Reviewerly/exHarmony huggingface dataset:
Fuzzy dynamic Markov model for Time Series Anomaly Detection

Dynamic Markov model are commonly utilized in time series anomaly detections. The traditional dynamic Markov models define data state using equal width interval segmentations. However, the state definition method is too rigorous. In real applications, the data may vary in a certain range and it cannot always belong to a specific state. Therefore, we proposed a new fuzzy interval segmentation scheme, which dispatch each data into three neighbored fuzzy states to tolerate data variations. Furthermore, we also introduced model updating schemes to adapt to the new data series. The comparison results demonstrated that our method obviously reduced the false detection rate of time series data compared with the traditional dynamic Markov models.
Design of Associate Content Based Classifier (ACBC) for Malicious URL Prediction by Rule Generation Algorithm

Recently, the internet is becoming as the most effective tool to interact with many foreign societies especially during COVID-19 pandemic. Moreover, the digital platform is increasing in many developing countries and at the same time, the chance of fraudulence is also increasing day by day. In the digital world, phishing assaults are emerging as the most common type of social engineering attack. Currently, many websites are targeting to acquire the confidential data, which is stored in websites. Recently, the classification techniques are employed to detect the phishing websites. Many tools are used for anti-phishing purposes; they are blacklist and antivirus software. The confidential data in a fake surrounding has intended the category of leaked data due to the action of attackers. In this scenario, machine learning method is observed as a very effective to classify the phishing and non-phishing web (Uniform Resource Locator) URLs. This classification struggles in classifying the leaked data content-based challenge. Therefore, the proposed algorithm is associated with the content-based classification method along with the rule-based generator algorithm. This research article integrates the content-based classification with a rule-based generator algorithm to improve the overall performance of the system. The updated public online repository called Mendeley dataset is used in the proposed research work. The proposed algorithm is used in 7k phishing and real websites content data for performing feature extraction. The extracted feature is then analyzed with our proposed algorithm to provide better prediction accuracy. Also, the proposed work has concluded that, the associate algorithm has achieved better accuracy, when compared to other existing methods.
AFM Signal Model for Digit Recognition

In this paper, a multicomponent amplitude and frequency modulated (AFM) signal model is proposed for isolated word (digit) recognition. The AFM signal model is designed at phoneme level, and features are extracted from each phoneme of the spoken word. The Fourier-Bessel expansion and discrete energy separation algorithm (FB-DESA) are used for the analysis of the signal model. The AM and FM parameters of the model are the features considered for digit recognition. The selected features are applied to the classifiers for word recognition. We achieved the best classification accuracy using the random forest decision tree classifier for speaker-independent data, and the best accuracy using the artificial neural network classifier for speaker-dependent data.
Information guiding and sharing enhanced simultaneous heat transfer search and its application to k-means optimization

Simultaneous Heat Transfer Search (SHTS) is a novel meta-heuristic algorithm proposed recently and it can solve some optimization problems. However, it still has some deficiencies, such as weak search ability owing to poor information exploitation so that it cannot solve complicated problems well. So an Information Guiding and Sharing enhanced SHTS (IGS-SHTS) is proposed in this paper. Firstly, Grey wolf optimizer is embedded into SHTS to enhance information sharing through the two different search methods. Secondly, an information sharing way through different agents is added. Thirdly, a sinusoidal crossover is utilized to fulfill information guiding through the historical individual-best population guiding the current one. Finally, an information guiding way, the global best agent guiding the worst one, is embedded to strengthen the worst agent. A large number of experimental results on the complex functions from CEC2017 test set and clustering optimization show that IGS-SHTS has stronger search ability compared with SHTS and quite a few state-of-the-art algorithms.
Performance Metric System for Malicious URL Data using Revised Random Forest Algorithm

