Reviewerly/exHarmony huggingface dataset:
A text-based syntax completion method using LR parsing

This paper presents a text-based syntax completion method using an LR parser. We propose formal definitions of candidate text to be completed based on the sentential forms, and we design algorithms for computing candidates through reductions in the LR parsing. This is in contrast to the existing methods that have not clearly stated what candidates they intend to produce. This is also different from a transformation approach using an LR parser, which transforms the grammar of the programming language, a burdensome task at this moment. The advantage of our method is that LR parsers can be adopted without modification, and a syntax completion system can be built using them, without incurring efforts. We implemented the algorithms as an Emacs server to demonstrate the feasibility of their application.
Node Classification and Geographical Analysis of the Lightning Cryptocurrency Network

Off-chain networks provide an attractive solution to the scalability challenges faced by cryptocurrencies such as Bitcoin. While first interesting networks are emerging, we currently have relatively limited insights into the structure and distribution of these networks. Such knowledge, however is useful, when reasoning about possible performance improvements or the security of the network. For example, information about the different node types and implementations in the network can help when planning the distribution of critical software updates.
Using OWA Approach to Solve Cold-Start Problem of Recommender Systems

The proliferating electronic commerce has led recommender systems to become impressive tools that their ability to leverage the power of data to benefit any enterprise is non-negligible. They are purposed to effectively proffer those items that meet the users' preferences best. A variety of techniques and methods have been designed and developed for recommender systems such as collaborative filtering and demographic-based filtering. This study proposes a new hybrid recommender system that its concentration is mainly on improving the performance and efficiency of operation under an undesirable condition called the "new user cold-start" which is caused by the existence of users that happen to have no ratings or only a small number of ratings. In this hybrid method, we have applied the optimistic exponential type of ordered weighted averaging (OWA) operator to combine the outcoming results of four recommender system strategies. Experiments were conducted over the MovieLens dataset and resulted in a predominance of the proposed hybrid approach dealing with the cold-start conditions.
A Model of Requirements Engineering on Agriculture Mobile Learning System Using Goal-Oriented Approach

Requirements engineering (RE) is an essential initial step in the software engineering process. This step requires good interaction and communication between stakeholders and system analysis. A user-oriented approach is very supportive in defining the requirements of a system. This paper contributes to the application of the goal-oriented method in RE process. Goal-oriented is applied through four steps such as elicitation, requirements definition, analysis, and specification. The elicitation step was carried out by involving fourteen stakeholders from the elements of farmers, agricultural extension workers, village government, and nongovernment employees. RE produces a specification list of functional and non-functional requirements, use case diagrams, and class diagrams. RE also creates a model design for applying the pear-to-pear interface architecture model for agriculture mobile learning. Through this research, it is known that the goal-oriented approach strongly supports a structured, systematic, and participatory RE process, making it easier to prepare requirements specifications that will become a reference at the system design and development step.
Real-time fog visibility range estimation for autonomous driving applications

In this paper, we present a novel fog visibility range estimation algorithm for autonomous driving application. The proposed method is based on a hybrid neural network for which localized image entropy and image-based features are given as an input. While entropy used for visibility estimation, image-based features act as a basis for distance calibration. The proposed network is tested on real data collected and calibrated on-road and performs very well with respect to accuracy. The proposed algorithm is also of low complexity and provides the result on a near real-time basis.
CognIDE

Wearable devices capable of capturing psychophysiological data are a reality today. Recent studies indicate that the developer's cognitive indicators (e.g., level of attention and meditation) might affect code comprehension and maintenance tasks. However, current Integrated Development Environments (IDEs) and code editors like Visual Studio (VS) Code fall short of providing contextual information of cognitive indicators located throughout source code. This article proposes CognIDE, a tool-supported approach for integrating psychophysiological data related to cognitive indicators into the VS Code. CognIDE help VS code to push a step forward, providing actionable contextual information alongside the evolving source code. The CognIDE was evaluated through a survey with 6 professionals and interviews for investigating its effects on their perception of usefulness, ease of use, and intention to use in real-world settings. With a high acceptance of the professionals, the emerging results show the potential for using CognIDE to identify and prioritize the review of source code with specific cognitive indicators, mainly related to bugs or inadequate understanding of code snippets.
Leveraging Linear Algebra to Count and Enumerate Simple Subgraphs

