, structural definition for information of things and concrete definition for information of things. The method can be a basis for standardization of things’ information, connection-technology-independent middleware and applications for IoT in future.
Tuning in on the Becoming of Music

Abstract In this article, I explore the music-in-becoming as a dialogue. The thesis of my inquiry is that during musical composition, the composer’s listening is marked by both activeness and receptiveness; actively structuring the sounding work, and receptively letting the work express itself as it takes its form. Composer and work merge in sudden moments of attunement, the sensation of coherence between the so-fare completed and the anticipation of the as-of-yet unformed work. Composition is all about balancing writing as a handicraft with those rare, unexpected moments of attunement. I discuss the emerging work’s invitational character and the fact that during composition, the piece seems to request the composer’s attention. The audibility of the work’s voice depends on the composer’s ability to listen. Finally, the methodological considerations concerning sonic-artistic research show that researching sonic experiences requires competencies of being attentive, responsive, and reflective. Attentiveness is related to thorough listening experiences of the emerging music, responsiveness appears as a vital skill in a composer’s dialoguing with the music-in-becoming, and reflectiveness is associated with empirically documenting the processes. At the heart of this sonic research project, I place my lived experience of composing and the intimate relation between sound and listening.
Intelligent Learning Architecture with Hybrid Features for Phishing Detection

This study proposes a novel machine learning architecture that uses deep learning technology to extract features from the structure of a web page and construct a model for phishing detection. Hackers can commit crimes through a variety of Internet technologies. In recent years, phishing incidents have become more frequent, and the rapid development of information technology has enabled hackers to develop more advanced phishing attacks. Furthermore, the release of phishing toolkits, which are collections of software tools, make it easier for people with minimal technical skills to launch their own phishing attacks. Therefore, more attention must be paid to the prevention of such attacks. Protection from phishing websites has various aspects, including user training, public awareness, technical security measures and others. In this research, we further improve the phishing detection on phishing kits. This research proposes to use the combination HTML structural feature with the features proposed by AI@ntiPhish1.0 to train the phishing detection model. Relevant experimental results demonstrate that the combination of AI@ntiPhish1.0 features with extracted HTML structural features is more effective on detecting the phishing kits, increasing the accuracy thereof from 82% to 87.2%.
Lidar Upsampling Using HSD Color Space Guided Image

This paper proposes a 3D spatial upsampling algorithm using a 2D LiDAR and a single camera. These two devices are placed on the same line, and both data are acquired by rotating the stage 360° around a vertical axis using a step motor. The obtained data is used to calibrate between the LiDAR and the camera. And a high-density 3D map is generated through a proposed two-step upsampling method using HSD-based guide image.
Privacy Preserving Machine Learning for Malicious URL Detection

Phishing remains the most prominent attack causing loss of billions of dollars for organizations and users every year. Attackers use phishing to obtain sensitive information from users, install malware and obtain control over their systems. Currently, web browsers counter this attack using blacklisting method, however it fails to detect newly generated malicious websites, hence ineffective. In the recent times, machine learning based URL classification techniques where trained models are deployed on server side, emerged as an effective solution to detect new malicious URLs and provide it as a service to the user. While malicious URL detection continues to be a problem, another potential concern is the user’s query privacy (when offered as malicious URL detection as a service, where server can learn about the URL) . Hence to address the query privacy, we propose privacy enabled malicious URL detection.In this work, we focus on privacy enabled malicious URL detection based on FHE using 3 methods (i) Deep Neural Network (DNN) (ii) Logistic regression (iii) Hybrid. In the hybrid approach, the feature extraction is done using DNN and classification is done using logistic regression model, gives practical performance. We designed the models based on split architecture (client/server). We present our experiments with the models trained using a dataset of 100,000 URLs (50,000 valid and 50,000 phished URLs). Our experiments show that malicious URL detection in encrypted domain is practical in terms of accuracy and efficiency.
Comparison of the visual texture calculation methods by image analysis, applied to mirror and scaled carp skin

Regions of interest (ROI) representative of the visual texture of images of mirror carp Cyprinus carpio carpio and scaled carp Cyprinus carpio were taken. Red, green, blue and grayscale (R, G, B, GS) histograms of these ROI were calculated. The following methods of visual texture calculations were performed on the ROIs: 1) image energy based on histograms, 2) image entropy based on histograms, 3) image energy based on co-occurrence matrices, 4) image entropy based on co-occurrence matrices, 5) texture based on fractal dimensions, 6) texture based on texture primitives method. Calculations were performed for color and grayscale images. The identification of the smoothest and roughest ROIs depended on the method used. The largest range between the minimum and maximum values was found in the co-occurrence matrix-based entropy calculation. A close second was the texture change index (TCI) method.
Implementation of Deep Learning Techniques for Text Summarization

To automatically summarize a piece of content, one must reduce the length of the original text while preserving the key informational elements and meaning of the content. Automatization of manual text summarization, which is a time-consuming and generally labor-intensive process, is gaining in popularity as a result of this, and is therefore a strong motivation for academic research. Abstracting and summarizing large texts is essential in today's world of data overload. Numerous methods of summarizing text have been developed over time. As a result of the redundancy and omission of the document summary relationship, traditional methods create a summary directly It has been shown that deep learning techniques are effective in generating summary. We focus on text summarization techniques based on deep learning that have been developed over the years.
Objective Ridge Regression System

In the actual system, it is very important to get enough training samples for the performance of the model. However, no matter how rich the collection is, there will be a problem: limited training sets cannot really explain the label of these. So we propose an Objective Ridge Regression System (ORRS) to solve this problem in the classification stage. First of all, according to different categories, training samples are regressing to the different objectives we defined by, to obtain a transform matrix. Secondly, all of the test samples are projected into the low dimensional space by this transform matrix. Thirdly, we developed a Diversity Factors Rule (DFR) to divide test samples into multiple batches. Finally, the Minimum Distance Classifier algorithm is used to classify each batch of test samples, and the class-centers are updated synchronously. Circulate the previous steps until all batch samples are classified. Experimental results on the Iris, Wine, ORL and AR databases show that our method outperforms some other algorithms.