Phishing alludes to drawing techniques utilized by character cheats to angle for individual data in a lake of clueless Internet clients. Phishers use ridiculed email, phishing programming to take Phishing costs, Internet clients, billions of dollars for every year. It alludes to attracting techniques utilized by character cheats to angle for individual data in a lake of clueless Internet clients. Phishers use satirize email, phishing programming to take budgetary record subtleties, and individual data, for example, usernames and passwords. This paper manages techniques for distinguishing phishing Web destinations by investigating different highlights of benevolent and phishing URLs by Machine learning calculations. We talk about the techniques utilized for the recognition of phishing Web locales dependent on have properties, axical highlights, and page significance properties. the Proposed model has been assessed utilizing five distinctive AI calculations provided the best performance and results. The tests were led with a few (angled and symmetrical) random forest (RF) method used to classy the data for site acknowledgment.
A new adaptive algorithm for v-support vector regression with feature selection using Harris hawks optimization algorithm

Abstract Support vector regression, especially, v-support vector regression (v-SVR) has been applied in several real problems. However, it is usually needed to tune manually the hyperparameter. In addition, v-SVR cannot perform feature selection. Nature-inspired algorithms were used as a feature selection and as an estimation for hyperparameter. In this paper, the Harris hawks optimization algorithm (HHOA) is proposed to optimize the hyperparameter of the v-SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on two datasets, show that our proposed algorithm performs better than other methods, in terms of prediction, number of selected features, and running time. In addition, the HHOA's experimental results confirm the efficiency of the proposed algorithm in improving prediction performance and computational time compared to other nature-inspired algorithms, which show case HHOA's ability to search for the best hyperparameter values and to select the most informative features for prediction tasks. Therefore the HHOA may likely be ideal for defining the data relationship between input features and the target variable as opposed to other algorithms. In other real applications this is highly effective in making predictions.
DP-VTON: Toward Detail-Preserving Image-Based Virtual Try-on Network

Image-based virtual try-on systems with the goal of transferring a target clothing item onto the corresponding region of a person have received great attention recently. However, it is still a challenge for the existing methods to generate photo-realistic try-on images while preserving non-target details(Fig. 1). To resolve this issue, we present a novel virtual try-on network, DP-VTON. First, a clothing warping module combines pixel transformation with feature transformation to transform the target clothing. Second, a semantic segmentation prediction module predicts a semantic segmentation map of the person wearing the target clothing. Third, an arm generation module generates arms of the reference image that will be changed after try-on. Finally, the warped clothing, semantic segmentation map, arms image and other non-target details (e.g. face, hair, bottom clothes) are fused together for try-on image synthesis. Extensive experiments demonstrate our system achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Computing Games: Bridging the Gap Between Search and Entertainment

Over history, games have served multiple purposes. It serves as a fun activity for players who need the entertainment to become test-beds for artificial intelligence. Solving games is beneficial in providing a better understanding of how information is progressing throughout the game. Uncertainty in games affects the way a game is solved and the way the game is experienced. Previous works have interpreted uncertainty in the game progress through various means, but there have been no clear links among those interpretations. In this paper, the probability-based proof number search (PPNS) and single conspiracy number (SCN) were used as the domain-independent indicators to analyze how uncertainty affects various game elements. PPNS exploits information from certain and uncertain information to reach convergence in solving games. Meanwhile, SCN evaluates the game states’ difficulty and describes game-playing patterns to understand play positions better. The study’s objective focuses on finding the optimal difficulty ordering of a game solver, defining the indicator for entertainment, and linking game-tree search and entertainment in different game environments. Experiments results demonstrate the link between the search indicators and the measure of entertainment where uncertainty plays a vital role in both contexts, verified from both two-person and single-agent games. Such a situation is also crucial for both computation and entertainment measures since it impacts both the quality of information and the expected game-playing experience.
Uncertainty of Rules Extracted from Artificial Neural Networks

Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image and speech recognition and translation. However, there is a problem that it is difficult for a person to understand what exactly the trained knowledge of an artificial neural network. As one of the methods for solving the problem of the artificial neural network, rule extraction methods had been devised. In this study, rules are extracted from artificial neural networks using ordered-attribute search (OAS) algorithm, which is one of the methods of extracting rules from trained neural networks, and the rules are analyzed to improve the extracted rules. As a result, we found that when the output value of the hidden layer has an intermediate value that is not close to 0 or 1 after passing through the sigmoid function, the problem of rule uncertainty occurs and affects the accuracy of the rules. In order to solve the uncertainty problem of the rules, we applied the hidden unit clarification method and suggested that it is possible to extract the efficient rule by binarizing the hidden layer output value. In addition, we extracted CDRPs (critical data routing paths) from the trained neural networks and used CDRPs to prune the extracted rules, which showed that the uncertainty problem of rules can be improved.
Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.
Research on Multi-granularity Ensemble Learning Based on Korean

Ensemble learning can train and combine multiple classifiers where the predictions are used as new features to train a meta-classifier. This improves the accuracy of the model. This paper proposes a multi granularity model based on Stacking ensemble learning for Korean text classification. Firstly, eojeol and subeojeol granularity is proposed according to the Korean language composition. Since different feature granularity contains different semantic information, compare the six different granularities of the phoneme, syllable, subword, word, subeojeol, and eojeol in Korean text classification task. Secondly, construct suffix words based on Korean grammatical morphology and compare the different granularities effects after suffix preprocessing. Finally, propose a multi granularity ensemble learning model based on Korean called MGEL-K. To enrich the diversity of ensemble learning using different granularities, making differences between learners. The results show that MGEL-K model proposed in this paper works best in the Korean text classification task with an accuracy of 92.33%.
Representation vs. Model: What Matters Most for Source Code Vulnerability Detection

Vulnerabilities in the source code of software are critical issues in the realm of software engineering. Coping with vulnerabilities in software source code is becoming more challenging due to several aspects of complexity and volume. Deep learning has gained popularity throughout the years as a means of addressing such issues. In this paper, we propose an evaluation of vulnerability detection performance on source code representations and evaluate how Machine Learning (ML) strategies can improve them. The structure of our experiment consists of 3 Deep Neural Networks (DNNs) in conjunction with five different source code representations; Abstract Syntax Trees (ASTs), Code Gadgets (CGs), Semantics-based Vulnerability Candidates (SeVCs), Lexed Code Representations (LCRs), and Composite Code Representations (CCRs). Experimental results show that employing different ML strategies in conjunction with the base model structure influences the performance results to a varying degree. However, ML-based techniques suffer from poor performance on class imbalance handling when used in conjunction with source code representations for software vulnerability detection.
“The Road not taken” statistically taken: A probabilistic analysis of Frost’s poem

“The Road Not Taken” is an exemplary poem composed by Robert Frost in 1916. The focal point of the paper is a careful statistical study of this poem intensifying several features such as: Whether the number of words per line follows uniform distribution or not.Studying the frequency distribution of word length and fitting a suitable probability model.Do the number of vowels follow a binomial or a quasi binomial model?Are the number of letters per line uniformly distributed?Computing the empirical probability of each alphabet in the poem.Analysis of rhythm in the poem.Analysis of parts of speech used in the poem. From the statistical analysis it is found that a Zero truncated Poisson distribution is a good fit for the frequency distribution of word length. The number of words per line may be taken as distributed uniformly in the poem which is tested by Chi Square goodness of fit test. Binomial distribution turns out to be a good fit for the number of vowels per stanza in the poem. The number of letters per line are distributed uniformly in the poem which is also tested by Chi Square goodness of fit test. The poem is following the rhythmic scheme abaab cdccd efeef ghggh. The number of words between two successive rhythmic words can be trichohotomized. The proportion of the first 13 letters (A to M) is more and significantly different as compared to that of the last 13 letters (N to Z). Finally verb is the maximum used part of speech in the poem.
Enabling Efficient and Expressive Spatial Keyword Queries On Encrypted Data