Even though subgraph counting and subgraph matching are well-known NP-Hard problems, they are foundational building blocks for many scientific and commercial applications. In order to analyze graphs that contain millions to billions of edges, distributed systems can provide computational scalability through search parallelization. One recent approach for exposing graph algorithm parallelization is through a linear algebra formulation and the use of the matrix multiply operation, which conceptually is equivalent to a massively parallel graph traversal. This approach has several benefits, including 1) a mathematically-rigorous foundation, and 2) ability to leverage specialized linear algebra accelerators and high-performance libraries. In this paper we explore and define a linear algebra methodology for performing exact subgraph counting and matching for 4-vertex subgraphs excluding the clique. Matches on these simple subgraphs can be joined as components for a larger subgraph. With thorough analysis we demonstrate that the linear algebra formulation leverages path aggregation which allows it to be up 2× to 5× more efficient in traversing the search space and compressing the results as compared to tree-based subgraph matching techniques.
Nscanner: Vulnerabilities Detection Tool for Web Application

Abstract Internet has been dominating the world nearly a decade. Web application is known to be the most widespread platform of the internet especially when it comes to share resources, e-commerce services, education and business platforms. Since the usage of web applications are increasing dramatically, it’s becoming more vulnerable for security attacks. Each year, organizations facing many security attacks towards their web applications. Although many security practices and mitigations have been applying in web application, however there are still some security loophole issues can be found in web application. For instance, these loopholes can be referred as lack of secure coding (standards) implemented in web application, lack of formal security training approach for web developers and improper security testing for their web application. Besides, social engineering attacks also tremendously increasing each year. Many organizations were compromised through phishing attacks due to lack of awareness among users (employees). As a solution to overcome the issues, a research project will be carried out to implement a system called Nscanner to detect Structured Query Language injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities for web application. Moreover, the developer also will design a malware detection feature based on machine learning approach to detect malware found in attachments from emails in order to prevent malware phishing attacks.
The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.
Evolving Evidence Gathering Process: Cloud Forensics

Identification of data which is stored in the cloud is complicated than what we expect and collection of evidence which is robust, tamper-proof and stands in the court of law is one of the most challenging aspects of the whole procedure. The data for which the whole process is being executed can be reserved on the data-centers, can be placed anywhere across the globe. Feasibility of accessing the data on the physical drive is much less when we see the scenario of cloud storage and processing. Case studies, guidelines and advisories till date majorly describes those traditional processes i.e maintaining chain of custody, search and seizure, etc. But the principle concern are gathering the data, integrity and reliability of the data that is to be analyzed after seizure and Formation of essential requirements like, Cloud Infrastructure, instances, services to run upon and most important—Client, for testing the procedure. Infrastructure to be used, compatibility issues, guidelines and advisories have been finalized to achieve the primary aspects that is: Assess the nobility and dependability of the extracted information that is not compromised, tampered.
Research on Alarm Association Mechanism of Information System Based on FP-growth Algorithm

Abstract With the continuous informatization advancement of State Grid Corporation, the scale of information networks has become increasingly complicated, therefore effective alarm filtering and correlation has become the focus of the operation and maintenance of information system. In this paper, FP-growth algorithm is used to mine the association rules of alarm data in information system. Aiming at the problems of short message gateway pressure, core alarm delay and even omission caused by large-scale network alarm, the calculation method of the confidence is optimized, which solves the problem of confidence calculation distortion caused by frequent alarm term. The experimental results show that the optimized FG-growth algorithm combines the alarm information effectively and has some reference significance for the actual operation and maintenance work.
Energy Separation Based Features for Replay Spoof Detection for Voice Assistant