Recently, spatial keyword query services have been widely deployed in real-life applications, such as location-based services and social networking. Several privacypreserving spatial keyword queries solutions were proposed to guarantee data security and query privacy on outsourced data. However, those solutions are either based on broken cryptographic tools or support a single query type, and hence cannot meet the security and functionality requirements in practical applications. In this paper, we propose a Secure Spatial Keyword Queries (SSKQ) construction supporting expressive query types. Specifically, we present a secure index structure for spatial-textual data based on the encrypted Quadtree and Bloom filter, which can prune the index tree dynamically and only reveal the files associated with a set of keywords. The security analysis and the experiments conducted on real-world datasets demonstrate the security and performance of our construction.
Edge intelligence‐enabled supply chain financial model based on Business‐to‐Business e‐business platforms

Summary Based on the analysis of the existing traditional supply chain financial model, this article proposes an edge intelligence‐enabled supply chain financial model based on Business‐to‐Business (B2B) platforms, combines the operation mechanism of the model and the quantitative analysis thinking of the traditional supply chain financing. This article uses the model to construct and evaluates the cost–benefit model of dealers, manufacturers, and B2B e‐business platforms in the perspective of the supply chain finance for B2B platforms. In order to further explore the operation strategy of supply chain member enterprises, this article also explores the selection of financing objects of B2B platforms, the optimization of financing cost rate formulated by the loan amount of financing enterprises, and the optimization of product order quantity. Numerical analysis shows that the proposed model can directly reflect the changing trend of the relevant parameters of B2B platforms' supply chain financial model and provide suggestions for the cooperation mechanism between supply chain and commercial banks.
Fast Under Water Image Enhancement for Real Time Applications

Ocean exploration is a major challenge that we are facing today. With advancements to fields of marine engineering and aquatic robotics, we are capable of performing autonomous and complex decision making deep underwater. Significance of online Underwater Computer Vision Algorithms is ever increasing. Underwater images, however, suffer from inaccurate colors, hazing, colour cast and degradations because of unequal absorption of light by water. Algorithms designed for detection/enhancement in the air are of no use underwater. Although a lot of underwater image enhancement algorithms have come up in recent times, most of them are not suitable for real-time applications like AUV, due to their high computational times. These algorithms are more suitable for offline analysis. In this paper, we propose an algorithm which is fast enough for real-time systems such as AUVs/ROVs and is comparable to the offline state of the art image enhancement algorithms. We will be exploring histogram equalization techniques for dehazing and automatic white balancing algorithms for color correction. UIEB (Underwater Image Enhancement Benchmark) is used for evaluation of our algorithm. The codes and results are available at https://github.com/opgp/underwater-image-processing.
Not Teaching Software Engineering Standards to Future Software Engineers-Malpractice?

Software engineering standards are essential sources of codified knowledge for all software engineers. Could the professors who are not teaching software engineering standards to software engineering students be accused of malpractice?
Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures

We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem. We demonstrate how prior information on the spectral signature of the post-change graph can be incorporated to implicitly denoise the observed sequential data, thus leading to a natural CUSUM-based algorithm for change-point detection. Numerical experiments illustrate the performance of our proposed approach, particularly underscoring the benefits of (potentially noisy) prior information.
How to Identify Boundary Conditions with Contrasty Metric?

The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences. In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (PPFc) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (JFc) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind JFc is that it considers the search bias toward contrastive BCs during identifying BCs, thereby pruning the BCs capturing the same divergence. Experiments confirm the improvements of JFc in identifying contrastive BCs.
Backward Weighted Coding

Extending recently suggested methods, a new dynamic compression algorithm is proposed, which assigns larger weights to characters that have just been coded by means of an increasing weight function. Empirical results present its efficient compression performance, which, for input files with locally skewed distributions, can improve beyond the lower bound given by the entropy for static encoding, at the price of slower running times for compression, and comparable time for decompression.