Voice Assistant (VA) now-a-days plays a very important role for the smart home applications. However, the VA along with ease also brings security issue too, such as possibility of being attacked by replay, hidden voice commands, etc. This paper presents replay Spoof Speech Detection (SSD) system for VA using Energy Separation Algorithm (ESA)-based features to capture Instantaneous Amplitude and Frequency Cepstral Coefficients (i.e., ESA-IACC and ESA-IFCC), and Gaussian Mixture Model (GMM) as a pattern classifier. Teager Energy Operator (TEO) has the characteristics to suppress the noise and hence, it is robust to noise sensitivity. For noisy acoustic environment, the ESA-based features that employ TEO perform well compared to the clean environment. We performed the experiments on the ReMASC database, which contains four different acoustic environments. Proposed features performed better in clean and noisy environments. In addition, to obtain possible complementary information, we performed score-level fusion of ESA-IACC and ESA-IFCC that resulted in low Equal Error Rate (EER) for different environments. Furthermore, we compared our proposed feature sets with Constant-Q Cepstral Coefficients (CQCC), and Linear Frequency Cepstral Coefficients (LFCC) resulting in an relative improvement of approximately 21.88 % for clean environments and 66.34 % for noisy environments (in EER), respectively.
PRNU Extraction from Stabilized Video: a Patch Maybe Better than a Bunch

This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity (PRNU) noise facing stabilized video.The stabilized video undergoes in-camera processing like rolling shutter correction.Thus, misalignment exists between the PRNU noises in the adjacent frames owing to the global and local frame registration performed by the in-camera processing.The misalignment makes the reference PRNU noise and the test PRNU noise unable to extract and match accurately.We design a computing method of maximum likelihood estimation algorithm for extracting the PRNU noise from stabilized video frames.Besides, unlike most prior arts tending to match the PRNU noise in whole frame, we propose a new patch-based matching strategy, aiming at reducing the influence from misalignment of frame the PRNU noise.After extracting the reference PRNU noise and the test PRNU noise, this paper adopts the reference and the test PRNU overlapping patch-based matching.It is different from the traditional matching method.This paper conducts different experiments on 224 stabilized videos taken by 13 smartphones in the VISION database.The area under curve of the algorithm proposed in this paper is 0.841, which is significantly higher than 0.805 of the whole frame matching in the traditional algorithm.Experimental results show good performance and effectiveness the proposed strategy by comparing with the prior arts.
Fast Semi-Supervised Anomaly Detection of Drivers’ Behavior using Online Sequential Extreme Learning Machine

With the wide spread of artificial intelligence (AI) technologies, many applications using AI are increasingly deployed in many fields. Specially anomaly detection is one of the key applications of AI. Among several targets, detecting anomaly behavior of drivers or vehicles has been attracting due to the growing demand of safety driving. It is crucial to study and evaluate techniques for anomaly driving detection with AI technologies. The Online Sequential Extreme Learning Machine (OS-ELM) is a recently attracting neural network model that has high memory efficiency and can perform highspeed sequential learning with streaming data. Though OSELM is known to be effective for anomaly detection, it has not yet been verified for non-stationary time series data such as driving sensor data. In this paper, we study the effectiveness of OS-ELM based anomaly driving behavior detector using sensor data of vehicles and compared the performance of it with a Hidden Markov Model (HMM) based and traditional Long Short-Term Memory (LSTM) based methods. Since the existing driving behavior benchmark data is not enough for evaluating anomaly driving, we also create a new dataset with a powered wheelchair. Throughout the evaluation, we show that the OS-ELM based anomaly driving detector has almost the same or even better accuracy in anomaly driving detection with much faster sequential learning speed compared with the HMM or LSTM based detector.
Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models

Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models.
No-Reference Stereoscopic Image Quality Assessment Based On Visual Attention Mechanism

In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM), which includes channel attention mechanism and spatial attention mechanism. The channel attention mechanism can give high weight to the features with large contribution to final quality, and small weight to features with low contribution. The spatial attention mechanism considers the inner region of a feature, and different areas are assigned different weights according to the importance of the region within the feature. In addition, data selection strategy is designed for CNN model. According to VAM, visual saliency is applied to guide data selection, and a certain proportion of saliency patches are employed to fine tune the network. The same operation is performed on the test set, which can remove data redundancy and improve algorithm performance. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art SIQA methods. Cross-database validation shows high generalization ability and high effectiveness of our model.
Dynamic K-Graphs: an Algorithm for Dynamic Graph Learning and Temporal Graph Signal Clustering

Graph signal processing (GSP) have found many applications in different domains. The underlying graph may not be available in all applications, and it should be learned from the data. There exist complicated data, where the graph changes over time. Hence, it is necessary to estimate the dynamic graph. In this paper, a new dynamic graph learning algorithm, called dynamic K -graphs, is proposed. This algorithm is capable of both estimating the time-varying graph and clustering the temporal graph signals. Numerical experiments demonstrate the high performance of this algorithm compared with other algorithms.
Performance Analysis of Predictive Association Rule Classifiers using Healthcare Datasets

In recent years, the use of data mining techniques has gained significance in healthcare applications. The appropriate data mining techniques extract interesting affinities/associations between patient's signs and symptoms, thus providing reasonable decision-making for the diagnosis and prognosis of the disease. Associative Classification (AC) is a contemporary technique, which uses the Class Association Rules (CARs) to build the classification system. Classification based on Predictive Association Rule (CPAR) is one of the popular AC algorithms, which utilise FOIL's Information Gain measure to select the best attributes for the generation of CARs. This work attempts to explore a suitable attribute selection measure and an error estimate measure that can best fit into the existing CPAR algorithm to construct an efficient rule-based classifier. Thus the performance of CPAR has been analyzed by applying alternative attribute selection measures such as Gain Ratio (GR) and Mutual Information Gain (IG) instead of FOIL's Information Gain. Moreover, two error estimate measures such as Laplace accuracy ("La") and Likelihood ratio statistic ("Lr") are used for rule evaluation and best k-rule selection tasks of CPAR. This work analyzes the performance of CPAR-GR and CPAR-IG with the existing CPAR algorithm in terms of classifier accuracy. From the results, it was found that the use of "GR" and "IG" within CPAR yields accuracy higher than the existing CPAR. Significant differences in the performances of CPAR, CPAR-GR, and CPAR-IG are identified and demonstrated by experiments, and statistical tests using healthcare datasets taken from the UCI machine learning repository.
Modeling and Analyzing Smart Contracts using Predicate Transition Nets

Blockchain technology has gained wide acceptance in recent years. Smart contracts facilitate the application of the blockchain technology. Smart contracts are programs running distributed environments and are thus error prone. Smart contracts often lack precise specifications and are written in high-level programming languages such as Solidity. In this paper, we present an approach to formally model and analyze smart contracts using predicate transitions nets. We use the blind auction smart contract to demonstrate our approach, which reveals some problematic implementation of some smart contract functions. We have applied predicate transition nets in modeling and analyzing all 11 smart contracts in Azure blockchain workbench. Although we cannot tell whether there is any problem in these smart contracts based on their informal descriptions and Solidity programs without designer input. Our experience has shown the applicability and suitability of predicate transition nets. We believe that our approach can help smart contract designers to detect and prevent early design problems in the current practice of using informal textual descriptions of smart contracts.
A Study on Effective analysis of Machine Learning algorithm towards the Women’s safety in Social Media

Social media is the most challenging one for analyzing the text and the social media application is a tool for expressing the opinion without facing any fear. At the same time, there will be a lot of crime and violence occurs on the internet. Especially the women who are in a popular position like celebrities, politicians, and also the common people are facing the threats of violence are in social media. So they feel insecure in society. Still, there will be a problem for analyzing the text. It is very too difficult to classifying the text on social media. Through the machine learning technique, it would be done effectively. It filtered the unwanted text and then it reports to the respective office. So it will help the government to protect the women in social media. In this paper, a survey on online abuse as well as a study about sentiment analysis. According to this paper, it helps many researchers to get an idea of the sentiment analysis in social media. Similarly, the survey is done in a combination of the different techniques and also drafted drawbacks.Keywords-Support vector machine (SVM), Naive Bayes classification, Sentiment analysis, Social networking site.